Citation
Evaluation of layer moduli in flexible pavement systems using nondestructive and penetration testing methods

Material Information

Title:
Evaluation of layer moduli in flexible pavement systems using nondestructive and penetration testing methods
Series Title:
Evaluation of layer moduli in flexible pavement systems using nondestructive and penetration testing methods
Creator:
Badu-Tweneboah, Kwasi,
Place of Publication:
Gainesville FL
Publisher:
University of Florida
Publication Date:

Subjects

Subjects / Keywords:
Asphalt ( jstor )
Base courses ( jstor )
Bituminous concrete pavements ( jstor )
Moduli of elasticity ( jstor )
Nondestructive testing ( jstor )
Pavements ( jstor )
Sensors ( jstor )
Stress tests ( jstor )
Structural deflection ( jstor )
Surface temperature ( jstor )
City of Madison ( local )

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright Kwasi Badu-Tweneboah. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Resource Identifier:
021938268 ( alephbibnum )
18287379 ( oclc )

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Full Text










EVALUATION OF LAYER MODULI IN FLEXIBLE PAVEMENT SYSTEMS
USING NONDESTRUCTIVE AND PENETRATION TESTING METHODS





By

KWASI BADU-TWENEBOAH


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY


UNIVERSITY OF FLORIDA


1987














DEDICATED TO MY FAMILY, ESPECIALLY MY SISTER, ABENA KYEM

AND GRANDFATHER, ATTA KWAME, FOR THEIR CONTINUAL PRAYERS,

ENCOURAGEMENT AND SUPPORT DURING THE COURSE OF MY EDUCATION.



"A MIND IS A TERRIBLE THING TO WASTE."














ACKNOWLEDGMENTS

I would like to express my gratitude to Dr. Byron E. Ruth, chairman

of my supervisory committee, for his guidance, encouragement and con-

structive criticisms in undertaking this research work. I am also

grateful to Drs. F. C. Townsend, J. L. Davidson, M. Tia, J. L. Eades,

and D. P. Spangler for serving on my graduate supervisory committee. I

consider myself honored to have had these distinguished men on my com-

mittee.

I also owe sincere thanks to Dr. J. H. Schaub, chairman of the

civil engineering department, for the many times he gave help during the

course of my studies here, especially in my obtaining the grant award to

participate in the 1986 APWA Congress in New Orleans, Louisiana.

I would like to express my appreciation to the Florida Department

of Transportation (FDOT) for providing the financial support, testing

facilities, materials, and personnel that made this research possible.

I would like to thank the many individuals at the Pavement Evaluation

and Bituminous Materials Research sections of the Bureau of Materials

and Research at FDOT who contributed significantly to the completion of

this work. In particular, I am indebted to Messrs. W. G. Miley, Ron

McNamara, Ed Leitner, Don Bagwell, and John Purcell for giving so

generously of their time.

A very special word of thanks goes to Dr. David Bloomquist for his

significant contributions in conducting the in situ penetration tests








and for his helpful suggestions, advice, and friendship. The assistance

of Mr. Ed Dobson in the field work is also appreciated.

I would also like to thank Dr. F. Balduzzi of the Institute of

Foundation Engineering and Soil Mechanics of the Federal Institute of

Technology, Zurich, Switzerland, for inviting me to the institute, and

consequently helping me develop the interest to pursue active research

and further studies.

Last, but far from being the least, I would like to thank

Ms. Candace Leggett for her expertise and diligent skill in typing this

dissertation.














TABLE OF CONTENTS


Page

ACKNOWLEDGMENTS................................... ................. ii

LIST OF TABLES................. ................... ...... ..... ...... ix

LIST OF FIGURES.......................................... E..... xiii

ABSTRACT............o ......... .... o ....... ...................... xx


CHAPTER

1 INTRODUCTION ..... .......... ........................ .. 1

1.1 Background........ ... .... ........ .................... 1
1.2 Study Objectives ... .... .............. ... ... ...... .... ... 3
1.3 Scope of Study................................. ... .. 4

2 LITERATURE REVIEW .. ....................... ........... ... ... 6

2.1 Introduction......................... .............. 6
2.2 Elastic-Layer Theory................... ................ 7
2.2.1 General............ ....... ...... ........... 7
2.2.2 One-Layer System................................ 8
2.2.3 Two-Layer System............................. 9
2.2.4 Three-Layer System........................... 10
2.2.5 Multilayered or N-Layered Systems.............. 11
2.3 Material Characterization Methods...................... 13
2.3.1 General......................................... 13
2.3.2 State-of-the-Art Nondestructive Testing......... 15
2.3.2.1 General............................... 15
2.3.2.2 Static Deflection Procedures........... 16
2.3.2.3 Steady-State Dynamic Force-
Deflection................................ 21
2.3.2.4 Dynamic Impact Load Response........... 24
2.3.2.5 Wave Propagation Technique............. 29
2.3.3 NDT Data-Interpretation Methods................ 32
2.3.3.1 General ...... ...................... ....... 32
2.3.3.2 Direct Solutions ...................... 36
2.3.3.3 Back-Calculation Methods............... 41
2.3.4 Other In Situ Methods...............t......... 44








Page

2.4 Factors Affecting Modulus of Pavement-Subgrade
Materials ................................................... 47
2.4.1 Introduction..................................... 47
2.4.2 Temperature.................................. .. 48
2.4.3 Stress Dependency............................. 49

3 EQUIPMENT AND FACILITIES.................................... 55

3.1 Description of Dynaflect Test System.................... 56
3.1.1 Description of Equipment....................... 56
3.1.2 Calibration.................... ............... 59
3.1.3 Testing Procedure .............................. 59
3.1.4 Limitations...................................... 60
3.2 Description of the Falling Weight Deflectometer
Testing System.......................................... 60
3.2.1 The 8002 FWD..................................... 61
3.2.2 The 8600 System Processor........................ 61
3.2.3 The HP-85 Computer.............................. 63
3.2.4 Testing Procedure............................... 63
3.2.5 Advantages...... ... .............................. 64
3.3 BISAR Computer Program ................................. 64
3.4 Description of Cone Penetration Test Equipment.......... 65
3.5 Marchetti Dilatometer Test Equipment.................... 67
3.6 Plate Bearing Test...................................... 70

4 SIMULATION AND ANALYSES OF NDT DEFLECTION DATA.............. 71

4.1 BISAR Simulation Study................................ 71
4.1.1 General .......................................... 71
4.1.2 Dynaflect Sensor Spacing........................ 74
4.1.3 FWD Sensor Spacing............................... 74
4.2 Sensitivity Analysis of Theretical NDT
Deflection Basins....................................... 76
4.2.1 Parametric Study............................... 76
4.2.2 Summary of Sensitivity Analysis.................. 88
4.3 Development of Layer Moduli Prediction Equations........ 91
4.3.1 General .......................................... 91
4.3.2 Development of Dynaflect Prediction Equations.... 95
4.3.2.1 Prediction Equations for E .............95
4.3.2.2 Prediction Equation for E2 for
Thin Pavements............... .......... 103
4.3.2.3 Prediction Equations for E3............. 105
4.3.2.4 Prediction Equations for E4 ............ 108
4.3.3 Development of FWD Prediction Equations.......... 110
4.3.3.1 Prediction Equations for E ............. 110
4.3.3.2 Prediction Equations for E ........... 111
4.3.3.3 Prediction Equations for E ............. 113
4.3.3.4 Prediction Equations for E ............. 114








Page

4.4 Accuracy and Reliability of NDT Prediction Equations.... 119
4.4.1 Prediction Accuracy of Dynaflect Equations....... 119
4.4.1.1 Asphalt Concrete Modulus, E ............ 119
4.4.1.2 Base Course Modulus, E2,
for Thin Pavements...................... 123
4.4.1.3 Stabilized Subgrade Modulus, E ......... 123
4.4.1.4 Subgrade Modulus, E .................... 125
4.4.2 Prediction Accuracy of FWD Equations............. 127
4.4.2.1 Asphalt Concrete Modulus, E ............ 127
4.4.2.2 Base Course Modulus, E2.............. 129
4.4.2.3 Stabilized Subgrade Modulus, E ......... 132
4.4.2.4 Subgrade Modulus, E ................... 134

5 TESTING PROGRAM............. ................. .. ......... 136

5.1 Introduction..... o ..................................... 136
5.2 Location and Characteristics of Test Pavements.......... 137
5.3 Description of Testingn Procedures..................... 140
5.3.1 General .......... ...................... ....... 140
5.3.2 Dynaflect Tests........................................... ....... 142
5.3.3 Falling Weight Deflectometer Tests............... 142
5.3.4 Cone Penetration Tests.......................... 144
5.3.5 Dilatometer Tests..... .......................... 145
5.3.6 Plate Loading Tests................................. 145
5.3.7 Asphalt Rheology Tests.......................... 147
5.3.8 Temperature Measurements........................ 151

6 ANALYSES OF FIELD MEASURED NDT DATA......................... 153

6.1 General ... ... .................. .. .. ............... ... 153
6.2 Linearity of Load-Deflection Response.................. 153
6.3 Prediction of Layer Moduli.............................. 169
6.3.1 General.......................................... 169
6.3.2 Dynaflect Layer Moduli Predictions............... 171
6.3.3 FWD Prediction of Layer Moduli..................... 174
6.4 Estimation of El from Asphalt Rheology Data............. 178
6.5 Modeling of Test Pavements.............................. 181
6.5.1 General .................... ............. ..... ... 181
6.5.2 Tuning of Dynaflect Deflection Basins........... 182
6.5.3 Tuning of FWD Deflection Basins.................. 205
6.5.4 Nonuniqueness of NDT Backcalculation
of Layer Moduli ................................. 231
6.5.5 Effect of Stress Dependency.................... 233
6.6 Comparison of NDT Devices.............................. 236
6.6.1 Comparison of Deflection Basins.................. 238
6.6.2 Comparison of Layer Moduli...................... 253
6.7 Analyses of Tuned NDT Data.............................. 265
6.7.1 General ...................o .... ...... ....... 265
6.7.2 Analysis of Dynaflect Tuned Data................. 266








Page
6.7.2.1 Comparison of Measured and
Predicted Deflections................... 266
6.7.2.2 Development of Simplified Layer
Moduli Equations........................ 271
6.7.3 Analysis of FWD Tuned Data....................... 279
6.7.3.1 Comparison of Measured and
Predicted Deflections................... 279
6.7.3.2 Development of Prediction Equations..... 282

7 INTERPRETATION OF IN SITU PENETRATION TESTS.................. 288

7.1 General ................. ............................... 288
7.2 Soil Profiling and Identification....................... 289
7.3 Correlation Between ED and qc........................... 292
7.4 Evaluation of Resilient Moduli for Pavement Layers...... 302
7.4.1 General .......................................... 302
7.4.2 Correlation of Resilient Moduli with
Cone Resistance.................................. 304
7.4.3 Correlation of Resilient Moduli with
Dilatometer Modulus.............................. 308
7.5 Variation of Subgrade Stiffness with Depth.............. 312

8 PAVEMENT STRESS ANALYSES..................................... 316

8.1 General ................................................. 316
8.2 Short-Term Load Induced Stress Analysis................. 318
8.2.1 Design Parameters................................ 318
8.2.2 Comparison of Pavement Response and
Material Properties.............................. 319
8.2.3 Summary ....................................... 330

9 CONCLUSIONS AND RECOMMENDATIONS.............................. 331

9.1 Conclusions................ ..... ...................... 331
9.2 Recommendations ........................ ......... 334

APPENDICES

A FIELD DYNAFLECT TEST RESULTS................................. 338

B FIELD FWD TEST RESULTS ...................................... 354

C COMPUTER PRINTOUT OF CPT RESULTS............................ 379

D COMPUTER PRINTOUT OF DMT RESULTS............................ 406

E RECOVERED ASPHALT RHEOLOGY TEST RESULTS...................... 432

F RECOMMENDED TESTING AND ANALYSIS PROCEDURES FOR THE
MODIFIED DYNAFLECT TESTING SYSTEM............................ 456

G PARTIAL LISTING OF DELMAPS1 COMPUTER PROGRAM................. 463


viii








Page

REFERENCES.......................................................... 478

BIOGRAPHICAL SKETCH................................................. 491














LIST OF TABLES


Table Page

2.1 Summary of Deflection Basin Parameters...................... 35

2.2 Summary of Computer Programs for Evaluation of
Flexible Pavement Moduli from NDT Devices.................... 42

4.1 Range of Pavement Layer Properties........................... 73

4.2 Sensitivity Analysis of FWD Deflections for ti = 3.0 in. .... 86

4.3 Sensitivity Analysis of FWD Deflections for t t ,
and t3....................................................... 87

4.4 Sensitivity Analysis of FWD Deflections for E =
600 ksi and ti = 3.0 in. ....... ...... ......... .......... 89

4.5 Pavements with Dynaflect E1 Predictions Having More
Than 10 Percent Error................ ....... ............. 121

4.6 Pavements with Dynaflect E2 Predictions Having More
Than 10 Percent Error.......................... ............ 124

4.7 Comparison of Actual and Predicted E3 Values for
Varying t ................................................... 126

4.8 Prediction Accuracy of Equation 4.18--Error
Distribution as a Function of t ............................. 128

4.9 Prediction Accuracy of Equation 4.19--Error
Distribution as a Function of t ............................ 130

4.10 Pavements with E1 Predictions Having 15 Percent
or More Error......................... .............. ....... 131

4.11 Pavements with E2 Predictions Having 20 Percent
or More Errors............................................... 133

5.1 Characteristics of Test Pavements.......................... 138

5.2 Summary of Tests Performed on Test Pavements................. 143

5.3 Plate Loading Test Results................................... 148








5.4 Viscosity-Temperature Relationships of Recovered
Asphalt from Test Pavements.................................. 149

5.5 Temperature Measurements of Test Pavement Sections........... 152

6.1 Typical Dynaflect Deflection Data from Test Sections......... 170

6.2 Typical FWD Data from Test Sections......................... 172

6.3 Layer Moduli Using Dynaflect Prediction Equations............ 173

6.4 Layer Moduli Using FWD Prediction Equations.................. 176

6.5 Comparison Between NDT and Rheology Predictions
of Asphalt Concrete Modulus................................. 180

6.6 Comparison of Field Measured and BISAR Predicted
Dynaflect Deflections ....................................... 183

6.7 Dynaflect Tuned Layer Moduli for Test Sections............... 203

6.8 Predicted Deflections from Tuned Layer Moduli................ 204

6.9 Comparison of Field Measured and BISAR Predicted
FWD Deflections ..................................... ...... 206

6.10 FWD Tuned Layer Moduli for Test Sections..................... 227

6.11 Predicted FWD Deflections from Tuned Layer Moduli............ 228

6.12 Comparison Between Re-Calculated and Tuned FWD
Layer Moduli............................................... 230

6.13 Illustration of Nonuniqueness of Backcalculation of
Layer Moduli from NDT Deflection Basin...................... 232

6.14 Comparison of Deflections Measured at Different Load
Levels .................................. ... ........ ........ 235

6.15 Comparison Between Tuned Layer Moduli and Applied
FWD Load............. ...................................... 237

6.16 Comparison of the Asphalt Concrete Modulus for
the Test Sections.............................................. 254

6.17 Comparison of the Base Course Modulus for the
Test Sections................................................ 258

6.18 Comparison of the Subbase Modulus for the
Test Sections................................................ 259

6.19 Comparison of the Subgrade Modulus for the
Test Sections ................................................ 260








6.20 Ratios of Dynaflect Moduli to FWD Moduli for Test Sections... 262

6.21 Correlation Between Measured and Predicted FWD
(9-kip Load) Deflections ......... ..... ........... ....... 281

7.1 Relationship Between ED and qc for Selected Test
Sections in Florida....................................... 298

7.2 Correlation of NOT Tuned Base Course Modulus (E2)
to Cone Resistance .......................................... 305

7.3 Correlation of NDT Tuned Subbase Modulus (E3) to
Cone Resistance......................................... ..... 306

7.4 Correlation of NDT Tuned Subgrade Modulus (E4) to
Cone Resistance............................... ..... ............ 307

7.5 Relationship Between Resilient Modulus, ER and
Cone Resistance, qc......... ...... ... ..... ................... 308

7.6 Correlation of NDT Tuned Subbase Modulus to
Dilatometer Modulus.......................................... 309

7.7 Correlation of NDT Tuned Subgrade Modulus to
Dilatometer Modulus ................... ................. ..... 310

7.8 Relationship Between Resilient Modulus, ER and
Dilatometer Modulus, ED...................................... 311

7.9 Effect of Varying Subgrade Stiffness on Dynaflect
Deflections on SR 26A...................................... 314

8.1 Material Properties and Results of Stress Analysis
for SR 26B (Gilchrist County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis....................... 320

8.2 Material Properties and Results of Stress Analysis
for SR 24 (Alachua County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis....................... 321

8.3 Material Properties and Results of Stress Analysis
for US 441 (Columbia County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis....................... 322

8.4 Material Properties and Results of Stress Analysis
for SR 15C (Martin County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis....................... 323

8.5 Material Properties and Results of Stress Analysis
for SR 80 (Palm Beach County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis....................... 324

8.6 Summary of Pavement Stress Analysis at Low Temperatures...... 326








8.7 Effect of Increased Base Course Modulus on Pavement
Response on SR 80; a) Input Parameters for BISAR;
b) Pavement Stress Analysis.................................. 329


xiii














LIST OF FIGURES


Figure Page

2.1 Well-Designed Pavement Deflection History Curve.............. 18

2.2 Typical Annual Deflection History for a Flexible Pavement.... 20

2.3 Typical Output of a Dynamic Force Generator.................. .. 22

2.4 Schematic Diagram of Impulse Load-Response Equipment......... 25

2.5 Characteristic Shape of Load Impulse......................... 26

2.6 Comparison of Pavement Response from FWD and
Moving-Wheel Loads. a) Surface Deflections;
b) Vertical Subgrade Strains................................. 28

2.7 Empirical Interpretation of Dynaflect Deflection
Basin. a) Basin Parameters; b) Criteria...................... 34

2.8 Dynaflect Fifth Sensor Deflection-Subgrade
Modulus Relationship.................. ........ ......... 40

2.9 Temperature Prediction Graphs. a) Pavements More
Than 2 in. Thick; b) Pavements Equal to or Less
Than 2 in. Thick............................................ 50

3.1 Typical Dynamic Force Output Signal of Dynaflect............ 57

3.2 Configuration of Dynaflect Load Wheels and Geophones
in Operating Position........................................ 58

3.3 Schematic of FWD Load-Geophone Configuration and
Deflection Basin ............................................ 62

3.4 Schematic of Marchetti Dilatometer Test Equipment............ 69

4.1 Four-Layer Flexible Pavement System Model.................... 72

4.2 Dynaflect Modified Geophone Positions........................ 75

4.3 Typical Four-Layer System Used for the Sensitivity
Analysis ..................................................... 77








4.4 Effect of Change of El on Theoretical FWD (9-kip Load)
Deflection Basin................................... ........ 79

4.5 Effect of Change of E2 on Theoretical FWD (9-kip Load)
Deflection Basin ............................................. 80

4.6 Effect of Change of E3 on Theoretical FWD (9-kip Load)
Deflection Basin ........................................... 81

4.7 Effect of Change of E4 on Theoretical FWD (9-kip Load)
Deflection Basin............................................. 82

4.8 Effect of Change of tI on Theoretical FWD (9-kip Load)
Deflection Basin............................................. 83

4.9 Effect of Change of t2 on Theoretical FWD (9-kip Load)
Deflection Basin............................................. 84

4.10 Effect of Change of t3 on Theoretical FWD (9-kip Load)
Deflection Basin............................................. 85

4.11 Effect of Varying Subgrade Thickness on Theoretical FWD
(9-kip Load) Deflection Basin................................ 90

4.12 Variation in Dynaflect Deflection Basin with Varying
E2 and E3 Values with t, = 3.0 in. ......................... 93

4.13 Variation in Dynaflect Deflection Basin with Varying
E and E4 Values with t, = 3.0 in. .......................... 94

4.14 Relationship Between El and D1 D4 for t, = 3.0 in ....... 96

4.15 Relationship Between E1 and D1 D4 for t, = 6.0 in. ........ 97

4.16 Relationship Between El and Di D4 for tI = 8.0 in. ........ 98

4.17 Variation of Ki with ti for Different E2 Values.............. 100

4.18 Variation of K2 with t for Different E2 Values.............. 101

4.19 Relationship Between E2 and Di D4 for t, = 1.0 in. ........ 104

4.20 Comparison of E4 Prediction Equations Using Modified
Sensor 10 Deflections .................................... 109

4.21 Relationship Between E4 and FWD Deflections for Fixed
E E and E Values with t1 = 3.0 in. .................... 116

4.22 Relationship Between E4 and FWD Deflections for Fixed
El, E2, and E3 Values with t1 = 6.0 in. ..................... 117

5.1 Location of Test Pavements in the State of Florida........... 139








5.2 Layout of Field Tests Conducted on Test Pavements........... 141


6.1 Surface Deflection as


6.2

6.3

6.4

6.5

6.6

6.7

6.8

6.9

6.10

6.11

6.12

6.13

6.14


Surface

Surface

Surface

Surface

Surface

Surface

Surface

Surface

Surface

Surface

Surface

Surface

Surface


Load on SR 26A........... 155


Deflection

Deflection

Deflection

Deflection

Deflection

Deflection

Deflection

Deflection

Deflection

Deflection

Deflection

Deflection

Deflection


as a Function of Load on


Function

Function

Function

Function

Function

Function

Function

Function

Function

Function

Function

Function

Function


6.15 Comparison of Measured and Predicted Dynaflect
Deflections for SR 26A--M.P. 11.912.........................

6.16 Comparison of Measured and Predicted Dynaflect
Deflections for SR 26B--M.P. 11.205..........................

6.17 Comparison of Measured and Predicted Dynaflect
Deflections for SR 26C--M.P. 10.168..........................

6.18 Comparison of Measured and Predicted Dynaflect
Deflections for SR 24--M.P. 11.112..........................

6.19 Comparison of Measured and Predicted Dynaflect
Deflections for US 301--M.P. 11.112.........................

6.20 Comparison of Measured and Predicted Dynaflect
Deflections for I-10A--M.P. 14.062...........................

6.21 Comparison of Measured and Predicted Dynaflect
Deflections for I-10B--M.P. 2.703............................

6.22 Comparison of Measured and Predicted Dynaflect
Deflections for I-10C--M.P. 32.071...........................


Load

Load

Load

Load

Load

Load

Load

Load

Load

Load

Load

Load


SR 26C...........

SR 24...........

US 301...........

US 441............

I-10A............

I-10B ............

I-10C.............

SR 715............

SR 12............

SR 15C ...........

SR 26B............

SR 15A............

SR 15B...........


156

157

158

159

160

161

162

163

164

165

166

167

168








6.23 Comparison of Measured and Predicted Dynaflect
Deflections for SR-15A--M.P. 6.549..........................

6.24 Comparison of Measured and Predicted Dynaflect
Deflections for SR 15B--M.P. 4.811...........................

6.25 Comparison of Measured and Predicted Dynaflect
Deflections for SR 715--M.P. 4.722............................

6.26 Comparison of Measured and Predicted Dynaflect
Deflections for SR 715--M.P. 4.720............................

6.27 Comparison of Measured and Predicted Dynaflect
Deflections for SR 12--M.P. 1.485.............................

6.28 Comparison of Measured and Predicted Dynaflect
Deflections for SR 80--Section 1............................

6.29 Comparison of Measured and Predicted Dynaflect
Deflections for SR 80--Section 2........... .................

6.30 Comparison of Measured and Predicted Dynaflect
Deflections for SR 15C--M.P. 0.055...........................

6.31 Comparison of Measured and Predicted Dynaflect
Deflections for SR 15C--M.P. 0.065...........................


6.32 Comparison of Measured and
(Normalized to 1-kip Load)

6.33 Comparison of Measured and
(Normalized to 1-kip Load)

6.34 Comparison of Measured and
(Normalized to 1-kip Load)

6.35 Comparison of Measured and
(Normalized to 1-kip Load)

6.36 Comparison of Measured and
(Normalized to 1-kip Load)

6.37 Comparison of Measured and
(Normalized to 1-kip Load)

6.38 Comparison of Measured and
(Normalized to 1-kip Load)

6.39 Comparison of Measured and
(Normalized to 1-kip Load)

6.40 Comparison of Measured and
(Normalized to 1-kip Load)


Predicted FWD Deflections
for SR 26A--M.P. 11.912........... 208

Predicted FWD Deflections
for SR 26B--M.P. 11.205........... 209

Predicted FWD Deflections
for SR 26C--M.P. 10.168........... 210

Predicted FWD Deflections
for SR 26C--M.P. 10.166........... 211

Predicted FWD Deflections
for SR 24--M.P. 11.112............ 212

Predicted FWD Deflections
for US 301--M.P. 21.585........... 213

Predicted FWD Deflections
for US 441--M.P. 1.236........... 214

Predicted FWD Deflections
for I-1OA--M.P. 14.062............ 215

Predicted FWD Deflections
for I-1OB--M.P. 2.703............. 216


xvii


194


195


196


197


198


199


200


201


202








6.41 Comparison of Measured and
(Normalized to 1-kip Load)

6.42 Comparison of Measured and
(Normalized to 1-kip Load)

6.43 Comparison of Measured and
(Normalized to 1-kip Load)

6.44 Comparison of Measured and
(Normalized to 1-kip Load)

6.45 Comparison of Measured and
(Normalized to 1-kip Load)

6.46 Comparison of Measured and
(Normalized to 1-kip Load)

6.47 Comparison of Measured and
(Normalized to 1-kip Load)

6.48 Comparison of Measured and
(Normalized to 1-kip Load)

6.49 Comparison of Measured and
(Normalized to 1-kip Load)


Predicted FWD Deflections
for I-10C--M.P. 32.071............ 217

Predicted FWD Deflections
for SR 15A--M.P. 6.546............ 218

Predicted FWD Deflections
for SR 15A--M.P. 6.549............ 219

Predicted FWD Deflections
for SR 15B--M.P. 4.811............ 220

Predicted FWD Deflections
for SR 715--M.P. 4.722............ 221

Predicted FWD Deflections
for SR 715--M.P. 4.720........... 222

Predicted FWD Deflections
for SR 12--M.P. 1.485............. 223

Predicted FWD Deflections
for SR 15C--M.P. 0.055............ 224

Predicted FWD Deflections
for SR 15C--M.P. 0.065........... 225


6.50 Comparison of Measured NDT Deflection Basins on SR 26C--
M.P. 10.166.................................................. 239

6.51 Comparison of Measured NDT Deflection Basins on US 301--
M.P. 21.585.................................................. 240

6.52 Comparison of Measured NDT Deflection Basins on US 441--
M.P. 1.237................................................... 241

6.53 Comparison of Measured NDT Deflection Basins on SR 12--
M.P. 1.485 ..................... ............ ........ ....... 242

6.54 Comparison of Measured NDT Deflection Basins on SR 26B--
M.P. 11.205.................................................. 243

6.55 Comparison of Measured NDT Deflection Basins on SR 15A--
M.P. 6.549................................................... 244

6.56 Comparison of Measured NDT Deflection Basins on SR 715--
M.P. 4.722................................................... 245

6.57 Comparison of Measured NDT Deflection Basins on SR 26A--
M.P. 11.912...................... ......... .................... 246

6.58 Comparison of Measured NDT Deflection Basins on SR 24--
M.P. 11.112....................................................... 247


xviii








6.59 Comparison of Measured NDT Deflection Basins on I-10A--
M.P. 14.062.................................................. 248

6.60 Comparison of Measured NDT Deflection Basins on I-10B--
M.P. 2.703................................................... 249

6.61 Comparison of Measured NDT Deflection Basins on I-1OC--
M.P. 32.071................................................. 250

6.62 Comparison of Measured NDT Deflection Basins on SR 15B--
M.P. 4.811.................................................... 251

6.63 Comparison of Measured NDT Deflection Basins on SR 15C--
M.P. 0.055....... ......... ............................... 252

6.64 Relationship Between Asphalt Concrete Modulus, E and
Mean Pavement Temperature.................................... 256

6.65 Comparison of Dynaflect and FWD Tuned Layer Moduli........... 261

6.66 Relationship Between Predicted and Measured Dynaflect
Modified Sensor 1 Deflections.............................. 267

6.67 Relationship Between Predicted and Measured Dynaflect
Modified Sensor 4 Deflections.............................. 268

6.68 Relationship Between Predicted and Measured Dynaflect
Modified Sensor 7 Deflections............................... 269

6.69 Relationship Between Predicted and Measured Dynaflect
Modified Sensor 10 Deflections .............................. 270

6.70 Relationship Between E12 (Using Equation 6.8) and D D .... 274


6.71

6.72

6.73

6.74

6.75

7.1

7.2

7.3

7.4

7.5


Relationship Between E12 (Using Equation 6.9) and D1 D ....

Relationship Between E3 and D4 D ..........................

Relationship Between E4 and D o..............................

Simplified Flow Chart of DELMAPS1 Program ...................

Relationship Between E4 and FWD D6 and D7....................

Variation of qc and FR with Depth on SR 12...................

Variation of ED and KD with Depth on SR 12...................

Variation of qc and ED with Depth on SR 26A..................

Variation of qc and ED with Depth on SR 26C..................

Variation of qc and ED with Depth on US 301...................


275

276

277

280

287

290

291

293

294

295


xix








7.6 Variation of qc and ED with Depth on US 441.................. 296

7.7 Variation of qc and ED with Depth on SR 12................... 297

7.8 Correlation of ED with qc.................................. 300














Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy


EVALUATION OF LAYER MODULI IN FLEXIBLE PAVEMENT SYSTEMS
USING NONDESTRUCTIVE AND PENETRATION TESTING METHODS

By

Kwasi Badu-Tweneboah

December 1987


Chairman: Byron E. Ruth
Major Department: Civil Engineering


A research study was conducted to develop procedures for the eval-

uation of layer moduli in flexible pavement systems using in situ non-

destructive (NDT) and penetration tests. The Bitumen Structure Analysis

in Roads (BISAR) elastic layer computer program was used to simulate

Dynaflect and Falling Weight Deflectometer (FWD) load-deflection

response for typical flexible pavements in the state of Florida. A

field testing program consisting of Dynaflect, FWD, cone penetration,

Marchetti Dilatometer, and plate bearing tests was conducted on fifteen

pavement sections in the state of Florida. Cores of the asphalt con-

crete pavement were collected for laboratory low-temperature rheology

tests. This provided a reliable and effective method of predicting the

asphalt concrete modulus.

A modified Dynaflect geophone configuration and simplified layer

moduli prediction equations which allow a layer-by-layer analysis of

Dynaflect deflection measurements were developed. Multiple linear








regression equations with relatively good prediction accuracy were

obtained from analyses of FWD deflection data. Different layer moduli

values were obtained from the Dynaflect and FWD deflection basins for

the various test sections.

The penetration tests provided means for identifying the soils and

also assessing the variability in stratigraphy of the test sites. Good

correlations between cone resistance, qc, and dilatometer modulus, ED,

for sandy soils and soils above the water table were obtained. Pavement

layer moduli determined from NDT data were regressed to qc and ED for

the various layers in the pavement. The correlations were better with

qc than with ED, and also for the base and subbase layers than the
variable subgrade layer. The penetration tests can be used to supple-

ment NDT evaluation of pavements especially in locating zones of weak-

ness in the pavement or underlying subgrade soils.

The effects of moisture, temperature, and the properties of the

asphalt binder on the performance and response characteristics of

flexible pavements were demonstrated using short-term load-induced

stress analyses on five of the test pavements.


xxii














CHAPTER 1
INTRODUCTION


1.1 Background

In recent years, the use of layered elastic theory to evaluate and

design highway and airfield pavements has become increasingly popular

vis-a-vis existing empirical methods. The elastic layer approach, also

called mechanistic analysis, has obvious advantages over empirical

methods which are based on the correlation between the maximum deflec-

tion under a load and pavement performance. It allows a rational eval-

uation of the mechanical properties of the materials in the pavement

structure.

An essential part of the mechanistic process is determining real-

istic elastic modulus values for the various layers in the pavement

structure. Current methods to determine the modulus of pavement

materials include various laboratory testing procedures, destructive

field tests, and in situ nondestructive tests (NDT). The problems

associated with the simulation of in situ conditions such as moisture

content, density, loading history and rate of loading of the pavement in

the laboratory are well known and recognized. Destructive field tests,

such as the California bearing ratio (CBR) and plate tests are expen-

sive, time-consuming, and generally involve trenching the pavement,

which has to be subsequently repaired.

Nondestructive testing generally involves applying some type of

dynamic load or shock waves to the surface of the pavement and measuring





2

the response of the pavement. Among such methods are various seismic

techniques and surface dynamic loading tests. The basic concept behind

seismic or wave propagation techniques is the use of vibratory loads and

the resulting identification and measurement of the waves that propagate

through the media. These methods have not gained wide acceptance,

partly because of the relative sophistication required in field opera-

tion and in the interpretation of test data.

Surface loading tests generally involve the use of measured surface

deflections to backcalculate the moduli of the pavement layers. Among

the numerous types of devices used are the Dynaflect, Road Rater, and

Falling Weight Deflectometer (FWD). Such techniques have gained wide-

spread popularity partly because they are simple, time-efficient, and

relatively inexpensive, and partly because of their ability to model

real traffic load intensities and durations. However, there are no

direct theoretical solutions available at present to evaluate the

various layer moduli of the pavement from the measured surface deflec-

tions which generally represent the overall combined stiffness of the

layers. Instead, computerized iterative solutions, graphical solutions,

and nomographs are currently used to backcalculate pavement layer

moduli. All these techniques basically consist of using linear-elastic

programs in which calculated versus measured deflections are matched by

adjustment of pavement layer moduli E-values.

Those methods which are based on iterative procedures may need a

large amount of computer time to arrive at the correct moduli for the

pavement materials. In some cases, the required computer may not be

accessible (e.g., for direct field evaluation) or the expertise required

may not be available. Also, due to the inherent problems associated








with iteration methods, unique solutions cannot be guaranteed and dif-

ferent sets of elastic moduli can produce results that are within the

specified (deflection or layer moduli) tolerance. In addition, elastic

layer programs generally assume an average (composite) modulus for the

subgrade layer without regard to the variation of the underlying soil

properties with depth. For sites with highly variable subgrade stiff-

nesses, it becomes very difficult to analytically match measured deflec-

tion basins using a composite modulus for the subgrade layer. There-

fore, there is a need to find a more viable way to determine the E-

values of pavement materials for a rational mechanistic analysis.

Recent advances in in situ testing in geotechnical engineering have

led to improvements in the determination of important soil parameters

such as strength and deformation moduli. Unfortunately, the application

of the improved techniques to evaluate or design pavements has been very

limited. The Marchetti Dilatometer test (DMT) offers significant pro-

mise for providing a reliable and economical method for obtaining in

situ moduli of pavement layers, especially of the subgrade. There is

also the potential of determining in situ moduli from the cone penetra-

tion test (CPT) since several correlations between different deformation

moduli and cone resistance have been reported in the geotechnical liter-

ature. The CPT and DMT provide detail information on site stratifica-

tion, identification, and classification of soil types which makes them

attractive tests for the evaluation and design of pavements.


1.2 Study Objectives

The primary objective of this study is to develop procedures for

the evaluation of material properties in layered pavement systems using








NDT deflection measurements. This includes the development of layer

moduli prediction equations from NOT deflections.

The secondary objective is to evaluate the feasibility of deter-

mining the modulus of pavement layers and underlying subgrade soils

using in situ penetration tests and to evaluate the possible effects of

stratigraphy, water table and underlying subgrade soil properties on

surface deflections obtained from NDT.


1.3 Scope of Study

This investigation is primarily concerned with predicting pavement

layer moduli from nondestructive and penetration tests. It is hoped

that this will lead to improvements in the determination of layer moduli

for mechanistic evaluation and design of flexible pavement systems. The

initial part of the study consisted of developing layer moduli predic-

tion equations from computer-simulated Dynaflect and FWD deflection

data. A modified Dynaflect load-sensor configuration was utilized in

the theoretical analysis.

Field tests were conducted on fifteen pavement sections in the

state of Florida. Tests conducted consisted of Dynaflect, FWD, elec-

tronic CPT, DMT, and plate bearing tests. Also, cores of asphalt con-

crete pavement were collected for laboratory low-temperature rheology

tests. These were used to establish viscosity-temperature relationships

of the recovered asphalts which were then used to predict the moduli of

the asphalt concrete layers.

The field measured NDT data were analyzed to establish layer moduli

values for the test pavement sections. The layer moduli derived from

the Dynaflect and FWD nondestructive tests were compared with each other








and correlated to the results of the penetration tests. Simplified

layer moduli prediction equations were developed for the modified

Dynaflect testing system.

Five of the test pavements were selected for short-term load

induced stress analysis using actual wheel loadings and low temperature

conditions. The effects of age-hardened asphalt, soil type, moisture

content, weak base course and subgrade characteristics on layer stiff-

nesses were evaluated to assess the stress-strain response of the

different pavements.














CHAPTER 2
LITERATURE REVIEW

2.1 Introduction

A mechanistic pavement design procedure consists of analyzing the

pavement on the basis of the predicted structural response (stresses,

strains, and deflections) of the system to moving vehicle loads. Pave-

ment-layer thicknesses (surface, base, and subbase) are selected such

that the predicted structural response would be acceptable for some

desired number of load repetitions and under existing environmental

conditions. This approach is consistent with the conditions established

by Yoder and Witczak that

for any pavement design procedure to be completely
rational in nature total consideration must be
given to three elements. These elements are
(1) the theory used to predict failure or distress
parameter, (2) the evaluation of the pertinent
material properties necessary for the theory
selected, and (3) the determination of the rela-
tionship between the magnitude of the parameter in
question to the failure or performance level
desired.
(133, p. 24-25)

In the last several years, a concentrated effort has been made to

develop a more rational analysis and design procedures for pavements.

For flexible or asphalt concrete pavements, layered (7,19,20) and finite

element (33) theories have been used with some success to analyze pave-

ment response. The use of either theory requires that the materials

that make up the pavement be suitably characterized. Layered and finite

element theories use Young's modulus and Poisson's ratio to characterize








the stress-strain behavior of pavement materials. While some success

has been made in developing design theories, their limitations must also

be understood. Most pavement material responses differ from the assump-

tions of the theory used, and the "true" values of stress, strain or

deflection may differ from the predicted levels. However, a great deal

of engineering reliance is being placed upon the use of multilayered

linear elastic theory (133) in which the elastic modulus is an important

input parameter.

The thesis presented here is aimed at determining realistic modulus

values for the structural characterization of flexible pavement systems

using layered elastic theory. This chapter reviews previous work on

layer-system solutions, the methods of determining the elastic modulus

of pavement-layer materials, and some important factors influencing the

modulus of elasticity.


2.2 Elastic-Layer Theory

2.2.1 General

The type of theory used in the analysis of a pavement-layered

system is generally distinguished by reference to three properties of

material behavior response (133). They are the relationship between

stress and strain (linear or nonlinear), the time dependency of strain

under constant stress level (viscous or nonviscous), and the degree to

which the material can rebound or recover strain after stress removal

(plastic or elastic). These concepts have been clearly elucidated by

Yoder and Witczak (133). Considerable effort has been expended to

analyze pavement response using the above concepts. For example, the

finite element method (33), elastic-layer analysis based on Burmister's








theory (18,19,20) and the visco-elastic layer analysis (7) are all based

on these three properties of material behavior. As previously noted,

the type of theory most widely used at the present time is the

multilayered linear elastic theory. The development of multilayered

elastic solutions is presented below.

2.2.2 One-Layer System

The mathematical solution of the elastic problem for a concentrated

load on a boundary of a semi-infinite body was given by Boussinesq in

1885 (13). His solution was based on the assumption that the material

is elastic, homogeneous, and isotropic. Boussinesq's equation (133;

p. 28) indicates that the vertical stress is dependent on the depth and

radial distance and is independent of the properties of the transmitting

medium. There are several limitations of this solution when applied to

pavements. For example, the type of surface loading usually encountered

in flexible pavements is not a point load but a load which is distri-

buted over an elliptical area (133).

Further work with the Boussinesq equation expanded the solutions

for a uniformly distributed circular load by integration. Newmark (85)

developed influence charts for determination of stresses in elastic soil

masses. The charts are widely used in foundation work. Love (60) used

the principle of superposition to extend Boussinesq's solution to solve

for a distributed load on a circular area. Foster and Ahlvin (36)

presented charts for computing vertical stress, horizontal stress, and

vertical elastic strains due to circular loaded plates, for a Poisson's

ratio of 0.5. This work was subsequently refined by Ahlvin and Ulery

(4) to allow for an extensive solution of the complete pattern of








stress, strain, and deflection at any point in the homogeneous mass for

any value of Poisson's ratio.

Although most asphalt pavement structures cannot be regarded as

being homogeneous, the use of these solutions are generally applicable

for subgrade stress, strain and deflection studies when the modular

ratio of the pavement and subgrade is close to unity. This condition is

probably most exemplified by conventional flexible granular base/subbase

pavement structures having a thin asphalt concrete surface course (133).

Normally, in deflection studies for this type of pavement, it is assumed

that the pavement portion (above the subgrade) does not contribute any

partial deflection to the total surface deflection.

2.2.3 Two-Layer System

Since Boussinesq's solution was limited to a one-layer system, a

need for a generalized multiple-layered system was recognized.

Moreover, typical flexible pavements are composed of layers such that

the moduli of elasticity decrease with depth (133). The effect is to

reduce stresses and deflections in the subgrade from those obtained for

the ideal homogeneous case.

Burmister (18,19,20) established much of the ground work for the

solution of elastic layers on a semi-infinite elastic layer. Assuming a

continuous interface, he first developed solutions for two layers, and

he conceptually established the solution for three-layer systems. The

basic assumption made was full continuity between the layers, which

implies that there is no slippage between the layers. Thus, Burmister

assumed that the strain in the bottom of one layer is equal to the

strain at the top of the next layer, but the stress levels in the two








layers will differ as a function of the modulus of elasticity of each

layer.

2.2.4 Three-Layer System

Although Burmister's work provided analytical expressions for

stresses and displacements in two- and three-layer elastic systems, Fox

(38) and Acum and Fox (2) produced the first extensive tabular summary

of normal and radial stresses in three-layer systems at the intersection

of the plate axis with the layer interfaces. Jones (52) and Peattie

(89) subsequently expanded these solutions to a much wider range of

solution parameters. Tables and charts for the various solutions can be

found in Yoder and Witzcak (133) and Poulos and Davis (92). It should

be noted that the figures and tables for stresses and displacements have

been developed, respectively, for Poisson's ratios of 0.5 and 0.35, for

all layers, and on the assumption of perfect friction at all interfaces.

Hank and Scrivner (42) presented solutions for full continuity and

zero continuity between layers. Their solutions indicate that the

stresses in the top layer for the frictionless case (zero continuity)

are larger than the stresses presented for the case of full continu-

ity. In an actual pavement, the layers are very likely to develop full

continuity; hence, full continuity between layers should probably be

assumed.

Schiffman (100) extended Burmister's solution to include shear

stress at the surface for a three-layer system. Mehta and Veletsos (73)

developed a more general elastic solution to a system with any number of

loads. They extended the solution presented by Burmister to include

tangential forces as well as normal forces.








2.2.5 Multilayered or N-Layered Systems

A general analysis of a multilayered system under general condi-

tions of surface loading or displacement, or both was developed indepen-
\
dently by Schiffman (99) and Verstraeten (125). Schiffman (99) con-

sidered the general solutions for stresses and displacements due to non-

uniform surface loads, tangential surface loads, and slightly inclined

loads, but no numerical evaluations were presented. Verstraeten (125)

presented a limited analysis of the four-layered elastic problem. He

first derived expressions for the stresses and displacements for the

general case and performed numerical calculations for the particular

case of four-layered systems with continuous interfaces. The analysis

by Verstraeten included not only a uniform normal surface stress, but

also two types of surface shear stresses: (1) uniform one-directional

shear stress and (2) a uniform centripetal shear stress.

Recently, the Chevron Research Corporation (74) and the Shell Oil

Company (32) have developed computer programs for multilayered solutions

of the complete state of stress and strain at any point in a pavement

structure. Notable programs of interest are the BISTRO and BISAR pro-

grams by Shell (32), and the various forms of CHEVRON program by the

Chevron Research Corporation. These computer solutions are essentially

an extension of Burmister's work that permit the analysis of a structure

consisting of any number of layers supported by a semi-infinite sub-

grade, and under various loading conditions. In reality, it is only the

CHEVRON N-LAYER program (74) which is suitable for any number of layers.

All the others are restricted to a maximum number of layers. BISAR

(32), for example, can handle nine pavement sub-layers of known thick-

nesses plus the semi-infinite subgrade or bottom layer.








Several investigators have verified the validity of Burmister's

theory with the actual mechanical response of flexible pavements.

Foster and Fergus (37) have compared the results of extensive test

measurements on a clayey silt subgrade to theoretical stresses and

deflections based on Burmister's theory and reported satisfactory

agreement. The discrepancy between actual and theoretical stresses and

displacements can be mainly attributed to the assumption of a homoge-

neous and isotropic material, the rate-dependent behavior of some

materials such as asphalt, and a circular loaded area representing the

wheel load. Nielsen (86) has made a detailed study in this area. His

review of the magnitude and distribution of stresses within a layered

system revealed regions where vertical and shearing stresses were criti-

cal. His studies concluded that the layered-elastic theory is in every

respect consistent and that it is possible to establish fundamental

patterns of pavement performance based upon this theory. This suggests

that the elasticity theory could be used more extensively.

The moderators of the Fifth International Conference on the Struc-

tural Design of Asphalt Pavements (76) concluded that the use of linear

elastic theory for determining stresses, strains, and deflections is

reasonable as long as the time-dependent and nonlinear response of the

paving materials are recognized. They noted that the papers presented

at the conference confirmed that multilayer elastic models generally

yield good results for asphalt concrete pavements.

Barksdale and Hicks (10) compared the multilayered elastic approach

with the finite element method and recommended the use of the former for

pavement analysis since only two variables are needed (modulus and

Poisson's ratio). Pichumani (91) used the BISAR computer program for








the numerical evaluation of stresses, strains, and displacements in a

linear elastic system. He demonstrated that predicted vertical, radial,

and shear stress distribution were noticeably affected by slight changes

in the assumed material moduli. Pichumani's work demonstrated the need

for proper and extensive material characterization.


2.3 Material Characterization Methods

2.3.1 General

The use of multilayered elastic theory has provided the engineer

with a rational and powerful basis for the structural design of pave-

ments, for pavement evaluation, and for overlay design. In this theory,

the complete stress, strain, and displacement pattern for a material

needs only two material properties for characterization, namely the

elastic modulus (E), and the Poisson's ratio (u). Generally, the effect

of Poisson's ratio is not as significant as the effect of the modulus

(133, pp. 280-282; 88; 59, p. 160). Thus, E is an important input

parameter for pavement analysis using the layer theory.

Many tests have been devised for measuring the elastic modulus of

paving materials. Some of the tests are arbitrary in the sense that

their usefulness lies in the correlation of their results with field

performance. To obtain reproducible results, the procedures must be

followed at all times. The various possible methods for determining the

elastic modulus of pavement materials include laboratory tests, destruc-

tive field tests, and in situ nondestructive tests.

Laboratory methods consist of conducting laboratory testing on

either laboratory-compacted specimens or undisturbed samples taken from

the pavement. Yoder and Witzcak (133) describe various laboratory








testing methods with the diametral resilient modulus test (8), indirect

tension test (9), and the triaxial resilient modulus test (1) being the

most popular. The latter is useful for unbound materials such as base

course and subgrade soils, while the other two are for bound materials

like asphalt concrete and stabilized materials. Monismith et al. (78)

studied the various factors that affect laboratory determination of the

moduli of pavement systems. They concluded that

. it is extremely difficult to obtain the same
conditions that exist in the road materials (mois-
ture content, density, etc.) and the same loadings
(including loading history) in the laboratory as
will be encountered in situ. . Thus the best
method of analysis would appear to be to determine
an equivalent modulus which when substituted into
expressions derived from the theory of elasticity,
will give a reasonable estimate of the probable
deformation.

(78, p. 112)

Destructive field tests include, among others, several different

plate load tests (8) and the California Bearing Ratio (CBR) test (8).

These tests require trench construction and subsequent repair of the

pavement, and like the laboratory test methods, usually call for an

elaborate and costly testing program. The delays associated with such

programs are prohibitive especially for routine pavement analysis

studies.

The third method involves the extraction of pavement-layer proper-

ties from in situ nondestructive testing (NDT). NDT methods have gained

wide popularity in the last few decades because of their ability to

collect data at many locations on a highway or airfield in a short

time. Therefore, a great deal of research effort has been concentrated

on this area. A review of the various types of NOT equipment available

and the associated interpretation tools is presented below.








2.3.2 State-of-the-Art of Nondestructive Testing

2.3.2.1 General. Nondestructive testing (NDT) consists of making

nondestructive measurements on a pavement's surface and inferring from

the responses the in situ characteristics related to the structural ade-

quacy or loading behavior (79). Among such methods are various seismic

techniques (associated with time measurements) and surface loading tests

(associated with deflection measurements). The latter is more popular

because surface deflection is the most easily measured structural

response of a pavement. The idea of using deflection measurements to

evaluate the structural integrity of pavements dates back to 1938 when

the California Division of Highways used electrical gages implanted in

roadways to measure displacements induced by truck loads (134).

There are currently several NDT procedures being used for pavement

investigations. Each of the procedures can be placed into one of the

following four general classes:

1. Static force-deflection,

2. Steady-state vibratoryy) dynamic force-deflection,

3. Dynamic impulse force-deflection, and

4. Wave propagation.

As their names imply, the first three categories are associated with

deflection measurements due to application of force or load. The fourth

category--wave propagation--measures the length and velocity of force-

induced waves traveling through the pavement system. A detailed

description and evaluation of many of these NDT devices and procedures

has been presented by Bush (21), Moore et al. (79), and Smith and Lytton

(109). In the following pages, a brief description of the principles

involved and equipment available for each class will be presented.








2.3.2.2 Static Deflection Procedures. Measurement systems that

determine the pavement response to slowly applied loads are generally

termed static deflection systems. In these systems, the loading.methods

may consist of slowly driving to or from a measurement point with a

loading vehicle, or by reacting against a stationary loading frame. The

maximum resilient or recoverable deflection at the surface of the pave-

ment is measured.

The most commonly used equipment in this class is the various forms

of the Benkelman beam devices. Other equipment that had been used

include the plate bearing test (8), Dehlen Curvature Meter, Traveling

Deflectometer, Lacroix-LCPC Deflectograph, and the French Curviameter.

The last three devices are essentially automation of the Benkelman beam

principle. The French Curviameter, for example, measures both the

deflection and curvature of the pavement, under an 18-kips rear axle

load, with tire pressure maintained at 100 psi (24). Most of the

automated devices have been used widely in Europe and other parts of the

world, except for the Traveling Deflectometer which was built for the

California Department of Transportation and has been in use by that

agency for several years (109).

The major advantages of the static deflection procedures are the

simplicity of the equipment and the large amount of data that has been

accumulated with these devices. The most serious problem with this type

of measurement technique is the difficulty in obtaining an immovable

reference point for making the deflection measurements. This makes the

absolute accuracy of this type of procedure questionable. In addition,

since most of the devices generally measure a single (maximum)








deflection only, it is impossible or difficult to determine the shape

and size of the deflection basin.

In spite of their shortcomings, the large amount of data developed

using static deflection techniques makes such procedures an important

part of structural pavement evaluation. For this reason, several inves-

tigators have attempted deflection comparison and correlations from the

static devices with those measured by the dynamic devices. The

following is a list of concepts developed from the deflection response

of a pavement using static NDT (79):

1. For adequately designed pavements, the deflections during the same

season of the year remain approximately constant for the life of the

pavement.

2. There is a tolerable level of deflection that is a function of

traffic type, volume and the structural capacity of the pavement as

determined by the pavement's structural section. This tolerable

level of deflection can be established through the use of fatigue

characteristics of the pavement structure.

3. Overlaying of a pavement will reduce its deflection. The thickness

required to reduce the deflection to a tolerable level can be esta-

blished.

4. The deflection history of a well-designed pavement undergoes three

phases in its behavior (71). A typical curve representing these

phases is shown in Figure 2.1.

a. In the initial phase, immediately after construction, the pave-

ment structure consolidates and the deflection shows a slight

decrease.
































TRAFFIC---


Figure 2.1


Well-Designed Pavement Deflection History Curve (71)








b. During the functional or service phase, the pavement carries the

anticipated traffic without undue deformation and the deflection

remains fairly constant or shows a slight decrease.

c. The failure phase occurs as a result of both traffic and envi-

ronmental factors. In this phase the deflection increases

rapidly and there is a rapid deterioration resulting in failure

of the pavement structure.

5. The deflection history of a pavement system varies throughout the

year due to the effects of frost, temperature, and moisture. A

typical annual deflection history of a pavement subjected to frost

action, as shown in Figure 2.2, can be divided into the following

four periods (103):

a. The period of deep frost when the pavement is the strongest.

b. The period during which the frost is beginning to disappear from

the pavement structure. During this period, the deflection

rises rapidly.

c. The period during which the water from the melting frost leaves

the pavement structure and the deflection begins to drop.

d. The period during which the deflection levels off with a general

downward trend as the pavement structure continues to slowly dry

out.

6. For a given flexible pavement structure it is generally known that

the magnitude of the deflection increases with an increase in the

temperature of the bituminous surfacing material. This is due to a

decrease in the stiffness of the bituminous surfacing. The effect

of temperature varies with the stiffness of the underlying layers.

As the stiffness of the underlying layers increases, the effect of















Cn 32

Period of : Period of Slow
Deep Frost ) Strength Recovery
C)


0


,.J
LL




--- I I 1 I I I I
DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV
MONTH


Figure 2.2 Typical Annual Deflection History for a Flexible Pavement (103)








an increase in temperature on deflection of the total structure

decreases.

2.3.2.3 Steady-State Dynamic Force-Deflection. Essentially, all

steady-state dynamic deflection measurement systems induce a steady

state sinusoidal vibration in the pavement with a dynamic force genera-

tor. The dynamic force is superimposed on the weight of the force gen-

erator, resulting in a variation of force with time as shown in Figure

2.3. The magnitude of the peak-to-peak dynamic force is less than twice

the static force to insure continuous contact of the vibrator with the

pavement (79). This means there must always be some amount of dead

weight or static force applied. As the dynamic peak-to-peak loading is

increased, this preload must also be increased (109).

Deflections are usually measured with inertial motion sensors. For

pure sinusoidal motions at any fixed frequency, the output of such sen-

sors is directly proportional to deflection. Thus, to measure deflec-

tion it is only necessary to determine the calibration factor (output

per unit of deflection) for the measurement frequency. In general,

either an accelerometer or a velocity sensor may be used to measure

deflections. The latter type is commonly called a geophone and is the

type normally employed in dynamic deflection measurements.

There are several different types of steady-state dynamic deflec-

tion equipment that are currently being used for nondestructive struc-

tural evaluation of pavements. Only two of them have been used exten-

sively and are available commercially--the Dynaflect and the Road

Rater. The others have been designed and constructed by agencies

involved in pavement research, namely the U.S. Army Waterways Experiment

Station (WES), the Federal Highway Administration (FHWA), the Illinois


































N)

o
0
U.








TIME -+



Figure 2.3 Typical Output of a Dynamic Force Generator (79)








Department of Transportation, and the Koninklijke/Shell Laboratorium,

Amsterdam, Holland. Detailed descriptions of the various vibratory

equipment can be found in References 21, 79, 109. The Dynaflect was

used in this study and a description of the device will be given later

in this report.

When one considers the difficulty in obtaining a reference point

for deflection measurements, the real advantage of a steady-state dyna-

mic deflection measurement system becomes apparent. An inertial refer-

ence can be employed to measure dynamic deflections. That is, the mag-

nitude of the deflection change (the peak-to-peak value) can be compared

directly to the magnitude of the dynamic force change (peak-to-peak

value). For a given value of dynamic force, the lower the deflection,

the stiffer the pavement is (79).

Although the dynamic response of a pavement system approaches its

static (or elastic) response at low frequencies, exactly what value of

driving frequency is low enough to determine the elastic characteristics

of a pavement is somewhat questionable. As the driving frequency be-

comes low it becomes difficult to generate dynamic forces and the output

of inertial motion sensors becomes very small. These factors combine to

make it difficult to obtain accurate low frequency dynamic deflection

measurements (79). Other technical limitations of vibratory equipment

include the need for a heavy static preload for the heavier devices and

the nonuniform loading configurations (109).

The deflection measurements that result represent the stiffness of

the entire pavement section. Although some significant accomplishments

have been made in separating the effects of major parts of the pavement

structure, the separation of the effects of all of the various








components of the structure with deflection basin measurements has not

yet been accomplished (63). The study presented herein is aimed at

developing an approach that would allow a layer-by-layer analysis of the

Dynaflect vibratory deflection basin.

2.3.2.4 Dynamic Impact Load Response. Essentially, all impact

load testing methods deliver some type of transient force impulse to the

pavement surface and measure its transient response. The equipment uses

a weight that is lifted to a given height on a guide system and is then

dropped. Figure 2.4 illustrates this schematically. By varying the

mass of the falling weight or the drop height or both, the impulse force

can be varied.

The width or duration of the loading pulse (loading time) is

controlled by the buffer characteristics, Figure 2.4, and it closely

approximates a half-sine wave (Figure 2.5). The duration of the force

is nominally 25-30 msec, Figure 2.5, which approximates the load

duration of a vehicle traveling 40 to 50 mph (123). The peak magnitude

of the force can be determined approximately by equating the initial

potential energy of the system to the stored strain energy of the

springs (buffer system) when the mass is momentarily brought to rest

(11,105). Thus


F = (2Mghk)1/2 Eqn. 2.1


where

M = mass of the falling weight,

h = drop height,

k = spring constant, and

g = acceleration due to gravity.























M



*


0oo o00 o oo
OD~ I- I^ 0 0


II,'Il
11111.


Figure 2.4 Schematic Diagram of Impulse Load-Response Equipment (105)


*1\jj\































TIME


Figure 2.5


Characteristic Shape of Load Impulse (105)








The response of the pavement to the impulse loading is normally

measured with a set of geophones placed at varying radial distances from

the center of the plate. These deflection measurements can, in princi-

ple, be used to characterize the structural properties of the pavement

layers.

Three manufacturers currently market impulse testing equipment in

the United States. These are the Dynatest, KUAB and Phoenix falling-

weight deflectometers. The Dynatest falling-weight deflectometer (FWD)

is the most widely used impulse loading device in North America and

Europe (109). Its newest version--the Dynatest 8000 FWD testing

system--was used in this study and will be described later in this

report. Other experimental impulse testing devices have been evaluated

by Washington State University and Cornell Aeronautical Laboratory (79).

The impulse testing machines have several advantages over other NDT

instruments. The magnitude of the force can be quickly and easily

changed to evaluate the stress sensitivity of the pavement materials

being tested. Perhaps the greatest advantage is the ability to simulate

vehicular loading conditions. Several investigators (11,35,46) have

compared pavement response in terms of stresses, strains, and deflec-

tions from an FWD-imposed load to the response of a moving wheel load.

All these comparisons have shown that the response to an FWD test is

quite close to the response of a moving wheel load with the same load

magnitude. Figure 2.6 shows such an example of pavement response com-

parison.

However, the deflection basin produced by an impulse loading device

is symmetrical about the load and Lytton et al. (63) have argued that

the deflection basin under a moving wheel is not symmetrical about the










750-




E 500-

00

OSL
O L.
M 250-
U.0
LL O


0 -

0


600

/

x< 0
400
/c
2 400


I- LLA



na
n --------I---


250 500
DEFLECTION (gm)
FROM MOVING WHEEL LOAD

a) Surface Deflections


200


400


VERTICAL STRAIN (x 10-6)
FROM MOVING WHEEL LOAD
b) Vertical Subgrade Strains


Figure 2.6


Comparison of Pavement Response from FWD and
Moving-Wheel Loads (35)


750


600








load in any pavement structure. Thus, the impulse load of a FWD is not

an exact representation of a moving wheel load. Moreover, the response

from the impact testing technique is similar to other types of dynamic

deflection testing in the sense that it represents characterization of

the entire structure. The technique does not provide information that

readily separates the effects of its various layers. Finally, the

parameters that cause plastic deformation in the structure are not

readily determinable from impact testing (79).

2.3.2.5 Wave Propagation Technique. Wave propagation provides

methods for the determination of the elastic properties of individual

pavement layers and subgrades. Unlike the three previous methods of

NDT, these methods are not concerned with the deflection response of the

pavement. Rather, they are concerned with the measurement of the velo-

city and length of the surface waves propagating away from the load

surface (127).

There are two basic techniques for propagating waves through pave-

ment structures: (1) steady-state vibration tests and (2) seismic

(impulse) tests. Generally, three types of waves are transmitted when a

pavement surface is subjected to vibration. These are

1. Compression or primary (P) waves,

2. Shear (S) waves, and

3. Rayleigh (R) waves.

The P and S waves are body waves while the R wave is a surface wave.

Raleigh waves are the dominant waves found in the dissipation of energy

input from a vibrator on a semi-infinite half-space (75). Also, because

P and S waves attenuate rapidly with radial distance from the vibration








source, R waves are the typical waves measured in the wave propagation

technique.

Wave propagation theory is based upon the fact that in a homoge-

neous isotropic half space subjected to an external disturbance, waves

travel at velocities that may be expressed as (59, p. 153; 79; 127)


Vs= (G) 2 -E /2 Eqn. 2.2a
p 2(1 + u)p

V = E(1 u) /2 Eqn. 2.2b
P p(1 + v)(1 2p)


VR = aVs Eqn. 2.2c


where

Vs = shear wave velocity,

Vp = compression wave velocity,

VR = Rayleigh wave velocity,
G = shear modulus,

E = Young's modulus,

P = Poisson's ratio,

p = mass density, and

a is a function of Poisson's ratio and varies from 0.875

for P = 0 to 0.995 for P = 0.5.

In general, R and S waves are not particularly dependent on Poisson's

ratio, but the value of compression wave velocity is strongly dependent

on Poisson's ratio (59,79).

Field test procedures for the wave propagation measurements involve

two general types of tests. Raleigh wave velocities are determined from








steady-state vibratory pavement responses and compression wave veloci-

ties are measured from impulse (seismic) tests. The former usually

follows procedures developed by researchers at the Royal Dutch Shell

Laboratory (43,53,93), the British Road Research Laboratory (79), and

the Waterways Experiment Station (79). They utilized a mechanical

vibrator for low-frequency vibrations (5-100 Hz) and a small electro-

magnetic vibrator for the high-frequency work (43,53). The general

procedure currently in use is to place the vibrator on the pavement

surface and set the equipment in operation at a constant frequency.

Details of the procedure can be found in Reference 79.

Seismic tests may be conducted to determine the velocity of com-

pression waves, which can be used with the shear wave (or Rayleigh wave)

velocity to compute Poisson's ratio. One such method is the hammer-

impulse technique in which the pavement is struck with a light hammer

and the resultant ground motion is observed at one or more points with

horizontal motion geophones. However, this method is only good for

soils where the velocity of the materials increases with depth. It is

not applicable to layered pavement systems where strong, high velocity

layers occur at the top and grow progressively weaker with depth. How-

ever, Moore et al. (79) report that this procedure has been used to

obtain compression wave velocities of pavement layers during construc-

tion.

A method of using surface waves to structurally characterize pave-

ments is currently in the research stage at the University of Texas at

Austin (80,81,82). The technique, called Spectral Analysis of Surface

Waves (SASW), determines shear wave velocity at soil or pavement sites.

The elastic shear and Young's moduli profiles are then calculated under








the assumption of homogeneous, isotropic, and elastic medium. The SASW

method is essentially a seismic procedure. An iterative inversion

process is used to interpret the shear wave velocity profiles (81).

Laboratory procedures are available for the determination of the

elastic properties of pavement and soil specimens using wave propagation

techniques. However, the laboratory procedures require that samples of

the pavement material be obtained for testing. Therefore, it may not be

considered as a nondestructive technique. Two laboratory procedures

that parallel the field vibratory procedures and which may be applicable

to pavement design are the resonant column and the pulse methods (79).

The most difficult aspect of the wave propagation techniques is

that of interpretation and analysis of test results. The wave propaga-

tion method of testing relies on the ability to interpret the data

obtained in the field so that the characteristics of the structure

beneath the surface may be determined (79). Because of the inherent

complexities involved, such techniques have not gained wide acceptance.

2.3.3 NDT Data-Interpretation Methods

2.3.3.1 General. Considerable emphasis has been placed upon

determining the elastic properties of pavement layers using NDT data.

Most of this work has been concentrated on the first three types of NDT

procedures, those associated with deflection measurements. The fourth

category, the wave propagation method, has not gained wide acceptance

because of the relative sophistication required in the field operation

and in the interpretation of test data. However, the interpretation of

measured surface deflection basins has gained widespread popularity with

the advent of NDT procedures. There is a general agreement among








pavement engineers that the measured surface deflection basins from NDT

can provide valuable information for structural evaluation of a pave-

ment.

Methods for the interpretation of NDT data can be placed into two

categories: empirical or mechanistic methods. Empirical procedures

directly relate NDT response parameters to the structural capacity of a

pavement. Most of these methods (48,56) do not involve direct or

indirect theoretical analysis. Instead, they are based upon the cor-

relation between the maximum deflection under a load (static NDT or

wheel load) and pavement performance.

In an attempt to improve the empirical procedures, other research-

ers have relied on the use of deflection basin parameters (90) or semi-

empirical correlations (79) for pavement evaluation. Figure 2.7 shows

an example of basin parameters and the criteria used to evaluate a pave-

ment. Table 2.1 lists some of the deflection basin parameters that have

been developed for NDT data evaluation of pavements (120). Most of the

basin parameters do not relate directly to the elastic parameters of the

pavement section.

Semi-empirical procedures usually involve correlation of modulus

values to other known pavement parameters. For example, Heukelom and

Foster (43) have developed a correlation between modulus E (in psi) from

wave propagation techniques and the California Bearing Ratio (CBR)

value. This correlation, though later refined by WES (79), is of the

form


E = 1500 (CBR)


Eqn. 2.3











ligid Force Wheels


SCI


DMD = Dynaflect Maximum Deflection (Numerical Value of Sensor No. 1)
SCI = Surface Curvature Index (Numerical Difference of Sensor No. 1 and
No. 2)
BCI = Base Curvature Index (Numerical Difference of Sensor No. 4 and
No. 5).
a) Basin Parameters


b) Criteria


Figure 2.7 Empirical Interpretation of Dynaflect Deflection Basin (90)


DMD SCI BCI CONDITION OF PAVEMENT STRUCTURE
GT 0.11 Pavement and Subgrade Weak
GT 0.48
GT 1.25 LT 0.11 Subgrade Strong, Pavement Weak
GT 0.11 Subgrade Weak, Pavement Marginal
LT 0.48
LT 0.11 DMD High, Structure Ok
GT 0.11 Structure Marginal, DMD Ok
GT 0.48
LT 1.25 LT 0.11 Pavement Weak, DMD Ok
GT 0.11 Subgrade Weak, DMD Ok
LT 0.11 Pavement and Subgrade Strong0.48
LT 0.11 Pavement and Subgrade Strong







Table 2.1 Summary of Deflection Basin Parameters


Parameter Definitiona NDT Deviceb


Dynaflect maximum deflection (DMD)

Surface curvature index (SCI)
Base curvature index (BCI)

Spreadability (SP)





Basin slope (SLOP)
Sensor 5 deflection (W )
5
Radius of curvature (R)
Deflection ratio (Qr)
Area, in inches (A)

Shape factors (F F )
1 2


Tangent slope (TS)


DMD = d
1
SCI = d d
1 2
BCI = d d
4 5
SP = ( Idi /5d )x 100
i=1 to 5
SP = ( Idi /4d ) x 100
i=1 to 4
SLOP = d d
1 5
W = d
5 5
R = r2/{2.dm[(dm/dr) 111]}

Qr = r/do
A = 6[1 + 2(d /d ) + 2(d /d ) +
2 1 3 1
F = (d d )/d
1 1 3 2
F = (d d )/d
2 2 4 3
TS = (d dx)/x


Dynaflect
Dynaflect, Road Rater model 400
Dynaflect

Dynaflect


Road Rater model 2008


Dynaflect
Dynaflect

Benkelman beam
FWD, Benkelman beam
(d /d )] Road Rater model 2008
4 1
Road Rater model 2008


a d = deflection; subscripts 1,2,3,4,5 = sensor locations; o = center of load; r = radial distance;
m = maximum deflection; x = distance of tangent point from the point of maximum deflection.
b The NDT device for which the deflection parameter was originally defined.

Source: Uddin et al. (120)








and is the most widely used correlation (133). Other correlations (79)

have been made between E and plate bearing subgrade modulus, K. It

should be recognized that the conditions of dynamic testing generally

yield moduli in the linear elastic range. Conventional tests such as

the CBR and plate bearing tests produce deformations that are not

completely recoverable and, therefore, are partly in the plastic range.

Thus, one would expect some variation in the correlation between E

modulus and pavement parameters, such as K and CBR.

Mechanistic analysis of NDT data is usually performed by one of the

following:

1. Direct relationship between deflection parameters and the

elastic moduli of the pavement layers.

2. Inverse application of a theoretical model by fitting a

measured deflection basin to a deflection basin using an

iterative procedure.

3. A combination of 1 and 2.

The above mechanistic methods employ deflection data from either vibra-

tory or impulse loading equipment. While these devices are dynamic in

nature, most of the mechanistic solutions are based on elasto-static

(19,32,74) and visco-elasto-static (7) models. Recently, an elasto-

dynamic model (54) has been used to interpret NDT data (66,67,105).

However, the use of dynamic analyses for interpretation of NDT data can

be considered to be in the research stage. Another significant obser-

vation is that almost all the mechanistic solutions available employ

layered theory or simplified versions of it. The only exception to this

is the use of a finite element model presented by Hoffman and Thompson

(45). A review of the various solutions is presented below.








2.3.3.2 Direct Solutions. Presently, there are no direct analy-

tical solutions that can uniquely determine the elastic moduli for a

multilayered pavement system using surface deflection measurements

alone. The so-called direct solutions have been developed for only two-

layer systems which usually involve graphical solutions, nomographs, or

in most cases only provide estimates for the subgrade modulus.

Scrivner et al. (102) presented an analytical technique for using

pavement deflections to determine the elastic moduli of the pavement and

subgrade assuming the structure is composed of two elastic layers.

Based upon the same assumption, Swift (113) presented a simple graphical

technique for determining the same two elastic moduli. Vaswani (124)

used Dynaflect basin parameters to develop charts for the structural

evaluation of the subgrade and its overlying layers for flexible pave-

ments in Virginia (see Table 2.1). The methods by Majidzadeh (64) and

Sharpe et al. (107), among others, employ similar basin parameters from

the Dynaflect or Road Rater to estimate the subgrade modulus and develop

charts to assess the overlying layers.

Jimenez (51) described a method for evaluating pavement-layer

modular ratios from Dynaflect deflections. The pavements were

considered to be three-layer systems, and the deflection data were used

to estimate ratios of the elastic moduli of the adjacent layers. The

ratios reduce the system from three values of elastic modulus to two

values of modular ratio. The major limitation of this method is that

the elastic modulus of the asphalt concrete layer must be known.

Wiseman (129) and Wiseman et al. (131) have, respectively, applied

the Hertz Theory of Plates and the Hogg Model to evaluate two-layered

flexible pavements using surface deflection basins. The Hertz theory is








an application of the analytical solution of a vertically loaded elastic

plate floating on a heavy fluid. The solution to this problem was

presented by Hertz in 1884 and was first applied to concrete pavement

analysis by Westergaard in 1926 (79). The Hogg model consists of an

infinite plate on an elastic subgrade. The subgrade can be either of

infinite extent or underlain by a perfectly rigid rough horizontal

bottom at a finite depth. Analysis of this model was reported by A.H.A.

Hogg in 1938 and 1944 (131). In both methods, the flexural rigidity of

the composite pavement which will best fit a measured deflection basin

is calculated.

Lytton et al. (62) and Alam and Little (5) have developed another

method based on elastic-layer theory for prediction of layer moduli from

surface deflections. This method makes use of the explicit expression

for deflection originally postulated by Vlasov and Leont'ev (126). The

major drawback of this technique is the need to develop several con-

stants, five in all, for which no analytical or test method exists as

yet. In applying this method, the authors (5,62) resorted to the use of

regression analyses and computer iterative solutions.

Cogill (28) presented a method which provides an estimate of the

stiffness of the pavement-layer materials. The method essentially is a

graphical presentation in which the deflections measured over a parti-

cular range of load spacing can be related to the stiffness of the pave-

ment material at a certain depth. The relationship is an approximate

one and is expressed with the aid of Boussinesq's formula.

All the methods presented above use deflection measurements

obtained from vibratory loading equipments--Dynaflect and Road Rater.

The only approach for the direct estimation of layer moduli from impulse








load-deflection response (such as an FWD deflection basin) is the

concept of equivalent layer thickness (121,122) in which the layered

pavement system is transformed into an equivalent Boussinesq (13)

system. This concept, originally proposed by Odemark (87), is based on

the assumption that the stresses, strains, and deflections below a given

layer interface depend on the stiffness and thickness of the layers

above that interface. Although this approach obtains an explicit

solution for the subgrade modulus (121), it relies on estimates of the

asphalt concrete layer modulus and also employs certain modular ratios

to obtain the moduli of the various layers above the subgrade (25). The

method of equivalent thicknesses (MET) has also been incorporated into

some iterative computer programs which are discussed in the next

section.

Several investigators have obtained equations to directly determine

the subgrade modulus from one or more sensor deflections. For example,

Figure 2.8 shows the relationship between the subgrade modulus and the

Dynaflect fifth sensor deflection as summarized by Way et al. (128).

Keyser and Ruth (55) developed a prediction equation from five test road

sections in the Province of Quebec, Canada, by using the BISAR elastic-

layer program to match measured Dynaflect deflection basins. The

equation is of the form


-1.0006
E = 5.3966(D ) Eqn. 2.4
4 5


where E4 is subgrade modulus in psi, and Ds is Dynaflect fifth sensor

deflection in inches. This equation had an R2 of 0.997 (55), and is

similar to that of Ullidtz (see Figure 2.8). Godwin and Miley (41) have













100,000 I I I 1 1 1 1 1



SMajidazdeh, Ref. 17
S-\ Esg =(6115 x D5)-0984

c0 -ADOT Hyperbolic -
SEsg =(0.000013 + 0.00016 x D )1
ADOT Power Equation
10,000 -UlIdtz, Ref. 20---- -= 8800 x D0.58
Sq xa2 x(1.0 Nu2) 5200xD10
g r x D5 x 0.001
S-q = Load Pressure = 159.16 psi
< -a = Load Pressure = -2. Inches
C Nu = Poisson's Ratio = VO-
0 r = Fifth Sensor Distance from Load = 49.0 Inches \

D -McCullough, Ref. 18
Esg =4000xD51.2

1,000 1 1 1 1 1 1 1 1 1 1 1 1 1 1 I
0.01 0.1 1.0 10.0
DYNAFLECT DEFLECTION, D5, mils


Dynaflect Fifth Sensor Deflection-Subgrade Modulus Relationship (128)


Figure 2.8








developed correlations between the base, subbase and subgrade moduli and

the second, third, and fourth Dynaflect sensor deflections, respec-

tively. However, the modulus values used in the correlation were the

surface moduli from plate bearing tests which suffers from the problem

of incorporating plastic and nonrecoverable deformations.

An approach using regression equations to estimate layer moduli has

been attempted by other investigators (83,120,132). This approach

usually involves analysis of computer-simulated NDT data using a theo-

retical model (usually layered elastic theory). The various investi-

gators reported success in the case of the subgrade modulus. To obtain

good correlations for the other layers (surface, base, subbase), certain

assumptions had to be made, such as the base course modulus being

greater than the modulus of the subgrade (83), or they had to resort to

computer-iteration programs (83,120).

2.3.3.3 Back-Calculation Methods. The method of iteratively

changing the layer moduli in a theoretical model to match the theoreti-

cal deflection basin to a measured basin is currently called back-

calculation in the literature. Initial developments of this procedure

utilized a trial-and-error approach (49,72) using the following steps:

1. Pavement-layer thicknesses, initial estimates of the pavement-layer

moduli, and the loading and deflection measurement configuration are

input into the model (usually a multilayer elastic computer

program).

2. The computed deflections at the geophone positions are compared with

those actually measured in the field.

3. The layer moduli used in the computer program are then adjusted to

improve the fit between the predicted and actual deflection basins.








4. This process is repeated until the two deflection basins are vir-

tually the same. The process may have to be repeated several times

before a reasonable fit is obtained.

Because of the time consuming nature (49) of the trial-and-error

method, many researchers have developed computer programs to perform the

iteration. Table 2.2 lists some of the self-iterative computer pro-

grams. The major differences among the various programs are the differ-

ent models, algorithms and tolerance levels used in the iteration pro-

cess. A few of these will be discussed here.

Anani (6) developed expressions for surface deflections in terms of

the modulus values of a four-layer pavement. However, he could not

obtain direct solutions to determine the moduli. Therefore he used an

iterative procedure to obtain the moduli from Road Rater deflection

basins. The computer programs reported by Tenison (114) and Mamlouk

(66) followed the successive approximation method of Anani (6). In the

overlay design program called OAF, Majidzadeh and Ilves (65) employed a

deflection matching technique for determining the in situ layer stiff-

nesses. While using field data to substantiate the applicability of the

procedure, they experienced difficulties and commented,

. the computed asphalt layer stiffness shows a
large variation, and in a few cases the asphalt is
stiffer than steel; nevertheless the values are
reasonable in a great majority of the cases . .

(65, p. 85)

The BISDEF computer program (23) is an improvement over the CHEVDEF

(22) to handle multiple loads and variable interface conditions. The

number of layers with unknown modulus values cannot exceed the number of

measured deflections. However, a maximum of four deflections are











Summary of Computer Programs for
of Flexible Pavement Moduli from


Evaluation
NDT Devices


Number Theoretical Applicable
Name Reference of Model Used NDT
Layers For Analysis Device


* Anani (6)


4
Layer


BISAR-Elastic


Road Rater 400


ISSEM4 Sharma and
Stubstad (106)

CHEVDEF Bush (22)


OAF Majidzadeh
and Ilves (65)


ILLI-PAVE


Hoffman and
Thompson (45)


Tenison (114)


FPEDD1 Uddin et al.
(118,120)

BISDEF Bush and
Alexander (23)


ELMOD Ullidtz and
Stubstad (123)

IMD Husain and
George (47)


DYNAMIC Mamlouk (66)


* not known or available


4 ELSYM5-Elastic
Layer


4(a)
Layer


CHEVRON-Elastic


3 or ELSYM5 Elastic
4 Layer

3 Finite Element


3 CHEVRON's N
Layer (Elastic)

3 or ELSYMS Elastic
4 Layer


4(a)


2, 3
or 4


BISAR Elastic
Layer
Vibrator, or FWD

MET-Boussinesq


3 or CHEVRON Elastic
4 Layer
for FWD

4 Elasto-dynamic


Road Rater 2008


Dynaflect,
Road Rater, or FWD

Road Rater 2008,
or FWD

Road Rater 2000


Dynaflect, FWD


Dynaflect,
Rater, WES


FWD


Road


Dynaflect, but
can be modified


Road Rater 400


(a) not to exceed number of deflections


Table 2.2








targeted during the iteration process, which is also limited to a

maximum of three loops. When applied to field measured deflections on

an airfield pavement in Florida (23), BISDEF predicted unreasonably high

values of the AC modulus for all the different NDT devices used in the

study. Also, Bush and Alexander (23) conceded that the program provides

the best results if the number of unknown layer moduli is three.

The ISSEM4 computer program (106) incorporates the principles of

the method of equivalent thicknesses (MET) into the ELSYM5 multilayered

elastic program to determine the in situ stress-dependent elastic

moduli. The parameters for the nonlinear stress-dependent relationships

(see Section 2.4.3) are established from FWD tests performed at differ-

ent load levels. The iteration process is seeded with a set of E-values

(106). The ELMOD program (123) also utilizes the MET principle and the

iteration procedure. Both programs provide relatively good solutions if

the asphalt concrete modulus is known.

ILLI-PAVE (45), the only program which utilizes a finite element

model, is specifically developed to handle Road Rater deflection data.

However, Road Rater deflection basins must be converted to equivalent

FWD deflection basins prior to being used in the program (45). Also,

the nonlinear stress-dependent material models incorporated into the

finite element method utilized relationships established from previous

laboratory material characterization procedures. It is also surprising

that the authors resulted to nomographs for specific applications of the

back-calculation model (45).

Most of the iteration programs listed above require a set of ini-

tial moduli--seed moduli--and therefore are user-dependent. Therefore

computational times and cost can be prohibitive. Unique solutions








cannot be guaranteed since an infinite number of layer modulus combina-

tions can provide essentially the same deflection basins. Moreover,

most of the iterative programs yield questionable base course and

subbase moduli. In some programs, adjustment of the field data are

required in order to improve the solution (6,47).

2.3.4 Other In Situ Methods

Cogill (27) presented a method involving the use of an ultrasonic

technique. The elastic modulus of the top layer can be accurately

determined; however, the modulus values for the other layers are

questionable. Kleyn et al. (58) and Khedr et al. (57) have developed

different forms of a portable cone penetrometer to evaluate the stiff-

ness of pavement layers and subgrade soils. However, these devices do

not provide direct modulus values but rather are based on correlations

with CBR and plate bearing parameters. Similarly, the Clegg Impact

tester, which was developed in Australia in the mid-1970s, relies on CBR

correlation for pavement evaluation applications (40). The problems of

the CBR and plate bearing tests have been discussed previously.

Maree et al. (70) presented an approach to determine pavement-layer

moduli based on a device developed to measure deflections at different

depths within a pavement structure. The device, called the multi-depth

deflectometer (MDD), is installed at various depths of an existing pave-

ment structure to measure the deflections from a heavy-vehicle simulator

(HVS) test. Maree et al. (70) suggested that effective moduli for use

in elastic-layer theory can be determined from correction factors esta-

blished from field measurements using the MDD at different times of the

year and under different conditions.








Molenaar and Beuving (77) described a methodology in which the FWD

and a dynamic cone penetrometer (DCP) were used to assess stress depen-

dent unbound pavement layers and the presence of soft interlayers.

However, the procedure does not provide any direct modulus correlation

but a graphical presentation of FWD surface modulus and DCP profiles.

Geotechnical engineers have, for several years, used various forms

of field tests to assess the engineering properties of soils for con-

struction purposes. Recent advances in exploration and interpretation

methods have led to improvements in the determination of important soil

parameters such as strength and deformation moduli. For example, the

following in situ techniques (26,30,50) are suitable for the determina-

tion of soil stiffness:

1. Menard Pressuremeter (PMT) and Self-Boring Pressuremeter (SBP)

tests.

2. Cone Penetration Test (CPT), including the mechanical,

electronic, and piezo-cone penetrometers.

3. Marchetti Dilatometer Test (DMT).

4. Plate Loading Tests (PLT), including Screw Plate Tests (SPL).

Some of these tests have the added advantage of providing detailed

information on site stratification, identification, and classification

of soil types. This is of great appeal since the variation of the

subgrade soil properties with depth can be accounted for rather than

assuming an average modulus value as conventionally used in multilayer

analysis.

Unfortunately, the application of the improved techniques to eval-

uate or design pavements has been very limited. As evident from the

previous sections, the material characterization part of a rational








pavement design program, though very important, is often treated with

considerable simplification and empiricism. Geotechnical engineers

often feel that structural engineers have little or no interest in those

parts of their work below the ground level. These feelings are cer-

tainly justified in the case of pavements (76). It is therefore not

surprising that most of the in situ geotechnical applications to pave-

ments rely on correlations with empirical pavement parameters such as

CBR to validate their proposed methods (40,57,58,77). The other known

applications of geotechnical in situ testing methods to evaluate the

stiffness of pavement structures are discussed below.

