Citation
Applications of the electronic cone penetration test for the geotechnical site investigation of Florida soils

Material Information

Title:
Applications of the electronic cone penetration test for the geotechnical site investigation of Florida soils
Creator:
Knox, Kenneth James, 1955- ( Dissertant )
Townsend, Frank C. ( Thesis advisor )
Davidson, John L. ( Reviewer )
McVay, Michael C. ( Reviewer )
Boomquist, David ( Reviewer )
Wilson, Joseph N. ( Reviewer )
Shafer, D. J. ( Degree grantor )
Place of Publication:
Gainesville, Fla.
Publisher:
University of Florida
Publication Date:
Copyright Date:
1989
Language:
English
Physical Description:
xvi, 246 leaves : ill. ; 28 cm.

Subjects

Subjects / Keywords:
Autocorrelation ( jstor )
Databases ( jstor )
Datasets ( jstor )
Landfills ( jstor )
Modeling ( jstor )
Penetrometers ( jstor )
Regression analysis ( jstor )
Silts ( jstor )
Soil properties ( jstor )
Soils ( jstor )
Civil Engineering thesis Ph. D
Dissertations, Academic -- Civil Engineering -- UF
Penetrometer ( lcsh )
Soil penetration test ( lcsh )
Soils -- Florida ( lcsh )
Soils -- Testing ( lcsh )
Genre:
bibliography ( marcgt )
non-fiction ( marcgt )
theses ( marcgt )

Notes

Abstract:
The purpose of this research project was to evaluate techniques to improve the application of in situ penetration testing to Florida soils, with emphasis on the electronic cone penetrometer test (ECPT). Topics addressed included describing the spatial variability of soil properties, classifying Florida soils with the ECPT, and correlating the ECPT with the standard penetration test (SPT). A collateral purpose was to create an in situ test data base consisting of 97 ECPT soundings and 79 SPT tests. This data base was subsequently evaluated using statistical analysis. The spatial variability study was carried out to evaluate methods of interpolation between test soundings. The techniques studied included three deterministic approaches (the mean, median, and a 10% trimmed average), three distance-weighting methods (two based on reciprocal distances, and linear interpolation), a random field model (a hybrid distance-weighting/regression model), and regression analysis. While none of the approaches stood out as consistently superior predictors, the deterministic approaches were generally inferior to the other, more sophisticated methods. The distance-weighting methods and the random field model performed comparably, but were sensitive to individual test soundings. The regression models predicted slightly better on the average, and with more stability. The ECPT classification study used parametric and nonparametric discriminant analysis of cone data on soils that had been identified from the SPT test. The ECPT was able to group soil accurately into one of seven categories (organics, clay, silt, clayey sand, silty sand, sand, weathered rock) approximately 40% of the time. This percentage increased to 70% when the three sand categories were combined, reflecting the SPT drillers' difficulties in discriminating silty soils. In the SPT-ECPT correlation study, average q^/N ratios for Florida soils were much higher than expected, possibly due to cementation or liquefaction. Regression analysis of the data suggested that the nature of the SPT-ECPT relationship is more a function of the magnitude of the tip resistance, and less of the actual soil type.
Thesis:
Thesis (Ph. D.)--University of Florida, 1989.
Bibliography:
Includes bibliographical references (leaves 238-243)
Additional Physical Form:
Also available on World Wide Web
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by Kenneth James Knox.

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University of Florida
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University of Florida
Rights Management:
Copyright [name of dissertation author]. 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:
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21887377 ( oclc )
AHC7193 ( notis )

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APPLICATIONS OF THE ELECTRONIC CONE PENETRATION TEST
FOR THE GEOTECHNICAL SITE INVESTIGATION OF FLORIDA SOILS














By

KENNETH JAMES KNOX














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

1989








































UNIVERSITY OF FLORIDA
I II llI I II2 l 0 ll lllll 1lllll
3 1262 08552 3198
























DEDICATED WITH ALL MY LOVE TO MY WIFE, PAT,

AND TO MY WONDERFUL CHILDREN, BRIAN AND KELLY,

FOR THEIR DEVOTED LOVE, PATIENCE, AND SUPPORT.












ACKNOWLEDGMENTS

So many people had a direct and significant impact on my studies

and research at the University of Florida that I am reluctant to attempt

to write the acknowledgments section for fear of omitting a key

contributor. Nevertheless, fear must never be allowed to impede

progress and worthwhile endeavors; therefore, please forgive my less-

than-perfect memory if I fail to acknowledge someone, and know that I am

deeply indebted to and appreciative of everyone I have been associated

with these past three years.

I would like to express my deepest gratitude to the members of my

supervisory committee. In particular, I would like to thank Dr. Frank

C. Townsend for serving as my chairman, and for being a true friend and

professional. While the wealth of knowledge I have managed to glean

from him will undoubtedly serve me well in the future, I value even more

his perspectives on the responsibilities of a doctorate, and on the

future of education in America.

I am also grateful to Dr. David Bloomquist not only for serving on

my committee, but also for the abundance of help he provided me,

especially regarding operation of the cone testing equipment and

preparation of this dissertation. "Dave's" amazing breadth of knowledge

and his "Let's do it!" attitude are invaluable assets to all who have

the pleasure of working with him. I would like to thank Dr. John L.

Davidson for serving on my committee, and for being a ready and willing

source of information. I also hope to absorb some of Dr. Davidson's






superb teaching style in my own return to teaching. Special thanks are

extended to Dr. Joseph N. Wilson of the Department of Computer and

Information Sciences for being an old friend of the family and for

serving as my external committee member.

I have purposely left Dr. Michael C. McVay to the end of my

committee members. Dr. McVay was singularly instrumental in, and the

driving force behind every phase of my research. He insured that I had

the resources I needed to accomplish the work. Dr. McVay constantly

challenged and encouraged me throughout the project, and the final

product is a direct result of his interest not only in the research, but

also in me. My deepest thanks are extended to Dr. McVay for his

support. I pray that some of Dr. McVay's thirst for knowledge will rub

off on me when I depart the University of Florida.

Many geotechnical engineers in the State of Florida unselfishly

offered extensive help in support of my research, and I am grateful.

This project would have been impossible without them. In particular, I

would like to thank Dr. Joseph A. Caliendo, Chief Geotechnical Engineer

with the Florida Department of Transportation (FDOT). He was a true

friend and invaluable resource. Equally invaluable was the unbelievable

assistance offered by Mr. William F. Knight and Mr. Sam Weede of the

FDOT's Chipley office. They literally opened up their entire operation

to me despite a crushing workload. My sincerest thanks are also

extended to Mr. Lincoln Morgado and Mr. Bob Raskin of the FDOT's Miami

office, Dr. John H. Schmertmann and Dr. David K. Crapps with Schmertmann

and Crapps of Gainesville, Mr. Bill Ryan with Ardaman and Associates of

Sarasota, Mr. Richard Stone, Jr., with Law Engineering of Naples, Mr.

Jay Casper with Jammal and Associates of Orlando, and Mr. Kevin Kett

with Law Engineering in Jacksonville.

iv







A key contributor to my research was Mr. Ed Dobson, Engineering

Technician with the Civil Engineering Department. Ed accompanied me on

all of the trips, and proved to be a hard and able worker. His humor

and contributions are greatly appreciated.

The friendship and support of my many graduate student colleagues

are also acknowledged. In particular, I would like to thank my mentor,

friend, and fellow Air Force officer, Dr. John Gill. His advice and

support were instrumental to my success. I also thank my other Air

Force friends, including Dr. Charlie Manzione, Greg Coker, and Bill

Corson. I thank Dr. Ramon Martinez, Fernando Parra, and Guillermo

Ramirez for their support, friendship, and patience with my Spanish. I

am also indebted to my friends Bob Casper, Curt Basnett, Chris Dumas,

David Springstead, Michelle Warner, and David Seed, all of whom directly

contributed to this research.

I would like to express my sincerest appreciation to the United

States Air Force for making this doctorate possible. In particular, I

would like to express my thanks to the U.S. Air Force Academy and

Colonel (Dr.) David 0. Swint, Professor and Head of the Academy

Department of Civil Engineering. They helped make my dream come true.

Lastly, but not least by a long shot, I would like to thank my

wonderful family for their endless devotion and support. Completion of

my doctorate would not have been possible without my wife Pat's undying

love, nurturing, prodding, scolding, supporting, and caring for me. My

little buddy, Brian, and my lovely little girl, Kelly, were bottomless

sources of joy to me when I most needed a lift. This doctorate truly

belongs to all of them.












TABLE OF CONTENTS
page

ACKNOWLEDGMENTS .................................................... iii

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

LIST OF FIGURES .................................................... xi

ABSTRACT ........................................................... xv

CHAPTERS

I INTRODUCTION ................................................ 1

Purpose of Research.......................................... 3
Research Methodology......................................... 4

2 PROJECT DATA BASE ........................................... 6

Introduction ................................................. 6
Extent of Data Base.......................................... 7
Site Descriptions ........................................... 9
Apalachicola River and Bay Bridges (Sites 001 003)....... 9
Overstreet Bridge (Sites 004 005)........................ 10
Sarasota Garage and Condo (Sites 006 008)................ 10
Sarasota Landfill (Site 009) ............................... 14
Fort Myers Interchange (Sites 010 011)................... 14
Fort Myers Airport (Site 012) .............................. 17
Port Orange (Sites 013 014) .............................. 17
West Palm 1-95 (Sites 015 018) ........................... 17
Choctawhatchee Bay (Sites 019 021)....................... 21
White City (Site 022) ...................................... 21
Orlando Arena (Site 023) ................................... 21
Orlando Hotel (Sites 024 026)............................ 24
Jacksonville Terminal (Sites 027 028).................... 24
Archer Landfill (Site 029) ................................. 26
West Bay (Site 030) ........................................ 26
Lake Wauberg (Site 031) .................................... 26
Collection of ECPT Data...................................... 28
Equipment .................................................. 28
Procedures ................................................. 31
Problems Encountered ...................................... 31

3 LOCAL VARIABILITY IN CONE PENETROMETER TEST MEASUREMENTS..... 40

Introduction ................................................. 40







Local Variability Data Base.................................. 41
Data Filter ................................................. 43
Evaluation of Data Scatter................................... 45

4 DESCRIBING THE SPATIAL VARIABILITY OF SOILS ................. 52

Introduction.................................... ............ 52
Descriptive Statistics for Spatial Variability............... 53
Summarizing a Data Set..................................... 53
Describing Variability..................................... 54
Measuring Association...................................... 55
Estimation Models. .......................................... 58
Traditional Choices ....................................... 58
Random Field Models..................... .................. 59

5 EVALUATION OF THE SPATIAL VARIABILITY MODELS................. 68

Application of Estimation Models............................. 68
Evaluation Criteria ........................................ 68
Data Manipulation .......................................... 69
Autocorrelation Function ................................. 71
Model Types ................................................ 72
Sites Investigated ......................................... 76
Results and Discussion ....................................... 83
Choctawhatchee Bay Site .................................... 83
Apalachicola River Site .................................... 97
Archer Landfill Site ....................................... 106
Discussion of Results ...................................... 112

6 COMPARISON OF 10-TON AND 15-TON FRICTION-CONE PENETROMETER
TIPS ....................................................... 124

Introduction ................................................ 124
Size Comparability Study Data Base .............. ............ 125
Evaluation of Data Scatter .............................. 126

7 CLASSIFICATION OF FLORIDA SOILS USING THE ECPT............... 129

Introduction ................................................ 129
Current Practice ............................................. 131
Measurement Considerations ................................. 131
Typical Classification Systems ........................... 132
Analysis Approach.................................... 134
Data Base Creation ................................... 134
Discriminant Analysis............................... 139
Results and Discussion............ ......................... .. 141
Data Transformation ........................................ 141
Data Sets .................................................. 142
Laboratory Data Analysis .... ................. 143
Discriminant Analysis of Field Measurements............... 149
Recommended Classification Scheme................ ..... 154

8 SPT-ECPT CORRELATIONS FOR FLORIDA SOILS..................... 159







Introduction ................................................ 159
SPT-ECPT Data Base........................................... 161
Data Analysis ............................................... 163
Exploratory Data Analysis.................................. 163
Regression Analysis........................................ 164

9 CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH.......... 168

Summary and Conclusions...................................... 168
Recommendations for Future Research ......................... 172

APPENDICES

A INDEX TO IN SITU TEST DATA BASE.............................. 175

B PENETROMETER TIP MEASUREMENTS AND UNEQUAL END AREA
CALCULATIONS .............................................. 191

C SUMMARY OF LABORATORY CLASSIFICATION OF SOILS................ 193

D DISCRIMINANT ANALYSIS CLASSIFICATION SUMMARIES............... 197

E COMPUTER PROGRAM LISTINGS .................................... 212

E-1. PROGRAM FILTER......................................... 213
E-2. PROGRAM NORMAL......................................... 214
E-3. PROGRAM RANDOM......................................... 218
E-4. PROGRAM AUTOCOR........................................ 220
E-5. PROGRAM AUTOCOR2....................................... 222

F STEPWISE REGRESSION SUMMARIES................................ 224

BIBLIOGRAPHY ....................................................... 238

BIOGRAPHICAL SKETCH................................................ 244












LIST OF TABLES


Table paqe

2-1. Data Base Summary ........................................... 9

3-1. Data Base for Local Variability Study ....................... 43

3-2. Results of Local Variability Study......................... 48

5-1. Deterministic Model Parameters for Choctawhatchee Bay Site.. 86

5-2. Regression Models for the Prediction of Cone Resistance at
the Choctawhatchee Bay Site ............................. 87

5-3. Regression Models for the Prediction of Friction Resistance
at the Choctawhatchee Bay Site........................... 88

5-4. Results of qc Analysis at Choctawhatchee Bay ............... 89

5-5. Results of fs Analysis at Choctawhatchee Bay ............... 89

5-6. Comparison of Transformed and Nontransformed Approaches at
Choctawhatchee Bay ........................................ 93

5-7. Deterministic Model Parameters for Apalachicola River Site.. 99

5-8. Regression Models for the Prediction of the SPT N-Value at
the Apalachicola River Site................ ............ 100

5-9. Results of Spatial Variability Study at Apalachicola River.. 101

5-10. Comparison of Transformed and Nontransformed Approaches at
Apalachicola River ........................................ 103