Briaud and Shields (14,15) have described the development and

procedure of a special pressuremeter test for pavement evaluation and

design. The pavement pressuremeter consists of a probe, tubing and a

control unit, and works on the same principle as the Menard pressure-

meter (30). They illustrated how the modulus values obtained from the

test can be used directly in multilayer mechanistic analysis. In order

to use empirical design charts, however, Briaud and Shields (15) also

developed a correlation between the pressuremeter modulus and the

bearing strength obtained from a Macleod plate test for two airport

pavements in Canada.

Borden et al. (12) have presented an experimental program in which

the dilatometer test (68) was used to determine pavement subgrade sup-

port characteristics. A major part of the testing program consisted of

conducting DMT and CBR tests in soil samples prepared in cylindrical

molds and also in a special rectangular chamber. A limited field test

was conducted on a compacted embankment constructed with one of the

soils used in the laboratory investigation. Although they report good








correlations between the dilatometer modulus and CBR value, the use of

the CBR test makes the study empirical, to say the least.



2.4 Factors Affecting Modulus of Pavement-Subgrade Materials

2.4.1 Introduction

The response characteristics of flexible pavement materials is a

complex function of many variables, which is far-fetched from the ideal

materials assumed in classical mechanics. In general, the behavior of

these materials is dependent upon many environmental and load vari-

ables. Specifically, the asphalt concrete response is primarily a

function of temperature and rate of loading. Due to its viscoelastic

nature (7,51), asphalt concrete materials become stiffer as the load

rate increases and the temperature decreases. The granular base course

and subgrade characteristics are dependent upon moisture content, dry

density, stress level, stress states, stress path, soil fabric, stress

history, and soil moisture tension (59,78,133).

Several researchers have presented relationships of resilient

modulus as a function of one or more variables, while keeping others

fixed or completely ignored. Most of these relationships were developed

from laboratory studies. A complete review of the relative effects of

the various factors on pavement-soil response, or the relationship

between modulus and other parameters measured in the laboratory can be

found in References 31, 44, 59, 78, 94, and 133. It is not the intent

of this discussion to review the various studies on this topic. The

discussion below will concentrate on two variables that are believed to

be very important in flexible pavement technology, especially when con-

sidering NDT and pavement evaluation. These factors are the temperature








of the asphalt concrete layer, and the stress dependency of base/subbase

and subgrade materials. This does not mean that the effects of the

other variables can be ignored or underestimated. For example, moisture

content has a considerable effect on the modulus of flexible-pavement

materials, especially for fine-grained subgrade soils (78).

2.4.2 Temperature

Temperature has a very important influence on the modulus of

asphalt-bound materials. The modulus of asphalt concrete decreases with

an increase in pavement temperature (51,78,111,133). The temperature of

the pavement also fluctuates with diurnal and seasonal temperature vari-

ations. In order to determine the variation of modulus with temperature

for flexible-pavement materials, the mean pavement temperature should be

established. Southgate and Deen (111) developed a method for estimating

the temperature at any depth in a flexible pavement up to 12 inches.

Figure 2.9 shows the graphical solution for the determination of the

mean pavement temperature with depth from the known temperatures. This

relationship has been recommended and in some cases incorporated into

many flexible pavement design procedures (47,65,107). Though, the

curves have been found to be reasonably accurate for other locations

(111), it would be more desirable to make a direct determination of this

temperature.

2.4.3 Stress Dependency

Laboratory studies presented in the literature (31,44,78,94) sug-

gest that the moduli of granular base materials and subgrade soils are

stress-dependent. The stiffness of the granular base has been found to

be a function of the bulk stress or first stress invariant. A stress-

stiffening model in which the modulus increases with the first stress











160
LL
0

a 120




I-
W 80


" 40

I-
0
0


40 80 120 160 200 240
PAVEMENT TEMPERATURE + 5
DAY MEAN AIR TEMPERATURE


a) Pavements More Than 2 in. Thick


o"- (b) Depth in
Pavement, -0
i- 0 Inches 1
S120- 2
-

t 80

c:
W 40- Depth in
0-
M 2 Pavement, Inches

0o l-- l I I0I I I
0 40 80 120 160 200 240
PAVEMENT TEMPERATURE + 5
DAY MEAN AIR TEMPERATURE

b) Pavements Equal to or Less Than 2 in. Thick


Figure 2.9 Temperature Prediction Graphs (111)








invariant is generally used to characterize granular base materials.

The relationship is of the form


K
E = K 9 2 Eqn. 2.5
1


where

E = granular base/subbase modulus,

6 = first stress invariant or bulk stress, and

K K = material constants
1 2

The subgrade stiffness, on the otherhand, has been found to be a

function of the deviator stress (stress difference). For fine-grained

soils, resilient modulus decreases with increase in stress difference

(78). The mathematical representation of the subgrade stiffness is of

the form



E = AaB Eqn. 2.6



where

E = subgrade modulus

a = stress difference, and

A, B = material constants for the subgrade

The constant B(slope) is less than zero for the stress-softening model,

while for the stress-stiffening model, the slope is greater than zero.

The stress-dependency approach of characterizing pavement materials

is of great importance for high traffic loadings. Situations in which

high traffic loadings occur are larger aircraft loadings in the case of

airfield pavements, and when heavy wheel loads and/or single tire








configurations (which result in higher stresses) are applied to flexible

highway pavements. For this reason, some of the NDT back-calculation

procedures have accounted for the stress dependency effect by incorpo-

rating Equations 2.5 and 2.6 into their algorithms (45,65,106). How-

ever, the problem of determining the material constants, A, B, K and
1
K still remains, especially when NDT deflection basins are used to
2
characterize the pavement. The most common approach is to use labora-

tory resilient moduli and regression analysis to determine these para-

meters (45,65,72). Thus, the material parameters will depend upon

sample preparation procedures, disturbance, prestress-strain conditions,

etc.

Other researchers (93,106,121) have suggested determining the mate-

rial constants from FWD tests conducted at three or more load levels.

However, it is not clear how viable this procedure is since the resul-

tant load-deflection response of a pavement is a combined effect of the

behavior of the individual layers. The relative contribution of each

layer is not clearly known. It is even more complex since the asphalt

concrete layer is dependent on the temperature and age-hardening

characteristics of the asphalt cement. Moreover, contrary to previous

belief, Thompson (116) has found that the material parameters are not

independent-of each other, especially for granular bases and subbases.

Uddin et al. (118,119,120) have applied the concepts of equivalent

linear analysis developed in soil dynamics and geotechnical earthquake

engineering to evaluate the nonlinear moduli. They concluded that the

in situ moduli derived from an FWD deflection basin (at 9000-1b. peak

force) are the effective nonlinear moduli and need no further correc-

tion. However, an equivalent linear analysis has to be performed to








correct the in situ moduli calculated for nonlinear granular materials

and subgrade soils from a Dynaflect deflection basin. These conclusions

were based on stress analysis comparisons of a single-axle 18-kip design

load, FWD (9000-lb. peak force) and Dynaflect loadings simulated in the

ELSYM5 elastic-layer program. An algorithm to perform this equivalent

linear correction has been incorporated into the FPPEDD1 self-iterative

computer program (120). However, results reported by Nazarian et al.

(81) tend to contradict the conclusions of Uddin et al. (120). Their

study involving FWD tests at 5- and 15-kip loads indicated that non-

linear behavior occurs at higher FWD loads, and is more predominant in

the base course layers than the subgrade.

These results and those from other research work indicate there is

disagreement as to what type of approach should be used when the effects

of nonlinearity and stress dependency are to be considered. There are

at least three schools of thought in this regard. The first group

believes that the use of an equivalent effective modulus in an elastic-

layer theory would provide reasonable response predictions. This

approach would eliminate the expense, time and complexity associated

with more rigorous methods such as finite element models (61). The

research works of Maree et al. (70), Roque (96), and Roque and Ruth (97)

on full-scale pavements tend to support this theory.

The second school of thought recommends that the nonlinear stress

dependent models (Equations 2.5 and 2.6) can be incorporated into an

elastic-layer program to predict reasonable response parameters. How-

ever, the asphalt concrete layer is treated as linear elastic. This

theory is supported by Monismith et al. (78), among others, and has been

used in iterative computer programs like OAF, ISSEM4, and IMD.








The third school of thought contends that layered elastic theory,

when used with certain combinations of pavement moduli, predicts tensile

stresses in granular base layers, even if gravity stresses are also

considered (16,45,112). Instead of using a layered approach, this group

prefers a finite element model in which the nonlinear responses of the

granular and subgrade materials are accurately characterized. Again,

the asphalt concrete layer is considered to be linear elastic. The

ILLI-PAVE finite element back-calculation program (45) is a classic

application of this theory.

In the finite element approach discussed above, researchers have

used, with limited success, various failure criteria and in some cases

arbitrary procedures to overcome the problem of tensile stresses

(16,112). For example, Brown and Pappin (16) used a finite element

program called SENOL with a K-0 contour model and found it to be capable

of determining surface deflections and asphalt tensile stresses but

unable to determine the stress conditions within the granular layer.

The asphalt layer was characterized as elastic with an equivalent linear

modulus. They therefore concluded that the simplest approach for design

calculations involves the use of a linear elastic-layered system pro-

vided adequate equivalent stiffnesses are used in the analysis. This

conclusion is shared by other investigators (10,61,96,97) and is the

philosophy behind the work presented in this dissertation.














CHAPTER 3
EQUIPMENT AND FACILITIES


Most of the methods available for determining the elastic moduli of

flexible pavements have been outlined in Chapter 2. These include the

use of nondestructive tests (NDT), laboratory methods and other in situ

test methods. The limitations of these methods and the need for a more

simple approach have also been highlighted. An approach which mechanis-

tically evaluates pavements with the use of NOT and/or in situ penetra-

tion tests is therefore developed in this study. This approach is

developed to simplify the mechanistic analysis and design process, and

to evaluate the effects of important variables involved in the determi-

nation of pavement layer moduli. The study consisted of the development

of moduli prediction equations from NDT data, field testing and analyses

of NDT and in situ penetration tests, and finally, comparison and eval-

uation of test data. Therefore, this chapter describes the equipment

and facilities used in the study.

The test equipment were either available at the Civil Engineering

Department of the University of Florida or at the Bureau of Materials

and Research, Florida Department of Transportation (FDOT). They are

essentially standard testing devices. This research was concerned with

their optimum use and application for a rational mechanistic design and

evaluation of asphalt concrete pavements.








3.1 Description of Dynaflect Test System

3.1.1 Description of Equipment

The Dynaflect, as previously mentioned, belongs to the dynamic

steady-state force-deflection group of NDT equipment. It is an electro-

mechanical device for measuring the dynamic deflection of a pavement

caused by oscillatory loading. The testing system (84,104,108) consists

of a dynamic force generator mounted on a small two-wheel trailer, a

control unit, a sensor assembly and a sensor (geophone) calibration

unit. The Dynaflect can be towed by and operated from any conventional

passenger carrying vehicle having a rigid trailer hitch and a 12-volt

battery system.

The oscillatory load is produced by a pair of counter weights

rotating in opposing directions and phased in such a manner that each

contributes to the vertical force of the other, but opposes the horizon-

tal force of the other, thereby canceling horizontal forces. The weight

of the unbalanced masses varies sinusoidally from 2500 Ibs. to 1500

Ibs., thereby producing a cyclic force of 1000 Ibs. peak-to-peak at a

frequency of 8 Hz (see Figure 3.1). The cyclic force is alternately

added to and subtracted from the 2000-1b. static weight of the trailer.

The 1000-1b. cyclic force is transmitted to the pavement through a pair

of polyurethane-coated steel wheels that are 4-in. wide and 16-in. in

outside diameter. These rigid wheels are spaced 20-in. center to center

(see Figure 3.2).

The pavement response to the dynamically applied load is measured

by five geophones located as shown in Figure 3.2. The first geophone

measures the deflection at a point midway between the rigid wheels while

the remaining four sensors measure the deflection occurring directly













1---1/f -A


f = Driving Frequency = 8 Hz



T
Peak-to-Peak
-- Dynamic Force
I= 1000 Ib


TIME


Typical Dynamic Force Output Signal of Dynaflect (108)


Figure 3.1














Housing and Tow Bar


Loading Wheels \ No.4 No.5
Geophones


(a) The Dynaflect System in Operating Position



Loading
Wheels Geophones


10 No.1 No. 2 No. 3 No. 4 No. 5
^ ~-A----^---*----^ --^
10" -12" 4-]-12" -H4-12" -I4-12" .J
4"
--T
h-n^

(b) Configuration of Load Wheels and Geophones.

Figure 3.2 Configuration of Dynaflect Load Wheels and
Geophones in Operating Position (108)








beneath their respective locations along the centerline of the trail-

er. However, the geophone configuration can be easily changed to a

desired pattern. Each geophone is equipped with a suitable base to

enable it to make proper contact with irregular surfaces (108).

Data are displayed by a digital readout for each sensor on the

control panel which is umbilically attached to the trailer and can be

placed on the seat of the towing vehicle beside the operator/driver.

All operations subsequent to calibration are performed from the control

panel by the operator/driver without leaving the towing vehicle.

3.1.2 Calibration

The Dynaflect unit is calibrated by placing the sensors on a cam-

actuated platform inside the calibrator furnished with each unit (108).

This platform provides a fixed 0.005-in. vertical motion at 8 cycles per

second. The corresponding meter reading of 5 mils is set in the control

unit by adjustment of an individual sensitivity control for each geo-

phone. Subsequent deflection measurements are thus comparisons against

this standard deflection.

3.1.3 Testing Procedure

The normal sequence of operation is to move the device to the test

point and hydraulically lower the loading wheels and geophones to the

pavement surface (84,108). A test is performed and the data of the 5

geophone deflection readings are recorded. At this point the operator

has the option of raising both the sensors and the loading wheels or

only the sensors. With the rigid wheels down and the pneumatic tires

lifted, the trailer may be moved short distances from one measuring

point to another at speeds up to 6 mph on the loading wheels (108).








When the rigid wheels are out of contact with the ground, the

trailer is supported on pneumatic tires for travel at normal vehicle

speeds. The sensors and loading wheels are raised and lowered by remote

control to enable such moves to be made quickly without need for the

operator/driver to leave the towing vehicle (84,104,108).

3.1.4 Limitations

The general limitations of dynamic steady-state NDT devices have

previously been described. In addition to those, the technical limi-

tations of the Dynaflect device include (109) peak-to-peak loading is

limited to 1000 lbs., load cannot be varied, frequency of loading cannot

be changed, the deflection directly under the load cannot be measured,

and it is difficult to determine the contact area.



3.2 Description of the Falling Weight
Deflectometer Testing System

The Falling Weight Deflectometer (FWD) is a deflection testing

device operating on the impulse loading principle. As described pre-

viously, there are various forms of the FWD, with the most widely used

one in the United States being the Dynatest Model 8000 FWD system. This

is the type used by the FDOT and in the study reported herein. There-

fore, this section describes the operating characteristics of the Dyna-

test FWD test system. Like the Dynaflect, the FWD is also trailer

mounted and can be easily towed by most conventional passenger cars or

vans.

The Dynatest 8000 FWD test system consists essentially of three

main components (34,110), namely

1. a Dynatest 8002 FWD,








2. a Dynatest 8600 System Processor, and

3. a Hewlett-Packard HP-85 Table Top Computer.

3.2.1 The 8002 FWD

The Dynatest 8002 FWD consists of a large mass that is constrained

to fall vertically under gravity onto a spring-loaded plate, 11.8 in. in

diameter, resting on the pavement surface (see Figure 2.4). A load

range of about 1500 to 24000 Ibs. can be achieved by adjusting the num-

ber of weights or height of drop or both. The impulse or impact load is

measured by using a strain-gauge-type load transducer (load cell). The

impact load closely approximates a half-sine wave (see Figure 2.5), with

a duration of 25-30 msec which closely approximates the effect of moving

dual-wheels with loads up to 24000 Ibs. (110).

Seven seismic deflection transducers or geophones in movable brack-

ets along a 2.25 m raise/lower bar are used to measure the response of

the pavement to the dynamically applied load. The geophones, which are

50 mm in diameter and 55 mm high, operate at a frequency range of 2 to

300 Hz (34). One of the geophones is placed at the center of the plate,

with the remainder placed at radial distances from the center of the

plate (see Figure 3.3). In its present form, the FDOT measures deflec-

tions at radial distances of 0, 7.87, 11.8, 19.7, 31.5, 47.2, and 63.0

in. from the plate center. These deflections are respectively called

D D D D D D and D in this study.
1 2 3 4 5 6 7
3.2.2 The 8600 System Processor

The Dynatest 8600 system processor is a microprocessor-based con-

trol and registration unit which is interfaced with the FWD as well as

the HP-85 computer (34,110). The processor is housed in a 19-in. wide












































Figure 3.3 Schematic of FWD Load-Geophone Configuration and
Deflection Basin (34)








case which is compact, light weight, and controls the FWD operation. It

also serves as a power supply unit for the HP-85 computer.

The system processor performs scanning and conditioning of the 8

transducer signals (1 load + 7 deflections). It also monitors the

status of the FWD unit to insure correct measurements.

3.2.3 The HP-85 Computer

The Hewlett-Packard Model 85 computer is used for input of control

and site/tests identification data as well as displaying, printing,

storing (on magnetic tape), editing, sorting, and further processing of

FWD test data (34,110).

3.2.4 Testing Procedure

The automatic test sequence is identified and programmed from the

HP-85 keyboard. This includes the input of site identification, height

and number of drops per test point, pavement temperature, etc. When the

operator enters a "START" command, the FWD loading plate and the bar

carrying the deflection transducers will be lowered to the pavement

surface, the weight will be dropped from the pre-programmed heightss,

and the plate and bar will be raised again. An audible "BEEP" signal

tells the operator that the sequence is complete, and that he/she may

drive onto the next test point. A complete measuring sequence normally

takes about one minute, exclusive of driving time between test points,

for three or four drops of the falling weight (34,110).

The measured set of data (1 load + 7 deflections) will be displayed

on the HP-85 for direct visual inspection, and the data will be stored

on the HP-85 magnetic tape cartridge, together with site identification

information, etc. The display, printed results, and stored results can

be in either metric or English units (34).








3.2.5 Advantages

The primary advantages of the Dynatest FWD, like many other impulse

deflection equipment, are that the created deflection basins closely

match those created by a moving wheel load of similar magnitude (11,45,

110,123), and the ability to apply variable and heavier dynamic loads to

assess stress sensitivity of pavement materials. The Dynatest FWD test

system has the added advantage that the resulting deflection basin is

constructed from seven deflection measurements compared to five and

three deflections in the KUAB and Phoenix Falling Weight Deflectometers,

respectively.



3.3 BISAR Computer Program

The analyses and evaluation of NDT deflection data in this study

involved the use of BISAR, an elastic multilayered computer program.

BISAR is an acronym for Bitumen Structures Analysis in Roads. The

program, developed by Koninklijke/Shell Laboratorium, Amsterdam,

Holland, is a general purpose program for computing stresses, strains

and displacements in elastic multilayered systems subjected to one or

more uniform loads, acting uniformly over circular surface areas (32).

The surface loads can be combinations of a vertical normal stress and

unidirectional tangential stress.

The use of BISAR to compute the state of stress or strain in a

pavement requires the following assumptions (32):

1. Each layer of pavement acts as a horizontally continuous, isotropic,

homogeneous, linearly elastic medium.

2. Each layer has finite thickness except for the lower layer, and all

are infinite in the horizontal direction.








3. The surface loading can be represented by uniformly distributed ver-

tical stresses over a circular area.

4. The interface conditions between layers can vary from perfectly

smooth (zero bond) to perfectly rough (complete bonding) conditions.

5. Inertial forces are negligible.

6. The stress solutions are characterized by two material properties,

Poisson's ratio and Young's modulus for each pavement layer.

BISAR was used over other layered-theory programs because of its

availability, tested--and proven--reliability and accuracy (72,91,96),

and, also, its ability to handle variable layer interface conditions.

For example, McCullough and Taute (72) found that the ELSYM5 program (3)

which is based on the CHEVRON program (74) predicts unrealistic deflec-

tions in the vicinity of the load. They therefore recommended the use

of BISAR in computing fitted deflection basins, especially if the

deflection measurements are made near the loading point. Also, Ruth et

al. (98) reported correspondence with Mr. Gale Ahlborn, who developed

the ELSYM5 program, that the program is unreliable for certain unpre-

dictable combinations of material properties.


3.4 Description of Cone Penetration Test Equipment

The cone penetration test equipment consisted of a truck-mounted

hydraulic penetration system, electronic cone penetrometers (95) and an

automated data acquisition system. Detailed descriptions of the truck

and its features have been presented by Davidson and Bloomquist (30).

The hydraulic system serves two functions (29): leveling the truck

and penetrating the cone. The leveling system consists of four








independently controlled jacks. The front two jacks are connected to a

2 ft. x 7 ft. reaction plate; the back two to separate 15-in. circular

pads. The vehicle is lifted off the ground and leveling assured by

means of a spirit level. The penetrating system consists of a 20-ton

ram assembly located in the truck to achieve maximum thrust from the

reaction mass of the vehicle. Two double-acting hydraulic cylinders

provide a useable vertical stroke of 1.22 m. Prior to testing, the rams

are used to raise the telescoping roof unit. When locked in the raised

position, the unit allows full travel of the rams (29,30).

The cone penetrometers are of the subtraction type configuration,

with tip and friction strain gauges mounted on the central shaft

(29,95). Cone bearing is sensed by compression in the first load cell,

while the sum of cone plus friction is sensed in the rear load cell.

The friction value is then obtained by subtraction, which is done

electronically (29). The cones used also measure pore water pressure

and inclination. A cable, threaded through the 1-meter long push rods,

transmits the field recording signals to the data acquisition system.

The University of Florida currently has three electric cones, with

rated capacities of 5, 10 and 15 metric tons. Each measures tip

resistance, local friction, pore pressure, and inclination. The 5- and

10-ton cones are of standard configuration with 10-cm tip areas and 150-

cm friction sleeves. The larger 15-ton cone has the capability of

testing in much stiffer soil materials. All three cones contain

precision optical inclinometers which output the angular deviation of

the cone from the vertical during penetration (30).








The electronic data acquisition system is capable of printing and

plotting penetration data directly on the job site. It consists of a

microprocessor with 128 k magnetic bubble memory, an operator's console

with keypad, an Okidata microline 82A printer and an HP 7470A graphics

plotter. The computer is programmed with preset limits defined to

protect the probe from overloading. If a limit is exceeded, the

computer automatically stops the test and displays the cause of the

abort (29,30).

The electronic cone penetration testing equipment has several

advantages, such as a rapid procedure, continuous recording, high

accuracy and repeatability, automatic data logging, reduction, and

plotting. The CPT provides detailed information on site stratification,

identification, and classification of soil types. Results have also

been correlated with several basic soil parameters, including different

deformation moduli. For example, Schmertmann's method (101) of

computing settlements in sands requires the in situ variation of Young's

modulus. This is obtained from the CPT cone bearing resistance.


3.5 Marchetti Dilatometer Test Equipment

The Marchetti Dilatometer test (DMT) is a form of penetration test

and is fully described in References 17, 29, 68 and 69. Basically, the

test consists of pushing into the ground a flat steel blade which has a

flush-mounted thin circular steel membrane on one face. At the desired

depth intervals (usually every 20 cms) penetration is stopped, and

measurements are taken of the gas pressure necessary to initiate

deflection and to deflect the center of the membrane 1.1 mm into the








soil. These two readings serve as a basis for predicting several

important geotechnical parameters, using experimentally and semi-

empirically derived correlations (17,50,68). The DMT sounding provides

indications of soil type, preconsolidation stress, lateral stress ratio

at rest (Ko), Young's modulus (E), constrained modulus (M), shear

strength in clays and angle of shearing resistance in sands.

The major components of the dilatometer test equipment are the

dilatometer blade, the gas-electric connecting cable, a gas-pressure

source, and the read-out (control) unit. Figure 3.4 shows a schematic

diagram of this equipment. In addition there is a calibration unit,

adaptors, electric ground cable and a tool kit containing special tools

and replacement parts. Detailed descriptions and functions of the

various components are presented by Bullock (17) and Marchetti and

Crapps (69).

The dilatometer blade, as shown in Figure 3.4, consists of a stain-

less steel blade, 94 mm wide and 14 mm thick, bevelled at the bottom

edge to provide an approximate 16-degree cutting edge. A 60 mm stain-

less steel circular membrane is centered on and flush with one side of

the blade.

The control unit, housed in an aluminum carrying case, contains

various indicators, a pressure gauge and the controls for running the

test. The control unit gauge used in the current study had a range of

0-40 bars. Higher and lower range units are also available. This gauge

provides the gas pressure readings for the dilatometer test.

The dilatometer blade is advanced into the ground using standard

field equipment. The blade can be pushed or driven by one of the

following methods (29):

















Pressure Gauge
Buzzer

High
Pressure
Tubing -


To Pressure
r Source

Regulator Valve


Drill Rods


SFriction Reducer Ring


(/ I --- Diaphragm (60mm diam)



'm-- ,DMT Blade
14mm-







Figure 3.4 Schematic of Marchetti Dilatometer Test Equipment (69)








1. Using a Dutch Cone Penetrometer rig. This method is believed to

yield the highest productivity, up to 250 or more tests per day.

2. Using the hydraulic capability of a drill rig.

3. Using the SPT rig hammer or similar lighter equipment.

4. With barge-mounted equipment or by wireline methods for underwater

testing.


3.6 Plate Bearing Test

The plate bearing test conceptually belongs to the static force-

deflection group of NDT procedures (79). However, it can also be con-

sidered as a destructive field test since the testing requires the

construction and subsequent repair of a trench or test pit. The plate

bearing test consisted of the repetitive-static type of load test out-

lined in ASTM Test Procedure D 1195-64 (8). The main objective in this

test is to measure the deformation characteristics of flexible pavements

under repeated loads applied to the pavement through rigid, circular

plates. Burmister's two-layer theory (18,19,20) is generally used to

interpret plate load testing results (133).

The test equipment used by the Florida Department of Transportation

consists of a 12-inch diameter steel plate, loading system, deflection

gauges and supports (41). A trailer loaded with a huge rubber container

filled with water is used as a reaction. A hydraulic jack assembly is

used to apply and release the load in increments. A detailed descrip-

tion of the repetitive-static plate load test is provided in ASTM test

standards (8, pp. 258-260).














CHAPTER 4
SIMULATION AND ANALYSES OF NDT DEFLECTION DATA


4.1 BISAR Simulation Study

4.1.1 General

The Dynaflect and FWD loading-geophone patterns were simulated in

the BISAR elastic-layer computer program to predict surface deflection

data for four-layer pavement systems. A flexible pavement structure was

modeled as a four-layer system with parameters shown in Figure 4.1.

The selection of layer thicknesses and moduli was based on typical

ranges in parameters representative of Florida's flexible pavement

systems. In general, the limerock base and stabilized subgrade thick-

nesses were fixed at 8 in. and 12 in., respectively. Table 4.1 lists

the range of layer parameters used in the theoretical analysis. The

subgrade was generally characterized as semi-infinite in thickness with

an average or composite modulus of elasticity. However, the effect of

bedrock at shallow depth was also assessed by varying depth to bedrock

in a five-layer system. Poisson's ratio was fixed at 0.35 for all the

pavement layers since it has negligible effect on computed deflections.

In using the layered theory to generate and analyze NDT deflection

data certain assumptions had to be made. The following assumptions were

made with the use of the BISAR program:

1. Pavement materials are homogeneous, isotropic, and linearly

elastic. Therefore, the principle of superposition is valid for

calculating response due to more than one load.





















I 1


E2 2 H12



E3, 3 H3
E4, P4 3 H3



E4,I H4 = .


Four-Layer Flexible Pavement System Model


Figure 4.1















Range of Pavement Layer Properties


Layer Layer Layer Poisson's Layer Modulus
Layer Layer Thickness
Number Type iness Ratio (ksi)
(in.)


1 Asphalt 1.0 10.0 0.35 75 1,200
Concrete


2 Limerock 8.0 0.35 10 170
Base


Stabilized
3 Subgrade 12.0 0.35 6 75
(Subbase)


4 Subgrade Semi-infinite 0.35 0.35 200
(Embankment)


Table 4.1








2. Pavement layers are continuously in contact at the interfaces with

shearing resistance fully mobilized between them, so that the four

layers act together as an elastic medium of composite nature with

full continuity of stresses and displacements.

3. The Dynaflect and FWD dynamic loads are modeled as static circular

loads. Thus, the peak-to-peak dynamic force of the Dynaflect is

modeled as two pseudo-static loads of 500 Ibs. each uniformly

distributed on circular areas. The peak dynamic force of the FWD is

assumed to be equal to a pseudo-static load uniformly distributed on

a circular area representing the FWD loading plate.

4. Thickness and Poisson's ratio of each layer are assumed to be known.

4.1.2 Dynaflect Sensor Spacing

In order to determine the optimum locations of the five Dynaflect

sensors, additional ones were included in the BISAR simulation study.

These sensors were placed at intermediate positions near the loaded

wheel and first two (standard) sensors, with hope of detecting the

primary response of the upper pavement layers (surface and base

course). Figure 4.2 illustrates the loading and modified geophone

array.

The Dynaflect was modeled in the BISAR program using two circular-

loaded areas, with deflection measurement positions as shown in Figure

4.2. Each load is 500 Ibs. in weight, and the contact area used in this

study was 64 in.2, resulting in an equivalent radius of 4.5 in.

4.1.3 FWD Sensor Spacing

The conventional sensor spacing used by the FDOT and four

additional sensor locations were utilized in the analytical study.

Sensors were placed at radial distances of 0, 7.87, 11.8, 16.0, 19.7,






















Spacing


GEOPHONE NO.
Conventional Modified


Figure 4.2 Dynaflect Modified Geophone Positions


@-X








24.0, 31.5, 36.0, 47.2, 63.0, and 72.0 in. from the center of the FWD

plate. The deflection measured by the last sensor (at radial distance

of 72.0 in.) is called D in this study. Sensors at 16, 24, and 36 in.

were only used to better define the deflection basin and were not incor-

porated in the analysis for prediction equations, which is described

later in this chapter. Thus, eight deflection locations were actually

used in the analysis. These are called D1, D2, D3, D4, D5, D6, D7, and

D8 to represent, respectively, radial distances of 0, 7.87, 11.8, 19.7,

31.5, 47.2, 63, and 72 in. from the center of the FWD plate.

The FWD was modeled in the BISAR program as a circular loaded area

with deflection measurement positions as stated previously. The radius

of the loaded area was 5.91 in., and a load of 9 kips was used which

corresponds to one-half of an 18-kip single-axle wheel loading.


4.2 Sensitivity Analysis of Theoretical NDT Deflection Basins

4.2.1 Parametric Study

Pavement surface deflection data generated from BISAR were ini-

tially evaluated to see the effect of rate of change of each layer

modulus and/or thickness on the NOT deflection basins. This was accom-

plished by using the pavement section shown in Figure 4.3 as a typical

Florida pavement under warm temperature conditions. Using the informa-

tion in Figure 4.3, each parameter (modulus and thickness) for a given

layer was doubled or halved while the others were kept unchanged. For

example, the E value of 10 ksi was increased to 20 ksi without changing

E E2, E3 and the layer thicknesses. BISAR was then used to calculate

the NDT deflections. The original E value was also halved to 5 ksi and

the theoretical deflections were computed with BISAR. This procedure was

















,',',',',ASPHALT CONCRETE' ,'
%%\` '\E1 = 150 ksi ^^^^^
\\%%^/^/^^ *VI\\\\\\^\\\\ \ .^ \\\ *
p p% ,p ,,,,,, I-1 pp ~P P K .
*,.b'U 0 /./,y --0 . 0. -,1oo.*o;
.O.' .:O. LIMEROCK BASE COURSE.'..O
C OO0O :E =85 ksi.o.k-c;.




.' .-. . ..* ....... ::. ..


SSTABILIZED SUBGRADEAY:', Y
(SUBB'AS E(SU B B AS E).





... ...-....... ....: ...::........
.. .. ........


Semi-Infinite


Figure 4.3 Typical Four-Layer System Used for the Sensitivity Analysis








repeated for all layer moduli and thicknesses. The NDT device used in

the parametric study was the FWD with a 9-kip loading and sensor spacing

as previously described. However, the findings also apply to the

Dynaflect loading system, under the principle of superposition and

linear elastic theory.

Figures 4.4 through 4.10 show the effect of change of either

modulus or thickness on the FWD deflection basins. The rate of change

of deflections is most pronounced with changes in E as compared with

the moduli of the upper pavement layers. In the case of the layer

thicknesses, the effect is most apparent with changes in the base course

thickness, t It is possible that the t2 effect was due to the high E2

relative to E3 and E4.

Table 4.2 shows the percent change in deflections as a result of

doubling or halving each layer modulus while keeping the other para-

meters unchanged for the pavement section shown in Figure 4.3. The

table shows that changes in E4 affect the deflections to the greatest

degree. The percent change in deflection is highest for any sensor

position when the E4 value is changed. This change with regard to E4

also increases substantially for the sensors further away from the load

center. The table thus suggests that E4 contributes the most to the FWD

deflections.

Similar comparisons were also made for changes in deflections for

t values of 1.5 and 6.0 in. The effect of layer thicknesses, ti, t2,

and t3, on the theoretical FWD deflections were also studied and the

results are summarized in Table 4.3. In this table, t2 seems to have

the most effect. The effect of tI on the deflections becomes negligible

when the original value (t. = 3.0 in.) is halved.




Full Text
Table B.10 Results of FWD Tests on SR 15A (Martin County)
Temperature (F):
Air = 88 Pvmt. Surf. = 110
Mid-Pvmt. = 120
Site
No.
Mile
Applied
Measured Deflections (mils)
Post
No.
Load
(kips)
.(a) 2 3
5 6
7
0.o(b) 7.87 11.8 19.7
31.5 47.2
63.0
4.894
10.04
6.61
5.51
4.29
3.35
2.76
2.28
6.546
7.198
16.42
11.73
10.08
8.07
6.46
5.12
4.17
9.280
22.48
16.26
14.21
11.42
8.94
7.05
5.63
4.672
5.75
4.76
4.33
3.78
3.19
2.72
2.32
6.549
6.976
10.43
8.90
8.19
7.09
5.94
4.96
4.21
9.026
14.45
12.36
11.30
9.84
8.15
6.54
5.35
CO
6.549
(OWP)
4.703
5.47
4.29
3.94
3.54
3.07
2.64
2.20
CT>
VO
7.008
10.04
8.11
7.60
6.97
6.06
5.08
4.21
9.057
13.15
10.59
9.92
9.06
7.80
6.42
5.31
4.656
6.38
5.43
4.92
4.21
3.43
2.76
2.28
6.551
7.008
11.89
10.35
9.41
8.11
6.65
5.24
4.37
9.026
15.51
13.50
12.24
10.47
8.50
6.57
5.28
4.608
6.93
5.35
4.88
4.13
3.50
2.91
2.36
6.556
7.023
13.23
10.55
9.65
8.39
6.97
5.83
4.72
8.994
17.44
13.98
12.80
11.02
9.13
7.36
5.98
4.513
9.92
6.85
5.94
5.12
4.29
3.54
2.91
6.560
6.912
17.56
12.83
11.30
9.88
8.15
6.69
5.35
8.962
22.83
16.89
14.96
12.95
10.55
8.50
6.69
6


65
3. The surface loading can be represented by uniformly distributed ver
tical stresses over a circular area.
4. The interface conditions between layers can vary from perfectly
smooth (zero bond) to perfectly rough (complete bonding) conditions.
5. Inertial forces are negligible.
6. The stress solutions are characterized by two material properties,
Poisson's ratio and Young's modulus for each pavement layer.
BISAR was used over other layered-theory programs because of its
availability, testedand proven--reliability and accuracy (72,91,96),
and, also, its ability to handle variable layer interface conditions.
For example, McCullough and Taute (72) found that the ELSY.M5 program (3)
which is based on the CHEVRON program (74) predicts unrealistic deflec
tions in the vicinity of the load. They therefore recommended the use
of BISAR in computing fitted deflection basins, especially if the
deflection measurements are made near the loading point. Also, Ruth et
al. (98) reported correspondence with Mr. Gale Ahlborn, who developed
the ELSYM5 program, that the program is unreliable for certain unpre
dictable combinations of material properties.
3.4 Description of Cone Penetration Test Equipment
The cone penetration test equipment consisted of a truck-mounted
hydraulic penetration system, electronic cone penetrometers (95) and an
automated data acquisition system. Detailed descriptions of the truck
and its features have been presented by Davidson and Bloomquist (30).
The hydraulic system serves two functions (29): leveling the truck
and penetrating the cone. The leveling system consists of four


482
44. Hicks, R.G., Factors Influencing the Resilient Properties of
Granular Materials, Ph.D. Dissertation, University of
California-Berkeley, 1970.
45. Hoffman, M.S., and Thompson, M.R., "Backcalculating Nonlinear
Resilient Moduli from Deflection Data," Transportation
Research Record 852, TRB, Washington, D.C., 1982, pp. 42-51.
46. Hoffman, M.S., and Thompson, M.R., "Comparative Study of Selected
Non-destructive Testing Devices," Transportation Research
Record 852, TRB, Washington, D.C., 1982, pp. 32-41.
47. Husain, S., and George, K.P., "In Situ Pavement Moduli From Dyna-
flect Deflections," Transportation Research Record 1043, TRB,
Washington, D.C, 1985, pp. 102-112.
48. Hveem, F.M., "Pavement Deflections and Fatigue Failures," Highway
Research Board Bulletin 114, HRB, Washington, D.C., 1965,
pp. 43-73.
49. Irwin, L.H., "Determination of Pavement Layer Moduli from Surface
Deflection Data for Pavement Performance Evaluation," Pro
ceedings Fourth International Conference on the Structural
Design of Asphalt Pavements, University of Michigan, Vol. 1,
1977, pp. 831-840.
50. Jamiolkowski, M., Ladd, C.C., Germaine, J.T., and Lancellota, R.,
"New Developments in Field and Laboratory Testing of Soils,"
Proceedings, Eleventh International Conference on Soil
Mechanics and Foundation Engineering, San Francisco, Califor
nia, Vol. 1, 1985, pp. 57-153.
51. Jimenez, R.A., "Pavement-Layer Modular Ratios from Dynaflect
Deflections," Transportation Research Record 671, TRB,
Washington, D.C., 1978, pp. 23-29.
52. Jones, A., "Tables of Stresses in Three-Layer Elastic Systems,"
Highway Research Board Bulletin 114, HRB, Washington, D.C.,
1955, pp. 176-214.
53. Kasianchuk, D.A., and Argue, G.H., "A Comparison of Plate Load
Testing with the Wave Propagation Technique," Proceedings,
Third International Conference on the Structural Design of
Asphalt Pavements, London, England, Vol. 1, September 11-15,
1972, pp. 444-454.
54. Kausel, E., and Peek, R., "Dynamic Loads in the Interior of a
Layered Stratum An Explicit Solution," Bulletin of the
Seismological Society of America, Vol. 72, No. 5, October
1982, pp. 1459-1481.


6.20 Ratios of Dynaflect Moduli to FWD Moduli for Test Sections... 262
6.21 Correlation Between Measured and Predicted FWD
(9-kip Load) Deflections 281
7.1 Relationship Between Eq and qc for Selected Test
Sections in Florida 298
7.2 Correlation of NDT Tuned Base Course Modulus (E2)
to Cone Resistance 305
7.3 Correlation of NDT Tuned Subbase Modulus (E3) to
Cone Resistance 306
7.4 Correlation of NDT Tuned Subgrade Modulus (E^) to
Cone Resistance 307
7.5 Relationship Between Resilient Modulus, E^ and
Cone Resistance, qc 308
7.6 Correlation of NDT Tuned Subbase Modulus to
Dilatometer Modulus 309
7.7 Correlation of NDT Tuned Subgrade Modulus to
Dilatometer Modulus 310
7.8 Relationship Between Resilient Modulus, E^ and
Dilatometer Modulus, ED 311
7.9 Effect of Varying Subgrade Stiffness on Dynaflect
Deflections on SR 26A 314
8.1 Material Properties and Results of Stress Analysis
for SR 26B (Gilchrist County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis 320
8.2 Material Properties and Results of Stress Analysis
for SR 24 (Alachua County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis 321
8.3 Material Properties and Results of Stress Analysis
for US 441 (Columbia County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis 322
8.4 Material Properties and Results of Stress Analysis
for SR 15C (Martin County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis 323
8.5 Material Properties and Results of Stress Analysis
for SR 80 (Palm Beach County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis 324
8.6 Summary of Pavement Stress Analysis at Low Temperatures 326
xii


DEFLECTION (mils)
80
RADIAL DISTANCE FROM LOAD (in)
0 10 20 30 40 50 60 70
Figure 4.5 Effect of Change of E on Theoretical FWD (9-kip Load)
Deflection Basin


TEST HO. 1
UN TV. CF FLORIDA CIVIL Etc. DETT. DR. B.E. RUTH
FILE NAME: FAVTTtENT-SUDGRAIlE MATERIALS CHARACTERIZATION
FILE NUMBER: 245-D51
RECORD OF DILATCMETER TEST NO. 1
USING DATA REDUCTION PROCEDURES IN MAR CUE TTI (ASCE, J-GED, MARCH 80)
K0 IN SANDS DETERMINED USING SCII1ERTMANN METHOD (1983)
FHI AJJGLE CALCULATION BASED ON DURGUNOGLU AND MITCHELL (ASCE.RALEIGH CONF.JUNE 75)
FHI ANGLE NORMALIZED TO 2.72 BARS USING EALIGH'S EXPRESSION (ASCE.J-GED,NOV 76)
MODIFIED MAYNE AND KULHAWY FORMUIA USED FOR OCR IN SANDS (ASCE,J-GED,JUNE 82)
LOCATION: SR 26A (GILCHRIST DO.) TEST SITE #3.5
FEP.FCRflEn DATE: 10-31-85
BY: JI.D/PGB/KBT
CALIBRATION INFORMATION:
TELTA
A 0.
22 BARS
DELTA B
* 0.45
BARS
GAGE 0
0.05
BARS
GOT DEPTH 1.57 M
RCP PIA. U.
80 CM
FR.l
RED.PIA.
. 3.70
CM
ROD WT.
.= 6.50
KG/M
DELTA/FUI 0.50
BLADE T=
13.70 m
1 BAR
1.019 KG/CM2 l.i
044 TSF
- 14.51 FSI
ANALYSIS USES
H20 UNIT WEIGHT =
1.000 T/M3
z
THRUST
A
B
ED
ID
KD
uo
GAFTIA
SV
rc
CCR
KO
CU FHI
M
SOIL TYFE
!M)
(EG)
(BAR)
(PAR)
(BAR)
(BAR)
(T/M3)
(BAR)
(BAR)
(BAR) (DEG)
(PAR)
* AAA*
* *V A A A
4**##
A A A A A
A A A A A
A A A A A
A A A A A A
A A A A A A
......
AAAA A
A AAAA
A AAAA
***** *****
>V A >V A A A
AAAAAA*AAAA*
0.63
4060.
n.RO
29.10
679.
2.16
73.48
0.000
2.150
0.123
57.93
A A A A A
8.71
42.1
2978.7
SILTY SAND
0.83
3403.
7.60
25.30
620.
2.58
42.64
0.000
2.000
0.162
26.77
A A A A A
5.01
42.3
2403.1
SILTY SAND
1.03
2630.
6.40
22.20
351.
2.73
28.85
0.000
2.000
0.202
16.30
80.88
3.42
41.2
1931.1
SILTY SAND
1.23
2355.
6.00
22.80
588.
3.16
22.28
0.000
2.000
0.241
12.05
50.07
2.67
40.7
1914.9
SILTY SAND
1.43
I960.
'<. 10
16.50
427.
3.34
13.25
0.000
1.900
0.278
4.87
17.53
1.56
41.4
1182.2
SAND
1.63
1005.
3.10
9.00
191.
1.83
9.70
0.006
1.900
0.309
3.70
11.94
1.32
36.5
471.0
SILTY SAND
1.83
290.
0.80
1.50
1.
0.03
2.95
0.026
1.500
0.319
0.59
1.84
0.77
0.114
1.4
MUD
2.03
375.
0.60
2.30
38.
1.61
2.03
0.045
1.600
0.331
0.41
1.24
0.47
33.5
36.5
SANDY SILT
2.23
1300.
2.30
15.10
442.
7.08
5.16
0.065
1.900
0.349
1.01
2.89
0.63
40.8
850.8
SAND
2.3
1270.
2.50
18.40
555.
8.77
4.98
0.084
1.900
0.366
1.04
2.83
0.63
40.4
1051.1
SAND
2.63
1045.
1.20
13.20
413.
17.01
1.83
0.104
1.800
0.382
0.13
0.34
0.21
41.3
423.2
SAND
2.83
1100.
0.50
9.10
289.
55.57
0.38
0.124
1.700
0.396
245.6
SAND
3.43
14 50.
1 70
11.30
326.
7.56
2.8?.
n. in:i
I. non
0.44 0
0.32
0.73
0.30
42.0
4 55.7
RAND
3.63
1040.
3.20
9.30
198.
1.97
6.33
0.202
1.900
0.458
1.95
4.27
0.76
41.3
409.9
SILTY SAND
3.83
1780.
6.10
14.40
278.
1.41
11.90
0.222
1.950
0.476
8.35
17.53
1.58
37.1
740.7
SANDY SILT
4.03
1535.
8.50
17.00
285.
1.02
16.24
0.241
1.950
0.495
12.98
26.23
2.46
844.2
SILT
4.23
1345.
7.30
14.00
220.
0.92
13.45
0.261
1.950
0.514
10.04
19.55
2.20
610.9
SILT
4.43
1090.
7.20
12.20
158.
0.66
12.91
0.281
1.950
0.532
9.77
18.35
2.15
1.205
432.6
CLAYEY SILT
4.63
905.
5.60
11.30
183.
1.01
9.52
0.300
1.800
0.548
6.25
11.41
1.78
449.2
SILT
4.83
930.
6.60
11.80
165.
0.76
10.98
0.320
1.950
0.567
8.08
14.26
1.95
1.048
427.3
CLAYEY SILT
5.03
1070.
0.20
19.00
333.
1.12
14.65
0.340
1.950
0.585
13.08
22.34
2.32
951.9
SILT
5.23
960.
5.SO
11.90
198.
1.07
8.89
0.359
1.800
0.601
6.15
10.24
1.71
471.4
SILT
5.43
820.
6.50
11.00
140.
0.66
9.89
0.379
1.800
0.617
7.47
12.11
1.83
1.001
347.3
CLAYEY SILT
5.63
830.
5.50
9.40
118.
0.66
8.08
0.398
1.800
0.632
5.58
8.83
1.61
0.797
268.6
CLAYEY SILT
END OF SCI "IDT NO
406


NORMALIZED DEFLECTION (mils)
246
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.57 Comparison of Measured NDT Deflection Basins on SR 26A
M.P. 11.912


336
4. Further work on the interpretation of the in situ penetration tests
is required to improve the correlations obtained in this study. The
use of the penetration tests to characterize the subgrade layer and
the feasibility of the DMT to predict the moduli of weak subgrade
soils should be investigated.
5. Additional stress analyses using load and thermal-induced stresses
should be performed to establish relationships between pavement
performance (stress-strain response) and material properties.
6. The algorithms obtained in this study should be used to develop a
computer program for mechanistic evaluation of flexible pavements.
This program should be able to predict the remaining life and/or
overlay thickness of a pavement subjected to the combined effects of
load and thermal-induced stresses.


104
D-j D4 (mils)
Figure 4.19 Relationship Between E2 and Dx D4 for tx = 1.0 in


Table A.2 Results of Dynaflect Tests on SR 26B (Gilchrist County)
Temperature (F): Air = 45 Pavement Surface = 48 Mid-Pavement = 59
Site
No.
Mile
Post No.
Type of
Data
Measured
Deflections
(mils) for
Sensor
Positions
*
1
2
3
4
5
6
7
8
9
10
0
11.213
Std

1.05
0.99
0.79
0.68
0.68
1
11.208
Std
1.24
1.16
0.92
0.78
0.68
1
11.208
Mod
1.31
1.30
1.06
0.79
0.69
1
11.208
Std
1.22
1.14
0.89
0.75
0.66
1
11.208
Mod
1.34
1.37
1.11
0.75
0.69
1.5
11.205
Std
1.18
1.12
0.90
0.77
0.68
1.5
11.205 .
Mod
1.28
1.23
0.99
0.76
0.67
2
11.202
Std
1.06
1.04
0.85
0.74
0.66
2
11.202
Mod
1.15
1.12
0.96
0.75
0.66
2.5
11.200
Std
1.12
1.09
0.89
0.78
0.70
2.5
11.200
Mod
1.25
1.21
1.02
0.81
0.73
3
11.197
Std
1.21
1.17
0.95
0.83
0.74
3
11.197
Mod
1.33
1.25
1.04
0.83
0.75
* Sensor positions correspond to the modified array in Figure 4.2


Table E.6--continued
Layer 3: Thickness =
Temperature
F (C)
Absolute
Viscosity
nj(Pa-s)
Complex
Flow (C)
Constant Power
Viscosity
"ioo(pa-s)
275 (135)
8.21 El
---
---
140 (60)
1.268 E3

1.268 E3
77 (25)(a)
8.10 E5
0.78
2.46 E6
60 (15.6)
5.83 E6
0.86
1.33 E7
41 ( 5)
1.23 E7
0.56
3.35 E8
23 (-5)
2.00 E8
0.86
7.99 E8
(a) Penetration
at 77F (25C)
= 30
Layer 4:
Thickness =
Temperature
F (C)
Absolute
Viscosity
n1(Pa-s)
Complex
Flow (C)
Constant Power
Viscosity
nioo(Pas)
275 (135)
5.83 El


140 (60)
8.032 E2

8.032 E2
77 (25)(a)
3.34 E5
0.82
7.45 E5
60 (15.6)
2.72 E6
0.91
4.40 E6
41 ( 5)
1.81 E7
0.68
1.98 E8
23 (-5)
1.25 E8
0.84
4.24 E8
(a) Penetration at 77F (25C) = 46


323
Table 8.4 Material Properties and Results of Stress
Analysis for SR 15C (Martin County)
a) Input Parameters for BISAR
Layer
Description
Thickness
Poisson s
Modulus
(psi)
(in.)
Ratio
Dynaflect
FWD
1
Asphalt Concrete
6.75
0.35
680,000 *
680,000 *
2
Limerock Base
12.5
0.35
105,000
50,000
3
Subbase
12.0
0.35
75,000
44,000
4
Subgrade
semi-
infinite
0.45
5,500
10,000
* Computed at 41F using rheology data (Table 5.4)
b) Pavement Stress Analysis
Response Parameters**
Dynaflect
FWD
Maximum Surface Deflection
(mils)
15.9
14.3
Radial stress, bottom of AC layer
(psi)
54.4
88.5
Radial strain, bottom of AC layer
(E-6
in./in.)
56.5
82.5
Vertical stress, top of base layer
(psi)
-23.0
-16.7
Radial stress, top of base layer
(psi)
-1.7
-1.6
Vertical stress, top of subbase layer
(psi)
-4.5
-4.8
Radial stress, top of subbase layer
(psi)
2.8
1.3
Vertical stress, top of subgrade
(psi)
-0.95
-1.8
Vertical strain, top of subgrade
(E-6
in./in.)
-161.0
-173.0
Deflection in AC layer
(*)
3.1
3.5
Deflection in base layer
(%)
8.8
16.1
Deflection in subbase layer
(%)
5.7
9.1
Deflection in subgrade layer
(%)
82.4 .
71.3
* Computed for single axle with dual wheels (6.0 kips/wheel)
NOTE: + = tensile
- = compressive


61
2. a Dynatest 8600 System Processor, and
3. a Hewlett-Packard HP-85 Table Top Computer.
3.2.1 The 8002 FWD
The Dynatest 8002 FWD consists of a large mass that is constrained
to fall vertically under gravity onto a spring-loaded plate, 11.8 in. in
diameter, resting on the pavement surface (see Figure 2.4). A load
range of about 1500 to 24000 lbs. can be achieved by adjusting the num
ber of weights or height of drop or both. The impulse or impact load is
measured by using a strain-gauge-type load transducer (load cell). The
impact load closely approximates a half-sine wave (see Figure 2.5), with
a duration of 25-30 msec which closely approximates the effect of moving
dual-wheels with loads up to 24000 lbs. (110).
Seven seismic deflection transducers or geophones in movable brack
ets along a 2.25 m raise/lower bar are used to measure the response of
the pavement to the dynamically applied load. The geophones, which are
50 mm in diameter and 55 mm high, operate at a frequency range of 2 to
300 Hz (34). One of the geophones is placed at the center of the plate,
with the remainder placed at radial distances from the center of the
plate (see Figure 3.3). In its present form, the FD0T measures deflec
tions at radial distances of 0, 7.87, 11.8, 19.7, 31.5, 47.2, and 63.0
in. from the plate center. These deflections are respectively called
D,D,D,D,D,D, and D in this study.
12 3 4 5 6 7
3.2.2 The 8600 System Processor
The Dynatest 8600 system processor is a microprocessor-based con
trol and registration unit which is interfaced with the FWD as well as
the HP-85 computer (34,110). The processor is housed in a 19-in. wide


458
3.Check whether measured deflections are within the following limits:
0.56 < Dj or D < 2.92 mil
0.27 < D3 < 2.07 mil
0.15 < D, < 1.50 mil
0.05 < D5 <1.00 mil
and also the following criteria are met:
0.09 < Dj + D2 2D3 < 0.85 mil
0.12 < D3 D4 < 0.57 mil.
These criteria conform approximately to the following range of layer
moduli and thicknesses:
65.0
< Ei
<
400
ksi
1.5
<
8.5 in.
26.0
E2
<
130
ksi
6.0
<
*2 <
24.0 in.
18.0
< E3
<
90.0
ksi
12.0
<
s<
36.0 in.
5.0
< E*t
<
105
ksi
%
=
semi
-infinite
Note that for the prediction equations, E.¡ is in ksi, t.¡ in inches,
and D.j in mils. Also extremely high or low D5 values, outside the
stipulated range, may be used for estimates of E4 from 1.0 to 200
ksi.
4. If the above conditions are satisfied, proceed to step 5. If not,
check deflection measurements and then go to step 3. If the checked
deflections do not meet conditions in step 3, proceed to step 5
considering that the predictions may be approximate or significantly
in error.
5. Obtain pavement layer thicknesses from construction drawings or by
coring.


TEST HO.
3
UNIV. OF FLORIDA CIVIL ENG. DEPT.- DR. B.E. RUTH
FILE NAME: PAVEMENT-SUBGRADE MATERIALS CHARACTERIZATION
FILE NUMBER: 245-D51
RECORD OF DILAICMETER TEST NO. 3
USING DATA REDUCTION PROCEDURES IN MARCHETTI CASCE.J-GED.MARCH 30)
KO IN SANDS DETERMINED USING SC33-RIMANN METHOD (1983)
PHI ANGLE CALCULATION BASED ON DURGUNOGLU AND MITCHELL (ASCE,RALEIGH CCNF.JUNE 75)
PHI ANGLE NORMALIZED TO 2.72 BARS USING BALIGHS EXPRESSION (ASCE,J-GED,NOV 76)
MODIFIED MAYNE AND KULHAWY FORMULA USED FOR OCR IN SANDS (ASCE,J-GED, JUNE 32)
LOCATION: US 301 (ALACHUA CO.) TEST SITE #4.0
PERFORMED DATE: 02-18-85
BY: JLD/DG3/KBT
CALIBRATION INFORMATION:
DELTA A 0.22 BARS
DELTA B
0.37 BARS
GAGE 0 0.05 BARS
GOT DEPTH- 1.14 M
ROD DIA.- 4.80 CM
FR.RED.DIA.-
3.70 CM
ROD WT.- 6.50 KG/M
DELTA/FHI- 0.50
BLADE T-13.70 Ml
1 BAR 1.019 KG/CM2
- 1.044 TSF -
14.51 PSI.
ANALYSIS USES
H20 UNIT WEIGHT -
1.000 T/M3
Z
THRUST
A
B
ED
ID
KD
UO
GAMA
SV
PC
OCR
KO
OJ
PHI
M
SOIL TYPE
(M)
(KG)
(BAR)
(BAR)
(BAR)
(BAR)
(T/M3)
(BAR)
(BAR)
(BAR)
(DEG)
(BAR)
*****
****
*****
*****
*****
*****
******
******
******
*****
*****
*****
*****
*****
******
************
0.50
2270.
3.40
22.60
678.
7.40
26.93
0.000
1.900
0.098
2331.2
SAND
0.70
5660.
11.80
40.00
1006.
2.74
75.53
0.000
2.150
0.140
66.10
*****
3.83
43.5
4440.7
SILTY SAND
0.90
3910.
13.50
38.60
893.
2.07
68.23
0.000
2.150
0.182
87.45
.....
8.25
38.9
3856.1
SILTY SAND
1.10
1790.
3.00
10.70
259.
2.65
12.81
0.000
1.900
0.220
3.30
15.03
1.43
42.5
708.4
SILTY SAND
1.30
1920.
2.30
11.80
325.
4.66
8.33
0.016
1.900
0.241
1.10
4.54
0.74
44.6
759.9
SAND
1.50
2605.
5.30
18.40
456.
2.73
18.43
0.035
2.000
0.261
8.53
32.71
2.15
42.0
1403.4
SILTY SAND
1.70
1800.
3.80
14.60
372.
3.15
12.22
0.055
1.900
0.279
4.25
15.26
1.45
41.0
1000.7
SILTY SAND
1.90
700.
1.80
5.80
124.
2.08
5.86
0.075
1.800
0.294
1.44
4.89
0.86
36.2
249.0
SILTY SAND
2.30
860.
1.90
8.60
223.
3.89
5.07
0.114
1.800
0.326
1.14
3.51
0.72
37.9
425.1
SAND
2.70
3310.
6.30
18.60
427.
2.15
15.88
0.153
2.000
0.361
8.78
24.31
1.84
42.4
1253.4
SILTY SAND
3.10
2305.
9.00
18.20
314.
1.06
21.41
0.192
1.950
0.399
16.12
40.37
2.89
1010.3
SILT
3.30
2335.
9.40
19.40
343.
1.11
21.27
0.212
1.950
0.418
16.70
39.96
2.88
1102.1
SILT
3.70
2005.
8.60
16.40
263.
0.93
17.92
0.251
1.950
0.455
13.93
30.60
2.61
801.8
SILT
3.90
1490.
7.80
13.60
190.
0.74
15.70
0.271
1.950
0.474
11.79
24.88
2.41
1.370
555.6
CLAYEY SILT
4.30
1325.
8.50
14.40
193.
0.69
15.84
0.310
1.950
0.511
12.39
25.22
2.43
1.494
567.9
CLAYEY SILT
4.70
1740.
7.60
17.40
336.
1.39
12.69
0.349
1.950
0.548
11.62
21.18
1.73
35.3
914.6
SANDY SILT
4.90
2415.
6.70
21.80
529.
2.54
10.17
0.369
2.000
0.568
6.87
12.09
1.31
39.4
1330.0
SILTY SAND
5.30
1655.
11.60
19.00
248.
0.65
18.18
0.408
1.950
0.606
18.96
31.28
2.63
2.105
760.7
CLAYEY SILT
5.70
4200.
10.40
29.00
656.
2.05
14.24
0.447
2.150
0.648
14.03
21.66
1.74
41.1
1860.1
SILTY SAND
5.90
1980.
11.20
17.40
204.
0.55
15.97
0.467
1.900
0.665
17.00
25.55
2.44
1.964
601.6
SILTY CLAY
6.10
2070.
7.30
19.00
405.
1.81
9.39
0.487
2.000
0.685
7.87
11.50
1.30
37.2
987.7
SILTY SAND
6.30
2625.
6.40
19.60
459.
2.44
7.71
0.506
2.000
0.704
5.01
7.10
1.01
40.0
1040.6
SILTY SAND
END OF SOUNDING


regression equations with relatively good prediction accuracy were
obtained from analyses of FWD deflection data. Different layer moduli
values were obtained from the Dynaflect and FWD deflection basins for
the various test sections.
The penetration tests provided means for identifying the soils and
also assessing the variability in stratigraphy of the test sites. Good
correlations between cone resistance, qc, and dilatometer modulus, Eg,
for sandy soils and soils above the water table were obtained. Pavement
layer moduli determined from NDT data were regressed to qc and Eg for
the various layers in the pavement. The correlations were better with
qc than with Eg, and also for the base and subbase layers than the
variable subgrade layer. The penetration tests can be used to supple
ment NDT evaluation of pavements especially in locating zones of weak
ness in the pavement or underlying subgrade soils.
The effects of moisture, temperature, and the properties of the
asphalt binder on the performance and response characteristics of
flexible pavements were demonstrated using short-term load-induced
stress analyses on five of the test pavements.
xxii


333
(Figure 3.3) are probably not suitable to allow the separation of
pavement layers from the interpretation of FWD deflections.
7. The use of viscosity-temperature relationship obtained from Schweyer
Rheometer tests on the recovered asphalts was found to be an effec
tive and reliable method for predicting the asphalt concrete modu
lus, Analysis of the NDT data resulted in the development of
Figure 6.64 which can be used as a simple and rapid method of esti
mating E from the mean pavement temperature during routine NDT
pavement evaluation studies.
8. Comparisons between Dynaflect and FWD tuned moduli for the various
test sections indicated that the Dynaflect predicted higher base
course and subbase (stabilized subgrade) moduli than the FWD. In
the case of the subgrade, no distinct trend between the two NDT
devices was found from the analyses. Therefore, the differences in
layer moduli predictions were attributable to the inherent differ
ences between the two NDT devices; namely, vibratory loading for the
Dynaflect versus impact loading for the FWD testing system.
9. The penetration tests provided means for identifying the soils and
also for assessing the variability in stratigraphy of test sites.
The cone resistance, qc, correlated well with the dilatometer modu
lus, Ed, especially for sandy soils and soils above the water
table. The correlation for clayey soils was poor, supporting the
argument that qc cannot be correlated to any drained soil modulus
for cohesive deposits.
10. Pavement layer moduli determined from NDT data were regressed to qc
and Eg for the various layers in the pavement. Good correlations
were obtained for qc as compared to Eg. However, the dilatometer


DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
ro
o
Figure 6.30 Comparison of Measured and Predicted Dynaflect Deflections for SR 15CM.P. 0.055


o o o
470
12
2
20
30
C-
C
C
C
C
C
C
C
C-
40
WRITE(NOT,4003) E(2),NU(2),THICK(2)
WRITE(*,4004) E(3),NU(3)
WRITE(NOT,4004) E(3),NU(3)
ELSE
DO 12 1=1,M
WRITE(*,5003) I,E(I),NU(I),THICK(I)
WRITE(NOT,5003) I,E(I),NU(I),THICK(I)
WRITE(*,5004) NLAYS,E(NLAYS),NU(NLAYS)
WRITE(NOT,5004) NLAYS,E(NLAYS),NU(NLAYS)
ENDIF
READ NUMBER OF LOADS AND THEIR PARAMETERS BI
NLOAD = 2
NZEP = .FALSE.
BI
NZEQ = .FALSE.
BI
IDENT = IREF1
DO 30 1=1,NLOAD
BI
LDSTRS(I) = 500.0
RADIUS(I) =4.5
HOSTR(I) =0.0
PSI(I) = 0.0
XCI) = 0.0
PSI(I)=.0174533*PS1(1) BI
IF(LDSTRS(I).GT.ACCUR(1)) NZEP= .TRUE. BI
IF(HOSTRd) .GT.ACCUR(l)) NZEQ = .TRUE. BI
IF(IDENT.EQ.IREF1) GO TO 20 BI
IF(IDENT.NE.IREF2) WRITE(NOUT,9040) LDSTRS(I),HOSTR(I) BI
GO TO 30 BI
LDSTRS(I) = LDSTRS(I)/(3.14159*RADIUS(I)*RADIUS(I)) BI
HOSTR(I) = HOSTR(I)/(3.14159*RADIUS(I)*RADIUS(I)) BI
CONTINUE BI
Y(1) = -10.0
Y(2) = 10.0
B1
TEST ON OBVIOUS MISTAKES IN SYSTEMS DATA-BI
CARDS. BI
WHEN IRED > 0 THE REDUCED SPRINGCOMPLIAN- BI
CE WAS READ. BI
A NON-VANISHING SLIPRESISTANCE IS SUBSTI- BI
TUTED TO PREVENT RIGID-BODY MOTION OF THE BI
TOPLAYERS BI
----BI
DO 50 J = 1,NLAYS BI
IF((1.0-NU(J)).LT.ACCUR(1)) GO TO 410 BI
IF(E(J).LT.ACCUR(1)) GO TO 420 BI
IF(J.EQ.NLAYS) GO TO 50 BI
IF(IRED.EQ.0) GO TO 40 BI
ALK(J) = AK(J) BI
IF(ALK(J).LT.1000.0.OR..NOT.NZEQ) GO TO 50 BI
ALK(J) = 1000.0 BI
AK(J) = 1000.0 BI
GO TO 50 BI
ALK(J) = AK(J)*E(J)/(1.0+NU(J)) BI
IF(ALK(J).LT.1000.0.OR..NOT.NZEQ) GO TO 50 BI
ALK(J) = 1000.0 BI


DEFLECTION (10 in)
155
Figure 6.1 Surface Deflection as a Function of Load on SR 26A


Table A. 12 Results of Dynaflect Tests on SR 715 (Palm Beach County)
Temperature (F): Air = 80 Pavement Surface = 88 Mid-Pavement = 111
Site
No.
Mi 1 e
Post No.
Type of
Data
Measured
Deflections
(mils) for
Sensor
Positions
*
1
2
3
4
5
6
7
8
9
10
1
4.732
Std
1.17
0.96
0.86
0.75
0.71
1
4.732
Mod
1.29
1.13
0.95
0.77
0.70
2
4.727
Std
1.20
0.97
0.87
0.80
0.72
2
4.727
Mod
1.27
1.15
0.94
0.79
0.71
3
4.722
Std
1.29
1.08
0.96
0.90
0.82
3
4.722
Mod
1.37
1.23
1.02
0.88
0.80
3.5
4.720
Std
1.38
1.15
1.07
0.99
0.90
3.5
4.720
Mod
1.45
1.36
1.19
1.00
0.91
4
4.717
Std
1.31
1.15
1.06
0.98
0.90
4
4.717
Mod
1.34
1.20
1.12
0.97
0.88
5
4.712
Std
1.41
1.21
1.11
1.03
0.94
5
4.712
Mod
1.41
1.35
1.19
1.02
0.93
* Sensor positions correspond to the modified array in Figure 4.2


329
Table 8.7 Effect of Increased Base Course Modulus
on Pavement Response on SR 80
a) Input Parameters for BISAR
Layer
Description
Thickness
Poisson's
Modulus (psi)
(in.)
Ratio
Dynaflect
1
Asphalt Concrete
1.5
0.35
642,540 *
2
Limerock Base
10.5
0.35
85,000 **
3
Subbase
36.0
0.40
18,000
4
Subgrade
semi-
infinite
0.45
5,750
* Computed at 23F using Equation ,6.3
** E2 value increased for illustration purposes
b) Pavement Stress Analysis
Response Parameters**
Dynaflect
Maximum Surface Deflection
(mils)
26.3
Radial stress, bottom of AC layer
Radial strain, bottom of AC layer
(E-6
(psi)
in./in.)
74.4
129.0
Vertical stress, top of base layer
Radial stress, top of base layer
(psi)
(psi)
-98.4
-31.1
Vertical stress, top of subbase layer
Radial stress, top of subbase layer
(psi)
(psi)
-10.0
-0.7
Vertical stress, top of subgrade
Vertical strain, top of subgrade
(E-6
(psi)
in./in.)
-1.0
-175.0
Deflection in AC layer
Deflection in base layer
Deflection in subbase layer
Deflection in subgrade layer
(X)
(X)
(X)
(X)
0
19.0
30.4
50.6
* Computed for single axle with dual wheels (6.0 kips/wheel)
NOTE: + = tensile
- = compressive


106
Case 1. For t. = 3.0 in., E = 100.0 ksi, 10.0 < E < 85.0 ksi,
i i 2
10.0 < E < 200.0 ksi, and with E. between 6.0 and 35.0 ksi,
4 3
-K
E = K (D D ) 2 Eqn. 4.11
3 14 10
K
1
"0.3562 9 0.7185
22.74(E ) + 3.503 x 10 (E )(E ) Eqn. 4.12
2 4 2
r 0 10183 V t )
K = [3.4455 + 0.00841(E )](E ) 2 Eqn. 4.13
2 2 4
The accuracy of the E3 prediction equation presented above appeared
good within the stipulated range of variables listed above. However, it
was not simplified enough to allow the development of a more comprehen
sive equation to include varying Ex and tx values. Multiple linear
regression analyses (39) were performed using various combinations of
variables and transformations in an attempt to develop a relatively
simple E3 prediction equation for t1 values of 3.0, 4.5, and 6.0 in.
The best results from these analyses produced a complex equation
containing 13 variables.
Case 2. For 3.0 < t < 6.0 in., 100.0 < E < 1000.0 ksi,
i l
10.0 < E < 85.0 ksi, and 0.35 < E < 200.0 ksi,
2 4


162


265
Dynaflect, FWD, and plate loading tests. Further research is required
to study the possible effects of the rigid FWD plate in interpreting
load-deflection response.
Table 6.19 shows that the subgrade moduli obtained from the plate
loading test were generally lower than those from the NDT devices. For
SR 12, the FWD and plate loading E^ values compare well. However, for
I-10A test site, which had very stiff layers, the plate bearing test
prediction of E4 was too low compared to the Dynaflect and FWD predic
tions. The poor prediction of the subgrade modulus from the plate load
tests is attributed to possible disturbance during trenching, measure
ment of plastic (or non-recoverable) deformation during testing, and the
static loading conditions used in the plate test as compared to the
dynamic NDT tests.
6.7 Analyses of Tuned NDT Data
6.7.1 General
Section 6.5 presented the modeling of the Dynaflect and FWD deflec
tions for the various test sections. The resultant moduli from modeling
have been referred to as tuned moduli for the respective NDT devices.
This section analyzes the moduli and corresponding BISAR predicted
deflections in the hope of developing simplified layer moduli prediction
equations, especially for the Dynaflect testing system. The predicted
deflections are used in this analysis because they matched (approxi
mately) the field measured values and more importantly meet the assump
tions inherent with the use of multilayered elastic theory. Details
pertaining to the analyses are presented for the Dynaflect and FWD in
Sections 6.7.2 and 6.7.3, respectively.


459
6. Calculate composite modulus, E12 using Equations 3 and 4.
E12 = 60.611(D1 + D2 2D3)0-831 Eqn. F.l
E12 = 59.174(D1 + D2 2D3)"-805 Eqn. F.2
7. Estimate E from recovered asphalt viscosity-temperature-modulus
relationships or from dynamic indirect tensile tests on pavement
cores (98,9). In Florida, E can be estimated using the relation
ships illustrated in Figure 6.64. The relationship for pavements
with no visible cracks can be used to determine E for the average
pavement temperature during Dynaflect testing. If the pavement
exhibits extensive cracking (e.g., alligator cracking), E will be
reduced considerably, even approaching the modulus (E2 of the
granular base course. It may be impossible to estimate a realistic
Ex value which would simulate the measured deflection basin using
elastic layer computer programs.
The relationship for considerable cracking in Figure 6.64 can
be used when pavement cracks are spaced sufficiently to eliminate
their influence on the Dynaflect deflections. This would apply to
pavement sections that have uncracked segments within cracked seg
ments. In this situation higher deflections and lower subgrade or
stabilized subgrade moduli may be the cause of overstressing of
these cracked segments. Analysis of the cracked pavements using
the Eimax relation from uncracked segments could be performed using
the E3 and E^ values predicted for cracked segments to verify high
stress levels and the cause of cracking.


303
referred to as resilient moduli since the load-deformation character
istics of flexible pavements are resilient (see Section 6.5.1). Also,
because different moduli values were obtained from the Dynaflect and FWD
tests, it was decided to correlate layer moduli predictions with the in
situ penetration tests for both NDT devices.
The correlations between pavement layer moduli and qc and Eq are
presented in Section 7.4.2 and 7.4.3, respectively. The average qc and
Eq values for each layer was correlated to the respective NDT tuned
moduli. The first layer was excluded, since the resilient characteris
tics of asphalt concrete materials are both temperature and rate of
loading dependent.
The effective pavement thickness (EPT) was defined to be 1 m from
the pavement surface. Materials below this were assumed to have no
effect on traffic-associated pavement performance. This was then used
to determine the thickness of the effective subgrade layer which was
therefore the difference between 1 m and the overlying pavement thick
ness. This assumption is consistent with the conventional design
practice in which subgrade samples immediately below the subbase layer
are tested for modulus values. Also, other workers have selected
similar depths for use with their penetration tests. For example, Kleyn
et al. (58) considered the depth of 0.8 m for the use of the dynamic
cone penetrometer in road pavements. Briaud and Shields (15) conducted
their pavement pressuremeter tests to a depth of 1.8 m in airport
pavements. Even though EPT could vary with pavement types and stiffness
characteristics of the pavement a value of 1 m was used in this
analysis. The possible effects of this and the general problem of
characterizing the subgrade layer are discussed in Section 7.5.


45
cannot be guaranteed since an infinite number of layer modulus combina
tions can provide essentially the same deflection basins. Moreover,
most of the iterative programs yield questionable base course and
subbase moduli. In some programs, adjustment of the field data are
required in order to improve the solution (6,47).
2.3.4 Other In Situ Methods
Cogill (27) presented a method involving the use of an ultrasonic
technique. The elastic modulus of the top layer can be accurately
determined; however, the modulus values for the other layers are
questionable. Kleyn et al. (58) and Khedr et al. (57) have developed
different forms of a portable cone penetrometer to evaluate the stiff
ness of pavement layers and subgrade soils. However, these devices do
not provide direct modulus values but rather are based on correlations
with CBR and plate bearing parameters. Similarly, the Clegg Impact
tester, which was developed in Australia in the mid-1970s, relies on CBR
correlation for pavement evaluation applications (40). The problems of
the CBR and plate bearing tests have been discussed previously.
Maree et al. (70) presented an approach to determine pavement-layer
moduli based on a device developed to measure deflections at different
depths within a pavement structure. The device, called the multi-depth
deflectometer (MDD), is installed at various depths of an existing pave
ment structure to measure the deflections from a heavy-vehicle simulator
(HVS) test. Maree et al. (70) suggested that effective moduli for use
in elastic-layer theory can be determined from correction factors esta
blished from field measurements using the MDD at different times of the
year and under different conditions.


183
Table 6.6 Comparison of Field Measured and BISAR Predicted
Dynaflect Deflections
Dynaflect Deflections (mils)
Road
Number
Type*
D.
3
4
D6
7
D8
9
.o
SR 26A
11.912
Measured
Predicted
0.87
0.90
0.81
0.80
0.77
0.79
0.68
0.70
0.61
0.65
0.53
0.57
0.45
0.47
0.39
0.40
SR 26B
11.205
Measured
Predicted
1.28
1.23
1.18
1.19
1.23
1.17
1.12
1.08
0.99
1.03
0.90
0.92
0.77
0.78
0.68
0.67
SR 26C
10.168
Measured
Predicted
0.89
0.77
0.77
0.66
0.77
0.63
0.62
0.48
0.53
0.41
0.37
0.31
0.24
0.22
0.16
0.17
SR 26C
10.166
Measured
Predicted
0.90
0.79
0.77
0.68
0.78
0.65
0.68
0.50
0.54
0.43
0.44
0.33
0.27
0.23
0.16
0.18
US 301
21.580
Measured
Predicted
0.56
0.58
0.50
0.50
0.49
0.48
0.37
0.38
0.34
0.34
0.27
0.27
0.20
0.20
0.15
0.16
US 301
21.585
Measured
Predicted
0.62
0.68
0.47
0.50
0.46
0.48
0.35
0.38
0.30
0.34
0.25
0.27
0.18
0.19
0.14
0.15
US 441
1.241
Measured
Predicted
0.73
0.80
0.63
0.64
0.57
0.61
0.45
0.50
0.40
0.45
0.32
0.37
0.25
0.28
0.20
0.22
I-10A
14.062
Measured
Predicted
0.30
0.23
0.29
0.22
0.28
0.22
0.18
0.17
0.16
0.15
0.10
0.11
0.07
0.08
0.05
0.05
I-10B
2.703
Measured
Predicted
0.44
0.40
0.46
0.36
0.40
0.35
0.29
0.28
0.25
0.24
0.17
0.18
0.12
0.13
0.09
0.09
I-10C
32.071
Measured
Predicted
0.70
0.76
0.46
0.49
0.43
0.47
0.30
0.38
0.29
0.34
0.22
0.27
0.18
0.20
0.15
0.16
SR 15B
4.811
Measured
Predicted
1.10
1.00
1.03
0.94
1.04
0.92
0.91
0.85
0.92
0.81
0.82
0.74
0.75
0.65
0.66
0.58
SR 15A
6.549
Measured
Predicted
1.50
1.04
1.46
1.03
1.48
1.02
1.40
0.98
1.36
0.95
1.27
0.90
1.14
0.83
1.04
0.76
SR 715
4.722
Measured
Predicted
1.37
1.27
1.29
1.14
1.23
1.11
1.08
0.98
1.02
0.92
0.96
0.83
0.89
0.74
0.81
0.66
SR 715
4.720
Measured
Predicted
1.45
1.28
1.38
1.19
1.36
1.16
1.15
1.03
1.19
0.96
1.07
0.87
1.00
0.77
0.91
0.70
SR 12
1.485
Measured
Predicted
0.86
0.99
0.68
0.82
0.65
0.79
0.44
0.61
0.42
0.53
0.36
0.41
0.27
0.29
0.21
0.22


Table B.10--continued
Temperature (F):
Air =
88
Pvmt. Surf.
= 110
Mid-Pvmt.
= 120
Site
No.
Mile
Applied
Measured
Deflections (mils)
Post
No.
Load
(kips)
Di(a)
3
5
Ds
7
0.o(b)
7.87
11.8
19.7
31.5
47.2
63.0
6.5
6.563
4.576
6.912
8.851
11.30
19.45
25.24
7.68
14.06
18.46
6.57
12.40
16.42
5.63
10.63
13.94
4.53
8.62
11.10
3.66
6.89
8.58
2.91
5.39
6.65
7
6.566
4.545
6.880
8.930
8.86
15.71
20.43
6.10
11.65
15.28
5.51
10.59
13.98
4.69
9.02
11.89
3.86
7.44
9.72
3.15
6.02
7.68
2.56
4.92
6.18
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load


Table B.13--continued
Temperature (F):
Air =
81
Pvmt. Surf.
= 91
Mid-Pvmt.
= 102
Site
No.
Mile
Applied
Measured
Deflections (mils)
Post
No.
Load
(kips)
D2
D3
05
D7
o.o 7.87
11.8
19.7
31.5
47.2
63.0
4.616
12.3
7.5
4.8
2.8
1.8
1.3
1.0
A
1.486
6.816
19.3
12.1
8.1
4.7
3.0
2.0
1.5
H
8.920
25.0
16.1
11.2
6.5
4.2
2.8
2.1
8.960
23.7
15.6
10.9
6.2
4.3
2.9
2.1
4.632
12.8
6.7
4.7
3.2
1.7
1.4
1.3
C
1.491
6.88
19.5
12.6
8.4
4.7
3.0
1.9
1.2
D
8.968
26.1
16.4
11.3
6.7
4.3
2.8
2.0
9.00
24.9
15.5
10.8
6.5
4.2
2.8
2.0
4.64
10.5
5.9
3.9
3.5
1.8
0.9
1.1
C
1.496
7.072
16.8
10.9
7.6
4.8
3.0
2.0
1.6
0
9.248
22.7
14.6
10.5
6.7
4.2
2.7
2.1
9.280
21.7
14.0
10.1
6.7
4.2
2.8
2.2
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load.


185
Tuning of the test sections was accomplished by adjusting'the input
moduli values until the BISAR predicted deflections closely matched the
field measured Dynaflect deflections. Figures 6.15 through 6.31 illus
trate measured and predicted deflection basins for all test sections.
The plots show that agreement between predicted and measured deflections
for SR 24, SR 12 and SR 80 (Section 2) as shown in Figures 6.18, 6.27,
and 6.29, respectively, was poor. It is suspected that the lack of fit
for these sites was probably due to the effects of variable foundation
soils or non-visible cracks. For SR 80-Section 2, it is clearly known
that the section had experienced problems including that of surface
cracks (see Section 5.2).
The layer moduli which produced the best fit of the measured Dyna
flect deflection basins are called tuned moduli and are listed in Table
6.7. The corresponding Dynaflect deflections predicted from BISAR are
listed in Table 6.8. Examination of the E values listed in Table 6.7
illustrates the good agreement between the final (or tuned) Ex values
with that obtained from the use of the modulus-viscosity-temperature
relationships. Slight adjustments occurred in the case of SR 268, SR
15A, SR 15B, and SR 15C. This could be due to the high mean pavement
temperature (except SR 26B) and also possibly high air void contents of
the asphalt concrete mixtures. Generally high air void contents tend to
result in a reduction in measured Ex values using the rheology rela
tionships. The actual air void contents of the mixtures were not known
to apply the correction factors suggested by Ruth et al. (98).
The other layer moduli values listed in Table 6.7 as the tuned
moduli do not differ much from the predicted values of Table 6.3. This
suggests the overall accuracy and reliabilty of the Dynaflect prediction


o o n
c *
C PROGRAM DELMAPS1 *
^ A ******* rtiSrVttfrVfrttfrft *
C PROGRAM DELMAPS1 *
C *
C DYNAFLECT EVALUATION OF LAYER MODULI IN ASPHALT PAVEMENT SYSTEMS *
C *
C VERSION 1 *
C *
£ ************************** A ********************************* ******* * *
C
C THIS PROGRAM IS A MODIFICATION TO BISAR PROGRAM TO
C CALCULATE LAYER MODULUS VALUES FROM DEFLECTIONS
C MEASURED WITH THE MODIFIED DYNAFLECT TESTING SYSTEM
C
c EX
C ARRAYS- DIMENSIONS AND DATA STATEMENTS
C
LOGICAL STRESS,EPS,RLOW,AID(27),N,L,N2,L2,NZEP,NZEQ BI
INTEGER REQEST(27),IQ(3),DATE(3),ISTRSS(27),INTV(10),IVERI(7), BI
&IVER2(10) BI
REAL NU,K5,MU,LDSTRS(10),HOSTR(10),LOAD,INT(17),V(15),X(10),Y(10),BI
&A(3,3),HH(3,3),W(3),C(39),B(3,3),TEXT(15 ) ,ACCUR(3),PSI(10),AK(9), BI
&ALK(9) BI
DOUBLE PRECISION CZ,ELLE,ELLK BI
COMMON/ASDT/LAYER,NLAYS,M,R,Z,NU(10),ACCUR,LOAD.HOSTRS,NZEROS,H(9)BI
&,K5(10),E(10),AL( 9) ,THICK(9),RADIUS(10 ) BI
COMMON/STRDTA/STRESS(27),EPS(17),RLOW,ST,CT,L,ACC BI
COMMON/CONST/CZ,ELLE,ELLK,ALMBDA BI
COMMON/CNTING/F10M1,F100,F101,F11M2,F11M1,F110,F111 BI
COMMON/TAPE/NOUT BI
CHARACTER*14 FILEN
CHARACTER*80 SITE
CHARACTER*1 KNOWN
CHARACTERS CHANGE
DIMENSION D(10),D12(10),D21(10),D34(10),E12(10),E21(10),AX(10),
&AY(10).DEPTH(10),ETA(10),DISP(10),DIFF(10),DISM(10),PECENT(10),
&E12MU0),E2M(10),E1T(10),E2T(10),112(10),E1C(10)
DATA NBLANK,ISTRSS,IREF1.IREF2/ BI
&*
t
,UR ,
,'UT ',
,'UZ ,
,SRR,
>SITi
,'SZZ,
,SRT,SRZ,
,STZ
,ERRBI
&,
ETT\
, EZZ,
,ERT,
,ERZ,
,ETZ,
,UX
,UY ,
,SXX,SXY,
,SXZ
,'SYYBI
&,
'SYZ',
,EXX',
,EXY,
,'EXZ',
,EYY,
,EY,
LOAD,
,'SIRS/
BI
DATA IVER1,IVER2/1,2,3,6,7,13,14,4,5,8,9,10,11,12,15,16,17/
BI
BEGIN PROGRAM STATEMENTS
WRITE(*,8000)
8000 FORMAT(1,6X,THIS IS A SELF-ITERATIVE AND INTERACTIVE PROGRAM',/
&,7X,SEED MODULI ARE CALCULATED USING PREDICTION EQUATIONS FROM',/
&,7X,THE MODIFIED DYNAFLECT DEFLECTION TESTING SYSTEM *,/,7X,BI
&SAR IS THEN USED TO COMPUTE DEFLECTIONS WHICH ARE COMPARED WITH,/
&,7X,MEASURED DEFLECTIONS : THE USER THEN ADJUST MODULI TO MATCH,
&/,7X,FIELD DEFLECTIONS AS REQUIRED,//,IX,ENTER A NAME FOR YOUR
& OUTPUT FILE TO BE COPIED TO (10 CHARACTERS OR LESS),/,IX,
&==>,$)
463


j'-O'*'*
CJ
Figure 5.1
Location of Test Pavements in the State of Florida


234
The stress dependency of the test pavements was evaluated by using
FWD deflections at different load levels to compute the layer moduli.
Five test pavements were selected for this analysis. Two of the pave
ments (SR 24 and SR 12) showed close resemblance to linearity. The
others were SR 26B which exhibited a stress-softening behavior; SR 15A
and SR 15B which behaved as a stress-stiffening material from their
load-deflection diagrams. The corresponding deflections measured at the
different load levels were normalized to 9 kips load level. These are
compared in Table 6.14 for each of the five test pavement sections.
Table 6.14 shows that deflections measured at the lowest load are
much different from the other high loads. This is especially true for
SR 26B, SR 15A, and SR 15B in which the deflections produced by the
lowest load are all less than those measured at the higher loads. Such
a result would agree with the stress-softening behavior of SR 26B, and
not for SR 15A and SR 15B, where a stress-stiffening phenomenon was
postulated from the load-deflection diagrams. The deflections for SR 24
and SR 12 compare well and the slight differences could be due to the
precision of the measuring devices, possibility of measuring close to
non-visible surface cracks (in the case of SR 12), and also due to the
resilient characteristics of the pavement materials. In general, there
is consistency in the normalized deflections at the two or more higher
loads even for the nonlinear pavements. Therefore, the nonlinearity
behavior of SR 26B, SR 15A, and SR 15B test sections only occur at loads
less than 6.0 kips.
The deflections listed in Table 6.14 were used to predict the
respective layer moduli. These were then input, using the E1 values
from rheology data, into BISAR to tune the various deflection basins.