5-11. Deterministic Model Parameters for Archer Landfill Site..... 108

5-12. Regression Models for the Prediction of Cone Resistance at
the Archer Landfill Site.................. .............. 108

5-13. Regression Models for the Prediction of Friction Resistance
at the Archer Landfill Site............................... 109

5-14. Results of Spatial Variability Study at Archer Landfill Site
Using Transformed Data .................................... 109

5-15. Comparison of Regression Model RMSE with Prediction RMSE.... 120







6-1. Data Base for Size Comparability Study..................... 126

6-2. Results of Size Comparability Study........................ 128

7-1. Soil Types in Classification Data Base..................... 137

7-2. Soil Classification Data Base ............................ 138

7-3. Summary of Laboratory Tests on SPT Samples ................. 139

7-4. Accuracy of SPT Soil Types................................ 143

7-5. Accuracy of Discriminant Analysis Approaches ............... 153

8-1. SPT-ECPT Data Base .......................................... 162

8-2. Exploratory Data Analysis of qc/N Ratios ................... 164

8-3. Results of SPT-ECPT Regression Analysis .................... 166

8-4. Descriptive'Statistics for log(N) in Units of log(blows/ft). 168












LIST OF FIGURES


Figure agqe

2-1. Cities Represented in Pile Data Base ....................... 8

2-2. Apalachicola River Bridge SPTs Used for Spatial Variability
Studies ................................................... 11

2-3. Apalachicola River Bridge Pier 3 Tests ....................... 11

2-4. Apalachicola River Bridge Flat Slab Bent 16 Tests........... 12

2-5. Apalachicola Bay Bridge Flat Slab Bent 22 Tests............. 12

2-6. Overstreet Bridge Pier 11 Tests............................. 13

2-7. Overstreet Bridge Pier 16 Tests ............................ 13

2-8. Sarasota Garage Tests ....................................... 15

2-9. Sarasota Condo Tests ........................................ 16

2-10. Fort Myers Interchange Tests ............................. 16

2-11. Port Orange Bent 19 Tests ................................ 18

2-12. Port Orange Bent 2 Tests .................................. 18

2-13. West Palm 1-95 Pier B-4 Tests............................. 19

2-14. West Palm 1-95 Pier B-6 Tests ............................. 19

2-15. West Palm 1-95 Pier B-9 Tests .............................. 20

2-16. West Palm 1-95 Pier C-2 Tests .............................. 20

2-17. Choctawhatchee Bay South Tests............................. 22

2-18. Choctawhatchee Bay North Tests............................. 22

2-19. White City South Tests ...................................... 23

2-20. White City North Tests ...................................... 23

2-21. Orlando Arena Tests ................ ......................... 25

2-22. Orlando Hotel Tests ......................................... 25
xi







2-23. Archer Landfill Tests ....................................... 27

2-24. West Bay Tests .............................................. 27

2-25. Subtraction-Type Electronic Friction-Cone P6netrometer...... 29

2-26. The UF Penetration Testing Vehicle.......................... 29

2-27. Calibration for 5-Ton Friction-Cone Penetrometer............ 34

2-28. Calibration for 10-Ton Friction-Cone Penetrometer........... 35

2-29. Calibration for 15-Ton Friction-Cone Penetrometer........... 36

3-1. Typical Matched Soundings for Local Variability Study....... 42

3-2. Cone Resistance Data for Local Variability Study............ 44

3-3. Friction Resistance Data for Local Variability Study........ 44

3-4. Effect of Average-Value Data Filter ........................ 46

3-5. Cone Resistance Data After Data Filtering .................. 47

3-6. Friction Resistance Data After Data Filtering............... 47

3-7. Residual Analysis and Proposed Standard Deviation for q .... 50

3-8. Residual Analysis and Proposed Standard Deviation for fs.... 51

4-1. Critical t-Values for Two-Sided Confidence Intervals........ 56

4-2. Critical Values for Testing Significance of Correlation
Coefficient ............................................... 56

4-3. Random Field Model Concept.................................. 60

4-4. Typical Experimental Semi-Variogram of Normalized Data...... 62

4-5. Typical Experimental Autocorrelation Function............... 64

5-1. Effect of Data Transformation on Cone Resistance Data at
Choctawhatchee Bay Site................................. 70

5-2. Spatial Variability Soundings at Choctawhatchee Bay......... 77

5-3. Autocorrelation Function for Normalized Raw Data at
Choctawhatchee Bay ........................................ 77

5-4. Spatial Variability Soundings at Apalachicola River......... 80

5-5. Autocorrelation Function for Normalized Raw Data at
Apalachicola River ........................................ 80







5-6. Spatial Variability Soundings at Archer Landfill............ 82

5-7. Autocorrelation Function for Normalized Raw Data at Archer
Landfill .................................................. 82

5-8. Final Autocorrelation Function for Choctawhatchee Bay....... 84

5-9. Final Autocorrelation Function for Choctawhatchee Bay Using
Transformed Data Set................ ................... 84

5-10. Prediction RMSEs for Cone Resistance at Choctawhatchee Bay.. 90

5-11. Prediction RMSEs for Friction Resistance at
Choctawhatchee Bay ........................................ 91

5-12. Prediction of fs for Sounding J Using a Weighting Model
(a/d) at Choctawhatchee Bay............................... 94

5-13. Prediction of qc for Sounding E Using Various Regression
Models at Choctawhatchee Bay.............................. 95

5-14. Prediction of fs for Sounding H Using High Term Regression
with Transformed and Regular Data at Choctawhatchee Bay... 96

5-15. Final Autocorrelation Function for Apalachicola River....... 98

5-16. Final Autocorrelation Function for Apalachicola River Using
Transformed Data Set ...................................... 98

5-17. Prediction RMSEs for SPT N-Values at Apalachicola River..... 102

5-18. Prediction of N for Sounding #16 Using Various Distance-
Weighting Models at Apalachicola River ................... 104

5-19. Prediction of N for Sounding #19 Using Various Regression
Models at Apalachicola River.............................. 105

5-20. Final Autocorrelation Function for Archer Landfill Using
Transformed Data Set ...................................... 107

5-21. Prediction RMSEs for Cone Resistance at Archer Landfill..... 110

5-22. Prediction RMSEs for Friction Resistance at Archer Landfill. 111

5-23. Prediction of qc for Sounding #5 Using Various Distance-
Weighting Models with Transformed Data at Archer Landfill. 113

5-24. Prediction of fs for Sounding #4 Using Various Regression
Models with Transformed Data at Archer Landfill........... 114

5-25. Comparison of Prediction Methods (Normalized) .............. 118

5-26. Example of Average Error Estimate--Prediction of qc at
Choctawhatchee Bay Sounding J Using Low Term Regression... 123







6-1. Cone Resistance Data for Size Comparability Study .......... 127

6-2. Friction Resistance Data for Size Comparability Study....... 127

7-1. Robertson and Campanella's Simple Soil Classification Chart. 133

7-2. Douglas and Olsen's More Complex Soil Classification Chart.. 133

7-3. Discrete Soil Classification Chart......................... 135

7-4. Soil Classification Chart Normalized for Overburden......... 136

7-5. Laboratory Classification Data Plotted with ECPT Data....... 145

7-6. Laboratory Classification Data Plotted with ECPT Data
Normalized for Overburden............................... 146