Table 2.2 Summary of Computer Programs for Evaluation
of Flexible Pavement Moduli from NOT Devices
Name
Reference
Number
of
Layers
Theoretical
Model Used
For Analysis
Applicable
NOT
Device
*
Anani (6)
4
Layer
BISAR-E1astic
Road Rater 400
ISSEM4
Sharma and
Stubstad (106)
4
ELSYM5-E1astic
Layer
FWD
CHEVDEF
Bush (22)
4(a)
Layer
CHEVR0N-E1astic
Road Rater 2008
OAF
Majidzadeh
and lives (65)
3 or
4
ELSYM5 Elastic
Layer
Dynaflect,
Road Rater, or FWD
ILLI-PAVE
Hoffman and
Thompson (45)
3
Finite Element
Road Rater 2008,
or FWD
*
Tenison (114)
3
Layer
CHEVRON'S n
(Elastic)
Road Rater 2000
FPEDD1
Uddin et al.
(118,120)
3 or
4
ELSYM5 Elastic
Layer
Dynaflect, FWD
BISDEF
Bush and
Alexander (23)
4(a)
BISAR Elastic
Layer
Vibrator, or FWD
Dynaflect, Road
Rater, WES
ELMOD
Ullidtz and
Stubstad (123)
2, 3
or 4
MET-Boussinesq
FWD
IMD
Husain and
George (47)
3 or
4
CHEVRON Elastic
Layer
for FWD
Dynaflect, but
can be modified
DYNAMIC
Mamlouk (66)
4
Elasto-dynamic
Road Rater 400
* not known or available
(a) not to exceed number of deflections


226
closely match measured values for all sensor locations. However, for
most of the other pavements, especially SR 26C, I 10, and SR 715, the
agreement between measured and predicted deflections was good for'D ,
Dg, and Dy measurements only. The difficulty in matching FWD deflec
tions with BISAR could be due to one or more of the following:
1. The pavements did not necessarily behave as linear elastic media.
2. It was improper to represent the FWD impulse or dynamic loadings
with a pseudo-static loading in BISAR.
3. Neglecting the inertia of the pavement in the simulation of NDT
response using multilayered linear elastic theory.
4. The FWD plate and loading are rigid rather than flexible as assumed
in the BISAR analysis.
5. The spacing of the geophones and loading configuration are probably
not suitable to allow the separation of pavement layers during the
interpretation of FWD deflections.
It is believed that the above reasons, especially the first three
apply to all NDT devices. It may be argued that the small load (1.0 kip
total) in the Dynaflect system is too small to produce any sensitivity
in the load-deflection response compared to the 9-kip load used in the
FWD. On the other hand, the use of dual loads in the Dynaflect testing
system with the modified sensor configuration enhances the separation of
layer response. Differences between the response of the test pavements
to the FWD and Dynaflect are discussed in detail in Section 6.6.
The layer moduli which best matched one or more of the FWD deflec
tions were selected as the pavement tuned layer moduli. These are
listed in Table 6.10. The predicted FWD deflections using the tuned
layer moduli are listed in Table 6.11. The tuned Ex values were not


442
Table E.6 Rheology and Penetration of Asphalt Recovered From I-10B
(Madison County)
Layer 1: Thickness =
Temperature
F (C)
Absolute
Viscosity
n1(Pa-s)
Complex
Flow (C)
Constant Power
Viscosity
nioo(Pa's>
275 (135)
7.11 El

140 (60)
7.863 E2

7.863 E2
77 (25)(a)
4.78 E5
0.73
1.79 E6
60 (15.6)
1.787 E6
0.78
6.00 E6
41 ( 5)
1.79 E7
0.67
1.95 E8
23 (-5)
1.08 E8
0.77
6.57 E8
(a) Penetration at
Layer 2:
77F (25C)
= 40
Thickness =
Temperature
Absolute
Comp!ex
Constant Power
F (C)
Viscosity
nj(Pa-s)
Flow (C)
Viscosity
nioo 275 (135)
7.241 El

...
140 (60)
7.912 E2

7.912 E2
77 (25)(a)
7.82 E5
0.77
2.51 E6
60 (15.6)
1.95 E6
0.79
6.21 E6
41 ( 5)
3.60 E7
0.74
2.44 E8
23 (-5)
1.18 E8
0.75
8.69 E8
(a) Penetration at 77F (25C) = 41


SUBGRADE MODULUS, E4 (ksi)
287
FWD (9 kips load) SENSOR DEFLECTION,
D6 or j (mils)
Figure 6.75 Relationship Between and FWD Dg and D?


454
Layer 5: Binder
Table
E.ll--continued
Thickness = 1/2"
Temperature
Absolute
Complex
Constant Power
F (C)
Viscosity
nj(Pa-s)
Flow (C)
Viscosity
n 10 0 275 (135)
2.168 E2


140 (60)
2.929 E4

2.929 E4
77 (25)(a)
1.232 E6
0.64
3.543 E6
60 (15.6)
2.404 E6
0.73
1.160 E7
41 ( 5)
4.073 E7
0.60
1.028 E9
23 (-5)
1.740 E8
0.73
1.639 E9
(a) Penetration at 77F (25C) = 11


58
Loading
(b) Configuration of Load Wheels and Geophones.
Figure 3.2 Configuration of Dynaflect Load Wheels and
Geophones in Operating Position (108)


39
load-deflection response (such as an FWD deflection basin) is the
concept of equivalent layer thickness (121,122) in which the layered
pavement system is transformed into an equivalent Boussinesq (13)
system. This concept, originally proposed by Odemark (87), is based on
the assumption that the stresses, strains, and deflections below a given
layer interface depend on the stiffness and thickness of the layers
above that interface. Although this approach obtains an explicit
solution for the subgrade modulus (121), it relies on estimates of the
asphalt concrete layer modulus and also employs certain modular ratios
to obtain the moduli of the various layers above the subgrade (25). The
method of equivalent thicknesses (MET) has also been incorporated into
some iterative computer programs which are discussed in the next
section.
Several investigators have obtained equations to directly determine
the subgrade modulus from one or more sensor deflections. For example,
Figure 2.8 shows the relationship between the subgrade modulus and the
Dynaflect fifth sensor deflection as summarized by Way et al. (128).
Keyser and Ruth (55) developed a prediction equation from five test road
sections in the Province of Quebec, Canada, by using the BISAR elastic-
layer program to match measured Dynaflect deflection basins. The
equation is of the form
-1.0006
E = 5.3966(D ) Eqn. 2.4
4 5
where E^ is subgrade modulus in psi, and D5 is Dynaflect fifth sensor
deflection in inches. This equation had an R2 of 0.997 (55), and is
similar to that of Ullidtz (see Figure 2.8). Godwin and Miley (41) have


41
developed correlations between the base, subbase and subgrade moduli and
the second, third, and fourth Dynaflect sensor deflections, respec
tively. However, the modulus values used in the correlation were the
surface moduli from plate bearing tests which suffers from the problem
of incorporating plastic and nonrecoverable deformations.
An approach using regression equations to estimate layer moduli has
been attempted by other investigators (83,120,132). This approach
usually involves analysis of computer-simulated NDT data using a theo
retical model (usually layered elastic theory). The various investi
gators reported success in the case of the subgrade modulus. To obtain
good correlations for the other layers (surface, base, subbase), certain
assumptions had to be made, such as the base course modulus being
greater than the modulus of the subgrade (83), or they had to resort to
computer-iteration programs (83,120).
2.3.3.3 Back-Calculation Methods. The method of iteratively
changing the layer moduli in a theoretical model to match the theoreti
cal deflection basin to a measured basin is currently called back-
calculation in the literature. Initial developments of this procedure
utilized a trial-and-error approach (49,72) using the following steps:
1. Pavement-layer thicknesses, initial estimates of the pavement-layer
moduli, and the loading and deflection measurement configuration are
input into the model (usually a multilayer elastic computer
program).
2. The computed deflections at the geophone positions are compared with
those actually measured in the field.
3. The layer moduli used in the computer program are then adjusted to
improve the fit between the predicted and actual deflection basins.


428
UJIXV. OF FLORIDA CIVIL ENG. DEPT.- DR. B.E. RUTH TEST NO. 3
FILE NAME: PAVEWENT-SUBGRADE MATERIALS CHARACTERIZATICN
FILE NUMBER: 245-D51
RECORD OF DILATCMETER TEST NO. 3
USING DATA REDUCTION PROCEDURES IN MARCHEITI (ASCE.J-(JED,MARCH dO)
KO IN SANDS DETERMINED USING SCIMERTMANN METHCO (1983)
EHI ANGLE CALCULATION BASED ON DURGUNOGLU AND MITCHELL (ASCE,RALEIGH CONF.JUNE 75)
FHI ANGLE NORMALIZED TO Z.72 BARS USING BALIGH'S EXPRESSION (ASCE,J-GED,NOV 76)
MODIFIED MAYNE AND KULHAWY FORMULA USED FOR OCR IN SANDS (ASCE.J-GED. JUNE 32)
LOCATION: SR 12 (GADSDEN CO.) TEST SITE #4. 5
PERFORMED DATE: 08-12-36
BY: DAVE/KWASI/ED
CALIBRATION INFORMATION:
DELTA A 0.22 BARS DELTA B 1.AO BARS
ROD DIA.- 3.70 CM FR.RED.DIA.- 3.70 CM
1 BAR
- 1.019 KG/CM2
- 1.044 TSF
- 14.51
. FSI
z
THRUST
A
B
ED
ID
KD
uo
GAf-MA
CM)
r****
(KG)
******
(BAR)
*****
(BAR)
*****
(BAR)
***** *****
*****
(BAR)
******
(T/M3)
******
0.24
1950.
4.10
13.50
466.
3.70
82.52
0.000
2.000
0.44
3225.
11.90
34.20
753.
1.97
0.000
2. 150
0.64
2510.
9.40
28.70
644 .
2. 14
67.65
0.000
2. 150
0.34
1640.
6.30
20.50
458.
2.26
34.84
0.000
2.000
1.04
1030.
4.30
15.10
334.
2.40
19.57
0.000
1.900
1.24
305.
3.10
11.90
262.
2.59
12.02
0.000
1.900
1.44
640.
2.50
10.10
213.
2.65
8.48
0.000
1.900
1.64
640.
2.50
9.80
207.
2.50
7.53
0.000
1.900
1.34
725.
3.00
11.20
240.
2.43
8.02
Q.OOQ
1.900
2.04
795.
3.00
11.30
243.
2.47
7.25
0.000
1.900
2.24
345.
3.00
11.70
258.
2.64
6.57
0.000
1.900
2.44
aso.
3.10
12.30
276.
2.75
6.20
0.000
1.900
2.64
995.
3.20
12.80
291.
2.32
5.90
0.000
1.900
2.84
1080.
3.70
14.10
320.
2.69
6.35
0.000
1.900
3.04
1115.
3.30
13.90
327.
3.12
5.23
0.000
1.900
3.24
1325.
4.00
15.20
349.
2.73
6.00
0.000
1.900
3.44
1510.
4.70
17.20
396.
2.64
6.61
0.000
2.000
3.64
1200.
3.00
14.40
324.
2.57
5.24
0.000
1.900
3.34
970.
3.90
13.30
302.
2.33
5.02
0.000
1.300
4.04
1030.
4.00
14.00
305.
2.35
4.90
0.000
1.900
4.24
1165.
5.00
16.50
360.
2.22
5.80
0.000
2.000
4,44
1625.
6.30
24.20
593.
3.02
6.70
0,000
2.000
4.64
2205.
6.70
23.80
564.
2.67
6.90
0.000
2.000
4.34
2345.
6.90
25.00
600.
2.77
6.76
0.000
2.000
5.04
2705.
7.60
27.30
659.
2.76
7.13
0.000
2.000
5.24
3130.
10.80
35.20
a3o.
2.43
9.78
0.000
2.150
5.44
2945.
9.20
30.10
702.
2.41
8.03
0.000
2.150
5.64
2565.
8.00
25.60
582.
2.28
6.79
0.000
2.000
GAGE 0
- 0.05
BARS
GWT DEFTH-10.
,00 M
RCO WT.
6.50
KG/M
DELTA/PHI- 0.
.50
BLADE T-13.70 PM
analysis uses
H20 UNIT WEIGHT -
1.000 T/M3
sv
rc
OCR
KO
cu
PHI
M
SOIL '
TYPE
(BAR)
(BAR)
(BAR)
(DEG)
(BAR)
******
*****
*****
*****
*****
*****
******
***********1
0.044
2094.5
BAND
0.066
15.41
J8.7
3702.5
SILTY
SAND
0. 126
61.70
8.20
Jd.o
2776.4
SILTY
SAND
0.168
22.64
4.29
37.1
1637.5
SILTY
SAND
0.205
9.32
45.46
2.50
35.9
1043.8
SILTY
SAND
0.242
4.47
18.45
1.62
35.0
699.5
SILTY
SAND
0.230
2.35
10.20
1.24
33.7
512.7
SILTY
SAND
0.317
2.67
8.42
1.13
33.3
464.5
SILTY
SAND
0.354
3.36
9.49
1.20
33.2
551.5
SILTY
SAND
0.391
3.06
7.81
1.09
34.0
537.7
SILTY
SAND
0.429
2.61
6.54
1.01
34.4
548.2
SILTY
SAND
0.466
2.77
5.94
0.96
34.5
573.a
SILTY
SAND
0.503
2.70
5.36
0.91
35.2
592.0
SILTY
SAND
0.541
3.31
6.12
0.97
35.1
670.5
SILTY
SAND
0.578
2.50
4.32
0.82
35.6
633.6
SILTY
SAND
0.615
3.30
5.36
0.91
36.3
714.6
SILTY
SAND
0.654
4.11
6.28
0.98
36.5
844.7
SILTY
SAND
0.692
3.13
4.53
0.85
35.1
621.5
SILTY
SAND
0.729
3. J5
4.60
0.88
32.7
564.9
SILTY
SAND
. 766
J.J1
4.32
0.85
33.5
564.6
SILTY
SAND
0.806
4.67
5.30
0.97
32.9
719.9
SILTY
SAND
0.845
5.30
6.37
1.03
35.1
1276.2
SILTY
SAND
0.884
5.37
6.64
1.00
37.5
1223.4
SILTY
SAND
0.923
5.37
6.36
0.98
37.8
1293.8
SILIY
SAND
0.963
6.54
6.79
1.00
38.5
1450.2
SILTY
SAND
1.005
12.48
12.42
1.34
37.3
2058.6
SILTY
SAND
1.047
9.01
8.60
1.13
38.0
1616.3
san
SAND
1.086
7.14
6.57
1.00
37.4
1248.3
SILTY
SAND
END OF SOUNDING


6.23 Comparison of Measured and Predicted Dynaflect
Deflections for SR-15A--M.P. 6.549 194
6.24 Comparison of Measured and Predicted Dynaflect
Deflections for SR 15BM.P. 4.811 195
6.25 Comparison of Measured and Predicted Dynaflect
Deflections for SR 715--M.P. 4.722 196
6.26 Comparison of Measured and Predicted Dynaflect
Deflections for SR 715--M.P. 4.720 197
6.27 Comparison of Measured and Predicted Dynaflect
Deflections for SR 12M.P. 1.485 198
6.28 Comparison of Measured and Predicted Dynaflect
Deflections for SR 80Section 1 199
6.29 Comparison of Measured and Predicted Dynaflect
Deflections for SR 80Section 2 200
6.30 Comparison of Measured and Predicted Dynaflect
Deflections for SR 15CM.P. 0.055 201
6.31 Comparison of Measured and Predicted Dynaflect
Deflections for SR 15C--M.P. 0.065 202
6.32 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 26A--M.P. 11.912 208
6.33 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 26BM.P. 11.205 209
6.34 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 26CM.P. 10.168 210
6.35 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 26CM.P. 10.166.... 211
6.36 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 24M.P. 11.112 212
6.37 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for US 301M.P. 21.585..... 213
6.38 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for US 441--M.P. 1.236 214
6.39 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for I-10AM.P. 14.062 215
6.40 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for I-10BM.P. 2.703 216
xv ii


NORMALIZED DEFLECTION (mils)
224
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.48 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 15CM.P. 0.055


420
UNIV. OF FLORIDA CIVIL ENG. DEPT.- DR. B.E. RUTH TEST NO. 3
FILE NAME: PAVEMENT SUBGRADE MATERIALS CHARACTERIZATION
FILE NUMBER: 245-D51
RECORD OF DILATCMETER TEST NO. 3
USING DATA REDUCTION PROCEDURES IN MARCHETTI (ASCE.J-GED,MARCH 80)
K0 IN SANDS DETERMINED USING SCHMERTMANN METHOD (1983)
PHI ANGLE CALCULATION BASED ON DURGUNOGLU AND MITCHELL (ASCE,RALEIGH CONF.JUNE 75)
PHI ANGLE NORMALIZED TO 2.72 BARS USING BALIGHS EXPRESSION (ASCE,J-GED,NOV 76)
MODIFIED MAYNE AND KULHAWY FORMULA USED FCR OCR IN SANDS (ASCE.J-GED,JUNE 82)
LOCATION: SR 15A (MARTIN CO.) TEST SITE #5.0
PERFORMED DATE: 04-29-86
BY: DAVE/KHASI/ED
CALIBRATION INFORMATION:
DELTA A = 0.17 BARS
DELTA B
0.35 BARS
GAGE 0 = 0.05 BARS
GWT DEPTH-
1.65 M
ROD DIA.- 3.70 CM
FR.RED.DIA.-
3.70 CM
ROD WT.- 6.50 KG/M
DELTA/PHI-
0.50
BLADE T-13.70 Ml
1 BAR = 1.019 KG/CM2
= 1.044 TSF =
14.51 PSI
ANALYSIS USES H20 UNIT WEIGHT =
1.000 T/M3
Z
THRUST
A
B
ED
ID
KD
UO
GAM4A
SV
PC
OCR
KO
CU
PHI
M
SOIL TYPE
(M)
(KG)
(BAR)
(BAR)
(BAR)
(BAR)
(T/M3)
(BAR)
(BAR)
(BAR)
(DEG)
(BAR)
*****
******
*****
*****
*****
*****
*****
******
******
******
*****
*****
*****
*****
*****
******
************
0.42
7335.
5.60
27.00
761.
4.69
53.75
0.000
2.000
0.087
3113.2
SAND
0.62
6835.
10.90
32.50
768.
2.22
77.14
0.000
2.150
0.129
55.55
*****
8.78
46.2
3405.7
SILTY SAND
0.82
3650.
6.90
26.10
681.
3.22
36.13
0.000
2.000
0.168
18.88
*****
4.13
43.7
2529.2
SILTY SAND
1.02
1670.
5.00
16.20
389.
2.45
22.08
0.000
2.000
0.208
10.79
51.93
2.70
39.0
1264.6
SILTY SAND
1.22
560.
1.00
3.40
68.
1.92
4.26
0.000
1.700
0.241
0.64
2.66
0.63
37.1
116.3
SILTY SAND
1.42
310.
0.80
1.70
14.
0.44
3.31
0.000
1.600
0.272
0.60
2.19
0.85
0.112
18.9
SILTY CLAY
1.62
255.
0.80
1.50
7.
0.21
3.02
0.000
1.500
0.302
0.57
1.90
0.79
0.111
8.3
MUD
1.82
270.
0.80
1.70
14.
0.45
2.79
0.017
1.600
0.317
0.53
1.68
0.74
0.106
16.5
SILTY CLAY
2.02
380.
0.85
1.60
8.
0.26
2.82
0.036
1.500
0.326
0.56
1.71
0.75
0.111
10.1
MUD
2.22
450.
0.40
1.60
25.
1.66
1.27
0.056
1.600
0.338
0.21
0.61
0.32
35.9
21.1
SANDY SILT
2.42
1225.
1.50
11.70
353.
9.58
3.00
0.076
1.800
0.354
0.27
0.77
0.31
42.0
512.5
SAND
2.62
1405.
3.40
12.50
313.
3.01
8.06
0.095
1.900
0.372
2.86
7.71
1.05
39.0
723.0
SILTY SAND
2.82
630.
1.00
2.40
32.
0.96
2.51
0.115
1.600
0.383
0.55
1.42
0.67
35.7
SILT
3.02
1430.
0.60
2.70
58.
3.28
1.28
0.134
1.700
0.397
48.9
SILTY SAND
3.22
5430.
3.60
21.50
633.
6.77
6.50
0.154
1.900
0.415
1346.3
SAND
3.42
5650.
7.00
24.60
622.
2.94
14.02
0.174
2.000
0.434
6.46
14.86
1.40
45.7
1755.1
SILTY SAND
3.62
3165.
2.10
12.40
356.
6.68
3.40
0.193
1.900
0.452
557.1
SAND
3.82
2180.
3.60
14.60
382.
3.69
6.35
0.213
1.900
0.470
1.89
4.02
0.73
42.0
804.0
SAND
4.02
5415.
11.20
40.00
1030.
3.07
19.65
0.233
2.150
0.492
18.29
37.15
2.29
42.9
3234.9
SILTY SAND
END OF SOUNDING


317
pavement with a weak foundation layer can produce high deflections but
lower load-induced stresses and strains than a so-called high quality
pavement.
However, the mechanistic process allows the engineer to base his
decision on a rational evaluation of the mechanical properties of the
materials in the existing pavement structure. The material properties,
for example moduli, are then used to calculate the response parameters
(stresses, strains, and displacements) under some determined loadings
and environmental conditions. Pavement layer thicknesses are modified
until the critical stresses or strains do not exceed permissible values.
The establishment of the allowable stresses or strains is the most dif
ficult part of the mechanistic approach. Therefore, empirical guide
lines and relationships continue to be used.
As mentioned previously, two forms of criteria are used; maximum
tensile stress or strain at the bottom of AC layer, and the vertical
stress or strain on top of the subgrade. These are used with empirical
relationships to compute the remaining life of the pavement and overlay
required to meet established criteria. The horizontal tensile stress at
the bottom of the bound layer is assumed critical in evaluating the
pavement's resistance to cracking. However, there are indications that
the mechanisms of cracking of asphalt concrete are not fully understood
and that the concepts used to analyze these cracks may not be completely
val id.
Existing pavement design procedures usually consider cracking of
asphalt-bound layers to be caused by traffic-load-induced fatigue.
Therefore, the allowable stress or strain criterion is based on the
number of repetitions of vehicular loadings to reach the fatigue level.


DEFLECTION (mils)
82
RADIAL DISTANCE FROM LOAD (in)
0 10 20 30 40 50 60 70
Figure 4.7 Effect of Change of E4 on Theoretical FWD (9-kip Load)
Deflection Basin


121
Table 4.5 Pavements with Dynaflect Ex Predictions Having More
Than 10 Percent Error (Case 1 Equations 4.1-4.7)
No.
Layer Moduli (ksi)
(in.)
Predicted percent
Difference
(ksi)
300
10
15
10
3.0
261.6
-12.8
1000
10
6
10
3.0
1128.9
12.9
1000
10
6
50
3.0
1128.9
12.9
1000
30
15
10
3.0
1124.8
12.5
1000
10
15
10
4.5
1224.8
22.5
1000
30
15
10
4.5
1144.3
14.4
100
40
15
10
4.5
87.9
-12.1
100
10
6
0.35
6.0
114.6
14.6
1000
30
15
10
6.0
1123.8
12.4
9


DEFLECTION (103in)
163
Figure 6.9 Surface Deflection as a Function of Load on SR 715
O O Q Q


175
computations, because the AC thickness generally exceeded 3.0 in. for
most of the test pavements. Equation 4.19 was used for the SR 24 test
site, while Equation 4.18 was used in the case of SR 12. The base
course modulus, E2, was computed from Equation 4.21, since Equation
4.22, which was more generalized than Equation 4.21, could not be used
because no D0 measurements were made during the FWD data collection. E
was calculated from Equation 4.24. This equation was considered to be
simplified enough and more generalized than Equation 4.23. Equations
4.25, 4.26, and 4.28 were used to make E4 computations. Equations 4.27
and 4.29 could not be used because measurements of D0 were not made
during FWD testing.
Table 6.4 lists the results of layer moduli predictions from the
FWD prediction equations. The asphalt concrete modulus, E seems to be
very high for most of the test sections. High Ex values are generally
typical of pavements tested under cold temperature conditions and/or
composed of very hard or brittle asphalt cements. The reliability or
accuracy of the FWD predicted Ex values, and that of the Dynaflect, are
compared in the next section with that determined from the rheology
tests.
The predicted E2 and E3 values seem to be of the order of magnitude
expected in practice, with the possible exception of SR 26C and SR 715.
For the latter, the high thickness of the base course layer (24.0 in.)
might have caused the peculiar predictions of E2 and E3. There were
also five test sections (SR 24, SR 15B, SR 715, SR 12, and SR 15C) in
which the predicted E2 values were lower than that of E3. Also SR 26C,
I-10A, US 301 and SR 15C test sections predicted considerably low E3
values. Unless the subbase layer of these pavements had failed, such


87
Table 4.3
Sensitivity
Analysis of FVJD Deflections
for tj
t. t2, and
Parameter
Percent
Change
in Deflections
Di
2
3
5
D6
7
8
V2 t:
9.8
7.3
6.3
4.4
2.2
0.55
-0.48 -
0.56
2ti
-15.0
-12.6
-10.5
- 7.9
-4.6
-1.6
0.0
0.83
V2 t2
19.1
18.9
15.9
9.7
1.8
0.37
-1.2 -
1.4
2 *2
-18.7
-22.0
-21.9
-18.1
-11.8
-5.1
-0.7
1.1
V2 tg
8.9
11.1
11.7
10.5
6.9
2.2
-0.48 -
1.4
2 t3
-9.8
-12.8
-14.11
-14.8
-12.7
-7.9
-3.4 -
1.4
Original pavement is composed of
E. = 150 ksi
E2 = 85 ksi
E3 = 30 ksi
E. -
tx = 3.0 in.
t2 = 8.0 in.
t = 12.0 in.
10 ksi
00


DEFLECTION (103¡n)
158
Figure 6.4 Surface Deflection as a Function of Load on US 301


403
JOB t 1 *=. R ISC.
DATE 00/30/80 13.80
LOCATION SR 13 (US 441)CSITe 2
FILE CPT171
LOCAL FRICTION FRICTION RATIO


NORMALIZED DEFLECTION (mils)
250
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.61 Comparison of Measured NDT Deflection Basins on I-10C--
M.P. 32.071


124
Table 4.6
Pavements with
Dynaflect
Predictions
Having More
Than
10
Percent
Error (Case
4--Equations
4.8-4.10)
No.
Layer Modul i
(ksi)
(in.)
Predicted
e2
(ksi)
Percent
Ei
E2
E3
E4
Difference
1
100
85
35
0.35
1.0
94.9
11.6
2
1000
10
35
0.35
1.0
11.3
13.0
3
100
85
15
10
2.0
75.5
-11.2
4
700
30
15
10
2.0
25.4
-15.3
5
700
85
15
10
2.0
96.0
12.9
6
1000
30
15
10
2.0
25.3
-15.7
7
1000
35
15
10
2.0
28.9
-17.4
8
1000
40
15
10
2.0
35.4
-11.5


184
Table 6.6--continued
Test
Mile Post
Dynaflect Deflections i
(mils)
Road
Number
Type*
Di
D3
D6
7
D8
9
D10
SR 80
Sec.l
Measured
Predicted
2.11
1.70
2.02
1.50
1.89
1.47
1.61
1.30
1.48
1.21
1.37
1.05
1.07
0.87
0.85
0.74
SR 80
Sec. 2
Measured
Predicted
2.41
2.10
2.15
1.78
2.05
1.72
1.61
1.47
1.48
1.33
1.22
1.11
0.96
0.87
0.74
0.71
SR 15C
0.055
Measured
Predicted
1.78
1.95
1.53
1.60
1.43
1.58
1.33
1.45
1.18
1.39
1.15
1.27
1.03
1.10
0.98
0.95
SR 15C
0.065
Measured
Predicted
1.42
1.50
1.42
1.34
1.26
1.32
1.20
1.22
1.13
1.17
1.10
1.08
1.03
0.97
0.98
0.86
* Predicted using Dynaflect layer moduli predictions listed in Table 6.3
as input into BISAR. E4 predictions from Equation 4.35 were used.


86
Table 4.
2 Sensitivity Analysis
of FWD
Deflections for t
o

CO
If
in.
Parameter
Percent
Change
in Deflections
Di
2
3
4
Ds
D6
7
Da
1/2 Ei
9.5
3.4
3.3
2.8
1.7
0.55
0.0
0.0
2 Ei
-7.7
-3.7
-3.3
-3.2
-2.2
-1.1
-0.48
0.0
1/2 e2
23.3
14.0
7.1
1.4
-0.26
-0.55
-0.48
-0.28
2 E2
-15.5
-9.7
-5.6
- 2.0
-0.13
0.37
0.24
0.28
1/2 E3
14.0
16.5
16.4
12.8
6.5
0.81
-1.8
-2.3
2 E3
-11.8
-13.7
-13.3
-10.1
-5.0
-0.64
1.6
2.2
1/2 E4
37.4
50.5
58.3
69.7
82.8
94.9
101.9
104.4
PO
m
-p
-23.1
-31.0
-35.4
-43.7
-46.8
-50.2
-51.2
-51.1
Original pavement is composed of
E = 150 ksi
E2 = 85 ksi
E3 = 30 ksi
t = 3.0 in.
t2 = 8.0 in.
t = 12.0 in.
10 ksi
00


385
J08 # 1
DATE 12/03/85 09(38
LOCATION SR-2*(WALDO R0> SlTt O-S
FILE # CPT 10
LOCAL FRICTION FRICTION RATIO


92
covering the range listed in Table 4.1. Typical theoretical FWD
deflection basins are shown in Figures 4.4 through 4.10. Figures 4.12
and 4.13 illustrate typical Dynaflect deflection basins for selected
combinations of moduli and asphalt concrete thickness. The BISAR
predicted deflection values served as a database for the development of
prediction equations which would hopefully provide the capability of
evaluating the structural capacity and deficiencies of in-service
pavements.
The deflection data generated from different combinations of layer
moduli and thicknesses were evaluated to determine if the deflection
response from one or more geophone positions in the Dynaflect or FWD
could provide a unique relationship for prediction of individual layer
moduli. The sensitivity analysis had indicated the possibility of
uniquely relating the farthest sensor in the Dynaflect or FWD system to
the subgrade modulus. Attempts were therefore made to identify similar
unique positions for the other layer modulus predictions. The lack of
sensitivity of the upper pavement layers with sensor deflections
suggested the difficulty of developing simple prediction equations.
However, it was found from the modified Dynaflect sensor system
(see Figure 4.2) that the difference between sensor 1 and 4 deflections
(i.e., Dx D^) tend to be uniquely related to the combined effect of
asphalt concrete and base courses. As shown in Figure 4.2, modified
sensor position 1 is located adjacent to one of the Dynaflect wheels,
6.0 in. transversely from standard sensor position i; and the modified
sensor 4 is positioned 4.0 in. longitudinally from the standard position
of sensor 1. This deflection difference essentially eliminated the
effect of the underlying layers and was primarily dependent upon the


328
essential that reduced stiffness be obtained by using softer and less
temperature-susceptible asphalts in the asphalt paving mixture.
The limerock base course moduli for the two SR 80 test sections
were considerably lower than the other pavements. Based on the results
of this investigation, a modulus value of 85,000 psi is considered
typical of well-placed limerock materials in the state of Florida.
Because the moduli of the extremely thick subbase (36 in.) and the
subgrade for SR 80 were relatively low, this pavement probably cracked
due to the lack of support from the upper pavement layers, primarily the
base course and the thin (1.5-in. thick) asphalt concrete layers. Field
observations indicated that either poor drainage conditions increased
the moisture content of the base course or the as-compacted quality of
the base material was poor and resulted in a substandard modulus for E2.
The base course modulus for SR 80 was increased to 85,000 psi
(standard base) for a stress analysis comparison to illustrate the
effect of an improved base course on pavement response and stresses.
The other layer moduli were kept constant and BISAR was used to compute
the response of this hypothetical pavement. These results are summa
rized in Table 8.7. Comparison of these results with the SR 80 test
sections indicates that the percent failure stress level drops to 18.6
percent. Thus, with a proper base course modulus, this pavement could
have yielded moderate stresses and good performance.
Table 8.6 shows that the vertical compressive strain on top of the
subgrade layer was of the order of 2.0 x 104 in./in. An axial compres
sive subgrade strain of 2.6 x 10"4 in./in. corresponds to a 108 repeti
tions of vehicular loading on flexible pavements (133). This limiting


CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
A mechanistic pavement design procedure consists of analyzing the
pavement on the basis of the predicted structural response (stresses,
strains, and deflections) of the system to moving vehicle loads. Pave
ment-layer thicknesses (surface, base, and subbase) are selected such
that the predicted structural response would be acceptable for some
desired number of load repetitions and under existing environmental
conditions. This approach is consistent with the conditions established
by Yoder and Witczak that
for any pavement design procedure to be completely
rational in nature total consideration must be
given to three elements. These elements are
(1) the theory used to predict failure or distress
parameter, (2) the evaluation of the pertinent
material properties necessary for the theory
selected, and (3) the determination of the rela
tionship between the magnitude of the parameter in
question to the failure or performance level
desired.
(133, p. 24-25)
In the last several years, a concentrated effort has been made to
develop a more rational analysis and design procedures for pavements.
For flexible or asphalt concrete pavements, layered (7,19,20) and finite
element (33) theories have been used with some success to analyze pave
ment response. The use of either theory requires that the materials
that make up the pavement be suitably characterized. Layered and finite
element theories use Young's modulus and Poisson's ratio to characterize
6


307
Table 7.4 Correlation of NDT Tuned Subgrade Modulus (E4)
to Cone Resistance
Test
Road
Mile Post
Number
Average qc
(psi)
Tuned E,
4
(psi)
Dynaflect
FWD
Dynaflect
FWD
SR 26A
11.912
2900
14600
18700
5.03
6.45
SR 26B
11.205
2610
7900
11000
3.03
4.21
SR 26C
10.168
2175
28500
25500
13.10
11.72
SR 26C
10.166
2175
28500
20000
13.10
9.20
SR 24
11.112
4712
38600
38600
8.20
8.20
US 301
21.580
1740
38600
25000
22.18
14.37
US 441
1.236
3190
27500
20000
8.62
6.27
I-10A
14.062
5293
105000
130,000
19.84
24.56
SR 15B
4.811
3625
8100
10200
2.23
2.81
SR 15A
6.549
*
4800
7500


SR 15A
6.546
*
5000
7000


SR 715
4.722
1015
6000
11000
5.91
10.84
SR 715
4.720
1015
5500
10500
5.42
10.34
SR 12
1.485
2175
26500
18500
12.18
8.51
SR 15C
0.055
2175
5500
9800
2.53
4.51
SR 15C
0.065
2175
5500
10000
2.53
4.60
* Average qc within the depth of the difference of 1 m (EPT) and the
overlying pavement
** Test not performed


Table A.13 Results of Dynaflect Tests on SR 12 (Gadsden County)
Temperature (F):
Air = 81
Pavement Surface = 91
Mid-Pavement = 102
Site
No.
Mile
Post No.
Type of
Measured
Deflections (mils) for Sensor
Positions*
Data
j**
2
3
4 5 6
1
8
9
10
1
1.472
Std
0.69
0.44
0.36
0.26
0.20
1
1.472
Mod
0.95
0.63
0.39
0.19
0.68
2
1.476
Std
0.75
0.47
0.37
0.28
0.21
2
1.476
Mod
1.20
0.70
0.47
0.21
0.78
3
1.481
Std
0.66
0.43
0.35
0.27
0.21
3
1.481
Mod
0.94
0.64
0.42
0.21
0.74
3.5
1.485
Std
0.68
0.44
0.36
0.27
0.21
3.5
1.485
Mod
1.00
0.65
0.42
0.20
0.71
.
4
1.486
Std
0.68
0.43
0.35
0.27
0.21
4
1.486
Mod
1.13
0.63
0.41
0.21
0.63
5
1.491
Std
0.67
0.43
0.35
0.26
0.20
5
1.491
Mod
1.00
0.66
0.42
0.20
0.70
,


47
pavement design program, though very important, is often treated with
considerable simplification and empiricism. Geotechnical engineers
often feel that structural engineers have little or no interest in those
parts of their work below the ground level. These feelings are cer
tainly justified in the case of pavements (76). It is therefore not
surprising that most of the in situ geotechnical applications to pave
ments rely on correlations with empirical pavement parameters such as
CBR to validate their proposed methods (40,57,58,77). The other known
applications of geotechnical in situ testing methods to evaluate the
stiffness of pavement structures are discussed below.
Briaud and Shields (14,15) have described the development and
procedure of a special pressuremeter test for pavement evaluation and
design. The pavement pressuremeter consists of a probe, tubing and a
control unit, and works on the same principle as the Menard pressure
meter (30). They illustrated how the modulus values obtained from the
test can be used directly in multilayer mechanistic analysis. In order
to use empirical design charts, however, Briaud and Shields (15) also
developed a correlation between the pressuremeter modulus and the
bearing strength obtained from a Macleod plate test for two airport
pavements in Canada.
Borden et al. (12) have presented an experimental program in which
the dilatometer test (68) was used to determine pavement subgrade sup
port characteristics. A major part of the testing program consisted of
conducting DMT and CBR tests in soil samples prepared in cylindrical
molds and also in a special rectangular chamber. A limited field test
was conducted on a compacted embankment constructed with one of the
soils used in the laboratory investigation. Although they report good


120
Equations 4.1 through 4.3 are applicable in this range. Therefore,
these equations were used to compute Ex values for asphalt concrete
thicknesses of 3.0, 4.5, and 6.0 in. Predictions were generally very
good and within +10 percent of the actual Ej value when the true value
of E2 was used. In general, the majority of the predicted E values
were within +5 percent, with the maximum error being +22.5 percent.
Pavements with Ex predictions above +10 percent are listed in Table
4.5. It can be seen from the table that these were very few considering
the size of the data set analyzed. Correlation between predicted and
actual E: values resulted in the following:
Ej (Predicted) = 9.988 + 0.933E1 (Actual) Eqn. 4.30
(N = 58, R2 = 0.992)
Attempts to predict outside of the designated thickness range
resulted in errors up to 70 percent. Thus, it is imperative that the E
prediction equations for Case 1 be used for the specified range.
Case 2: 32.0 < E < 85.0 ksi; and 2.0 < t < 6.0 in.
2 l
E: is predicted from Equations 4.1, 4.4, and 4.5. Comparisons between
predicted and actual E1 values indicated that errors up to 90 percent
could be obtained with the use of these equations. The higher errors
occurred in pavements with extreme values of E^ (0.35 and 200 ksi).
When these pavements were deleted, predictive errors were generally
within the range of +20 percent, with a few cases going as high
as +30 percent.


DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.19 Comparison of Measured and Predicted Dynaflect Deflections for US 301M.P. 11.112


398
JOB # i 1
PATE 10/01/M 142S
LOCATION SR 71S SITE #4.9
FILE # CPT17*
LOCAL FRICTION FRICTION RATIO


NORMALIZED DEFLECTION (mils)
211
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.35
Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 26C--M.P. 10.166


285
log E, = 4.970 + 0.1773(t.) 1.6966 log(t) 0.1069(DJ
O i 1 H
+ 0.2552(D7) 2.6546 1og(D1) 3.9906 log(D3)
+ 1.8241 log(Dc) + 3.5092 log(D0 D_)
b o
Eqn. 6.13
R2 = 0.887 and N = 22
Error analysis indicated that one pavement (SR 15B) had -34.6
percent prediction error. The actual Eg value was 50.0 ksi, while
the predicted value was 32.7 ksi. Others had prediction errors
generally less than +15 percent.
Equation 6.13 for the prediction of Eg applies to a slightly
wider range of variables than Equation 4.24 which was selected from
the theoretical analysis. However, the latter is more simplified
and contains fewer variables than Equation 6.13. Also, since the
R2 value is greater, Equation 4.24 should be used for E3 predic
tions.
6.7.3.2.4 Subgrade modulus, E Regression analyses of E^
against either Dg, or Dy, or both resulted in the following equa
tions :
E = 53.697(0 )"i.04i Eqn. 6>14
4 6
(R2 = 0.997 and N = 22)
E = 39.690(D )1.004 Eqn< 6>15
(R2 = 0.999 and N = 22)


305
Table 7.2 Correlation of NDT Tuned Base Course Modulus (E2)
to Cone Resistance
Test
Road
Mile Post
Number
Average qc
(psi)
Tuned E2
(psi)
e2/9
c
Dynaflect
FWD
Dynaflect
FWD
SR 26A
11.912
6525
105000
75000
16.09
11.49
SR 26B
11.205
6380
90000
90000
14.11
14.11
SR 26C
10.168
5075
55000
45000
10.84
8.87
SR 26C
10.166
5075
55000
55000
10.84
10.84
SR 24
11.112
8338
105000
55000
12.59
6.60
US 301
21.580
8048
120000
45000
14.91
5.59
US 441
1.236
7250
85000
55000
11.72
7.59
I-10A
14.062
7685
95000
90000
12.36
11.71
SR 15B
4.811
6815
120000
52800
17.61
7.75
SR 15A
6.549
*
120000
95000


SR 15A
6.546
*
85000
45000


SR 715
4.722
*
75000
45000


SR 715
4.720
*
65000
65000

SR 12
1.485
1958
120000
31000
61.29
15.83
SR 15C
0.055
3625
105000
35000
28.97
9.66
SR 15C
0.065
5510
105000
50000
19.06
9.07
* Test not performed


439
Table E.5 Rheology and Penetration of Asphalt Recovered From I-10A
(Madison County)
Layer 1: Thickness =
Temperature
F (C)
Absolute
Viscosity
n1(Pa-s)
Complex
Flow (C)
Constant Power
Viscosity
nioo(Pa-5)
275 (135)
5.68 El


140 (60)
3.81 E2

3.81 E2
77 (25)(a)
2.86 E5
0.98
3.10 E5
60 (15.6)
1.29 E6
0.88
2.36 E6
41 ( 5)
1.30 E7
0.68
1.23 E8
23 (-5)
1.03 E8
0.79
5.23 E8
(a) Penetration
at 77F (25C)
= 59
Layer 2:
Thickness =
Temperature
Absolute
Complex
Constant Power
F (C)
Viscosity
Oj(Pa-s)
Flow (C)
Viscosity
nioo(Pa-s)
275 (135)
5.89 El


140 (60)
4.648 E2

4.648 E2
77 (25) 4.32 E5
0.92
6.12 E5
60 (15.6)
2.24 E6
0.95
2.90 E6
41 ( 5)
3.02 E7
0.79
1.33 E8
23 (-5)
2.00 E8
0.88
5.05 E8
(a) Penetration at 77F (25C) = 50


279
procedure in Appendix F have been incorporated into the BISAR elastic
layer computer program to perform the iteration after the initial
computation of the "seed moduli." Figure 6.74 shows a simplified flow
chart of the modified BISAR program utilizing the modified Dynaflect
testing system. The modified program is referred to as DELMAPS1 which
is an acronym for Dynaflect Evaluation of _Layer Moduli for Asphalt
_Pavement Systems Version l_. The iteration part of the program is
interactive and user-specified with respect to the modulus value to be
adjusted to achieve the desired tuning. A partial listing of the
DELMAPS1 program is presented in Appendix G.
6.7.3 Analysis of FWD Tuned Data
6.7.3.1 Comparison of Measured and Predicted Deflections. It was
explained in Section 6.5.3 that tuning of the FWD deflection basins was
extremely difficult for most of the test pavements. It was relatively
easy to match D. and D. or D_, and generally difficult to simulate the
intermediate sensor deflections. This is demonstrated in Table 6.21 in
which correlation between measured and predicted FWD deflections is
presented. The R2 values are generally good for the first two and last
two sensors, and poor for sensors 3, 4, and 5 deflections. Also,
sensors 3, 4 and 5 have slopes lower than unity in their regression
equations. Comparison between BISAR predicted and field measured FWD
deflections indicated that percent errors as high as +35 percent were
obtained at sensor positions 3, 4, and 5. However, the difference in
deflections at sensors 1, 2, 6, and 7 were generally of the order of +10
percent.


486
89. Peattie, K.R., "Stress and Strain Factors for Three-Layer Elastic
Systems," Highway Research Board Bulletin 342, HRB,
Washington, D.C., 1962, pp. 215-253.
90. Petersen, G., and Shepherd, L.W., "Deflection Analysis of Flexible
Pavements," Report 906, Utah State Highway Department, Provo,
Utah, January, 1972.
91. Pichumani, R., "Application of Computer Codes to the Analysis of
Flexible Pavements," Proceedings, Third International Con
ference on the Structural Design of Asphalt Pavements,
University of Michigan, Vol. 1, 1972, pp. 506-520.
92. Poulos, H.G., and Davis, E.H., Elastic Solutions for Soils and
Rock Mechanics, John Wiley & Sons, Inc., New York, 1974,
411 pp.
93. Pronk, A.C., and Buiter, R., "Aspects of the Interpretation and
Evaluation of Falling Weight Deflection (FWD) Measurements,"
Proceedings, Fifth International Conference on the Structural
Design of Asphalt Pavements, Delft, The Netherlands, 1982,
pp. 461-474.
94. Rada, G., and Witczak, M.W., "Comprehensive Evaluation of Labora
tory Resilient Moduli Results for Granular Material,"
Transportation Research Record 810, TRB, Washington, D.C.,
1981, pp. 23-33.
95. Robertson, P.K., and Campanella, R.G., "Guidelines for Use and
Interpretation of the Electronic Cone Penetration Test," Soil
Mechanics Series No. 69, Department of Civil Engineering,
University of British Columbia, Columbia, Vancouver, 1984.
96. Roque, R., Low Temperature Response of Asphalt Concrete Pavements,
Ph.D., Dissertation, Department of Civil Engineering, Univer-
sity of Florida, Gainesville, Florida, 1986, 444 pp.
97. Roque, R., and Ruth, B.E., "Materials Characterization and Re
sponse of Flexible Pavements at Low Temperatures," presented
at the 62nd Annual Meeting of the Association of Asphalt
Paving Technologists, Reno, Nevada, 1987.
98. Ruth, B.E., Bloy, L.A.K., and Avital, A.A., "Prediction of Pave
ment Cracking at Low Temperatures," Proceedings, Association
of Asphalt Paving Technologists, Vol. 51, 1982, pp. 53-90.
99. Schiffman, R.L., "General Analysis of Stresses and Displacements
in Layered Elastic Systems," Proceedings, First International
Conference on the Structural Design of Asphalt Pavements,
University of Michigan, Ann Arbor, Michigan, 1963, pp. 365-
375.


53
correct the in situ moduli calculated for nonlinear granular materials
and subgrade soils from a Dynaflect deflection basin. These conclusions
were based on stress analysis comparisons of a single-axle 18-kip design
load, FWD (9000-lb. peak force) and Dynaflect loadings simulated in the
ELSYM5 elastic-layer program. An algorithm to perform this equivalent
linear correction has been incorporated into the FPPE0D1 self-iterative
computer program (120). However, results reported by Nazarian et al.
(81) tend to contradict the conclusions of Uddin et al. (120). Their
study involving FWD tests at 5- and 15-kip loads indicated that non
linear behavior occurs at higher FWD loads, and is more predominant in
the base course layers than the subgrade.
These results and those from other research work indicate there is
disagreement as to what type of approach should be used when the effects
of nonlinearity and stress dependency are to be considered. There are
at least three schools of thought in this regard. The first group
believes that the use of an equivalent effective modulus in an elastic-
layer theory would provide reasonable response predictions. This
approach would eliminate the expense, time and complexity associated
with more rigorous methods such as finite element models (61). The
research works of Maree et al. (70), Roque (96), and Roque and Ruth (97)
on full-scale pavements tend to support this theory.
The second school of thought recommends that the nonlinear stress
dependent models (Equations 2.5 and 2.6) can be incorporated into an
elastic-layer program to predict reasonable response parameters. How
ever, the asphalt concrete layer is treated as linear elastic. This
theory is supported by Moni smith et al. (78), among others, and has been
used in iterative computer programs like OAF, ISSEM4, and IMD.


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21
an increase in temperature on deflection of the total structure
decreases.
2.3.2.3 Steady-State Dynamic Force-Deflection. Essentially, all
steady-state dynamic deflection measurement systems induce a steady
state sinusoidal vibration in the pavement with a dynamic force genera
tor. The dynamic force is superimposed on the weight of the force gen
erator, resulting in a variation of force with time as shown in Figure
2.3. The magnitude of the peak-to-peak dynamic force is less than twice
the static force to insure continuous contact of the vibrator with the
pavement (79). This means there must always be some amount of dead
weight or static force applied. As the dynamic peak-to-peak loading is
increased, this preload must also be increased (109).
Deflections are usually measured with inertial motion sensors. For
pure sinusoidal motions at any fixed frequency, the output of such sen
sors is directly proportional to deflection. Thus, to measure deflec
tion it is only necessary to determine the calibration factor (output
per unit of deflection) for the measurement frequency. In general,
either an accelerometer or a velocity sensor may be used to measure
deflections. The latter type is commonly called a geophone and is the
type normally employed in dynamic deflection measurements.
There are several different types of steady-state dynamic deflec
tion equipment that are currently being used for nondestructive struc
tural evaluation of pavements. Only two of them have been used exten
sively and are available commerciallythe Dynaflect and the Road
Rater. The others have been designed and constructed by agencies
involved in pavement research, namely the U.S. Army Waterways Experiment
Station (WES), the Federal Highway Administration (FHWA), the Illinois


36
and is the most widely used correlation (133). Other correlations (79)
have been made between E and plate bearing subgrade modulus, K. It
should be recognized that the conditions of dynamic testing generally
yield moduli in the linear elastic range. Conventional tests such as
the CBR and plate bearing tests produce deformations that are not
completely recoverable and, therefore, are partly in the plastic range.
Thus, one would expect some variation in the correlation between E
modulus and pavement parameters, such as K and CBR.
Mechanistic analysis of NDT data is usually performed by one of the
following:
1. Direct relationship between deflection parameters and the
elastic moduli of the pavement layers.
2. Inverse application of a theoretical model by fitting a
measured deflection basin to a deflection basin using an
iterative procedure.
3. A combination of 1 and 2.
The above mechanistic methods employ deflection data from either vibra
tory or impulse loading equipment. While these devices are dynamic in
nature, most of the mechanistic solutions are based on elasto-static
(19,32,74) and visco-elasto-static (7) models. Recently, an elasto-
dynamic model (54) has been used to interpret NDT data (66,67,105).
However, the use of dynamic analyses for interpretation of NDT data can
be considered to be in the research stage. Another significant obser
vation is that almost all the mechanistic solutions available employ
layered theory or simplified versions of it. The only exception to this
is the use of a finite element model presented by Hoffman and Thompson
(45). A review of the various solutions is presented below.


76
24.0, 31.5, 36.0, 47.2, 63.0, and 72.0 in. from the center of the FWD
plate. The deflection measured by the last sensor (at radial distance
of 72.0 in.) is called Dn in this study. Sensors at 16, 24, and 36 in.
o
were only used to better define the deflection basin and were not incor
porated in the analysis for prediction equations, which is described
later in this chapter. Thus, eight deflection locations were actually
used in the analysis. These are called Dj, D2, D3, D^, D5, Dg, D?, and
Dg to represent, respectively, radial distances of 0, 7.87, 11.8, 19.7,
31.5, 47.2, 63, and 72 in. from the center of the FWD plate.
The FWD was modeled in the BISAR program as a circular loaded area
with deflection measurement positions as stated previously. The radius
of the loaded area was 5.91 in., and a load of 9 kips was used which
corresponds to one-half of an 18-kip single-axle wheel loading.
4.2 Sensitivity Analysis of Theoretical NDT Deflection Basins
4.2.1 Parametric Study
Pavement surface deflection data generated from BISAR were ini
tially evaluated to see the effect of rate of change of each layer
modulus and/or thickness on the NDT deflection basins. This was accom
plished by using the pavement section shown in Figure 4.3 as a typical
Florida pavement under warm temperature conditions. Using the informa
tion in Figure 4.3, each parameter (modulus and thickness) for a given
layer was doubled or halved while the others were kept unchanged. For
example, the E4 value of 10 ksi was increased to 20 ksi without changing
Ex, E2, Eg and the layer thicknesses. BISAR was then used to calculate
the NDT deflections. The original E4 value was also halved to 5 ksi and
the theortical deflections were computed with BISAR. This procedure was


452
Table E.ll Rheology and Penetration of Asphalt Recovered From SR 15C
(Martin County)
Layer 1: Shell Mix Thickness = 1.5"
Temperature
F (C)
Absolute
Viscosity
n^Pa-s)
Complex
Flow (C)
Constant Power
Viscosity
ni00 275 (135)
4.167 E2


140 (60)
3.843 E2

3.843 E3
77 (25)(a)
1.497 E6
0.69
8.732 E6
60 (15.6)
2.024 E6
0.69
1.248 E7
41 ( 5)
9.143 E7
0.66
1.521 E9
23 (-5)
2.399 E8
0.76
1.778 E9
(a) Penetration
at 77F (25C)
= 11
Layer 2: Type
II Mix
Thickness = 1/2"
Temperature
F (C)
Absolute
Viscosity
n1(Pa-s)
Complex
Flow (C)
Constant Power
Vi scosity
n100(Pa-s)
275 (135)
6.770 El


140 (60)
1.379 E3

1.379 E3
77 (25) 5.286 E5
0.76
1.702 E6
60 (15.6)
1.424 E6
0.67
9.425 E6
41 ( 5)
5.641 E6
0.52
1.786 E8
23 (-5)
6.465 E7
0.70
6.854 E8
(a) Penetration at 77F (25C) = 34


413
UNIV. OF FLORIDA CIVIL ENG. DEPT.- DR. B.E. RUTH TEST NO. 2
FILE NAME: PAVEMENT-SUBGHADE MATERIALS CHARACTERIZATION
FILS NUMBER: 245-D51
RECORD OF DILAICHETER TEST NO. 2
USING DATA REDUCTION PROCEDURES IN MARCHETTI (ASCE.J-GED,MARCH 80)
KO IN SANDS DETERMINED USING SCHMERTMANN METHOD (1983)
HI ANGLE CALCULATION 3ASED ON DURGUNOGLU AND MITCHELL (ASCE,RALEIGH CONF.JUNE 75)
rHI ANGLE NORMALIZED TO 2.72 BARS USING BALIGHS EXPRESSION (ASCE.J-GED,NOV 76)
MODIFIED MAYNE AND KULHAWY FORMULA USED FCR OCR IN SANDS (ASCE,J-GED, JUNE 82)
LOCATION: US 301 (ALACHUA CO.) TEST SIIE #3.0
PERFORMED DATE: 02-18-85
BY: JLD/DGB/KBT
CALIBRATION INFORMATION:
DELTA A 0.22 BARS
DELTA B
0.37 BARS
GAGE 0 0.05 BARS
GWT DEPTH-
1.14 M
ROD DIA.- 4.80 CM
FR.RED.DIA.-
3.70 at
ROD WT.- 6.50 KG/M
DELTA/PHI-
0.50
BLADE T-13.70 MM
1 BAR 1.019 KG/CM2
- 1.044 TSF -
14.51 PSI
ANALYSIS USES
H20 UNIT WEIGHT =
1.000 T/M3
Z
THRUST
A
B
ED
ID
KD
UO
GAMMA
SV
PC
OCR
KO
CU
PHI
M
SOIL TYPE
(M)
(KG)
(BAR)
(BAR)
(BAR)
(BAR)
(T/M3)
(BAR)
(BAR)
(BAR)
(DEG)
(BAR)
*****
******
*****
*****
*****
*****
*****
******
******
******
*****
*****
*****
*****
*****
******
************
0.50
2500.
5.20
26.60
758.
5.05
44.18
0.000
2.000
0.098
2962.0
SAND
0.70
4405.
8.20
30.20
780.
3.08
53.18
0.000
2.000
0.137
31.71
*****
6.12
44.2
3184.4
SILTY SAND
0.90
2945.
9.00
25.30
572.
1.97
47.50
0.000
2.000
0.177
40.18
*****
5.73
39.6
2275.4
SILTY SAND
1.10
1335.
2.70
10.20
252.
2.37
11.81
0.000
1.900
0.214
3.02
14.13
1.40
40.7
669.0
SILTY SAND
1.30
800.
1.00
5.20
132.
3.89
4.17
0.016
1.800
0.233
0.43
1.84
0.50
40.5
228.9
SAND
1.50
1240.
1.90
9.20
244.
4.15
6.82
0.035
1.800
0.249
1.10
4.41
0.76
41.6
529.9
SAND
1.70
930.
2.20
8.20
197.
2.78
7.66
0.055
1.900
0.267
1.90
7.13
1.01
36.2
446.5
SILTY SAND
1.90
730.
1.00
4.50
106.
3.22
3.39
0.075
1.700
0.281
0.44
1.56
0.47
39.0
165.3
SILTY SAND
2.10
740.
0.90
4.30
121.
4.29
2.75
0.094
1.700
0.294
0.31
1.05
0.38
39.4
166.4
' SAND
2.30
655.
0.90
5.00
128.
4.72
2.53
0.114
1.700
0.308
0.33
1.08
0.40
38.2
167.3
SAND
2.70
1220.
5.60
10.80
168.
0.90
15.96
0.153
1.800
0.337
8.62
25.54
2.44
0.996
494.3
CLAYEY SILT
3.10
1765.
11.20
19.80
292.
0.78
28.99
0.192
1.950
0.372
24.08
64.78
3.42
2.313
1023.7
CLAYEY SILT
3.50
1620.
11.70
20.60
303.
0.78
27.43
0.232
1.950
0.409
24.32
59.45
3.32
2.376
1046.2
CLAYEY SILT
3.70
1770.
11.80
21.50
332.
0.85
26.15
0.251
2.100
0.431
23.76
55.17
3.23
2.356
1131.9
CLAYEY SILT
4.10
3640.
11.60
13.20
219.
0.56
23.79
0.290
1.900
0.470
22.36
47.59
3.07
2.284
727.1
SILTY CLAY
4.50
3640.
10.40
32.80
795.
2.50
17.93
0.330
2.150
0.510
17.72
34.73
2.21
40.2
2426.1

SILTY SAND'
4.70
2575.
10.40
17.80
248.
0.72
18.68
0.349
1.950
0.529
17.27
32.65
2.67
1.900
767.1
CLAYEY SILT
4.90
2390.
10.20
30.40
714.
2.28
16.36
0.369
2.150
0.551
18.27
33.13
2.14
36.6
2119.2
SILTY SAND
5.10
3035.
5.80
14.00
277.
1.54
9.12
0.339
1.950
0.570
4.96
8.70
1.09
41.7
668.7
SANDY SILT
5.50
3810.
10.20
30.30
711.
2.28
14.67
0.428
2.150
0.611
14.30
23.40
1.81
40.6
2035.0
SILTY SAND
5.90
2025.
7.20
21.50
500.
2.32
9.51
0.467
2.000
0.653
7.66
11.72
1.31
37.2
1226.1
SILTY SAND
6.30
2705.
5.00
18.20
459.
3.28
5.82
0.506
2.000
0.693
2.60
3.75
0.72
41.5
932.3
SILTY SAND
END OF SOUNDING


73
Table 4.1
Range of Pavement
Layer Properties
Layer
Number
Layer
Type
Layer
Thickness
(in.)
Poisson's
Ratio
Layer Modulus
(ksi)
1
Asphalt
Concrete
1.0 10.0
0.35
75 1,200
2
Limerock
Base
8.0
0.35
10 170
3
Stabilized
Subgrade
(Subbase)
12.0
0.35
6 75
4
Subgrade
(Embankment)
Semi-infinite
0.35
0.35 200


COMPOSITE MODULUS, E12 (ksi)
274
D1 D4 (mils)
Figure 6.70 Relationship Between E12 (Using Equation 6.8) and Dx -


404
joe # 1 Sc.
ATE oa/30/80 15a 51
LOCATION SR 15-441 SITE 3
FILE # CPT17Z
LOCAL FRICTION FRICTION RATIO


147
The modulus values calculated from Equation 5.1 for each layer are
essentially surface or composite modulus for the particular layer and
the underlying layer(s). Burmister's two layer theory (133, pp. 40-44)
was then used to obtain the modulus of each layer. Table 5.3 lists
results of the plate loading tests.
5.3.7 Asphalt Rheology Tests
The core samples of asphalt concrete obtained from each test pave
ment section were separated in the laboratory according to each layer
(lift) or type of asphalt concrete mix. These were then heated and
broken down for extraction using Method B (Reflux) of ASTM D 2172 for
Quantitative Extraction of Bitumen from Bituminous Paving Mixtures
(8). The asphalt cement was recovered using the Abson method, ASTM D
1856 (8).
Low-temperature rheology tests were performed at different tempera
tures on the recovered asphalt cement samples. This involved viscosity
determination at different shear stresses and test temperatures using
the Schweyer Constant Stress Rheometer. Details pertaining to the
physical characteristics, operation, and computational methods of the
Schweyer Constant Stress Rheometer are presented by Tia and Ruth (117).
Absolute and constant power viscosities (117) were computed from
Schweyer Rheometer test data at temperatures of 140, 77, 60, 41, and
23F. The results for each test pavement site are listed in Appendix
E. Linear regression analyses of constant power viscosity (n ) with
10 0
absolute temperature resulted in regression constants as listed in Table
5.4. These were then used in previously established modulus-viscosity-
temperature relationships (98,117) to compute the modulus of the


DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.29 Comparison of Measured and Predicted Dynaflect Deflections for SR 80Section 2
200


DEFLECTION (mils)
94
DISTANCE FROM CENTER OF LOADED AREA (in)
0 10 20 30 40 50
Figure 4.13 Variation in Dynaflect Deflection Basin with Varying E3
and E4 Values with tx = 3.0 in.


301
The regression equation for sandy soils is almost identical to
Equation 7.2 which included all the test data. The correlation for the
clayey soils is poor. This poor correlation tends to agree with obser
vations of Jamiolkowski et al. (50) that qc cannot be correlated to any
drained soil modulus for cohesive deposits.
The combined test data was also separated into above and below
water table categories for the purpose of assessing whether or not the
saturation state of the soils affected the Eg-qc correlations. The
regression equation for above water table conditions,
Ed = 3.64 qc Eqn. 7.5
(R2 = 0.926, N = 102)
is similar to Equation 7.3 for sandy soils. The equation obtained for
soils below water table,
Ed = 3.946 qc Eqn. 7.6
(R2 = 0.778, N = 122)
is almost the same as Equation 7.4 for cohesive soils. It is evident
that the correlations were affected by the development of excess pore
water pressures in the clayey soils during penetration. An attempt was
made to measure pore water pressures using the piezocone. This was
unsuccessful due to the clogging of the porous stone by fine sand
particles.


SUBGRADE MODULUS, E4, IN ksi
117
SENSOR DEFLECTION, D6 D? D8 (mils)
Figure 4.22 Relationship Between E4 and FWD Deflections for Fixed
Ej, E2, and Eg Values with tx = 6.0 in.


DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
VO
-Pi
Figure 6.23 Comparison of Measured and Predicted Dynaflect Deflections for SR-15AM.P. 6.549


310
Table 7.7
Correlation
Dilatometer
of NDT Tuned Subgrade
Modulus
Modulus to
Test
Mile Post
Average Eq*
Tuned E,
r 4
(psi)
E4
./Ed
Road
Number
(psi)
Dynaflect
FWD
Dynaflect
FWD
SR 26A
11.912
8990
14600
18700
1.62
2.08
SR 26B
11.205
11528
7900
11000
0.69
0.95
SR 26C
10.168
9933
28500
25500
2.87
2.57
SR 26C
10.166
9933
28500
20000
2.87
2.01
SR 24
11.112
**
38600
38600


US 301
21.580
9752
38600
25000
3.96
2.56
US 441
1.236
7975
27500
20000
3.45
2.51
I-10A
14.062

105000
130000


SR 15B
4.811
11165
8100
10200
0.73
0.91
SR 15A
6.549
5365
4800
7500
0.89
1.40
SR 15A
6.546
5365
5000
7000
0.93
1.30
SR 715
4.722
7250
6000
11000
0.83
1.52
SR 715
4.720
7250
5500
10500
0.76
1.45
SR 12
1.485
7323
26500
18500
3.62
2.53
SR 15C
0.055
7250
5500
9800
0.76
1.35
SR 15C
0.065
7250
5500
10000
0.76
1.38
* Average Eq within the depth of the difference of 1 m (EPT) and the
overlying pavement.
** Test not performed


102
Case 1
K =
i
K =
2
Case 2
K =
l
K =
2
Case 3
: For 10.0 < E < 30.0 ksi and 3.0 t < 6.0 in.,
2 1
.0.794 (31.0 E )
7.99 + 26.64 (8/t ) 2
1
0.9362
Eqn. 4.2
0.569 1.08 t 4.5 0*28^2^
0.7828(E ) [t -(_!)]
2 1 6
0.3463
Eqn. 4.3
For 32.0 < E < 85.0 ksi, and 2.0 in. < t < 6.0 in.,
2 1
, .0.3826
32.93 1.636 (E 30.15) (6.2 t )
2 1
Eqn. 4.4
0.3463
0.621 1.08 t 4.5 "*289^E2^
0.6779 (E ) [t HO ] Eqn. 4.5
2 1 6
For 10.0 < E < 85.0 ksi, and 7.0 < t < 10.0 in.,
2 1
K = 36.661 0.1218(E )
1 2
Eqn. 4.6


NORMALIZED DEFLECTION (mils)
225
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.49 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 15CM.P. 0.065


EVALUATION OF LAYER MODULI IN FLEXIBLE PAVEMENT SYSTEMS
USING NONDESTRUCTIVE AND PENETRATION TESTING METHODS
By
KWASI BADU-TWENEBOAH
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1987

DEDICATED TO MY FAMILY, ESPECIALLY MY SISTER, ABENA KYEM
AND GRANDFATHER, ATTA KWAME, FOR THEIR CONTINUAL PRAYERS,
ENCOURAGEMENT AND SUPPORT DURING THE COURSE OF MY EDUCATION.
"A MIND IS A TERRIBLE THING TO WASTE
II

ACKNOWLEDGMENTS
I would like to express my gratitude to Dr. Byron E. Ruth, chairman
of my supervisory committee, for his guidance, encouragement and con
structive criticisms in undertaking this research work. I am also
grateful to Drs. F. C. Townsend, J. L. Davidson, M. Tia, J. L. Eades,
and D. P. Spangler for serving on my graduate supervisory committee. I
consider myself honored to have had these distinguished men on my com
mittee.
I also owe sincere thanks to Dr. J. H. Schaub, chairman of the
civil engineering department, for the many times he gave help during the
course of my studies here, especially in my obtaining the grant award to
participate in the 1986 APWA Congress in New Orleans, Louisiana.
I would like to express my appreciation to the Florida Department
of Transportation (FDOT) for providing the financial support, testing
facilities, materials, and personnel that made this research possible.
I would like to thank the many individuals at the Pavement Evaluation
and Bituminous Materials Research sections of the Bureau of Materials
and Research at FDOT who contributed significantly to the completion of
this work. In particular, I am indebted to Messrs. W. G. Miley, Ron
McNamara, Ed Leitner, Don Bagwell, and John Purcell for giving so
generously of their time.
A very special word of thanks goes to Dr. David Bloomquist for his
significant contributions in conducting the in situ penetration tests

and for his helpful suggestions, advice, and friendship. The assistance
of Mr. Ed Dobson in the field work is also appreciated.
I would also like to thank Dr. F. Balduzzi of the Institute of
Foundation Engineering and Soil Mechanics of the Federal Institute of
Technology, Zurich, Switzerland, for inviting me to the institute, and
consequently helping me develop the interest to pursue active research
and further studies.
Last, but far from being the least, I would like to thank
Ms. Candace Leggett for her expertise and diligent skill in typing this
dissertation.
iv

TABLE OF CONTENTS
Page
ACKNOWLEDGMENTS ii
LIST OF TABLES ix
LIST OF FIGURES xi 11
ABSTRACT xx
CHAPTER
1 INTRODUCTION 1
1.1 Background 1
1.2 Study Objectives 3
1.3 Scope of Study 4
2 LITERATURE REVIEW 6
2.1 Introduction 6
2.2 Elastic-Layer Theory 7
2.2.1 General 7
2.2.2 One-Layer System 8
2.2.3 Two-Layer System 9
2.2.4 Three-Layer System 10
2.2.5 Multilayered or N-Layered Systems 11
2.3 Material Characterization Methods 13
2.3.1 General 13
2.3.2 State-of-the-Art Nondestructive Testing 15
2.3.2.1 General 15
2.3.2.2 Static Deflection Procedures 16
2.3.2.3 Steady-State Dynamic Force-
Deflection 21
2.3.2.4 Dynamic Impact Load Response 24
2.3.2.5 Wave Propagation Technique 29
2.3.3 NDT Data-Interpretation Methods 32
2.3.3.1 General 32
2.3.3.2 Direct Solutions 36
2.3.3.3 Back-Calculation Methods 41
2.3.4 Other In Situ Methods 44
v

Page
2.4Factors Affecting Modulus of Pavement-Subgrade
Materials 47
2.4.1 Introduction 47
2.4.2 Temperature 48
2.4.3 Stress Dependency 49
3 EQUIPMENT AND FACILITIES 55
3.1 Description of Dynaflect Test System 56
3.1.1 Description of Equipment 56
3.1.2 Calibration 59
3.1.3 Testing Procedure 59
3.1.4 Limitations 60
3.2 Description of the Falling Weight Deflectometer
Testing System 60
3.2.1 The 8002 FWD 61
3.2.2 The 8600 System Processor 61
3.2.3 The HP-85 Computer 63
3.2.4 Testing Procedure 63
3.2.5 Advantages 64
3.3 BISAR Computer Program 64
3.4 Description of Cone Penetration Test Equipment 65
3.5 Marchetti Dilatometer Test Equipment 67
3.6 Plate Bearing Test 70
4 SIMULATION AND ANALYSES OF NDT DEFLECTION DATA 71
4.1 BISAR Simulation Study 71
4.1.1 General 71
4.1.2 Dynaflect Sensor Spacing 74
4.1.3 FWD Sensor Spacing 74
4.2 Sensitivity Analysis of Theretical NDT
Deflection Basins 76
4.2.1 Parametric Study 76
4.2.2 Summary of Sensitivity Analysis 88
4.3 Development of Layer Moduli Prediction Equations. 91
4.3.1 General 91
4.3.2 Development of Dynaflect Prediction Equations.... 95
4.3.2.1 Prediction Equations for Ex 95
4.3.2.2 Prediction Equation for E£ for
Thin Pavements 103
4.3.2.3 Prediction Equations for E3 105
4.3.2.4 Prediction Equations for E^ 108
4.3.3 Development of FWD Prediction Equations 110
4.3.3.1 Prediction Equations for E1 110
4.3.3.2 Prediction Equations for E£ Ill
4.3.3.3 Prediction Equations for E3 113
4.3.3.4 Prediction Equations for E^ 114
vi

Page
4.4Accuracy and Reliability of NDT Prediction Equations.... 119
4.4.1 Prediction Accuracy of Dynaflect Equations 119
4.4.1.1 Asphalt Concrete Modulus, E: 119
4.4.1.2 Base Course Modulus, E2,
for Thin Pavements 123
4.4.1.3 Stabilized Subgrade Modulus, E 123
4.4.1.4 Subgrade Modulus, E4 125
4.4.2 Prediction Accuracy of FWD Equations 127
4.4.2.1 Asphalt Concrete Modulus, E 127
4.4.2.2 Base Course Modulus, E2 129
4.4.2.3 Stabilized Subgrade Modulus, E3 132
4.4.2.4 Subgrade Modulus, E4 134
5 TESTING PROGRAM 136
5.1 Introduction 136
5.2 Location and Characteristics of Test Pavements 137
5.3 Description of Testingn Procedures 140
5.3.1 General 140
5.3.2 Dynaflect Tests 142
5.3.3 Falling Weight Deflectometer Tests 142
5.3.4 Cone Penetration Tests 144
5.3.5 Dilatometer Tests 145
5.3.6 Plate Loading Tests 145
5.3.7 Asphalt Rheology Tests 147
5.3.8 Temperature Measurements 151
6 ANALYSES OF FIELD MEASURED NDT DATA 153
6.1 General 153
6.2 Linearity of Load-Deflection Response 153
6.3 Prediction of Layer Moduli 169
6.3.1 General 169
6.3.2 Dynaflect Layer Moduli Predictions 171
6.3.3 FWD Prediction of Layer Moduli 174
6.4 Estimation of Ex from Asphalt Rheology Data 178
6.5 Modeling of Test Pavements 181
6.5.1 General 181
6.5.2 Tuning of Dynaflect Deflection Basins 182
6.5.3 Tuning of FWD Deflection Basins 205
6.5.4 Nonuniqueness of NDT Backcalculation
of Layer Modul i 231
6.5.5 Effect of Stress Dependency 233
6.6 Comparison of NDT Devices 236
6.6.1 Comparison of Deflection Basins 238
6.6.2 Comparison of Layer Moduli 253
6.7 Analyses of Tuned NDT Data 265
6.7.1 General 265
6.7.2 Analysis of Dynaflect Tuned Data 266
vii

Page
6.7.2.1 Comparison of Measured and
Predicted Deflections 266
6.7.2.2 Development of Simplified Layer
Moduli Equations 271
6.7.3Analysis of FWD Tuned Data 279
6.7.3.1 Comparison of Measured and
Predicted Deflections 279
6.7.3.2 Development of Prediction Equations 282
7 INTERPRETATION OF IN SITU PENETRATION TESTS 288
7.1 General 288
7.2 Soil Profiling and Identification 289
7.3 Correlation Between ED and qc 292
7.4 Evaluation of Resilient Moduli for Pavement Layers 302
7.4.1 General 302
7.4.2 Correlation of Resilient Moduli with
Cone Resistance 304
7.4.3 Correlation of Resilient Moduli with
Dilatometer Modulus 308
7.5 Variation of Subgrade Stiffness with Depth 312
8 PAVEMENT STRESS ANALYSES 316
8.1 General 316
8.2 Short-Term Load Induced Stress Analysis 318
8.2.1 Design Parameters 318
8.2.2 Comparison of Pavement Response and
Material Properties 319
8.2.3 Summary 330
9 CONCLUSIONS AND RECOMMENDATIONS 331
9.1 Conclusions 331
9.2 Recommendations 334
APPENDICES
A FIELD DYNAFLECT TEST RESULTS 338
B FIELD FWD TEST RESULTS 354
C COMPUTER PRINTOUT OF CPT RESULTS 379
D COMPUTER PRINTOUT OF DMT RESULTS 406
E RECOVERED ASPHALT RHEOLOGY TEST RESULTS 432
F RECOMMENDED TESTING AND ANALYSIS PROCEDURES FOR THE
MODIFIED DYNAFLECT TESTING SYSTEM 456
G PARTIAL LISTING OF DELMAPS1 COMPUTER PROGRAM 463
viii

Page
REFERENCES 478
BIOGRAPHICAL SKETCH 491
ix

LIST OF TABLES
Table Page
2.1 Summary of Deflection Basin Parameters 35
2.2 Summary of Computer Programs for Evaluation of
Flexible Pavement Moduli from NOT Devices 42
4.1 Range of Pavement Layer Properties 73
4.2 Sensitivity Analysis of FWD Deflections for tx = 3.0 in 86
4.3 Sensitivity Analysis of FWD Deflections for t t
and t3 87
4.4 Sensitivity Analysis of FWD Deflections for E =
600 ksi and tx = 3.0 in 89
4.5 Pavements with Dynaflect Ex Predictions Having More
Than 10 Percent Error 121
4.6 Pavements with Dynaflect E2 Predictions Having More
Than 10 Percent Error 124
4.7 Comparison of Actual and Predicted E Values for
Varying tx 126
4.8 Prediction Accuracy of Equation 4.18--Error
Distribution as a Function of tl 128
4.9 Prediction Accuracy of Equation 4.19--Error
Distribution as a Function of tj 130
4.10 Pavements with Ex Predictions Having 15 Percent
or More Error 131
4.11 Pavements with E2 Predictions Having 20 Percent
or More Errors 133
5.1 Characteristics of Test Pavements 138
5.2 Summary of Tests Performed on Test Pavements 143
5.3 Plate Loading Test Results 148
x

5.4 Viscosity-Temperature Relationships of Recovered
Asphalt from Test Pavements 149
5.5 Temperature Measurements of Test Pavement Sections 152
6.1 Typical Dynaflect Deflection Data from Test Sections.. 170
6.2 Typical FWD Data from Test Sections 172
6.3 Layer Moduli Using Dynaflect Prediction Equations 173
6.4 Layer Moduli Using FWD Prediction Equations 176
6.5 Comparison Between NDT and Rheology Predictions
of Asphalt Concrete Modulus 180
6.6 Comparison of Field Measured and BISAR Predicted
Dynaflect Deflections 183
6.7 Dynaflect Tuned Layer Moduli for Test Sections 203
6.8 Predicted Deflections from Tuned Layer Moduli 204
6.9 Comparison of Field Measured and BISAR Predicted
FWD Deflections 206
6.10 FWD Tuned Layer Moduli for Test Sections 227
6.11 Predicted FWD Deflections from Tuned Layer Moduli 228
6.12 Comparison Between Re-Calculated and Tuned FWD
Layer Moduli 230
6.13 Illustration of Nonuniqueness of Backcalculation of
Layer Moduli from NDT Deflection Basin 232
6.14 Comparison of Deflections Measured at Different Load
Levels 235
6.15 Comparison Between Tuned Layer Moduli and Applied
FWD Load 237
6.16 Comparison of the Asphalt Concrete Modulus for
the Test Sections 254
6.17 Comparison of the Base Course Modulus for the
Test Sections 258
6.18 Comparison of the Subbase Modulus for the
Test Sections 259
6.19 Comparison of the Subgrade Modulus for the
Test Sections 260
XI

6.20 Ratios of Dynaflect Moduli to FWD Moduli for Test Sections... 262
6.21 Correlation Between Measured and Predicted FWD
(9-kip Load) Deflections 281
7.1 Relationship Between Eq and qc for Selected Test
Sections in Florida 298
7.2 Correlation of NDT Tuned Base Course Modulus (E2)
to Cone Resistance 305
7.3 Correlation of NDT Tuned Subbase Modulus (E3) to
Cone Resistance 306
7.4 Correlation of NDT Tuned Subgrade Modulus (E^) to
Cone Resistance 307
7.5 Relationship Between Resilient Modulus, E^ and
Cone Resistance, qc 308
7.6 Correlation of NDT Tuned Subbase Modulus to
Dilatometer Modulus 309
7.7 Correlation of NDT Tuned Subgrade Modulus to
Dilatometer Modulus 310
7.8 Relationship Between Resilient Modulus, E^ and
Dilatometer Modulus, ED 311
7.9 Effect of Varying Subgrade Stiffness on Dynaflect
Deflections on SR 26A 314
8.1 Material Properties and Results of Stress Analysis
for SR 26B (Gilchrist County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis 320
8.2 Material Properties and Results of Stress Analysis
for SR 24 (Alachua County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis 321
8.3 Material Properties and Results of Stress Analysis
for US 441 (Columbia County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis 322
8.4 Material Properties and Results of Stress Analysis
for SR 15C (Martin County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis 323
8.5 Material Properties and Results of Stress Analysis
for SR 80 (Palm Beach County); a) Input Parameters
for BISAR; b) Pavement Stress Analysis 324
8.6 Summary of Pavement Stress Analysis at Low Temperatures 326
xii

8.7 Effect of Increased Base Course Modulus on Pavement
Response on SR 80; a) Input Parameters for BISAR;
b) Pavement Stress Analysis
329
xm

LIST OF FIGURES
Figure Page
2.1 Well-Designed Pavement Deflection History Curve 18
2.2 Typical Annual Deflection History for a Flexible Pavement.... 20
2.3 Typical Output of a Dynamic Force Generator 22
2.4 Schematic Diagram of Impulse Load-Response Equipment 25
2.5 Characteristic Shape of Load Impulse 26
2.6 Comparison of Pavement Response from FWD and
Moving-Wheel Loads, a) Surface Deflections;
b) Vertical Subgrade Strains 28
2.7 Empirical Interpretation of Dynaflect Deflection
Basin, a) Basin Parameters; b) Criteria 34
2.8 Dynaflect Fifth Sensor Deflection-Subgrade
Modulus Relationship 40
2.9 Temperature Prediction Graphs, a) Pavements More
Than 2 in. Thick; b) Pavements Equal to or Less
Than 2 in. Thick 50
3.1 Typical Dynamic Force Output Signal of Dynaflect 57
3.2 Configuration of Dynaflect Load Wheels and Geophones
in Operating Position 58
3.3 Schematic of FWD Load-Geophone Configuration and
Deflection Basin 62
3.4 Schematic of Marchetti Dilatometer Test Equipment 69
4.1 Four-Layer Flexible Pavement System Model 72
4.2 Dynaflect Modified Geophone Positions 75
4.3 Typical Four-Layer System Used for the Sensitivity
Analysis 77
xiv