7-7. Discriminant Analysis of Laboratory Data Using NEIGHBOR
Procedure................................................. 147

7-8. Discriminant Analysis Using NEIGHBOR Procedure of Laboratory
Data Normalized for Overburden........................... 148

7-9. Discriminant Analysis Using DISCRIM Procedure of Laboratory
Data Normalized for Overburden........................... 150

7-10. General Trends of the Soil Classification Data Set.......... 151

7-11. DISCRIM Discriminant Analysis on Data Classified by Category 155

7-12. NEIGHBOR Discriminant Analysis on Data Classified by
Category.................................................. 156

7-13. Recommended ECPT Soil Classification Chart for Florida Soils 158

8-1. Variation of qc/N Ratio with Mean Grain Size ............... 160

8-2. Results of qc/N Ratio Study................................. 165












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

APPLICATIONS OF THE ELECTRONIC CONE PENETRATION TEST
FOR THE GEOTECHNICAL SITE INVESTIGATION OF FLORIDA SOILS

By

KENNETH JAMES KNOX

August 1989


Chairman: Dr. Frank C. Townsend
Major Department: Civil Engineering

The purpose of this research project was to evaluate techniques to

improve the application of in situ penetration testing to Florida soils,

with emphasis on the electronic cone penetrometer test (ECPT). Topics

addressed included describing the spatial variability of soil

properties, classifying Florida soils with the ECPT, and correlating the

ECPT with the standard penetration test (SPT). A collateral purpose was

to create an in situ test data base consisting of 97 ECPT soundings and

79 SPT tests. This data base was subsequently evaluated using

statistical analysis.

The spatial variability study was carried out to evaluate methods

of interpolation between test soundings. The techniques studied

included three deterministic approaches (the mean, median, and a 10%

trimmed average), three distance-weighting methods (two based on

reciprocal distances, and linear interpolation), a random field model (a

hybrid distance-weighting/regression model), and regression analysis.

While none of the approaches stood out as consistently superior







predictors, the deterministic approaches were generally inferior to the

other, more sophisticated methods. The distance-weighting methods and

the random field model performed comparably, but were sensitive to

individual test soundings. The regression models predicted slightly

better on the average, and with more stability.

The ECPT classification study used parametric and nonparametric

discriminant analysis of cone data on soils that had been identified

from the SPT test. The ECPT was able to group soil accurately into one

of seven categories organicc, clay, silt, clayey sand, silty sand,

sand, weathered rock) approximately 40% of the time. This percentage

increased to 70% when the three sand categories were combined,

reflecting the SPT drillers' difficulties in discriminating silty soils.

In the SPT-ECPT correlation study, average qc/N ratios for Florida

soils were much higher than expected, possibly due to cementation or

liquefaction. Regression analysis of the data suggested that the nature

of the SPT-ECPT relationship is more a function of the magnitude of the

tip resistance, and less of the actual soil type.












CHAPTER 1
INTRODUCTION


Seeking solutions to the problems of transferring superstructure

loads to the supporting ground is typically the responsibility of the

geotechnical engineer. Solutions to this interface problem are many and

diverse depending on the nature and magnitude of the loads involved; the

geology of the site; and the economic, environmental, and political

climate of the project. The economic impact of foundations can be

considerable. Vanikar reports that nearly 20% of approximately 2.6

billion dollars worth of highway construction by the Federal Highway

Administration and the state transportation departments in fiscal year

1984 was spent on foundations (62).

In all but the simplest of projects, a site investigation of the

underground conditions is necessary. This investigation, which usually

costs between 0.5 and 1% of the total construction costs (8), should

provide the geotechnical engineer with enough information to

characterize the site geology, select the type of foundation required,

determine the load capacity of the soil and/or rock, and estimate the

settlements of the superstructure. There is a large number of in situ

tests and equipment available to help obtain this information, including

the standard penetration test (SPT), the cone penetration test (CPT),

the Marchetti dilatometer test (DMT), the Menard and the self-boring

pressuremeters, the vane shear test, and others.






2

The Florida Department of Transportation (FOOT) uses the SPT and

the CPT in the design of axially loaded pile foundations (53). In the

standard penetration test, a standard split-barrel sampler is attached

to drill rods and inserted into a predrilled borehole. The sampler is

then driven 45.7 cm (18 in) using a 63.6 kg (140 lb) hammer and a 76.2

cm (30 in) drop height. The split-barrel sampler is then withdrawn and

opened, providing a physical sample of the soil. The SPT "N-value"

equals the number of blows for the final 30.5 cm (12 in) of penetration.

These N-values have been correlated to many soil parameters despite

considerable criticism as to their reproducibility. The SPT is

standardized by the American Society for Testing and Materials (ASTM)

Standard Method D 1586 (2).

In the cone penetration test using an electronic cone penetrometer

(designated ECPT), a cylindrical rod with a conical point is pushed into

the ground at a constant, slow rate, and the force on the point is

measured by an internal strain gauge. A second strain gauge measures

the force caused by friction on a free-floating friction sleeve. The

ECPT provides an accurate description of the subsurface stratification

and, from simple correlations, an estimate of the soil type. Also, many

soil properties have been correlated with the ECPT measurements. The

principal disadvantages of the cone penetration test are the lack of a

soil sample from the test, and the penetrometer's limited ability to

penetrate stiff soil layers. The CPT is standardized by the ASTM

Standard Method D 3441 (2).

The design procedures for pile foundations depend on an accurate

representation of the soil at the location of the pile, both in terms of

the measured or estimated soil properties,_and the type of soil.





3

Uncertainty in the input parameters determined by the SPT or CPT will

naturally result in uncertainty in the calculated pile load capacity.

The need exists to describe and quantify the uncertainty in the input

parameters, as well as to use procedures which minimize the uncertainty

associated with a site investigation program.


Purpose of Research


The purpose of this research project is to evaluate methods to

improve the use of in situ penetration tests for the geotechnical site

investigation of soils indigenous to Florida. In support of the

University of Florida's driven pile study, the project concentrates on

construction sites employing driven pile foundations. The primary in

situ device to be evaluated is the electronic cone penetrometer, which

is thought to model a pile foundation. This emphasis is the result of

the ECPT's faster speed, better reproducibility, and lower cost relative

to the standard penetration test.

Specifically, methods to describe the spatial variability of soil

properties will be evaluated with the purpose of determining the method

which can best interpolate test measurements between soundings. The

ability of the ECPT to classify Florida soil types will also be

evaluated, and procedures recommended to improve current Florida

practice. Finally, correlations between the SPT N-values and the ECPT

cone resistance and friction resistance will be determined. These

correlations will be valuable in situations when the cone penetrometer

test cannot be used due to stiff soil layers or difficult access.

A collateral purpose for this research project is to develop a data

base of pile load tests and in situ tests for Florida. Such a data base






4

will prove extremely valuable to future geotechnical research on Florida

soils.

Research Methodoloav


The initial phase of the research project involved setting up a

data base of pile load tests and in situ tests performed throughout

Florida. A letter soliciting data and site access was sent to all of

the FDOOT district geotechnical engineers, and to many private

geotechnical consulting firms. As a result of the letter and follow-up

telephone contacts, a significant amount of information was collected.

These data included site plans, pile load tests, pile driving records,

standard penetration tests, mechanical and electronic cone penetration

tests, wave equation analyses (CAPWAPC), and Marchetti dilatometer data.

Numerous trips to sites with driven pile load tests were also made in

order to collect electronic cone penetration test (ECPT) data using the

University of Florida cone penetration testing vehicle and equipment.

In order to handle this large data base and to run statistical

analyses on the data, the SASTM System was used (SAS is a registered

trademark of the SAS Institute Inc., of Cary, North Carolina). The SAS

System is computer software that provides data retrieval and management,

reporting and graphics capabilities, and an extensive array of

elementary and advanced statistical analysis procedures (47,48,49,51).

As the data were collected, they were encoded and stored on a computer

for future analysis. To date the encoded data base includes pile load

tests (PLTs), electronic cone penetration tests (ECPTs), standard

penetration tests (SPTs), and some mechanical cone penetration tests

(MCPTs). Additional data are on file at the University of Florida, and






5

can be encoded as required by future research. Chapter 2 describes the

data base used by this research project.

Once the in situ test data were available to the SAS System, the

individual data sets were combined into larger sets (depending on the

nature of the study) for statistical analysis. The spatial variability

studies were accomplished using the SAS data manipulation and reporting

capabilities, coupled with regression analysis and exploratory data

analysis. The soil classification study employed the SAS discriminant

analysis procedures. The SPT/ECPT correlation study used exploratory

data analysis and regression analysis.












CHAPTER 2
PROJECT DATA BASE

Introduction


The data base was created in support of the University of Florida

Department of Civil Engineering's Deep Foundations Project, sponsored by

the Florida Department of Transportion. The specific focus of this

phase of the project is the design of axially-loaded driven piles and

pile groups. As a result, data were solicited on construction sites

having driven pile load test data. Letters and telephone calls were

made to all of the FDOT district geotechnical engineers, and to many

geotechnical consultants in Florida. When suitable sites were

identified, all available geotechnical data were obtained.

In order to obtain electronic cone penetration test (ECPT) data

coinciding with the pile load tests (PLTs), site visits were made to

perform ECPTs if the data were not otherwise available (which was

generally the case). ECPT soundings were made near the pile load tests,

and also adjacent to standard penetration test borings that were near

the PLTs. These latter soundings were designed to support the

classification study of the ECPT.

This chapter describes the nature and extent of the entire project

data base. Subsequent chapters describe the parts of the data base used

for the individual analyses. This chapter also describes the procedures

and equipment used for the ECPTs performed by the University of Florida,





7

including a discussion of some of the problems and limitations

associated with the electronic cone penetration test.

Extent of Data Base


Figure 2-1 is a map of the State of Florida, showing the thirteen

cities where test data were collected. Table 2-1 summarizes the number

of tests at each site that have been entered into the computer data

base. Note that multiple pile load tests at a site usually indicate

multiple tests on the same pile (either the pile was redriven, or a

tension test was performed). Note also that additional data from many

of the sites are available, but have not yet been encoded and stored in

the computer. These tests are generally either not pertinent to this

study (the Marchetti dilatometer tests for instance), or are not close

to pile load tests of interest. The majority of these data is comprised

of SPT and MCPT data.

A more extensive description of the Table 2-1 data base is located

in Appendix A, which is an index of the data base. This index is

organized by location (generally of the pile load test). Each

individual test is identified by a prefix to identify the type of test,

a number to identify the location, and a suffix to identify individual

tests. The prefixes are shown below the test abbreviations in Table

2-1. For instance, C001B is an electronic cone penetration test (the

prefix C) at Pier 3 of the Apalachicola River bridge (the number 001),

and is the second test at that location (the suffix B). The index

includes information on general soil conditions, a description of the

pile used in the pile load test, the file name used by the source of the

data, and any important additional comments. The data base itself is

contained in Knox (25).
























:ksonville

e

\Port Orange


Beach


C. "


Figure 2-1. Cities Represented in Pile Data Base


Over3treet
White (







Table 2-1. Data Base Summary


LOCATION PLTs ECPTs SPTs MCPTs
NUMBER SITE P) LCu iSl i(M)


001 Apalachicola River Bridge--Pier 3 1 2 20 4
002 Apalachicola River Bridge--Bent 16 1 2 0 2
003 Apalachicola Bay Bridge--Bent 22 1 2 0 4
004 Overstreet Bridge--Pier 11 1 4 2 0
005 Overstreet Bridge--Pier 16 1 4 4 0
006 Sarasota Garage--SP7 2 4 4 0
007 Sarasota Garage--SP5 2 4 5 0
008 Sarasota Condo 2 2 5 0
009 Sarasota Landfill 0 3 3 0
010 Fort Myers--Concrete Pile 2 8 2 0
011 Fort Myers--Steel Pile 1 0 0 0
012 Fort Myers Airport 0 2 2 0
013 Port Orange--Bent 19 1 2 0 2
014 Port Orange--Bent 2 1 1 1 2
015 West Palm 1-95--Pier B-4 1 3 1 0
016 West Palm I-95--Pier B-6 0 3 1 0
017 West Palm I-95--Pier B-9 1 2 1 0
018 West Palm I-95--Pier C-2 1 0 1 2
019 Choctawhatchee Bay--Pier 1 1 13 3 1
020 Choctawhatchee Bay--Pier 4 1 2 1 2
021 Choctawhatchee Bay--Bent 26 1 4 1 3
022 White City 0 3 3 3
023 Orlando Arena 2 4 5 0
024 Orlando Hotel South 1 2 2 0
025 Orlando Hotel North 1 1 1 0
026 Orlando Hotel Northeast I 1 1 0
027 Jacksonville Terminal B-20 2 3 1 0
028 Jacksonville Terminal B-21 2 2 1 0
029 Archer Landfill 0 7 2 0
030 West Bay Bridge 0 6 6 0
031 Lake Wauberg 0 1 0 0
TOTALS 31 97 79 25


Site Descriptions


Apalachicola River and Bay Bridges (Sites 001 003)


The Apalachicola River and Bay bridges are replacement structures

for older bridges on U.S. Highway 98 in Apalachicola. Both are FOOT

projects. The Apalachicola River bridge is a 1153 m (3783 ft) structure






10

running generally east and west,' with a turn to the north on its western

end. The Apalachicola Bay bridge is a 4321 m (14175 ft) structure

traversing the bay east and west.

The available test data for these sites include test pile driving

records, CAPWAPC analyses, pile load tests (PLTs), standard penetration

tests (SPTs), Marchetti dilatometer tests (DMTs), mechanical cone

penetration tests (MCPTs), and University of Florida electronic cone

penetration tests (ECPTs). The soils are predominantly clays, sands,

and clay/sand mixtures. Figure 2-2 locates the Apalachicola River

bridge SPTs used in the spatial variability studies. Figures 2-3

through 2-5 locate the available in situ soil test data available near

the pile load tests in the data base.