4.4 Effect of Change of Ex on Theoretical FWD (9-kip Load)
Deflection Basin 79
4.5 Effect of Change of E2 on Theoretical FWD (9-kip Load)
Deflection Basin 80
4.6 Effect of Change of E on Theoretical FWD (9-kip Load)
Deflection Basin 81
4.7 Effect of Change of E4 on Theoretical FWD (9-kip Load)
Deflection Basin 82
4.8 Effect of Change of tj on Theoretical FWD (9-kip Load)
Deflection Basin 83
4.9 Effect of Change of t2 on Theoretical FWD (9-kip Load)
Deflection Basin 84
4.10 Effect of Change of t3 on Theoretical FWD (9-kip Load)
Deflection Basin 85
4.11 Effect of Varying Subgrade Thickness on Theoretical FWD
(9-kip Load) Deflection Basin 90
4.12 Variation in Dynaflect Deflection Basin with Varying
E2 and E3 Values with tx = 3.0 in 93
4.13 Variation in Dynaflect Deflection Basin with Varying
E3 and E4 Values with tx = 3.0 in 94
4.14 Relationship Between Ex and Dx D4 for tx = 3.0 in 96
4.15 Relationship Between Ex and Dx D4 for tx = 6.0 in 97
4.16 Relationship Between Ex and Dx D4 for tx = 8.0 in 98
4.17 Variation of K: with tx for Different E2 Values 100
4.18 Variation of K2 with tx for Different E2 Values 101
4.19 Relationship Between E2 and Dx D4 for tx = 1.0 in 104
4.20 Comparison of E4 Prediction Equations Using Modified
Sensor 10 Deflections 109
4.21 Relationship Between E and FWD Deflections for Fixed
ElS E2, and Eg Values with tx = 3.0 in 116
4.22 Relationship Between E and FWD Deflections for Fixed
Ex, E2, and Eg Values with t: = 6.0 in 117
5.1 Location of Test Pavements in the State of Florida 139
xv

5.2 Layout of Field Tests Conducted on Test Pavements 141
6.1 Surface Deflection as a Function of Load on SR 26A 155
6.2 Surface Deflection as a Function of Load on SR 26C 156
6.3 Surface Deflection as a Function of Load on SR 24 157
6.4 Surface Deflection as a Function of Load on US 301 158
6.5 Surface Deflection as a Function of Load on US 441 159
6.6 Surface Deflection as a Function of Load on I-10A 160
6.7 Surface Deflection as a Function of Load on I-10B 161
6.8 Surface Deflection as a Function of Load on I-10C 162
6.9 Surface Deflection as a Function of Load on SR 715 163
6.10 Surface Deflection as a Function of Load on SR 12 164
6.11 Surface Deflection as a Function of Load on SR 15C 165
6.12 Surface Deflection as a Function of Load on SR 26B 166
6.13 Surface Deflection as a Function of Load on SR 15A 167
6.14 Surface Deflection as a Function of Load on SR 15B 168
6.15 Comparison of Measured and Predicted Dynaflect
Deflections for SR 26AM.P. 11.912 186
6.16 Comparison of Measured and Predicted Dynaflect
Deflections for SR 26BM.P. 11.205 187
6.17 Comparison of Measured and Predicted Dynaflect
Deflections for SR 26CM.P. 10.168 188
6.18 Comparison of Measured and Predicted Dynaflect
Deflections for SR 24M.P. 11.112 189
6.19 Comparison of Measured and Predicted Dynaflect
Deflections for US 301M.P. 11.112 190
6.20 Comparison of Measured and Predicted Dynaflect
Deflections for I-10AM.P. 14.062 191
6.21 Comparison of Measured and Predicted Dynaflect
Deflections for I-10BM.P. 2.703 192
6.22 Comparison of Measured and Predicted Dynaflect
Deflections for I-10CM.P. 32.071 193
xvi

6.23 Comparison of Measured and Predicted Dynaflect
Deflections for SR-15A--M.P. 6.549 194
6.24 Comparison of Measured and Predicted Dynaflect
Deflections for SR 15BM.P. 4.811 195
6.25 Comparison of Measured and Predicted Dynaflect
Deflections for SR 715--M.P. 4.722 196
6.26 Comparison of Measured and Predicted Dynaflect
Deflections for SR 715--M.P. 4.720 197
6.27 Comparison of Measured and Predicted Dynaflect
Deflections for SR 12M.P. 1.485 198
6.28 Comparison of Measured and Predicted Dynaflect
Deflections for SR 80Section 1 199
6.29 Comparison of Measured and Predicted Dynaflect
Deflections for SR 80Section 2 200
6.30 Comparison of Measured and Predicted Dynaflect
Deflections for SR 15CM.P. 0.055 201
6.31 Comparison of Measured and Predicted Dynaflect
Deflections for SR 15C--M.P. 0.065 202
6.32 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 26A--M.P. 11.912 208
6.33 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 26BM.P. 11.205 209
6.34 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 26CM.P. 10.168 210
6.35 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 26CM.P. 10.166.... 211
6.36 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 24M.P. 11.112 212
6.37 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for US 301M.P. 21.585..... 213
6.38 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for US 441--M.P. 1.236 214
6.39 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for I-10AM.P. 14.062 215
6.40 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for I-10BM.P. 2.703 216
xv ii

6.41 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for I-10C--M.P. 32.071 217
6.42 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 15A--M.P. 6.546 218
6.43 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 15A--M.P. 6.549 219
6.44 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 15B--M.P. 4.811 220
6.45 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 715M.P. 4.722 221
6.46 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 715M.P. 4.720 222
6.47 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 12M.P. 1.485 223
6.48 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 15CM.P. 0.055 224
6.49 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 15CM.P. 0.065 225
6.50 Comparison of Measured NDT Deflection Basins on SR 26C
M.P. 10.166 239
6.51 Comparison of Measured NDT Deflection Basins on US 301
M.P. 21.585 240
6.52 Comparison of Measured NDT Deflection Basins on US 441
M.P. 1.237 241
6.53 Comparison of Measured NDT Deflection Basins on SR 12
M.P. 1.485 242
6.54 Comparison of Measured NDT Deflection Basins on SR 26B
M.P. 11.205 243
6.55 Comparison of Measured NDT Deflection Basins on SR 15A
M.P. 6.549 244
6.56 Comparison of Measured NDT Deflection Basins on SR 715
M.P. 4.722 245
6.57 Comparison of Measured NDT Deflection Basins on SR 26A
M.P. 11.912 246
6.58 Comparison of Measured NDT Deflection Basins on SR 24
M.P. 11.112 247
xviii

6.59 Comparison of Measured NDT Deflection Basins on I-10A--
M.P. 14.062 248
6.60 Comparison of Measured NDT Deflection Basins on I-10B
M.P. 2.703 249
6.61 Comparison of Measured NDT Deflection Basins on I-10C
M.P. 32.071 250
6.62 Comparison of Measured NDT Deflection Basins on SR 15B
M.P. 4.811 251
6.63 Comparison of Measured NDT Deflection Basins on SR 15C
M.P. 0.055 252
6.64 Relationship Between Asphalt Concrete Modulus, E and
Mean Pavement Temperature 256
6.65 Comparison of Dynaflect and FWD Tuned Layer Moduli 261
6.66 Relationship Between Predicted and Measured Dynaflect
Modified Sensor 1 Deflections 267
6.67 Relationship Between Predicted and Measured Dynaflect
Modified Sensor 4 Deflections 268
6.68 Relationship Between Predicted and Measured Dynaflect
Modified Sensor 7 Deflections 269
6.69 Relationship Between Predicted and Measured Dynaflect
Modified Sensor 10 Deflections 270
6.70 Relationship Between E (Using Equation 6.8) and D2 D^.... 274
6.71 Relationship Between E12 (Using Equation 6.9) and Dx D^.... 275
6.72 Relationship Between E3 and D^ D? 276
6.73 Relationship Between E4 and D1(J 277
6.74 Simplified Flow Chart of DELMAPS1 Program 280
6.75 Relationship Between E4 and FWD Dg and D? 287
7.1 Variation of qc and FR with Depth on SR 12 290
7.2 Variation of Eq and Kg with Depth on SR 12 291
7.3 Variation of qc and Eq with Depth on SR 26A 293
7.4 Variation of qc and Eq with Depth on SR 26C 294
7.5 Variation of qc and Eq with Depth on US 301 295
xix

7.6 Variation of qc and ED with Depth on US 441 296
7.7 Variation of qc and Eg with Depth on SR 12 297
7.8 Correlation of Eg with qc 300
xx

Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
EVALUATION OF LAYER MODULI IN FLEXIBLE PAVEMENT SYSTEMS
USING NONDESTRUCTIVE AND PENETRATION TESTING METHODS
By
Kwasi Badu-Tweneboah
December 1937
Chairman: Byron E. Ruth
Major Department: Civil Engineering
A research study was conducted to develop procedures for the eval
uation of layer moduli in flexible pavement systems using in situ non
destructive (NDT) and penetration tests. The _Bitumen structure Analysi
in Roads (BISAR) elastic layer computer program was used to simulate
Dynaflect and Falling Weight Deflectometer (FWD) load-deflection
response for typical flexible pavements in the state of Florida. A
field testing program consisting of Dynaflect, FWD, cone penetration,
Marchetti Dilatometer, and plate bearing tests was conducted on fifteen
pavement sections in the state of Florida. Cores of the asphalt con
crete pavement were collected for laboratory low-temperature rheology
tests. This provided a reliable and effective method of predicting the
asphalt concrete modulus.
A modified Dynaflect geophone configuration and simplified layer
moduli prediction equations which allow a layer-by-layer analysis of
Dynaflect deflection measurements were developed. Multiple linear

regression equations with relatively good prediction accuracy were
obtained from analyses of FWD deflection data. Different layer moduli
values were obtained from the Dynaflect and FWD deflection basins for
the various test sections.
The penetration tests provided means for identifying the soils and
also assessing the variability in stratigraphy of the test sites. Good
correlations between cone resistance, qc, and dilatometer modulus, Eg,
for sandy soils and soils above the water table were obtained. Pavement
layer moduli determined from NDT data were regressed to qc and Eg for
the various layers in the pavement. The correlations were better with
qc than with Eg, and also for the base and subbase layers than the
variable subgrade layer. The penetration tests can be used to supple
ment NDT evaluation of pavements especially in locating zones of weak
ness in the pavement or underlying subgrade soils.
The effects of moisture, temperature, and the properties of the
asphalt binder on the performance and response characteristics of
flexible pavements were demonstrated using short-term load-induced
stress analyses on five of the test pavements.
xxii

CHAPTER 1
INTRODUCTION
1.1 Background
In recent years, the use of layered elastic theory to evaluate and
design highway and airfield pavements has become increasingly popular
vis-a-vis existing empirical methods. The elastic layer approach, also
called mechanistic analysis, has obvious advantages over empirical
methods which are based on the correlation between the maximum deflec
tion under a load and pavement performance. It allows a rational eval
uation of the mechanical properties of the materials in the pavement
structure.
An essential part of the mechanistic process is determining real
istic elastic modulus values for the various layers in the pavement
structure. Current methods to determine the modulus of pavement
materials include various laboratory testing procedures, destructive
field tests, and in situ nondestructive tests (NDT). The problems
associated with the simulation of in situ conditions such as moisture
content, density, loading history and rate of loading of the pavement in
the laboratory are well known and recognized. Destructive field tests,
such as the California bearing ratio (CBR) and plate tests are expen
sive, time-consuming, and generally involve trenching the pavement,
which has to be subsequently repaired.
Nondestructive testing generally involves applying some type of
dynamic load or shock waves to the surface of the pavement and measuring
1

2
the response of the pavement. Among such methods are various seismic
techniques and surface dynamic loading tests. The basic concept behind
seismic or wave propagation techniques is the use of vibratory loads and
the resulting identification and measurement of the waves that propagate
through the media. These methods have not gained wide acceptance,
partly because of the relative sophistication required in field opera
tion and in the interpretation of test data.
Surface loading tests generally involve the use of measured surface
deflections to backcalculate the moduli of the pavement layers. Among
the numerous types of devices used are the Dynaflect, Road Rater, and
Falling Weight Deflectometer (FWD). Such techniques have gained wide
spread popularity partly because they are simple, time-efficient, and
relatively inexpensive, and partly because of their ability to model
real traffic load intensities and durations. However, there are no
direct theoretical solutions available at present to evaluate the
various layer moduli of the pavement from the measured surface deflec
tions which generally represent the overall combined stiffness of the
layers. Instead, computerized iterative solutions, graphical solutions,
and nomographs are currently used to backcalculate pavement layer
moduli. All these techniques basically consist of using linear-elastic
programs in which calculated versus measured deflections are matched by
adjustment of pavement layer moduli E-values.
Those methods which are based on iterative procedures may need a
large amount of computer time to arrive at the correct moduli for the
pavement materials. In some cases, the required computer may not be
accessible (e.g., for direct field evaluation) or the expertise required
may not be available. Also, due to the inherent problems associated

3
with iteration methods, unique solutions cannot be guaranteed and dif
ferent sets of elastic moduli can produce results that are within the
specified (deflection or layer moduli) tolerance. In addition, elastic
layer programs generally assume an average (composite) modulus for the
subgrade layer without regard to the variation of the underlying soil
properties with depth. For sites with highly variable subgrade stiff
nesses, it becomes very difficult to analytically match measured deflec
tion basins using a composite modulus for the subgrade layer. There
fore, there is a need to find a more viable way to determine the E-
values of pavement materials for a rational mechanistic analysis.
Recent advances in in situ testing in geotechnical engineering have
led to improvements in the determination of important soil parameters
such as strength and deformation moduli. Unfortunately, the application
of the improved techniques to evaluate or design pavements has been very
limited. The Marchetti Dilatometer test (DMT) offers significant pro
mise for providing a reliable and economical method for obtaining in
situ moduli of pavement layers, especially of the subgrade. There is
also the potential of determining in situ moduli from the cone penetra
tion test (CPT) since several correlations between different deformation
moduli and cone resistance have been reported in the geotechnical liter
ature. The CPT and DMT provide detail information on site stratifica
tion, identification, and classification of soil types which makes them
attractive tests for the evaluation and design of pavements.
1.2 Study Objectives
The primary objective of this study is to develop procedures for
the evaluation of material properties in layered pavement systems using

4
NDT deflection measurements. This includes the development of layer
moduli prediction equations from NDT deflections.
The secondary objective is to evaluate the feasibility of deter
mining the modulus of pavement layers and underlying subgrade soils
using in situ penetration tests and to evaluate the possible effects of
stratigraphy, water table and underlying subgrade soil properties on
surface deflections obtained from NDT.
1.3 Scope of Study
This investigation is primarily concerned with predicting pavement
layer moduli from nondestructive and penetration tests. It is hoped
that this will lead to improvements in the determination of layer moduli
for mechanistic evaluation and design of flexible pavement systems. The
initial part of the study consisted of developing layer moduli predic
tion equations from computer-simulated Dynaflect and FWD deflection
data. A modified Dynaflect load-sensor configuration was utilized in
the theoretical analysis.
Field tests were conducted on fifteen pavement sections in the
state of Florida. Tests conducted consisted of Dynaflect, FWD, elec
tronic CPT, DMT, and plate bearing tests. Also, cores of asphalt con
crete pavement were collected for laboratory low-temperature rheology
tests. These were used to establish viscosity-temperature relationships
of the recovered asphalts which were then used to predict the moduli of
the asphalt concrete layers.
The field measured NDT data were analyzed to establish layer moduli
values for the test pavement sections. The layer moduli derived from
the Dynaflect and FWD nondestructive tests were compared with each other

5
and correlated to the results of the penetration tests. Simplified
layer moduli prediction equations were developed for the modified
Dynaflect testing system.
Five of the test pavements were selected for short-term load
induced stress analysis using actual wheel loadings and low temperature
conditions. The effects of age-hardened asphalt, soil type, moisture
content, weak base course and subgrade characteristics on layer stiff
nesses were evaluated to assess the stress-strain response of the
different pavements.

CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
A mechanistic pavement design procedure consists of analyzing the
pavement on the basis of the predicted structural response (stresses,
strains, and deflections) of the system to moving vehicle loads. Pave
ment-layer thicknesses (surface, base, and subbase) are selected such
that the predicted structural response would be acceptable for some
desired number of load repetitions and under existing environmental
conditions. This approach is consistent with the conditions established
by Yoder and Witczak that
for any pavement design procedure to be completely
rational in nature total consideration must be
given to three elements. These elements are
(1) the theory used to predict failure or distress
parameter, (2) the evaluation of the pertinent
material properties necessary for the theory
selected, and (3) the determination of the rela
tionship between the magnitude of the parameter in
question to the failure or performance level
desired.
(133, p. 24-25)
In the last several years, a concentrated effort has been made to
develop a more rational analysis and design procedures for pavements.
For flexible or asphalt concrete pavements, layered (7,19,20) and finite
element (33) theories have been used with some success to analyze pave
ment response. The use of either theory requires that the materials
that make up the pavement be suitably characterized. Layered and finite
element theories use Young's modulus and Poisson's ratio to characterize
6

7
the stress-strain behavior of pavement materials. While some success
has been made in developing design theories, their limitations must also
be understood. Most pavement material responses differ from the assump
tions of the theory used, and the "true" values of stress, strain or
deflection may differ from the predicted levels. However, a great deal
of engineering reliance is being placed upon the use of multilayered
linear elastic theory (133) in which the elastic modulus is an important
input parameter.
The thesis presented here is aimed at determining realistic modulus
values for the structural characterization of flexible pavement systems
using layered elastic theory. This chapter reviews previous work on
layer-system solutions, the methods of determining the elastic modulus
of pavement-layer materials, and some important factors influencing the
modulus of elasticity.
2.2 Elastic-Layer Theory
2.2.1 General
The type of theory used in the analysis of a pavement-layered
system is generally distinguished by reference to three properties of
material behavior response (133). They are the relationship between
stress and strain (linear or nonlinear), the time dependency of strain
under constant stress level (viscous or nonviscous), and the degree to
which the material can rebound or recover strain after stress removal
(plastic or elastic). These concepts have been clearly elucidated by
Yoder and Witczak (133). Considerable effort has been expended to
analyze pavement response using the above concepts. For example, the
finite element method (33), elastic-layer analysis based on Burmister's

8
theory (18,19,20) and the visco-elastic layer analysis (7) are all based
on these three properties of material behavior. As previously noted,
the type of theory most widely used at the present time is the
multilayered linear elastic theory. The development of multilayered
elastic solutions is presented below.
2.2.2 One-Layer System
The mathematical solution of the elastic problem for a concentrated
load on a boundary of a semi-infinite body was given by Boussinesq in
1885 (13). His solution was based on the assumption that the material
is elastic, homogeneous, and isotropic. Boussinesq's equation (133;
p. 28) indicates that the vertical stress is dependent on the depth and
radial distance and is independent of the properties of the transmitting
medium. There are several limitations of this solution when applied to
pavements. For example, the type of surface loading usually encountered
in flexible pavements is not a point load but a load which is distri
buted over an elliptical area (133).
Further work with the Boussinesq equation expanded the solutions
for a uniformly distributed circular load by integration. Newmark (85)
developed influence charts for determination of stresses in elastic soil
masses. The charts are widely used in foundation work. Love (60) used
the principle of superposition to extend Boussinesq's solution to solve
for a distributed load on a circular area. Foster and Ahlvin (36)
presented charts for computing vertical stress, horizontal stress, and
vertical elastic strains due to circular loaded plates, for a Poisson's
ratio of 0.5. This work was subsequently refined by Ahlvin and Ulery
(4) to allow for an extensive solution of the complete pattern of

9
stress, strain, and deflection at any point in the homogeneous mass for
any value of Poisson's ratio.
Although most asphalt pavement structures cannot be regarded as
being homogeneous, the use of these solutions are generally applicable
for subgrade stress, strain and deflection studies when the modular
ratio of the pavement and subgrade is close to unity. This condition is
probably most exemplified by conventional flexible granular base/subbase
pavement structures having a thin asphalt concrete surface course (133).
Normally, in deflection studies for this type of pavement, it is assumed
that the pavement portion (above the subgrade) does not contribute any
partial deflection to the total surface deflection.
2.2.3 Two-Layer System
Since Boussinesq's solution was limited to a one-layer system, a
need for a generalized multiple-layered system was recognized.
Moreover, typical flexible pavements are composed of layers such that
the moduli of elasticity decrease with depth (133). The effect is to
reduce stresses and deflections in the subgrade from those obtained for
the ideal homogeneous case.
Burmister (18,19,20) established much of the ground work for the
solution of elastic layers on a semi-infinite elastic layer. Assuming a
continuous interface, he first developed solutions for two layers, and
he conceptually established the solution for three-layer systems. The
basic assumption made was full continuity between the layers, which
implies that there is no slippage between the layers. Thus, Burmister
assumed that the strain in the bottom of one layer is equal to the
strain at the top of the next layer, but the stress levels in the two

10
layers will differ as a function of the modulus of elasticity of each
layer.
2.2.4 Three-Layer System
Although Burmister's work provided analytical expressions for
stresses and displacements in two- and three-layer elastic systems, Fox
(38) and Acum and Fox (2) produced the first extensive tabular summary
of normal and radial stresses in three-layer systems at the intersection
of the plate axis with the layer interfaces. Jones (52) and Peattie
(89) subsequently expanded these solutions to a much wider range of
solution parameters. Tables and charts for the various solutions can be
found in Yoder and Witzcak (133) and Poulos and Davis (92). It should
be noted that the figures and tables for stresses and displacements have
been developed, respectively, for Poisson's ratios of 0.5 and 0.35, for
all layers, and on the assumption of perfect friction at all interfaces.
Hank and Scrivner (42) presented solutions for full continuity and
zero continuity between layers. Their solutions indicate that the
stresses in the top layer for the frictionless case (zero continuity)
are larger than the stresses presented for the case of full continu
ity. In an actual pavement, the layers are very likely to develop full
continuity; hence, full continuity between layers should probably be
assumed.
Schiffman (100) extended Burmister's solution to include shear
stress at the surface for a three-layer system. Mehta and Veletsos (73)
developed a more general elastic solution to a system with any number of
loads. They extended the solution presented by Burmister to include
tangential forces as well as normal forces.

11
2.2.5 Multilayered or N-Layered Systems
A general analysis of a multilayered system under general condi
tions of surface loading or displacement, or both was developed indepen-
dently by Schiffman (99) and Verstraeten (125). Schiffman (99) con
sidered the general solutions for stresses and displacements due to non-
uniform surface loads, tangential surface loads, and slightly inclined
loads, but no numerical evaluations were presented. Verstraeten (125)
presented a limited analysis of the four-layered elastic problem. He
first derived expressions for the stresses and displacements for the
general case and performed numerical calculations for the particular
case of four-layered systems with continuous interfaces. The analysis
by Verstraeten included not only a uniform normal surface stress, but
also two types of surface shear stresses: (1) uniform one-directional
shear stress and (2) a uniform centripetal shear stress.
Recently, the Chevron Research Corporation (74) and the Shell Oil
Company (32) have developed computer programs for multilayered solutions
of the complete state of stress and strain at any point in a pavement
structure. Notable programs of interest are the BISTRO and BISAR pro
grams by Shell (32), and the various forms of CHEVRON program by the
Chevron Research Corporation. These computer solutions are essentially
an extension of Burmister's work that permit the analysis of a structure
consisting of any number of layers supported by a semi-infinite sub
grade, and under various loading conditions. In reality, it is only the
CHEVRON N-LAYER program (74) which is suitable for any number of layers.
All the others are restricted to a maximum number of layers. BISAR
(32), for example, can handle nine pavement sub-layers of known thick
nesses plus the semi-infinite subgrade or bottom layer.

12
Several investigators have verified the validity of Burmister's
theory with the actual mechanical response of flexible pavements.
Foster and Fergus (37) have compared the results of extensive test
measurements on a clayey silt subgrade to theoretical stresses and
deflections based on Burmister's theory and reported satisfactory
agreement. The discrepancy between actual and theoretical stresses and
displacements can be mainly attributed to the assumption of a homoge
neous and isotropic material, the rate-dependent behavior of some
materials such as asphalt, and a circular loaded area representing the
wheel load. Nielsen (86) has made a detailed study in this area. His
review of the magnitude and distribution of stresses within a layered
system revealed regions where vertical and shearing stresses were criti
cal. His studies concluded that the layered-elastic theory is in every
respect consistent and that it is possible to establish fundamental
patterns of pavement performance based upon this theory. This suggests
that the elasticity theory could be used more extensively.
The moderators of the Fifth International Conference on the Struc
tural Design of Asphalt Pavements (76) concluded that the use of linear
elastic theory for determining stresses, strains, and deflections is
reasonable as long as the time-dependent and nonlinear response of the
paving materials are recognized. They noted that the papers presented
at the conference confirmed that multilayer elastic models generally
yield good results for asphalt concrete pavements.
Barksdale and Hicks (10) compared the multilayered elastic approach
with the finite element method and recommended the use of the former for
pavement analysis since only two variables are needed (modulus and
Poisson's ratio). Pichumani (91) used the BISAR computer program for

13
the numerical evaluation of stresses, strains, and displacements in a
linear elastic system. He demonstrated that predicted vertical, radial,
and shear stress distribution were noticeably affected by slight changes
in the assumed material moduli. Pichumani's work demonstrated the need
for proper and extensive material characterization.
2.3 Material Characterization Methods
2.3.1 General
The use of multilayered elastic theory has provided the engineer
with a rational and powerful basis for the structural design of pave
ments, for pavement evaluation, and for overlay design. In this theory,
the complete stress, strain, and displacement pattern for a material
needs only two material properties for characterization, namely the
elastic modulus (E), and the Poisson's ratio (u). Generally, the effect
of Poisson's ratio is not as significant as the effect of the modulus
(133, pp. 280-282; 88; 59, p. 160). Thus, E is an important input
parameter for pavement analysis using the layer theory.
Many tests have been devised for measuring the elastic modulus of
paving materials. Some of the tests are arbitrary in the sense that
their usefulness lies in the correlation of their results with field
performance. To obtain reproducible results, the procedures must be
followed at all times. The various possible methods for determining the
elastic modulus of pavement materials include laboratory tests, destruc
tive field tests, and in situ nondestructive tests.
Laboratory methods consist of conducting laboratory testing on
either laboratory-compacted specimens or undisturbed samples taken from
the pavement. Yoder and Witzcak (133) describe various laboratory

14
testing methods with the diametral resilient modulus test (8), indirect
tension test (9), and the triaxial resilient modulus test (1) being the
most popular. The latter is useful for unbound materials such as base
course and subgrade soils, while the other two are for bound materials
like asphalt concrete and stabilized materials. Monismith et al. (78)
studied the various factors that affect laboratory determination of the
moduli of pavement systems. They concluded that
. . it is extremely difficult to obtain the same
conditions that exist in the road materials (mois
ture content, density, etc.) and the same loadings
(including loading history) in the laboratory as
will be encountered in situ. . Thus the best
method of analysis would appear to be to determine
an equivalent modulus which when substituted into
expressions derived from the theory of elasticity,
will give a reasonable estimate of the probable
deformation.
(78, p. 112)
Destructive field tests include, among others, several different
plate load tests (8) and the California Bearing Ratio (CBR) test (8).
These tests require trench construction and subsequent repair of the
pavement, and like the laboratory test methods, usually call for an
elaborate and costly testing program. The delays associated with such
programs are prohibitive especially for routine pavement analysis
studies.
The third method involves the extraction of pavement-layer proper
ties from in situ nondestructive testing (NDT). NDT methods have gained
wide popularity in the last few decades because of their ability to
collect data at many locations on a highway or airfield in a short
time. Therefore, a great deal of research effort has been concentrated
on this area. A review of the various types of NDT equipment available
and the associated interpretation tools is presented below.

15
2.3.2 State-of-the-Art of Nondestructive Testing
2.3.2.1 General. Nondestructive testing (NDT) consists of making
nondestructive measurements on a pavement's surface and inferring from
the responses the in situ characteristics related to the structural ade
quacy or loading behavior (79). Among such methods are various seismic
techniques (associated with time measurements) and surface loading tests
(associated with deflection measurements). The latter is more popular
because surface deflection is the most easily measured structural
response of a pavement. The idea of using deflection measurements to
evaluate the structural integrity of pavements dates back to 1938 when
the California Division of Highways used electrical gages implanted in
roadways to measure displacements induced by truck loads (134).
There are currently several NDT procedures being used for pavement
investigations. Each of the procedures can be placed into one of the
following four general classes:
1. Static force-deflection,
2. Steady-state (vibratory) dynamic force-deflection,
3. Dynamic impulse force-deflection, and
4. Wave propagation.
As their names imply, the first three categories are associated with
deflection measurements due to application of force or load. The fourth
category--wave propagationmeasures the length and velocity of force-
induced waves traveling through the pavement system. A detailed
description and evaluation of many of these NDT devices and procedures
has been presented by Bush (21), Moore et al. (79), and Smith and Lytton
(109). In the following pages, a brief description of the principles
involved and equipment available for each class will be presented.

16
2.3.2.2 Static Deflection Procedures. Measurement systems that
determine the pavement response to slowly applied loads are generally
termed static deflection systems. In these systems, the loading, methods
may consist of slowly driving to or from a measurement point with a
loading vehicle, or by reacting against a stationary loading frame. The
maximum resilient or recoverable deflection at the surface of the pave
ment is measured.
The most commonly used equipment in this class is the various forms
of the Benkelman beam devices. Other equipment that had been used
include the plate bearing test (8), Dehlen Curvature Meter, Traveling
Deflectometer, Lacroix-LCPC Deflectograph, and the French Curviameter.
The last three devices are essentially automation of the Benkelman beam
principle. The French Curviameter, for example, measures both the
deflection and curvature of the pavement, under an 18-kips rear axle
load, with tire pressure maintained at 100 psi (24). Most of the
automated devices have been used widely in Europe and other parts of the
world, except for the Traveling Deflectometer which was built for the
California Department of Transportation and has been in use by that
agency for several years (109).
The major advantages of the static deflection procedures are the
simplicity of the equipment and the large amount of data that has been
accumulated with these devices. The most serious problem with this type
of measurement technique is the difficulty in obtaining an immovable
reference point for making the deflection measurements. This makes the
absolute accuracy of this type of procedure questionable. In addition,
since most of the devices generally measure a single (maximum)

17
deflection only, it is impossible or difficult to determine the shape
and size of the deflection basin.
In spite of their shortcomings, the large amount of data developed
using static deflection techniques makes such procedures an important
part of structural pavement evaluation. For this reason, several inves
tigators have attempted deflection comparison and correlations from the
static devices with those measured by the dynamic devices. The
following is a list of concepts developed from the deflection response
of a pavement using static NDT (79):
1. For adequately designed pavements, the deflections during the same
season of the year remain approximately constant for the life of the
pavement.
2. There is a tolerable level of deflection that is a function of
traffic type, volume and the structural capacity of the pavement as
determined by the pavement's structural section. This tolerable
level of deflection can be established through the use of fatigue
characteristics of the pavement structure.
3. Overlaying of a pavement will reduce its deflection. The thickness
required to reduce the deflection to a tolerable level can be esta-
blished.
4. The deflection history of a well-designed pavement undergoes three
phases in its behavior (71). A typical curve representing these
phases is shown in Figure 2.1.
a. In the initial phase, immediately after construction, the pave
ment structure consolidates and the deflection shows a slight
decrease.

DEFLECTION
Initial
Phase
Functional Phase
TRAFFIC
Failure Phase
Figure 2.1 Wei 1-Designed Pavement Deflection History Curve (71)

19
b. During the functional or service phase, the pavement carries the
anticipated traffic without undue deformation and the deflection
remains fairly constant or shows a slight decrease.
c. The failure phase occurs as a result of both traffic and envi
ronmental factors. In this phase the deflection increases
rapidly and there is a rapid deterioration resulting in failure
of the pavement structure.
5. The deflection history of a pavement system varies throughout the
year due to the effects of frost, temperature, and moisture. A
typical annual deflection history of a pavement subjected to frost
action, as shown in Figure 2.2, can be divided into the following
four periods (103):
a. The period of deep frost when the pavement is the strongest.
b. The period during which the frost is beginning to disappear from
the pavement structure. During this period, the deflection
rises rapidly.
c. The period during which the water from the melting frost leaves
the pavement structure and the deflection begins to drop.
d. The period during which the deflection levels off with a general
downward trend as the pavement structure continues to slowly dry
out.
6. For a given flexible pavement structure it is generally known that
the magnitude of the deflection increases with an increase in the
temperature of the bituminous surfacing material. This is due to a
decrease in the stiffness of the bituminous surfacing. The effect
of temperature varies with the stiffness of the underlying layers.
As the stiffness of the underlying layers increases, the effect of

DEFLECTION
MONTH

Figure 2.2 Typical Annual Deflection History for a Flexible Pavement (103)

21
an increase in temperature on deflection of the total structure
decreases.
2.3.2.3 Steady-State Dynamic Force-Deflection. Essentially, all
steady-state dynamic deflection measurement systems induce a steady
state sinusoidal vibration in the pavement with a dynamic force genera
tor. The dynamic force is superimposed on the weight of the force gen
erator, resulting in a variation of force with time as shown in Figure
2.3. The magnitude of the peak-to-peak dynamic force is less than twice
the static force to insure continuous contact of the vibrator with the
pavement (79). This means there must always be some amount of dead
weight or static force applied. As the dynamic peak-to-peak loading is
increased, this preload must also be increased (109).
Deflections are usually measured with inertial motion sensors. For
pure sinusoidal motions at any fixed frequency, the output of such sen
sors is directly proportional to deflection. Thus, to measure deflec
tion it is only necessary to determine the calibration factor (output
per unit of deflection) for the measurement frequency. In general,
either an accelerometer or a velocity sensor may be used to measure
deflections. The latter type is commonly called a geophone and is the
type normally employed in dynamic deflection measurements.
There are several different types of steady-state dynamic deflec
tion equipment that are currently being used for nondestructive struc
tural evaluation of pavements. Only two of them have been used exten
sively and are available commerciallythe Dynaflect and the Road
Rater. The others have been designed and constructed by agencies
involved in pavement research, namely the U.S. Army Waterways Experiment
Station (WES), the Federal Highway Administration (FHWA), the Illinois

Figure 2.3 Typical Output of a Dynamic Force Generator (79)

23
Department of Transportation, and the Koninklijke/Shel1 Laboratorium,
Amsterdam, Holland. Detailed descriptions of the various vibratory
equipment can be found in References 21, 79, 109. The Dynaflect was
used in this study and a description of the device will be given later
in this report.
When one considers the difficulty in obtaining a reference point
for deflection measurements, the real advantage of a steady-state dyna
mic deflection measurement system becomes apparent. An inertial refer
ence can be employed to measure dynamic deflections. That is, the mag
nitude of the deflection change (the peak-to-peak value) can be compared
directly to the magnitude of the dynamic force change (peak-to-peak
value). For a given value of dynamic force, the lower the deflection,
the stiffer the pavement is (79).
Although the dynamic response of a pavement system approaches its
static (or elastic) response at low frequencies, exactly what value of
driving frequency is low enough to determine the elastic characteristics
of a pavement is somewhat questionable. As the driving frequency be
comes low it becomes difficult to generate dynamic forces and the output
of inertial motion sensors becomes very small. These factors combine to
make it difficult to obtain accurate low frequency dynamic deflection
measurements (79). Other technical limitations of vibratory equipment
include the need for a heavy static preload for the heavier devices and
the nonuniform loading configurations (109).
The deflection measurements that result represent the stiffness of
the entire pavement section. Although some significant accomplishments
have been made in separating the effects of major parts of the pavement
structure, the separation of the effects of all of the various

24
components of the structure with deflection basin measurements has not
yet been accomplished (63). The study presented herein is aimed at
developing an approach that would allow a layer-by-layer analysis of the
Dynaflect vibratory deflection basin.
2.3.2.4 Dynamic Impact Load Response. Essentially, all impact
load testing methods deliver some type of transient force impulse to the
pavement surface and measure its transient response. The equipment uses
a weight that is lifted to a given height on a guide system and is then
dropped. Figure 2.4 illustrates this schematically. By varying the
mass of the falling weight or the drop height or both, the impulse force
can be varied.
The width or duration of the loading pulse (loading time) is
controlled by the buffer characteristics, Figure 2.4, and it closely
approximates a half-sine wave (Figure 2.5). The duration of the force
is nominally 25-30 msec, Figure 2.5, which approximates the load
duration of a vehicle traveling 40 to 50 mph (123). The peak magnitude
of the force can be determined approximately by equating the initial
potential energy of the system to the stored strain energy of the
springs (buffer system) when the mass is momentarily brought to rest
(11,105). Thus
F = (2Mghk)1/z
Eqn. 2.1
where
M = mass of the falling weight,
h = drop height,
k = spring constant, and
g = acceleration due to gravity.

25
' / / / / /
s s s \ \ \
*/////
\ s s s \ \
/////
/////#
N N \ N S S
//////
\ S N S N S
/////#
Figure 2.4 Schematic Diagram of Impulse Load-Response Equipment (105)

Figure 2.5 Characteristic Shape of Load Impulse (105)

27
The response of the pavement to the impulse loading is normally
measured with a set of geophones placed at varying radial distances from
the center of the plate. These deflection measurements can, in princi
ple, be used to characterize the structural properties of the pavement
layers.
Three manufacturers currently market impulse testing equipment in
the United States. These are the Dynatest, KUA3 and Phoenix falling-
weight deflectometers. The Dynatest falling-weight deflectometer (FWD)
is the most widely used impulse loading device in North America and
Europe (109). Its newest version--the Dynatest 8000 FWD testing
system--was used in this study and will be described later in this
report. Other experimental impulse testing devices have been evaluated
by Washington State University and Cornell Aeronautical Laboratory (79).
The impulse testing machines have several advantages over other NDT
instruments. The magnitude of the force can be quickly and easily
changed to evaluate the stress sensitivity of the pavement materials
being tested. Perhaps the greatest advantage is the ability to simulate
vehicular loading conditions. Several investigators (11,35,46) have
compared pavement response in terms of stresses, strains, and deflec
tions from an FWD-imposed load to the response of a moving wheel load.
All these comparisons have shown that the response to an FWD test is
quite close to the response of a moving wheel load with the same load
magnitude. Figure 2.6 shows such an example of pavement response com
parison.
However, the deflection basin produced by an impulse loading device
is symmetrical about the load and Lytton et al. (63) have argued that
the deflection basin under a moving wheel is not symmetrical about the

28
a) Surface Deflections
600
Q
^g 400
DC
c/> EL
go 200
b DC
I LL.
DC
LLI
>
0
/
/
/
/
/
/ o
/ O
o
/
A
D

9*
/
/

0
200
400
600
VERTICAL STRAIN (x lO"6)
FROM MOVING WHEEL LOAD
b) Vertical Subgrade Strains
Figure 2.6 Comparison of Pavement Response from FWD and
Moving-Wheel Loads (35)

29
load in any pavement structure. Thus, the impulse load of a FWD is not
an exact representation of a moving wheel load. Moreover, the response
from the impact testing technique is similar to other types of dynamic
deflection testing in the sense that it represents characterization of
the entire structure. The technique does not provide information that
readily separates the effects of its various layers. Finally, the
parameters that cause plastic deformation in the structure are not
readily determinable from impact testing (79).
2.3.2.5 Wave Propagation Technique. Wave propagation provides
methods for the determination of the elastic properties of individual
pavement layers and subgrades. Unlike the three previous methods of
NOT, these methods are not concerned with the deflection response of the
pavement. Rather, they are concerned with the measurement of the velo
city and length of the surface waves propagating away from the load
surface (127).
There are two basic techniques for propagating waves through pave
ment structures: (1) steady-state vibration tests and (2) seismic
(impulse) tests. Generally, three types of waves are transmitted when a
pavement surface is subjected to vibration. These are
1. Compression or primary (P) waves,
2. Shear (S) waves, and
3. Rayleigh (R) waves.
The P and S waves are body waves while the R wave is a surface wave.
Raleigh waves are the dominant waves found in the dissipation of energy
input from a vibrator on a semi-infinite half-space (75). Also, because
P and S waves attenuate rapidly with radial distance from the vibration

30
source, R waves are the typical waves measured in the wave propagation
technique.
Wave propagation theory is based upon the fact that in a homoge
neous isotropic half space subjected to an external disturbance, waves
travel at velocities that may be expressed as (59, p. 153; 79; 127)
V, £) )/2 Eqn. 2.2a
p 2(1 + u)p
v = ( HV-J1.) )x/2 Eqn. 2>2b
P p(l + y)(l 2y)
VR = aV$ Eqn. 2.2c
where
Vs = shear wave velocity,
Vp = compression wave velocity,
Vr = Rayleigh wave velocity,
G = shear modulus,
E = Young's modulus,
y = Poisson's ratio,
p = mass density, and
a is a function of Poisson's ratio and varies from 0.875
for y = 0 to 0.995 for y = 0.5.
In general, R and S waves are not particularly dependent on Poisson's
ratio, but the value of compression wave velocity is strongly dependent
on Poisson's ratio (59,79).
Field test procedures for the wave propagation measurements involve
two general types of tests. Raleigh wave velocities are determined from

31
steady-state vibratory pavement responses and compression wave veloci
ties are measured from impulse (seismic) tests. The former usually
follows procedures developed by researchers at the Royal Dutch Shell
Laboratory (43,53,93), the British Road Research Laboratory (79), and
the Waterways Experiment Station (79). They utilized a mechanical
vibrator for low-frequency vibrations (5-100 Hz) and a small electro
magnetic vibrator for the high-frequency work (43,53). The general
procedure currently in use is to place the vibrator on the pavement
surface and set the equipment in operation at a constant frequency.
Details of the procedure can be found in Reference 79.
Seismic tests may be conducted to determine the velocity of com
pression waves, which can be used with the shear wave (or Rayleigh wave)
velocity to compute Poisson's ratio. One such method is the hammer-
impulse technique in which the pavement is struck with a light hammer
and the resultant ground motion is observed at one or more points with
horizontal motion geophones. However, this method is only good for
soils where the velocity of the materials increases with depth. It is
not applicable to layered pavement systems where strong, high velocity
layers occur at the top and grow progressively weaker with depth. How
ever, Moore et al. (79) report that this procedure has been used to
obtain compression wave velocities of pavement layers during construc
tion.
A method of using surface waves to structurally characterize pave
ments is currently in the research stage at the University of Texas at
Austin (80,81,82). The technique, called Spectral Analysis of Surface
Waves (SASW), determines shear wave velocity at soil or pavement sites.
The elastic shear and Young's moduli profiles are then calculated under

32
the assumption of homogeneous, isotropic, and elastic medium. The SASW
method is essentially a seismic procedure. An iterative inversion
process is used to interpret the shear wave velocity profiles (81).
Laboratory procedures are available for the determination of the
elastic properties of pavement and soil specimens using wave propagation
techniques. However, the laboratory procedures require that samples of
the pavement material be obtained for testing. Therefore, it may not be
considered as a nondestructive technique. Two laboratory procedures
that parallel the field vibratory procedures and which may be applicable
to pavement design are the resonant column and the pulse methods (79).
The most difficult aspect of the wave propagation techniques is
that of interpretation and analysis of test results. The wave propaga
tion method of testing relies on the ability to interpret the data
obtained in the field so that the characteristics of the structure
beneath the surface may be determined (79). Because of the inherent
complexities involved, such techniques have not gained wide acceptance.
2.3.3 NDT Data-Interpretation Methods
2.3.3.1 General. Considerable emphasis has been placed upon
determining the elastic properties of pavement layers using NDT data.
Most of this work has been concentrated on the first three types of NDT
procedures, those associated with deflection measurements. The fourth
category, the wave propagation method, has not gained wide acceptance
because of the relative sophistication required in the field operation
and in the interpretation of test data. However, the interpretation of
measured surface deflection basins has gained widespread popularity with
the advent of NDT procedures. There is a general agreement among

33
pavement engineers that the measured surface deflection basins from NDT
can provide valuable information for structural evaluation of a pave
ment.
Methods for the interpretation of NDT data can be placed into two
categories: empirical or mechanistic methods. Empirical procedures
directly relate NDT response parameters to the structural capacity of a
pavement. Most of these methods (48,56) do not involve direct or
indirect theoretical analysis. Instead, they are based upon the cor
relation between the maximum deflection under a load (static NDT or
wheel load) and pavement performance.
In an attempt to improve the empirical procedures, other research
ers have relied on the use of deflection basin parameters (90) or semi-
empirical correlations (79) for pavement evaluation. Figure 2.7 shows
an example of basin parameters and the criteria used to evaluate a pave
ment. Table 2.1 lists some of the deflection basin parameters that have
been developed for NDT data evaluation of pavements (120). Most of the
basin parameters do not relate directly to the elastic parameters of the
pavement section.
Semi-empirical procedures usually involve correlation of modulus
values to other known pavement parameters. For example, Heukelom and
Foster (43) have developed a correlation between modulus E (in psi) from
wave propagation techniques and the California Bearing Ratio (CBR)
value. This correlation, though later refined by WES (79), is of the
form
E
1500 (CBR)
Eqn. 2.3

34
DMD = Dynaflect Maximum Deflection (Numerical Value of Sensor No. 1)
SCI = Surface Curvature Index (Numerical Difference of Sensor No. 1 and
No. 2)
BCI = Base Curvature Index (Numerical Difference of Sensor No. 4 and
No. 5).
a) Basin Parameters
(b)
DMD
SCI
BCI
CONDITION OF PAVEMENT STRUCTURE
GT 1.25
GT 0.48
GT 0.11
Pavement and Subgrade Weak
LT 0.11
Subgrade Strong, Pavement Weak
LT 0.48
GT 0.11
Subgrade Weak, Pavement Marginal
LT 0.11
DMD High, Structure Ok
LT 1.25
GT 0.48
GT 0.11
Structure Marginal, DMD Ok
LT 0.11
Pavement Weak, DMD Ok
LT 0.48
GT 0.11
Subgrade Weak, DMD Ok
LT 0.11
Pavement and Subgrade Strong
b) Criteria
Figure 2.7 Empirical Interpretation of Dynaflect Deflection Basin (90)

Table 2.1 Summary of Deflection Basin Parameters
Parameter
Definition3
NDT Device13
Dynaflect maximum deflection (DMD)
DMD
=
d
l
d-d
1 2
d d
4 5
Dynaflect
Surface curvature index (SCI)
SCI
=
Dynaflect, Road Rater model 400
Base curvature index (BCI)
BCI
=
Dynaflect
Spreadability (SP)
SP
=
( Id. /56J x
100
Dynaflect
i=l to 5
SP
=
( Idi /4di) x
i=l to 4
100
Road Rater model 2008
Bsin slope (SLOP)
SLOP
=
d-d
Dynaflect
Sensor 5 deflection (W )
5
W
5
=
1 !)
d
5
Dynaflect
Radius of curvature (R)
R
=
r2/f2-dm[(Vdr)
-1]}
Benkelman beam
Deflection ratio (Qr)
Qr
=
r/dQ
FWD, Benkelman beam
Area, in inches (A)
A
=
6[ 1 + 2(d /d ) +
2 1
2(d /d )
3 1
+ (d /d )] Road Rater model 2008
4 1
Shape factors (F F )
1 2
F
l
=
(d d )/d
1 3 2
Road Rater model 2008
F
2
=
(d d )/d
2 4 3
Tangent slope (TS)
TS
=
(dm dxVx

a d = deflection; subscripts 1,2,3,4,5 = sensor locations; o = center of load; r = radial distance;
m = maximum deflection; x = distance of tangent point from the point of maximum deflection.
b The NDT device for which the deflection parameter was originally defined.
Source: Uddin et al. (120)

36
and is the most widely used correlation (133). Other correlations (79)
have been made between E and plate bearing subgrade modulus, K. It
should be recognized that the conditions of dynamic testing generally
yield moduli in the linear elastic range. Conventional tests such as
the CBR and plate bearing tests produce deformations that are not
completely recoverable and, therefore, are partly in the plastic range.
Thus, one would expect some variation in the correlation between E
modulus and pavement parameters, such as K and CBR.
Mechanistic analysis of NDT data is usually performed by one of the
following:
1. Direct relationship between deflection parameters and the
elastic moduli of the pavement layers.
2. Inverse application of a theoretical model by fitting a
measured deflection basin to a deflection basin using an
iterative procedure.
3. A combination of 1 and 2.
The above mechanistic methods employ deflection data from either vibra
tory or impulse loading equipment. While these devices are dynamic in
nature, most of the mechanistic solutions are based on elasto-static
(19,32,74) and visco-elasto-static (7) models. Recently, an elasto-
dynamic model (54) has been used to interpret NDT data (66,67,105).
However, the use of dynamic analyses for interpretation of NDT data can
be considered to be in the research stage. Another significant obser
vation is that almost all the mechanistic solutions available employ
layered theory or simplified versions of it. The only exception to this
is the use of a finite element model presented by Hoffman and Thompson
(45). A review of the various solutions is presented below.

37
2.3.3.2 Direct Solutions. Presently, there are no direct analy
tical solutions that can uniquely determine the elastic moduli for a
multilayered pavement system using surface deflection measurements
alone. The so-called direct solutions have been developed for only two-
layer systems which usually involve graphical solutions, nomographs, or
in most cases only provide estimates for the subgrade modulus.
Scrivner et al. (102) presented an analytical technique for using
pavement deflections to determine the elastic moduli of the pavement and
subgrade assuming the structure is composed of two elastic layers.
Based upon the same assumption, Swift (113) presented a simple graphical
technique for determining the same two elastic moduli. Vaswani (124)
used Dynaflect basin parameters to develop charts for the structural
evaluation of the subgrade and its overlying layers for flexible pave
ments in Virginia (see Table 2.1). The methods by Majidzadeh (64) and
Sharpe et al. (107), among others, employ similar basin parameters from
the Dynaflect or Road Rater to estimate the subgrade modulus and develop
charts to assess the overlying layers.
Jimenez (51) described a method for evaluating pavement-layer
modular ratios from Dynaflect deflections. The pavements were
considered to be three-layer systems, and the deflection data were used
to estimate ratios of the elastic moduli of the adjacent layers. The
ratios reduce the system from three values of elastic modulus to two
values of modular ratio. The major limitation of this method is that
the elastic modulus of the asphalt concrete layer must be known.
Wiseman (129) and Wiseman et al. (131) have, respectively, applied
the Hertz Theory of Plates and the Hogg Model to evaluate two-layered
flexible pavements using surface deflection basins. The Hertz theory is

38
an application of the analytical solution of a vertically loaded elastic
plate floating on a heavy fluid. The solution to this problem was
presented by Hertz in 1884 and was first applied to concrete pavement
analysis by Westergaard in 1926 (79). The Hogg model consists of an
infinite plate on an elastic subgrade. The subgrade can be either of
infinite extent or underlain by a perfectly rigid rough horizontal
bottom at a finite depth. Analysis of this model was reported by A.H.A.
Hogg in 1938 and 1944 (131). In both methods, the flexural rigidity of
the composite pavement which will best fit a measured deflection basin
is calculated.
Lytton et al. (62) and Alam and Little (5) have developed another
method based on elastic-layer theory for prediction of layer moduli from
surface deflections. This method makes use of the explicit expression
for deflection originally postulated by Vlasov and Leont'ev (126). The
major drawback of this technique is the need to develop several con
stants, five in all, for which no analytical or test method exists as
yet. In applying this method, the authors (5,62) resorted to the use of
regression analyses and computer iterative solutions.
Cogill (28) presented a method which provides an estimate of the
stiffness of the pavement-layer materials. The method essentially is a
graphical presentation in which the deflections measured over a parti
cular range of load spacing can be related to the stiffness of the pave
ment material at a certain depth. The relationship is an approximate
one and is expressed with the aid of Boussinesq's formula.
All the methods presented above use deflection measurements
obtained from vibratory loading equipments--Dynaflect and Road Rater.
The only approach for the direct estimation of layer moduli from impulse

39
load-deflection response (such as an FWD deflection basin) is the
concept of equivalent layer thickness (121,122) in which the layered
pavement system is transformed into an equivalent Boussinesq (13)
system. This concept, originally proposed by Odemark (87), is based on
the assumption that the stresses, strains, and deflections below a given
layer interface depend on the stiffness and thickness of the layers
above that interface. Although this approach obtains an explicit
solution for the subgrade modulus (121), it relies on estimates of the
asphalt concrete layer modulus and also employs certain modular ratios
to obtain the moduli of the various layers above the subgrade (25). The
method of equivalent thicknesses (MET) has also been incorporated into
some iterative computer programs which are discussed in the next
section.
Several investigators have obtained equations to directly determine
the subgrade modulus from one or more sensor deflections. For example,
Figure 2.8 shows the relationship between the subgrade modulus and the
Dynaflect fifth sensor deflection as summarized by Way et al. (128).
Keyser and Ruth (55) developed a prediction equation from five test road
sections in the Province of Quebec, Canada, by using the BISAR elastic-
layer program to match measured Dynaflect deflection basins. The
equation is of the form
-1.0006
E = 5.3966(D ) Eqn. 2.4
4 5
where E^ is subgrade modulus in psi, and D5 is Dynaflect fifth sensor
deflection in inches. This equation had an R2 of 0.997 (55), and is
similar to that of Ullidtz (see Figure 2.8). Godwin and Miley (41) have

SUBGRADE MODULUS, Esg> psi
100,000
10,000
1,000
Figure
DYNAFLECT DEFLECTION, D5, mils
.8 Dynaflect Fifth Sensor Deflection-Subgrade Modulus Relationship (128)

41
developed correlations between the base, subbase and subgrade moduli and
the second, third, and fourth Dynaflect sensor deflections, respec
tively. However, the modulus values used in the correlation were the
surface moduli from plate bearing tests which suffers from the problem
of incorporating plastic and nonrecoverable deformations.
An approach using regression equations to estimate layer moduli has
been attempted by other investigators (83,120,132). This approach
usually involves analysis of computer-simulated NDT data using a theo
retical model (usually layered elastic theory). The various investi
gators reported success in the case of the subgrade modulus. To obtain
good correlations for the other layers (surface, base, subbase), certain
assumptions had to be made, such as the base course modulus being
greater than the modulus of the subgrade (83), or they had to resort to
computer-iteration programs (83,120).
2.3.3.3 Back-Calculation Methods. The method of iteratively
changing the layer moduli in a theoretical model to match the theoreti
cal deflection basin to a measured basin is currently called back-
calculation in the literature. Initial developments of this procedure
utilized a trial-and-error approach (49,72) using the following steps:
1. Pavement-layer thicknesses, initial estimates of the pavement-layer
moduli, and the loading and deflection measurement configuration are
input into the model (usually a multilayer elastic computer
program).
2. The computed deflections at the geophone positions are compared with
those actually measured in the field.
3. The layer moduli used in the computer program are then adjusted to
improve the fit between the predicted and actual deflection basins.

42
4. This process is repeated until the two deflection basins are vir
tually the same. The process may have to be repeated several times
before a reasonable fit is obtained.
Because of the time consuming nature (49) of the trial-and-error
method, many researchers have developed computer programs to perform the
iteration. Table 2.2 lists some of the self-iterative computer pro
grams. The major differences among the various programs are the differ
ent models, algorithms and tolerance levels used in the iteration pro
cess. A few of these will be discussed here.
Anani (6) developed expressions for surface deflections in terms of
the modulus values of a four-layer pavement. However, he could not
obtain direct solutions to determine the moduli. Therefore he used an
iterative procedure to obtain the moduli from Road Rater deflection
basins. The computer programs reported by Tenison (114) and Mamlouk
(66) followed the successive approximation method of Anani (6). In the
overlay design program called OAF, Majidzadeh and lives (65) employed a
deflection matching technique for determining the in situ layer stiff
nesses. While using field data to substantiate the applicability of the
procedure, they experienced difficulties and commented,
. . the computed asphalt layer stiffness shows a
large variation, and in a few cases the asphalt is
stiffer than steel; nevertheless the values are
reasonable in a great majority of the cases . .
(65, p. 85)
The BISDEF computer program (23) is an improvement over the CHEVDEF
(22) to handle multiple loads and variable interface conditions. The
number of layers with unknown modulus values cannot exceed the number of
measured deflections. However, a maximum of four deflections are

Table 2.2 Summary of Computer Programs for Evaluation
of Flexible Pavement Moduli from NOT Devices
Name
Reference
Number
of
Layers
Theoretical
Model Used
For Analysis
Applicable
NOT
Device
*
Anani (6)
4
Layer
BISAR-E1astic
Road Rater 400
ISSEM4
Sharma and
Stubstad (106)
4
ELSYM5-E1astic
Layer
FWD
CHEVDEF
Bush (22)
4(a)
Layer
CHEVR0N-E1astic
Road Rater 2008
OAF
Majidzadeh
and lives (65)
3 or
4
ELSYM5 Elastic
Layer
Dynaflect,
Road Rater, or FWD
ILLI-PAVE
Hoffman and
Thompson (45)
3
Finite Element
Road Rater 2008,
or FWD
*
Tenison (114)
3
Layer
CHEVRON'S n
(Elastic)
Road Rater 2000
FPEDD1
Uddin et al.
(118,120)
3 or
4
ELSYM5 Elastic
Layer
Dynaflect, FWD
BISDEF
Bush and
Alexander (23)
4(a)
BISAR Elastic
Layer
Vibrator, or FWD
Dynaflect, Road
Rater, WES
ELMOD
Ullidtz and
Stubstad (123)
2, 3
or 4
MET-Boussinesq
FWD
IMD
Husain and
George (47)
3 or
4
CHEVRON Elastic
Layer
for FWD
Dynaflect, but
can be modified
DYNAMIC
Mamlouk (66)
4
Elasto-dynamic
Road Rater 400
* not known or available
(a) not to exceed number of deflections

44
targeted during the iteration process, which is also limited to a
maximum of three loops. When applied to field measured deflections on
an airfield pavement in Florida (23), BISDEF predicted unreasonably high
values of the AC modulus for all the different NDT devices used in the
study. Also, Bush and Alexander (23) conceded that the program,,provides
the best results if the number of unknown layer moduli is three.
The ISSEM4 computer program (106) incorporates the principles of
the method of equivalent thicknesses (MET) into the ELSYM5 multilayered
elastic program to determine the in situ stress-dependent elastic
moduli. The parameters for the nonlinear stress-dependent relationships
(see Section 2.4.3) are established from FWD tests performed at differ
ent load levels. The iteration process is seeded with a set of E-values
(106). The ELMOD program (123) also utilizes the MET principle and the
iteration procedure. Both programs provide relatively good solutions if
the asphalt concrete modulus is known.
ILLI-PAVE (45), the only program which utilizes a finite element
model, is specifically developed to handle Road Rater deflection data.
However, Road Rater deflection basins must be converted to equivalent
FWD deflection basins prior to being used in the program (45). Also,
the nonlinear stress-dependent material models incorporated into the
finite element method utilized relationships established from previous
laboratory material characterization procedures. It is also surprising
that the authors resulted to nomographs for specific applications of the
back-calculation model (45).
Most of the iteration programs listed above require a set of ini
tial moduli--seed moduli--and therefore are user-dependent. Therefore
computational times and cost can be prohibitive. Unique solutions

45
cannot be guaranteed since an infinite number of layer modulus combina
tions can provide essentially the same deflection basins. Moreover,
most of the iterative programs yield questionable base course and
subbase moduli. In some programs, adjustment of the field data are
required in order to improve the solution (6,47).
2.3.4 Other In Situ Methods
Cogill (27) presented a method involving the use of an ultrasonic
technique. The elastic modulus of the top layer can be accurately
determined; however, the modulus values for the other layers are
questionable. Kleyn et al. (58) and Khedr et al. (57) have developed
different forms of a portable cone penetrometer to evaluate the stiff
ness of pavement layers and subgrade soils. However, these devices do
not provide direct modulus values but rather are based on correlations
with CBR and plate bearing parameters. Similarly, the Clegg Impact
tester, which was developed in Australia in the mid-1970s, relies on CBR
correlation for pavement evaluation applications (40). The problems of
the CBR and plate bearing tests have been discussed previously.
Maree et al. (70) presented an approach to determine pavement-layer
moduli based on a device developed to measure deflections at different
depths within a pavement structure. The device, called the multi-depth
deflectometer (MDD), is installed at various depths of an existing pave
ment structure to measure the deflections from a heavy-vehicle simulator
(HVS) test. Maree et al. (70) suggested that effective moduli for use
in elastic-layer theory can be determined from correction factors esta
blished from field measurements using the MDD at different times of the
year and under different conditions.

46
Molenaar and Beuving (77) described a methodology in which the FWD
and a dynamic cone penetrometer (DCP) were used to assess stress depen
dent unbound pavement layers and the presence of soft interlayers.
However, the procedure does not provide any direct modulus correlation
but a graphical presentation of FWD surface modulus and DCP profiles.
Geotechnical engineers have, for several years, used various forms
of field tests to assess the engineering properties of soils for con
struction purposes. Recent advances in exploration and interpretation
methods have led to improvements in the determination of important soil
parameters such as strength and deformation moduli. For example, the
following in situ techniques (26,30,50) are suitable for the determina
tion of soil stiffness:
1. Menard Pressuremeter (PMT) and Self-Boring Pressuremeter (SBP)
tests.
2. Cone Penetration Test (CPT), including the mechanical,
electronic, and piezo-cone penetrometers.
3. Marchetti Dilatometer Test (DMT).
4. Plate Loading Tests (PLT), including Screw Plate Tests (SPL).
Some of these tests have the added advantage of providing detailed
information on site stratification, identification, and classification
of soil types. This is of great appeal since the variation of the
subgrade soil properties with depth can be accounted for rather than
assuming an average modulus value as conventionally used in multilayer
analysis.
Unfortunately, the application of the improved techniques to eval
uate or design pavements has been very limited. As evident from the
previous sections, the material characterization part of a rational

47
pavement design program, though very important, is often treated with
considerable simplification and empiricism. Geotechnical engineers
often feel that structural engineers have little or no interest in those
parts of their work below the ground level. These feelings are cer
tainly justified in the case of pavements (76). It is therefore not
surprising that most of the in situ geotechnical applications to pave
ments rely on correlations with empirical pavement parameters such as
CBR to validate their proposed methods (40,57,58,77). The other known
applications of geotechnical in situ testing methods to evaluate the
stiffness of pavement structures are discussed below.
Briaud and Shields (14,15) have described the development and
procedure of a special pressuremeter test for pavement evaluation and
design. The pavement pressuremeter consists of a probe, tubing and a
control unit, and works on the same principle as the Menard pressure
meter (30). They illustrated how the modulus values obtained from the
test can be used directly in multilayer mechanistic analysis. In order
to use empirical design charts, however, Briaud and Shields (15) also
developed a correlation between the pressuremeter modulus and the
bearing strength obtained from a Macleod plate test for two airport
pavements in Canada.
Borden et al. (12) have presented an experimental program in which
the dilatometer test (68) was used to determine pavement subgrade sup
port characteristics. A major part of the testing program consisted of
conducting DMT and CBR tests in soil samples prepared in cylindrical
molds and also in a special rectangular chamber. A limited field test
was conducted on a compacted embankment constructed with one of the
soils used in the laboratory investigation. Although they report good

48
correlations between the dilatometer modulus and CBR value, the use of
the CBR test makes the study empirical, to say the least.
2.4 Factors Affecting Modulus of Pavement-Subgrade Materials
2.4.1 Introduction
The response characteristics of flexible pavement materials is a
complex function of many variables, which is far-fetched from the ideal
materials assumed in classical mechanics. In general, the behavior of
these materials is dependent upon many environmental and load vari
ables. Specifically, the asphalt concrete response is primarily a
function of temperature and rate of loading. Due to its viscoelastic
nature (7,51), asphalt concrete materials become stiffer as the load
rate increases and the temperature decreases. The granular base course
and subgrade characteristics are dependent upon moisture content, dry
density, stress level, stress states, stress path, soil fabric, stress
history, and soil moisture tension (59,78,133).
Several researchers have presented relationships of resilient
modulus as a function of one or more variables, while keeping others
fixed or completely ignored. Most of these relationships were developed
from laboratory studies. A complete review of the relative effects of
the various factors on pavement-soil response, or the relationship
between modulus and other parameters measured in the laboratory can be
found in References 31, 44, 59, 78, 94, and 133. It is not the intent
of this discussion to review the various studies on this topic. The
discussion below will concentrate on two variables that are believed to
be very important in flexible pavement technology, especially when con
sidering NDT and pavement evaluation. These factors are the temperature

49
of the asphalt concrete layer, and the stress dependency of base/subbase
and subgrade materials. This does not mean that the effects of the
other variables can be ignored or underestimated. For example, moisture
content has a considerable effect on the modulus of flexible-pavement
materials, especially for fine-grained subgrade soils (78).
2.4.2 Temperature
Temperature has a very important influence on the modulus of
asphalt-bound materials. The modulus of asphalt concrete decreases with
an increase in pavement temperature (51,78,111,133). The temperature of
the pavement also fluctuates with diurnal and seasonal temperature vari
ations. In order to determine the variation of modulus with temperature
for flexible-pavement materials, the mean pavement temperature should be
established. Southgate and Deen (111) developed a method for estimating
the temperature at any depth in a flexible pavement up to 12 inches.
Figure 2.9 shows the graphical solution for the determination of the
mean pavement temperature with depth from the known temperatures. This
relationship has been recommended and in some cases incorporated into
many flexible pavement design procedures (47,65,107). Though, the
curves have been found to be reasonably accurate for other locations
(111), it would be more desirable to make a direct determination of this
temperature.
2.4.3 Stress Dependency
Laboratory studies presented in the literature (31,44,78,94) sug
gest that the moduli of granular base materials and subgrade soils are
stress-dependent. The stiffness of the granular base has been found to
be a function of the bulk stress or first stress invariant. A stress-
stiffening model in which the modulus increases with the first stress

TEMPERATURE AT DEPTH, F TEMPERATURE AT DEPTH,F
50
PAVEMENT TEMPERATURE + 5
DAY MEAN AIR TEMPERATURE
a) Pavements More Than 2 in. Thick
PAVEMENT TEMPERATURE + 5
DAY MEAN AIR TEMPERATURE
b) Pavements Equal to or Less Than 2 in. Thick
Figure 2.9 Temperature Prediction Graphs (111)

51
invariant is generally used to characterize granular base materials.
The relationship is of the form
K
E = K^e 2 Eqn. 2.5
where
E = granular base/subbase modulus,
0 = first stress invariant or bulk stress, and
K K = material constants
1 2
The subgrade stiffness, on the otherhand, has been found to be a
function of the deviator stress (stress difference). For fine-grained
soils, resilient modulus decreases with increase in stress difference
(78). The mathematical representation of the subgrade stiffness is of
the form
E
Eqn. 2.6
where
E = subgrade modulus
a = stress difference, and
A, B = material constants for the subgrade
The constant B(slope) is less than zero for the stress-softening model,
while for the stress-stiffening model, the slope is greater than zero.
The stress-dependency approach of characterizing pavement materials
is of great importance for high traffic loadings. Situations in which
high traffic loadings occur are larger aircraft loadings in the case of
airfield pavements, and when heavy wheel loads and/or single tire

52
configurations (which result in higher stresses) are applied to flexible
highway pavements. For this reason, some of the NDT back-calculation
procedures have accounted for the stress dependency effect by incorpo
rating Equations 2.5 and 2.6 into their algorithms (45,65,106). How
ever, the problem of determining the material constants, A, B, K and
K still remains, especially when NDT deflection basins are used to
2
characterize the pavement. The most common approach is to use labora
tory resilient moduli and regression analysis to determine these para
meters (45,65,72). Thus, the material parameters will depend upon
sample preparation procedures, disturbance, prestress-strain conditions,
etc.
Other researchers (93,106,121) have suggested determining the mate
rial constants from FWD tests conducted at three or more load levels.
However, it is not clear how viable this procedure is since the resul
tant load-deflection response of a pavement is a combined effect of the
behavior of the individual layers. The relative contribution of each
layer is not clearly known. It is even more complex since the asphalt
concrete layer is dependent on the temperature and age-hardening
characteristics of the asphalt cement. Moreover, contrary to previous
belief, Thompson (116) has found that the material parameters are not
independent-of each other, especially for granular bases and subbases.
Uddin et al. (118,119,120) have applied the concepts of equivalent
linear analysis developed in soil dynamics and geotechnical earthquake
engineering to evaluate the nonlinear moduli. They concluded that the
in situ moduli derived from an FWD deflection basin (at 9000-lb. peak
force) are the effective nonlinear moduli and need no further correc
tion. However, an equivalent linear analysis has to be performed to

53
correct the in situ moduli calculated for nonlinear granular materials
and subgrade soils from a Dynaflect deflection basin. These conclusions
were based on stress analysis comparisons of a single-axle 18-kip design
load, FWD (9000-lb. peak force) and Dynaflect loadings simulated in the
ELSYM5 elastic-layer program. An algorithm to perform this equivalent
linear correction has been incorporated into the FPPE0D1 self-iterative
computer program (120). However, results reported by Nazarian et al.
(81) tend to contradict the conclusions of Uddin et al. (120). Their
study involving FWD tests at 5- and 15-kip loads indicated that non
linear behavior occurs at higher FWD loads, and is more predominant in
the base course layers than the subgrade.
These results and those from other research work indicate there is
disagreement as to what type of approach should be used when the effects
of nonlinearity and stress dependency are to be considered. There are
at least three schools of thought in this regard. The first group
believes that the use of an equivalent effective modulus in an elastic-
layer theory would provide reasonable response predictions. This
approach would eliminate the expense, time and complexity associated
with more rigorous methods such as finite element models (61). The
research works of Maree et al. (70), Roque (96), and Roque and Ruth (97)
on full-scale pavements tend to support this theory.
The second school of thought recommends that the nonlinear stress
dependent models (Equations 2.5 and 2.6) can be incorporated into an
elastic-layer program to predict reasonable response parameters. How
ever, the asphalt concrete layer is treated as linear elastic. This
theory is supported by Moni smith et al. (78), among others, and has been
used in iterative computer programs like OAF, ISSEM4, and IMD.

54
The third school of thought contends that layered elastic theory,
when used with certain combinations of pavement moduli, predicts tensile
stresses in granular base layers, even if gravity stresses are also
considered (16,45,112). Instead of using a layered approach, this group
prefers a finite element model in which the nonlinear responses of the
granular and subgrade materials are accurately characterized. Again,
the asphalt concrete layer is considered to be linear elastic. The
ILLI-PAVE finite element back-calculation program (45) is a classic
application of this theory.
In the finite element approach discussed above, researchers have
used, with limited success, various failure criteria and in some cases
arbitrary procedures to overcome the problem of tensile stresses
(16,112). For example, Brown and Pappin (16) used a finite element
program called SENOL with a K-0 contour model and found it to be capable
of determining surface deflections and asphalt tensile stresses but
unable to determine the stress conditions within the granular layer.
The asphalt layer was characterized as elastic with an equivalent linear
modulus. They therefore concluded that the simplest approach for design
calculations involves the use of a linear elastic-layered system pro
vided adequate equivalent stiffnesses are used in the analysis. This
conclusion is shared by other investigators (10,61,96,97) and is the
philosophy behind the work presented in this dissertation.

CHAPTER 3
EQUIPMENT AND FACILITIES
Most of the methods available for determining the elastic moduli of
flexible pavements have been outlined in Chapter 2. These include the
use of nondestructive tests (NDT), laboratory methods and other in situ
test methods. The limitations of these methods and the need for a more
simple approach have also been highlighted. An approach which mechanis
tically evaluates pavements with the use of NDT and/or in situ penetra
tion tests is therefore developed in this study. This approach is
developed to simplify the mechanistic analysis and design process, and
to evaluate the effects of important variables involved in the determi
nation of pavement layer moduli. The study consisted of the development
of moduli prediction equations from NDT data, field testing and analyses
of NDT and in situ penetration tests, and finally, comparison and eval
uation of test data. Therefore, this chapter describes the equipment
and facilities used in the study.
The test equipment were either available at the Civil Engineering
Department of the University of Florida or at the Bureau of Materials
and Research, Florida Department of Transportation (FDOT). They are
essentially standard testing devices. This research was concerned with
their optimum use and application for a rational mechanistic design and
evaluation of asphalt concrete pavements.
55

56
3.1 Description of Dynaflect Test System
3.1.1 Description of Equipment
The Dynaflect, as previously mentioned, belongs to the dynamic
steady-state force-deflection group of NDT equipment. It is an electro
mechanical device for measuring the dynamic deflection of a pavement
caused by oscillatory loading. The testing system (84,104,108) consists
of a dynamic force generator mounted on a small two-wheel trailer, a
control unit, a sensor assembly and a sensor (geophone) calibration
unit. The Dynaflect can be towed by and operated from any conventional
passenger carrying vehicle having a rigid trailer hitch and a 12-volt
battery system.
The oscillatory load is produced by a pair of counter weights
rotating in opposing directions and phased in such a manner that each
contributes to the vertical force of the other, but opposes the horizon
tal force of the other, thereby canceling horizontal forces. The weight
of the unbalanced masses varies sinusoidally from 2500 lbs. to 1500
lbs., thereby producing a cyclic force of 1000 lbs. peak-to-peak at a
frequency of 8 Hz (see Figure 3.1). The cyclic force is alternately
added to and subtracted from the 2000-lb. static weight of the trailer.
The 1000-lb. cyclic force is transmitted to the pavement through a pair
of polyurethane-coated steel wheels that are 4-in. wide and 16-in. in
outside diameter. These rigid wheels are spaced 20-in. center to center
(see Figure 3.2).
The pavement response to the dynamically applied load is measured
by five geophones located as shown in Figure 3.2. The first geophone
measures the deflection at a point midway between the rigid wheels while
the remaining four sensors measure the deflection occurring directly

FORCE EXERTED ON PAVEMENT
f = Driving Frequency = 8 Hz
1/f ]
TIME
Figure 3.1 Typical Dynamic Force Output Signal of Dynaflect (108)

58
Loading
(b) Configuration of Load Wheels and Geophones.
Figure 3.2 Configuration of Dynaflect Load Wheels and
Geophones in Operating Position (108)

59
beneath their respective locations along the centerline of the trail
er. However, the geophone configuration can be easily changed to a
desired pattern. Each geophone is equipped with a suitable base to
enable it to make proper contact with irregular surfaces (108).
Data are displayed by a digital readout for each sensor on the
control panel which is umbilically attached to the trailer and can be
placed on the seat of the towing vehicle beside the operator/driver.
All operations subsequent to calibration are performed from the control
panel by the operator/driver without leaving the towing vehicle.
3.1.2 Calibration
The Dynaflect unit is calibrated by placing the sensors on a cam-
actuated platform inside the calibrator furnished with each unit (108).
This platform provides a fixed 0.005-in. vertical motion at 8 cycles per
second. The corresponding meter reading of 5 mils is set in the control
unit by adjustment of an individual sensitivity control for each geo
phone. Subsequent deflection measurements are thus comparisons against
this standard deflection.
3.1.3 Testing Procedure
The normal sequence of operation is to move the device to the test
point and hydraulically lower the loading wheels and geophones to the
pavement surface (84,108). A test is performed and the data of the 5
geophone deflection readings are recorded. At this point the operator
has the option of raising both the sensors and the loading wheels or
only the sensors. With the rigid wheels down and the pneumatic tires
lifted, the trailer may be moved short distances from one measuring
point to another at speeds up to 6 mph on the loading wheels (108).

60
When the rigid wheels are out of contact with the ground, the
trailer is supported on pneumatic tires for travel at normal vehicle
speeds. The sensors and loading wheels are raised and lowered by remote
control to enable such moves to be made quickly without need for the
operator/driver to leave the towing vehicle (84,104,108).
3.1.4 Limitations
The general limitations of dynamic steady-state NDT devices have
previously been described. In addition to those, the technical limi
tations of the Dynaflect device include (109) peak-to-peak loading is
limited to 1000 lbs., load cannot be varied, frequency of loading cannot
be changed, the deflection directly under the load cannot be measured,
and it is difficult to determine the contact area.
3.2 Description of the Falling Weight
Deflectometer Testing System
The Falling Weight Deflectometer (FWD) is a deflection testing
device operating on the impulse loading principle. As described pre
viously, there are various forms of the FWD, with the most widely used
one in the United States being the Dynatest Model 8000 FWD system. This
is the type used by the FD0T and in the study reported herein. There
fore, this section describes the operating characteristics of the Dyna
test FWD test system. Like the Dynaflect, the FWD is also trailer
mounted and can be easily towed by most conventional passenger cars or
vans.
The Dynatest 8000 FWD test system consists essentially of three
main components (34,110), namely
1. a Dynatest 8002 FWD,

61
2. a Dynatest 8600 System Processor, and
3. a Hewlett-Packard HP-85 Table Top Computer.
3.2.1 The 8002 FWD
The Dynatest 8002 FWD consists of a large mass that is constrained
to fall vertically under gravity onto a spring-loaded plate, 11.8 in. in
diameter, resting on the pavement surface (see Figure 2.4). A load
range of about 1500 to 24000 lbs. can be achieved by adjusting the num
ber of weights or height of drop or both. The impulse or impact load is
measured by using a strain-gauge-type load transducer (load cell). The
impact load closely approximates a half-sine wave (see Figure 2.5), with
a duration of 25-30 msec which closely approximates the effect of moving
dual-wheels with loads up to 24000 lbs. (110).
Seven seismic deflection transducers or geophones in movable brack
ets along a 2.25 m raise/lower bar are used to measure the response of
the pavement to the dynamically applied load. The geophones, which are
50 mm in diameter and 55 mm high, operate at a frequency range of 2 to
300 Hz (34). One of the geophones is placed at the center of the plate,
with the remainder placed at radial distances from the center of the
plate (see Figure 3.3). In its present form, the FD0T measures deflec
tions at radial distances of 0, 7.87, 11.8, 19.7, 31.5, 47.2, and 63.0
in. from the plate center. These deflections are respectively called
D,D,D,D,D,D, and D in this study.
12 3 4 5 6 7
3.2.2 The 8600 System Processor
The Dynatest 8600 system processor is a microprocessor-based con
trol and registration unit which is interfaced with the FWD as well as
the HP-85 computer (34,110). The processor is housed in a 19-in. wide

62
Figure 3.3 Schematic of FWD Load-Geophone Configuration and
Deflection Basin (34)

63
case which is compact, light weight, and controls the FWD operation. It
also serves as a power supply unit for the HP-85 computer.
The system processor performs scanning and conditioning of the 8
transducer signals (1 load + 7 deflections). It also monitors the
status of the FWD unit to insure correct measurements.
3.2.3 The HP-85 Computer
The Hewlett-Packard Model 85 computer is used for input of control
and site/tests identification data as well as displaying, printing,
storing (on magnetic tape), editing, sorting, and further processing of
FWD test data (34,110).
3.2.4 Testing Procedure
The automatic test sequence is identified and programmed from the
HP-85 keyboard. This includes the input of site identification, height
and number of drops per test point, pavement temperature, etc. When the
operator enters a "START" command, the FWD loading plate and the bar
carrying the deflection transducers will be lowered to the pavement
surface, the weight will be dropped from the pre-programmed height(s),
and the plate and bar will be raised again. An audible "BEEP" signal
tells the operator that the sequence is complete, and that he/she may
drive onto the next test point. A complete measuring sequence normally
takes about one minute, exclusive of driving time between test points,
for three or four drops of the falling weight (34,110).
The measured set of data (1 load + 7 deflections) will be displayed
on the HP-85 for direct visual inspection, and the data will be stored
on the HP-85 magnetic tape cartridge, together with site identification
information, etc. The display, printed results, and stored results can
be in either metric or English units (34).

64
3.2.5 Advantages
The primary advantages of the Dynatest FWD, like many other impulse
deflection equipment, are that the created deflection basins closely
match those created by a moving wheel load of similar magnitude (11,45,
110,123), and the ability to apply variable and heavier dynamic loads to
assess stress sensitivity of pavement materials. The Dynatest FWD test
system has the added advantage that the resulting deflection basin is
constructed from seven deflection measurements compared to five and
three deflections in the KUAB and Phoenix Falling Weight Deflectometers,
respectively.
3.3 BISAR Computer Program
The analyses and evaluation of NDT deflection data in this study
involved the use of BISAR, an elastic multilayered computer program.
BISAR is an acronym for _Bitumen structures Analysis in _Roads. The
program, developed by Koninklijke/Shell Laboratorium, Amsterdam,
Holland, is a general purpose program for computing stresses, strains
and displacements in elastic multilayered systems subjected to one or
more uniform loads, acting uniformly over circular surface areas (32).
The surface loads can be combinations of a vertical normal stress and
unidirectional tangential stress.
The use of BISAR to compute the state of stress or strain in a
pavement requires the following assumptions (32):
1. Each layer of pavement acts as a horizontally continuous, isotropic,
homogeneous, linearly elastic medium.
2. Each layer has finite thickness except for the lower layer, and all
are infinite in the horizontal direction.

65
3. The surface loading can be represented by uniformly distributed ver
tical stresses over a circular area.
4. The interface conditions between layers can vary from perfectly
smooth (zero bond) to perfectly rough (complete bonding) conditions.
5. Inertial forces are negligible.
6. The stress solutions are characterized by two material properties,
Poisson's ratio and Young's modulus for each pavement layer.
BISAR was used over other layered-theory programs because of its
availability, testedand proven--reliability and accuracy (72,91,96),
and, also, its ability to handle variable layer interface conditions.
For example, McCullough and Taute (72) found that the ELSY.M5 program (3)
which is based on the CHEVRON program (74) predicts unrealistic deflec
tions in the vicinity of the load. They therefore recommended the use
of BISAR in computing fitted deflection basins, especially if the
deflection measurements are made near the loading point. Also, Ruth et
al. (98) reported correspondence with Mr. Gale Ahlborn, who developed
the ELSYM5 program, that the program is unreliable for certain unpre
dictable combinations of material properties.
3.4 Description of Cone Penetration Test Equipment
The cone penetration test equipment consisted of a truck-mounted
hydraulic penetration system, electronic cone penetrometers (95) and an
automated data acquisition system. Detailed descriptions of the truck
and its features have been presented by Davidson and Bloomquist (30).
The hydraulic system serves two functions (29): leveling the truck
and penetrating the cone. The leveling system consists of four

66
independently controlled jacks. The front two jacks are connected to a
2 ft. x 7 ft. reaction plate; the back two to separate 15-in. circular
pads. The vehicle is lifted off the ground and leveling assured by
means of a spirit level. The penetrating system consists of a 20-ton
ram assembly located in the truck to achieve maximum thrust from the
reaction mass of the vehicle. Two double-acting hydraulic cylinders
provide a useable vertical stroke of 1.22 m. Prior to testing, the rams
are used to raise the telescoping roof unit. When locked in the raised
position, the unit allows full travel of the rams (29,30).
The cone penetrometers are of the subtraction type configuration,
with tip and friction strain gauges mounted on the central shaft
(29,95). Cone bearing is sensed by compression in the first load cell,
while the sum of cone plus friction is sensed in the rear load cell.
The friction value is then obtained by subtraction, which is done
electronically (29). The cones used also measure pore water pressure
and inclination. A cable, threaded through the 1-meter long push rods,
transmits the field recording signals to the data acquisition system.
The University of Florida currently has three electric cones, with
rated capacities of 5, 10 and 15 metric tons. Each measures tip
resistance, local friction, pore pressure, and inclination. The 5- and
10-ton cones are of standard configuration with 10-cm tip areas and 150-
cm friction sleeves. The larger 15-ton cone has the capability of
testing in much stiffer soil materials. All three cones contain
precision optical inclinometers which output the angular deviation of
the cone from the vertical during penetration (30).