Overstreet Bridge (Sites 004 005)

The Overstreet bridge is a 962 m (3157 ft) structure over the

Intracoastal Waterway on State Road 386, near the town of Overstreet,

Florida. This FDOT project is a replacement for an old floating pivot

bridge. The available test data include test pile driving records,

PLTs, SPTs, MCPTs, and ECPTs. The soils are mostly sand, with some

clayey sand and clay. Figures 2-6 and 2-7 locate the available in situ

soil test data near the pile load tests in the data base.

Sarasota Garage and Condo (Sites 006 008)

The Sarasota parking garage (Sites 006 and 007) and the Sarasota

condo site (Site 008) are supported by a pile foundation designed by

Ardaman & Associates of Sarasota. The available test data include test

pile driving records, PLTs, SPTs, and ECPTs. The soils at the parking



















BORING NUMBERS

14 16 18 20
. m m 0 m


i / 110
I I
I -JI
LU

a


Figure 2-2.


! PLT

A MCPT


STATIONS

Cl STATION = 30.5 m)


Apalachicola River Bridge SPTs Used for
Spatial Variability Studies


0 ECPT


0 DMT


S001C *


M001A
A CO01B
,- 0


22.8 m


Figure 2-3. Apalachicola River Bridge Pier 3 Tests


. SPTs


I I2


22
-m U


M00IB

P001 O MT
C001A c


4.1 m 5.0 m













m PLT


o ECPT


M002R


2.7 m


A MCPT


0 C002B


POO2


C002A
---- 0


Figure 2-4. Apalachicola River Bridge Flat Slab Bent 16 Tests


k PLT


C003R


0 ECPT


A MCPT


Figure 2-5. Apalachicola Bay Bridge Flat Slab Bent 22 Tests


0



C003B o0






P003
M003C A 0"

14.B .I n]













i PLT o ECPT U SPT
N

C0048


S0048 I P004 S0048B
C004A o 0
S5C004C
22.1 m 3.8 4.4 : 0 12.3 m




Co 0040




Figure 2-6. Overstreet Bridge Pier 11 Tests




N
A PLT o ECPT U SPT


C0050


S0058
*


0058
C05B
o


S005C
a


11.8 m


Figure 2-7. Overstreet Bridge Pier 16 Tests






14

garage are mostly sand overlying limestone rock at approximately 7.6 m

depth (25 ft). The condo site is predominantly fine sand and clayey

sand overlying limestone at approximately 5.5 m depth (18 ft). Figures

2-8 and 2-9 locate the available in situ soil test data near the pile

load tests in the data base.

Sarasota Landfill (Site 009)

The Sarasota (Manatee County) landfill is located north of

Sarasota. No pile load tests are available for this site, but Ardaman &

Associates of Sarasota provided some SPT data, which were supplemented

with UF ECPT soundings. ECPT sounding C009A is 0.76 m (2.5 ft) from SPT

sounding S009A; 122 m (400 ft) southeast, C009B is 0.76 m from S009B;

61 m (200 ft) further southeast, C009C is 0.5 m (1.5 ft) from S009C.

The soils at the landfill are mostly clayey fine sand, with some clay

and sandy clay.

Fort Myers Interchange (Sites 010 011)

The Fort Myers site is a highway interchange project designed by

Greiner Engineering of Tampa, with Law Engineering Testing Company of

Naples serving as the geotechnical consultant. Available test data

include test pile driving logs, pile load tests, SPTs, and ECPTs.

Several of the ECPTs were rate-controlled tests (0.5 to 2.0 cm/s),

although the nonstandard tests were not used in this project. The soil

is sand and sand/clay mixture overlying cemented clayey sand at a depth

of 31 m (102 ft). Figure 2-10 locates the test data in the data base.












* PLT

*~ UNTESTED PILE


007C
5007C


C007B


C007A
- 0 0


C0070




C007C 0 -


5007R .
INSET A


C00O60
o -


SOO6R

INSET B


Figure 2-8. Sarasota Garage Tests


0 ECPT


* SPT


0078
S007B


-0.5 a


-0.3 *









0 ECPT


POO8T *-E


1, 2.3 m


0
C008R


COOBB


0.0


Figure 2-9. Sarasota Condo Tests


A PLT


0 ECPT


C010C CO100
0 0





P011 S010A
_?_ .


0.5 m
CO I OF
COIOE0 0


0.3 m
C010B
o


COl0R o0 '


Figure 2-10. Fort Myers Interchange Tests


P008


m SPT


K PLT







Fort Myers Airport (Site 012)


The Fort Myers airport site is an interchange project, with Law

Engineering Testing Company of Naples serving as the geotechnical

consultant. Available test data include SPTs, ECPTs, and some

laboratory test data. The soil is comprised of sand and sand/silt/clay

mixtures, interbedded with weak to competent limestone layers. Two SPT

sites were used, separated by approximately 23 m (75 ft). ECPT sounding

C012A is 1.37 m (4.5 ft) from SPT S012A, and C012B is 1.52 m (5 ft) from

SO12B.

Port Orange (Sites 013 014)

The Port Orange site is an FDOT bridge on State Road AlA over the

Halifax River. The foundation for this bridge uses driven piles on the

approaches and drilled shafts under the main spans. The data base

includes PLTs, SPTs, MCPTs, ECPTs (both UF and FDOT), CAPWAPC analyses,

and laboratory analyses. The soil is mostly shelly sand and sandy silt,

with a 4.5 to 6m (15 to 20 ft) thick clay layer overlying limestone at

approximately 26 m (85 ft) in depth. Figures 2-11 and 2-12 identify the

test data near the pile load tests in the data base.

West Palm 1-95 (Sites 015 018)

This recently-completed project consisted of ramps and overpasses

for Interstate 95 in Palm Beach County. The data base include test pile

driving records, PLTs, SPTs, ECPTs (for the P.G.A. Boulevard ramp, Sites

015 017), and MCPTs (for the Military Trail overpass, Site 018). The

soil is fine sand with a small amount of clayey fine sand. Figures 2-13

through 2-16 locate the test data near the pile load tests.











w PLT


4/






MO13A
A --


o ECPT


18.3 m


A MCPT



-o CO I 3


P013
-w 0

8 .' m -


Figure 2-11. Port Orange Bent 19 Tests

PLT 0 ECPT A MCPT

SPT


M014R


C013R


4/


Figure 2-12. Port Orange Bent 2 Tests










0 ECPT


C015
---7-- 0


SPT


C015A o
o --/E-


C


2.4 i


P015


\V X 'k a


3.2 m


Figure 2-13.




A PLT


West Palm 1-95 Pier B-4 Tests


0 ECPT


n SPT


C016C 0
2.2 m


SO IBA
-

.6 m\ 3.7 m


o C01OR


Figure 2-14. West Palm 1-95 Pier B-6 Tests


CD 015B


S015A


3.4 m


M->* -A


x PLT


-----x








s PLT


CO7A
0


S017A

0.4 4m


P017 m -


IC


0 ECPT


* SPT



o C017B


4/


Figure 2-15. West Palm 1-95 Pier B-9 Tests

U PLT A MrPT U SPT


M018B I


SO018A


Figure 2-16. West Palm 1-95 Pier C-2 Tests


------X---------------------------------------------T--





21

Choctawhatchee Bay (Sites 019 021)

The Choctawhatchee Bay bridge is a replacement structure for an

older bridge on State Road 83 (U.S. 331). The bridge portion of this

FDOT project is approximately 2296 m (7534 ft) long, running north and

south. Available test data include PLTs, SPTs, MCPTs, ECPTs (both FDOT

and UF), DMTs (available from FDOT), and laboratory test data performed

by both the FDOT and the University of Florida. The soils are

predominantly sand overlying some clays and clayey sand on the southern

approach to the bridge, with the clays increasing as you proceed north.