67
The electronic data acquisition system is capable of printing and
plotting penetration data directly on the job site. It consists of a
microprocessor with 128 k magnetic bubble memory, an operator's console
with keypad, an Okidata microline 82A printer and an HP 7470A graphics
plotter. The computer is programmed with preset limits defined to
protect the probe from overloading. If a limit is exceeded, the
computer automatically stops the test and displays the cause of the
abort (29,30).
The electronic cone penetration testing equipment has several
advantages, such as a rapid procedure, continuous recording, high
accuracy and repeatability, automatic data logging, reduction, and
plotting. The CPT provides detailed information on site stratification,
identification, and classification of soil types. Results have also
been correlated with several basic soil parameters, including different
deformation moduli. For example, Schmertmann's method (101) of
computing settlements in sands requires the in situ variation of Young's
modulus. This is obtained from the CPT cone bearing resistance.
3.5 Marchetti Dilatometer Test Equipment
The Marchetti Dilatometer test (DMT) is a form of penetration test
and is fully described in References 17, 29, 68 and 69. Basically, the
test consists of pushing into the ground a flat steel blade which has a
flush-mounted thin circular steel membrane on one face. At the desired
depth intervals (usually every 20 cms) penetration is stopped, and
measurements are taken of the gas pressure necessary to initiate
deflection and to deflect the center of the membrane 1.1 mm into the

68
soil. These two readings serve as a basis for predicting several
important geotechnical parameters, using experimentally and semi-
empirically derived correlations (17,50,68). The DMT sounding provides
indications of soil type, preconsolidation stress, lateral stress ratio
at rest (KQ), Young's modulus (E), constrained modulus (M), shear
strength in clays and angle of shearing resistance in sands.
The major components of the dilatometer test equipment are the
dilatometer blade, the gas-electric connecting cable, a gas-pressure
source, and the read-out (control) unit. Figure 3.4 shows a schematic
diagram of this equipment. In addition there is a calibration unit,
adaptors, electric ground cable and a tool kit containing special tools
and replacement parts. Detailed descriptions and functions of the
various components are presented by Bullock (17) and Marchetti and
Crapps (69).
The dilatometer blade, as shown in Figure 3.4, consists of a stain
less steel blade, 94 mm wide and 14 mm thick, bevelled at the bottom
edge to provide an approximate 16-degree cutting edge. A 60 mm stain
less steel circular membrane is centered on and flush with one side of
the blade.
The control unit, housed in an aluminum carrying case, contains
various indicators, a pressure gauge and the controls for running the
test. The control unit gauge used in the current study had a range of
0-40 bars. Higher and lower range units are also available. This gauge
provides the gas pressure readings for the dilatometer test.
The dilatometer blade is advanced into the ground using standard
field equipment. The blade can be pushed or driven by one of the
following methods (29):

69
To Pressure
Figure 3.4 Schematic of Marchetti Dilatometer Test Equipment (69)

70
1. Using a Dutch Cone Penetrometer rig. This method is believed to
yield the highest productivity, up to 250 or more tests per day.
2. Using the hydraulic capability of a drill rig.
3. Using the SPT rig hammer or similar lighter equipment.
4. With barge-mounted equipment or by wireline methods for underwater
testing.
3.6 Plate Bearing Test
The plate bearing test conceptually belongs to the static force-
deflection group of NDT procedures (79). However, it can also be con
sidered as a destructive field test since the testing requires the
construction and subsequent repair of a trench or test pit. The plate
bearing test consisted of the repetitive-static type of load test out
lined in ASTM Test Procedure D 1195-64 (8). The main objective in this
test is to measure the deformation characteristics of flexible pavements
under repeated loads applied to the pavement through rigid, circular
plates. Burmister's two-layer theory (18,19,20) is generally used to
interpret plate load testing results (133).
The test equipment used by the Florida Department of Transportation
consists of a 12-inch diameter steel plate, loading system, deflection
gauges and supports (41). A trailer loaded with a huge rubber container
filled with water is used as a reaction. A hydraulic jack assembly is
used to apply and release the load in increments. A detailed descrip
tion of the repetitive-static plate load test is provided in ASTM test
standards (8, pp. 258-260).

CHAPTER 4
SIMULATION AND ANALYSES OF NDT DEFLECTION DATA
4.1 BISAR Simulation Study
4.1.1 General
The Dynaflect and FWD loading-geophone patterns were simulated in
the BISAR elastic-layer computer program to predict surface deflection
data for four-layer pavement systems. A flexible pavement structure was
modeled as a four-layer system with parameters shown in Figure 4.1.
The selection of layer thicknesses and moduli was based on typical
ranges in parameters representative of Florida's flexible pavement
systems. In general, the limerock base and stabilized subgrade thick
nesses were fixed at 8 in. and 12 in., respectively. Table 4.1 lists
the range of layer parameters used in the theoretical analysis. The
subgrade was generally characterized as semi-infinite in thickness with
an average or composite modulus of elasticity. However, the effect of
bedrock at shallow depth was also assessed by varying depth to bedrock
in a five-layer system. Poisson's ratio was fixed at 0.35 for all the
pavement layers since it has negligible effect on computed deflections.
In using the layered theory to generate and analyze NDT deflection
data certain assumptions had to be made. The following assumptions were
made with the use of the BISAR program:
1. Pavement materials are homogeneous, isotropic, and linearly
elastic. Therefore, the principle of superposition is valid for
calculating response due to more than one load.
71

72
IDJCP
¡ i
"i*1 i
i
! '
E2 ^2 }
¡ i
i
h2
r
1 i
1
1
E3 ^3 '
1
1 '
i
H3
f
j i
i
^4 M4 !
! 1
l
1
1
i
H4 = OO
r
I
Figure 4.1 Four-Layer Flexible Pavement System Model

73
Table 4.1
Range of Pavement
Layer Properties
Layer
Number
Layer
Type
Layer
Thickness
(in.)
Poisson's
Ratio
Layer Modulus
(ksi)
1
Asphalt
Concrete
1.0 10.0
0.35
75 1,200
2
Limerock
Base
8.0
0.35
10 170
3
Stabilized
Subgrade
(Subbase)
12.0
0.35
6 75
4
Subgrade
(Embankment)
Semi-infinite
0.35
0.35 200

74
2. Pavement layers are continuously in contact at the interfaces with
shearing resistance fully mobilized between them, so that the four
layers act together as an elastic medium of composite nature with
full continuity of stresses and displacements.
3. The Dynaflect and FWD dynamic loads are modeled as static circular
loads. Thus, the peak-to-peak dynamic force of the Dynaflect is
modeled as two pseudo-static loads of 500 lbs. each uniformly
distributed on circular areas. The peak dynamic force of the FWD is
assumed to be equal to a pseudo-static load uniformly distributed on
a circular area representing the FWD loading plate.
4. Thickness and Poisson's ratio of each layer are assumed to be known.
4.1.2 Dynaflect Sensor Spacing
In order to determine the optimum locations of the five Dynaflect
sensors, additional ones were included in the BISAR simulation study.
These sensors were placed at intermediate positions near the loaded
wheel and first two (standard) sensors, with hope of detecting the
primary response of the upper pavement layers (surface and base
course). Figure 4.2 illustrates the loading and modified geophone
array.
The Dynaflect was modeled in the BISAR program using two circular-
loaded areas, with deflection measurement positions as shown in Figure
4.2. Each load is 500 lbs. in weight, and the contact area used in this
study was 64 in.2, resulting in an equivalent radius of 4.5 in.
4.1.3 FWD Sensor Spacing
The conventional sensor spacing used by the FDOT and four
additional sensor locations were utilized in the analytical study.
Sensors were placed at radial distances of 0, 7.87, 11.8, 16.0, 19.7,

y
Figure 4.2 Dynaflect Modified Geophone Positions

76
24.0, 31.5, 36.0, 47.2, 63.0, and 72.0 in. from the center of the FWD
plate. The deflection measured by the last sensor (at radial distance
of 72.0 in.) is called Dn in this study. Sensors at 16, 24, and 36 in.
o
were only used to better define the deflection basin and were not incor
porated in the analysis for prediction equations, which is described
later in this chapter. Thus, eight deflection locations were actually
used in the analysis. These are called Dj, D2, D3, D^, D5, Dg, D?, and
Dg to represent, respectively, radial distances of 0, 7.87, 11.8, 19.7,
31.5, 47.2, 63, and 72 in. from the center of the FWD plate.
The FWD was modeled in the BISAR program as a circular loaded area
with deflection measurement positions as stated previously. The radius
of the loaded area was 5.91 in., and a load of 9 kips was used which
corresponds to one-half of an 18-kip single-axle wheel loading.
4.2 Sensitivity Analysis of Theoretical NDT Deflection Basins
4.2.1 Parametric Study
Pavement surface deflection data generated from BISAR were ini
tially evaluated to see the effect of rate of change of each layer
modulus and/or thickness on the NDT deflection basins. This was accom
plished by using the pavement section shown in Figure 4.3 as a typical
Florida pavement under warm temperature conditions. Using the informa
tion in Figure 4.3, each parameter (modulus and thickness) for a given
layer was doubled or halved while the others were kept unchanged. For
example, the E4 value of 10 ksi was increased to 20 ksi without changing
Ex, E2, Eg and the layer thicknesses. BISAR was then used to calculate
the NDT deflections. The original E4 value was also halved to 5 ksi and
the theortical deflections were computed with BISAR. This procedure was

77
Figure 4.3
Typical Four-Layer System Used for the Sensitivity Analysis

78
repeated for all layer moduli and thicknesses. The NDT device used in
the parametric study was the FWD with a 9-kip loading and sensor spacing
as previously described. However, the findings also apply to the
Dynaflect loading system, under the principle of superposition and
linear elastic theory.
Figures 4.4 through 4.10 show the effect of change of either
modulus or thickness on the FWD deflection basins. The rate of change
of deflections is most pronounced with changes in E4, as compared with
the moduli of the upper pavement layers. In the case of the layer
thicknesses, the effect is most apparent with changes in the base course
thickness, t2. It is possible that the t2 effect was due to the high E2
relative to E3 and E .
Table 4.2 shows the percent change in deflections as a result of
doubling or halving each layer modulus while keeping the other para
meters unchanged for the pavement section shown in Figure 4.3. The
table shows that changes in E4 affect the deflections to the greatest
degree. The percent change in deflection is highest for any sensor
position when the E4 value is changed. This change with regard to E4
also increases substantially for the sensors further away from the load
center. The table thus suggests that E4 contributes the most to the FWD
deflections.
Similar comparisons were also made for changes in deflections for
tl values of 1.5 and 6.0 in. The effect of layer thicknesses, tx, t2,
and t3, on the theoretical FWD deflections were also studied and the
results are summarized in Table 4.3. In this table, t2 seems to have
the most effect. The effect of tx on the deflections becomes negligible
when the original value (tx = 3.0 in.) is halved.

DEFLECTION (mils)
79
RADIAL DISTANCE FROM LOAD (in)
0 10 20 30 40 50 60 70
Figure 4.4 Effect of Change of Ex on Theoretical FWD (9-kip Load)
Deflection Basin

DEFLECTION (mils)
80
RADIAL DISTANCE FROM LOAD (in)
0 10 20 30 40 50 60 70
Figure 4.5 Effect of Change of E on Theoretical FWD (9-kip Load)
Deflection Basin

DEFLECTION (mils)
81
RADIAL DISTANCE FROM LOAD (in)
0 10 20 30 40 50 60 70
Figure 4.6 Effect of Change of E3 on Theoretical FWD (9-kip Load)
Deflection Basin

DEFLECTION (mils)
82
RADIAL DISTANCE FROM LOAD (in)
0 10 20 30 40 50 60 70
Figure 4.7 Effect of Change of E4 on Theoretical FWD (9-kip Load)
Deflection Basin

DEFLECTION (mils)
83
RADIAL DISTANCE FROM LOAD (in)
0 10 20 30 40 50 60 70
Figure 4.8 Effect of Change of ton Theoretical FWD (9-kip Load)
Deflection Basin

DEFLECTION (mils)
84
RADIAL DISTANCE FROM LOAD (in)
0 10 20 30 40 50 60 70
Figure 4.9 Effect of Change of t2 on Theoretical FWD (9-kip Load)
Deflection Basin

DEFLECTION (mils)
85
RADIAL DISTANCE FROM LOAD (in)
0 10 20 30 40 50 60 70
Figure 4.10 Effect of Change of t3 on Theoretical FWD (9-kip Load)
Deflection Basin

86
Table 4.
2 Sensitivity Analysis
of FWD
Deflections for t
o

CO
If
in.
Parameter
Percent
Change
in Deflections
Di
2
3
4
Ds
D6
7
Da
1/2 Ei
9.5
3.4
3.3
2.8
1.7
0.55
0.0
0.0
2 Ei
-7.7
-3.7
-3.3
-3.2
-2.2
-1.1
-0.48
0.0
1/2 e2
23.3
14.0
7.1
1.4
-0.26
-0.55
-0.48
-0.28
2 E2
-15.5
-9.7
-5.6
- 2.0
-0.13
0.37
0.24
0.28
1/2 E3
14.0
16.5
16.4
12.8
6.5
0.81
-1.8
-2.3
2 E3
-11.8
-13.7
-13.3
-10.1
-5.0
-0.64
1.6
2.2
1/2 E4
37.4
50.5
58.3
69.7
82.8
94.9
101.9
104.4
PO
m
-p
-23.1
-31.0
-35.4
-43.7
-46.8
-50.2
-51.2
-51.1
Original pavement is composed of
E = 150 ksi
E2 = 85 ksi
E3 = 30 ksi
t = 3.0 in.
t2 = 8.0 in.
t = 12.0 in.
10 ksi
00

87
Table 4.3
Sensitivity
Analysis of FVJD Deflections
for tj
t. t2, and
Parameter
Percent
Change
in Deflections
Di
2
3
5
D6
7
8
V2 t:
9.8
7.3
6.3
4.4
2.2
0.55
-0.48 -
0.56
2ti
-15.0
-12.6
-10.5
- 7.9
-4.6
-1.6
0.0
0.83
V2 t2
19.1
18.9
15.9
9.7
1.8
0.37
-1.2 -
1.4
2 *2
-18.7
-22.0
-21.9
-18.1
-11.8
-5.1
-0.7
1.1
V2 tg
8.9
11.1
11.7
10.5
6.9
2.2
-0.48 -
1.4
2 t3
-9.8
-12.8
-14.11
-14.8
-12.7
-7.9
-3.4 -
1.4
Original pavement is composed of
E. = 150 ksi
E2 = 85 ksi
E3 = 30 ksi
E. -
tx = 3.0 in.
t2 = 8.0 in.
t = 12.0 in.
10 ksi
00

88
The asphalt concrete modulus of 150 ksi used in the above para
metric study generally corresponds to pavements under warm temperature
conditions. To demonstrate the effect of low temperature or hard
asphalts on NDT deflections, the E1 value was increased to 600 ksi while
keeping the other parameters constant. Table 4.4 lists typical percent
changes in deflection for a tx value of 3.0 in. The trend is similar to
that of the E1 value of 150 ksi. Therefore, E4 has the greatest effect
on deflections whether the asphalt concrete modulus is high or low.
The effect of a bedrock or rigid layer at some finite depth beneath
the subgrade was briefly assessed by varying the subgrade thickness from
zero to infinity. The zero case corresponded to the case of the rock
layer being the subgrade, while the infinity case represented the
situation of no bedrock. The latter case--semi-infinite subgrade
thickness--is the classical representation with the use of layered
elastic theory. Figure 4.11 illustrates the variation of theoretical
FWD deflection basins with subgrade thickness. The figure shows that
the rock layer has considerable effect on NDT deflections unless the
depth of bedrock is 30 ft. or more.
4.2.2 Summary of Sensitivity Analysis
From the parametric study presented above, the following basic
conclusions can be made concerning the use of NDT and layered elastic
theory to determine pavement layer moduli.
1. E4 contributes significantly to NDT deflections, as compared with
the moduli of the upper layers. Small changes in E4 would have a
significant effect on deflections and vice versa.
2. The percent change in deflections from E4 increases from the first
NDT sensor to the last one. This suggests that the pavement

89
Table 4.4 Sensitivity Analysis of FWD Deflections for E = 600 ksi
and t1 = 3.0 in.
Parameter
Percent Change in Deflections
D1
2
3
D4
5
6
7
8
x/2 E,
8.1
3.8
3.1
2.95
2.2
1.5
0.73
0.56
2 Ei
-7.8
-4.0
-2.7
-2.1
-1.8
-1.1
-0.73
-0.56
V2 E2
20.3
15.3
10.4
3.6
0.28
-0.75
-1.0
-0.84
2 E2
-15.7
-11.6
-8.0
-3.4
-0.55
0.94
1.2
1.1
/2 e3
13.7
15.3
15.5
13.3
7.9
2.4
-0.73
-1.4
2 E3
-12.2
-13.5
-13.4
-11.0
-6.6
-2.3
0.49
1.7
V2 e4
38.8
48.3
55.4
66.7
79.4
91.9
99.8
102.8
2 E4
-24.3
-30.0
-34.1
-40.1
-45.8
-49.4
-50.9
-51.1
Original
pavement is composed of
Ej = 600 ksi
E2 = 85 ksi
E3 = 30 ksi
E, =
tx = 3.0 in.
t2 = 8.0 in.
tg = 12.0 in.
10 ksi
00

DEFLECTION (mils)
90
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 4.11 Effect of Varying Subgrade Thickness on Theoretical FWD
(9-kip Load) Deflection Basin

91
response measured in the furthest sensor is mainly due to the
subgrade with little or no effect from the overlying pavement.
Thus, the subgrade modulus may be uniquely related to deflection
measured at the farthest sensor in the Dynaflect and FWD testing
systems.
3. The effect in the rate of change of Ex increases as t: increases.
4. Minor changes in E2, or Eg would not have a significant effect
on NDT deflection basins. This lack of sensitivity suggests that it
would probably be difficult to develop simplified Ex, E2, and Eg
prediction equations from individual sensor deflections with a high
degree of accuracy.
5. The thickness of the base course, t2, has a substantially greater
effect on NDT deflections than changes in tx or t3. However, this
could be due to the relative stiffness of the pavement layers,
especially when Ex and E2 are high. In any case, the base course
thickness in Florida does not differ much from 8.0 in. Therefore,
the t2 value was fixed at 8.0 in. in the development of layer moduli
prediction equations.
6. The presence of a rigid layer at a shallow depth below the subgrade
layer can result in a deflection basin which is quite different from
the conventional case of a semi-infinite subgrade. This difference
becomes negligible with subgrade thickness of 30 ft. or more.
4.3 Development of Layer Moduli Prediction Equations
4.3.1 General
The elastic-layer program BISAR was used to generate Dynaflect and
FWD deflections for different combinations of moduli and thicknesses

92
covering the range listed in Table 4.1. Typical theoretical FWD
deflection basins are shown in Figures 4.4 through 4.10. Figures 4.12
and 4.13 illustrate typical Dynaflect deflection basins for selected
combinations of moduli and asphalt concrete thickness. The BISAR
predicted deflection values served as a database for the development of
prediction equations which would hopefully provide the capability of
evaluating the structural capacity and deficiencies of in-service
pavements.
The deflection data generated from different combinations of layer
moduli and thicknesses were evaluated to determine if the deflection
response from one or more geophone positions in the Dynaflect or FWD
could provide a unique relationship for prediction of individual layer
moduli. The sensitivity analysis had indicated the possibility of
uniquely relating the farthest sensor in the Dynaflect or FWD system to
the subgrade modulus. Attempts were therefore made to identify similar
unique positions for the other layer modulus predictions. The lack of
sensitivity of the upper pavement layers with sensor deflections
suggested the difficulty of developing simple prediction equations.
However, it was found from the modified Dynaflect sensor system
(see Figure 4.2) that the difference between sensor 1 and 4 deflections
(i.e., Dx D^) tend to be uniquely related to the combined effect of
asphalt concrete and base courses. As shown in Figure 4.2, modified
sensor position 1 is located adjacent to one of the Dynaflect wheels,
6.0 in. transversely from standard sensor position i; and the modified
sensor 4 is positioned 4.0 in. longitudinally from the standard position
of sensor 1. This deflection difference essentially eliminated the
effect of the underlying layers and was primarily dependent upon the

DEFLECTION (mils)
93
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 4.12 Variation in Dynaflect Deflection Basin with Varying E2
and E3 Values with tx = 3.0 in.

DEFLECTION (mils)
94
DISTANCE FROM CENTER OF LOADED AREA (in)
0 10 20 30 40 50
Figure 4.13 Variation in Dynaflect Deflection Basin with Varying E3
and E4 Values with tx = 3.0 in.

95
moduli and thickness of layers 1 and 2. Figures 4.14, 4.15, and 4.16
show that for a fixed t1 the relationships between Ex and D D^ are
not significantly affected by E3 and E^. Also, the plots suggest that
the effect of E2 becomes negligible as the thickness of the asphalt
concrete increases. This relationship was therefore used to develop
power law equations to predict E: with an estimate of E2 for different
layer combinations using the Dynaflect modified system.
The sequential development of layer moduli prediction equations
using the BISAR generated Dynaflect and FWD deflections is presented in
Section 4.3.2 for both NDT devices. Equations for the FWD used theore
tical deflections from a 9-kip FWD load. The methodology employed
involved the use of simple power law relationships and multiple linear
regression analysis (39) procedures.
4.3.2 Development of Dynaflect Prediction Equations
4.3.2.1 Prediction Equations for E^ Initial analysis of theo
retical Dynaflect deflection basins had indicated that E: and E2 were
essentially independent of E3 and E4 using Dj D^ as shown in Figures
4.14 through 4.16. Thus Ex could be expressed as a function of E2, t15
t and D D where D, and D, are deflections at sensors 1 and 4,
respectively, in the modified Dynaflect sensor array (see Figure 4.2).
The use of constant base course thickness (Table 4.1) removes the
influence of t2.
Power law relationships between E1 and Dx D^ for several combina
tions of t and E2 were developed. Regression analyses were then per
formed to establish intercepts and slopes for the linear trends. The
basic form of the regression equation used in the analyses was

96
D-j D4 (mils)
Figure 4.14 Relationship Between Ex and Dx D4 for t: = 3.0 in.

97
D1 D4 (mils)
Figure 4.15 Relationship Between E1 and Dx D4 for t:
= 6.0 in.

98
D-, D4 (mils)
Figure 4.16 Relationship Between Ex and Dx D4 for tx = 8.0 in

99
-K
E = K (D D ) 2 Eqn. 4.1
1114
where
Ex = asphalt concrete modulus in ksi,
Di ~ 4 = d1fference in Dynaflect deflections in mils,
Kl = intercept, and
K2 = slope or exponent.
Figures 4.17 and 4.18 show the variation of Kx and K2 with t ,
respectively, for different E2 values. The figures indicate that the
effect of E2 on the regression constants reduces significantly beyond t1
values of 6.0 in. Equations were developed for each value of E2 using
curve fitting techniques to obtain the primary constants and coeffi
cients. It was necessary in several instances to limit the applicable
range of tl to achieve reasonable prediction accuracy. The next step
was to combine some of the regression equations to cover the range of
E2. This process was found to be extremely complex and difficult.
The complexity of obtaining a combined equation for the various
values of Ex was resolved by developing equations to cover different
ranges in E2 and t1 values. This approach assumed a priori that these
parameters could be estimated for computation of E1. Three equations
were developed to represent the range of parameters listed in Table 4.1,
with Equation 4.1 being the basic form. The K2 and K2 equations for the
applicable range of parameters are presented below.

INTERCEPT, K1 (X 1000)
100
ASPHALT, CONCRETE THICKNESS, t-, (in)
Figure 4.17 Variation of Kx with tx for Different E2 Values

SLOPE (K2)
101
ASPHALT CONCRETE THICKNESS, t, (in)
Figure 4.18 Variation of K2 with t1 for Different E2 Values

102
Case 1
K =
i
K =
2
Case 2
K =
l
K =
2
Case 3
: For 10.0 < E < 30.0 ksi and 3.0 t < 6.0 in.,
2 1
.0.794 (31.0 E )
7.99 + 26.64 (8/t ) 2
1
0.9362
Eqn. 4.2
0.569 1.08 t 4.5 0*28^2^
0.7828(E ) [t -(_!)]
2 1 6
0.3463
Eqn. 4.3
For 32.0 < E < 85.0 ksi, and 2.0 in. < t < 6.0 in.,
2 1
, .0.3826
32.93 1.636 (E 30.15) (6.2 t )
2 1
Eqn. 4.4
0.3463
0.621 1.08 t 4.5 "*289^E2^
0.6779 (E ) [t HO ] Eqn. 4.5
2 1 6
For 10.0 < E < 85.0 ksi, and 7.0 < t < 10.0 in.,
2 1
K = 36.661 0.1218(E )
1 2
Eqn. 4.6

103
K
2
0.9399 + 0.00112(E )
2
Eqn. 4.7
In Equations 4.1 through 4.7, Ej and E2 are in ksi; t1 is in inches; and
is in mils. The equations for Case 3 appear to be simple com
pared to those for Cases 1 and 2. The accuracy of these equations,
neglecting the estimation error of E2, is discussed later in this chap
ter. It will also be shown later that the equations can be combined
into a simpler form when field measured Dynaflect deflection data are
evaluated.
4.3.2.2 Prediction Equation for E2 for Thin Pavements. Analysis
of data for thin asphalt concrete pavements (1.0 and 2.0 in.) indicated
that El has little effect on Dx for any specific value. Figure
4.19 shows that E2 versus D: D4 is not sensitive to changes in Er
This lack of sensitivity suggested that it would be more reliable to
develop an equation for the prediction of E2 using estimated values of
Ex without introducing significant errors. Therefore, E2 was esta
blished to be a function of E^ tlS and D: D4. The general form of
the equation is
Eqn. 4.8
where E2 is modulus of base course in ksi, and Kx and K2 relationships
are as shown in Case 4.

104
D-j D4 (mils)
Figure 4.19 Relationship Between E2 and Dx D4 for tx = 1.0 in

105
Case 4. For 100.0 < E < 1000.0 ksi, 10.0 < E <85.0 ksi,
1 2
and t < 2.5 in.,
l
K
1
2.5239
-0.1961 r-0.00037(E )(t )
24.782(t ) eL 1 1
]
Eqn. 4.9
K
2
1.1341(t )
l
0.1173
2 9599
+ 0.000114(E )(t ) *
l l
Eqn. 4.10
Again modulus E^ is in ksi, deflection Dj in mils, and thickness of
asphalt concrete t: is in,inches.
4.3.2.3 Prediction Equations for E3 The initial analysis of
Dynaflect data was performed in an attempt to select some combinations
of sensor deflection response which would provide a simple, straight
forward method for the prediction of moduli for the 12-in. thick
stabilized subgrade. This layer was found to be the most difficult
layer for developing a rational prediction equation. Three equations
were obtained for various ranges of variables. It will later be shown
that the third Eg prediction equation presented herein was simplified
enough and had the capability of being expanded to cover a larger range
of variables.
Considerable effort was expended in analyzing the relationship
between E3 and D1(). This relationship was significantly affected
by Ep E2, E4, and tx. Preliminary analysis resulted in the development
of an equation for fixed values of E1 and t and variable and E^
values.

106
Case 1. For t. = 3.0 in., E = 100.0 ksi, 10.0 < E < 85.0 ksi,
i i 2
10.0 < E < 200.0 ksi, and with E. between 6.0 and 35.0 ksi,
4 3
-K
E = K (D D ) 2 Eqn. 4.11
3 14 10
K
1
"0.3562 9 0.7185
22.74(E ) + 3.503 x 10 (E )(E ) Eqn. 4.12
2 4 2
r 0 10183 V t )
K = [3.4455 + 0.00841(E )](E ) 2 Eqn. 4.13
2 2 4
The accuracy of the E3 prediction equation presented above appeared
good within the stipulated range of variables listed above. However, it
was not simplified enough to allow the development of a more comprehen
sive equation to include varying Ex and tx values. Multiple linear
regression analyses (39) were performed using various combinations of
variables and transformations in an attempt to develop a relatively
simple E3 prediction equation for t1 values of 3.0, 4.5, and 6.0 in.
The best results from these analyses produced a complex equation
containing 13 variables.
Case 2. For 3.0 < t < 6.0 in., 100.0 < E < 1000.0 ksi,
i l
10.0 < E < 85.0 ksi, and 0.35 < E < 200.0 ksi,
2 4

107
log (E ) = 3.28273 2.1435 log (D D ) + 0.3655 log (D D )
3 4 10 14
- 2.10305 log (t ) + 0.266174 log (E /E )
l l 2
+ 0.005057 (E ) log (D D ) + 5.54315 (l/E )
2 4 10 4
- 3.78662 (l/t ) + (0.278664 x 10'6)(E E )
l l 4
- 9.41778 x 10"6(E E ) + 0.0015423 (E /E )
2 4 12
- 7.34825 x 105(E t ) 0.024136 (E /E )
11 2 4
- 0.135502 (E /E ) log (D D ) Eqn. 4.14
2 4 4 10
Equation 4.14 has an R2 of 0.933 with a total of 134 number of cases.
The reliability of this equation is discussed under Prediction Accuracy.
The third Eg prediction equation was developed from a limited range
of variables using D? as the basic independent variable. The
resulting equation (Equation 4.15) was later found to be applicable to a
wider range of variables when field measured Dynaflect deflections were
analyzed.
Case 3. For 1.0 < t < 3.0 in., 150.0 < E < 500.0 ksi,
i l
38.0 < E < 55.0 ksi, 18.0 < E < 30.0 ksi.,
2 3
and 14.0 < E <22.0 ksi,
4
E
3
3.606 (0
4
D )
1.1256
Eqn. 4.15
(N = 12, R2 = 0.991)

108
4.3.2.4 Prediction Equations for E It was concluded from the
sensitivity analysis that the subgrade modulus, E4, could be uniquely
related to the furthest NDT sensor deflection. Therefore, deflections
at the modified sensor 10 position, D1(), which were generated from the
BISAR simulation run, were regressed to their corresponding E4 values.
Two equations were essentially obtained from the regression analysis
(39).
Case 1. For 0.35 < E,< 200.0 ksi,
4
1.0299
E = 5.198 (D ) Eqn. 4.16
4 10
(N = 266, R2 = 0.984)
Case 2. For 10.0 < E< 50.0 ksi,
4
, -0.9745
E = 5.85l(D ) Eqn. 4.17
4 10
(N = 193, R2 = 0.9924)
Figure 4.20 compares Equations 4.16 and 4.17 for E4 values ranging
from 0.1 to 200 ksi. Also shown in Figure 4.20 is a simplified equation
which was originally developed from analysis of Dynaflect tests on pave
ment sections in Quebec, Canada, and Florida (55). It is seen from the
figure that within the E4 range of 10 to 100 ksi, the three equations
are practically the same. Thus, unless the subgrade modulus is

EMBANKMENT MODULUS, E4 in ksi
DYNAFLECT MODIFIED SENSOR 10 DEFLECTION, D10, in mils
Figure 4.20 Comparison of E^ Prediction Equations Using Modified Sensor 10 Deflections

110
extremely low or high, a simple power law equation can be used to
predict E4 from the modified Dynaflect sensor 10 deflection.
4.3.3 Development of FWD Prediction Equations
4.3.3.1 Prediction Equations for E^ It was observed from the
sensitivity analysis that the magnitude of E^ did not produce much
change in the FWD deflections. The maximum change occurred at Dx with
reduced sensitivity for deflections D2 through D5. Beyond D5 there was
essentially no response effect. Therefore, initial effort was expended
in developing simple power law equations between Ex and Dx for fixed
values of E2> Eg, and E^. The resulting power law relationships
provided excellent prediction reliability with R2 values greater than
0.95. However, efforts to achieve a generalized E1 prediction equation
with Dx as the main independent variable was complicated by the inter
action of thickness and layer moduli values. Therefore, the difference
in deflections between D1 and D2 through D5 were analyzed to determine
if any of these values could be used to characterize E^ A multiple
linear regression analysis (39) was used to obtain three equations for
different ranges of t1 values. These E prediction equations are
presented below and hold for deflections obtained with a 9-kip FWD load.
Case 1. For 1.5 < t < 8.0 in.,
l
log (E ) = 1.87689 0.41689(t ) 23.8735 log D D )
1 l 12
+ 43.582 log (D D ) 29.7179 log (D^ D )
- 8.80 log (D D )
(N = 400 and R2 = 0.932)
Eqn. 4.18

Ill
Case 2. For 3.0 < t < 8.0 in.,
l
log (Ei)
1.4506 19.6499 log (D D ) + 32.5256 log (D D )
12 13
- 18.6215 log (D D ) 4.78148 log (D D )
14 15
Eqn. 4.19
(N = 320 and R2 = 0.979)
Case 3. For 4.5 < t < 8.0 in.,
l
log (E ) = 1.79194 10.8459 log (D D ) + 13.6157 log (D D )
- 3.65434 log (D D )
1 4
Eqn. 4.20
(N = 240 and R2 = 0.993)
The above equations hold for their respective range in tx values
and the following range in moduli:
75.0 < E < 1200.0 ksi, 42.5 < E < 85.0 ksi,
l 2
30.0 < E < 60.0 ksi, and 5.0 < E < 40.0 ksi.
3 4
The accuracy and reliability of these equations will be discussed later
using both theoretical and field FWD deflection measurements.
4.3.3.2 Prediction Equations for E2. The sensitivity analysis
presented in Section 4.2 suggested that it would be difficult to develop
E2 prediction equations from FWD deflections. This was due to the
relative lack of sensitivity of E on the theoretical FWD deflection

112
basins. Multiple regression analyses similar to those for Ex prediction
equations were performed to develop equations suitable for the predic
tion of E2. The results of these analyses indicated the following two
equations provided the best overall accuracy for the range in variables
used in the study.
Case 1. For 150.0 < E < 300.0 ksi, 1.5 < t < 6.0 in.,
1 l
42.5 < E < 170.0 ksi, 30.0 < E < 60.0 ksi,
2 3
and 10.0 < E < 40.0 ksi,
4
log (El
2
2.9271 0.109 t + 0.5104 log (t ) 0.3997 log (D )
+ 4.213 log (D D ) 17.149 log (D D )
12 14
+ 12.295 log (D D )
(N = 240, and R2 = 0.962)
Eqn. 4.21
Case 2. For 1.5 < t < 8.0 in., 5.0 < E < 40.0 ksi, and others as
1 4
listed for Equation 4.21,
log (E ) = 3.06546 0.08134 t 0.1256 D + 0.2793 D 0.19322 D
2 1 1 2 5
+ 0.2998 log t + 3.5381 log (D )
+ 2.1045 log (D ) 2.4614 log (D )
5 8
+ 6.76643 log (D D ) 11.0912 log (D D )
13 14
Eqn. 4.22
(N = 400, and R2
0.953)

113
The 10-variable regression equation presented above has some of the
variables in Equation 4.21. The log (D D ) and log (D D ) terms
14 12
were found to make the most significant contribution to the R2 value of
0.953 (39). It must be noted that D0 is in Equation 4.22. This sensor
deflection measurement is currently not made with the conventional
sensor array utilized by the FOOT (see Section 3.2.1).
4.3.3.3 Prediction Equations for E?. The modulus of the sta
bilized subgrade layer, E3, does not contribute any significant change
on FWD deflection basins. As in the case of the Dynaflect, this layer
was found to be difficult to develop moduli prediction equations. From
the sensitivity analysis, the maximum percent change in deflections
occurred at FWD deflections D2 and D3 when the original E3 value was
doubled or halved (Tables 4.2 and 4.4). Therefore initial analyses
involved the examination of the relationships between Eg and D2 or D3
using log-log plots. Simple power law equations with R2 values greater
than 0.998 were generally obtained for various levels of Ex, E2, E^ and
t These parameters were found to have considerable influence on the
intercepts (Kx) and slopes (K2) of the power law relationships. Because
of the complex interactions involved, it was difficult to combine some
of the power law equations.
Multiple linear regression analysis was performed on a subset of
the theoretical deflections. An 8-variable prediction equation was
obtained from the analysis. This is shown in Case 1.
Case 1. For 150.0 < E < 300.0 ksi, 1.5 < t < 6.0 in.,
1 l
42.5 < E < 170.0 ksi, 30.0 < E < 75.0 ksi,
2 3
and 10.0 < E < 40.0 ksi,
4

114
log (E3)
0.587 0.037 ti 10.19 + 8.01 log^) + 2.226 log(Dg)
- 5.119 log (D D ) + 17.255 log (D D )
13 15
- 6.101 log (D D ) 7.051 log D D )
2 5 4 7
(N = 192, and R2 = 0.958)
Eqn. 4.23
The database used to develop Equation 4.23 was then expanded to
include E3 = 15 ksi, E4 = 5 ksi and tx = 8.0 in. combinations.
Subsequent regression analysis resulted in a 5-variable prediction
equation listed under Case 2.
Case 2. For 150.0 < E < 300.0 ksi, 1.5 < t < 8.0 in.,
1 i
42.5 < E < 85.0 ksi, 15.0 < E < 75.0 ksi,
2 3
and 5.0 < E < 40.0 ksi,
4
log (E ) = 3.8646 0.27061 log (t ) + 1.1212 log (D )
3 16
- 1.849 log (D D ) + 12.009 log (D D )
2 3 2 4
- 12.3637 log (D D ) Eqn. 4.24
2 5
(N = 400, and R2 = 0.935)
4.3.3.4 Prediction Equations for E,t. The subgrade modulus, E ,
was found from the sensitivity analysis to contribute significantly to
the NDT deflections compared to the moduli of the upper layers. Changes
in E4 affected the deflections to the greatest degree. Using the 9-kip

115
FWD deflections, the percent change in deflections due to changes in E4
increased from Dx to Dg. However, the rate of increase seemed to level
off from Dc to D. (see Tables 4.2 and 4.4). These are the farthest
O O
sensor deflections used in the FWD theoretical study.
Figures 4.21 and 4.22 show, for example, the relationships between
E, and D., D_, or D for fixed levels of E,, E, E, and t,. The
Hb/o 1231
subgrade modulus, E4, ranges from 5 to 100 ksi. Regression equations
and R2 values for each power equation are indicated on the plots.
Similar relationships with a high degree of correlation were obtained
for pavements with tx values ranging from 1.5 to 10 in.
Based on the unique relationships obtained for E4 and the last
three FWD sensor deflections, the database was combined and also
expanded to cover a large range of parameters listed in Table 4.1. The
following power law equations were obtained by regressing E4 against Dg,
or D?, or Dg.
General Case: For 1.5 < t < 8.0 in., 75.0 < E < 1200.0 ksi,
1 i
42.5 < E < 85.0 ksi, 30.0 < E < 60.0 ksi,
2 3
and 5.0 < E <40.0 ksi,
4
54.6122 (D )
1.03065
E
Eqn. 4.25
6
(N = 400, and R2 = 0.9974)
39.9899 (D )
7
0.98912
E
(N = 400 and R2 = 0.9992)
Eqn. 4.26

SUBGRADE MODULUS, E4 IN ksi
116
SENSOR DEFLECTION, D6 D7 D8 (mils)
Figure 4.21 Relationship Between E and FWD Deflections for Fixed
E1> E2, and E3 Values with tx = 3.0 in.

SUBGRADE MODULUS, E4, IN ksi
117
SENSOR DEFLECTION, D6 D? D8 (mils)
Figure 4.22 Relationship Between E4 and FWD Deflections for Fixed
Ej, E2, and Eg Values with tx = 6.0 in.

118
, 0.97871
E = 34.8891 (D ) Eqn. 4.27
4 8
(N = 400 and R2 = 0.9996)
In Equations 4.25 through 4.27, like the previous equations, modulus is
in ksi, and deflections are in mils. Again, Dg and D? are deflections
from sensors located at radial distances of 47.2 and 63.0 in., respec
tively, in the conventional geophone spacings utilized by the FD0T.
However, Dg is an additional sensor located at a radial distance of 72.0
in. which was included in this study. The FD0T does not have sensor 8
measurement (Dg) in their current sensor array system.
Additional multiple linear regression analyses (39) were performed
to see if there were any interactions among Dg, D?, and Dg which could
provide an optimum relationship for the prediction of E^. The following
two logarithmic equations were obtained:
log (E ) = 1.51999 + 0.622145 log (D ) 1.58542 log (D ) Eqn. 4.28
4 6 7
(N = 400 and R2 = 0.9995)
log (E ) = 1.48914 + 0.893689 log (D ) 1.86276 log (D )
(N = 400 and R2 = 0.9997) Eqn. 4.29
An equation combining all three independent variables Dg, D?, and
Dq was not found from the multiple linear regression analysis. The
applicable range of parameters (layer moduli and thicknesses) for
Equations 4.25 through 4.29 are presented above. However, due to the
unique relationship between E4 and the FWD sensor deflections, these

119
equations could be applied to an expanded range of parameters without
introducing much error. The prediction accuracy of these equations and
others are discussed in the next section.
4.4 Accuracy and Reliability of NDT Prediction Equations
The previous section presented several layer moduli prediction
equations for both the Dynaflect and FWD testing systems. The equations
are theoretical in the sense that they were developed from theoreti
cal ly-derived deflections (using the BISAR computer program), layer
thicknesses and moduli. The prediction accuracy of these equations are
assessed in this section.
The various equations were used to predict and evaluate prediction
errors. This was achieved by relating predicted moduli values to the
true or actual values. High prediction errors were deleted from the
data set and the remaining values were correlated to the actual values
of moduli originally selected for analysis. In general, prediction
errors of the order of 10 percent were considered to be compatible with
the variabilities commonly noted in field deflection-basin measurements.
4.4.1 Prediction Accuracy of Dynaflect Equations
4.4.1.1 Asphalt Concrete Modulus, Er The complexity of devel
oping a generalized equation to predict Ex for varying t1 and from
D -D^ resulted in the development of three equations to cover the dif
ferent ranges of E2 and tx (Equations 4.1 through 4.7). The accuracy of
these equations is discussed below.
Case 1: 10.0 < E < 30.0 ksi; and 3.0 < t < 6.0 in.
2 1

120
Equations 4.1 through 4.3 are applicable in this range. Therefore,
these equations were used to compute Ex values for asphalt concrete
thicknesses of 3.0, 4.5, and 6.0 in. Predictions were generally very
good and within +10 percent of the actual Ej value when the true value
of E2 was used. In general, the majority of the predicted E values
were within +5 percent, with the maximum error being +22.5 percent.
Pavements with Ex predictions above +10 percent are listed in Table
4.5. It can be seen from the table that these were very few considering
the size of the data set analyzed. Correlation between predicted and
actual E: values resulted in the following:
Ej (Predicted) = 9.988 + 0.933E1 (Actual) Eqn. 4.30
(N = 58, R2 = 0.992)
Attempts to predict outside of the designated thickness range
resulted in errors up to 70 percent. Thus, it is imperative that the E
prediction equations for Case 1 be used for the specified range.
Case 2: 32.0 < E < 85.0 ksi; and 2.0 < t < 6.0 in.
2 l
E: is predicted from Equations 4.1, 4.4, and 4.5. Comparisons between
predicted and actual E1 values indicated that errors up to 90 percent
could be obtained with the use of these equations. The higher errors
occurred in pavements with extreme values of E^ (0.35 and 200 ksi).
When these pavements were deleted, predictive errors were generally
within the range of +20 percent, with a few cases going as high
as +30 percent.

121
Table 4.5 Pavements with Dynaflect Ex Predictions Having More
Than 10 Percent Error (Case 1 Equations 4.1-4.7)
No.
Layer Moduli (ksi)
(in.)
Predicted percent
Difference
(ksi)
300
10
15
10
3.0
261.6
-12.8
1000
10
6
10
3.0
1128.9
12.9
1000
10
6
50
3.0
1128.9
12.9
1000
30
15
10
3.0
1124.8
12.5
1000
10
15
10
4.5
1224.8
22.5
1000
30
15
10
4.5
1144.3
14.4
100
40
15
10
4.5
87.9
-12.1
100
10
6
0.35
6.0
114.6
14.6
1000
30
15
10
6.0
1123.8
12.4
9

122
Ex (Predicted) = 75.612 + 0.848E1 (Actual) Eqn. 4.31
(N = 97, R2 = 0.848)
Deleting the cases with E^ equal to 0.35 and 200 ksi resulted in
Ex (Predicted) = 28.707 + 0.989E1 (Actual) Eqn. 4.32
(N = 90, R2 = 0.934)
The improvement in R2 value suggests that the Ex prediction equa
tions for Case 2 should be used with caution when E, values are
extremely low or high.
Case 3: 10.0 < E < 85.0 ksi; and 7.0 < t < 10.0 in.
2 l
For this case, Equations 4.1, 4.6, and 4.7 were derived to predict E .
As mentioned previously, these equations appear to be simple compared
with those for Cases 1 and 2. The relatively simple Ex prediction
equations for Case 3 were developed using data for tx = 8.0 in. only,
but it was found to be applicable for thicknesses between 7 and 10 in.
This also suggests that for thicker pavements, the effect of tx on Ej,
E2, and becomes negligible, as shown in Figures 4.17 and 4.18.
The percent difference between actual and predicted Ex values for
Case 3 were within +6 percent. Only three pavements exceeded this,
having less than +10 percent error. When the predicted and true moduli
values were regressed, the following equation was obtained.
E1 (Predicted) = 2.896 + 0.991E1 (Actual) Eqn. 4.33
(N = 22, R2 = 0.998)

123
4.4.1.2 Base Course Modulus, E2, for Thin Pavements. For pave
ments with very thin asphalt concrete layers, tx < 2.5 in., Equations
4.8 through 4.10 can be used to predict E2 with an estimate of E .
Analysis of predicted E2 values indicated that errors were generally
within the order of 10 percent for tx values of 1.0, 2.0, and 2.5 in. A
few cases (8 out of 66) had errors up to +20 percent. These are listed
in Table 4.6.
Correlation between predicted and actual E2 values yielded the
following equation:
E2 (Predicted) = 2.3745 + 0.962E2 (Actual) Eqn. 4.34
(N = 66, R2 = 0.982)
An attempt was made to extrapolate the equations to predict pavements
with 3 inches or more of asphalt concrete layer. Errors as high as 40
percent were obtained, thus emphasizing the need to apply the equations
to the stipulated range.
4.4.1.3 Stabilized Subgrade Modulus, Eg. The stabilized subgrade
layer was initially found to be the most difficult layer for developing
a rational prediction equation. Equations 4.11 through 4.13 which were
developed for Case 1 with fixed tx and E1 values (3.0 in. and 100.0 ksi,
respectively) had 24 out of 88 cases with predictive errors exceeding
15 percent. When the equations were used to predict pavements with Ex
value of 1000.0 ksi, errors as high as 75 percent were obtained.
Equation 4.14 (for Case 2), which was developed from a multilinear
regression analysis, applies to pavements with AC thicknesses ranging

124
Table 4.6
Pavements with
Dynaflect
Predictions
Having More
Than
10
Percent
Error (Case
4--Equations
4.8-4.10)
No.
Layer Modul i
(ksi)
(in.)
Predicted
e2
(ksi)
Percent
Ei
E2
E3
E4
Difference
1
100
85
35
0.35
1.0
94.9
11.6
2
1000
10
35
0.35
1.0
11.3
13.0
3
100
85
15
10
2.0
75.5
-11.2
4
700
30
15
10
2.0
25.4
-15.3
5
700
85
15
10
2.0
96.0
12.9
6
1000
30
15
10
2.0
25.3
-15.7
7
1000
35
15
10
2.0
28.9
-17.4
8
1000
40
15
10
2.0
35.4
-11.5

125
from 3.0 to 6.0 in. This equation had an R2 of 0.933 for 134 number of
cases. Predictions were generally within 10 percent of actual Eg
values. Only 22 of the 134 total observations had more than 20 percent
predictive error. It must also be noted that the use of this equation
requires knowledge of the other layer moduli; namely E2 and E4. If
any of these is off, then the E3 value predicted from Equation 4.14
would probably be in error. Thus, the reliability of the E3 prediction
is dependent on the accurate estimates of the other layer moduli.
Equation 4.15, which predicts E3 from D4-D?, is a relatively sim
plified equation. Table 4.7 shows the high degree of accuracy of this
equation to predict E Correlation between predicted and actual E
values resulted in an R2 of 0.991. Extrapolation of this equation to
predict outside the stipulated range resulted in errors as high as +60
percent. However, it will be shown later that this equation format,
using field measured Oynaflect deflections, is applicable to a wider
range of variables than those listed under Case 3.
4.4.1.4 Subgrade Modulus, E Equation 4.16, derived to predict
the subgrade modulus, holds for a wide range of E4 (0.35 to 200.0
ksi). This equation has an R2 of 0.984 for 266 number of cases.
Because of the wide range of E4, the equation tends to underpredict
slightly for intermediate E4 values. Also, predictive errors greater
than +60 percent were found to be typical for the extreme values of E4
(0.35 and 200.0 ksi). Therefore, those data were deleted and the
remaining data set regressed to obtain Equation 4.17 with an R2 of 0.992
and N equal to 193. Equation 4.17 holds for E4 values between 10.0 and
50.0 ksi. Only 4 out of 193 E4 predictions fell above +15 percent.

126
Table 4.7 Comparison of Actual and Predicted E Values for
Varying t2 (Case 3Equation 4.15)
No.
Layer Moduli (ksi)
E1 E2 E 3 E*t
*1
(in.)
Predicted
E3
(ksi)
Percent
Difference
1
150
38
18
14
1.0
17.2
-4.4
2
500
38
18
14
1.0
18.0
0
3
150
55
30
22
1.0
28.8
-4.0
4
500
55
30
22
1.0
30.0
0
5
150
38
18
14
2.0
17.9
-0.6
6
500
38
18
14
2.0 .
17.7
-1.7
7
150
55
30
22
2.0
30.1
0.3
8
500
55
30
22
2.0
29.8
-0.7
9
150
38
18
14
3.0
13.6
3.3
10
500
38
18
14
3.0
18.8
4.4
11
150
55
30
22
3.0
30.6
2.0
12
500
55
30
22
3.0
30.4
1.3

127
It was shown in Figure 4.20 that a simplified equation originally
developed from field measured Dynaflect data could be used to predict E4
values varying from 10.0 to 100.0 ksi. The simplified equation
= 5.4 (D T1-0 Eqn. 4.35
was used to predict all the data set originally used to develop Equation
4.16. The R2 value obtained from linear regression of predicted and
actual E values was 0.830. However, when the extreme E values (0.35
and 200.0 ksi) were deleted, the R2 increased to 0.982. Thus, within
the range of 10.0 to 100.0 ksi of subgrade modulus, Equation 4.35 is
considered to be simple and accurate enough to predict E4.
4.4.2 Prediction Accuracy of FWD Equations
4.4.2.1 Asphalt Concrete Modulus, E1: Three equations were devel
oped to predict Ex for different ranges of asphalt concrete thickness,
tj. Equation 4.18 is valid for t: values varying from 1.5 to 8.0 in.
The prediction accuracy of this equation is summarized in Table 4.8. In
this table, the number of cases with percent predictive errors falling
within certain limits are listed for varying tx values. The table
illustrates that higher errors occur at tx values of 1.5 and 3.0 in. As
tx increases, the percent error reduces substantially with t1 equal to
6.0 in. having most cases with less than 20 percent prediction error.
Because of the high errors obtained with tx values of 1.5 in. in
Equation 4.18, these values were deleted from the data set and the
remaining data were analyzed to obtain Equation 4.19. This equation
thus holds for tx values from 3.0 to 8.0 in., and had an R2 of 0.979 for
320 number of cases. The prediction accuracy of Equation 4.19 is also

128
Table 4.8 Prediction Accuracy of Equation 4.18--Error
Distribution as a Function of tx
Number
of Cases
with Percent Error
Within
(in.)
> 60
50-60
40-50
30-40
20-30
15-20
< 15
1.5
2
9
12
18
10
5
24
3.0
3
9
9
7
19
10
23
4.5
0
0
7
10
21
9
33
6.0
0
0
0
1
8
4
67
8.0
0
0
0
3
27
20
30
Total
5
18
28
39
85
48
177

129
listed in Table 4.9. Only 14 out of 320 cases had errors above 30
percent but they did not exceed 40 percent. These cases occurred when
tx value was 3.0 in. (10 cases) and 4.5 in. (4 cases). Table 4.9 also
shows that only six cases with 20 to 30 percent predictive error
occurred for tx value of 6.0 and 8.0 in. More importantly, the percent
error decreased substantially for tx equal to 8.0 in. The majority of
the pavements with 8.0 in. asphalt concrete thickness had less than 10
percent prediction error.
The general tendency for Equations 4.18 and 4.19 was to predict
with a higher degree of accuracy for pavements with thick asphalt
concrete layers. Because of the high prediction errors from 3.0 in.
asphalt concrete pavements, all the 3.0 in. pavements were also deleted
from the data set. Subsequent regression analysis of the remaining data
set yielded Equation 4.20, with a significantly improved correlation (R2
= 0.993, and N = 240). Error analysis revealed that only 2 out of 240
cases had errors of 20 percent or more. Pavements with E predictions
having errors of 15 percent or more are listed in Table 4.10. This
table indicates that only the 4.5- and 6.0-in. pavements fall in this
category. The 4.5-in. pavements all underpredicted Ex when the actual
value was 1200 ksi. However, for the 6.0-in. pavements, Ex values of
150 and 300 ksi were generally overpredicted. Again, predictions were
very good for the 8.0-in. asphalt concrete pavements. This supports the
findings with the Dynaflect that for thicker pavements, the effect of tx
on E is small.
4.4.2.2 Base Course Modulus, E2 Two equations were derived to
predict the base course modulus using theoretical 9-kip FWD load deflec
tions. Equation 4.21, which was developed from a subset of the data

130
Table
4.9
Prediction Accuracy of Equation 4.19-'
as a Function of tl
-Error
Distribution
*1
Number
of Cases
With Percent Error
Within
(in.)
> 60
50-60
40-50
30-40
20-30
15-20
< 15
3.0
0
0
0
10
7
9
54
4.5
0
0
0
4
12
12
52
6.0
0
0
0
0
5
19
56
8.0
0
0
0
0
1
2
77
Total
0
0
0
14
25
42
239

131
Table 4.10 Pavements with Ex Predictions Having 15 Percent or More
Error (Equation 4.20Case 3)
No.
Layer Modul i
E! E2
(ksi)
E3
E4
*1
(in.)
Predicted
E.
(ksi)
Percent
Difference
1
1200
42.5
30
5
4.5
951.7
-20.7
2
1200
42.5
30
10
4.5
976.8
-18.6
3
1200
42.5
30
20
4.5
1013.2
-15.6
4
1200
42.5
60
5
4.5
960.3
-20.0
5
1200
42.5
60
10
4.5
977
-18.6
6
1200
42.5
60
20
4.5
996.8
-16.9
7
1200
85.
30
5
4.5
966.1
-19.5
8
1200
85.
30
10
4.5
995.4
-17.0
9
1200
85.
30
40
4.5
976.1
-18.7
10
1200
85.
60
5
4.5
985.4
-17.9
11
150
42.5
30
20
6.0
174.3
16.2
12
300
42.5
30
20
6.0
349.5
16.5
13
150
42.5
30
40
6.0
175.5
17
14
300
42.5
30
40
6.0
351.5
17.0
15
300
42.5
60
10
6.0
346.7
15.6
16
300
42.5
60
40
6.0
347.8
15.9

132
set, had an R2 of 0.962 for 240 number of cases. Error analysis indi
cated that 11 out of the 240 cases had errors exceeding 20 percent, and
these were cases with tx equal to 1.5 or 6.0 in. These were the
extremes of the range of tx used to develop Equation 4.21.
When the data set was expanded to include tx and E4 values of 8.0
in. and 5.0 ksi, respectively, Equation 4.22 was obtained. This equa
tion had an R2 of 0.953 for 400 number of observations. Error analysis
indicated that 30 out of 400 observations had predictive errors above 20
percent. These simulated pavements are listed in Table 4.11, which
shows that two pavements (numbers 24 and 28) had E2 predictions with 30
to 40 percent error. One pavement (number 27) had 54 percent prediction
error. When all three pavements with 30 percent or more predictive
errors were deleted from the data set, the R2 value increased from 0.953
to 0.956. This increase was considered not significant enough to war
rant changing the Equation 4.22 format.
The most interesting thing from Equation 4.22 was that most of the
errors occurred at the extreme limits of tx (1.5 and 8.0 in.). As shown
in Table 4.11, most of these high errors were pavements with E2 values
of 42.5 and 175.0 ksi. Predictions were generally good for intermediate
t1 values, especially 4.5 and 6.0 in. Moreover, Equation 4.22 contains
sensor 8 deflection measurement (Dg) which is currently not made with
the conventional sensor array utilized by the FD0T. Therefore, the
reliability of this equation is contingent upon measurement of 0o. In
o
the absence of D measurement, Equation 4.21 would be the best choice
8
for prediction of the base course modulus.
4.4.2.3 Stabilized Subgrade Modulus, E^. Two equations were also
developed to predict E3. Equation 4.23 which holds for t values from

133
Table 4.11 Pavements with E. Predictions Having 20 Percent or More
Errors (Equation4.22--Case 2)
No.
Layer Moduli (ksi)
E1 E2 E3 E4
*1
(in.)
Predicted
E.
(ks1)
Percent
Difference
1
150
170.
30
5
1.5
133.9
-21.2
2
300
170.
30
5
1.5
127.9
-24.8
3
150
85.
60
5
1.5
109.7
29.1
4
300
170.
30
10
1.5
134.5
-20.9
5
150
85.
60
10
1.5
107.4
26.3
6
150
120.
60
10
1.5
145.
20.8
7
150
170.
30
20
1.5
133.7
-21.4
8
150
85.
60
20
1.5
102.5
20.6
9
150
42.5
30
40
1.5
33.5
-21.2
10
150
170.
30
40
1.5
128.9
-24.2
11
300
170.
30
40
1.5
128.4
-24.5
12
300
170.
30
5
3.0
135.2
-20.4
13
150
120.
60
10
3.0
144.5
20.4
14
150
170.
30
5
8.0
132.4
-22.1
15
300
42.5
30
5
8.0
52.2
22.9
16
300
42.5
60
5
8.0
54.6
28.4
17
300
60.
60
5
8.0
72.3
20.4
18
300
120.
60
5
8.0
144.1
20.1
19
300
170.
60
5
8.0
214.1
26.0
20
150
170.
30
10
8.0
131.9
-22.4
21
300
42.5
30
10
8.0
54.7
28.6
22
150
170.
60
10
8.0
133.8
-21.3
23
300
42.5
60
10
8.0
53.5
25.8
24
300
42.5
30
20
8.0
56.5
33.0
25
150
170.
60
20
8.0
128.8
-24.3
26
150
42.5
30
40
8.0
54.5
28.2
27
300
42.5
30
40
8.0
65.4
54.
28
300
60.
30
40
8.0
78.5
30.8
29
150
170.
60
40
8.0
134.2
-21.1
30
300
42.5
60
40
8.0
53.9
26.9

134
1.5 to 6.0 in. had an R2 of 0.958 with 192 number of cases analyzed.
Error analysis indicated that only 9 out of the 192 total cases had
predictive errors above 15 percent but all did not exceed 20 percent.
These occurred in pavements with value of 40 ksi and tx of 1.5 or 6.0
in.
Because of the high reliability of Equation 4.23, the database was
expanded to include other moduli and thickness combinations. Analysis
of the large data set (N = 400) resulted in Equation 4.24 with an R2 of
0.935. In 62 out of 400 cases, predictive errors were more than 20 per
cent. Three pavements had 40 percent or more prediction errors with one
of the them being 55.6 percent.
Additional evaluation of the practical limits of Equation 4.24
indicated that prediction accuracy was satisfactory even when the Ex
range was expanded to 600 ksi. Errors were generally less than 30 per
cent and only 7 out of 80 cases had predictive errors between 30 and 43
percent using Ex = 600 ksi. Overall assessment of this E3 prediction
suggests that the best accuracy is obtained from 3 to 6 in. of asphalt
concrete.
4.4.2.4 Subgrade Modulus, E,. Five equations were derived to pre
dict subgrade modulus from the 9-kip FW0 load deflections. The first
three equations (4.25 to 4.27) were essentially dependent upon individ
ual sensor deflections, Dg to D8, respectively. The other equations
(4.28 and 4.29) included two sensor deflections in each equation. All
five equations had R2 values greater than 0.997 for N = 400.
Considering the high R2 values obtained for Equations 4.25 through
4.29, their prediction accuracy is expected to be good. Also, a high
degree of accuracy is required for E^ prediction since small changes

135
would affect the FWD deflections and vice versa (see Section 4.2).
Error analysis of these equations revealed that predictive errors were
generally below 20 percent, with most cases actually being less than 10
percent. The highest errors occurred with Equation 4.25, and the least
in Equation 4.29. This trend was in agreement with that of the R2
values.
The lowest and least number of prediction errors always occurred
using the equations with Dg as an independent variable(s). These are
Equations 4.27 and 4.29. The FWD sensor for D measurement is located
o
72.0 in. from the center of the loading plate and is not a conventional
sensor spacing. The high degree of prediction accuracy with this sensor
deflection emphasizes the need to incorporate its measurement in the FWD
test system. However, in the absence of Dg, Equation 4.28 which incor
porates both Dg and D? would be the best equation compared to the
others. With the use of two sensor deflections, this equation should
minimize the potential for prediction error due to measurement
variability.

CHAPTER 5
TESTING PROGRAM
5.1 Introduction
The field testing program involved the acquisition of Dynaflect,
Falling Weight Deflectometer (FWD), cone penetration test (CPT), Mar-
chetti Dilatometer test (DMT), and plate bearing test data from selected
sections of asphalt concrete pavements in the state of Florida. The
test sections generally incorporated a wide range of deflection charac
teristics resulting from the properties of the materials in the pavement
layered systems.
The NDT data generally served as a tool for assessing the applica
bility of the prediction equations presented in Section 4.3 to actual
field-measured NDT data. Also the deflection data were used to deter
mine the actual moduli of the pavement systems at the time of NDT
testing. The penetration tests also provided a means of determining
layer thicknesses and moduli profiles. In addition, the CPT and DMT
were used to evaluate the stratigraphy of the subsurface. This infor
mation is valuable when performing elastic layer analyses to determine
the moduli of pavement layers from NDT data. Details pertaining to
testing procedures are presented in the ensuing discussion.
Specific test areas for selected pavements were chosen on the basis
of uniformity in response characteristics as determined from preliminary
NDT measurements. More than one test site (section) was established
when significant differences in NDT deflection response were observed
136

137
over the length of a highway pavement. The characteristics of each test
section are described in detail in Section 5.2.
Cores of the asphalt concrete layer were also obtained for labora
tory tests on the rheological properties of the recovered asphalt. The
rheology tests generally consisted of establishing the low temperature-
viscosity characteristics of the recovered asphalt (117) which were then
used to predict the modulus of the asphalt concrete layer. Other perti
nent data collected as part of this program include the location of
water table, pavement temperatures, and physical properties of the base,
subbase, and subgrade soil materials. Detailed information on these
testing procedures are presented in this chapter.
5.2 Location and Characteristics of Test Pavements
Most of the pavement sections used in the study had been scheduled
for evaluation by the FDOT. These sections, as listed in Table 5.1, are
representative of pavement deflection response, type of construction,
and soil-moisture conditions. Also shown in Table 5.1 are the year each
pavement was originally built and the last time it was resurfaced with
an overlay. Figure 5.1 shows the locations of the test pavement sec
tions on the map of Florida.
The thicknesses of surface and base layers for each pavement sec
tion are also listed in Table 5.1. The asphalt concrete thickness was
generally within the range listed in Table 4.1 for the theoretical anal
ysis. However, the base course thickness differed slightly from the 8.0
in. used in Table 4.1, except for the 24 in. at the SR 715 test site.
The subbase thicknesses were generally found to be 12.0 in., except for

138
Table 5.1 Characteristics of Test Pavements
Test
Road
County
Mile Post
Number
Year*
Pavement Thickness
(in.)
Water
Table
AC
Base
(in.)
SR 26A
Gilchrist
11.8-12.0
1930(1982)
8.0
9.0
62
SR 26B
Gilchrist
11.1-11.3
1930(1982)
8.0
7.5
44
SR 26C
Gilchrist
10.1-10.2
1930(1982)
6.5
8.5
33
SR 24
Alachua
11.1-11.2
1976
2.5
11.0
NE**
US 301
A1achua
21.5-21.8
1966
4.5
8.5
45
US 441
Columbia
1.2- 1.4
1960
3.0
9.0
NE
I-10A
Madison
14.0-14.1
1973(1980)
8.0
10.4
NE
I-10B
Madison
2.7- 2.8
1973(1980)
7.0
10.2
NE
I-10C
Madison
32.0-32.1
1973(1980)
5.5
10.2
NE
SR 15A
Martin
6.5- 6.6
1973
8.5
12.5
65
SR 15B
Martin
4.8- 5.0
1973
7.0
12.0
65
SR 715
Palm Beach
4.7- 4.8
1969
4.5
24.0
NE
SR 12
Gadsden
1.4- 1.6
1979
1.5
6.0
NE
SR 80
Palm Beach
Sec. 1 & 2
1986
1.5
10.5
NE
SR 15C
Martin
0.05- 0.065 1973
6.75
12.5
NE
* Year represents the approximate date the road was built. Dates in
parentheses are the latest year of reconstruction-overlay, surface
treatment, etc.
** Water table not encountered at depth up to 18 ft. Measurements were
made using a moisture meter inserted in the holes produced from cone
penetration test (CPT).

j'-O'*'*
CJ
Figure 5.1
Location of Test Pavements in the State of Florida

140
the SR 24 and SR 80 test sites where construction drawings indicated
thicknesses of 17.0 in. and 36.0 in., respectively.
The base course material consisted of limerock except for SR 12
which was constructed with a sand-clay mixture. The subbase material
was in most cases stabilized, either mechanically or chemically with
lime or cement. This layer is conventionally called "stabilized sub
grade" by the FOOT. The underlying subgrade soils were generally sands
with clay/silt layers often encountered at depth, as indicated from the
penetration tests. The locations of the water table which were inferred
from the CPT holes are also listed in Table 5.1.
Most of the pavement sections were uncracked or had limited (hair
line) longitudinal and/or transverse cracking. However, the US 441 test
section did exhibit block cracking even though the pavement structure
was stiff. Also SR 80, a recently constructed highway was included in
the study for the following reason. Some segments were highly dis
tressed due to construction problems which had resulted in potholes,
ponding of water and cracking of the asphalt concrete surface. There
fore, two segments of this roadway were included in this study; Section
1 in which there was no visible surface distress, and Section 2 in which
cracks and potholes were present. Only Dynaflect test data was collec
ted on SR 80.
5.3 Description of Testing Procedures
5.3.1 General
Figure 5.2 shows the array and layout of tests performed for most
of the test pavement sections. In general, the NOT tests were performed
at 25-ft. spacings, while the penetration tests were restricted to sites

/
/
3
4

5 6
7
8
9
10
1
2

0
/





11
12
1
1

i
i
1
i
/
I
i
1
i

i
1
i
i
\4 25'] \+\2*\
1,
12
Q
0
/
/
/
/
FWD and Dynaflect Tests.
Cone Penetration Tests.
Marchelti Dilatometer Test.
Trench for Plate Loading Test.
Figure 5.2 Layout of Field Tests Conducted on Test Pavements

142
exhibiting uniformity in Dynaflect deflection measurements. Table 5.2
lists those tests performed on each test pavement section.
5.3.2 Dynaflect Tests
Testing with the Dynaflect was accomplished using the standard
sensor spacing to identify segments of pavement with fairly uniform
deflection response. Each segment was tested at 25-ft. spacings until
three or more locations provided essentially identical deflection
basins. The modified Dynaflect sensor array was then used to obtain
deflection measurements. The sensors were positioned by hand at loca
tions designated as 1, 4, 7, and 10, as shown in Figure 4.2. These were
the best positions, based on the theoretical study, for separation of
layer response.
The initial part of the field testing involved placing the extra
sensor at position 9 in the modified system (standard position 4). This
procedure was later changed to placing one sensor near each Dynaflect
loading wheel and the remaining sensors placed at modified positions 4,
7, and 10. In the later case, an average value of Dx was used in the
analysis. Appendix A lists Dynaflect deflection measurements from each
test section.
5.3.3 Falling Weight Deflectometer Tests
The FWD measurements were conducted at approximately the same loca
tions as the Dynaflect. In the tests the height of drop and the weight
were adjusted to produce different load levels. Two to three drops
(load levels) were usually made, with the highest load often repeated.
The highest load was generally close to 9 kips. The exact magnitude of
each load applied was registered by a load cell located just above the
loading plate.

143
Table 5.2 Summary of Tests Performed on Test Pavements
Test Road
Test Date
Types of
Tests
Performed
Dynaflect
FWD
CPT
DMT
PLT
Rheology
SR 26A
10-31-85
X
X
X
X
0
X
SR 26B
11-05-85
X
X
X
X
0
X
SR 26C
11-05-85
X
X
X
X
0
X
SR 24
12-03-85
X
X
X
0
0
X
US 301
02-18-86
X
X
X
X
X
X
US 441
02-26-86
X
X
X
X
X
X
I-10A
03-18-86
X
X
X
0
X
X
I-10B
03-25-86
X
X
0
0
X
X
I-10C
03-26-86
X
X
0
0
X
X
SR 15A
04-28-86
X
X
0
X
0
X
SR 15B
04-28-86
X
X
X
X
0
X
SR 715
04-29-86
X
X
X
X
0
X
SR 12
08-12-86
X
X
X
X
X
0
SR 80
08-19-86
X
0
0
0
0
0
SR 15C
09-30-86
X
X
X
X
0
X
X Test performed
0 Test not performed

144
Deflections were measured with geophones at the conventional posi
tions used by the FDOT. The measurements were made at radial distances
of 0, 7.87, 11.8, 19.7, 31.5, 47.2, and 63.0 in. from the center of the
FWD loading plate. It was not feasible to obtain deflection measure
ments at sensor 8 (radial distance of 72.0 in.), although D8 had been
used in developing some of the FWD layer moduli prediction equations.
Appendix B lists test results for each test pavement section.
5.3.4 Cone Penetration Tests
The cone penetration test consisted of penetrating the pavement at
a rate of 2 cm/sec with an electronic friction cone. The University of
Florida cone truck was used to conduct the tests. Three to four CPT
soundings were conducted, spaced in between the NDT test locations, as
illustrated in Figure 5.2. Each test was performed to a depth of
approximately 18 ft., unless bedrock or a hard layer was encountered.
Values of tip resistance, local friction, and friction ratio were
obtained.
The CPT data were generally collected from the surface of the
asphalt concrete layer through the pavement to the final depth of
exploration. Generally the 15 metric ton cone was used to conduct the
CPT tests, especially in testing through the asphalt concrete and base
course layers. The data acquisition system was used to obtain plots of
the CPT test results. Appendix C shows profiles of tip resistance,
local friction, and friction ratio for the tests conducted on each test
pavement. The hole created by the CPT was used to determine the loca
tion of the water table. This was performed by inserting a moisture
meter into the hole. Table 5.1 lists the depth of water table for each
test site.

145
5.3.5 Dilatometer Tests
The dilatometer test was conducted according to the procedures
described by Marchetti and Crapps (69). The University of Florida cone
truck was also used to advance the dilatometer blade into the ground.
Two to three DMT tests were conducted at each pavement test section.
These were staggered between the CPT holes (Figure 5.2). Each test was
also restricted to a depth of about 18 ft.
The DMT tests were conducted only in the subbase and underlying
subgrade soils. Because of the high stiffness of the overlying asphalt
concrete and base course layers, these materials had to be cored out
before conducting the DMT tests. The DMT data were reduced and inter
preted with the computer program described by Marchetti and Crapps (69).
The results of the DMT data reduction for each test are listed in
Appendix D.
5.3.6 Plate Loading Tests
The plate loading test was conducted on a limited number of test
pavements as shown in Table 5.2. The testing procedure generally
followed ASTM D 1195-64 (8) with slight modifications. The asphalt
concrete layer was removed from approximately a 4-ft. wide strip across
the traffic lane. In making the trench site, great care was taken to
prevent disturbance of the material in the layer to be tested.
The 12-in. plate was placed at a location within the test area.
Molding plaster (hydrocal) was used to insure uniform loading on a level
surface. The loading system was placed over the plate center. Two
deflection gauges, one on each side of the 12-in. plate were then
zeroed. The load increments depended upon the total load which was
expected to cause an approximate 0.03-in. deflection. Five load levels

146
between the initial zero and the final 0.03-in. deflection were obtained
during the test. Three cycles of loading were applied at each load
level.
After each load increment was applied, the load and deflection
readings were recorded as soon as the gauge movement had stabilized.
With this load in place, the next load increment was applied until the
average deflection of the two gauges reached the 0.03 in. The load was
then released and readings of any permanent deformation was recorded.
All gauges were then zeroed and the above preloading sequence repeated
two more times.
After the third preload, the load was then increased in increments
until a total deflection of 0.05 in. was reached. This deflection and
corresponding load were then recorded. This process was repeated on the
base, subbase, and subgrade layers. In all cases nuclear density tests
were conducted, and soil samples were taken for moisture content and
soil classification tests.
A plot of load versus deflection was made to establish the linear
ity of the load-deflection response. The modulus of elasticity (E) was
then calculated using the following equation:
E = 1.18 Eqn. 5.1
where A = deflection of 0.05 in.
p = total load at the 0.05 in deflection in psi.
a = radius of plate (6.0 in.).

147
The modulus values calculated from Equation 5.1 for each layer are
essentially surface or composite modulus for the particular layer and
the underlying layer(s). Burmister's two layer theory (133, pp. 40-44)
was then used to obtain the modulus of each layer. Table 5.3 lists
results of the plate loading tests.
5.3.7 Asphalt Rheology Tests
The core samples of asphalt concrete obtained from each test pave
ment section were separated in the laboratory according to each layer
(lift) or type of asphalt concrete mix. These were then heated and
broken down for extraction using Method B (Reflux) of ASTM D 2172 for
Quantitative Extraction of Bitumen from Bituminous Paving Mixtures
(8). The asphalt cement was recovered using the Abson method, ASTM D
1856 (8).
Low-temperature rheology tests were performed at different tempera
tures on the recovered asphalt cement samples. This involved viscosity
determination at different shear stresses and test temperatures using
the Schweyer Constant Stress Rheometer. Details pertaining to the
physical characteristics, operation, and computational methods of the
Schweyer Constant Stress Rheometer are presented by Tia and Ruth (117).
Absolute and constant power viscosities (117) were computed from
Schweyer Rheometer test data at temperatures of 140, 77, 60, 41, and
23F. The results for each test pavement site are listed in Appendix
E. Linear regression analyses of constant power viscosity (n ) with
10 0
absolute temperature resulted in regression constants as listed in Table
5.4. These were then used in previously established modulus-viscosity-
temperature relationships (98,117) to compute the modulus of the

148
Table
5.3 Plate
Loading Test
Results
Test Road
Mile Post
No.
Layer
Type
Layer
Thickness
(in.)
Composite
Modulus
(ksi)
Layer
Modulus
(ksi)
Base
8.5
28.92
55.96
US 301
21.583
Subbase
12.0
18.65
27.75
Subgrade
S.F.*
11.56
11.56
Base
9.0
28.42
40.31
US 441
1.236
Subbase
12.0
20.16
29.70
Subgrade
S.F.
11.60
11.60
Base
10.4
48.20
93.77. ,
I-10A
14.062
Subbase
12.0
26.79
Subgrade
S.F.
31.80
32.80
Base
10.1
34.68
80.13, .
(a)
I-10B
2.703
Subbase
12.0
20.03
Subgrade
S.F.
21.16
21.16
Base
10.1
47.26
66.48
I-10C
32.071
Subbase
12.0
36.93
44.60
Subgrade
S.F.
29.74
29.74
Base
6.0
28.30
43.42
SR 12
1.485
Subbase
12.0
25.54
46.10
Subgrade
S.F.
15.37
15.37
* S.F. Semi-infinite layer
(a) Deflection Factor, F2 (133, Fig. 2.7) greater than 1.0, thus
calling for extrapolation. This would mean subbase layer weaker
than subgrade or plastic deformation occurred during load test.

149
Table 5.4 Viscosity-Temperature Relationships of Recovered
Asphalt from Test Pavements
Test
AC Description
Regression
Coefficients

County
Road
Layer
Mix Type
a
b
R2
n
1
S-I
182.62
71.27
0.996
4
SR 26
Gilchrist
2
I
165.83
64.49
0.997
4
3
II
142.83
54.98
0.992
5
SR 24
A1 achua
_
166.49
64.85
0.999
5


179.70
70.13
0.996
5
1
II
152.81
59.18
0.994
5
US 301
A1 achua
2
3
I
Binder
102.77
96.39
38.58
36.06
0.976
0.979
4
3
4
Binder
129.36
49.29
0.994
3
Surface
I
96.39
36.06
0.979
3
US 441
Columbia
1
I
129.36
49.29
0.994
3
2
Binder
137.67
53.05
0.975
4
1
171.16
66.88
0.989
5
2
--
162.95
63.55
0.999
4
I-10A
Madison
3
--
167.19
65.23
0.989
5
4
--
144.87
56.01
0.989
5
5

171.54
69.12
0.998
4
1
_ _
164.95
64.22
0.991
5
I-10B
Madison
2
3

167.59
174.44
65.25
67.93
0.988
0.997
5
4
4

162.41
63.24
0.986
5
1
163.83
63.83
0.986
4
I-10C
Madison
2
--
148.83
57.58
0.983
5
3

154.79
60.03
0.985
5
1
Shell
155.38
59.99
1.000
3
2
Shell
105.57
39.73
0.966
4
SR 15B
Martin
3
II
139.84
53.83
0.998
3
4
I
141.45
54.49
0.989
5
5
Shell
98.83
37.07
0.975
4
2
Shell
155.35
60.18
0.995
4
SR 15A
Martin
3
4
II
I
146.64
139.85
56.64
53.98
0.988
1.000
5
3
5
Shell
107.17
40.46
0.995
3

150
Table 5.4--continued
Test
AC Description
Regression Coefficients*
Road
uuunujr
Layer
Mix Type
a
b
R2
n
SR 715
Palm Beach
1
2
I
Shell
141.90
141.51
54.62
54.49
0.993
0.991
4
4
1
Shell
153.45
59.44
0.997
3
2
II
158.82
61.70
0.995
5
SR 15C
Martin
3
S-I
157.73
61.25
0.996
5
4
Shell
144.24
55.71
1.000
3
5
Binder
153.69
59.53
0.981
3
* log n- = a b log T

where n,- = constant power viscosity, n (Pa-sec)
J 100
T = temperature in K (K = 273 + C)
a, b = linear regression constants
R2 = coefficient of determination
n = number of observations used

151
asphalt concrete layer. Details pertinent to these calculations are
described in the next chapter.
5.3.8 Temperature Measurements
Temperature measurements were obtained for the air (ambient), the
surface of pavement, and in the middle of the asphalt concrete pavement
layer. These measurements were accomplished with the aid of a tempera
ture probe. The mean asphalt pavement temperatures were taken using the
probe to measure the temperature of motor oil that had been poured into
a drilled hole in the pavement. The various temperature measurements
are listed in Table 5.5. The mean pavement temperature measurements
were necessary for the prediction of asphalt concrete moduli from the
low-temperature viscosity data of the asphalts recovered from pavement
cores (98,117).

152
Table
5.5 Temperature Measurements of
Test Pavement
Sections
Test
Mile Post
Test
Temperature
(F)
Road
Number
Date
Air
Surface
Mean
SR 26A
11.912
10-31-85
79
82
81
SR 26B
11.205
11-05-85
45
48
59
SR 26C
10.168
11-05-85
60
60
82
SR 24
11.102
12-03-85
57
55
57
US 301
21.580
2-18-86
63
65
69
US 441
1.236
2-26-86
51
56
79
I-10A
14.062
3-18-86
84
106
104
I-10B
2.703
3-25-86
80
101
88
I-10C
32.071
3-26-86
82
99
106
SR 15A
6.549
4-28-86
88
110
120
SR 15B
4.811
4-28-86
' 93
111
127
SR 715
4.722
4-29-86
80
88
111
SR 12
1.485
8-12-86
81
91
102
SR 80
Sec 1 & 2
8-19-86
84
96
94
SR 15C
0.055
9-30-86
82
90
105

CHAPTER 6
ANALYSES OF FIELD MEASURED NDT DATA
6.1 General
A complete description of the testing program has been presented in
Chapter 5. The results of the field measured Dynaflect and FWD data are
listed in Appendices A and B, respectively. These data were used with
the prediction equations presented in Chapter 4 to determine the moduli
of the pavement layers for the test sections. The analyses of the NDT
data with regard to determining the elastic characteristics of the test
pavements are presented in this chapter.
6.2 Linearity of Load-Deflection Response
The underlying assumption in the theoretical analysis for the
interpretation of NDT measurements is that each pavement layer acts as
an isotropic, homogeneous, and linearly elastic medium. For this
reason, a multilayered elastic computer program, BISAR, was used for the
simulation and development of NDT and pavement layer moduli prediction
equations, respectively. Therefore, the applicability of multilayered
linear elastic theory to the test pavements is verified in this section.
FWD measurements carried out at different load levels and tem
peratures provided the means of checking whether it was feasible to
adopt a linear elastic model to Florida's flexible pavement systems.
The plot of load levels versus FWD deflections is a simple way to assess
153

154
linearity. Figures 6.1 through 6.14 shows surface deflection as a
function of applied load, as obtained from the FWD tests, for all test
sections except SR 80. FWD tests were not performed on this pavement
section.
The plots indicate that most of the test pavements (9 out of 14)
had measured deflections showing strong linearity with the applied load
for the seven different sensor locations. Test pavements exhibiting
linear load-deflection response are SR 26A, SR 26C, SR 24, US 301, US
441, 1-10 A, B and C, and SR 715 as illustrated in Figures 6.1 to 6.9,
respectively. However, it must be noted that the plots did not
necessarily pass through the origin of the load-deflection diagrams.
This would suggest that the load-deflection response was probably
affected by other factors such as the initial static load in the FWD
testing system. This will be explained later in this chapter.
There were two other test pavements which showed a tendency towards
linear load-deflection response with some slight deviations. These are
SR 12 and SR 15C (Figures 6.10 and 6.11). For SR 12, Figure 6.10, the
repeated test at the highest load level always produced lower deflec
tions, especially for D., D and D However, the deflections measured
at the fourth to seventh sensors showed close linearity to applied loads
than the first three sensors. The decrease in deflection with repeated
load on SR 12 could be due to seating effect, surface cracks, delayed
recovery, and/or stiffening of the sand-clay materials beneath the thin
(1.5-in. thick) asphalt concrete layer. Unlike SR 12, the first four
sensor deflections measured at SR 15C showed strong linearity with the
applied load (Figure 6.11). The last three sensors exhibited

DEFLECTION (10 in)
155
Figure 6.1 Surface Deflection as a Function of Load on SR 26A

156
Figure 6.2 Surface Deflection as a Function of Load on SR 26C

DEFLECTION (10 in)
157
Figure 6.3 Surface Deflection as a Function of Load on SR 24

DEFLECTION (103¡n)
158
Figure 6.4 Surface Deflection as a Function of Load on US 301

DEFLECTION (103¡n)
159
Figure 6.5 Surface Deflection as a Function of Load on US 441
o a

DEFLECTION (10'3in)
160

MOA (Madison Co.)
M.P. 14.062
1 2
4 6
FWD LOAD (KIPS)
7-^
Figure 6.6 Surface Deflection as a Function of Load on I-10A
out

DEFLECTION (10'3in)
161
Figure 6.7 Surface Deflection as a Function of Load on I-10B
Q QQ

162

DEFLECTION (103in)
163
Figure 6.9 Surface Deflection as a Function of Load on SR 715
O O Q Q

SENSOR DEFLECTION, 8¡ IN MILS
164
Figure 6.10 Surface Deflection as a Function of Load on SR 12

DEFLECTION (10'3¡n)
165
Figure 6.11 Surface Deflection as a Function of Load on SR 15C
o o

DEFLECTION (10 in)
166
Figure 6.12 Surface Deflection as a Function of Load on SR 26B

DEFLECTION (lO"'5 in)
167
Figure 6.13 Surface Deflection as a Function of Load on SR 15A

DEFLECTION (10*3¡n)
168
Figure 6.14 Surface Deflection as a Function of Load on SR 15B

169
nonlinearity like that of SR 26B. It is not known whether the anomaly
on the SR 15C was caused by geophone sensitivity errors or possibly the
sensors being placed on or near cracks in the asphalt concrete pavement.
Three test pavements exhibited nonlinear response from the load-
deflection diagrams. These are SR 26B, SR 15A, and SR 15B, as shown in
Figures 6.12, 6.13, and 6.14, respectively. The SR 26B test section
showed a stress-softening behavior in which deflections increased at a
higher rate than the applied FWD load. The other two pavements, SR 15A
and SR 15B, showed a stress-stiffening behavior from the FWD measure
ments. Though, the plots of Figures 6.12 to 6.14 indicated that these
pavements had a nonlinear response, a linear elastic model (BISAR) was
used to analyze the behavior of all test pavements. The implications of
this assumption are discussed in the following sections.
6.3 Prediction of Layer Moduli
6.3.1 General
The developed equations presented in Section 4.3 were used to pre
dict layer moduli for the test sections using the Dynaflect and FWD
field measured deflections. Typical deflections were selected from each
test site for the moduli computations. Predictions were performed for
more than one test location when a test site showed excessive variabi
lity in NDT deflections. Table 6.1 lists typical Dynaflect deflection
data for the different test sections. The deflection data comprised of
the modified and standard sensor configurations. Although the latter
was not used in the prediction equations, it assisted in the initial
analysis, especially in modeling the pavements.