Many of the ECPTs on the south side of the bridge were used in the

spatial variability studies. Figures 2-17 and 2-18 identify the in situ

test data in the data base.

White City (Site 022)

The White City bridge is a replacement structure over the

Intracoastal Waterway on State Road 71. The bridge portion of this FDOT

project is approximately 549 m (1800 ft) long, running north and south.

Available test data include SPTs, MCPTs, ECPTs, and laboratory data.

Pile load test data should be available in the near future. The soils

are mostly sand, with some clayey sand. Figures 2-19 and 2-20 locate

the available test data near the UF ECPTs.

Orlando Arena (Site 023)

The Orlando Arena is a 15,000 plus-seat structure constructed by

the City of Orlando. Jammal & Associates of Orlando performed the

geotechnical investigation, and kindly provided all of the test data

used in this project. Available data include test pile driving records,









" PROPOSED PLT o ECPT

* SPT o DMT


A MCPT


112 114 118 11 120
STATION (I STATION 30.5 m)

Note: See Appendix A for test Identification.


Figure 2-17. Choctawhatchee Bay South Tests


* PROPOSED PLT o ECPT

* SPT


A MCPT


u an
178 tIo 182 184 188
STATION (I STRTION 30.5 m)

Note: See Appendix A for test identification.


Figure 2-18. Choctawhatchee Bay North Tests


S0 00


A A A


13U.i










A MCPT


=*z


C022A


S022R


M022R -
A


1".5 m


Figure 2-19. White City South Tests


A MCPT


0 ECPT


S022B


a SPT


M022C
A --


Figure 2-20. White City North Tests


MO22B A


0 ECPT


a SPT





24
PLTs, auger borings, SPTs, ECPTs, and laboratory test data. The site is

mainly sand overlying mixed clay and sand at depths of 12 to 18 m (40 to

60 ft), with consolidated clays and silts being encountered at depths of

approximately 33.5 m (110 ft). Figure 2-21 locates the in situ test

data used in this project.

Orlando Hotel (Sites 024 026)


The Orlando Hotel is a proposed high-rise structure in downtown

Orlando. Jammal & Associates of Orlando performed the geotechnical

investigation, and provided all of the test data used in this project.

Available data include test pile driving records, PLTs, SPTs, ECPTs, and

laboratory test data. The site is comprised of a surficial sand fill

overlying fine sand with some silt and clay to a depth of 13 to 16 m (43

to 53 ft). Below this depth are mixed sands, silts, and clays

characteristic of the Hawthorn Formation. Figure 2-22 identifies the

test data used in this project.


Jacksonville Terminal (Sites 027 028)


This project was the addition of a coal conveyer system to the

St. John's River Coal Terminal on Blount Island. The geotechnical

consultant for the project was Law Engineering of Jacksonville. The

available data include test pile driving records, PLTs, CAPWAPC

analyses, SPTs, and ECPTs. The exact location of the PLTs and SPTs

could only be estimated at the time of the electronic cone penetration

tests, but all tests are believed to be very near one another. The

three ECPTs were spaced in a line at 1.5 m (5 ft) increments for Site

027, whereas the two ECPTs at Site 028 were 1.8 m (6 ft) apart. The

soils are predominantly fine sand and silty sand.







I PLT


0 GCPT


I SPT


0 60 FEET

0 I .3 METERS
SCALE


Figure 2-21.


OECPT


0 20 FEET

0 6.1 METERS
SCALE


Orlando Arena Tests


M SPT



v==5


S024B .C0248
4C024B


C024R


C025R


P025


SO26A


C02AR P026

Figure 2-22. Orlando Hotel Tests


SPLT


P024
024
S024A


---I


F-





26

Archer Landfill (Site 029)

This Alachua County landfill site is covered by ancient sand dunes

which overlie limestone at approximately 15 m (50 ft) of depth. The

source for the data at this site is a Master's thesis by Basnett (7).

The site is remarkably uniform, and was used for the spatial variability

studies. Available soils data include SPTs, ECPTs, UF laboratory data,

and DMTs. Figure 2-23 identifies the test sites pertinent to this

study.

West Bay (Site 030)

The West Bay site is an FDOOT bridge on State Road 79. All of the

in situ test data for this site was provided by the FDOOT, and includes

approximately 29 SPTs and 14 ECPTs. Laboratory data from both FDOT and

UF are also available. The soils are mostly fine sand with some silts

and clays. Some of the silty sand is slightly cemented. Figure 2-24

locates the test data used in this project.

Lake Wauberg (Site 031)

The Lake Wauberg site is located on University of Florida property

south of Gainesville. The ECPT sounding for this site came from Basnett

(7). This sounding was correlated with the results of UF laboratory

analyses on recovered samples of highly plastic clays and elastic silts,

the results of which are included in the classification studies.












0 ECPT



I SPT


0 100 FEET

0 30.5 METERS


CO29H C02G
0 0
C0201
0 v C0293
CO0L


0


CO29K 0 CO29E
0
C029R 0
C0298


I S02BR


Figure 2-23. Archer Landfill Tests


o ECPT PT P Z




0 C0300
0 15
v S030C
S0300 a o C030E
0 2 12
-.


S5030A
SG30B 0 C030C
' o 0 c030B S030E
C030R S030F
.0


0* C030F 0 a 0

282 2B 290 294 298 302 30B 310
STATION (I STATION 30.5 m)


Figure 2-24. West Bay Tests


I S029B


CO29F


0 CO2OC

0 C0290





28

Collection of ECPT Data


Equipment

All of the electronic cone penetration test data was obtained using

University of Florida equipment, with the exception of two of the Port

Orange soundings (source: FDOT), the Orlando data (source: private

consultant), and the West Bay data (source: FDOT). Three electronic

friction-cone penetrometers were used in the research, rated at 5-tons

(metric), 10-tons, and 15-tons respectively. All three are subtraction-

type friction-cone penetrometer tips marketed by Hogentogler and

Company, Inc. of Columbia, Maryland. Figure 2-25 is a schematic drawing

of a subtraction-type penetrometer tip.

The American Society of Testing and Materials (ASTM) has

standardized the cone penetrometer and the cone penetration test in ASTM

Standard D 3441 (2). The standard penetrometer tip has a 60 cone with

a base diameter of 35.7 mm (1.406 in.), resulting in a projected area of

10 cm2 (1.55 in.2). The standard friction sleeve has the same outside

diameter as the cone, and a surface area of 150 cm2 (23.2 in.2). The UF

5-ton and 10-ton penetrometer tips conform to this standard, whereas the

15-ton penetrometer's 60 cone has a base diameter of 43.7 mm (1.72 in.)

for a projected area of 15 cm2 (2.33 in.2). The friction sleeve,

however, has the standard 150 cm2 surface area.