170
Table 6.1 Typical Dynaflect Deflection Data from Test Sections
Test
Road
Mile Post
Number
Deflections
(mils)

Di
D3
6
7
D8
9
D,o
SR 26A
11.912
0.87
0.81
0.77
0.68
0.61
0.53
0.45
0.39
SR 26B
11.205
1.28
1.18
1.23
1.12
0.99
0.90
0.77
0.68
SR 26C
10.168
0.89
0.77
0.77
0.62
0.53
0.37
0.24
0.16
SR 26C
10.166
0.90
0.77
0.78
0.68
0.54
0.44
0.27
0.17
SR 24
11.112
0.50
0.51
0.50
0.33
0.28
0.22
0.18
0.15
US 301
21.580
0.56
0.50
0.49
0.37
0.34
0.27
0.20
0.15
US 301
21.585
0.62
0.47
0.46
0.35
0.30
0.25
0.18
0.14
US 301
21.593
0.39
0.43
0.42
0.33
0.27
0.23
0.17
0.14
US 441
1.236
0.65
0.68
0.64
0.52
0.45
0.34
0.26
0.22
US 441
1.241
0.73
0.63
0.57
0.45
0.40
0.32
0.25
0.20
I-10A
14.062
0.30
0.29
0.28
0.18
0.16
0.10
0.07
0.05
I-10B
2.703
0.44
0.46
0.40
0.29
0.25
0.17
0.12
0.09
I-10C
32.071
0.70
0.46
0.43
0.30
0.29
0.22
0.18
0.15
SR 15B
4.811
1.10
1.03
1.04
0.91
0.92
0.82
0.75
0.66
SR 15A
6.549
1.50
1.46
1.48
1.40
1.36
1.27
1.14
1.04
SR 715
4.722
1.37
1.29
1.23
1.08
1.02
0.96
0.89
0.81
SR 715
4.720
1.45
1.38
1.36
1.15
1.19
1.07
1.00
0.91
SR 12
1.485
0.86
0.68
0.65
0.44
0.42
0.36
0.27
0.21
SR 80
Sec.l
2.11
2.02
1.89
1.61
1.48
1.37
1.07
0.85
SR 80
Sec.2
2.41
2.15
2.05
1.61
1.48
1.22
0.96
0.74
SR 15C
0.055
1.78
1.53
1.43
1.33
1.18
1.15
1.03
0.98
SR 15C
0.065
1.42
1.42
1.26
1.20
1.13
1.10
1.03
0.98
NOTE: Deflections are for both modified and standard geophone positions

171
FWD deflections from the highest load were used as input into the
equations to predict layer moduli. However, these deflections were
adjusted to equivalent 9-kip deflections to enhance the use of the
prediction equations. Typical deflections, at the highest load level,
were selected from each test site for the moduli computations. Table
6.2 lists the FWD deflections which have been adjusted to a 9-kip load
level.
6.3.2 Dynaflect Layer Moduli Predictions
The Dynaflect data listed in Table 6.1 were used with the appro
priate equations to compute the respective layer moduli for each test
pavement section. The prediction of Ex or E required an estimate of E2
or E respectively. Therefore, the modulus values which were later
determined to be "true" moduli from modeling Dynaflect deflection basins
were used as input to calculate the other modulus value (e.g., Ex or
e2).
For E3 computations, the simplified equation (Equation 4.15) was
used for all test pavements without regard to the limitations of the
equation. This was to illustrate that Equation 4.15 could be applied to
a wider range without much problems. Three equations were used to com
pute the subgrade modulus, E4. These are Equations 4.16 and 4.17,
developed from the theoretical analyses, and Equation 4.35 which was
originally developed from the analysis of field measured Dynaflect
deflections on test pavements from Quebec, Canada and Florida.
Table 6.3 lists layer moduli predictions from the Dynaflect equa
tions. The asphalt concrete modulus, E using "true" E2 values as
input were computed for most of the sections. Although most of the E2

172
Table 6.2 Typical FWD Data from Test Sections
Test
Road
Mile Post
No.
Load
(kips)
Equivalent
9-kips
Deflections
(mils)
*
D!
2
3
4
5
6
7
SR 26A
11.912
9.08
10.41
8.52
7.24
5.55
3.96
2.68
2.18
SR 26B
11.205
9.096
10.79
9.70
9.00
7.72
6.33
4.75
3.76
SR 26C
10.168
9.008
13.39
11.39
9.89
7.49
4.80
2.60
1.60
SR 26C
10.166
8.936
13.90
11.38
10.07
7.65
5.04
2.82
1.81
SR 24
11.112
8.808
13.23
8.99
6.23
3.47
2.25
1.53
1.12
US 301
21.58
9.16
14.49
10.80
8.55
5.58
3.15
1.89
1.17
US 441
1.236
9.18
15.66
10.62
8.37
5.79
3.96
2.70
1.98
I 10A
14.062
9.116
8.01
4.32
3.33
1.89
0.84
0.44
0.35
I 10B
2.703
8.95
11.79
7.29
5.31
3.15
1.71
1.11
0.90
I IOC
32.071
9.008
10.26
7.47
5.76
3.51
2.07
1.26
0.90
SR 15B
4.811
8.962
18.47
13.13
10.76
8.22
6.45
5.06
4.04
SR 15A
6.546
9.280
21.80
15.77
13.78
11.08
8.67
6.84
5.46
SR 15A
6.549
9.026
14.41
12.32
11.27
9.81
8.13
6.52
5.33
SR 715
4.722
9.026
20.88
12.16
8.33
5.45
4.75
4.16
3.69
SR 715
4.720
8.803
16.38
10.91
8.01
5.80
4.95
4.35
3.86
SR 12
1.485
9.232
29.15
18.52
11.80
6.43
4.09
2.73
2.14
SR 15C
0.055
8.867
25.33
19.22
16.11
11.63
7.28
4.95
4.12
SR 15C
0.065
8.803
16.83
12.80
10.87
8.13
6.07
4.47
3.95
* Adjustment made on the assumption of linear load-deflection response

173
Table 6.3 Layer Moduli Using Dynaflect Prediction Equations
Test
Road
Mile Post
No.
S
(in.)
Predicted Moduli (ksi)
E (a)
l
e2
E (b)
3
E (c)
4
E (d)
4
E (e)
4
SR 26A
11.912
8.0
272.5
105.0
67.7
13.7
14.6
13.8
SR 26B
11.205
8.0
580.6
90.0
42.9
7.7
8.5
7.9
SR 26C
10.168
6.5
250.5
55.0
42.9
34.3
34.9
33.8
SR 26C
10.166
6.5
250.5
55.0
42.9
32.2
32.9
31.8
SR 24
11.112
2.5
*
*
47.3
36.7
37.2
36.0
US 301
21.580
4.5
454.2
120.0
72.8
36.7
37.2
36.0
US 301
21.585
4.5
164.7
120.0
67.7
39.4
39.7
38.6
US 301
21.593
4.5
*
130.0
72.8
39.4
39.7
38.6
US 441
1.236
3.0
*
85.0
55.8
24.7
25.6
24.5
US 441
1.241
3.0
221.1
120.0
63.2
27.3
28.1
27.0
I-10A
14.062
8.0
1503.6
95.0
93.6
113.7
108.4
108.0
I-10B
2.703
7.0
740.0
80.0
72.8
62.1
61.1
60.0
I-10C
32.071
5.5
99.8
105.0
78.7
36.7
37.2
36.0
SR 15A
6.549
8.5
1474.0
120.0
93.6
5.0
5.6
5.2
SR 15B
4.811
7.0
452.8
120.0
93.6
8.0
8.8
8.2
SR 715
4.722
4.5
276.2
75.0
49.9
6.5
7.2
6.7
SR 715
4.720
4.5
533.3
65.0
63.2
5.7
6.4
5.9
SR 12
1.485
1.5
400.0
122.9
45.0
25.9
26.8
25.7
SR 80
Sec.l
1.5
100.0
132.4
23.5
6.1
6.9
6.4
SR 80
Sec. 2
1.5
100.0
72.3
16.2
7.1
7.8
7.3
SR 15C
0.055
6.75
72.4
105.0
41.0
5.3
6.0
5.5
SR 15C
0.065
6.75
165.8
105.0
85.5
5.3
6.0
5.5
a) Actual or calculated modulus
b) E3 calculated from Equation 4.15
c) Equation 4.16 used to predict E4
d) Equation 4.17 used to predict E4
e) Equation 4.35 used to predict E4
* Prediction equations could not be used due to negative values
of D, D,

174
values exceeded the range established for the Ex prediction equations,
the predicted Ex values seem to be reasonable except for five test sec
tions. For these sections (I-10A, I-10B, SR 15A and SR 715) the pre
dicted asphalt concrete modulus tends to be high, considering the mean
pavement temperature and corresponding viscosity of the asphalt binder
during NDT testing. The reliability or accuracy of the predicted E2
values, neglecting estimation errors of E2, are discussed in Section
6.4.
The base course modulus was computed for only three sites (SR 12,
and SR 80 Sections 1 and 2, using an estimate of E Two of the pre
dicted E2 values exceeded the limits originally established for the
prediction equations (Case 4 Equations 4.8 to 4.10). The E3 predic
tions seem to be reasonable and of the order of magnitude expected in
practice. The accuracy of these values will be determined when the
deflections are modeled using BISAR in Section 6.5.
Table 6.3 shows that the three E4 predictions are in close agree
ment, especially for Equations 4.16 and 4.35. The good agreement
occurred for E4 values from 5.0 to about 40.0 ksi. However, beyond E^
values of 40.0 ksi, the agreement is good between Equations 4.17 and
4.35. A typical example is the I-10A test pavement in which the 108.4
and 108.0 ksi predictions from Equations 4.17 and 4.35, respectively,
are far closer than that of 113.7 ksi from Equation 4.16.
6.3.3 FWD Prediction of Layer Moduli
The FWD prediction equations and the deflection data listed in
Table 6.2 were used to compute each layer modulus for the various test
pavement sections. Equation 4.20 was used to make most of the E

175
computations, because the AC thickness generally exceeded 3.0 in. for
most of the test pavements. Equation 4.19 was used for the SR 24 test
site, while Equation 4.18 was used in the case of SR 12. The base
course modulus, E2, was computed from Equation 4.21, since Equation
4.22, which was more generalized than Equation 4.21, could not be used
because no D0 measurements were made during the FWD data collection. E
was calculated from Equation 4.24. This equation was considered to be
simplified enough and more generalized than Equation 4.23. Equations
4.25, 4.26, and 4.28 were used to make E4 computations. Equations 4.27
and 4.29 could not be used because measurements of D0 were not made
during FWD testing.
Table 6.4 lists the results of layer moduli predictions from the
FWD prediction equations. The asphalt concrete modulus, E seems to be
very high for most of the test sections. High Ex values are generally
typical of pavements tested under cold temperature conditions and/or
composed of very hard or brittle asphalt cements. The reliability or
accuracy of the FWD predicted Ex values, and that of the Dynaflect, are
compared in the next section with that determined from the rheology
tests.
The predicted E2 and E3 values seem to be of the order of magnitude
expected in practice, with the possible exception of SR 26C and SR 715.
For the latter, the high thickness of the base course layer (24.0 in.)
might have caused the peculiar predictions of E2 and E3. There were
also five test sections (SR 24, SR 15B, SR 715, SR 12, and SR 15C) in
which the predicted E2 values were lower than that of E3. Also SR 26C,
I-10A, US 301 and SR 15C test sections predicted considerably low E3
values. Unless the subbase layer of these pavements had failed, such

176
Table 6.4 Layer Moduli Using FWD Prediction Equations
Test
Road
Mile Post
No.
*1 -
(in.)
Predicted Moduli (ksi)
E (a)
E (b)
2
E (c)
3
E (d)
4
^(f)
SR 26A
11.912
8.0
1278.0
53.0
27.0
19.8
18.5
17.8
SR 26B
11.205
8.0
1118.5
118.4
50.6
11.0
10.8
10.7
SR 26C
10.168
6.5
1312.0
121.4
5.7
20.4
25.1
28.5
SR 26C
10.166
6.5
295.6
166.6
7.6
18.8
22.2
24.6
SR 24
11.112
2.5
1451.0
20.3
65.1
35.2
35.8
36.1
US 301
21.580
4.5
508.7
64.4
11.0
28.3
34.2
38.4
US 441
1.236
3.0
200.0
58.4
39.8
19.6
20.4
20.8
I-10A
14.062
8.0
78.1
130.0
16.0
127.3
113.0
105.0
I-10B
2.703
7.0
216.3
44.0
20.7
49.0
44.4
41.8
I-10C
32.071
5.5
664.3
40.6
29.4
43.0
44.4
45.8
SR 15A
6.546
8.5
75.0
35.5
34.1
7.5
7.5
7.4
SR 15A
6.549
8.5
461.1
95.2
39.2
7.9
7.6
7.5
SR 15B
4.811
7.0
192.9
23.1
68.3
10.3
10.1
9.9
SR 715
4.722
4.5
161.2
9.4
300.3
12.6
11.0
10.6
SR 715
4.720
4.5
405.1
12.4
296.0
12.0
10.5
9.7
SR 12
1.485
1.5
2038.0
9.7
27.7
19.4
18.8
18.5
SR 15C
0.055
6.75
177.1
43.2
5.8
10.5
9.9
9.5
SR 15C
0.065
6.75
223.4
31.7
43.8
11.7
10.3
9.5
a) Equation 4.20 used to predict Ex except for SR 24 and SR 12 in which
Equations 4.19 and 4.18, respectively, were used.
b) Equation 4.21 used to predict E2#
c) Equation 4.24 used to predict E3.
d), (e) and (f) Equations 4.25, .4.26 and 4.28, respectively, were used
to predict E4.

177
predictions could be considered to be in error. These will be verified
when the FWD deflection basins are modeled using BISAR in Section 6.5.
The subgrade modulus, E4, computed from the three applicable equa
tions tends to be in favorable agreement for most of the test pavement
sections. The agreement in the three equation predictions of E4 could
be attributed to the high degree of accuracy of the developed equations.
It can also be considered as an indicator of the homogeneity of the sub
grade soils. Where they differed, for example, SR 26C, US 301, and
I-10A, it is possible that the stiffness or strength of the underlying
soils vary with depth. The lack of D8 measurements prevented means of
assessing the equivalence of E4 predictions from the deflections at
varying radial distances. It is postulated that knowledge of Dg, Dy,
and D could assist in indicating the variability of the properties of
O
the subgrade soils with depth.
Tables 6.2 and 6.4 suggest that small changes in deflections
greatly affected the predicted moduli. This occurred on SR 26C, SR 15A,
SR 715, and SR 15C test sections in which two adjacent deflection
mneasurements were interpreted. However, the sensitivity analysis of
Section 4.2 had indicated that large changes (50 and 100 percent) in E^,
E2, and E3 did not have a large effect on FWD deflections. This was
assessed by changing one variable while keeping the others fixed. It
was not possible to assess the combined effect of the various layers on
the deflections. However, the equations developed to predict Ej, E2,
and E3 were dependent on almost all sensor deflection measurements. In
this case, any changes in one or more sensor deflections would have a
significant effect on the predicted modulus value. Therefore, the

178
equations for computing pavement layer moduli may be considered to be
very sensitive to small changes in FWD deflections.
6.4 Estimation of Ex from Asphalt Rheology Data
The predicted asphalt concrete modulus, E listed in Tables 6.3
and 6.4 were noted to be unusually high, considering the test tempera
tures, for most of the test pavement sections. The equations used to
compute these E values had a high degree of prediction accuracy from
their R2 values. For example, Equation 4.20, used for most of the FWD
predictions, had an R2 value of 0.993. The compatibility of the predic
tion equations to the field measured NDT deflections is discussed in
this and subsequent sections.
As discussed previously, the resilient characteristics of asphalt
concrete materials are generally dependent on both temperature and rate
of loading. The modulus of asphalt concrete pavements are usually
determined from indirect tensile tests (8) using either laboratory-
prepared specimens or cored specimens from in-service pavements. An
indirect method which uses low-temperature rheology tests and previously
established correlations by Ruth et al. (98) has been found to effec
tively predict asphalt concrete modulus, E1 (96,97,117).
Asphalt cement samples recovered from the cores taken during NDT
tests were tested to establish their viscosity-temperature relation
ships. The results of the low-temperature rheology tests performed at
different temperatures are listed in Appendix E. The resulting regres
sion equations are also shown in Table 5.4. Using the mean pavement
temperature recorded at the time of NDT tests, the corresponding E
value was calculated using the procedures described by Tia and Ruth

179
(117). The following equations (98,117) were used in the computations:
For n < 9.18 E8 Pa.s:
100
log E = 7.18659 + 0.30677 log(n ) Eqn. 6.1
1 100
For n > 9.19 E8 Pa.s:
100
log E = 9.15354 + 0.04716 log(n ) Eqn. 6.2
1 100
Equations 6.1 or 6.2 were used to compute E1 values for the various
layers (lifts) in the AC layer. The average Ex value for the total AC
layer was computed using the weighted average technique. Where lift
thicknesses were not known, a common averaging technique was employed.
The computed asphalt concrete moduli for the various test sections are
listed in Table 6.5, and compared with those determined from the theo
retically developed NDT prediction equations.
Table 6.5 shows significant differences between the NDT and rheo
logy methods. It will be shown in Section 6.5 that the indirect method
which uses modulus-temperature-viscosity relationships is reliable in
modeling NDT deflection basins. Therefore, the other Ex values deter
mined from the NDT prediction equations could be doubtful.
Higher differences occur with the FWD than the Dynaflect E1 predic
tions. The discrepancy in the Dynaflect predictions could be attributed
to the estimated E2 values exceeding the limits of developed equations
and also the use of a single Dx measurement in most of the test sites.
When two sensor deflection measurements are used (sensors placed next to

180
Table 6.5 Comparison Between NDT and Rheology Predictions of
Asphalt Concrete Modulus
Test
Road
Mile Post
Number
*1
(in.)
Mean
Temperature
(F)
AC Modulus, E(ksi)
Rheology*
Dynaflect
FWD
SR 26A
11.912
8.0
81
171.3
272.5
1278.0
SR 26B
11.205
3.0
59
406.5
580.6
1118.5
SR 26C
10.168
6.5
82
171.3
250.5
1312.0
SR 26C
10.166
6.5
82
171.3
250.5
295.6
SR 24
11.112
2.5
57
338.3

1451.0
US 301
21.580
4.5
69
256.6
454.2
508.7
US 301
21.585
4.5
69
256.6
164.7
573.5
US 441
1.236
3.0
79
289.6

200.0
US 441
1.241
3.0
79
289.6
221.1
1092.5
I-10A
14.062
8.0
104
60.8
1503.6
78.1
I-10B
2.703
7.0
88
113.2
740.0
216.3
I-10C
32.071
5.5
106
66.9
99.8
664.3
SR 15A
6.549
8.5
120
85.0
1474.0
461.1
SR 15B
4.811
7.0
127
90.5
452.8
192.9
SR 715
4.722
4.5
111
92.6
276.2
161.2
SR 715
4.720
4.5
111
92.6
533.3
405.1
SR 15C
0.055
6.75
105
80.3
72.4
177.1
SR 15C
0.065
6.75
105
80.3
165.8
222.3
* Weighted average values using Equations 6.1 and 6.2

181
each Dynaflect loading wheel), the potential for eccentric loading and
its subsequent effect on Dx is reduced with the use of an average value
from the two sensor deflections. In the case of the FWD, the prediction
equations had a high degree of accuracy. Therefore, the suspect E
values using FWD predictions could be affected by other factors which
are discussed in the subsequent section.
6.5 Modeling of Test Pavements
6.5.1 General
The theory of a linear elastic model generally implies that defor
mations (or strains) are proportional to the loads applied to the medium
or media. For flexible pavements, recoverable deformations are con
sidered elastic even though they are not necessarily proportional to
stress nor instantaneous. In accordance with the terminology first
introduced by Hveem (48), recoverable deformations are referred to as
resilient deformations and the corresponding moduli as resilient moduli.
Analysis of the load-deflection response of the FWD measurements
had indicated that a linear elastic model could be used to analyze most
of the test pavements. Therefore, the BISAR elastic layer computer pro
gram was used to determine the moduli of the pavement layers from the
Dynaflect and FWD deflection basins. The subgrade was characterized as
a composite value, as conventionally done in multi-layer analyses.
The layer moduli determined from the prediction equations and
summarized in Tables 6.3 and 6.4 were used as input into the BISAR
computer program to compute Dynaflect and FWD deflections, respec
tively. These modulus values plus layer thicknesses (Table 5.1) and
Poisson's ratio (Table 4.1) served as input data for BISAR. The

182
interface conditions between layers were represented as perfectly rough
(complete bonding). If the BISAR predicted deflections "closely"
matched the measured deflections, then the input layer moduli were
considered the correct pavement layer moduli which accurately model the
pavement's NDT response.
On the other hand if the measured and predicted deflections did not
match, the input moduli values were adjusted until a suitable match of
the measured deflection basin was achieved.
This process of adjusting or "juggling" E-values is referred to, in
this discussion, as tuning. However, this inverse technique of matching
deflections does not yield unique solutions. Several moduli combina
tions can produce the same deflection basin. This will be demonstrated
in Section 6.5.4 using some of the test sections. Details pertaining to
the modeling of the Dynaflect and FWD deflection basins of the test
pavements are presented in Sections 6.5.2 and 6.5.3, respectively.
6.5.2 Tuning of Dynaflect Deflection Basins
The layer moduli predictions listed in Table 6.3 were input into
BISAR to predict Dynaflect deflections for comparison to the field
measured values. Table 6.6 shows that the predicted deflections were
very close to measured values with prediction errors of the order of 5
percent or less. However, there were some pavements with prediction
errors as high as 30 percent, especially for Dx through Dg. These were
the pavements (I-10A, I-10B, and SR 15A) which had considerably high E1
predictions. Therefore, the values were replaced by those predicted
from rheology tests, as listed in Table 6.5, in modeling the Dynaflect
deflection basins.

183
Table 6.6 Comparison of Field Measured and BISAR Predicted
Dynaflect Deflections
Dynaflect Deflections (mils)
Road
Number
Type*
D.
3
4
D6
7
D8
9
.o
SR 26A
11.912
Measured
Predicted
0.87
0.90
0.81
0.80
0.77
0.79
0.68
0.70
0.61
0.65
0.53
0.57
0.45
0.47
0.39
0.40
SR 26B
11.205
Measured
Predicted
1.28
1.23
1.18
1.19
1.23
1.17
1.12
1.08
0.99
1.03
0.90
0.92
0.77
0.78
0.68
0.67
SR 26C
10.168
Measured
Predicted
0.89
0.77
0.77
0.66
0.77
0.63
0.62
0.48
0.53
0.41
0.37
0.31
0.24
0.22
0.16
0.17
SR 26C
10.166
Measured
Predicted
0.90
0.79
0.77
0.68
0.78
0.65
0.68
0.50
0.54
0.43
0.44
0.33
0.27
0.23
0.16
0.18
US 301
21.580
Measured
Predicted
0.56
0.58
0.50
0.50
0.49
0.48
0.37
0.38
0.34
0.34
0.27
0.27
0.20
0.20
0.15
0.16
US 301
21.585
Measured
Predicted
0.62
0.68
0.47
0.50
0.46
0.48
0.35
0.38
0.30
0.34
0.25
0.27
0.18
0.19
0.14
0.15
US 441
1.241
Measured
Predicted
0.73
0.80
0.63
0.64
0.57
0.61
0.45
0.50
0.40
0.45
0.32
0.37
0.25
0.28
0.20
0.22
I-10A
14.062
Measured
Predicted
0.30
0.23
0.29
0.22
0.28
0.22
0.18
0.17
0.16
0.15
0.10
0.11
0.07
0.08
0.05
0.05
I-10B
2.703
Measured
Predicted
0.44
0.40
0.46
0.36
0.40
0.35
0.29
0.28
0.25
0.24
0.17
0.18
0.12
0.13
0.09
0.09
I-10C
32.071
Measured
Predicted
0.70
0.76
0.46
0.49
0.43
0.47
0.30
0.38
0.29
0.34
0.22
0.27
0.18
0.20
0.15
0.16
SR 15B
4.811
Measured
Predicted
1.10
1.00
1.03
0.94
1.04
0.92
0.91
0.85
0.92
0.81
0.82
0.74
0.75
0.65
0.66
0.58
SR 15A
6.549
Measured
Predicted
1.50
1.04
1.46
1.03
1.48
1.02
1.40
0.98
1.36
0.95
1.27
0.90
1.14
0.83
1.04
0.76
SR 715
4.722
Measured
Predicted
1.37
1.27
1.29
1.14
1.23
1.11
1.08
0.98
1.02
0.92
0.96
0.83
0.89
0.74
0.81
0.66
SR 715
4.720
Measured
Predicted
1.45
1.28
1.38
1.19
1.36
1.16
1.15
1.03
1.19
0.96
1.07
0.87
1.00
0.77
0.91
0.70
SR 12
1.485
Measured
Predicted
0.86
0.99
0.68
0.82
0.65
0.79
0.44
0.61
0.42
0.53
0.36
0.41
0.27
0.29
0.21
0.22

184
Table 6.6--continued
Test
Mile Post
Dynaflect Deflections i
(mils)
Road
Number
Type*
Di
D3
D6
7
D8
9
D10
SR 80
Sec.l
Measured
Predicted
2.11
1.70
2.02
1.50
1.89
1.47
1.61
1.30
1.48
1.21
1.37
1.05
1.07
0.87
0.85
0.74
SR 80
Sec. 2
Measured
Predicted
2.41
2.10
2.15
1.78
2.05
1.72
1.61
1.47
1.48
1.33
1.22
1.11
0.96
0.87
0.74
0.71
SR 15C
0.055
Measured
Predicted
1.78
1.95
1.53
1.60
1.43
1.58
1.33
1.45
1.18
1.39
1.15
1.27
1.03
1.10
0.98
0.95
SR 15C
0.065
Measured
Predicted
1.42
1.50
1.42
1.34
1.26
1.32
1.20
1.22
1.13
1.17
1.10
1.08
1.03
0.97
0.98
0.86
* Predicted using Dynaflect layer moduli predictions listed in Table 6.3
as input into BISAR. E4 predictions from Equation 4.35 were used.

185
Tuning of the test sections was accomplished by adjusting'the input
moduli values until the BISAR predicted deflections closely matched the
field measured Dynaflect deflections. Figures 6.15 through 6.31 illus
trate measured and predicted deflection basins for all test sections.
The plots show that agreement between predicted and measured deflections
for SR 24, SR 12 and SR 80 (Section 2) as shown in Figures 6.18, 6.27,
and 6.29, respectively, was poor. It is suspected that the lack of fit
for these sites was probably due to the effects of variable foundation
soils or non-visible cracks. For SR 80-Section 2, it is clearly known
that the section had experienced problems including that of surface
cracks (see Section 5.2).
The layer moduli which produced the best fit of the measured Dyna
flect deflection basins are called tuned moduli and are listed in Table
6.7. The corresponding Dynaflect deflections predicted from BISAR are
listed in Table 6.8. Examination of the E values listed in Table 6.7
illustrates the good agreement between the final (or tuned) Ex values
with that obtained from the use of the modulus-viscosity-temperature
relationships. Slight adjustments occurred in the case of SR 268, SR
15A, SR 15B, and SR 15C. This could be due to the high mean pavement
temperature (except SR 26B) and also possibly high air void contents of
the asphalt concrete mixtures. Generally high air void contents tend to
result in a reduction in measured Ex values using the rheology rela
tionships. The actual air void contents of the mixtures were not known
to apply the correction factors suggested by Ruth et al. (98).
The other layer moduli values listed in Table 6.7 as the tuned
moduli do not differ much from the predicted values of Table 6.3. This
suggests the overall accuracy and reliabilty of the Dynaflect prediction

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.15 Comparison of Measured and Predicted Dynaflect Deflections for SR 26AM.P. 11.912

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.16 Comparison of Measured and Predicted Dynaflect Deflections for SR 26B--M.P. 11.205

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
0 10 20 30 40 50
Figure 6.17 Comparison of Measured and Predicted Dynaflect Deflections for SR 26CM.P. 10.168

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.18 Comparison of Measured and Predicted Oynaflect Deflections for SR 24M.P. 11.112

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.19 Comparison of Measured and Predicted Dynaflect Deflections for US 301M.P. 11.112

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.20 Comparison of Measured and Predicted Dynaflect Deflections for I-10AM.P. 14.062

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (In)
Figure 6.21 Comparison of Measured and Predicted Dynaflect Deflections for I-10BM.P. 2.703

DEFLECTION (mils)
50
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.22 Comparison of Measured and Predicted Dynaflect Deflections for I-10CM.P. 32.071

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
VO
-Pi
Figure 6.23 Comparison of Measured and Predicted Dynaflect Deflections for SR-15AM.P. 6.549

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.24 Comparison of Measured and Predicted Dynaflect Deflections for SR 15B-M.P. 4.811

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
O'
Figure 6.25 Comparison of Measured and Predicted Dynaflect Deflections for SR 715M.P. 4.722

DEFLECTION (mils)
50
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.26 Comparison
of Measured and Predicted Dynaflect Deflections for SR 715M.P. 4.720

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.27 Comparison of Measured and Predicted Dynaflect Deflections for SR 12M.P. 1.485

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
VO
VO
Figure 6.28 Comparison of Measured and Predicted Dynaflect Deflections for SR 80Section 1

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.29 Comparison of Measured and Predicted Dynaflect Deflections for SR 80Section 2
200

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
ro
o
Figure 6.30 Comparison of Measured and Predicted Dynaflect Deflections for SR 15CM.P. 0.055

DEFLECTION (mils)
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.31 Comparison
of Measured and Predicted Dynaflect Deflections for SR 15CM.P. 0.065
202

203
Table 6.7 Dynaflect Tuned Layer Moduli for Test Sections
Test
Road
Mile Post
Number
Layer Moduli
(ksi)
Ei
E2
E 3
SR 26A
11.912
171.5
105.0
70.0
14.6
SR 26B
11.205
360.0
90.0
60.0
7.9
SR 26C
10.168
171.5
55.0
35.0
28.5
SR 24
11.112
338.3
105.0
75.0
38.6
US 301
21.580
250.0
120.0
60.0
38.6
US 301
21.585
250.0
120.0
75.0
42.0
US 301
21.593
250.0
130.0
80.0
44.0
US 441
1.236
290.0
85.0
60.0
27.5
US 441
1.241
290.0
120.0
75.0
28.5
I-10A
14.062
65.0
95.0
89.4
105.0
I-10B
2.703
113.0
80.0
65.0
60.0
I-10C
32.071
67.0
105.0
85.0
40.0
SR 15B
4.811
150.0
120.0
75.0
8.1
SR 15A
6.549
150.0
120.0
40.0
4.8
SR 715
4.722
92.6
75.0
50.0
6.0
SR 715
4.720
92.6
65.0
45.0
5.5
SR 12
1.485
400.0
120.0
75.0
26.5
SR 80
Sec.l
100.0
45.0
18.0
5.75
SR 80
Sec. 2
100.0
26.5
18.0
5.75
SR 15C
0.055
80.0
105.0
75.0
5.5
SR 15C
0.065
150.0
105.0
75.0
5.5

Table 6.8 Predicted Deflections from Tuned Layer Moduli
Test Mile Post
Deflections (mils)
Road
Number
D
l
D
2
D
3
D
4
D
5
D
6
D
7
D
3
D
9
D
10
SR 26A
11.912
0.95
0.82
0.80
0.78
0.74
0.69
0.64
0.56
0.46
0.38
SR 26B
11.205
1.25
1.19
1.18
1.16
1.12
1.06
1.01
0.90
0.77
0.66
SR 26C
10.168
0.93
0.79
0.76
0.73
0.65
0.56
0.49
0.37
0.26
0.20
SR 24
11.102
0.62
0.50
0.47
0.45
0.40
0.35
0.31
0.25
0.19
0.15
US 301
21.580
0.65
0.54
0.52
0.50
0.45
0.39
0.34
0.27
0.19
0.15
US 301
21.585
0.59
0.49
0.46
0.44
0.40
0.35
0.31
0.24
0.18
0.14
US 301
21.593
0.56
0.46
0.44
0.42
0.38
0.33
0.29
0.23
0.17
0.13
US 441
1.236
0.85
0.71
0.68
0.64
0.58
0.51
0.45
0.36
0.27
0.21
US 441
1.241
0.73
0.62
0.59
0.57
0/52
0.46
0.42
0.34
0.26
0.20
I-10A
14.062
0.70
0.35
0.30
0.27
0.22
0.18
0.15
0.11
0.07
0.05
I-10B
2.703
0.66
0.45
0.42
0.39
0.34
0.28
0.24
0.18
0.13
0.10
I-10C
32.071
0.83
0.50
0.46
0.44
0.398
0.35
0.31
0.25
0.19
0.15
SR 15B
4.811
1.25
1.10
1.07
1.05
1.00
0.96
0.91
0.83
0.72
0.63
SR 15A
6.549
1.71
1.56
1.54
1.52
1.47
1.42
0.36
0.26
0.13
1.00
SR 715
4.722
1.57
1.31
1.27
1.23
1.17
1.10
1.05
0.96
0.86
0.76
SR 715
4.720
1.71
1.45
1.40
1.37
1.29
1.22
1.15
1.05
0.94
0.83
SR 12
1.485
0.87
0.73
0.70
0.67
0.61
0.54
0.48
0.38
0.29
0.22
SR 80
Sec 1
2.49
2.09
2.01
1.94
1.80
1.63
1.49
1.25
1.01
0.85
SR 80
Sec 2
2.92
2.30
2.16
2.07
1.87
1.67
1.50
1.25
1.01
0.86
SR 15C
0.055
1.77
1.49
1.45
1.42
1.37
1.31
1.26
1.16
1.03
0.91
SR 15C
0.065
1.56
1.41
1.38
1.36
1.31
1.26
1.20
1.11
0.99
0.88
ro
o
-P

205
equations. The results listed in Tables 6.7 and 6.8 will be used later
in this chapter to develop simplified moduli prediction equations, espe
cially in the case of and E2.
6.5.3 Tuning of FWD Deflection Basins
The layer moduli predictions from the FWD equations and listed in
Table 6.4 were also used as input into BISAR to compute the field
measured FWD deflections. Table 6.9 compares measured and predicted FWD
deflections. The table shows considerable differences between the two
especially in Dx through D5 responses. In most cases there was a
tendency to underpredict the deflections. Beyond D5, the agreement was
generally good. This could be due to the fact that the predicted Ex
values were too high to offset the influence of the underlying layer
stiffnesses. Since Dg and Dy are mainly affected by E4, the good agree
ment between measured and predicted deflections reflects the accuracy of
the E4 prediction equations.
The input moduli were adjusted until BISAR predicted deflections
were in close agreement with measured values. Like the Dynaflect, Ex
values used in the tuning process were those estimated from the rheology
tests. Modeling of FWD deflection basins was found to be extremely
difficult for most of the test sections. In general, it was relatively
easy to match Dx and Dg or D?, and difficult to simulate the interme
diate sensor deflections. This could be due to the accuracy of the Ex
and E4 values compared to the other layer moduli predictions.
Figures 6.32 through 6.49 illustrate the modeling of the test
pavement sections using the FWD deflection basins. The deflections are
normalized to 1-kip load level. The figures show that for some of the
pavements such as US 441, SR 12, and SR 15C, the predicted deflections

Table
6.9 Comparison
of Field
Measured
and BISAR
Predicted
FWD Deflections
Test
Mile Post
FWD (9-kip Load) Deflections (mils)
Road
Number
Type*
Di
2
3
4
5
6
7
SR 26A
11.912
Measured
10.41
8.52
7.24
5.55
3.96
2.68
2.18
Predicted
7.55
6.74
6.24
5.30
4.14
3.04
2.31
SR 26B
11.205
Measured
10.79
9.70
9.00
7.72
6.33
4.75
3.76
Predicted
8.82
8.01
7.55
6.72
5.67
4.56
3.70
SR 26C
10.168
Measured
13.39
11.39
9.89
7.49
4.80
2.60
1.60
Predicted
8.36
7.40
6.76
5.56
4.06
2.63
1.73
SR 26C
10.166
Measured
13.90
11.38
10.07
7.65
5.04
2.82
1.81
Predicted
10.77
8.45
7.44
5.92
4.21
2.70
1.82
SR 24
. 11.122
Measured
13.23
8.99
6.23
3.47
2.25
1.53
1.12
Predicted
15.61
10.38
7.14
3.49
1.94
1.41
1.09
US 301
21.580
Measured
14.49
10.80
8.55
5.58
3.15
1.89
1.17
Predicted
12.41
9.35
7.54
4.96
2.77
1.46
0.96
US 441
1.236
Measured
15.66
10.62
8.37
5.79
3.96
2.70
1.98
Predicted
15.98
10.04
7.62
5.34
3.73
2.58
1.93
I-10A
14.062
Measured
8.01
4.32
3.33
1.89
0.84
0.44
0.35
Predicted
11.28
5.14
3.60
2.33
1.28
0.62
0.37
I-10B
2.703
Measured
11.79
7.29
5.31
3.15
1.71
1.11
0.90
Predicted
11.19
7.60
5.96
3.84
2.22
1.31
0.93
I-10C
32.071
Measured
10.26
7.47
5.76
3.51
2.07
1.26
0.90
Predicted
8.73
6.60
5.31
3.43
1.94
1.15
0.84
SR 15B
4.811
Measured
18.47
13.13
10.76
8.22
6.45
5.06
4.04
Predicted
18.17
13.73
11.56
8.67
6.44
4.92
3.92

Table 6.9--continued
Test
Mile Post
FWD (9-kip Load)
Deflections (mils)
Road
Number
Type*
Di
D2
3
5
6
7
SR 15A
6.546
Measured
21.80
15.77
13.78
11.08
8.67
6.84
5.46
Predicted
23.73
16.09
13.52
10.80
8.57
6.69
5.35
SR 15A
6.549
Measured
14.41
12.32
11.27
9.81
8.13
6.52
5.33
Predicted
11.70
10.17
9.47
8.41
7.23
6.01
5.04
SR 715
4.722
Measured
20.88
12.16
8.33
5.45
4.75
4.16
3.69
Predicted
36.59
25.36
18.76
10.48
5.77
4.16
3.48
SR 715
4.720
Measured
16.38
10.91
8.01
5.80
4.95
4.35
3.86
Predicted
24.54
18.90
15.20
9.83
6.02
4.37
3.65
SR 12
1.485
Measured
29.15
18.52
11.80
6.43
4.09
2.73
2.14
Predicted
33.19
20.55
13.33
6.55
4.03
2.75
2.05
SR 15C
0.055
Measured
25.33
19.22
16.11
11.63
7.28
4.95
4.12
Predicted
21.51
17.02
14.87
11.81
8.72
6.07
4.40
SR 15C
0.065
Measured
16.83
12.80
10.87
8.13
6.07
4.47
3.95
Predicted
17.25
13.37
11.46
8.89
6.77
5.18
4.11
* Predicted using FWD
layer moduli
predictions
listed in
Table 6.4 as
input
into BISAR.
E predictions
from Equation 4.28 were used.
207

NORMALIZED DEFLECTION (mils)
208
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.32
Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 26A M.P. 11.912

NORMALIZED DEFLECTION (mils)
209
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.33 Comparison of Measured and Predicted FWD Deflections
(Normalized to l-kip Load) for SR 26BM.P. 11.205

NORMALIZED DEFLECTION (mils)
210
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.34
Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 26C--M.P. 10.168

NORMALIZED DEFLECTION (mils)
211
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.35
Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 26C--M.P. 10.166

NORMALIZED DEFLECTION (mils)
212
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.36
Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 24M.P. 11.112

NORMALIZED DEFLECTION (mils)
213
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.37 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for US 301M.P. 21.585

DEFLECTION (mils)
214
DISTANCE FROM CENTER OF
LOAD AREA (in)
Figure 6.38 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for US 441M.P. 1.236

NORMALIZED DEFLECTION (mils)
215
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.39 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for I-10AM.P. 14.062

NORMALIZED DEFLECTION (mils)
216
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.40
Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for I-10B--M.P. 2.703

DEFLECTION (mils)
217
DISTANCE FROM CENTER OF
LOAD AREA (in)
Figure 6.41 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for I-10CM.P. 32.071

NORMALIZED DEFLECTION (mils)
218
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.42 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 15AM.P. 6.546

NORMALIZED DEFLECTION (mils)
219
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.43
Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 15AM.P. 6.549

NORMALIZED DEFLECTION (mils)
220
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.44 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 15BM.P. 4.811

NORMALIZED DEFLECTION (mils)
221
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.45 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 715M.P. 4.722

NORMALIZED DEFLECTION (mils)
222
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.46
Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 715M.P. 4.720

NORMALIZED DEFLECTION (mils)
223
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.47 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 12M.P. 1.485

NORMALIZED DEFLECTION (mils)
224
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.48 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 15CM.P. 0.055

NORMALIZED DEFLECTION (mils)
225
DISTANCE FROM CENTER OF LOADED AREA (in)
Figure 6.49 Comparison of Measured and Predicted FWD Deflections
(Normalized to 1-kip Load) for SR 15CM.P. 0.065

226
closely match measured values for all sensor locations. However, for
most of the other pavements, especially SR 26C, I 10, and SR 715, the
agreement between measured and predicted deflections was good for'D ,
Dg, and Dy measurements only. The difficulty in matching FWD deflec
tions with BISAR could be due to one or more of the following:
1. The pavements did not necessarily behave as linear elastic media.
2. It was improper to represent the FWD impulse or dynamic loadings
with a pseudo-static loading in BISAR.
3. Neglecting the inertia of the pavement in the simulation of NDT
response using multilayered linear elastic theory.
4. The FWD plate and loading are rigid rather than flexible as assumed
in the BISAR analysis.
5. The spacing of the geophones and loading configuration are probably
not suitable to allow the separation of pavement layers during the
interpretation of FWD deflections.
It is believed that the above reasons, especially the first three
apply to all NDT devices. It may be argued that the small load (1.0 kip
total) in the Dynaflect system is too small to produce any sensitivity
in the load-deflection response compared to the 9-kip load used in the
FWD. On the other hand, the use of dual loads in the Dynaflect testing
system with the modified sensor configuration enhances the separation of
layer response. Differences between the response of the test pavements
to the FWD and Dynaflect are discussed in detail in Section 6.6.
The layer moduli which best matched one or more of the FWD deflec
tions were selected as the pavement tuned layer moduli. These are
listed in Table 6.10. The predicted FWD deflections using the tuned
layer moduli are listed in Table 6.11. The tuned Ex values were not

227
Table 6.10
FWD Tuned
Layer Moduli
for Test Sections
Test
Road
Mile Post
Number
Layer Modulus (ksi)
Ei
E2
E3
E4
SR 26A
11.912
171.5
75.0
45.0
18.7
SR 26B
11.205
360.0
90.0
45.0
11.0
SR 26C
10.168
171.5
45.0
27.5
25.5
SR 26C
10.166
171.5
55.0
22.0
20.0
SR 24
11.112
338.3
55.0
40.0
38.6
US 301
21.580
250.0
45.0
35.0
25.0
US 441
1.236
290.0
55.0
35.0
20.0
I-10A
14.062
100.0
90.0
80.0
130.0
I-10B
2.703
100.0
60.0
50.0
43.0
I-10C
32.071
120.0
85.0
50.0
46.0
SR 15B
4.811
90.5
52.8
50.0
10.2
SR 15A
6.546
85.0
45.0
35.0
7.0
SR 15A
6.549
150.0
95.0
39.5
7.5
SR 715
4.722
92.6
45.0
25.0
11.0
SR 715
4.720
92.6
65.0
26.0
10.5
SR 12
1.585
400.0
31.0
20.0
18.5
SR 15C
0.055
80.0
35.0
12.0
9.8
SR 15C
0.065
150.0
50.0
44.0
10.0

228
Table 6.11 Predicted FVJD Deflections from Tuned Layer Moduli
Test
Road
Mile Post
Number
BISAR Deflections (mils)
Di
2
3
D4
Ds
6
7
SR 26A
11.912
11.42
7.92
6.65
5.18
3.89
2.85
2.18
SR 26B
11.205
11.59
9.61
8.72
7.39
5.97
4.62
3.65
SR 26C
10.168
13.60
9.17
7.24
4.91
3.18
2.11
1.56
SR 26C
10.166
14.44
10.15
8.30
5.98
4.05
2.72
2.01
SR 24
11.112
13.55
7.73
5.29
3.24
2.06
1.38
1.03
US 301
21.580
14.18
9.53
7.28
4.78
3.13
2.12
1.58
US 441
1.236
16.12
10.58
8.08
5.60
3.87
2.67
1.99
I-10A
14.062
8.58
3.51
2.16
1.16
0.65
0.41
0.31
I-10B
2.703
11.97
6.30
4.50
2.88
1.89
1.26
0.94
I-10C
32.071
10.35
5.57
4.07
2.69
1.76
1.18
0.87
SR 15B
4.811
18.51
12.16
10.09
8.04
6.42
5.02
4.02
SR 15A
6.546
21.99
15.35
13.16
10.82
8.80
6.99
5.65
SR 15A
6.549
15.38
11.67
10.45
9.09
7.72
6.32
5.21
SR 715
4.722
19.95
12.39
9.74
7.36
5.76
4.47
3.57
SR 715
4.720
17.11
10.75
8.77
7.06
5.76
4.60
3.73
SR 12
1.485
29.54
16.64
11.29
6.84
4.26
2.81
2.09
SR 15C
0.055
25.38
17.35
14.30
10.80
7.82
5.49
4.08
SR 15C
0.065
16.60
12.05
10.24
8.14
6.42
4.98
3.97

229
much different from those obtained from the rheology tests (Table 6.5).
This does not only confirm the reliability of the indirect method of
predicting Ej, but also suggests that the E values obtained from the
prediction equations are suspect. Also the tuned and E3 values are
much different from the predicted values listed in Table 6.4. However,
the subgrade modulus values compare well which emphasizes the uniqueness
of the relationship between E4 and the furthest NDT sensor deflec
tion^).
The use of the field measured FWD deflections as input into the
prediction equations did not yield reasonable layer moduli, especially
for Ex. Predicted deflections were generally different from the field
measured values and likewise the tuned moduli differed considerably from
the predicted, with the possible exception of E4. It was suggested that
this discrepancy was due to the fact that FWD measurements do not neces
sarily satisfy the assumptions of layered theory used in the theoretical
analysis. To support this, the predicted deflections obtained from
BISAR using the tuned moduli were then used as input into the developed
equations to compute the layer moduli. These deflections, listed in
Table 6.11, are for all purposes BISAR generated deflections and should
satisfy the assumptions inherent in the theoretical analysis.
Table 6.12 lists the calculated (predicted) moduli compared with
the tuned values. There is favorable agreement between the two for each
layer modulus. For the SR 12 tests site, the considerable difference in
E values is due to the poor prediction accuracy of Equation 4.18 for
very thin asphalt concrete layers. The relatively good agreement
between predicted and tuned moduli in Table 6.12 indicates that the
prediction equations could be used to estimate layer modlui. However,

230
Table 6.12 Comparison Between Re-Calculated and Tuned FWD Layer Moduli
Test
Road
Mile Post
Number
Tuned Moduli^1) (ksi)
Predicted Moduli^2)
(ksi)
Ei
E2
E3
E.
Ei
E2
E3
E4
SR 26A
11.912
171.5
75.0
45.0
18.7
167.4
65.5
51.4
18.5
SR 26B
11.205
360.0
90.0
45.0
11.0
340.0
117.8
48.0
11.0
SR 26C
10.168
171.5
45.0
27.5
25.5
194.6
49.1
26.7
26.0
SR 26C
10.166
171.5
55.0
22.0
20.0
188.3
59.2
23.7
20.4
SR 24
11.112
338.3
55.0
40.0
38.6
428.9
54.4
51.7
38.6
US 301
21.580
250.0
45.0
35.0
25.0
262.0
43.9
36.4
25.6
US 441
1.236
290.0
55.0
35.0
20.0
286.4
51.1
42.5
20.5
I-10A
14.062
100.0
90.0
80.0
130.0
91.2
66.0
57.4
122.0
I-10B
2.703
100.0
60.0
50.0
43.0
101.5
47.9
52.7
42.2
I-10C
32.071
120.0
85.0
50.0
46.0
113.9
81.6
56.7
45.8
SR 15B
4.811
90.5
52.8
50.0
10.2
90.5
35.8
69.0
10.0
SR 15A
6.546
85.0
45.0
35.0
7.0
84.1
27.5
51.8
7.1
SR 15A
6.549
85.0
95.0
39.5
7.5
74.0
39.3
79.4
7.6
SR 715
4.722
92.6
45.0
25.0
11.0
96.0
35.4
80.0
11.2
SR 715
4.720
92.6
65.0
26.0
10.5
90.7
51.5
115.0
10.6
SR 12
1.485
400.0
31.0
20.0
18.5
201.5
18.6
23.3
19.6
SR 15C
0.055
80.0
35.0
12.0
9.8
88.9
33.6
18.7
10.3
SR 15C
0.065
150.0
50.0
44.0
10.0
160.7
43.1
60.0
10.1
(1) BISAR tuned moduli (Table 6.10)
(2) Moduli obtained using deflections in Table 6.11 as input into FWD
prediction equations

231
to use the equations with field measured FWD deflections may result in
substantial prediction errors. This suggests that the measured FWD
deflections should be adjusted to compensate for the possible effects of
the rigid plate and other variables prior to the application of the
developed equations which were based on multilayered elastic theory.
6.5.4 Nonuniqueness of NDT Backcalculation of Layer Moduli
One of the major problems associated with backcalculation of layer
moduli from NDT deflection basins is the nonuniqueness of moduli. Theo
retically, an infinite number of moduli combinations can produce the
same deflection basin. There are no closed-form solutions at present to
compute layer moduli if the NDT deflections are known. Therefore, a
completely erroneous set of moduli could be determined for a pavement
using the trial-and-error approach of matching measured deflection
basins. This is demonstrated in Table 6.13 using field measured Dyna-
flect basins from some of the test sections.
Table 6.13 shows that for the US 301 test section, the use of two
different combinations of Ex and E2 and same Eg and E^ values produced
practically the same deflection basin. The Ex value of 250.0 ksi is
that obtained from the modulus-viscosity-temperature relationships.
However, an E1 value which is five times as high as the above value also
produced similar deflections, with a slight reduction in E2.
In the case of US 441, when the Ej value was reduced from 290.0 ksi
to 100.0 ksi, predicted deflections were close to measured. It was
initially believed that the extensive block cracking on this site (Sec
tion 5.2) had caused the reduction in the asphalt concrete modulus as
predicted from rheology data. However, because a third set of moduli
combination produced similar deflections indicate that the problem could

Table 6.13 Illustration of Nonuniqueness of Backcalculation of Layer Moduli from NDT Deflection Basin
Test
Layer Modul i
(ksi)
Dynaflect Deflections
(mils)
Road
No.*
E
E
E
E
D
D
D
D
D
D
D
D
l
2
3
4
l
3
4
6
7
8
9
10
0
0.56
0.50
0.49
0.37
0.34
0.27
0.20
0.15
US 301
1
250.0
120.0
60.0
38.6
0.65
0.52
0.50
0.39
0.34
0.27
0.20
0.15
2
1211.2
85.0
60.0
38.6
0.55
0.52
0.50
0.40
0.34
0.26
0.19
0.15
0
MB _
0.65
0.68
0.64
0.52
0.45
0.34
0.26
0.22
US 441
1
290.0
85.0
60.0
27.5
0.85
0.68
0.64
0.51
0.45
0.36
0.27
0.21
2
100.0
85.0
60.0
27.5
0.97
0.69
0.66
0.52
0.46
0.37
0.27
0.21
3
290.0
120.0
60.0
25.5
0.80
0.67
0.64
0.53
0.47
0.38
0.29
0.23
0
__
1.50
1.46
1.48
1.40
1.36
1.27
1.14
1.04
SR 15A
1
150.0
105.0
55.0
4.7
1.69
1.52
1.49
1.39
1.34
1.24
1.11
1.00
2
150.0
120.0
40.0
4.8
1.71
1.54
1.52
1.42
1.36
1.26
1.13
1.00
0

__
0.87
0.81
0.77
0.68
0.61
0.53
0.45
0.39
SR 26A
1
171.5
105.0
85.0
14.0
0.98
0.82
0.80
0.71
0.66
0.57
0.47
0.39
2
171.5
105.0
65.0
14.5
0.97
0.81
0.79
0.70
0.65
0.56
0.46
0.39
3
171.5
105.0
70.0
14.6
0.95
0.80
0.78
0.69
0.64
0.56
0.46
0.38
* 0 Field-measured Dynaflect deflections
1,2,3 Tuned moduli and corresponding BISAR deflections

233
be due to nonuniqueness in the backcalculation of layer moduli. Table
6.13 also shows that slight changes in E3 and E4 (SR 15A and SR 26A)
could also lead to the same deflection solutions.
The problem of nonuniqueness in layer moduli determination is pre
vented with the use of the prediction equations. The subgrade modulus
has already been shown to be uniquely related to the deflection(s) at
the farthest sensor in the Dynaflect and FWD testing systems. Also the
high degree of reliability of predicting E1 from asphalt rheology rela
tionship generally fixes the £l value to be used in the tuning of
deflection basin. The use of the prediction equations in addition to E1
predictions from rheology tests eliminate guesswork in selecting initial
moduli. Therefore the methodology presented in this dissertation
ensures unique solutions and is not user-dependent with regard to
selecting input moduli values.
6.5.5 Effect of Stress Dependency
As previously mentioned, laboratory studies generally suggest that
the moduli of subgrade materials and granular bases are stress depen
dent. One of the advantages of the FWD testing system is its ability to
apply variable and heavier loads to assess the stress dependency of
pavement materials. The load-deflection response shown in Figures 6.1
through 6.14 indicated that FWD deflections were within reason linearly
related to the applied loads for most of the test sections except for SR
26B, SR 15A and SR 15B. For these pavement sections, the tendency was
toward a nonlinear response. However, in all cases the load-deflection
response did not pass through the origin. This could be due to the
inertia of the pavement system to loading, and perhaps the influence of
the static loading due to the plate.

234
The stress dependency of the test pavements was evaluated by using
FWD deflections at different load levels to compute the layer moduli.
Five test pavements were selected for this analysis. Two of the pave
ments (SR 24 and SR 12) showed close resemblance to linearity. The
others were SR 26B which exhibited a stress-softening behavior; SR 15A
and SR 15B which behaved as a stress-stiffening material from their
load-deflection diagrams. The corresponding deflections measured at the
different load levels were normalized to 9 kips load level. These are
compared in Table 6.14 for each of the five test pavement sections.
Table 6.14 shows that deflections measured at the lowest load are
much different from the other high loads. This is especially true for
SR 26B, SR 15A, and SR 15B in which the deflections produced by the
lowest load are all less than those measured at the higher loads. Such
a result would agree with the stress-softening behavior of SR 26B, and
not for SR 15A and SR 15B, where a stress-stiffening phenomenon was
postulated from the load-deflection diagrams. The deflections for SR 24
and SR 12 compare well and the slight differences could be due to the
precision of the measuring devices, possibility of measuring close to
non-visible surface cracks (in the case of SR 12), and also due to the
resilient characteristics of the pavement materials. In general, there
is consistency in the normalized deflections at the two or more higher
loads even for the nonlinear pavements. Therefore, the nonlinearity
behavior of SR 26B, SR 15A, and SR 15B test sections only occur at loads
less than 6.0 kips.
The deflections listed in Table 6.14 were used to predict the
respective layer moduli. These were then input, using the E1 values
from rheology data, into BISAR to tune the various deflection basins.

Table 6.14 Comparison of Deflections Measured at Different Load Levels
Test !
Mile Post
Load
Normalized Deflections* (mils)
Road
Number
(kips)
Di
2
3
4
5
6
7
4.760
14.94
9.45
6.05
3.03
1.89
1.32
0.95
SR 24
11.112
7.176
13.55
9.16
6.27
3.26
2.01
1.38
1.13
8.816
13.27
8.98
6.23
3.47
2.25
1.53
1.12
4.696
32.77
18.21
10.54
6.13
3.83
2.49
2.49
SR 12
1.485
6.920
29.52
18.08
11.19
6.63
3.64
2.47
1.82
9.232
29.15
13.52
11.80
6.43
4.09
2.73
2.14
9.288
28.68
17.44
11.24
6.30
4.17
2.71
2.33
4.656
8.12
6.77
6.38
5.03
3.67
2.51
1.55
SR 26B
11.205
6.880
10.33
9.16
8.50
7.33
5.89
4.45
3.53
9.096
10.79
9.70
9.00
7.72
6.33
4.75
3.76
9.112
10.57
9.48
8.79
7.51
6.12
4.64
3.65
4.656
12.33
10.50
9.51
8.14
6.63
5.34
4.47
SR 15A
6.551
7.008
15.27
13.29
12.09
10.42
8.54
6.73
5.67
9.026
15.46
13.46
12.21
10.44
8.48
6.55
5.26
4.513
16.65
10.99
8.24
6.04
4.87
4.17
3.53
SR 15B
4.811
6.769
18.84
13.30
10.75
8.00
6.24
5.03
4.08
8.962
18.47
13.13
10.76
8.22
6.45
5.06
4.04
* Deflections normalized to 9 kips load

236
The resultant layer moduli are listed in Table 6.15. The modulus of the
asphalt concrete was generally found to be the same and close to the
value determined from rheology data. The base course modulus, E2,
decreased with load in the case of SR 15A and SR 15B test sections.
This again confirms that the pavement materials were stress-softening
rather than stress-stiffening. The subbase and subgrade layers had
modulus values showing no particular trends, except that higher values
were obtained at the lowest load. At higher loads the moduli for the
subbase and subgrade layers were very consistent.
Tables 6.14 and 6.15 suggest that FWD deflections should be mea
sured at higher loads, preferably 9 kips to minimize the effects of
nonlinearity. This also implies that equivalent moduli can be deter
mined using the theory of elasticity. The equivalent moduli would,
hopefully, produce a reasonable estimate of the deformations and strains
in the field.
6.6 Comparison of NDT Devices
The previous discussion indicated that different responses were
observed with the Dynaflect and FWD deflection basins. These differ
ences can be attributed to the magnitude of loading applied by each
device as well as differences between both devices. In this section a
comparison is made between the moduli predictions using Dynaflect and
FWD testing system. The layer moduli obtained from the plate loading
tests on some of the test sections will also be compared with the dyna
mic NDT devices.

Table 6.15 Comparison Between Tuned Layer Moduli and Applied FWD Load
Test Mile Post
Load
Load
Layer Moduli (ksi)
Road
Number
Deflection
Response
(kips)
Ei
E2
E3
E4
4.760
338.3
46.0
45.0
41.5
SR 24
11.112
linear
7.176
338.3
55.0
45.0
37.5
8.816
338.3
55.0
40.0
38.6
4.696
315.0
35.0
25.0
18.0
SR 12
1.485
quasi-
6.920
400.0
35.0
22.0
21.0
linear
9.232
400.0
31.0
20.0
18.5
9.288
400.0
31.0
20.0
17.4
non!inear
4.656
360.0
110.0
35.0
25.0
SR 26B
11.205
(stress
softening)
6.880
9.096
360.0
360.0
110.0
90.0
40.0
45.0
11.5
11.0
9.112
360.0
120.0
50.0
11.0
nonlinear
4.656
200.0
71.0
57.0
9.0
SR 15A
6.551
(stress-
7.008
200.0
55.0
50.0
6.9
stiffening)
9.026
200.0
50.0
42.0
7.2
nonlinear
4.513
90.5
75.0
60.0
11.5
SR 15B
4.811
(stress-
6.769
90.5
60.0
50.0
9.6
stiffening)
8.962
90.5
52.8
50.0
10.2

238
6.6.1 Comparison of Deflection Basins
Figures 6.50 through 6.63 show comparisons of the field measured
FWD and Dynaflect deflection basin for each test section. The deflec
tions are normalized to an equivalent 1000-lb. load level. Deflection
basins normalized with respect to a standard load level are very helpful
in comparing NDT devices that apply different loads to the pavement.
Normalization of the FWD deflection basins, as illustrated in the plots,
is also another way of assessing linearity and stress dependency of the
load-deflection response. The figures indicate that FWD deflections
measured at about 7.0 kips load levels are very close to those measured
around 9.0 kips for all test sections. This would suggest that line
arity is achieved at FWD loads beyond 6.0 kips as postulated in Section
6.5.5.
In comparing the Dynaflect deflection basin to the FWD deflection
basins for each test section, the following three trends can be differ
entiated from the normalized plots:
1. Pavements which have FWD deflections greater than the Dynaflect
deflections. Test sections in this group are SR 26C, US 301, US
441, and SR 12. The normalized plots for these pavement sections
are illustrated in Figures 6.50 to 6.53.
2. Pavements which exhibit the reverse of the above, that is, Dynaflect
deflections are greater than those of the FWD. These are SR 26B, SR
15A, and SR 715 test sites and they are shown in Figures 6.54, 6.55,
and 6.56, respectively. Note that the SR 715 site (Figure 6.56) had
similar FWD normalized deflection basins, confirming the linear load
response diagram of Figure 6.9. The different Dynaflect deflection
basin could therefore be due to differences in NDT devices.

NORMALIZED DEFLECTION (mils)
239
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.50 Comparison of Measured NDT Deflection Basins on SR 26C
M.P. 10.166

NORMALIZED DEFLECTION (mils)
240
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.51 Comparison of Measured NDT Deflection Basins on US 301
M.P. 21.585

NORMALIZED DEFLECTION (mils)
241
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.52 Comparison of Measured NDT Deflection Basins on US 441
M.P. 1.237

NORMALIZED DEFLECTION (mils)
242
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.53 Comparison of Measured NDT Deflection Basins on SR 12
M.P. 1.485

NORMALIZED DEFLECTION (mils)
243
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.54 Comparison of Measured NDT Deflection Basins on SR 26B
M.P. 11.205

NORMALIZED DEFLECTION (mils)
244
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.55 Comparison of Measured NDT Deflection Basins on SR 15A
M.P. 6.549

NORMALIZED DEFLECTION (mils)
245
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.56 Comparison of Measured NDT Deflection Basins on SR 715
M.P. 4.722

NORMALIZED DEFLECTION (mils)
246
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.57 Comparison of Measured NDT Deflection Basins on SR 26A
M.P. 11.912

NORMALIZED DEFLECTION (mils)
247
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.58 Comparison of Measured NDT Deflection Basins on SR 24
M.P. 11.112

NORMALIZED DEFLECTION (mils)
248
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.59 Comparison of Measured NDT Deflection Basins on I-10A--
M.P. 14.062

NORMALIZED DEFLECTION (mils)
249
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.60 Comparison of Measured NDT Deflection Basins on I-10B
M.P. 2.703

NORMALIZED DEFLECTION (mils)
250
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.61 Comparison of Measured NDT Deflection Basins on I-10C--
M.P. 32.071

NORMALIZED DEFLECTION (mils)
251
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.62 Comparison of Measured NOT Deflection Basins on SR 15B
M.P. 4.811

NORMALIZED DEFLECTION (mils)
252
RADIAL DISTANCE FROM CENTER OF LOADED AREA (in.)
Figure 6.63 Comparison of Measured NDT Deflection Basins on SR 15C
M.P. 0.055

253
3. Those pavement sections in which FWD and Dynaflect deflection basins
are similar, especially when the higher FWD load deflections are
used in the comparison. Figures 6.57 through 6.63 show that test
pavements SR 26A, SR 24, I-10A, I-10B, I-10C, SR 15B, and SR 15C
fall into this group. It must be emphasized that similarity used
here implies that deflection basins from the Dynaflect and the FWD
are either close to each other or cross over (overlap). A typical
example of the latter is SR 15C. Thus group 3 is an overlap between
groups 1 and 2.
The differences between the Dynaflect and FWD deflection basins
from the normalized plots suggest that response of the pavements is
affected by type of NDT device and loading mode. The resultant effect
on the moduli required to model the pavements is discussed in the next
section.
6.6.2 Comparison of Layer Moduli
The tuned layer moduli obtained from modeling of the Dynaflect and
FWD deflection basins are compared below. The comparison here applies
to only the highest (9-kip) FWD deflections. However, Section 6.5.5 had
shown that the effects of nonlinearity and stress dependency in FWD
load-deflection response is reduced considerably at FWD loads greater
than 6.0 kips. Also the use of the 9 kips FWD load response charac
teristics approximates the rear-axle loading of an 18-kip single axle
design load. Plate loading test results listed in Table 5.3 are also
compared here with the NDT moduli predictions for those pavement sec
tions in which tests were performed.
Tables 6.16 through 6.19 compare moduli from the Dynaflect and FWD
deflections for the asphalt concrete, base course, subbase, and subgrade

254
Table 6.16 Comparison of the Asphalt Concrete Modulus
for the Test Sections
Test
Road
Mile Post
Number
S
(in.)
Mean
Temperature
(F)
AC Modulus, Ex (ksi)
Rheology
Dynaf1ect
FWD
SR 26A
11.912
8.0
81
171.3
171.5
171.5
SR 26B
11.205
8.0
59
406.5
360.0
360.0
SR 26C
10.168
6.5
82
171.3
171.5
171.5
SR 24
11.112
2.5
57
338.3
338.3
338.3
US 301
21.580
4.5
69
256.6
250.0
250.0
US 441
1.236
3.0
79
289.6
290.0
290.0
I-10A
14.062
8.0
104
60.8
65.0
100.0*
I-10B
2.703
7.0
88
113.2
113.0
100.0
I-10C
32.071
5.5
106
66.9
67.0
120.0*
SR 15B
4.811
7.0
127
90.5
150.0*
90.5
SR 15A
6.546
8.5
120
85.0
150.0*
150.0*
SR 715
4.722
4.5
111
92.6
92.6
92.6
SR 715
4.720
4.5
111
92.6
92.6
92.6
SR 15A
6.546
8.5
120
85.0
85.0
85.0
SR 12
1.485
1.5
102

400.0
400.0
SR 15C
0.055
6.75
105
80.3
80.0
80.0
SR 15C
0.065
6.75
105
80.3
150.0
150.0
* Significant differences between E1 predicted from rheology of
recovered asphalt and Dynaflect or FWD E1 values.

255
layers, respectively. In Table 6.16, the modulus of the asphalt con
crete determined from rheology test data (Table 6.5) are also listed for
comparison purposes. The table shows favorable agreement between the
NDT moduli and the indirect method of using low-temperature viscosity
relationship. Slight differences occur at SR 26B in which the rheology
moduli prediction was reduced by about 12 percent in tuning the NDT
deflection basins. In some test sections where there were differences,
it was found that the Dynaflect moduli matched the rheology value while
the FWD required some adjustment in the E2 or vice versa. These always
occurred with pavements tested at higher temperatures (90F or more).
It is generally believed that the indirect method of estimating Ex works
best at mean pavement temperatures less than 80F or where E is greater
than 100 ksi.
The NDT Zl values listed in Table 6.16 were correlated to the
respective mean pavement temperatures to establish a simple and rapid
method of estimating E: values for routine applications. The resulting
relationship is shown in Figure 6.64. The asphalt concrete modulus, E ,
of pavements with no visible cracks can be determined from the mean
pavement temperature, T, using the following equation
Log Ex = 6.4147 0.0148 T Eqn. 6.3
In Equation 6.3, Ex is in psi, and T is in F, as illustrated in Figure
6.64. The relationship for considerable cracking in Figure 6.64 can be
used when pavement cracks are spaced sufficiently to eliminate their
influence on the NDT deflections. This would apply to pavement sections
that have uncracked segments within cracked segments. However, if the

RESILIENT MODULUS, E., (psi)
256
Figure 6.64 Relationship Between Asphalt Concrete Modulus, E1,
and Mean Pavement Temperature

257
pavement exhibits extensive cracking (e.g., alligator cracking), Ex will
be reduced considerably. Details pertaining to the use of Figure 6.64
will be presented in Chapter 8.
Tables 6.17 and 6.18 show that the base course and subbase moduli
from the Dynaflect are, respectively, higher than those for the FWD.
The corresponding comparison for the subgrade is shown in Table 6.19 and
this does not necessarily follow the trend of the base and subbase
layers. A comparison for all three layer moduli is shown in Figure 6.65
and Table 6.20. In Table 6.20, the ratio of the Dynaflect to FWD moduli
for all test sections are listed for the various layers. The good
agreement for the AC layer has already been discussed. For the base
course layer, the ratio ranges from 1.0 to 3.87. The 3.87 value oc
curred with the SR 12 test section in which the base course material was
a sand-clay mixture instead of the limerock material used for the rest
of the test pavements. The higher predictions of E2 from the Dynaflect
than FWD agrees with the findings of Bush and Alexander (23), and Wise
man et al. (130). These researchers found that the moduli of the base
course determined from FWD deflection basins were significantly lower
than those obtained from analyzing deflection basins produced by either
the Dynaflect, Road Rater, Pavement Profiler, or 16-kip vibrator. The
Pavement Profiler and 16-kip vibrator also operate on the steady-state
vibratory loading principles of the Dynaflect and Road Rater. The base
course moduli predictions obtained from this research therefore support
the belief that deflection basins produced by steady-state vibratory
loading devices result in higher base course moduli than those produced
by impulse loading devices such as the FWD.

258
Table 6.17 Comparison of the Base Course Modulus for the Test Sections
Test
Road
Mile Post
Base Course Modulus, E
(ksi)
Number
Dynaflect
FWD
PLT
SR 26A
11.912
105.0
75.0

SR 26B
11.205
90.0
90.0

SR 26C
10.168
55.0
45.0

SR 24
11.112
105.0
75.0

US 301
21.580
120.0
45.0
56.0
US 441
1.236
85.0
55.0
40.3
I-10A
14.062
95.0
90.3
93.8
I-10B
2.703
80.0
60.0
80.1
I-10C
32.071
105.0
85.0
66.5
SR 15B
4.811
120.0
52.8

SR 15A
6.549
120.0
95.0

SR 715
4.722
75.0
45.0

SR 715
4.720
65.0
65.0

SR 12
1.485
120.0
31.0
43.4
SR 15C
0.055
105.0
35.0

SR 15C
0.065
105.0
50.0

SR 15A
6.546
85.0
45.0
__

259
Table
6.18 Comparison
of the Subbase
Modulus for the Test
Sections
Test
Road
Mile Post
Subbase Modulus, E3 (ksi)
Number
Dynaflect
FWD
PLT
SR 26A
11.912
70.0
45.0

SR 26B
11.205
60.0
45.0
--
SR 26C
10.168
35.0
27.5

SR 24
11.112
75.0
40.0
--
US 301
21.580
60.0
35.0
27.8
US 441
1.236
60.0
35.0
29.7
I-10A
14.062
89.4
80.0

I-10B
2.703
65.0
50.0

I-10C
32.071
85.0
50.0
44.6
SR 15B
4.811
75.0
50.0

SR 15A
6.549
40.0
39.5

SR 15A
6.546
65.0
35.0

SR 715
4.722
50.0
25.0

SR 715
4.720
45.0
26.0
--
SR 12
1.485
75.0
20.0
46.10
SR 15C
0.055
75.0
12.0

SR 15C
0.065
75.0
44.0

260
Table 6
.19 Comparison
of the Subgrade Modulus for the
Test Sections
Test
Road
Mile Post
Subgrade Modulus
Number
Dynaflect
FWD
PLT
SR 26A
11.912
14.6
18.7

SR 26B
11.205
7.9
11.0

SR 26C
10.168
28.5
25.5

SR 24
11.112
38.6
38.6

US 301
21.580
38.6
25.0
11.6
US 441
1.236
27.5
20.0
11.6
I-10A
14.062
105.0
130.0
32.8
I-10B
2.703
60.0
43.0
21.2
I-10C
32.071
40.0
46.0
29.7
SR 15B
4.811
8.1
10.2

SR 15A
6.549
4.8
7.5

SR 15A
6.546
5.0
7.0

SR 715
4.722
6.0
11.0

SR 715
4.720
5.5
10.5
--
SR 12
1.485
26.5
18.5
15.4
SR 15C
0.055
5.5
9.8

SR 15C
0.065
5.5
10.0


DYNAFLECT (1-KIP LOAD) MODULUS (KSI)
Figure 6.65 Comparison of Dynaflect and FWD Tuned Layer Moduli

262
Table 6
.20 Ratios of Dynaflect
Moduli to
FWD Moduli for
Test Sections
Test
Road
Mile Post
Number
E(DYN)/E(FWD)
AC
Base
Subbase
Subgrade
SR 26A
11.912
1.00
1.40
1.56
0.78
SR 26B
11.205
1.00
1.00
1.33
0.72
SR 26C
10.168
1.00
1.22
1.27
1.12
SR 24
11.112
1.00
1.40
1.88
1.00
US 301
21.580
1.00
2.67
1.71
1.54
US 441
1.236
1.00
1.55
1.71
1.38
I-10A
14.062
0.65
1.05
1.12
0.81
I-10B
2.703
1.13
1.33
1.30
1.40
I-10C
32.071
0.56
1.24
1.70
0.87
SR 15B
4.811
1.66
2.27
1.50
0.79
SR 15A
6.549
1.00
1.26
1.01
0.64
SR 15A
6.546
1.00
1.89
1.86
0.71
SR 715
4.722
1.00
1.67
2.00
0.55
SR 715
4.720
1.00
1.00
1.73
0.52
SR 12
1.485
1.00
3.87
3.75
1.43
SR 15C
0.055
1.00
3.00
6.25
0.56
SR 15C
0.065
1.00
2.10
1.70
0.55

263
Correlation of the base course modulus (E2) from the Dynaflect and
FWD for the various test sections resulted to the following equation:
E2 (Dynaflect) = 1.380 E2 (FWD) Eqn. 6.4
(R2 = 0.871, N = 17)
Table 6.20 shows that the ratio of the subbase modulus from the
Dynaflect to the FWD ranges from 1.01 to 3.75, except for SR 15C M.P.
0.055 in which a ratio of 6.25 was obtained. This value was considered
to be an outlier (though both deflection basins tuned well) in subse
quent analysis. The trend for the subbase is similar to that of the
base course. Therefore, the same conclusions, with regard to the effect
of the type of NDT device on granular base course materials, applies to
the subbase layer. Regression analysis of Dynaflect and FWD data in
Table 6.18 resulted in the following equation:
E3 (Dynaflect) = 1.488 E$ (FWD) Eqn. 6.5
(R2 = 0.930, N = 16)
It is interesting to note that not only does the R2 value increase, but
also the regression coefficient increased in Equation 6.5 compared to
Equation 6.4. Thus, higher ratios (see Table 6.20) were obtained for
the subbase than the base course.
The comparison between Dynaflect and FWD predictions of the sub
grade modulus (E^) is presented in Tables 6.19 and 6.20. The ratio

264
of the Dynaflect to FWD E4 values ranges from 0.55 to 1.54. In only 6
out of 17 cases did the Dynaflect predict higher E4 values than the
FWD. Some researchers argue that the light loading produced by the
Dynaflect would result in higher modulus values than the moduli obtained
with the heavy loads used in the FWD. The findings from this study tend
to contradict this argument in the case of the subgrade layer. Also,
this is the only layer which has unique relationship between the modulus
and the farthest sensor deflection(s) for each NDT device. The subgrade
modulus comparison does not indicate any effect of nonlinearity in the
subgrade soil materials. Therefore, the differences in the moduli pre
dictions could be attributed to the inherent differences between the two
NDT devices; viz, vibratory loading in the Dynaflect versus impulse
loading in the FWD.
Regression analysis of Dynaflect E4 values to FWD E4 values
resulted in the following equation:
E4 (Dynaflect) = 0.933 E4 (FWD) Eqn. 6.6
(R2 = 0.929, N = 17)
Tables 6.17 to 6.19 illustrate the comparison of the moduli ob
tained from plate loading tests and the NDT devices for the base, sub
base, and subgrade layers, respectively. Tables 6.17 and 6.18 indicate
that the plate bearing E2 and E3 values are closer to the FWD than the
Dynaflect predictions. This seems to support the argument that FWD
deflection response is influenced by the rigid plate effects. For
example, I-10A which was very stiff produced similar E2 values using the

265
Dynaflect, FWD, and plate loading tests. Further research is required
to study the possible effects of the rigid FWD plate in interpreting
load-deflection response.
Table 6.19 shows that the subgrade moduli obtained from the plate
loading test were generally lower than those from the NDT devices. For
SR 12, the FWD and plate loading E^ values compare well. However, for
I-10A test site, which had very stiff layers, the plate bearing test
prediction of E4 was too low compared to the Dynaflect and FWD predic
tions. The poor prediction of the subgrade modulus from the plate load
tests is attributed to possible disturbance during trenching, measure
ment of plastic (or non-recoverable) deformation during testing, and the
static loading conditions used in the plate test as compared to the
dynamic NDT tests.
6.7 Analyses of Tuned NDT Data
6.7.1 General
Section 6.5 presented the modeling of the Dynaflect and FWD deflec
tions for the various test sections. The resultant moduli from modeling
have been referred to as tuned moduli for the respective NDT devices.
This section analyzes the moduli and corresponding BISAR predicted
deflections in the hope of developing simplified layer moduli prediction
equations, especially for the Dynaflect testing system. The predicted
deflections are used in this analysis because they matched (approxi
mately) the field measured values and more importantly meet the assump
tions inherent with the use of multilayered elastic theory. Details
pertaining to the analyses are presented for the Dynaflect and FWD in
Sections 6.7.2 and 6.7.3, respectively.

266
6.7.2 Analysis of Dynaflect Tuned Data
6.7.2.1 Comparison of Measured and Predicted Deflections. It was
mentioned in Section 6.5.2 that the measured and predicted Dynaflect
deflections (Figures 6.15 to 6.31) were generally in good agreement for
the test pavement sections. The measured and predicted deflections
listed in Tables 6.1 and 6.8, respectively, were regressed to evaluate
the reliability of the BISAR predicted Dynaflect deflections. The
regression analyses utilized only sensors 1, 4, 7, and 10 deflections.
These were the sensor deflections selected from the theoretical analysis
(Chapter 4) to be related to the moduli of specific layers.
Figures 6,66, 6.67, 6.68 and 6.69 present the relationship between
BISAR predicted and field measured Dynaflect deflections at modified
sensor locations 1, 4, 7, and 10, respectively. In all cases, the high
R2 value (R2 > 0.96) indicated an expectionally good correlation between
predicted and measured deflections. The regression equations for D^ and
Dy (Figures 6.67 and 6.68) provided an almost perfect correlation with
the intercept and slope being within 0.015 mils of zero and 0.018 mils
of unity, respectively.
The Dx values (Figure 6.66) tended to yield a slightly higher
intercept (0.065) and slope (1.107) which results in the predicted
deflections being slightly greater than those measured. There were four
test sites where predicted Dx values were about 0.2 to 0.3 mils greater
than the measured D1 values. This difference may be due to sensor
placement variation, the use of single Dx measurement in the earlier
tests, variation in measured Dx response according to wheel positioning,
or where complete tuning was not achieved (e.g., SR 24 and SR 12).

PREDICTED D! (mils)
267
0 0.5 1.0 1.5 2.0 2.5 3.0
MEASURED D1 (mils)
Figure 6.66 Relationship Between Predicted and Measured Dynaflect
Modified Sensor 1 Deflections

PREDICTED D4 (mils)
268
Figure 6.67 Relationship Between Predicted and Measured Dynaflect
Modified Sensor 4 Deflections

PREDICTED Dy (mils)
269
Figure 6.68 Relationship Between Predicted and Measured Dynaflect
Modified Sensor 7 Deflections

PREDICTED D1 0 (mils)
270
Figure 6.69 Relationship Between Predicted and Measured Dynaflect
Modified Sensor 10 Deflections

271
Other than one test site (SR 80 Section 2), the D10 values pro
vided an excellent, highly reliable relationship (Figure 6.69). How
ever, the slope of 0.95 suggests that predicted deflections are about 5
percent less than measured 01Q values. The discrepancy occurs because
the straight line log-log relationship for predicting E4 from D1() (stan
dard D5) tends to be a curvilinear (hyperbolic) relationship for E4
values below 10.0 ksi or above 100.0 ksi (see Figure 2.8).
6.7.2.2 Development of Simplified Layer Moduli Equations. Si nee
/
the tuned layer moduli provided predicted Dynaflect deflections which
correlated exceedingly well with the measured deflections, regression
analyses were performed to assess the relationship between
a) Composite modulus of asphalt concrete and base course layers
(E12) and Dj D4
b) Subbase or stabilized subgrade modulus (Eg) and D4 D?
c) Subgrade modulus (E4) and D1Q.
As mentioned previously, these sensor deflections were selected from the
analytical study on the basis of being related to the moduli of specific
layers. It was necessary to combine the asphalt concrete and base
course moduli because the analyses had indicated that no sensor location
or combination of sensor deflections were suitable for separation of Ex
and E2. The series of equations (Equation 4.1 through 4.10) developed
for prediction of either Ej or E2 from D1 D4, with a reasonable esti
mate of E2 or E respectively, albeit their high degree of prediction
accuracy, were considered to be too complex for routine evaluation of
pavements. Therefore, it was necessary to simplify the various £1 and
E2 prediction equations. The approach used is described below.

272
It was shown in Figures 4.14 to 4.19 that Dx in the modified
Dynaflect sensor array system was dependent only on the surface and base
course layers. Thus,
Dx d4 = fn (Ej, tlf E2, t2) = Eeq or E12 Eqn. 6.7
where Dx 04 is the difference in modified Dynaflect sensor deflections
(see Figure 4.2),
Ej is modulus of the asphalt concrete layer,
E2 is modulus of the base course layer,
tx is the thickness of the asphalt concrete layer,
t2 is the thickness of the base course layer, and
E12 (or Eeq) is a composite (or an equivalent) asphalt concrete
and base course modulus.
Two equations were employed to combine the surface and base course
layer stiffnesses into a composite E12 value. The first formula is
essentially a weighted average formula, and is of the form
E t + E t
E = '1 22- Eqn. 6.8
12 t + t
1 2
Equation 6.8, which is a commonly-used weighting formula, has pre
viously been utilized by Vaswani (124) to combine pavement layers over
the subgrade. The second method used to combine E and E2 follows the
approximation suggested by Thenn de Barros (115). The equation is of
the form

273
E
12
+ t 3 ST
2
Eqn. 6.9
2
Figures 6.70 and 6.71 present the relationships between E and
Dx D4 for each of the weighting methods. There is very little
difference between modulus-deflection relationship for the standard
weighting method (Equation 6.8) and the Thenn de Barros1 formula
(Equation 6.9), as shown in Figure 6.70 and Figure 6.71, respectively.
It would appear that either method would be suitable for defining E12
although the difference between methods becomes significant at low E
values and high Dx D4 values (e.g., E12 < 34.0 ksi, and D1 D4 > 1.0
mil). Thus knowing E from Dj D^, and Ej from asphalt rheology (or
using Figure 6.64), E2 can be calculated using Equations 6.8 and 6.9. A
procedure incorporating the above and other layer moduli predictions for
routine pavement evaluation studies is presented in Appendix F.
The relationship between E3 and 04 D? is illustrated in Figure
6.72. The figure shows that the simplified format of Equation 4.15
could be used for a wider range of E3 values. Even though the results
of the regression analysis of Figure 6.72 is fairly good, the range in
E3 values is still narrow and limited to only two values below 20.0
ksi. Additional test data in the lower range would be helpful in either
verifying the validity of the E3 prediction equation or modifying the
regression equation.
Subgrade modulus prediction equations and the modified Dynaflect
sensor 10 deflection values are shown in Figure 6.73. The simplified
equation (Equation 4.35), as previously explained, was originally
developed using data collected in Quebec, Canada and Florida. Figure

COMPOSITE MODULUS, E12 (ksi)
274
D1 D4 (mils)
Figure 6.70 Relationship Between E12 (Using Equation 6.8) and Dx -

COMPOSITE MODULUS, E1 2 (ksi)
275
- D4 (mils)
Figure 6.71 Relationship Between E12 (Using Equation 6.9) and Dx D4

276
D4 D? (mils)
Figure 6.72 Relationship Between E3 and D4 D?

SUBGRADE MODULUS, E4 (ksi)
277
DYNAFLECT MODIFIED SENSOR
DEFLECTION, D ^ 0 (mils)
Figure 6.73 Relationship Between and D1(J

278
6.73 therefore compares the relationship- between the results from that
study and those obtained from the test pavement sections used in this
study. From a practical standpoint, there is very little difference
between the E4 prediction equations. This difference is not significant
enough to warrant the use of one equation in preference to the other,
except when D10 is less than 0.06 mils or much greater than 1.0 mil. It
was shown in Section 4.4.1.4 that the use of the simplified E4 predic
tion equation result in the overprediction of weak subgrades (E4 < 10.0
ksi) and underprediction of high or stiff subgrades (E4 > 100.0 ksi).
The results obtained with the use of the modified Dynaflect system
indicate that separation of loaded areas produces double bending which
allows for the optimal placement of sensors to separate the response of
the different pavement layers. This somewhat unique load-sensor config
uration makes it possible to develop simplified (power law) equations
for prediction of layer moduli. If desired, the predicted layer moduli
can be used as "seed moduli" in iterative elastic multi-layer computer
programs (e.g., BISDEF). This would help to ensure that unique
solutions are obtained. It has been demonstrated in Section 6.5 that
predicted Ex and E4 values are reliable and seldom require much adjust
ment or tuning to match the measured deflection basin. Therefore, it
appears that the most desirable approach in computer simulation is to
use E2 and E3 values as "seed moduli" for any iterative or judgment
modified analysis.
Appendix F describes a recommended testing and analysis procedures
using the modified Dynaflect testing system. The positions of the five
geophones in the conventional system were modified into the form shown
in Figure F.l. The computational algorithms of the recommended

279
procedure in Appendix F have been incorporated into the BISAR elastic
layer computer program to perform the iteration after the initial
computation of the "seed moduli." Figure 6.74 shows a simplified flow
chart of the modified BISAR program utilizing the modified Dynaflect
testing system. The modified program is referred to as DELMAPS1 which
is an acronym for Dynaflect Evaluation of _Layer Moduli for Asphalt
_Pavement Systems Version l_. The iteration part of the program is
interactive and user-specified with respect to the modulus value to be
adjusted to achieve the desired tuning. A partial listing of the
DELMAPS1 program is presented in Appendix G.
6.7.3 Analysis of FWD Tuned Data
6.7.3.1 Comparison of Measured and Predicted Deflections. It was
explained in Section 6.5.3 that tuning of the FWD deflection basins was
extremely difficult for most of the test pavements. It was relatively
easy to match D. and D. or D_, and generally difficult to simulate the
intermediate sensor deflections. This is demonstrated in Table 6.21 in
which correlation between measured and predicted FWD deflections is
presented. The R2 values are generally good for the first two and last
two sensors, and poor for sensors 3, 4, and 5 deflections. Also,
sensors 3, 4 and 5 have slopes lower than unity in their regression
equations. Comparison between BISAR predicted and field measured FWD
deflections indicated that percent errors as high as +35 percent were
obtained at sensor positions 3, 4, and 5. However, the difference in
deflections at sensors 1, 2, 6, and 7 were generally of the order of +10
percent.

280
Figure 6.74 Simplified Flow Chart of DELMAPS1 Program

281
Table 6.21 Correlation Between Measured and Predicted FWD
(9-kip Load) Deflections
Sensor
N
R2
Regression
Equation
1
18
0.993
D i P =
-0.683
+ 1.025 DlM
2
18
0.964
D2p =
0.385
+ 1.005 D2M
3
18
0.914
3P =
1.239
+ *947 D3M
4
18
0.873
4P =
0.822
+ 0.925 D4M
5
18
0.933
5P =
0.453
+ *915 D5M
6
18
0.983
6p
0.090
+ 0.955 D6jv|
7
18
0.993
7P =
-0.032
+ 1.010 D7M
The relatively high R2 values for sensors 1, 2, 6, and 7 indicates
good prediction accuracy for E1 and E4 values as compared to E2 and E3.
Generally, the E4 value contributes about 60 percent to the entire
deflection basin depending on the stiffness of the pavement structure.
Therefore, if the intermediate and last sensor deflections had been
matched, lower deflections would probably have been measured at Dx and
D2 due to the influence of rigid plate effects in the FWD system which
is modeled as a flexible plate in elastic layer programs. An attempt
was made to demonstrate this effect using a rigid plate loading approxi
mation in the elastic layer program which would produce the same deflec
tion as measured with the FWD. Although, this technique has previously
been employed by Roque (96), it was found to be too cumbersome to
pursue. Nevertheless, the rigid plate appears to influence FWD load-
deflection response. It is also believed that the FWD load-geophone

282
configuration does not provide separation of the layers' response as was
obtained with the Dynaflect. A modified FWD system utilizing a dual
loading system appears worth pursuing in future NDT research.
6.7.3.2 Development of Prediction Equations. Although tuning of
the FWD deflection basins was not as good as that of the Dynaflect, the
tuned data were used to develop new equations in the hope of improving
those presented in Section 4.3.3. The FWD prediction equations pre
sented in this section and that of Section 4.3.3 may be used for the
prediction of the initial or "seed" moduli for subsequent use in itera
tive elastic multilayered computer programs.
The range of layer moduli and thicknesses are as follows:
80.0 < Ex < 400.0 ksi and 1.5 < tx < 8.5 in.
31.0 < E2 < 95.0 ksi and 6.0 < t < 24.0 in.
12.0 < E3 < 80.0 ksi and 12.0 < t3 < 17.0 in.
7.0 < E4 < 130.0 ksi and
t, = semi-infinite.
4
The corresponding range of deflections (9-kip FWD load) as listed
in Table 6.11 are:
3.58 < D < 29.54 mils
3.51 < D2 < 17.81 mils
2.16 < D3 < 14.67 mils
1.16 < D, < 11.03 mils
0.65 < 05 < 8.80 mils
0.41 < D. < 6.99 mils
6
0.31 < D? < 5.56 mils
The range of parameters listed above are slightly different from those
used in the theoretical analysis. The subbase and subgrade moduli
ranges are increased here. Also, the base course thickness is increased

283
from the 8.0 in. used in the theoretical study to a high value of 24.0
in. (SR 715). Since these values represent actual test pavements, the
resultant prediction equations would be more reliable than those of
Section 4.3.3, unless of course their prediction accuracies are lower.
6.7.3.2.1 Asphalt concrete modulus, Ex Multiple linear
regression analysis of the test data resulted in the following
equation:
log E1 = 3.229 1.0683 log {tj 2.8217 log (D1 D2)
+ 1.008 log (Dx D3) + 0.8835 log (D1 05)
Eqn. 6.10
(R2 = 0.885 and N = 22)
Error analysis indicated that 5 out of 22 pavements had pre
dictions with errors greater than 20 percent with one pavement
having as high as 44 percent prediction error. This pavement was
SR 12 which had an asphalt concrete thickness of 1.5 in. As
explained in Section 4.4.3, the SR 12 pavement was deleted from the
data base and the remaining data analyzed to obtain Equation 6.11.
log Ej = 2.215 0.2481 log (tj 12.445 log (Dx D2)
+ 17.205 log (Dx D3) 5.871 log (Dx DJ
Eqn. 6.11
(R2 = 0.959 and N = 21)

284
This equation is similar to Equation 4.20 except that the
regression coefficients and range of parameters applicable are
different. Two pavements had prediction errors of -21.8 and 16.8
percent. These were SR 24 and US 301, with asphalt concrete thick
nesses of 2.5 and 4.5 in., respectively. All other pavements had
E: predictions of the order of 10 percent error. Therefore, Equa
tion 6.11 may be preferred over Equations 4.19 and 4.20, since it
covers a broader range of variables and also developed from tuned
test data.
6.7.3.2.2 Base course modulus, E2 Analysis of the tuned
data using E2 as the dependent variable resulted to an equation
similar to Equation 4.21 which was obtained from the theoretical
analysis.
log E2 = 3.280 0.03326(t ) 0.1179 log (D?)
+ 3.3562 log (Dx 0 ) 9.0167 log (D1 DJ
- 4.8423 log (Dj Dg) Eqn. 6.12
(R2 = 0.959 and N = 22)
Error analysis indicated that only two pavements (SR 15A M.P.
6.549 and 6.546) had -15.6 and 15.8 percent prediction errors.
Prediction errors for the others were 10 percent or less. Equation
6.12 should be used in place of Equation 4.21 unless the applicable
range of the former is excessively exceeded.
6.7.3.2.3 Stabilized subgrade modulus, E Multiple regres
sion analysis of the data set resulted in the following equation

285
log E, = 4.970 + 0.1773(t.) 1.6966 log(t) 0.1069(DJ
O i 1 H
+ 0.2552(D7) 2.6546 1og(D1) 3.9906 log(D3)
+ 1.8241 log(Dc) + 3.5092 log(D0 D_)
b o
Eqn. 6.13
R2 = 0.887 and N = 22
Error analysis indicated that one pavement (SR 15B) had -34.6
percent prediction error. The actual Eg value was 50.0 ksi, while
the predicted value was 32.7 ksi. Others had prediction errors
generally less than +15 percent.
Equation 6.13 for the prediction of Eg applies to a slightly
wider range of variables than Equation 4.24 which was selected from
the theoretical analysis. However, the latter is more simplified
and contains fewer variables than Equation 6.13. Also, since the
R2 value is greater, Equation 4.24 should be used for E3 predic
tions.
6.7.3.2.4 Subgrade modulus, E Regression analyses of E^
against either Dg, or Dy, or both resulted in the following equa
tions :
E = 53.697(0 )"i.04i Eqn. 6>14
4 6
(R2 = 0.997 and N = 22)
E = 39.690(D )1.004 Eqn< 6>15
(R2 = 0.999 and N = 22)

286
E = 39.427(D )*023(D p-026 Eqn. 6.16
4 6 7
(R2 = 0.999 and N = 22)
The percent prediction errors of Equations 6.14 to 6.16 were
generally below +10 percent. The maximum prediction error of Equa
tion 6.14 was 7.0 percent while the other two had 9.0 percent pre
diction errors. However, Equations 6.15 and 6.16 had greater pre
diction accuracy than Equation 6.14, with prediction errors gener
ally less than +4 percent.
The relationship between E^ and Dg or D? is illustrated in
Figure 6.75. The corresponding equations (Equations 6.14 and 6.15)
apply to a wider range of E4 values than those obtained from the
theoretical analysis. Also, the slopes of Equations 6.14 and 6.15
are close to unity, approaching the format of the Dynaflect simpli
fied E^ prediction equation.
It was initially believed (in Section 4.4.2.4) that the use of
Equations 4.28 or 6.16, which incorporate two sensor deflections,
should generally minimize the potential for prediction error due to
measurement variablity. However, it was found that variation in D.
6
as high as 100 percent would have about one percent change in the
predicted E^ value with the use of Equation 6.16. Thus, Equation
6.16 is not very sensitive to changes in D. as compared to that of
O
D?. Therefore, Equations 6.14 and 6.15 (Figure 6.75) should be
used for E4 predictions, and whenever possible, an average value
should be used. Where surface cracks exist, one of the equations
might be preferable to the other depending on the ability of the
pavement to transfer loads to the geophone locations.

SUBGRADE MODULUS, E4 (ksi)
287
FWD (9 kips load) SENSOR DEFLECTION,
D6 or j (mils)
Figure 6.75 Relationship Between and FWD Dg and D?

CHAPTER 7
INTERPRETATION OF IN SITU PENETRATION TESTS
7.1 General
The testing program for the penetration tests (CPT and DMT) has
been presented in Chapter 5. The electric cone penetration test, with
the computerized data acquisition system, yielded cone resistance (qc),
local friction (fs), and their ratio FR = fs/qc (called the friction
ratio) values for the various depths of penetration. The plots of these
profiles, using the HP 7470A graphics plotter, are presented in Appendix
C for each pavement section.
The dilatometer test consisted of two basic readings which are,
1) the gas pressure required to lift the membrane off its seating, and
2) the pressure to deflect the center of the membrane 1.1 mm. From
these two readings and two initial calibration readings, three dilato
meter parameters Ig (Material Index), Kg (Horizontal Stress Index), and
Eg (Dilatometer Modulus) are calculated. A number of useful empirical
and experimental correlations between these parameters and important
geotechnical parameters have been developed by Marchetti (68). This
allows a comprehensive characterization of the penetrated deposit.
Using the data reduction program described by Marchetti and Crapps (69),
the DMT results were reduced and presented in Appendix D.
This chapter presents an analysis of the penetration tests con
ducted on the pavement sections. The feasibility of determining the
modulus of the pavement layers and underlying subgrade soils from the
288

289
penetration tests are assessed. Also, the effects of the subgrade
stratigraphy on the NDT deflection basins are evaluated.
7.2 Soil Profiling and Identification
Based upon continuous monitoring of cone penetration tests on
various soils, many attempts have been made by engineers to develop
classification charts relating the soil type to the measured cone pene
tration parameters. Among those is the classification chart based on qc
and FR developed by Robertson and Campanella (95). Similarly, an iden
tification chart for soil type using Ig and Kg from dilatometer tests
has been developed by Marchetti (68). Both procedures were used to
identify the stratigraphy of the test pavement sites, especially for the
\
underlying subgrade soils. There was good agreement between the two
penetration tests in providing delineation between sandy and clayey/
silty soils. However, the DMT could not identify sand-clay mixes which
were interpolated from the CPT identification charts. Because the para
meter Ig increases continuously with grain size, Marchetti's correlation
cannot identify a sand-clay mixture (29). Even though no detailed labo
ratory classification tests were conducted, these results were used in
subsequent analysis of the penetration tests.
The accuracy and repeatability of the test soundings were evaluated
from the multiple tests performed on each site. Figure 7.1 shows, for
example, the variation of qc and FR with depth for SR 12. For the same
test pavement the variation of Eg and Kg with depth is also shown in
Figure 7.2. These plots indicate the reproducibility of the penetration
tests considering spatial soil variability. The CPT and DMT profiles
seemed to agree with the variability of the test sites as inferred from

CONE TIP RESISTANCE, Cfc (MPa)
FRICTION RATIO (%)

ro

o
Figure 7.1 Variation of qc and FR with Depth on SR 12

DI L ATOM ETER MODULUS, ED (MPa) HORIZONTAL STRESS INDEX, KD
Figure 7.2 Variation of Eg and Kg with Depth on SR 12

292
the deflections measured by the nondestructive tests for all test pave
ments.
The variability in the subgrade or foundation soils is also illus
trated by the CPT and DMT logs in Figures 7.3 through 7.7 for some of
the test pavements. There was a general tendency for the strength and
modulus of the subgrade to decrease significantly to a depth of about
2.0 m. The qc and Eq profiles, as illustrated in Figures 7.1 through
7.7, can also be used to evaluate the structural condition of a pave
ment. These profiles of stiffness would assist the engineer in iden
tifying possible zones of weakness in the pavement or subgrade. Figure
7.3 shows, for example, that a weak (soft) layer exists at a depth of
2.0 m in the subgrade for SR 26A. Figure 7.4 also suggests that a stiff
layer (hardpan) exists below 4 m depth on SR 26C. The stiffness pro
files provided means of identifying the layered systems within the sub
grade or foundation as opposed to the infinitely thick homogeneous
subgrades conventionally assumed in layered-theory analysis. The dif
ferences between the two are demonstrated in Section 7.5.
7.3 Correlation Between Ep and gc
The dilatometer modulus, Eq, obtained from the DMT was developed by
Marchetti using the theory of elasticity. It is related to Young's mod
ulus of elasticity, E, and Poisson's ratio, u, as follows:
En = Eqn. 7. 1
[1 2)
Thus, with a reasonable estimate of u one can determine the in situ
elastic modulus E. However, Jamiolkowski et al. (50) have reported

Cone Tip Resistance, q (MPa) Dilatometer Modulus, E0 (MPa)
V
0 20 40 60 0 20 40 60 80 100
Figure 7.3 Variation of qc and Eq with Depth on SR 26A

100
Cone Tip Resistance, q c (MPa) Dilatometer Modulus, ED (MPa)
0 20 40 60 0 20 40 60 80
Figure 7.4 Variation of qc and Eg with Depth on SR 26C

Cone Tip Resistance, qc (MPa) Dilatometer Modulus, ED (MPa)
0 20 40 60 0 20 40 60 80 100
ro
cn
Figure 7.5 Variation of qc and Eq with Depth on US 301

Cone Tip Resistance, q c (MPa) Dilatometer Modulus, ED (MPa)
Figure 7.6 Variation of qc and ED with Depth on US 441

Cone Tip Resistance, q (MPa)
Dilatometer Modulus, ED(MPa)
po
KO
Figure 7.7 Variation of qc and Eg with Depth on SR 12

298
results from calibration chamber tests in which Eg was found to be equi
valent to the secant modulus at 25 percent stress level (E'25) for
normally consolidated cohesionless soils. Similar correlations between
E'25 and qc suggest that the ratio of E'25 to qc varies from 2 to 10 or
more depending on the stress history, relative density and mineralgica!
composition of the soils (50). This suggests that Eg and qc are related
which is generally verified by comparing the trends illustrated in
Figures 7.3 to 7.7.
Regression analysis of Eg and qc values were performed for each
test pavement site. The results of these analyses are given in Table
7.1. Note that the analysis utilized average values of Eg and qc from
each test site. Regression equations for the three SR 26 sites and SR
15C were almost identical although their R2 values varied from 0.75 to
0.97. The other test sites had higher constants with the exception of
SR 15B in which the lowest slope--2.519--was obtained.
Table 7.1 Relationship Between Eg and qc for Selected
Test Sections in Florida
Test Road
County
Number of
Observations
R2
Regression Equation
SR 26A
Gilchrist
24
0.850
=
3.199
c
SR 26B
Gilchrist
26
0.860
ed
=
3.201
qc
SR 26C
Gilchrist
21
0.969
ed
=
3.384
qc
US 301
Alachua
31
0.878
eD
=
5.351
qc
US 441
Columbia
26
0.881
Ed
=
4.188
%
SR 12
Gadsden
28
0.915
ed
=
4.191
qc
SR 15B
Martin
21
0.900
ed
=
2.519
qc
SR 15C
Martin
22
0.752
ed
=
3.300
qc
SR 715
Palm Beach
25
0.850
eD
=
5.330
qc

299
Figure 7.8 illustrates the plot of Eg against qc for most of the
test sites. The scatter of the data may be attributed to spatial soil
variability within and between test sites. Also, the variability in the
relationship between Eg and qc could be affected by soil type, moisture
content, and stress history. Regression analysis of the combined data
resulted in the following equation:
Eqn. 7.2
Eg = 3.46 qc
(R2 = 0.830, N = 224)
Due to the possible effect of soil type on the Eg-qc correlations,
further regression analyses were performed to establish separate rela
tionships for sandy and for clayey soils. The regression equations are:
For sandy soils:
Eg = 3.423 qc
Eqn. 7.3
(R2 = 0.852, N =154)
For clayey soils:
Eg = 4.141 qc
Eqn. 7.4
(R2
0.637, N = 70)

DILATOMETER MODULUS, E
^ 100
60
40
20
0
0 10 20 30 40
CONE TIP RESISTANCE, qc, (MPa)
** i
A0
A
*
O
O
X
SR26A
SR26B
SR26C
US 301
US 441
SR 12
SR15B
I
CO
o
o
Figure 7.8 Correlation of ED with qc

301
The regression equation for sandy soils is almost identical to
Equation 7.2 which included all the test data. The correlation for the
clayey soils is poor. This poor correlation tends to agree with obser
vations of Jamiolkowski et al. (50) that qc cannot be correlated to any
drained soil modulus for cohesive deposits.
The combined test data was also separated into above and below
water table categories for the purpose of assessing whether or not the
saturation state of the soils affected the Eg-qc correlations. The
regression equation for above water table conditions,
Ed = 3.64 qc Eqn. 7.5
(R2 = 0.926, N = 102)
is similar to Equation 7.3 for sandy soils. The equation obtained for
soils below water table,
Ed = 3.946 qc Eqn. 7.6
(R2 = 0.778, N = 122)
is almost the same as Equation 7.4 for cohesive soils. It is evident
that the correlations were affected by the development of excess pore
water pressures in the clayey soils during penetration. An attempt was
made to measure pore water pressures using the piezocone. This was
unsuccessful due to the clogging of the porous stone by fine sand
particles.

302
Even though these correlations are probably site specific, the
ratio of Eg to qc obtained in this investigation were within the range
of values reported in the literature. However, since the CPT and DMT
were performed side by side, these correlations are unique and represent
improvements over other correlations which are based on comparison be
tween field measured qc and laboratory determined deformation moduli.
It also tends to support the argument by many engineers that the cone
resistance, which is primarily an indicator of bearing capacity, can be
related to soil deformation moduli.
7.4 Evaluation of Resilient Moduli for Pavement Layers
7.4.1 General
One of the main objectives of this study was to assess the feasibi
lity of determining the modulus of pavement layers and underlying sub
grade soils using in situ penetration tests. However, Section 7.3
indicated that the parameters from the penetration tests, especially the
dilatometer modulus (Eg), are related to the secant modulus at 25 per
cent stress levels (E'2S) The modulus obtained from NOT generally
represents the initial tangent modulus of the resultant stress-strain
relationship. Because NDT and wheel loadings are generally applied in
short duration of time periods, lower strain levels are obtained.
Therefore, the use of in situ penetration tests' modulus values in
multilayer elastic analysis would be very conservative, and overpredict
pavement response.
Because qc and Eg relate directly to the in situ deformation char
acteristics of the soils, it was decided to correlate these parameters
to the NDT tuned layer moduli. The tuned layer moduli have been

303
referred to as resilient moduli since the load-deformation character
istics of flexible pavements are resilient (see Section 6.5.1). Also,
because different moduli values were obtained from the Dynaflect and FWD
tests, it was decided to correlate layer moduli predictions with the in
situ penetration tests for both NDT devices.
The correlations between pavement layer moduli and qc and Eq are
presented in Section 7.4.2 and 7.4.3, respectively. The average qc and
Eq values for each layer was correlated to the respective NDT tuned
moduli. The first layer was excluded, since the resilient characteris
tics of asphalt concrete materials are both temperature and rate of
loading dependent.
The effective pavement thickness (EPT) was defined to be 1 m from
the pavement surface. Materials below this were assumed to have no
effect on traffic-associated pavement performance. This was then used
to determine the thickness of the effective subgrade layer which was
therefore the difference between 1 m and the overlying pavement thick
ness. This assumption is consistent with the conventional design
practice in which subgrade samples immediately below the subbase layer
are tested for modulus values. Also, other workers have selected
similar depths for use with their penetration tests. For example, Kleyn
et al. (58) considered the depth of 0.8 m for the use of the dynamic
cone penetrometer in road pavements. Briaud and Shields (15) conducted
their pavement pressuremeter tests to a depth of 1.8 m in airport
pavements. Even though EPT could vary with pavement types and stiffness
characteristics of the pavement a value of 1 m was used in this
analysis. The possible effects of this and the general problem of
characterizing the subgrade layer are discussed in Section 7.5.

304
7.4.2 Correlation of Resilient Moduli with Cone Resistance
The average qc values determined from each layer were compared to
the respective NDT tuned layer moduli. The results of these comparisons
are illustrated in Tables 7.2, 7.3, and 7.4, for the base course, sub
base, and subgrade layers, respectively. The ratios of tuned moduli to
cone resistance presented in Tables 7.2 to 7.4 indicate that variability
increases from E2 through Eg to E^. However, significantly high ratios
were obtained for the base course and subbase layers for some of the
pavements. For example, the SR 12 pavement had a ratio of 61.29 using
the Dynaflect tuned E2 (Table 7.2). The corresponding FWD E2 to qc
ratio, 15.83, does not differ much from the others in the FWD column.
Also, the plate loading test results gave an E2 value of 43,000 psi (see
Table 6.17) as compared to the NDT tuned values of 120,000 and 31,000
psi, respectively, with the Dynaflect and FWD deflection responses.
Thus, the FWD prediction of the base course modulus on SR 12 test
section may be more realistic than that of the Dynaflect. Pavement
sections and layers having extremely high or low ratios were excluded in
subsequent analysis of the data.
Regression analyses were performed between resilient (or tuned NDT)
moduli and cone resistance. The results are summarized in Table 7.5.
The results suggest that the correlations are good for the base and
subbase layers but poor for the subgrade. Also, the results for the
combined data are similar to those for the base and subbase layers as
compared to the subgrade layer. The poor correlation in the subgrade
could be due to the natural variability in the subgrade soils compared
to the essentially homogeneous base and subbase materials. The tech
nique of determining the subgrade layer might have also affected the

305
Table 7.2 Correlation of NDT Tuned Base Course Modulus (E2)
to Cone Resistance
Test
Road
Mile Post
Number
Average qc
(psi)
Tuned E2
(psi)
e2/9
c
Dynaflect
FWD
Dynaflect
FWD
SR 26A
11.912
6525
105000
75000
16.09
11.49
SR 26B
11.205
6380
90000
90000
14.11
14.11
SR 26C
10.168
5075
55000
45000
10.84
8.87
SR 26C
10.166
5075
55000
55000
10.84
10.84
SR 24
11.112
8338
105000
55000
12.59
6.60
US 301
21.580
8048
120000
45000
14.91
5.59
US 441
1.236
7250
85000
55000
11.72
7.59
I-10A
14.062
7685
95000
90000
12.36
11.71
SR 15B
4.811
6815
120000
52800
17.61
7.75
SR 15A
6.549
*
120000
95000


SR 15A
6.546
*
85000
45000


SR 715
4.722
*
75000
45000


SR 715
4.720
*
65000
65000

SR 12
1.485
1958
120000
31000
61.29
15.83
SR 15C
0.055
3625
105000
35000
28.97
9.66
SR 15C
0.065
5510
105000
50000
19.06
9.07
* Test not performed

306
Table 7.3 Correlation of NDT Tuned Subbase Modulus (E3)
to Cone Resistance
Test
Road
Mile Post
Number
Average qc
(psi)
Tuned Eg
(psi)
E,
Dynaflect
FWD
Dynaflect
FWD
SR 26A
11.912
4532
70000
45000
15.45
9.93
SR 26B
11.205
4118
60000
45000
14.57
10.93
SR 26C
10.168
4350
35000
27500
8.05
6.32
SR 26C
10.166
4350
35000
22000
8.05
5.06
SR 24
11.112
6344
75000
40000
11.82
6.31
US 301
21.580
3154
75000
35000
23.78
11.10
US 441
1.236
4350
60000
35000
13.79
8.05
I-10A
14.062
5655
89400
80000
15.81
14.15
SR 15B
4.811
6090
75000
50000
12.32
8.21
SR 15A
6.549
*
40000
39500


SR 15A
6.546
*
65000
35000


SR 715
4.722
3843
50000
25000
13.01
10.28
SR 715
4.720
3843
45000
26000
11.71
6.77
SR 12
1.485
3915
75000
20000
19.16
5.11
SR 15C
0.055
2175
75000
12000
34.48
5.52
SR 15C
0.065
2175
75000
44000
34.48
20.23

Test not performed

307
Table 7.4 Correlation of NDT Tuned Subgrade Modulus (E4)
to Cone Resistance
Test
Road
Mile Post
Number
Average qc
(psi)
Tuned E,
4
(psi)
Dynaflect
FWD
Dynaflect
FWD
SR 26A
11.912
2900
14600
18700
5.03
6.45
SR 26B
11.205
2610
7900
11000
3.03
4.21
SR 26C
10.168
2175
28500
25500
13.10
11.72
SR 26C
10.166
2175
28500
20000
13.10
9.20
SR 24
11.112
4712
38600
38600
8.20
8.20
US 301
21.580
1740
38600
25000
22.18
14.37
US 441
1.236
3190
27500
20000
8.62
6.27
I-10A
14.062
5293
105000
130,000
19.84
24.56
SR 15B
4.811
3625
8100
10200
2.23
2.81
SR 15A
6.549
*
4800
7500


SR 15A
6.546
*
5000
7000


SR 715
4.722
1015
6000
11000
5.91
10.84
SR 715
4.720
1015
5500
10500
5.42
10.34
SR 12
1.485
2175
26500
18500
12.18
8.51
SR 15C
0.055
2175
5500
9800
2.53
4.51
SR 15C
0.065
2175
5500
10000
2.53
4.60
* Average qc within the depth of the difference of 1 m (EPT) and the
overlying pavement
** Test not performed

308
Table 7.5 Relationship Between Resilient Modulus,
and Cone Resistance, qc
Dynaflect
Moduli
FWD Moduli
Layer
Regression*
Equation
N
R2
Regression*
Equation
N
R2
Base
E2 = 13.992 qc
i0(a)
0.971
E2 = 9.073 qc
12
0.921
Subbase
E3 = 12.987 qc
11(b)
0.954
E3 = 7.467 qc
12(c)
0.942
Subgrade
E, = 6.699 qc
12(d)
0.770
E^ = 6.853 qc
13(e)
0.856
ALL
Er = 12.881 qc
33
0.931
E^ = 8.356 qc
37
0.910
* Some of the test pavements were deleted in the regression analysis.
Pavements deleted are:
(a) SR 12 and SR 15C M.P. 0.055
(b) US 301 and SR 15C M.P. 0.055 and 0.065
(c) SR 15C M.P. 0.065
(d) US 301 and I-10A
(e) I-10A
correlation since a composite (average) subgrade modulus from the NOT
was used in the analysis.
7.4.3 Correlation of Resilient Moduli with Dilatometer Modulus
Similar comparisons with the dilatometer modulus, Eq, for the sub
base and subgrade layers were made. These are presented in Tables 7.6
and 7.7, respectively. The ratios of tuned moduli to Eq for the subbase
vary more than that of the subgrade. Also the ratios obtained for the
subgrade are close to unity, especially for the pavement sections with
weak subgrade layers. This suggests that the dilatometer modulus may be
directly related to the in situ elastic (resilient) modulus of soft
subgrade soils.

309
Table 7.6
Correlation
Dilatometer
of NOT Tuned
Modulus
Subbase
Modulus to
Test
Mile Post
Average Eq
Tuned E3
(psi)
E
3/ed
Road
Number
(psi)
Dynaflect
FWD
Dynaflect
FWD
SR 26A
11.912
9860
70000
45000
7.10
4.56
SR 26B
11.205
11455
60000
45000
5.24
3.93
SR 26C
10.168
13340
35000
27500
2.62
2.06
SR 26C
10.166
13340
35000
22000
2.62
1.65
SR 24
11.112
*
75000
40000


US 301
21.580
8700
75000
35000
8.62
4.02
US 441
1.236
12615
60000
35000
4.76
2.77
I-10A
14.062

89400
80000


SR 15B
4.811
14065
75000
50000
5.33
3.55
SR 15A
6.549
12470
40000
39500
3.21
3.17
SR 15A
6.546
12470
65000
35000
5.21
2.81
SR 715
4.722
15718
50000
25000
3.18
1.59
SR 715
4.720
15718
45000
26000
2.86
1.65
SR 12
1.485
11673
75000
20000
6.43
1.71
SR 15C
0.055
3335
75000
12000
22.49
3.60
SR 15C
0.065
3335
75000
44000
22.49
13.19
* Test not performed

310
Table 7.7
Correlation
Dilatometer
of NDT Tuned Subgrade
Modulus
Modulus to
Test
Mile Post
Average Eq*
Tuned E,
r 4
(psi)
E4
./Ed
Road
Number
(psi)
Dynaflect
FWD
Dynaflect
FWD
SR 26A
11.912
8990
14600
18700
1.62
2.08
SR 26B
11.205
11528
7900
11000
0.69
0.95
SR 26C
10.168
9933
28500
25500
2.87
2.57
SR 26C
10.166
9933
28500
20000
2.87
2.01
SR 24
11.112
**
38600
38600


US 301
21.580
9752
38600
25000
3.96
2.56
US 441
1.236
7975
27500
20000
3.45
2.51
I-10A
14.062

105000
130000


SR 15B
4.811
11165
8100
10200
0.73
0.91
SR 15A
6.549
5365
4800
7500
0.89
1.40
SR 15A
6.546
5365
5000
7000
0.93
1.30
SR 715
4.722
7250
6000
11000
0.83
1.52
SR 715
4.720
7250
5500
10500
0.76
1.45
SR 12
1.485
7323
26500
18500
3.62
2.53
SR 15C
0.055
7250
5500
9800
0.76
1.35
SR 15C
0.065
7250
5500
10000
0.76
1.38
* Average Eq within the depth of the difference of 1 m (EPT) and the
overlying pavement.
** Test not performed

311
Regression analyses were also performed between the resilient (NDT
tuned) and dilatometer moduli. Table 7.8 lists results of the analysis
for the subbase and subgrade layers. The correlation coefficients are
lower than those obtained for the cone resistance in Table 7.5. Thus,
the CPT may be more reliable than the DMT in predicting the modulus of
layered pavement systems and subgrade soils. The ability of the CPT to
test stiffer soils such as base course materials make it more attractive
than the DMT. However, for weak subgrade soils the DMT may yield
reasonable modulus predictions. Further work on the interpretation of
both CPT and DMT results may be worth pursuing.
Table 7.8 Relationship Between Resilient Modulus, ER
and
Dilatometer
Modulus,
ed
Dynaflect
Moduli
FWD Moduli
Layer
Regression
Equation
N
R2
Regression
Equation
N
R2
Base
*


*


Subbase
E3 = 4.317
eD
12(a)
0.874
E3 = 2.576 Ed
13(b)
0.879
Subgrade
E, = 1.855
eD
14
0.697
Eii = 1-749 Ed
14
0.882
ALL
E, = 3.476
4
ed
26
0.767
e4 = 2.294 Ed
27
0.854
* Dilatometer test not conducted in base course layer
NOTE: Some pavements were deleted in the regression analysis. Those
pavements are:
(a) SR 15C M.P. 0.055 and 0.065
(b) SR 15C M.P. 0.065

312
7.5 Variation of Subgrade Stiffness with Depth
The analysis presented in Section 7.4 indicated the potential for
CPT prediction of the modulus of layered pavement systems as compared to
the DMT. However, the correlation of NDT tuned moduli to CPT qc values
were generally better for the base and subbase layers than the subgrade
layer (see Table 7.5). The poor correlation for the subgrade was attri
buted, among others, to the technique of determining the effective
subgrade layer. This layer was assumed to extend from the subbase-
subgrade interface to a depth of 1 m from the surface of the pavement
without regard to the stratification of the underlying subsoils. It is
argued that depending on the relative stiffnesses of the upper layers,
the zone of influence of the dynamic loads from NDT or actual wheel
loadings could exceed the EPT value of 1 m used in the analysis.
For a pavement section with very weak subsoil conditions, the
effective subgrade layer used in the analysis is in reality an embank
ment which was placed on the natural subgrade to facilitate construction
of the pavement. Therefore, this layer would not be a true represen
tation of the subsoil conditions on the site. However, the subgrade
modulus determined from the NDT deflections and used in the correlation
represents a homogeneous and isotropic semi-infinite layer, as pre
viously explained. In reality, infinitely thick subgrades and homo
geneous subgrades with a well-defined depth seldom exist in the field.
This is substantiated by the stiffness profiles presented in Figures 7.1
to 7.7 in which there was a general tendency for the stiffness to
decrease significantly to a depth of 2 m. This would mean that in
principle the subgrade must be considered as two or more sublayers
depending on the stratigraphy, and consequently, increasing the total

313
number of layers in the pavement structure. The possible effects of
subgrade stratification on NDT deflections and pavement response are
briefly discussed below.
The CPT log for SR 26A, as shown in Figure 7.3, was used to assess
the variability of the subgrade layer. This test section had a clay
layer (approximately 0.6 m thick) at a depth of about 2 m. The water
table was at a depth of 1.58 m. The CPT log was used to divide the sub-
grade layer into 4 sublayers and an average qc value was assigned to
each layer. The Dynaflect modulus-qc equation for the combined data in
Table 7.5 was used to predict the respective layer modulus. The number
of pavement layers was assumed to vary from 4 to 7, depending on the
number of layers in the subgrade. BISAR was then used to predict Dyna
flect deflections for the various layers. In all cases, the moduli
values for the AC, base and subbase layers were kept constant.
Table 7.9 shows the results of Dynaflect deflections predicted by
BISAR for the various numbers of layers. The actual field measured
deflections are also listed in Table 7.9. It is seen from the table
that significantly different deflections were predicted by BISAR
depending on the number of layers and the modulus value of the semi-
infinite subgrade layer. For example, when the weak clay layer was
considered as the semi-infinite layer in a 6-layer system, the predicted
deflections were more than twice the measured values. However, when
this weak clay layer was underlain by a relatively stiff subgrade in the
7-layer system, predicted and measured deflections were comparable.
The above illustration tends to support the argument that founda
tion layers with highly variable moduli significantly influence the
response characteristics of nondestructive tests. The presence of a

Table 7.9 Effect of Varying Subgrade Stiffness on Dynaflect Deflections on SR 26A
Number
of
Layers
Subgrade
Modulus*
(ksi)
Dynaflect Deflections (mils)
D
l
D
2
D
3
D
4
D
5
D
6
D
7
D
8
D
9
D
10
**

0.87

0.81
0.77

0.68
0.61
0.53
0.45
0.39
4(a)
37.4
0.63
0.49
0.47
0.45
0.41
0.36
0.32
0.26
0.20
0.15
50>)
18.9
0.80
0.66
0.64
0.62
0.58
0.53
0.49
0.42
0.34
0.29
6(c)
2.6
1.84
1.71
1.69
1.67
1.62
1.57
1.52
1.44
1.34
1.25
7(d)
23.1
0.93
0.79
0.77
0.75
0.71
0.66
0.62
0.54
0.45
0.38
*
**
(a)
(b)
(c)
(d)
Semi-infinite subgrade modulus using Dynaflect modulus qc correlation in Table 7.5 with
Ex = 171.5 ksi; E2 = 105.0 ksi; E3 = 70.0 ksi; t: = 8.0 in.; t2 = 9.0 in.; t3 = 12.0 in.
Field measured Dynaflect deflections
4-layer: E4 = 37.4 ksi and t4 =
5-layer: E4 = 37.4 ksi; E5 = 18.9 ksi; t4 = 10.4 in. and tg = >
6-layer: E4 = 37.4 ksi; E5 = 18.9 ksi; Eg = 2.6 ksi; t4 = 10.4 in.; tg = 19.7 in. and tg =
7-layer: E4 = 37.4 ksi; Eg = 18.9 ksi; Eg = 2.6 ksi; E? = 23.1 ksi; t4 10.4 in.; tg = 19.7 in.;
t6 = 29.5 in. and t? =
00

315
clay layer at shallow depth and near the water table, as in the case of
the SR 26A site, would not only contribute to a loss in foundation
support, but would also channel water to the upper layers by means of
capillary rise. Pore pressures could also be generated in the submerged
layers which would also contribute to a reduction in foundation support.
In general, a weak subgrade would result in high deflections. However,
it will be shown in Chapter 8 that high deflections do not necessarily
mean that the pavement is structurally deficient. Depending on the
relative stiffnesses of the upper layers (AC, base and subbase), a pave
ment with a weak subgrade (or high deflections) could yield moderate
stresses in the various layers. But when the subbase and base course
layers also become weak as a result of moisture intrusion, the entire
pavement could deteriorate structurally.
The variation of the subgrade stiffness with depth is also impor
tant for the design of new pavements. Table 7.9 suggests that predicted
deflections could differ significantly from field measured values
depending on the subgrade modulus. In most design procedures, the
thicknesses of the upper layers are selected based on the subgrade
modulus. Therefore, if samples of the subgrade from immediately below
the subbase layer are tested for modulus, use of this modulus could
result in a completely wrong design. Knowledge of the stratigraphy
would help to arrive at an optimum and efficient design. It is believed
that the CPT has the capability of providing such important informa
tion. Also, the correlations presented in Table 7.5 could be used to
estimate the subgrade modulus. This could avoid the problems of deter
mining subgrade modulus in the laboratory.

CHAPTER 8
PAVEMENT STRESS ANALYSES
8.1 General
The mechanistic approach for evaluation and design of pavement sys
tems contains an important empirical component. It relies on empirical
relationships between pavement response and pavement performance. In
general, two relationships are used, one for predicting cracking of
bound layers (e.g., asphalt concrete) and one for predicting permanent
deformations (roughness or rutting) of the base course and subgrade
layers. These forms of deterioration are, respectively, referred to as
structural and functional by Ullidtz and Stubstad (123). The horizontal
tensile stress or strain at the bottom of the asphalt concrete and the
vertical strain or stress on top of the subgrade are both considered in
the evaluation of flexible pavements. Essentially, these relate to the
bearing capacity and riding quality, respectively, of the pavement. As
suggested by Ullidtz and Stubstad (123), it would be preferable to
interrelate the two relationships such that cracking is considered in
the model for predicting roughness and rutting. However, this is seldom
attempted in practice.
Despite the empirical component, the mechanistic design process has
obvious advantages over existing empirical methods which are based on
the correlation between the maximum deflection under a load and pavement
performance. The use of maximum deflection as an indicator of struc
tural capacity may be misleading, depending upon the stiffness of the
pavement layers relative to the subgrade moduli. For example, a stiff
316

317
pavement with a weak foundation layer can produce high deflections but
lower load-induced stresses and strains than a so-called high quality
pavement.
However, the mechanistic process allows the engineer to base his
decision on a rational evaluation of the mechanical properties of the
materials in the existing pavement structure. The material properties,
for example moduli, are then used to calculate the response parameters
(stresses, strains, and displacements) under some determined loadings
and environmental conditions. Pavement layer thicknesses are modified
until the critical stresses or strains do not exceed permissible values.
The establishment of the allowable stresses or strains is the most dif
ficult part of the mechanistic approach. Therefore, empirical guide
lines and relationships continue to be used.
As mentioned previously, two forms of criteria are used; maximum
tensile stress or strain at the bottom of AC layer, and the vertical
stress or strain on top of the subgrade. These are used with empirical
relationships to compute the remaining life of the pavement and overlay
required to meet established criteria. The horizontal tensile stress at
the bottom of the bound layer is assumed critical in evaluating the
pavement's resistance to cracking. However, there are indications that
the mechanisms of cracking of asphalt concrete are not fully understood
and that the concepts used to analyze these cracks may not be completely
val id.
Existing pavement design procedures usually consider cracking of
asphalt-bound layers to be caused by traffic-load-induced fatigue.
Therefore, the allowable stress or strain criterion is based on the
number of repetitions of vehicular loadings to reach the fatigue level.

318
However, Ruth and his co-workers (97,98) have hypothesized that cracking
is a short-term phenomenon that occurs when the combined effect of tem
perature and traffic loads exceed the failure limit of the asphalt
concrete pavement. The research work of Roque (96) on a full-scale
pavement tested at low temperatures indicated that rapid cooling pro
duces sufficient temperature differential to result in thermal rippling
of the pavement. Rippling of the asphalt concrete occurs when portions
of the pavement lift off from the base course forming a wave-like
pattern (96,97). Therefore, heavy wheel loadings applied at different
temperatures can greatly influence the stress levels and cracking poten
tial of asphalt concrete pavements. Also, Ullidtz and Stubstad (123)
argue that field observations suggest that cracking of an asphalt layer
often originates at the top of the layer and not at the bottom, as
conventionally assumed.
A complete review of the mechanisms of cracking is beyond the scope
of the work presented herein. However, this chapter demonstrates how
the moduli determined from NDT can be used to analyze the response of a
pavement to load at low temperatures. Also, it is intended to show how
empirical interpretation of NDT deflections can often be misleading in
assessing the structural adequacy of a pavement. A rigorous analysis is
not presented here. Only the short-term loading is considered, and the
effect of any long-term loading (thermal stresses) are neglected.
8.2 Short-Term Load Induced Stress Analysis
8.2.1 Design Parameters
The BISAR elastic layer computer program was used to calculate
pavement response induced by vehicular loadings at low temperature

319
conditions. A 24-kip single axle with dual tires at 120 psi (13.0 in.
between wheel centers) was used to represent truck loading conditions.
Five test pavements were selected for the analysis. These are SR 26B,
SR 24, US 441, SR 15C, and SR 80. Asphalt concrete moduli for the
various pavement sections were computed using the rheology relationship
at two low temperatures. These are 23F and 41F to represent winter
conditions in northern (SR 26B, SR 24, and US 441), and southern (SR 15C
and SR 80) Florida, respectively.
The other pavement layer moduli were obtained from the NDT test
results (Tables 6.7 and 6.10). With the exception of SR 80, the com
putations were made using both Dynaflect and FWD tuned moduli for
comparison purposes. For SR 80, the comparison was made for both
sections, with the objective of verifying the cracking problems of
Section 2. The input parameters for BISAR are listed in Tables 8.1
through 8.5 for each test site.
The computer program was used to compute the critical response
parameters (stresses and strains) at the bottom of the AC layer, and at
the top of the base, subbase, and subgrade layers. Also computed were
the maximum surface deflection under load and the percent compression of
each layer. The values computed at the center of one wheel are listed
in Tables 8.1 to 8.5 for each pavement site. The tabulated results
generally constituted the critical responses for the various pavement
systems. The interaction between pavement response and material proper
ties are presented in the ensuing discussion.
8.2.2 Comparison of Pavement Response and Material Properties
The results of the stress analysis for the five pavements are
listed in Tables 8.1 to 8.5. The tables indicate that the responses

320
Table 8.1 Material Properties and Results of Stress
Analysis for SR 26B (Gilchrist County)
a) Input Parameters for BISAR
Layer
Description
Thickness
Poisson's
Modulus
(psi)
(in.)
Ratio
Dynafl ect
FWD
1
Asphalt Concrete
8.0
0.35
1,315,600 *
1,315,600 *
2
Limerock Base
7.5
0.35
90,000
90,000
3
Subbase
12.0
0.35
60,000
45,000
4
Subgrade
semi-
infinite
0.45
7,900
11,000
* Computed at 23F using rheology data (Table 5.4)
b) Pavement Stress Analysis
Response Parameters**
Dynaflect
FWD
Maximum Surface Deflection
(mils)
13.0
11.1
Radial stress, bottom of AC layer
(psi)
89.0
90.7
Radial strain, bottom of AC layer
(E-6
in./in.)
42.2
42.9
Vertical stress, top of base layer
(psi)
-11.1
-10.8
Radial stress, top of base layer
(psi)
0.7
1.0
Vertical stress, top of subbase layer
(psi)
-4.3
-4.2
Radial stress, top of subbase layer
(psi)
2.0
1.3
Vertical stress, top of subgrade
(psi)
-1.3
-1.6
Vertical strain, top of subgrade
(E-6
in./in.)
-166.0
-151.0
Deflection in AC layer
(%)
2.3
2.7
Deflection in base layer
(%)
6.3
7.2
Deflection in subbase layer
(%)
8.4
9.7
Deflection in subgrade layer
(%)
83.0
80.4
* Computed for single axle with dual wheels (6.0 kips/wheel)
NOTE: + = tensile
- = compressive

321
Table 8.2 Material Properties and Results of Stress
Analysis for SR 24 (Alachua County)
a) Input Parameters for BISAR
Layer
Description
Thickness
(in.)
Poisson's
Ratio
Modulus
(psi)
Dynaflect
FWD
1
Asphalt Concrete
2.5
0.35
1,288,100 *
1,288,100
2
Limerock Base
11.0
0.35
105,000
55,000
3
Subbase
17.0
0.35
75,000
40,000
4
Subgrade
00
0.35
38,600
38,600
* Computed at 23F using
rheology
data (Table
5.4)
b) Pavement Stress Analysis
Response Parameters**
Dynaflect
FWD
Maximum Surface Deflection
(mils)
8.1
11.7
Radial stress, bottom of AC layer
(psi)
182.0
278.0
Radial strain, bottom of AC layer
(E-6 in
./in.)
101.0
140.0
Vertical stress, top of base layer
(psi)
-58.3
-46.0
Radial stress, top of base layer
(psi)
-12.2
-10.5
Vertical stress, top of subbase layer
(psi)
-12.2
-11.8
Radial stress, top of subbase layer
(psi)
-0.4
-0.7
Vertical stress, top of subgrade
(psi)
-3.2
-3.9
Vertical strain, top of subgrade
(E-6 in
./in.)
-87.7
-105.0
Deflection in AC layer
(X)
1.1
0.9
Deflection in base layer
(X)
34.1
38.4
Deflection in subbase layer
(X)
19.1
25.2
Deflection in subgrade layer
(X)
44.9
35.5
* Computed for single axle with dual
wheels
(6.0 kips/wheel)
NOTE: + = tensile
- = compressive

322
Table 8.3 Material Properties and Results of Stress
Analysis for US 441 (Columbia County)
a) Input Parameters for BISAR
Layer
Description
Thickness
Poisson's
Modulus
(psi)
(in.)
Ratio
Dynaflect
FWD
1
Asphalt Concrete
3.0
0.35
1,453,200 *
1,453,200 *
2
Limerock Base
9.0
0.35
85,000
55,000
3
Subbase
12.0
0.35
60,000
35,000
4
Subgrade
semi-
infinite
0.40
27,500
20,000
* Computed at 23F using log = 6.4167 0.01106T for pavements at
incipient failure.
b) Pavement Stress Analysis
Response Parameters**
Dynaflect
FWD
Maximum Surface Deflection
(mils)
10.0
12.4
Radial stress, bottom of AC layer
(psi)
208.0
267.0
Radial strain, bottom of AC layer
(E-6
in./in.)
93.9
115.0
Vertical stress, top of base layer
(psi)
-42.0
35.0
Radial stress, top of base layer
(psi)
-8.0
-7.1
Vertical stress, top of subbase layer
(psi)
-12.1
-11.4
Radial stress, top of subbase layer
(psi)
0.02
-0.5
Vertical stress, top of subgrade
(psi)
-4.2
-4.8
Vertical strain, top of subgrade
(E-6
in./in.)
-155.0
-176.0
Deflection in AC layer
(%)
1.0
0.8
Deflection in base layer
(%)
23.8
25.8
Deflection in subbase layer
(%)
17.0
21.6
Deflection in subgrade layer
(%)
58.2
51.8
* Computed for single axle with dual wheels (6.0 kips/wheel)
NOTE: + = tensile
- = compressive

323
Table 8.4 Material Properties and Results of Stress
Analysis for SR 15C (Martin County)
a) Input Parameters for BISAR
Layer
Description
Thickness
Poisson s
Modulus
(psi)
(in.)
Ratio
Dynaflect
FWD
1
Asphalt Concrete
6.75
0.35
680,000 *
680,000 *
2
Limerock Base
12.5
0.35
105,000
50,000
3
Subbase
12.0
0.35
75,000
44,000
4
Subgrade
semi-
infinite
0.45
5,500
10,000
* Computed at 41F using rheology data (Table 5.4)
b) Pavement Stress Analysis
Response Parameters**
Dynaflect
FWD
Maximum Surface Deflection
(mils)
15.9
14.3
Radial stress, bottom of AC layer
(psi)
54.4
88.5
Radial strain, bottom of AC layer
(E-6
in./in.)
56.5
82.5
Vertical stress, top of base layer
(psi)
-23.0
-16.7
Radial stress, top of base layer
(psi)
-1.7
-1.6
Vertical stress, top of subbase layer
(psi)
-4.5
-4.8
Radial stress, top of subbase layer
(psi)
2.8
1.3
Vertical stress, top of subgrade
(psi)
-0.95
-1.8
Vertical strain, top of subgrade
(E-6
in./in.)
-161.0
-173.0
Deflection in AC layer
(*)
3.1
3.5
Deflection in base layer
(%)
8.8
16.1
Deflection in subbase layer
(%)
5.7
9.1
Deflection in subgrade layer
(%)
82.4 .
71.3
* Computed for single axle with dual wheels (6.0 kips/wheel)
NOTE: + = tensile
- = compressive

324
Table 8.5 Material Properties and Results of Stress
Analysis for SR 80 (Palm Beach County)
a) Input Parameters for BISAR
Layer
Description
Thickness
(in.)
Poisson's
Modulus
(psi)
Ratio
Sec. 1
Sec. 2
1
Asphalt Concrete
1.5
0.35
642,540 *
642,540 *
2
Limerock Base
10.5
0.35
45,000
26,500
3
Subbase
36.0
0.40
18,000
18,000
4
Subgrade
CO
0.45
5,750
5,750
* Computed at 41F using Equation 6.3
b) Pavement Stress Analysis
Response Parameters**
Dynaflect
FWD
Maximum Surface Deflection
(mils)
32.0
38.5
Radial stress, bottom of AC layer
(psi)
207.0
344.0
Radial strain, bottom of AC layer
(E-6
in./in.)
251.0
374.0
Vertical stress, top of base layer
(psi)
-87.4
-76.4
Radial stress, top of base layer
(psi)
-25.0
-21.8
Vertical stress, top of subbase layer
(psi)
-12.6
-14.7
Radial stress, top of subbase layer
(psi)
-1.0
-1.6
Vertical stress, top of subgrade
(psi)
-1.11
-1.12
Vertical strain, top of subgrade
(E-6
in./in.)
-191.0
-206.0
Deflection in AC layer
(X)
0
0.3
Deflection in base layer
(%)
27.2
41.6
Deflection in subbase layer
(X)
29.4
33.4
Deflection in subgrade layer
(X)
43.4
24.7
* Computed for single axle with dual wheels (6.0 kips/wheel)
NOTE: + = tensile
- = compressive

325
predicted using Dynaflect and FWD tuned moduli are comparable for SR
26B, US 441, and SR 15C test sites. The similarity in response predic
tions for the two NDT devices may be attributed to the high stiffness of
layers 1, 2, and 3 relative to the underlying layer support values.
However, Table 8.2 shows that significant differences between Dynaflect
and FWD occurred on SR 24 because of large differences in base course
and subbase moduli even though the asphalt concrete and subgrade moduli
were identical for both NDT devices.
Tensile stresses were predicted in the base course and subbase
layers of SR 26B and SR 15C. However, the magnitude of these stresses
are too low (2.8 psi maximum at SR 15C) to be of concern. Also, verti
cal stresses on top of the base course layer were higher for thin pave
ments (SR 24, US 441, and SR 80) than pavements with thick AC layers (SR
26B and SR 15C). The vertical subgrade stresses were generally low,
with a maximum value of 4.8 psi obtained for US 441. This value consti
tutes 4.0 percent of the total vertical stress of 120 psi applied by
each wheel. This value is less than the limiting 10 percent stress
level conventionally used with the classical Boussinesq's solution
(59,133). Thus, the relatively high stiffnesses of the pavement layers
(i.e., the asphalt concrete, base, and subbase) result in a reduction in
the stresses and strains on top of the subgrade.
Table 8.6 lists a summary of the stress analyses for each pavement.
The maximum surface deflection ranges from 8.1 to 38.5 mils. The major
ity of these maximum deflections occurred in the subgrade layer, as
indicated by the corresponding percent compression values. In general,
high deflections were associated with pavements with low subgrade
moduli. However, the high deflections obtained in most of these

Table 8.6 Summary of Pavement Stress Analysis at Low Temperatures
Test
Road
Temp.
(F)
NDT
Device
Maximum
Deflection
(mils)
Subgrade
Compression
(%)
Subgrade
Strain*
(0.0001 in./in.)
AC
Stress**
(psi)
Percent of
AC Failure
Stress++
QD 9£R
O9
Dynaflect
13.0
83.0
1.66
89.0
22.3
OK OD
Co
FWD
11.1
80.4
1.51
90.7
22.7
SR 24
Dynaflect
8.1
44.9
0.88
182.0
45.5
FWD
11.7
35.5
1.05
278.0
69.5
IIS 441
Dynaflect
10.0
58.2
1.55
208.0
52.0
O
FWD
12.4
51.8
1.76
267.0
66.8
SR 15C
41
Dynaflect
15.9
82.4
1.61
54.4
13.6
FWD
14.3
71.3
1.73
88.5
22.1
SR-80-1
41
Dynaflect
32.0
43.4
1.91
207.0
51.8
SR-80-2
41
Dynaflect
38.5
24.7
2.06
344.0
86.0
SR 80+
41
Dynaflect+
26.3
50.6
1.75
74.4
18.6
* Vertical compressive strain on top of subgrade layer
** Tensile stress at bottom of AC layer
+ Base course modulus increased to 85.0 ksi
++ Failure tensile stress of 400 psi

327
pavements did not necessarily mean that the pavements were structurally
deficient.
A maximum failure tensile stress of 400 psi can be considered a
typical value for fracture of dense graded asphalt concrete mixtures
when subjected to a short-term loading at low temperatures (98). Using
the tensile stress at the bottom of the AC layer, the percent failure
stress for each pavement was computed. This is summarized in Table 8.6
for both NOT devices. Prior investigations (98) indicate that pavements
can crack when stress levels are in the range of 64 percent to 70 per
cent of the failure stress. Thus, when the combined effect of load and
thermal-induced stresses are considered, three out of the five pave
ments, if not already cracked, would be susceptible to cracking. These
are SR 24, US 441, and SR 80 test pavement sections.
Field observations (Section 5.2) indicated that US 441 and Section
2 of SR 80 test pavements exhibited considerable longitudinal, trans
verse, and block forms of cracking. However, the SR 24 pavement had
limited or hairline forms of cracking. At the time of testing, Section
1 of SR 80 was uncracked. However, it was confirmed by Mr. W. G. Miley
of the FD0T that cracks had subsequently appeared on Section 1 of SR 80
(personal communication, 1987).
Table 8.3 indicates that the US 441 test pavement had relatively
stiff layers and a low vertical stress (4.8 psi) on top of the subgrade.
This pavement probably cracked because the viscosity of the age-hardened
asphalt binder was high and the fracture strain was very low at the
lower in situ temperatures. Therefore, in those environmental situa
tions where rates of cooling and thermal stresses are very high, it is

328
essential that reduced stiffness be obtained by using softer and less
temperature-susceptible asphalts in the asphalt paving mixture.
The limerock base course moduli for the two SR 80 test sections
were considerably lower than the other pavements. Based on the results
of this investigation, a modulus value of 85,000 psi is considered
typical of well-placed limerock materials in the state of Florida.
Because the moduli of the extremely thick subbase (36 in.) and the
subgrade for SR 80 were relatively low, this pavement probably cracked
due to the lack of support from the upper pavement layers, primarily the
base course and the thin (1.5-in. thick) asphalt concrete layers. Field
observations indicated that either poor drainage conditions increased
the moisture content of the base course or the as-compacted quality of
the base material was poor and resulted in a substandard modulus for E2.
The base course modulus for SR 80 was increased to 85,000 psi
(standard base) for a stress analysis comparison to illustrate the
effect of an improved base course on pavement response and stresses.
The other layer moduli were kept constant and BISAR was used to compute
the response of this hypothetical pavement. These results are summa
rized in Table 8.7. Comparison of these results with the SR 80 test
sections indicates that the percent failure stress level drops to 18.6
percent. Thus, with a proper base course modulus, this pavement could
have yielded moderate stresses and good performance.
Table 8.6 shows that the vertical compressive strain on top of the
subgrade layer was of the order of 2.0 x 104 in./in. An axial compres
sive subgrade strain of 2.6 x 10"4 in./in. corresponds to a 108 repeti
tions of vehicular loading on flexible pavements (133). This limiting

329
Table 8.7 Effect of Increased Base Course Modulus
on Pavement Response on SR 80
a) Input Parameters for BISAR
Layer
Description
Thickness
Poisson's
Modulus (psi)
(in.)
Ratio
Dynaflect
1
Asphalt Concrete
1.5
0.35
642,540 *
2
Limerock Base
10.5
0.35
85,000 **
3
Subbase
36.0
0.40
18,000
4
Subgrade
semi-
infinite
0.45
5,750
* Computed at 23F using Equation ,6.3
** E2 value increased for illustration purposes
b) Pavement Stress Analysis
Response Parameters**
Dynaflect
Maximum Surface Deflection
(mils)
26.3
Radial stress, bottom of AC layer
Radial strain, bottom of AC layer
(E-6
(psi)
in./in.)
74.4
129.0
Vertical stress, top of base layer
Radial stress, top of base layer
(psi)
(psi)
-98.4
-31.1
Vertical stress, top of subbase layer
Radial stress, top of subbase layer
(psi)
(psi)
-10.0
-0.7
Vertical stress, top of subgrade
Vertical strain, top of subgrade
(E-6
(psi)
in./in.)
-1.0
-175.0
Deflection in AC layer
Deflection in base layer
Deflection in subbase layer
Deflection in subgrade layer
(X)
(X)
(X)
(X)
0
19.0
30.4
50.6
* Computed for single axle with dual wheels (6.0 kips/wheel)
NOTE: + = tensile
- = compressive

330
strain criteria, established by the Shell Oil Company, ensures that per
manent deformation in the subgrade will not lead to excessive rutting at
the pavement surface (133). The results summarized in Table 8.6 suggest
that these pavements are not susceptible to functional deterioration,
provided the desirable levels of moisture and loading conditions are
maintained.
8.2.3 Summary
The previous discussion has demonstrated that layer moduli from NOT
tests can be valuable in predicting the response of a pavement to the
combined effects of load and environment. Also, the use of the maximum
deflections from NDT equipment as an indicator of structural adequacy
may lead to erroneous interpretation. High deflections do not necessar
ily mean high stresses or a structurally deficient pavement.
The computations for US 441 and SR 80 suggest that the performance
and response characteristics of pavement materials are highly dependent
upon the effects of moisture, temperature, and the properties of the
asphalt binder. A pavement's resistance to load-induced cracking can be
improved by either increasing the stiffness of support layers or using a
lower viscosity asphalt to eliminate excessively high asphalt concrete
moduli at the minimum pavement temperatures. In those environmental
situations where rates of cooling and thermal stresses are very high, it
is important that both factors, improved stiffness of underlying support
layers, and softer asphalts, be incorporated in the pavement's struc
ture. Provision of proper drainageadequate camber, side drains, free-
draining materials, etc.--would help to ensure that the in-place
materials would maintain desirable levels of strength.

CHAPTER 9
CONCLUSIONS AND RECOMMENDATIONS
9.1 Conclusions
A research study was conducted to evaluate NDT deflection response
of flexible pavements and to develop procedures for the prediction of
layer moduli. The investigation included analyses of both computer-
simulated and field-measured NDT data on fifteen flexible pavement
sections in the state of Florida. In situ cone penetration, Marchetti
dilatometer and plate bearing tests were performed on most of these
pavement sections. The following conclusions were derived from the
results obtained in this investigation:
1. A simplified approach which allows a layer-by-layer analysis of the
Dynaflect deflection basin has been developed. This technique,
which utilizes a modified Dynaflect geophone configuration (Figure
F.l), provides the capability to separate the deflection response
contributed by the subgrade, stabilized subgrade, and the combina
tion of base and asphalt concrete layers for typical Florida flexi
ble pavement systems.
2. Analyses of Dynaflect data from the fifteen in-service flexible
pavements using the BISAR elastic layer computer program resulted in
the development of simple power law regression equations to predict
layer moduli from modified sensor deflections. A recommended test
ing and analysis procedures using the modified Dynaflect testing
system has been presented in Appendix F. The computational
331

332
algorithms of the recommended procedure were incorporated into the
BISAR computer program to perform the iteration after the initial
computation of the seed moduli. A partial listing of the DELMAPS1--
Dynaflect Evaluation of Layer Moduli for Asphalt Pavement Systems
Version l--computer program is also presented in Appendix G.
3. Sensitivity analyses of theoretically-derived FWD data indicated
that minor changes in Ej, E2, and E3 would not have a significant
effect on the FWD deflections. Multiple linear regression analysis
was utilized to develop layer moduli prediction equations from FWD
deflection basins.
4. Analyses of load-deflection response from FWD tests conducted on the
test pavements suggested that most of the pavement sections behave
linearly. Only three pavement sections (SR 26B, SR 15A, and SR 15B)
exhibited nonlinear response from the load-deflection diagrams.
Further analyses revealed that nonlinearity only occurred at loads
less than 6.0 kips. Therefore, FWD deflections should be measured
at higher loads, preferably 9 kips to minimize the effects of
nonlinearity.
5. Modeling of the FWD deflection basins for most of the test pavements
was found to be very difficult as compared to the deflections ob
tained with the Dynaflect. The difficulty in tuning the FWD deflec
tions was attributed to the FWD plate and loading system being rigid
rather than flexible.
6. Analyses of the tuned FWD data for the test sections did not result
in any simplified relations for layer moduli predictions, with the
possible exception of E^. Therefore, the current spacing of the
geophones and loading configuration of the FWD testing system

333
(Figure 3.3) are probably not suitable to allow the separation of
pavement layers from the interpretation of FWD deflections.
7. The use of viscosity-temperature relationship obtained from Schweyer
Rheometer tests on the recovered asphalts was found to be an effec
tive and reliable method for predicting the asphalt concrete modu
lus, Analysis of the NDT data resulted in the development of
Figure 6.64 which can be used as a simple and rapid method of esti
mating E from the mean pavement temperature during routine NDT
pavement evaluation studies.
8. Comparisons between Dynaflect and FWD tuned moduli for the various
test sections indicated that the Dynaflect predicted higher base
course and subbase (stabilized subgrade) moduli than the FWD. In
the case of the subgrade, no distinct trend between the two NDT
devices was found from the analyses. Therefore, the differences in
layer moduli predictions were attributable to the inherent differ
ences between the two NDT devices; namely, vibratory loading for the
Dynaflect versus impact loading for the FWD testing system.
9. The penetration tests provided means for identifying the soils and
also for assessing the variability in stratigraphy of test sites.
The cone resistance, qc, correlated well with the dilatometer modu
lus, Ed, especially for sandy soils and soils above the water
table. The correlation for clayey soils was poor, supporting the
argument that qc cannot be correlated to any drained soil modulus
for cohesive deposits.
10. Pavement layer moduli determined from NDT data were regressed to qc
and Eg for the various layers in the pavement. Good correlations
were obtained for qc as compared to Eg. However, the dilatometer

334
modulus compared favorably with the NDT moduli of weak (soft) sub
grade soils. Also, lower correlation coefficients were obtained for
the subgrade layer, suggesting the variability of this layer as
compared to the upper pavement layers.
11. Analysis of the in situ penetration tests indicated that these
tests, especially the CPT, can be used to supplement NDT for the
evaluation of pavements especially in locating zones of weakness in
the pavement or underlying subgrade soils.
12. Stress analysis conducted on some of the pavements provided a means
of establishing the possible causes of failure and/or surface
cracking on US 441 and SR 80. The analysis suggested that the
performance and response characteristics of pavement materials are
highly dependent upon the effects of moisture, temperature, and the
properties of the asphalt binder. High deflections do not necessar
ily mean high stresses or a structurally deficient pavement.
9.2 Recommendations
The ultimate goal of the techniques developed in this study is to
alleviate the problems of determining realistic pavement layer moduli
using current methods. The following recommendations for further study,
based on the results obtained in this investigation, are suggested:
1. The simplified approach using the modified Dynaflect testing system
would allow a large number of test points to be analyzed, and conse
quently enhance our ability to carry out mechanistic pavement evalu
ation on a routine basis. Therefore, it is recommended that the
FDOT (and other agencies) modify one of their Dynaflect units to
meet the system described herein. The modified and standard system

335
can be used side by side in research and routine pavement evaluation
studies. This should include an on-board computer (PC) which can
compute moduli for a four-layer pavement system using the regression
equations presented in Appendix F. The PC should be capable of
printing out deflection response and layer moduli profiles which
could be superimposed in graphic format for visual interpretation of
lineal segments of highway pavements.
2. The field tests reported herein were carried out on fifteen pavement
sections at fixed levels of temperature and moisture conditions. It
is recommended that additional work be conducted on other flexible
pavements at various seasons of the year to establish the effects
(if any) of moisture and temperature on the prediction equations.
3. The FWD layer moduli prediction equations were found to be reliable,
provided the field measured deflections simulated the conditions
used to develop the equations. It was suggested, among other fac
tors, that the rigidity of the plate, and the load-geophone configu
ration in the FWD testing system influenced field measured deflec
tions. This influence makes FWD deflections deviate from those
simulated from the multilayered linear elastic theory. It is there
fore recommended that further study be conducted to establish proce
dures to adjust field measured FWD deflections which would enhance
the use of the developed layer moduli prediction equations.
It is also believed that the FWD load-configuration does not
provide separation of the pavement layers' response as was obtained
with the Dynaflect. A modified FWD system utilizing a dual loading
system appears worth pursuing in future NDT research.

336
4. Further work on the interpretation of the in situ penetration tests
is required to improve the correlations obtained in this study. The
use of the penetration tests to characterize the subgrade layer and
the feasibility of the DMT to predict the moduli of weak subgrade
soils should be investigated.
5. Additional stress analyses using load and thermal-induced stresses
should be performed to establish relationships between pavement
performance (stress-strain response) and material properties.
6. The algorithms obtained in this study should be used to develop a
computer program for mechanistic evaluation of flexible pavements.
This program should be able to predict the remaining life and/or
overlay thickness of a pavement subjected to the combined effects of
load and thermal-induced stresses.

APPENDIX A
FIELD DYNAFLECT TEST RESULTS

Table A.l Results of Dynaflect Tests on SR 26A (Gilchrist County)
Temperature (F): Air = 79 Pavement Surface = 82 Mid-Pavement = 81
Site
No.
Mile
Post No.
Type of
Data
Measured Deflections (mils) for
Sensor
Positions
*
1
2
3
4
5 6
7
8
9
10
1
11.927
Std
0.83
0.74
0.58
0.50
0.43
2
11.922
Std
0.80
0.70
0.50
0.41
0.34
3
11.917
Std
0.81
0.68
0.54
0.45
0.38
3
11.917
Mod
1.12
0.78
0.64
0.46
0.39
3.5
11.914
Mod
0.86
0.79
0.65
0.47
0.40
4
11.912
Std
0.81
0.68
0.53
0.45
0.39
4
11.912
Mod
0.87
0.77
0.61
0.44
0.38
4.5
11.909
Mod
0.97
0.82
0.67
0.51
0.44
5
11.906
Std
0.86
0.74
0.62
0.53
0.46
6
11.901
Std
0.98
0.86
0.70
0.61
0.54
7
11.896
Std
0.97
0.87
0.74
0.67
0.60
8
11.891
Std
0.92
0.81
0.65
0.57
0.50
* Sensor positions correspond to the modified array in Figure 4.2

Table A.2 Results of Dynaflect Tests on SR 26B (Gilchrist County)
Temperature (F): Air = 45 Pavement Surface = 48 Mid-Pavement = 59
Site
No.
Mile
Post No.
Type of
Data
Measured
Deflections
(mils) for
Sensor
Positions
*
1
2
3
4
5
6
7
8
9
10
0
11.213
Std

1.05
0.99
0.79
0.68
0.68
1
11.208
Std
1.24
1.16
0.92
0.78
0.68
1
11.208
Mod
1.31
1.30
1.06
0.79
0.69
1
11.208
Std
1.22
1.14
0.89
0.75
0.66
1
11.208
Mod
1.34
1.37
1.11
0.75
0.69
1.5
11.205
Std
1.18
1.12
0.90
0.77
0.68
1.5
11.205 .
Mod
1.28
1.23
0.99
0.76
0.67
2
11.202
Std
1.06
1.04
0.85
0.74
0.66
2
11.202
Mod
1.15
1.12
0.96
0.75
0.66
2.5
11.200
Std
1.12
1.09
0.89
0.78
0.70
2.5
11.200
Mod
1.25
1.21
1.02
0.81
0.73
3
11.197
Std
1.21
1.17
0.95
0.83
0.74
3
11.197
Mod
1.33
1.25
1.04
0.83
0.75
* Sensor positions correspond to the modified array in Figure 4.2

Table A.3 Results of Dynaflect Tests on SR 26C (Gilchrist County)
Temperature (F): Air = 60 Pavement Surface = 60 Mid-Pavement = 82
Site
No.
Mi 1 e
Post No.
Type of
Data
Measured
Deflections
(mils) for Sensor
Positions
*
1
2 3
4
5
6
7
8
9
10
1
10.183
Std
0.73
0.63
0.41
0.25
0.21
2
10.178
Std
0.72
0.62
0.41
0.29
0.20
3
10.173
Std
0.79
0.69
0.45
0.28
0.18
4
10.168
Std
0.77
0.62
0.37
0.24
0.16
4
10.168
Mod
0.89
0.77
0.53
0.24
0.16
4.5
10.166
Std
0.77
0.68
0.44
0.27
0.17
4.5
10.166
Mod
0.90
0.78
0.54
0.26
0.17
5
10.163
Std
0.74
0.66
0.41
0.25
0.16
5
10.163
Mod
0.82
0.75
0.51
0.25
0.15
5.5
10.160
Std
0.76
0.65
0.42
0.27
0.18
5.5
10.160
Mod
0.75
0.79
0.55
0.27
0.18
6
10.158
Std
0.73
0.62
0.39
0.25
0.16
6
10.158
Mod
0.70
0.76
0.53
0.25
0.16
* Sensor positions correspond to the modified array in Figure 4.2

Table A.4 Results of Dynaflect Tests on SR 24 (Alachua County)
Temperature (F):
Air = 55
Pavement Surface
= 55
Mid-Pavement
= 57
Site
Mile
Type of
Measured
Deflections
(mils) for
Sensor
Positions*
No.
Post No.
Data
1
2
3
4 5
6
7
8 9
10
6.1
11.107
Std
0.50
0.32
0.21 0.17
0.14
6.1
11.107
Mod
0.47
0.48
0.27
0.18
0.14
6.2
11.112
Std
0.51
0.33
0.22 0.18
0.15
6.2
11.112
Mod
0.50
0.50
0.28
0.18
0.15
6.3
11.117
Std
0.51
0.33
0.21 0.18
0.14
6.3
11.117
Mod
0.44
0.51
0.28
0.17
0.14
*
Sensor positions correspond to the modified array in Figure 4.2

Table A.5 Results of Dynaflect Tests on US 301 (Alachua County)
Temperature (F): Air = 63 Pavement Surface = 65 Mid-Pavement = 69
Site
No.
Mile
Post No.
Type of
Data
Measured
Deflections
(mils) for
Sensor
Positions
*
1
2
3
4
5
6
7
8
9
10
1
21.575
Std
0.42
0.32
0.22
0.16
0.13
1
21.575
Mod
0.39
0.41
0.25
0.16
0.13
2
21.580
Std
0.49
0.37
0.26
0.19
0.14
2
21.580
Mod
0.54
0.48
0.34
0.21
0.15
3
21.585
Std
0.47
0.35
0.25
0.18
0.13
3
21.585
Mod
0.62
0.46
0.30
0.23
0.14
4
21.589
Std
0.51
0.37
0.27
0.20
0.15
4
21.589
Mod
0.57
0.50
0.34
0.20
0.18
5
21.593
Std
0.43
0.33
0.24
0.18
0.15
5
21.593
Mod
0.39
0.42
0.28
0.18
0.14
* Sensor positions correspond to the modified array in Figure 4.2

Table A.6 Results of Dynaflect Tests on US 441 (Columbia County)
Temperature (F): Air = 51 Pavement Surface = 56 Mid-Pavement = 79
Site
No.
Mile
Post No.
Type of
Data
Measured
Deflections
(mils) for Sensor
Positions
*
1
2
3
4
5
6
7
8
9
10
3
1.231
Std
0.69
0.51
0.36
0.27
0.22
3
1.231
Mod
0.64
0.62
0.44
0.27
0.22
3.5
1.236
Std
0.67
0.44
0.33
0.26
0.22
3.5
1.236
Mod
0.65
0.65
0.54
0.26
0.22
4
1.237
Std
0.68
0.52
0.34
0.26
0.22
4
1.237
Mod
0.66
0.65
0.45
0.26
0.22
5
1.241
Std
0.64
0.43
0.33
0,26
0.21
5
1.241
Mod
0.70
0.56
0.38
0.25
0.21
6
1.246
Std
0.63
0.47
0.31
0.24
0.19
6
1.246
Mod
0.71
0.59
0.41
0.23
0.19
7
1.251
Std
0.63
0.44
0.32
0.24
0.19
7
1.251
Mod
0.77
0.55
0.33
0.24
0.19
* Sensor positions correspond to the modified array in Figure 4.2

Table A.7 Results of Dynaflect Tests on I-10A (Madison County)
Temperature (F): Air = 84 Pavement Surface = 106 Mid-Pavement = 104
Site
No.
Mi 1 e
Post No.
Type of
Data
Measured
Deflections
(mils) for
Sensor
Positions
*
1
2
3
4
5
6
7
8
9
10
1
14.079
Std
0.29
0.18
0.11
0.08
0.06
1
14.079
Mod
0.31
0.27
0.14
0.08
0.05
2
14.075
Std
0.28
0.17
0.10
0.07
0.05
2
14.075
Mod
0.35
0.27
0.14
0.07
0.05
3
14.069
Std
0.28
0.19
0.11
0.08
0.06
3
14.069
Mod
0.30
0.28
0.14
0.08
0.06
4
14.065
Std
0.28
0.18
0.11
0.07
0.05
4
14.065
Mod
0.30
0.27
0.15
0.08
0.05
4.5
14.062
Std
0.29
0.18
0.10
0.07
0.05
4.5
14.062
Mod
0.30
0.28
0.16
0.07
0.05
5
14.06
Std
0.27
0.16
0.10
0.06
0.05
5
14.06
Mod
0.29
0.26
0.14
0.07
0.05
6
14.055
Std
0.27
0.17
0.10
0.06
0.05
6
14.055
Mod
0.28
0.25
0.14
0.07
0.05
* Sensor positions correspond to the modified array in Figure 4.2

Table A.8 Results of Dynaflect Tests on I-10B (Madison County)
Temperature (F): Air = 80 Pavement Surface = 101 Mid-Pavement = 88
Site
No.
Mi 1 e
Post No.
Type of
Measured Deflections
(mils) for
Sensor
Positions*
Data j
2
3 4
5
6
7
8
9
10
1
2.703
Std
0.46
0.29
0.17
0.12
0.09
1
2.703
Mod 0.44
0.40
0.25
0.12
0.09
1
2.703
Std
0.45
0.28
0.17
0.12
0.09
(OWP)
* Sensor positions correspond to the modified array in Figure 4.2

Table A.9 Results of Oynaflect Tests on I-10C (Madison County)
Temperature (F):
Air = 82
Pavement Surface
= 99
Mid-Pavement
= 106
Site
Mi 1 e
Type of
Measured Deflections
(mils) for
Sensor
Positions*
No.
Post No.
Data j
2
3 4 5
6
7
8 9
10
1
32.071
Std
0.46
0.30
0.22 0.18
0.15
1
32.071
Mod 0.70
0.43
0.29
0.18
0.15
1
32.071
Std
0.40
0.28
0.22 0.18
0.15
(OWP)
* Sensor positions correspond to the modified array in Figure 4.2

Table A.10 Results of Dynaflect Tests on SR 15A (Martin County)
Temperature (F): Air = 88 Pavement Surface = 110 Mid-Pavement = 120
Site
No.
Mile
Post No.
Type of
Data
Measured
Deflections
(mils) for
Sensor
Positions*
1
2
3
4
5
6
7
8
9
10
3
6.546
Std
1.60
1.52
1.38
1.22
1.10
3
6.546
Mod
1.54
1.53
1.40
1.13
1.03
3.5
6.549
Std
1.46
1.40
1.27
1.14
1.04
3.5
6.549
Mod
1.50
1.48
1.36
1.13
1.03
4
6.551
Std
1.48
1.44
1.29
1.14
1.03
4
6.551
Mod
1.52
1.50
1.39
1.15
1.02
5
6.556
Std
1.58
1.51
1.38
1.25
1.14
5
6.556
Mod
1.57
1.55
1.45
1.21
1.11
6
6.560
Std
1.84
1.79
1.64
1.46
1.31
6
6.560
Mod
1.80
1.84
1.70
1.40
1.26
6.5
6.563
Std
2.18
1.99
1.72
1.50
1.32
6.5
6.563
Mod
2.09
2.15
1.89
1.47
1.30
7
6.566
Std
1.83
1.74
1.53
1.36
1.22
7
6.566
Mod
1.83
1.86
1.69
1.36
1.20
* Sensor positions correspond to the modified array in Figure 4.2

Table A. 11 Results of Dynaflect Tests on SR 15B (Martin County)
Temperature (F): Air = 93 Pavement Surface = 111 Mid-Pavement = 127
Site
No.
Mi 1 e
Post No.
Type of
Data
Measured
Deflections
(mils) for
Sensor
Positions
*
1
2 3
4
5
6
7
8
9
10
1
4.803
Std
1.04
0.93
0.84
0.77
0.70
1
4.803
Mod
1.11
1.03
0.91
0.76
0.68
2
4.808
Std
1.02
0.93
0.84
0.77
0.68
2
4.808
Mod
1.09
1.01
0.92
0.76
0.67
2.5
4.811
Std
1.03
0.91
0.82
0.75
0.65
2.5
4.811
Mod
1.10
1.04
0.92
0.75
0.67
3
4.813
Std
1.01
0.90
0.80
0.73
0.65
3
4.813
Mod
1.06
1.01
0.91
0.72
0.64
4
4.818
Std
1.03
0.91
0.84
0.74
0.64
4
4.818
Mod
1.13
1.01
0.92
0.72
0.64
5
4.823
Std
1.08
0.98
0.88
0.79
0.69
5
4.823
Mod
1.09
1.06
0.95
0.76
0.67
* Sensor positions correspond to the modified array in Figure 4.2

Table A. 12 Results of Dynaflect Tests on SR 715 (Palm Beach County)
Temperature (F): Air = 80 Pavement Surface = 88 Mid-Pavement = 111
Site
No.
Mi 1 e
Post No.
Type of
Data
Measured
Deflections
(mils) for
Sensor
Positions
*
1
2
3
4
5
6
7
8
9
10
1
4.732
Std
1.17
0.96
0.86
0.75
0.71
1
4.732
Mod
1.29
1.13
0.95
0.77
0.70
2
4.727
Std
1.20
0.97
0.87
0.80
0.72
2
4.727
Mod
1.27
1.15
0.94
0.79
0.71
3
4.722
Std
1.29
1.08
0.96
0.90
0.82
3
4.722
Mod
1.37
1.23
1.02
0.88
0.80
3.5
4.720
Std
1.38
1.15
1.07
0.99
0.90
3.5
4.720
Mod
1.45
1.36
1.19
1.00
0.91
4
4.717
Std
1.31
1.15
1.06
0.98
0.90
4
4.717
Mod
1.34
1.20
1.12
0.97
0.88
5
4.712
Std
1.41
1.21
1.11
1.03
0.94
5
4.712
Mod
1.41
1.35
1.19
1.02
0.93
* Sensor positions correspond to the modified array in Figure 4.2

Table A.13 Results of Dynaflect Tests on SR 12 (Gadsden County)
Temperature (F):
Air = 81
Pavement Surface = 91
Mid-Pavement = 102
Site
No.
Mile
Post No.
Type of
Measured
Deflections (mils) for Sensor
Positions*
Data
j**
2
3
4 5 6
1
8
9
10
1
1.472
Std
0.69
0.44
0.36
0.26
0.20
1
1.472
Mod
0.95
0.63
0.39
0.19
0.68
2
1.476
Std
0.75
0.47
0.37
0.28
0.21
2
1.476
Mod
1.20
0.70
0.47
0.21
0.78
3
1.481
Std
0.66
0.43
0.35
0.27
0.21
3
1.481
Mod
0.94
0.64
0.42
0.21
0.74
3.5
1.485
Std
0.68
0.44
0.36
0.27
0.21
3.5
1.485
Mod
1.00
0.65
0.42
0.20
0.71
.
4
1.486
Std
0.68
0.43
0.35
0.27
0.21
4
1.486
Mod
1.13
0.63
0.41
0.21
0.63
5
1.491
Std
0.67
0.43
0.35
0.26
0.20
5
1.491
Mod
1.00
0.66
0.42
0.20
0.70
,

Table A.13--continued
Temperature (F):
Air = 81
Pavement Surface = 91
Mid-Pavement
= 102
Site
Mi 1 e
Type of
Measured
Deflections (mils) for
Sensor
Positions*
No.
Post No.
Data
]**
2
3
4 5 6
7
8 9
10
6
1.496
Std
0.73
0.44
0.37 0.27
0.21
6
1.496
Mod
0.78
0.64
0.44
0.22
0.78
* Sensor positions correspond to the modified array in Figure 4.2
** Two geophones were placed near both wheels

Table
A. 14
Results
of Dynaflect Tests on
SR 15C (Martin County)
Temperature (F):
Air = 82
Pavement Surface
= 90
Mid-
Pavement
= 105
Site
Mile
Type of
Measured Deflections
(mils) for Sensor
Positions
*
No.
Post No.
Data
2**
2
3 4 5
6 7
8
9
10
1
0.050
Std
1.59
1.30
1.13
1.07
1.06
1
0.050
Mod
2.31
1.49
1.22
1.05
0.99
2
0.055
Std
1.53
1.33
1.15
1.03
1.00
2
0.055
Mod
2.19
1.43
1.18
0.96
1.36
3
0.060
Std
1.08
0.95
0.87
0.85
0.83
3
0.060
Mod
1.68
0.97
0.87
0.82
1.04
4
0.065
Std
1.42
1.20
1.10
1.03
1.00
4
0.065
Mod
1.79
1.26
1.13
0.96
1.04
* Sensor positions correspond to the modified array in Figure 4.2
** Two geophones were placed near both wheels

APPENDIX B
FIELD FWD TEST RESULTS

CO
OI
45*
Table B.l Results of FWD Tests on SR 26A (Gilchrist County)
Temperature (F): Air = 79 Pvmt. Surf. = 82 Mid-Pvmt. = 81
Site
No.
Mile
Post
No.
Measured Deflections (mils)
Load
(kips)
D,(a)
2
3
5
6
7
o


1
1
7.87
11.8
19.7
31.5
47.2
63.0
4.48
4.4
3.8
3.2
2.5
1.7
1.2
1.0
6.928
7.3
6.3
5.3
4.2
2.9
2.0
1.6
9.096
10.2
8.8
7.6
6.0
4.3
2.9
2.4
9.104
10.2
8.8
7.6
6.0
4.3
3.0
2.4
4.656
4.6
3.8
3.2
2.4
1.6
1.2
1.0
7.152
7.9
6.5
5.6
4.3
3.1
2.1
1.8
9.096
11.0
9.0
7.7
6.0
4.3
3.0
2.5
9.136
10.9
8.9
7.7
6.0
4.4
3.1
2.5
4.536
4.3
3.5
3.0
2.2
1.6
1.2
1.0
6.912
7.2
6.1
5.2
4.1
3.0
2.1
1.8
8.968
9.8
8.2
7.1
5.6
4.1
2.8
2.4
8.936
9.7
8.1
7.0
5.5
4.0
2.9
2.4
4.512
4.5
3.7
3.0
2.2
1.5
1.1
0.9
7.008
7.7
6.3
5.3
4.0
2.8
2.0
1.6
9.080
10.5
8.6
7.3
5.6
4.0
2.7
2.2
9.128
10.6
8.7
7.4
5.6
4.0
2.7
2.2
4.536
4.0
3.3
2.9
2.1
1.6
1.1
1.0
7.056
6.8
5.8
5.0
3.9
3.0
2.1
1.8
9.048
9.3
7.7
6.8
5.4
4.0
2.8
2.3
9.072
9.1
7.7
6.7
5.3
4.0
2.8
2.3
11.922
11.917
3.5
11.914
11.912
11.909
4.5

Table B.¡--continued
Temperature (F):
Air =
79
Pvmt. Surf.
= 82
Mid-Pvmt.
= 81
Site
No.
Mile
Applied
Measured
Deflections (mils)
Post
No.
Load
(kips)
D^a)
Da
3
5
6
7
O 1
o
7.87
11.8
19.7
31.5
47.2
63.0
4.512
4.1
3.4
2.8
2.1
1.5
1.1
1.0
c
11.906
6.992
6.9
5.8
5.0
3.9
2.9
2.0
1.8
D
9.080
9.4
7.9
6.8
5.4
4.0
2.8
2.3
9.088
9.3
7.8
6.7
5.2
3.9
2.8
2.3
4.528
4.8
4.0
3.3
2.5
1.9
1.5
1.2
c
11.901
7.00
8.2
6.9
5.9
4.6
3.4
2.5
2.1
0
9.064
11.1
9.4
8.1
6.3
4.8
3.4
2.9
9.080
11.0
9.2
7.9
6.3
4.8
3.5
3.0
4.480
4.1
3.4
2.8
2.2
1.7
1.4
1.2
7
11.896
7.072
6.8
5.7
4.9
3.9
3.0
2.2
1.8
/
9.032
9.3
7.8
6.7
5.4
4.1
2.9
2.5
9.048
9.2
7.7
6.6
5.4
4.1
3.1
2.5
4.456
4.9
4.0
3.3
2.4
1.7
1.3
1.1
8
11.891
6.920
8.4
7.0
5.8
4.4
3.2
2.2
1.9
9.088
11.4
9.4
7.9
6.1
4.4
3.1
2.5
9.072
11.2
9.2
7.8
5.9
4.4
3.1
2.6
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load.

Table B.2 Results of FWD Tests on SR 26B (Gilchrist County)
Site
No.
0
0.5
1
1.5
Temperature (F):
Air = 45
Pvmt. Surf. = 48
Mid-Pvmt. = 59
Mile
Post
No.
Measured Deflections (mils)
Load
(kips)
Di(a)
2
3
D
Ds
D6
7
0.o(b)
7.87
11.8
19.7
31.5
47.2
63.0
4.872
5.2
4.6
4.3
3.5
2.7
2.1
1.7
7.016
8.2
7.4
6.9
5.8
4.6
3.5
2.8
9.128
11.4
10.2
9.6
8.0
6.5
4.8
3.9
9.184
11.3
10.1
9.4
7.9
6.4
4.8
3.9
4.736
5.8
5.3
5.0
4.2
3.5
2.8
1.8
7.072
9.4
8.6
8.1
7.0
5.7
4.2
3.1
9.048
12.9
11.9
11.3
9.7
7.9
5.7
4.2
9.040
12.8
11.8
11.1
9.6
7.8
5.7
4.1
4.728
6.3
5.6
5.1
4.1
3.2
2.4
2.0
6.952
10.1
9.0
8.2
6.8
5.3
4.0
3.1
9.112
14.2
12.6
11.5
9.6
7.6
5.6
4.4
9.128
14.0
12.4
11.4
9.6
7.5
5.6
4.4
4.656
4.2
3.5
3.3
2.6
1.9
1.3
0.8
6.880
7.9
7.0
6.5
5.6
4.5
3.4
2.7
9.096
10.9
9.8
9.1
7.8
6.4
4.8
3.8
9.112
10.7
9.6
8.9
7.6
6.2
4.7
3.7
4.816
4.3
3.8
3.7
3.1
2.6
2.1
1.7
7.160
6.8
6.2
5.9
5.1
4.2
3.3
2.7
9.104
9.8
8.8
8.5
7.4
6.2
4.8
3.9
9.096
9.6
8.8
8.3
7.3
6.1
4.7
3.8
11.213
11.210
11.208
11.205
11.203
GO
in
o
2

Table B.2--continued
Temperature (F):
Air =
45
Pvmt. Surf.
= 48
Mid-Pvmt.
= 59
Site
No.
Mile
Applied
Measured
Deflections (mils)
Post
No.
Load
(kips)
Di(a)
a
D3
D,
5
6
7
o

O
cr
7.87
11.8
19.7
31.5
47.2
63.0
4.664
4.8
4.3
4.0
3.5
2.8
2.2
1.8
0 c
11.200
7.072
7.6
7.0
6.5
5.6
4.7
3.5
2.8
9.048
10.5
9.8
9.1
8.0
6.5
5.0
3.9
9.080
10.50
9.7
9.0
7.9
6.6
5.1
4.0
4.552
4.8
4.3
4.0
3.3
2.7
2.1
1.8
Q
11.197
6.880
7.6
6.7
6.3
5.3
4.2
3.2
2.6
0
9.104
10.6
9.5
8.8
7.5
6.1
4.6
3.7
9.128
10.5
9.4
8.8
7.5
6.1
4.7
3.8
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load

Table B.3 Results of FWD Tests on SR 26C (Gilchrist County)
Temperature (F):
Air = 60
Pvmt. Surf.
= 60
Mid-Pvmt. = 82
Site
No.
Mile
Applied
Measured
Deflections (mils)
Post
No.
Load
(kips)
D2
3
\
5 D6
07
0.0(b) 7.87
11.8
19.7
31.5 47.2
63.0
4.592
5.6
4.7
4.1
3.1
2.0
1.2
0.8
6.864
8.8
7.5
6.7
5.0
3.3
2.0
1.4
9.112
12.1
10.4
9.1
7.0
4.6
2.8
2.0
9.080
11.9
10.2
8.9
6.9
4.6
2.9
2.0
4.696
5.3
4.5
4.1
3.1
2.1
1.3
0.8
6.912
8.1
7.1
6.3
5.0
3.4
2.0
1.4
9.088
11.1
9.7
8.7
6.9
4.8
2.9
2.0
9.056
11.0
9.6
8.6
6.8
4.8
2.9
2.0
4.592
6.4
5.4
' 4.7
3.5
2.2
1.3
0.9
6.736
9.9
8.4
7.4
5.5
3.5
2.0
1.3
9.056
13.5
11.4
10.1
7.6
4.9
2.8
1.9
9.032
13.2
11.2
9.9
7.4
4.8
2.8
1.8
4.576
6.1
5.2
4.5
3.3
2.1
1.1
0.7
6.744
9.5
8.1
7.1
5.2
3.3
1.8
1.1
9.064
13.0
11.0
9.6
7.2
4.6
2.5
1.6
9.032
12.8
10.8
9.4
7.0
4.5
2.5
1.5
4.40
6.3
5.3
4.7
3.4
2.1
1.1
0.8
6.984
10.4
8.8
7.7
5.8
3.6
2.0
1.3
9.008
13.4
11.4
9.9
7.5
4.8
2.6
1.6
9.048
13.3
11.3
9.8
7.4
4.8
2.7
1.7
10.183
10.178
10.173
3.5
10.17
10.168
CO
cn
00
4

Table B.3--continiied
Temperature (F):
Air =
60
Pvmt. Surf.
= 60
Mid-Pvmt.
= 82
Mile
Applied
Measured
Deflections (mils)
j 1 vL
No.
Post
Load
0i D3
D,
5
D6
7
Ma
ho*
IKips;
0.o(fa)
7.87
11.8
19.7
31.5
47.2
63.0
4.432
6.3
5.3
4.6
3.5
2.3
1.3
0.90
A K
m i cc
6.920
10.2
8.8
7.7
5.8
3.8
2.2
1.4
4 j
1U.IDO
8.936
13.8
11.3
10.0
7.6
5.0
2.8
1.8
8.952
13.1
11.2
9.8
7.6
5.0
2.8
1.8
4.56
5.7
4.9
4.3
3.2
2.1
1.1
0.7
c
1 n i co
7.096
9.3
8.2
7.2
5.5
3.5
2.0
1.2
0
111* 10 J
8.96
12.3
10.6
9.4
7.2
4.7
2.6
1.7
8.968
12.1
10.5
9.3
7.1
4.6
2.6
1.6
4.464
5.7
5.0
4.5
3.5
2.3
1.4
0.9
r E
1 n i cn
7.096
9.4
8.2
7.4
5.8
4.0
2.3
1.5
b J
1U.loU
8.928
12.3
10.8
9.6
7.6
5.3
3.1
2.0
8.968
12.1
10.6
9.5
7.5
5.2
3.1
2.0
4.496
5.4
4.7
4.1
3.2
2.1
1.2
0.7
c
in i co
7.112
8.9
7.9
7.0
5.4
3.6
2.0
1.2
0
lU1Ju
8.952
11.8
10.4
9.2
7.3
4.8
2.7
1.7
8.968
11.6
10.2
9.1
7.2
4.8
2.8
1.7
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load

Table B.4
Results of
FWD Tests on SR 24
(Alachua County)
Temperature (F):
Air =
57
Pvmt. Surf.
= 55
Mid-Pvmt.
= 57
Site
No.
Mile
Applied
Measured
Deflections (mils)
Post
No.
Load
(kips)
0i(a)
2
3
5
6
7
0.o(b)
7.87
11.8
19.7
31.5
47.2
63.0
4.832
8.6
5.3
3.4
1.6
1.0
0.8
0.6
6.1
11.107
7.288
11.5
7.5
5.1
2.7
1.6
1.1
0.8
9.144
14.1
9.4
6.6
3.6
2.2
1.5
1.1
4.760
7.9
5.0
3.2
1.6
1.0
0.7
0.5
6.2
11.112
7.176
10.8
7.3
5.0
2.6
1.6
1.1
0.9
8.816
13.0
8.8
6.1
3.4
2.2
1.5
1.1
4.688
7.8
4.9
3.2
1.6
1.0
0.7
0.5
6.3
11.117
7.048
10.7
7.1
4.9
2.6
1.5
1.0
0.8
8.80
12.9
8.8
6.2
3.4
2.1
1.4
1.1
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load

Table B.5 Results of FWD Tests on US 301 (Alachua County)
Temperature (F):
Air =
63
Pvmt. Surf.
= 65
Mid-Pvmt.
= 69
Site
No.
Mi 1 e
Applied
Measured
Deflections (mils)
Post
No.
Load
(kips)
D3
\
D5
6
7
O

o
cr
7.87
11.8
19.7
31.5
47.2
63.0
4.984
7.6
5.3
4.0
2.5
1.5
0.9
0.6
1
21.575
9.024
14.8
11.0
8.7
5.6
3.2
1.9
1.2
9.160
14.5
10.8
8.6
5.6
3.2
1.9
1.2
5.120
7.3
5.5
4.2
2.5
1.5
0.9
0.6
2
21.580
9.160
14.8
11.4
9.0
5.6
3.1
1.9
1.3
9.200
14.6
11.3
8.9
5.6
3.1
1.9
1.3
5.00
7.5
5.6
4.3
2.8
1.6
0.9
0.7
2.5
21.583
9.072
15.2
12.0
9.6
6.3
3.5
2.0
1.3
9.00
14.8
11.7
9.3
6.1
3.5
2.0
1.3
21.583
(OWP)
5.032
6.8
5.3
4.2
2.9
1.8
1.1
0.7
2.5
8.992
14.4
11.5
9.5
6.7
4.1
2.4
1.5
8.936
14.3
11.5
9.5
6.7
4.1
2.4
1.6
4.96
8.5
6.0
4.5
2.8
1.6
0.9
0.6
3
21.585
8.816
16.8
12.5
9.7
6.2
3.6
2.0
1.3
8.824
15.7
11.7
9.2
5.9
3.4
2.0
1.3
GJ
CT>

Table B.5--continued
Temperature (F):
Air =
63
Pvmt. Surf.
= 65
Mid-Pvmt.
= 69
Site
No.
Mile
Applied
Measured
Deflections (mils)
Post
No.
Load
(kips)
Di(a)
2
3
d5
6
D7
-Q
O

O
7.87
11.8
19.7
31.5
47.2
63.0
5.008
7.5
5.4
4.1
2.6
1.3
0.7
0.8
4
21.589
8.936
15.1
11.2
9.0
5.9
3.5
2.0
1.4
8.968
14.1
10.6
8.4
5.6
3.4
2.0
1.4
4.80
7.2
4.9
3.6
2.2
1.2
0.7
0.6
5
21.593
9.12
15.9
11.4
8.8
5.6
3.0
1.7
1.3
9.104
15.6
11.3
8.7
5.6
3.2
1.8
1.1
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load

Table B.6 Results of FWD Tests on US 441 (Columbia County)
Temperature (F): Air = 51 Pvmt. Surf. = 56 Mid-Pvmt. = 79
Site
No.
Mile
Applied
Measured
Deflections (mils)
Post
No.
Load
(kips)
Dx(a) D2
3
4
D D
5 6
7
0.0(b) 7.87
11.8
19.7
31.5 47.2
63.0
5.224
8.4
6.3
5.0
3.2
2.1
1.4
1.1
1.231
8.944
15.8
12.2
9.8
6.7
4.2
2.8
2.1
8.920
15.6
12.0
9.7
6.8
4.4
2.8
2.1
5.416
9.0
5.5
4.2
2.9
2.0
1.4
1.1
CO
cr>
1.236
9.20
16.3
11.0
8.7
6.0
4.0
2.7
2.0
9.152
15.6
10.6
8.4
5.8
4.0
2.8
2.0
CO
1.236
(OWP)
5.312
9.7
6.9
5.0
3.0
1.8
1.3
1.0
8.824
16.4
12.2
9.4
6.1
3.8
2.6
1.9
8.808
16.4
12.2
9.4
6.1
3.8
2.6
1.9
5.208
8.4
6.2
4.7
2.5
2.0
1.3
1.0
1.237
8.976
16.1
12.3
9.7
5.4
3.8
2.6
1.9
8.976
15.4
11.8
9.4
5.2
3.9
2.7
2.0
5.48
7.8
5.7
4.5
3.0
2.0
1.5
1.0
1.241
9.152
13.9
10.6
8.5
5.9
3.9
2.8
1.9
9.104
13.6
10.5
8.4
5.9
4.0
2.7
1.9
5

Table B.6--continued
Temperature (F):
Air =
51
Pvmt. Surf.
= 56
Mid-Pvmt.
= 79
Site
Mo.
Mile
Applied
Measured
Deflections (mils)
Post
No.
Load
(kips)
Di(a)
D2
3
D*
5
6
7
o

o
cr
7.87
11.8
19.7
31.5
47.2
63.0
5.336
7.9
6.1
4.8
2.7
1.8
1.2
1.0
6
1.246
8.936
14.6
11.5
9.3
5.7
3.5
2.2
1.5
8.928
14.1
11.1
8.9
5.6
3.4
2.3
1.6
5.448
7.8
5.9
4.5
2.8
1.8
1.3
1.0
7
1.251
8.872
13.3
10.2
8.0
5.1
3.4
2.2
1.6
8.872
13.0
10.1
7.9
5.1
3.5
2.3
1.6
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load

Table B.7 Results of FWD Tests on I-10A (Madison County)
Temperature (F): Air = 84 Pvmt. Surf. = 106 Mid-Pvmt. = 104
Site
Mo.
Mile
Applied
Measured
Deflections (mils)
Post
M/%
Load
(kips)
D D
1 2
3
D,
5 D6
7
llO
0.0(b) 7.87
11.8
19.7
31.5 47.2
63.0
4.672
5.4
2.3
1.6
0.8
0.5
0.2
0.3
14.079
8.960
10.0
4.5
3.2
1.8
1.0
0.6
0.4
8.984
9.3
4.4
3.1
1.8
1.0
0.5
0.4
4.784
4.9
2.3
1.6
0.9
0.4
0.2
0.2
14.075
9.04
8.9
4.4
3.2
1.8
0.9
0.5
0.3
8.952
8.7
4.3
3.2
1.8
0.9
0.4
0.3
U>
4.592
4.5
2.2
1.6
0.9
0.5
0.2
0.2
CJ1
14.069
8.992
8.4
4.2
3.2
1.8
1.0
0.4
0.3
9.000
8.2
4.2
3.2
1.8
0.9
0.4
0.3
4.736
4.8
2.3
1.7
0.9
0.4
0.2
0.2
14.065
9.120
8.7
4.5
3.3
1.9
0.9
0.4
0.3
9.056
8.5
4.4
3.2
1.9
0.8
0.4
0.3
4.664
4.5
2.2
1.6
0.9
0.4
0.2
0.1
14.062
9.128
8.2
4.4
3.4
1.9
0.8
0.4
0.3
9.104
8.0
4.4
3.4
1.9
0.9
0.5
0.4
1 A nco
4.92
3.5
2.2
1.5
0.8
0.4
0.2
0.1
14* Ub
/ r\i m\
9.152
6.4
4.0
2.9
1.6
0.8
0.4
0.3
(OWP)
9.064
6.4
4.0
2.9
1.6
0.8
0.4
0.3
4.5

Table B.7--continued
Temperature (F):
Air = 84
Pvmt. Surf.
= 106
Mid-Pvmt.
= 104
Site
No.
Mile
Applied
Measured
Deflections (mils)
Post
No.
Load
(kips)
D](a)
2
3
4
D5
6
D7
0.o(b)
7.87
11.8
19.7
31.5
47.2
63.0
4.72
4.5
2.4
1.5
0.6
0.0
0.5
0.1
5
14.06
9.136
7.8
4.5
3.2
1.7
0.8
0.4
0.3
9.136
7.7
4.5
3.2
1.8
0.7
0.4
0.4
4.824
4.2
2.0
1.4
0.2
0.0
0.8
0.5
6
14.055
8.872
7.0
4.3
3.1
1.7
0.8
0.4
0.3
8.808
7.0
4.3
3.1
1.7
0.8
0.6
0.4
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load

Table B.8
Results of
FWD Tests on I-10B
(Madison County)
Temperature (F):
Air =
80
Pvmt. Surf.
= 101
Mid-Pvmt.
= 88
Site
Mile
Applied
Measured
Deflections (mils)
No.
Post
No.
Load
(kips)
2
3
4
5
6
7
o.o(b)
7.87
11.8
19.7
31.5
47.2
63.0
2.703
(OWP)
4.40
6.3
3.5
2.5
1.3
0.8
0.4
0.4
1
8.928
8.968
12.0
11.5
7.3
7.1
5.3
5.2
3.2
3.0
1.7
1.7
1.1
1.1
0.9
0.9
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load.

Table B.9 Results of FWD Tests on I-10C (Madison County)
Temperature (F):
Air =
82
Pvmt. Surf.
= 99
Mid-Pvmt.
= 106
Site
No.
Mile
Applied
Measured
Deflections (mils)
Post
No.
Load
(kips)
D2
3
5
6
D7
o.o 7.87
11.8
19.7
31.5
47.2
63.0
1
32.071
4.592
8.984
9.032
5.1
10.4
10.1
3.6
7.7
7.3
2.6
5.9
5.7
1.5
3.6
3.4
1.0
2.1
2.1
0.7
1.3
1.3
0.4
0.9
0.9
(a)
(b)
Sensor No.
Radial Distance in inches from center of FWD load

Table B.10 Results of FWD Tests on SR 15A (Martin County)
Temperature (F):
Air = 88 Pvmt. Surf. = 110
Mid-Pvmt. = 120
Site
No.
Mile
Applied
Measured Deflections (mils)
Post
No.
Load
(kips)
.(a) 2 3
5 6
7
0.o(b) 7.87 11.8 19.7
31.5 47.2
63.0
4.894
10.04
6.61
5.51
4.29
3.35
2.76
2.28
6.546
7.198
16.42
11.73
10.08
8.07
6.46
5.12
4.17
9.280
22.48
16.26
14.21
11.42
8.94
7.05
5.63
4.672
5.75
4.76
4.33
3.78
3.19
2.72
2.32
6.549
6.976
10.43
8.90
8.19
7.09
5.94
4.96
4.21
9.026
14.45
12.36
11.30
9.84
8.15
6.54
5.35
CO
6.549
(OWP)
4.703
5.47
4.29
3.94
3.54
3.07
2.64
2.20
CT>
VO
7.008
10.04
8.11
7.60
6.97
6.06
5.08
4.21
9.057
13.15
10.59
9.92
9.06
7.80
6.42
5.31
4.656
6.38
5.43
4.92
4.21
3.43
2.76
2.28
6.551
7.008
11.89
10.35
9.41
8.11
6.65
5.24
4.37
9.026
15.51
13.50
12.24
10.47
8.50
6.57
5.28
4.608
6.93
5.35
4.88
4.13
3.50
2.91
2.36
6.556
7.023
13.23
10.55
9.65
8.39
6.97
5.83
4.72
8.994
17.44
13.98
12.80
11.02
9.13
7.36
5.98
4.513
9.92
6.85
5.94
5.12
4.29
3.54
2.91
6.560
6.912
17.56
12.83
11.30
9.88
8.15
6.69
5.35
8.962
22.83
16.89
14.96
12.95
10.55
8.50
6.69
6

Table B.10--continued
Temperature (F):
Air =
88
Pvmt. Surf.
= 110
Mid-Pvmt.
= 120
Site
No.
Mile
Applied
Measured
Deflections (mils)
Post
No.
Load
(kips)
Di(a)
3
5
Ds
7
0.o(b)
7.87
11.8
19.7
31.5
47.2
63.0
6.5
6.563
4.576
6.912
8.851
11.30
19.45
25.24
7.68
14.06
18.46
6.57
12.40
16.42
5.63
10.63
13.94
4.53
8.62
11.10
3.66
6.89
8.58
2.91
5.39
6.65
7
6.566
4.545
6.880
8.930
8.86
15.71
20.43
6.10
11.65
15.28
5.51
10.59
13.98
4.69
9.02
11.89
3.86
7.44
9.72
3.15
6.02
7.68
2.56
4.92
6.18
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load

Table B.ll Results of FWD Tests on SR 15B (Martin County)
Temperature (F):
Air = 93 Pvmt. Surf. = 111
Mid-Pvmt.
= 127
Site
No.
Mile
Applied
Measured Deflections (mils)
Post
No.
Load
(kips)
Dl(a) 02 d3 d.
DS
7
0.o(b) 7.87 11.8 19.7
31.5
47.2
63.0
4.433
8.86
5.83
4.61
3.35
2.56
2.13
1.73
4.803
6.801
14.65
10.04
8.35
6.14
4.69
3.86
3.11
8.994
19.29
13.35
11.10
8.43
6.50
5.24
4.17
4.465
6.77
4.69
3.66
2.76
2.28
1.97
1.69
4.808
6.865
12.13
8.74
7.17
5.67
4.49
3.62
2.99
9.026
15.98
11.50
9.49
7.52
6.06
4.84
4.02
4.513
8.35
5.51
4.13
3.03
2.44
2.09
1.77
4.811
6.769
14.17
10.0
7.95
6.02
4.69
3.78
3.07
8.962
18.39
13.07
10.71
8.19
6.42
5.04
4.02
4.811
(OWP)
4.354
5.94
4.61
3.98
3.31
2.80
2.44
1.97
6.928
10.87
8.58
7.56
6.34
5.08
4.09
3.27
9.026
14.49
11.38
10.12
8.54
6.89
5.43
4.37
4.481
7.68
4.88
3.90
2.95
2.52
2.01
1.77
4.813
6.817
13.27
9.17
7.56
5.92
4.69
3.74
3.11
8.946
17.32
12.17
10.20
8.07
6.42
5.12
4.13
3

Table B.ll--continued
Temperature (F):
Air =
93
Pvmt. Surf.
= 111
Mid-Pvmt.
= 127
Site
Mile
Applied
Measured
Deflections (mils)
D[(a)
No.
Post
No.
Load
(kips)
2
3
D4
DS
6
7
o

o
1 2
7.87
11.8
19.7
31.5
47.2
63.0
4.529
10.59
6.42
4.80
3.31
2.56
2.24
1.89
4
4.818
7.214
16.81
10.98
8.70
6.42
4.84
3.78
3.19
9.057
21.34
14.33
11.65
8.66
6.50
5.08
4.17
4.560
10.75
5.98
4.65
3.31
2.44
2.17
1.85
5
4.823
6.928
17.60
10.83
8.86
6.65
5.04
3.90
3.19
8.867
22.52
14.17
12.05
9.06
6.73
5.31
4.29
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load

Table B.12 Results of FWD Tests on SR 715 (Palm Beach County)
Temperature (F):
Air =
80 Pvmt. Surf.
= 88
Mid-Pvmt.
= 111
Site
No.
Mile
Post
No.
Applied
Load
(kips)
Measured
Deflections (mils)
DU)
2
D3
D,
D5
D7
i
^ o

! o
1
7.87
11.8
19.7
31.5
47.2
63.0
4.703
9.13
5.08
3.43
2.32
2.05
1.85
1.69
1
4.732
7.039
14.21
8.23
5.75
4.02
3.54
3.15
2.83
9.010
18.35
10.79
7.52
5.24
4.69
4.06
3.54
4.672
10.55
5.47
3.58
2.44
2.09
1.85
1.61
2
4.727
7.103
16.14
8.62
5.91
3.98
3.46
3.07
2.76
9.042
20.47
11.18
7.68
5.24
4.49
3.94
3.46
4.56
10.59
6.10
4.09
2.60
2.28
2.01
1.85
3
4.722
7.039
16.34
9.49
6.42
4.25
3.74
3.39
3.07
9.026
20.94
12.20
8.35
5.47
4.76
4.17
3.70
4.815
8.54
5.47
3.78
2.72
2.24
2.05
1.85
3.5
4.720
7.246
12.99
8.62
6.22
4.37
3.78
3.39
2.91
8.803
16.02
10.67
7.83
5.67
4.84
4.25
3.78
4.735
8.82
5.47
3.74
2.72
2.32
2.17
1.97
4
4.717
7.135
13.27
8.43
5.94
4.37
3.82
3.50
3.11
9.026
17.17
11.02
7.99
5.91
5.16
4.57
4.09
4.783
7.76
5.31
3.90
2.87
2.48
2.24
1.97
5
4.712
7.151
12.36
8.58
6.38
4.69
4.09
3.58
3.15
8.978
16.42
11.42
8.62
6.46
5.55
4.92
4.25
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load.

Table B.13 Results of FWD Tests on SR 12 (Gadsden County)
Temperature (F): Air = 81 Pvmt. Surf. = 91 Mid-Pvmt. = 102
Site
No.
1
2
3
3.5
Mile
Post
No.
Measured Deflections (mils)
Load
(kips)
Di(a)
d2
3
5
6
7
0.o(b)
7.87
11.8
19.7
31.5
47.2
63.0
4.568
15.3
7.4
4.9
2.7
1.8
1.3
0.9
6.952
27.3
12.5
8.3
4.7
2.9
1.9
1.6
9.160
31.3
16.7
11.4
6.6
4.2
2.8
2.1
9.168
27.2
16.1
11.1
6.6
4.3
3.0
2.2
4.48
17.0
8.5
4.9
3.3
1.8
1.2
1.1
6.624
25.7
14.0
8.7
4.6
3.1
2.2
1.4
8.688
32.6
18.4
11.8
6.5
4.4
2.9
2.2
8.744
29.3
17.5
11.3
6.3
4.4
3.0
2.3
4.592
14.5
8.4
4.9
2.7
1.9
1.3
1.0
6.872
21.7
13.1
8.0
4.4
3.0
2.0
1.6
9.032
28.0
17.2
10.9
6.2
4.4
2.8
2.0
9.120
26.0
16.2
10.3
6.0
4.2
2.9
2.1
4.696
17.1
9.5
5.5
3.2
2.0
1.3
1.3
6.920
22.7
13.9
8.6
5.1
2.8
1.9
1.4
9.232
29.9
19.0
12.1
6.6
4.2
2.8
2.2
9.288
29.6
18.0
11.6
6.5
4.3
2.8
2.4
4.568
11.4
6.8
4.7
3.0
2.4
1.4
1.0
6.696
21.7
11.7
8.1
4.6
2.6
2.0
1.6
8.76
25.0
15.5
11.0
6.6
4.2
2.8
2.2
8.776
22.6
15.1
10.8
6.6
4.2
2.9
2.3
1.472
1.476
1.481
1.485
1.485
(OWP)
GJ
45
3.5

Table B.13--continued
Temperature (F):
Air =
81
Pvmt. Surf.
= 91
Mid-Pvmt.
= 102
Site
No.
Mile
Applied
Measured
Deflections (mils)
Post
No.
Load
(kips)
D2
D3
05
D7
o.o 7.87
11.8
19.7
31.5
47.2
63.0
4.616
12.3
7.5
4.8
2.8
1.8
1.3
1.0
A
1.486
6.816
19.3
12.1
8.1
4.7
3.0
2.0
1.5
H
8.920
25.0
16.1
11.2
6.5
4.2
2.8
2.1
8.960
23.7
15.6
10.9
6.2
4.3
2.9
2.1
4.632
12.8
6.7
4.7
3.2
1.7
1.4
1.3
C
1.491
6.88
19.5
12.6
8.4
4.7
3.0
1.9
1.2
D
8.968
26.1
16.4
11.3
6.7
4.3
2.8
2.0
9.00
24.9
15.5
10.8
6.5
4.2
2.8
2.0
4.64
10.5
5.9
3.9
3.5
1.8
0.9
1.1
C
1.496
7.072
16.8
10.9
7.6
4.8
3.0
2.0
1.6
0
9.248
22.7
14.6
10.5
6.7
4.2
2.7
2.1
9.280
21.7
14.0
10.1
6.7
4.2
2.8
2.2
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load.

Table B.14 Results of FWD Tests on SR 15C (Martin County)
Temperature (F): Air = 82 Pvmt. Surf. = 90 Mid-Pvmt. = 105
Site
No.
1
1
2
2
Mile
Post
No.
0.050
0.050
(0WP)
0.055
0.055
(0WP)
0.060
Measured Deflections (mils)
Load
(kips)
D^a)
2
3
D
5
6
D7
' O

o
O"
7.87
11.8
19.7
31.5
47.2
63.0
4.862
10.87
8.50
6.89
4.92
3.46
2.36
2.44
6.801
15.59
12.01
9.72
6.97
4.57
3.11
2.91
8.819
20.67
16.06
13.19
9.61
6.46
4.76
4.29
8.835
20.31
15.71
13.07
9.41
6.50
4.84
4.21
4.815
11.02
8.39
6.54
4.49
3.27
2.56
2.17
6.801
15.87
12.05
9.45
6.50
4.45
3.07
2.48
8.898
21.30
16.34
12.99
9.02
6.46
4.69
3.82
8.898
20.75
15.87
12.60
' 8.70
6.22
4.65
3.82
4.831
13.54
10.0
8.11
5.75
3.58
2.56
2.32
6.769
18.82
14.02
11.57
8.15
4.92
3.23
2.68
8.867
24.96
18.94
15.87
11.46
7.17
4.88
4.06
8.883
24.20
18.46
15.47
11.26
7.09
4.92
4.13
4.926
11.14
8.70
7.20
5.16
3.43
2.60
2.09
6.817
15.67
12.56
10.16
7.28
4.76
2.91
2.24
8.883
20.94
17.01
13.94
10.28
6.89
4.49
3.58
8.898
20.51
16.73
13.74
10.04
6.73
4.49
3.70
4.862
12.17
9.25
7.40
5.08
3.23
2.40
2.05
6.769
16.97
12.87
10.43
7.05
4.13
2.64
2.13
8.835
22.52
17.36
14.25
9.96
6.14
4.17
3.43
8.867
21.81
16.81
13.86
9.69
5.98
4.09
3.35
CJ
CT>
3

Table B.14--continued
Temperature (F):
Air =
82
Pvmt. Surf.
= 90
Mid-Pvmt.
, = 105
Site
No.
Mile
Applied
Measured
Deflections (mils)
Post
Load
D2
3
D4
5
6
7
No.
IKips;
o

o
! o
i
7.87
11.8
19.7
31.5
47.2
63.0
4.799
9.25
7.01
5.98
4.53
3.15
2.32
2.01
3
0.060
6.785
13.07
10.04
8.46
6.38
4.25
2.76
2.13
(OWP)
8.883
17.80
13.78
11.81
8.94
6.14
4.21
3.39
8.867
17.24
13.31
11.34
8.58
5.83
4.09
3.35
4.831
8.90
6.61
5.51
4.09
3.11
2.36
2.20
A
n n£tr
6.737
12.32
9.25
7.83
5.71
4.17
3.03
2.60
4
U UuO
8.803
16.46
12.52
10.63
7.95
5.94
4.37
3.86
8.851
15.98
12.24
10.31
7.80
5.83
4.45
3.94
4.719
7.83
6.42
5.12
4.09
3.07
2.56
2.36
4
0.065
6.801
11.26
9.17
7.36
5.75
4.17
2.99
2.60
(OWP)
8.883
15.83
12.80
10.63
8.54
6.38
4.88
4.17
8.851
15.04
11.73
9.96
7.80
5.94
4.69
4.02
(a) Sensor No.
(b) Radial Distance in inches from center of FWD load

APPENDIX C
COMPUTER PRINTOUT OF CPT RESULTS

JOB 0 2
PATE i 10-31-85 11.00
LOCATION. SR 26 A SITE ITS
FILE 0 CPT 87
LOCAL FNJCriUN FRICTION NAT1U
0 TIP RESISTANCE 50 0 500 0 (PERCENT) 8
379

380
0 TIP RESISTANCE (MN/m'2)
JD0 0 3
DATE i 10-31-63 13.30
LOCATION i SR 26^- SvTC
FILE # i CPT 70
LOCAL FRICTION FRICTION RATIO
50 0 (kN/m2) 500 0 (PERCENT) 8
14

381
joa § i 6
DATE i 11/4/65 ID. 45
LOCATION SR-26e SITe I-IS
FILE # i CPT 83
LOCAL FRICTION FRICTION RATIO

382
JOB 5
PATE 02/15/02 23*591
LOCATION. SR-26A S\TE 3-0
FILE 0 CPT 80
LOCAL FRICTION FRICTION RATIO

383
JOB i 9
OATE 11/4/65 tZ.OO
LOCATION. SR-26C ilTE 4-.2.S
FILE 0 CPT 86

joq 0 i e
PATE i 11/4/8S 11.40
LOCATION i SR-26C SITE S'- O
FILE 0 i CPT 85
LOCAL FRICTION FRICTION RATIO

385
J08 # 1
DATE 12/03/85 09(38
LOCATION SR-2*(WALDO R0> SlTt O-S
FILE # CPT 10
LOCAL FRICTION FRICTION RATIO

386
JOB 0 I
PATE i 12/03/03 00 34
LOCATION SR2* (WALDO R0> &V*V£ 2 S
FILE 0 CPT tZ
LOCAL FRICTION FRICTION RATIO

387
JOB 0 1
DATE i 02-18-06 10*49
LOCATION. SR 301 SvTE 3 5
FILE 0 CPT 93
LOCAL FRICTION FRICTION RATIO
0 TIP RESISTANCE (MN/m"2) 50 0 (hN/m*2) SOO 0 (PERCENT) 8

388
JOB 0 t 1
DATE i 02-10-86 11.29
LOCATION SR 301 Si "HE. 3-5
FILE 0 i CPT 9S
LOCAL FRICTION FRICTION RATIO
0 TIP RESISTANCE
389
JOB # i l
PATE i 02-18-66 11.29
location'. ck aui SiTE tf.s
FILE 0 CPT 6
LOCAL FRICTION FRICTION RATIO
0 TIP RESISTANCE
390
JOB # t 2
DATE 02-26-86 Ui 45
LOCATION i US 441 ST6 S
FILE 0 CPTIOO
LOCAL FRICTION FRICTION RATIO

391
JOB # i 4
DATE 02/26/66 12.52
LOCATION US 441 SlT fe-S
FILE # CPT104
LOCAL FRICTION FRICTION RATIO
0 TIP RESISTANCE 500 0 (PERCENT) 6

392
JOB # i 1
PATE i 03/18/80 D9t 23
LOCATION 1-10* S|T£
FILE 0 CPT107
LOCAL FRICTION FRICTION RATIO
0 TIP RESISTANCE
393
JOB i 2
PATS. i 03/18/flB 08*48
LOCATION I-1UA Slfe f.S
FILE i CPTlOa
LOCAL FRICTION FRICTION RATIO

394
JOS #
DATE
LOCATION
FILE
4
03/18/00 10.41
I-lOfc (=*S
CPT112
LOCAL FRICTION FRICTION RATIO

395
JOB 0 i
DATE i 04/29/66 09, 11
LOCATION SR IS (US SITE \ S
FILE 0 CPT121
LOCAL FRICTION FRICTION RATIO

396
JOB 0 i 3-SITE 3.5
PATE 04/29/66 11.03
LOCATION i SR 15 CUS 441)-B SITE 3. g*
FILE 0 CPT126
LOCAL FRICTION
FRICTION RATIO

397
JOB # 4-SITE 4.5
PATE 04/29/86 11*30
LOCATION SN 15(US 441J-B -StTC H--S
FILE d* i CPT129
LOCAL FRICTION FRICTION RATIO

398
JOB # i 1
PATE 10/01/M 142S
LOCATION SR 71S SITE #4.9
FILE # CPT17*
LOCAL FRICTION FRICTION RATIO

399
JOS # #1
pate a-iz-ae lo.o*
LOCATION SR 12 SITE #2
FILE 0 CPT1BQ
LOCAL FRICTION FRICTION RATIO

400
JOS # #1
ATE i a-12-66 10.45
LOCATION SR 12 SITE #3
FILE # CPT101
LOCAL FRICTION FRICTION RATIO

401
JOB 0 al
DATE a 8-12-80 1la 35
LOCATION a SR 12 SITE #5
FILE # i CPT105
LOCAL FRICTION FRICTION RATIO

402
JOB 1 SR tfi-C
DATE 08/30/M 1*. S3
LOCATION SR 13 OIS AlJClTT-l
FILE i CPT170
LOCAL FRICTION FRICTION RATIO

403
JOB t 1 *=. R ISC.
DATE 00/30/80 13.80
LOCATION SR 13 (US 441)CSITe 2
FILE CPT171
LOCAL FRICTION FRICTION RATIO

404
joe # 1 Sc.
ATE oa/30/80 15a 51
LOCATION SR 15-441 SITE 3
FILE # CPT17Z
LOCAL FRICTION FRICTION RATIO

APPENDIX D
COMPUTER PRINTOUT OF DMT RESULTS

TEST HO. 1
UN TV. CF FLORIDA CIVIL Etc. DETT. DR. B.E. RUTH
FILE NAME: FAVTTtENT-SUDGRAIlE MATERIALS CHARACTERIZATION
FILE NUMBER: 245-D51
RECORD OF DILATCMETER TEST NO. 1
USING DATA REDUCTION PROCEDURES IN MAR CUE TTI (ASCE, J-GED, MARCH 80)
K0 IN SANDS DETERMINED USING SCII1ERTMANN METHOD (1983)
FHI AJJGLE CALCULATION BASED ON DURGUNOGLU AND MITCHELL (ASCE.RALEIGH CONF.JUNE 75)
FHI ANGLE NORMALIZED TO 2.72 BARS USING EALIGH'S EXPRESSION (ASCE.J-GED,NOV 76)
MODIFIED MAYNE AND KULHAWY FORMUIA USED FOR OCR IN SANDS (ASCE,J-GED,JUNE 82)
LOCATION: SR 26A (GILCHRIST DO.) TEST SITE #3.5
FEP.FCRflEn DATE: 10-31-85
BY: JI.D/PGB/KBT
CALIBRATION INFORMATION:
TELTA
A 0.
22 BARS
DELTA B
* 0.45
BARS
GAGE 0
0.05
BARS
GOT DEPTH 1.57 M
RCP PIA. U.
80 CM
FR.l
RED.PIA.
. 3.70
CM
ROD WT.
.= 6.50
KG/M
DELTA/FUI 0.50
BLADE T=
13.70 m
1 BAR
1.019 KG/CM2 l.i
044 TSF
- 14.51 FSI
ANALYSIS USES
H20 UNIT WEIGHT =
1.000 T/M3
z
THRUST
A
B
ED
ID
KD
uo
GAFTIA
SV
rc
CCR
KO
CU FHI
M
SOIL TYFE
!M)
(EG)
(BAR)
(PAR)
(BAR)
(BAR)
(T/M3)
(BAR)
(BAR)
(BAR) (DEG)
(PAR)
* AAA*
* *V A A A
4**##
A A A A A
A A A A A
A A A A A
A A A A A A
A A A A A A
......
AAAA A
A AAAA
A AAAA
***** *****
>V A >V A A A
AAAAAA*AAAA*
0.63
4060.
n.RO
29.10
679.
2.16
73.48
0.000
2.150
0.123
57.93
A A A A A
8.71
42.1
2978.7
SILTY SAND
0.83
3403.
7.60
25.30
620.
2.58
42.64
0.000
2.000
0.162
26.77
A A A A A
5.01
42.3
2403.1
SILTY SAND
1.03
2630.
6.40
22.20
351.
2.73
28.85
0.000
2.000
0.202
16.30
80.88
3.42
41.2
1931.1
SILTY SAND
1.23
2355.
6.00
22.80
588.
3.16
22.28
0.000
2.000
0.241
12.05
50.07
2.67
40.7
1914.9
SILTY SAND
1.43
I960.
'<. 10
16.50
427.
3.34
13.25
0.000
1.900
0.278
4.87
17.53
1.56
41.4
1182.2
SAND
1.63
1005.
3.10
9.00
191.
1.83
9.70
0.006
1.900
0.309
3.70
11.94
1.32
36.5
471.0
SILTY SAND
1.83
290.
0.80
1.50
1.
0.03
2.95
0.026
1.500
0.319
0.59
1.84
0.77
0.114
1.4
MUD
2.03
375.
0.60
2.30
38.
1.61
2.03
0.045
1.600
0.331
0.41
1.24
0.47
33.5
36.5
SANDY SILT
2.23
1300.
2.30
15.10
442.
7.08
5.16
0.065
1.900
0.349
1.01
2.89
0.63
40.8
850.8
SAND
2.3
1270.
2.50
18.40
555.
8.77
4.98
0.084
1.900
0.366
1.04
2.83
0.63
40.4
1051.1
SAND
2.63
1045.
1.20
13.20
413.
17.01
1.83
0.104
1.800
0.382
0.13
0.34
0.21
41.3
423.2
SAND
2.83
1100.
0.50
9.10
289.
55.57
0.38
0.124
1.700
0.396
245.6
SAND
3.43
14 50.
1 70
11.30
326.
7.56
2.8?.
n. in:i
I. non
0.44 0
0.32
0.73
0.30
42.0
4 55.7
RAND
3.63
1040.
3.20
9.30
198.
1.97
6.33
0.202
1.900
0.458
1.95
4.27
0.76
41.3
409.9
SILTY SAND
3.83
1780.
6.10
14.40
278.
1.41
11.90
0.222
1.950
0.476
8.35
17.53
1.58
37.1
740.7
SANDY SILT
4.03
1535.
8.50
17.00
285.
1.02
16.24
0.241
1.950
0.495
12.98
26.23
2.46
844.2
SILT
4.23
1345.
7.30
14.00
220.
0.92
13.45
0.261
1.950
0.514
10.04
19.55
2.20
610.9
SILT
4.43
1090.
7.20
12.20
158.
0.66
12.91
0.281
1.950
0.532
9.77
18.35
2.15
1.205
432.6
CLAYEY SILT
4.63
905.
5.60
11.30
183.
1.01
9.52
0.300
1.800
0.548
6.25
11.41
1.78
449.2
SILT
4.83
930.
6.60
11.80
165.
0.76
10.98
0.320
1.950
0.567
8.08
14.26
1.95
1.048
427.3
CLAYEY SILT
5.03
1070.
0.20
19.00
333.
1.12
14.65
0.340
1.950
0.585
13.08
22.34
2.32
951.9
SILT
5.23
960.
5.SO
11.90
198.
1.07
8.89
0.359
1.800
0.601
6.15
10.24
1.71
471.4
SILT
5.43
820.
6.50
11.00
140.
0.66
9.89
0.379
1.800
0.617
7.47
12.11
1.83
1.001
347.3
CLAYEY SILT
5.63
830.
5.50
9.40
118.
0.66
8.08
0.398
1.800
0.632
5.58
8.83
1.61
0.797
268.6
CLAYEY SILT
END OF SCI "IDT NO
406

407
UHIV. OF FLORIDA CIVIL ERG. DEPT.- DR. B.E. RUTH TEST NO. 2
FILE NAME: PAVHENT-SUEGRADE MATERIALS CHARACTERIZATION
FILE NUMBER: 245-D51
RECORD OF DILATCMETER TEST NO. 2
USING DATA REDUCTION PROCEDURES IN MARCHETTI (ASCE,J-GED,MARCH 80)
K0 IN SANDS DETERMINED USING SCHMERTMANN METHOD (1983)
IHI ANGLE CALCULATION BASED ON DURGUNOGLU AND MITCHELL (ASCE,RALEIGH CONF.JUNE 75)
IHI ANGLE NORMALIZED TO 2.72 BARS USING BALIGHS EXPRESSION (ASCE,J-GED,NOV 76)
MODIFIED MAYNE AND KULHAWY FORMULA USED FOR OCR IN SANDS (ASCE,J-GED,JUNE 82)
LOCATION: 2SR 26A (GILCHRIST CO.) TEST SITE #4.5
PERFORMED DATE: 10-31-85
BY: JLD/DGB/KBT
CALIBRATION INFORMATION:
DELTA A = 0.22 BARS DELTA B = 0.45 BARS GAGE 0 0.05 BARS GWT DEPTH- 1.57 M
ROD DIA.= 4.80 CM FR.RED.DIA.= 3.70 CM ROD WT. 6.50 KG/M DELTA/FHI= 0.50 BLADE 1=13.70 M
1 BAR = 1.019 KG/CM2 1.044 TSF 14.51 PSI ANALYSIS USES H20 UNIT WEIGHT = 1.000 T/M3
z
THRUST
A
B
ED
ID
KD
UO
GAM4A
SV
PC
OCR
KO
CU
PHI
M
SOIL TYPE
(M)
(KG)
(BAR)
(BAR)
(BAR)
(BAR)
(T/M3)
(BAR)
(BAR)
(BAR)
(DEG)
(BAR)
*****
******
*****
*****
*****
*****
*****
******
******
******
*****
*****
*****
*****
*****
******
************
1.03
3295.
10.40
32.00
763.
2.31
49.60
0.000
2.150
0.192
48.00
*****
5.99
39.5
3062.7
SILTY SAND
1.43
2390.
4.10
16.20
416.
3.24
13.62
0.000
1.900
0.271
4.61
16.99
1.52
42.8
1163.0
SILTY SAND
2.03
645.
0.80
5.80
158.
6.42
2.13
0.045
1.700
0.332
0.29
0.87
0.36
38.0
182.6
SAND
2.43
1920.
3.80
21.60
624.
5.94
8.33
0.084
1.900
0.364
2.55
7.01
0.98
41.6
1461.1
SAND
3.03
3890.
7.40
33.90
941.
4.42
14.62
0.143
2.000
0.420
8.41
20.03
1.66
43.1
2691.1
SAND
3.63
1595.
4.80
14.00
311.
2.06
9.13
0.202
1.900
0.476
4.98
10.47
1.23
37.7
750.6
SILTY SAND
4.03
805.
2.60
5.00
63.
0.74
4.82
0.241
1.700
0.507
2.00
3.94
1.13
0.335
110.8
CLAYEY SILT
END OF SOUNDING

408
UNIV. OF FLORIDA CIVIL EDO. DEFT.- DR. B.E. RUTH TEST NO. 1
FILE NAME: PAVEMENT-SUBGRADE MATERIALS CHARACTERIZATION
FILE NUMBER: 245-D51
RECORD OF PIIARTIETER TEST NO. 1
US I NO DATA REDICTION rROCEDURES IN MARCIIETTI (ABCE.J GED, MARCH BO)
KO IN SANDS DETERMINED USING SCIHERTT1ANN METHOD (1983)
mi ANGLE CALCULATION BASED ON DURGUUOGLU AND MITCHELL (ASCE.RALEIGH CONF.JUNE 75)
FHI ANGLE NORMALIZED TO 2.72 BARS USING BALIGHS EXFRESSION (ASCE, J-GED,NOV 76)
MODIFIED MAYNE AND KULHAWY FORMULA USED FOR OCR IN SANDS (ASCE,J-GED.JUNE 82)
LOCATION: SR 26B (GILCHRIST CO.) TEST SITE #1.0
PERFORMED DATE:
11-05-85
BY:
JLD/DGB/KBT
CALIBRATION INFORMATION:
DELTA A 0.2.8 BARS DELTA B
0.32 BARS
GAGE 0
- 0.05 BARR
GWT DEFTH-* 1.05 M
Ron dta.- *.nn cm
FR.RFn.nTA."
3.70 m
Ron wr,
6.50 KG/M
pFT.TA/nu- n.5o nr.ape T-13.70 m
1 PAR 1.01 KG/CM2
= 1.0*4 TRF -
14.51 FSI
ANALYSIS USES
H20 UNIT WEIGHT 1.000 T/M3
z
THRUST
A
B
ED
ID
KD
UO
GAEMA
sv
FC
OCR
KO
CU
FHI
M
SOIL TYPE
(M)
(KG)
(BAR)
(BAR)
(BAR)
(BAR)
(T/M3)
(BAR)
(BAR)
(BAR)
(DEG)
(BAR)
******
*****
*****
*****
*****
******
******
******
*****
*****
*****
*****
******
************
0.59
2615.
* .80
27.10
791.
5.78
34.30
0.000
2.000
0.115
10.41
90.56
3.72
45.2
2899.3
SAND
0.79
3515.
17.60
0.00
794.
1.37
*****
0.000
2.100
0.156
*****
*****
13.04
34.9
3769.2
SANDY SILT
0.99
3270.
5.40
26.60
751.
4.70
23.53
0.000
2.000
0.195
8.99
46.01
2.57
44.4
2484.5
SAND
1.19
1900.
5.40
17.60
423.
2.42
22.79
0.014
2.000
0.221
12.10
54.75
2.78
39.3
1386.2
SILTY SAND
1.39
760.
1.30
3.90
73.
1.50
5.95
0.033
1.700
0.235
1.01
4.32
0.78
38.6
145.6
SANDY SILT
1.59
335.
0.20
1.00
7.
0.57
1.50
0.053
1.500
0.245
0.16
0.64
0.40
0.038
6.2
MUD
1.79
260.
0.30
1.20
11.
0.71
1.74
0.073
1.500
0.254
0.20
0.80
0.47
0.047
9.3
MUD
1.99
1060.
7.10
11.80
149.
0.61
25.76
0.092
1.950
0.273
14.71
53.89
3.21
1.466
507.3
CLAYEY SILT
2.19
2155.
4.60
18.60
488.
3.48
13.83
0.112
2.000
0.293
5.63
19.25
1.64
41.3
1370.6
SAND
2.39
810.
2.20
3.90
40.
0.51
7.32
0.132
1.700
0.306
2.32
7.57
1.51
0.341
87.4
SILTY CLAY
2.59
70.
2.30
4.20
47.
0.59
7.23
0.151
1.700
0.320
2.38
7.42
1.49
0.351
102.7
SILTY CLAY
2.79
500.
2.00
4.50
69.
1.02
5.88
0.171
1.700
0.334
1.80
5.38
1.30
136.4
SILT
2.99
595.
2.70
5.30
73.
0.80
7.59
0.190
1.700
0.348
2.79
8.02
1.54
0.405
161.9
CLAYEY SILT
3.19
1010.
3.00
6.10
91.
0.91
8.01
0.210
1.700
0.361
3.15
8.71
1.60
207.4
SILT
3.39
2630.
4.00
20.60
583.
5.25
8.40
0.230
2.000
0.381
2.27
5.95
0.88
43.6
1369.2
SAND
3.59
035.
7.20
26.00
663.
3.05
15.65
0.249
2.000
0.401
9.12
22.76
1.78
43.2
1939.2
SILTY SAND
3.79
2935.
6.40
24.10
623.
3.26
13.10
0.269
2.000
0.420
7.35
17.50
1.56
41.6
1717.0
SILTY SAND
3.99
1850.
5.70
17.90
423.
2.41
11.51
0.289
2.000
0.440
6.92
15.74
1.50
38.3
1112.8
SILTY SAND
A. 19
3235.
4.70
9.50
153.
1.00
9.69
0.308
1.800
0.456
5.34
11.71
1.80
377.7
SILT
.39
1355.
5.10
9.60
142.
0.85
10.20
0.328
1.800
0.471
5.99
12.70
1.86
0.795
357.9
CLAYEY SILT
.59
1065.
4.40
9.40
160.
1. 14
8.34
0.34 7
1.800
0.487
4.53
9.28
1.64
372.0
SILT
79
830.
4.40
9 /.II
160
1.14
0.04
0.36 7
l. nnn
0.503
4.41
8.77
1.60
366.2
SILT
4.90
1665,
3.00
11 40
755
7.73
6 33
0 38 7
1.900
0.470
7.. 63
4. (13
(1.85
39.7.
530.5
SILTY SAND
5.19
1 760.
3.70
13.60
339.
3. 19
5.69
0.406
1.900
0.538
2.14
3.97
0.75
39.9
680.9
SILTY SAND
5.39
1185.
2.90
7.10
131.
1.50
4.58
0.47.6
1.700
0.552
1.78
3.22
0.70
37.3
228.8
SANDY SILT
5.59
1000.
2.50
6.00
106.
1.42
3.78
0.446
1.700
0.565
1.43
2.52
0.64
36.4
164.4
SANDY SILT
END OF SOUNDING

409
UNIV. OF FLORIDA CIVIL ENG. DEPT.- DR. B.E. RUTH TEST NO. 2
FILE NAME: PAVEMENT-SUBGRADE MATERIALS CHARACTERIZATION
FILE NUMBER: 245-D51
RECORD OF DILATCMETER TEST NO. 2
USING DATA REDUCTION PROCEDURES IN MARCHETTI (ASCE,J-GED, MARCH 80)
K0 IN SANDS DETERMINED USING SCHMERIMANN METHOD (1983)
PHI ANGLE CALCULATION BASED ON DURGUNOGLU AND MITCHELL (ASCE,RALEIGH CONF.JUNE 75)
PHI ANGLE NORMALIZED TO 2.72 BARS USING BALIGHS EXPRESSION (ASCE,J-GED,NOV 76)
MODIFIED MAYNE AND KULHAWY FORMULA USED FOR OCR IN SANDS (ASCE,J-GED,JUNE 82)
LOCATION: SR 26B (GILCHRIST CO.) TEST SITE #1.5
PERFORMED DATE: 11-05-85
BY: JLD/DGB/KBT
CALIBRATION INFORMATION:
DELTA A 0.28 BARS DELTA B 0.32 BARS
ROD DIA.= A.80 CM FR.RED.DIA.= 3.70 CM
1 BAR
= 1.019
KG/CM2
= 1.044 TSF
= 14.51 PSI
z
THRUST
A
B
ED
ID
KD
UO
GAAMA
(M)
(KG)
(BAR)
(BAR)
(BAR)
(BAR)
(T/M3)
*****
******
*****
*****
***** 1
*****
*****
******
******
0.68
3900.
7.10
35.80
1024.
4.98
44.55
0.000
2.000
0.88
4740.
16.30
40.00
842.
1.58
88.25
0.000
2.100
1.08
3580.
10.20
32.80
802.
2.48
43.69
0.003
2.150
1.28
2070.
4.50
18.00
470.
3.33
17.43
0.023
2.000
1.A8
1045.
2.80
8.10
171.
1.79
11.06
0.042
1.800
1.68
235.
2.40
4.70
62.
0.72
9.46
0.062
1.700
2.08
785.
0.70
3.50
80.
3.21
2.48
0.101
1.700
2.28
1615.
5.40
16.40
379.
2.19
16.11
0.121
2.000
2.A8
660.
1.90
3.20
26.
0.38
6.08
0.140
1.600
2.88
430.
2.50
4.50
51.
0.59
7.15
0.180
1.700
3.08
490.
2.40
4.70
62.
0.76
6.50
0.199
1.700
3.48
1925.
3.60
9.30
186.
1.60
8.55
0.238
1.800
3.71
1600.
3.60
6.40
80.
0.67
8.50
0.261
1.700
3.88
1145.
3.40
6.20
80.
0.71
7.74
0.278
1.700
4.28
800.
3.60
7.30
113.
0.97
7.49
0.317
1.800
4.48
875.
4.10
8.40
135.
1.02
8.21
0.337
1.800
4.71
730.
3.90
7.40
106.
0.84
7.52
0.359
1.800
4.88
1160.
3.70
7.90
131.
1.12
6.81
0.376
1.800
5.48
1130.
3.70
7.80
128.
1.11
6.12
0.435
1.800
GAGE 0 0.05 BARS GOT DEPTH- 1.05 M
ROD WT. 6.50 KG/M DELTA/PHI- 0.50 BLADE T-13.70 tM
ANALYSIS USES H20 UNIT WEIGHT = 1.000 T/M3
SV
PC
OCR
KO
CU
PHI
M
SOIL TYPE
(BAR)
(BAR)
(BAR)
(DEG)
(BAR)
******
*****
*****
*****
*****
*****
******
************
0.133
20.97
*****
5.01
44.9
4007.7
SAND
0.174
*****
*****
10.66
38.9
3839.3
SANDY SILT
0.213
40.41
*****
5.25
40.4
3123.0
SILTY SAND
0.233
6.97
29.90
2.05
41.4
1422.2
SAND
0.249
3.58
14.38
1.43
37.8
444.5
SANDY SILT
0.263
2.96
11.29
1.78
0.403
151.3
CLAYEY SILT
0.290
0.22
0.74
0.32
40.2
103.3
SILTY SAND
0.310
9.33
30.12
2.05
37.6
1118.4
SILTY SAND
0.321
1.82
5.67
1.33
0.284
50.8
SILTY CLAY
0.347
2.53
7.30
1.48
0.375
110.0
SILTY CLAY
0.361
2.27
6.29
1.39
0.347
127.8
CLAYEY SILT
0.390
3.03
7.77
1.04
40.9
436.7
SANDY SILT
0.407
3.89
9.55
1.66
0.546
187.1
CLAYEY SILT
0.419
3.46
8.26
1.56
0.500
179.5
CLAYEY SILT
0.448
3.52
7.85
1.53
249.7
SILT
0.464
4.20
9.05
1.62
310.5
SILT
0.482
3.81
7.90
1.53
0.555
233.8
CLAYEY SILT
0.495
3.35
6.77
1.44
278.0
SILT
0.542
3.11
5.73
1.34
256.7
SILT
END OF SOUNDING

410
UNIV. OF FLORIDA CIVIL ENG. DEFT.- DR. B.E. RUTH TEST NO. 1
FILE NAME: PAVEMENT-SUBGRADE MATERIALS CHARACTERIZATION
FILE NUMBER: 245-D51
RECORD OF DILATCMETER TEST NO. 1
USING DATA REDUCTION PROCEDURES IN MARCHETTI (ASCE.J-GED,MARCH 80)
KO IN SANDS DETERMINED USING SCEMERIMANN METHOD (1983)
PHI ANGLE CALCULATION BASED ON DURGUNOGLU AND MITCHELL (ASCE,RALEIGH CONF.JUNE 75)
ffll ANGLE NORMALIZED TO 2.72 BARS USING BALIGHS EXPRESSION (ASCE,J-GED,NOV 76)
MODIFIED MAYNE AND KULHAWY FORMULA USED FCR OCR IN SANDS (ASCE,J-GED,JUNE 82)
LOCATION: SR 26C (GILCHRIST CO.) TEST SITE #4.0
PERFORMED DATE: 11-05-85
BY: JLD/DGB/KBT
CALIBRATION INFORMATION:
DELTA A 0.28 BARS DELTA B 0.32 BARS GAGE 0 = 0.05 BARS GWI DEPTH- 1.05 M
ROD DIA.= 4.80 CM FR.RED.DIA.- 3.70 CM ROD WT.= 6.50 KG/M DELTA/FHI= 0.50 BLADE 1=13.70 FM
1 BAR 1.019 KG/CM2 1.044 TSF = 14.51 PSI
z
THRUST
A
B
ED
ID
KD
UO
GAFMA
(M)
(KG)
(BAR)
(BAR)
(BAR)
(BAR)
(T/M3)
*****
******
*****
*****
*****
*****
*****
******
******
0.58
4150.
8.20
34.20
925.
3.72
63.93
0.000
2.150
0.78
3290.
6.80
26.00
678.
3.20
40.33
0.000
2.000
0.98
1360.
4.60
13.40
299.
1.95
23.44
0.000
1.900
1.18
860.
1.30
7.40
200.
4.65
5.88
0.013
1.800
1.38
1745.
2.20
11.80
328.
4.85
8.51
0.032
1.900
1.58
2030.
3.80
15.20
393.
3.30
13.95
0.052
1.900
1.78
1045.
2.60
11.20
291.
3.56
8.93
0.072
1.900
1.98
1180.
2.60
9.40
226.
2.68
8.62
0.091
1.900
2.18
1600.
2.60
9.50
230.
2.75
8.03
0.111
1.900
2.38
1680.
1.60
9.30
259.
5.54
4.27
0.131
1.800
2.58
1370.
1.90
8.40
215.
3.68
5.09
0.150
1.800
2.78
1040.
1.40
7.20
189.
4.55
3.46
0.170
1.800
2.98
1050.
1.50
8.60
237.
5.61
3.36
0.189
1.800
3.18
1115.
1.20
6.20
160.
4.62
2.65
0.209
1.800
3.38
985.
1.30
5.80
142.
3.70
2.81
0.229
1.800
3.58
1125.
1.40
6.70
171.
4.30
2.80
0.248
1.800
3.78
1325.
1.40
7.70
208.
5.56
2.53
0.268
1.800
3.98
1855.
2.00
9.20
240.
4.30
3.66
0.288
1.800
4.18
2540.
2.40
11.30
302.
4.57
4.16
0.307
1.900
4.38
3440.
2.80
13.00
350.
4.53
4.67
0.327
1.900
4.58
7640.
7.80
32.40
874.
3.89
13.08
0.346
2.000
ANALYSIS USES H20 UNIT WEIGHT = 1.000 T/M3
SV
PC
OCR
KO
CU
PHI
M
SOIL TYPE
(BAR)
(BAR)
(BAR)
(DEG)
(BAR)
******
*****
*****
*****
*****
*****
kirftirtrk
*r**r*n***r*r**r*
0.112
36.02
*****
7.36
44.6
3939.1
SAND
0.151
21.65
*****
4.68
43.0
2589.0
SILTY SAND
0.189
11.56
61.33
2.92
37.4
987.9
SILTY SAND
0.211
0.75
3.58
0.69
40.5
408.7
SAND
0.229
1.20
5.26
0.81
44.0
773.9
SAND
0.246
4.59
18.61
1.60
42.0
1107.8
SILTY SAND
0.264
2.47
9.35
1.15
38.5
700.0
SAND
0.282
2.39
8.48
1.09
39.2
534.9
SILTY SAND
0.299
1.90
6.36
0.93
41.6
529.3
SILTY SAND
0.315
0.26
0.83
0.30
44.4
455.3
SAND
0.331
0.84
2.54
0.58
41.6
411.4
SAND
0.347
0.49
1.42
0.44
40.3
299.2
SAND
0.362
0.50
1.38
0.44
40.2
367.5
SAND
0.378
0.29
0.76
0.32
41.1
215.8
SAND
0.394
0.44
1.11
0.40
39.6
198.6
SAND
0.409
0.40
0.98
0.37
40.4
238.8
SAND
0.425
0.26
0.61
0.28
41.8
271.6
SAND
0.441
0.42
0.95
0.34
43.2
391.1
SAND
0.458
0.30
0.65
0.27
45.2
525.8
SAND
0.476
643.1
SAND
0.496
4.85
9.78
1.10
47.4
2408.3
SAND
END OF SOUNDING

411
UNIV. OF FLORIDA CIVIL ENG. DEPT.- DR. B.E. RUTH TEST NO. 2
FILE NAME: PAVEMENT-SUBGRADE MATERIALS CHARACTERIZATION
FILE NUMBER: 245-D51
RECORD OF DILATCMETER TEST NO. 2
USING DATA REDUCTION PROCEDURES IN MARCHETTI (ASCE,J-GED,MARCH 80)
KO IN SANDS DETERMINED USING SCEMERTMANN METHOD (1983)
PHI ANGLE CALCULATION BASED ON DURGUNOGLU AND MITCHELL (ASCE,RALEIGH CONF.JUNE 75)
PHI ANGLE NORMALIZED TO 2.72 BARS USING BALIGHS EXPRESSION (ASCE,J-GED,NOV 76)
MODIFIED MAYNE AND KULHAWY FORMULA USED FOR OCR IN SANDS (ASCE,J-GED,JUNE 82)
LOCATION: >SR 26C (GILCHRIST CO.) TEST SITE #4.5
PERFORMED DATE: 11-05-85
BY: JLD/DGB/KBT
CALIBRATION INFORMATION:
DELTA A = 0.28 BARS DELTA B = 0.32 BARS
ROD DIA.- 4.80 CM FR.RED.DIA.- 3.70 CM
GAGE 0 = 0.05 BARS GMT DEPTH- 1.05 M
ROD WT.= 6.50 KG/M DELTA/PHI- 0.50 BLADE T-13.70 Ml
1 BAR = 1.019 KG/CM2 = 1.044 TSF = 14.51 PSI
ANALYSIS USES H20 UNIT WEIGHT = 1.000 T/M3
z
THRUST
A
B
ED
ID
KD
UO
GAM1A
SV
PC
OCR
KO
CU
PHI
M
SOIL TYPE
(M)
(KG)
(BAR)
(BAR)
(BAR)
(BAR)
(T/K3)
(BAR)
(BAR)
(BAR)
(DEG)
(BAR)
*****
******
*****
*****
*****
*****
*****
******
******
******
*****
*****
*****
*****
*****
******
************
0.58
5375.
3.60
31.60
998.
11.70
21.96
0.000
2.000
0.112
3239.5
SAND
0.78
4528.
10.30
35.20
885.
2.74
60.41
0.000
2.150
0.154
49.57
*****
7.13
42.5
3721.0
SILTY SAND
0.98
2095.
6.10
19.80
477.
2.42
29.34
0.000
2.000
0.193
17.18
88.81
3.55
39.5
1679.6
SILTY SAND
1.18
1060.
2.60
10.20
255.
2.98
11.32
0.013
1.900
0.218
3.11
14.27
1.42
38.8
667.5
SILTY SAND
1.38
1045.
2.40
9.80
248.
3.16
9.58
0.032
1.900
0.236
2.44
10.34
1.21
39.0
610.2
SILTY SAND
1.58
1095.
2.50
10.00
251.
3.11
9.21
0.052
1.900
0.253
2.44
9.62
1.16
39.0
610.5
SILTY SAND
1.78
820.
2.00
8.40
211.
3.26
6.95
0.072
1.800
0.269
1.66
6.16
0.95
37.5
461.4
SILTY SAND
1.98
900.
1.80
7.70
193.
3.32
5.88
0.091
1.800
0.285
1.22
4.30
0.78
38.6
393.7
SAND
2.18
1110.
1.70
8.40
222.
4.23
5.04
0.111
1.800
0.300
0.83
2.75
0.61
40.6
423.4
SAND
2.38
1280.
2.20
10.10
266.
3.96
6.08
0.131
1.900
0.318
1.27
4.00
0.74
40.6
550.0
SAND
2.98
1370.
2.00
10.00
270.
4.65
4.50
0.189
1.900
0.371
0.78
2.11
0.53
41.2
487.2
SAND
3.18
2715.
4.00
17.20
459.
3.90
8.72
0.209
1.900
0.389
2.54
6.52
0.92
43.5
1093.2
SAND
3.78
3735.
6.00
22.60
583.
3.25
11.61
0.268
2.000
0.445
5.38
12.09
1.27
43.6
1539.7
SILTY SAND
4.18
6125.
9.00
28.80
700.
2.53
16.35
0.307
2.150
0.487
11.17
22.94
1.78
44.8
2074.7
SILTY SAND
4.38
7615.
13.40
38.70
900.
2.15
23.69
0.327
2.150
0.509
26.16
51.35
2.72
44.0
2984.7
SILTY SAND
4.58
8700.
14.20
40.00
918.
2.06
24.10
0.346
2.150
0.532
27.60
51.89
2.74
44.5
3060.2
SILTY SAND
END OF SOUNDING

412
UNIV. OF FLORIDA CIVIL ENG. DEPT. DR. B.E. RUTH TEST NO. 1
FILE NAME: PAVEKENT-SUBGRADE MATERIALS CHARACTERIZATION
FILE NUMBER: 245-D51
RECORD OF DILATCMETER TEST NO. 1
USING DATA REDUCTION PROCEDURES IN MARCHETTI (ASCE,J-GED,MARCH 80)
KO IN SANDS DETERMINED USING SCEMERIMANN METHOD (1983)
ESI ANGLE CALCULATION BASED ON DURGUNOGLU AND MITCHELL (ASCE,RALEIGH CONF.JUNE 75)
PHI ANGLE NORMALIZED TO 2.72 BARS USING BALIGH'S EXPRESSION (ASCE.J-GED.NOV 78)
MODIFIED MAYNE AND KULHAWY FORMULA USED FCR OCR IN SANDS (ASCE,J-GED. JUNE 82)
LOCATION: US 301 (ALACHUA CO.) TEST SITE #2.0
PERFORMED DATE: 02-18-85
BY: JLD/DGB/KBT
CALIBRATION INFORMATION:
DELTA A 0.22 BARS
DELTA B
0.37 BARS
GAGE 0 0.05 BARS
GOT DEPTH- 1.14 M
ROD DIA.- 4.80 CM
FR.RED.DIA.-
3.70 CM
ROD WT.- 6.50 KG/M
DELTA/PHI- 0.50
BLADE T-13.70 tfi
1 EAR 1.019 KG/CM2
- 1.044 TSF -
14.51 PSI
ANALYSIS USES
320 UNIT WEIGHT -
1.000 T/M3
z
THRUST
A
B
ED
ID
KD
UQ
GAM4A
SV
PC
OCR
KO
CU
PHI
M
SOIL TYPE
(M)
(KG)
(BAR)
(BAR)
(BAR)
(BAR)
CT/K3)
(BAR)
(BAR)
(BAR)
(DEG)
(BAR)
*****
******
*****
*****
*****
*****
*****
******
******
******
...**
*****
*****
*****
*****
******
************
0.50
1180.
3.20
14.20
379.
3.84
29.08
0.000
1.900
0.098
7.91
80.67
3.43
40.6
1331.5
SAND
0.70
2760.
2.60
19.70
602.
8.92
14.37
0.000
1.900
0.135
1710.4
SAND
0.90
1595.
4.40
15.00
365.
2.58
23.31
0.000
2.000
0.175
9.78
56.05
2.82
39.6
1204.1
SILTY SAND
1.10
575.
0.70
3.20
70.
2.59
3.73
0.000
1.700
0.208
0.37
1.76
0.50
38.9
112.4
SILTY SAND
1.30
265.
0.40
2.30
48.
2.81
2.17
0.016
1.700
0.22S
0.29
1.31
0.48
33.1
55.0
SILTY SAND
1.50
325.
0.40
3.60
95.
6.78
1.69
0.035
1.700
0.239
0.20
0.85
0.38
35.1
90.3
SAND
1.70
440.
0.30
4.60
117.
4.47
2.98
0.055
1.700
0.253
0.42
1.68
0.52
35.8
169.5
SAND
1.90
440.
0.70
4.10
102.
4.51
2.45
0.075
1.700
0.267
0.34
1.27
0.45
36.0
131.1
SAND
2.10
680.
1.10
6.00
157.
4.71
3.40
0.094
1.300
0.282
0.48
1.68
0.50
38.3
245.4
SAND
2.30
750.
1.10
6.70
183.
5.81
3.04
0.114
1.800
0.298
0.38
1.29
0.43
39.1
267.4
SAND
2.50
1020.
2.50
9.20
223.
2.88
7.06
0.133
1.900
0.316
1.97
6.24
0.95
38.1
488.7
SILTY SAND
2.70
1450.
3.60
10.10
215.
1.87
9.96
0.153
1.900
0.334
3.81
11.42
1.27
39.0
537.5
SILTY SAND
2.90
2380.
7.30
19.60
427.
1.83
19.01
0.173
2.000
0.353
14.12
39.98
2.37
38.9
1326.0
SILTY SAND
3.10
2795.
7.20
17.80
365.
1.57
17.96
0.192
1.950
0.372
12.72
34.21
2.19
40.2
1114.0
SANDY SILT
3.30
2290.
7.30
17.80
361.
1.54
17.32
0.212
1.950
0.390
13.31
34.08
2.18
38.5
1090.4
SANDY SILT
3.50
1910.
9.20
19.40
350.
1.17
21.16
0.232
1.950
0.409
16.22
39.66
2.87
1123.9
SILT
3.70
1680.
12.10
21.90
336.
0.84
26.84
0.251
2.100
0.431
24.74
57.44
3.28
2.433
1152.6
CLAYEY SILT
3.90
1615.
7.20
16.40
314.
1.36
14.84
0.271
1.950
0.449
12.78
28.45
1.99
35.1
901.5
SANDY SILT
4.10
1335.
7.80
14.40
219.
0.86
15.77
0.290
1.950
0.468
11.73
25.06
2.42
1.360
641.8
CLAYEY SILT
4.30
2475.
10.80
18.70
266.
0.75
21.16
0.310
1.950
0.487
19.29
39.63
2.87
2.042
854.8
CLAYEY SILT
4.50
3660.
7.60
25.80
642.
2.82
12.96
0.330
2.000
0.506
8.54
16.87
1.53
42.1
1761.4
SILTY SAND
4.70
5435.
15.60
37.80
787.
1.58
27.17
0.349
2.100
0.528
40.98
77.64
3.31
40.6
2713.4
SANDY SILT
4.90
2910.
9.70
23.80
492.
1.61
16.15
0.369
1.950
0.546
16.51
30.21
2.06
38.5
1454.0
SANDY SILT
5.10
2190.
9.10
21.80
441.
1.54
14.64
0.389
1.950
0.565
15.26
26.99
1.94
36.3
1262.4
SANDY SILT
5.30
2805.
7.80
26.00
642.
2.77
11.43
0.408
2.000
0.585
8.69
14.87
1.45
39.8
1685.0
SILTY SAND
5.50
2740.
3.00
16.20
277.
1.09
12.20
0.428
1.950
0.603
10.13
16.79
2.08
745.4
SILT
5.70
2025.
10.40
18.70
281.
0.83
15.65
0.447
1.950
0.622
15.41
24.77
2.41
1.791
821.5
CLAYEY SILT
5.90
2115.
7.80
19.40
401.
1.66
10.85
0.467
1.950
0.641
9.65
15.06
1.47
37.0
1033.9
SANDY SILT
6.10
2565.
7.40
20.80
467.
2.09
9.76
0.487
2.000
0.660
7.56
11.45
1.28
39.1
1156.2
SILTY SAND
6.30
2855.
5.50
18.60
456.
2.89
6.67
0.506
2.000
0.680
3.32
4.88
0.82
41.5
978.2
SILTY SAND
6.50
3280.
7.10
21.80
514.
2.45
8.63
0.526
2.000
0.700
5.72
8.18
1.07
41.3
1216.8
SILTY SAND
END OF SOUNDING

413
UNIV. OF FLORIDA CIVIL ENG. DEPT.- DR. B.E. RUTH TEST NO. 2
FILE NAME: PAVEMENT-SUBGHADE MATERIALS CHARACTERIZATION
FILS NUMBER: 245-D51
RECORD OF DILAICHETER TEST NO. 2
USING DATA REDUCTION PROCEDURES IN MARCHETTI (ASCE.J-GED,MARCH 80)
KO IN SANDS DETERMINED USING SCHMERTMANN METHOD (1983)
HI ANGLE CALCULATION 3ASED ON DURGUNOGLU AND MITCHELL (ASCE,RALEIGH CONF.JUNE 75)
rHI ANGLE NORMALIZED TO 2.72 BARS USING BALIGHS EXPRESSION (ASCE.J-GED,NOV 76)
MODIFIED MAYNE AND KULHAWY FORMULA USED FCR OCR IN SANDS (ASCE,J-GED, JUNE 82)
LOCATION: US 301 (ALACHUA CO.) TEST SIIE #3.0
PERFORMED DATE: 02-18-85
BY: JLD/DGB/KBT
CALIBRATION INFORMATION:
DELTA A 0.22 BARS
DELTA B
0.37 BARS
GAGE 0 0.05 BARS
GWT DEPTH-
1.14 M
ROD DIA.- 4.80 CM
FR.RED.DIA.-
3.70 at
ROD WT.- 6.50 KG/M
DELTA/PHI-
0.50
BLADE T-13.70 MM
1 BAR 1.019 KG/CM2
- 1.044 TSF -
14.51 PSI
ANALYSIS USES
H20 UNIT WEIGHT =
1.000 T/M3
Z
THRUST
A
B
ED
ID
KD
UO
GAMMA
SV
PC
OCR
KO
CU
PHI
M
SOIL TYPE
(M)
(KG)
(BAR)
(BAR)
(BAR)
(BAR)
(T/M3)
(BAR)
(BAR)
(BAR)
(DEG)
(BAR)
*****
******
*****
*****
*****
*****
*****
******
******
******
*****
*****
*****
*****
*****
******
************
0.50
2500.
5.20
26.60
758.
5.05
44.18
0.000
2.000
0.098
2962.0
SAND
0.70
4405.
8.20
30.20
780.
3.08
53.18
0.000
2.000
0.137
31.71
*****
6.12
44.2
3184.4
SILTY SAND
0.90
2945.
9.00
25.30
572.
1.97
47.50
0.000
2.000
0.177
40.18
*****
5.73
39.6
2275.4
SILTY SAND
1.10
1335.
2.70
10.20
252.
2.37
11.81
0.000
1.900
0.214
3.02
14.13
1.40
40.7
669.0
SILTY SAND
1.30
800.
1.00
5.20
132.
3.89
4.17
0.016
1.800
0.233
0.43
1.84
0.50
40.5
228.9
SAND
1.50
1240.
1.90
9.20
244.
4.15
6.82
0.035
1.800
0.249
1.10
4.41
0.76
41.6
529.9
SAND
1.70
930.
2.20
8.20
197.
2.78
7.66
0.055
1.900
0.267
1.90
7.13
1.01
36.2
446.5
SILTY SAND
1.90
730.
1.00
4.50
106.
3.22
3.39
0.075
1.700
0.281
0.44
1.56
0.47
39.0
165.3
SILTY SAND
2.10
740.
0.90
4.30
121.
4.29
2.75
0.094
1.700
0.294
0.31
1.05
0.38
39.4
166.4
' SAND
2.30
655.
0.90
5.00
128.
4.72
2.53
0.114
1.700
0.308
0.33
1.08
0.40
38.2
167.3
SAND
2.70
1220.
5.60
10.80
168.
0.90
15.96
0.153
1.800
0.337
8.62
25.54
2.44
0.996
494.3
CLAYEY SILT
3.10
1765.
11.20