Two primary measurements are made by the friction-cone

penetrometer. The cone resistance, qc, is defined as the vertical force

applied to the cone divided by its projected area. The friction

resistance, fs, is the vertical force applied to the friction sleeve

divided by its surface area. The friction resistance is comprised of

both frictional and adhesive forces.









CONE RESISTANCE STRAIN GAUGE


FRICTION SLEEVE


CONE RESISTANCE AND FRICTION
RESISTANCE STRAIN GAUGE


ELECTRONIC CABLE


CONE





60'


13.41 cmr
(5.28 in)




Figure 2-25. Subtraction-Type Electronic Friction-Cone Penetrometer Tip


Figure 2-26. The UF Penetration Testing Vehicle


I, 'MM





30

One of the advantages of electronic penetrometers is that other

electrical measuring devices can be incorporated into the tip housing to

provide additional and specialized information about the soil being

penetrated. The UF penetrometer tips incorporate two additional

devices, an inclinometer and a pore pressure transducer.

The precision optical inclinometer is primarily a safety device.

It measures the angular deviation of the penetrometer tip from vertical

during penetration, warning the operator of possible drifting during

penetration of stiff layers.

Dynamic pore pressures are measured using a small pressure

transducer mounted within the penetrometer tip. The plastic porous

filter element is located immediately behind the cone- The filter

element is carefully boiled in a water/glycerin mixture to completely

saturate it. Saturation of the tip is maintained prior to use by a

rubber sheath around the filter element.

Insertion of the penetrometer tip and collection of the data were

accomplished using the University of Florida's cone penetrometer testing

truck. This vehicle includes a 20-metric-ton hydraulic ram assembly,

four independently-controlled jacks for leveling, and a computer-

operated data acquisition system. The data acquisition system is

comprised of a microprocessor with a 128k magnetic bubble memory, a

keyboard, a printer, and a graphics plotter. The system permits real

time monitoring of the ECPT test, built-in overload factors for safety,

and permanent recording of the data. The system is described in detail

in Davidson and Bloomquist (11). Figure 2-26 shows the UF penetrometer

testing vehicle.







Procedures

The test procedures used to collect the ECPT data follow the ASTM

Standard D 3441 (2) and the manufacturer's recommended guidelines (41).

In summary, the porous filter elements for the pore pressure

measurements are saturated by boiling in a water/glycerin mixture prior

to the test, and stored in the same mixture until needed. At the test

site, the truck is positioned over the sounding location and leveled. A

friction reducer and the first drill rod are attached to the

penetrometer tip, and are hung in the jaws of the hydraulic ram's

automatic clamp. After the tip has warmed up for at least 20 minutes,

an initial no-load baseline reading is taken of all of the data channels

(cone resistance, friction resistance, pore pressure, and inclination).

Once the baseline is taken, the actual test may begin. The

penetrometer tip is pushed into the ground at a rate of 2 cm/s (0.79

in./s). Measurement signals are constantly being received from the tip,

but are actually recorded every 5 cm (1.97 in.). During penetration,

the next one-meter length of drill rod can be added, allowing for nearly

continuous penetration (except for the time required to raise the

automatic clamp to grab the next drill rod). Once the test is complete,

the automatic clamp is reversed and the rods retracted. Once the

penetrometer tip is clear of the ground, it is quickly wiped off and a

final baseline reading taken. This final baseline is compared with the

initial one to evaluate the quality of the sounding.

Problems Encountered

Minor problems. Several difficulties were encountered in the

course of collecting the ECPT data for this project. Problems included




W


32

numerous instances of reaching the thrust limits of the hydraulic ram

system, and of unacceptable inclinations as the probe veered from

vertical. These problems were a predictable result of the inherent

limitations of the equipment. The cone penetration test is not suitable

for all geology, a fact well-understood by experienced operators. In

locations having competent near-surface limestone formations, highly

cemented sands, heavily overconsolidated clays, and similar stiff

subsurface soils, the ECPT will necessarily have to give way to more

robust in situ testing methods such as the standard penetration test.

More troublesome, however, were the less-predictable problems

encountered. The friction reducer is a special rod with small

projections welded to it. It follows the tip, and its purpose is to

enlarge the hole and reduce the friction on the subsequent drill rods,

thus permitting deeper soundings. Twice during testing, the friction

reducer cold-welded itself to the penetrometer tip, resulting in costly

repairs and equipment downtime. Future problems were avoided by careful

attention to cleanliness in the threads of the tip, and by the use of an

anti-seize compound on the threads.

Calibration. The most insidious problems were associated with the

quality of the measurements themselves. The usual method of evaluating

a device's accuracy is by calibration against a known quantity. The UF

penetration testing vehicle contains a field calibration device. This

device employs a hand pump to hydraulically apply a force to either the

cone or the friction sleeve. The force is measured with a load cell,

and compared with the readings from the data acquisition system.

Unfortunately, only standard-size penetrometer tips can be calibrated in

this device; therefore the 15-ton tip was calibrated by the






33

manufacturer. Figures 2-27 through 2-29 show the results of the

calibrations on the three UF cone penetrometer tips.

The calibration for the 5-ton penetrometer (Figure 2-27) showed

that the qc readings were high by generally less than 2%, although the

readings were off as much as 10% on the high side for cone resistances

less than 7 MPa (73 tsf). The friction resistance "noise" refers to the

measured friction when only the cone is loaded. This noise, which was

generally linear with increasing qc, would result in friction readings

that were too low at the rate of approximately 0.34 kPa/MPa. For

example, for a moderate cone resistance of 15 MPa (157 tsf), the

friction resistance would be too low by about 5.1 kPa (0.053 tsf) due to

the cross-channel noise. The fs calibration was similar, reporting

friction resistance values generally 1.5% too low, but ranging as high

as 7% low for friction resistances less than 100 kPa. The qc noise rate

was a low 0.00018 MPa/kPa.

The 10-ton penetrometer tip was calibrated twice during the field

testing phase of the project. The qc measurements were generally within

1% on the low side of the actual load for cone resistances greater than

10 MPa (105 tsf), and within 4% for smaller qc's. The friction noise

ranged as high as 9 kPa (0.094 tsf). The friction resistance was

usually within 1 to 3% of the true value. The qc noise rate was an

acceptable 0.00046 MPa/kPa. Overall, the calibration for this

penetrometer tip was the most acceptable of the three instruments used

in the project.

The 15-ton penetrometer tip was calibrated before and after repair

by the manufacturer in August, 1988. The cone resistance calibration

showed an excellent 0.6% error both before and after repair. The









o SEPT 88 CALIBRATION


0 5 10 15 20 25
RCTURL Sc (MPa)

(R) Go Calibration



o SEPT 88 CRLIBRRTION 0 Gc CHANNEL NOISE




D 0.20


-J-
S-0.15







,d'

400 o


o 0n 200 30 4 5
ACTURL Fs kP.a)


(B) Fe Calibratlon


Figure 2-27. Calibration for 5-Ton Friction-Cone Penetrometer
Figure 2-27. Calibration for 5-Ton Friction-Cone Penetrometer


0 ,
no






10,-


= FRICTION NOISE







o SEPT 88 CALIBRATION
A NOV 88 CALIBRATION


o SEPT 88 Fe NOISE
o NOV 88 Fe NOISE


0- 15


50 *55


I0 0 0 0
40 0 0 a
3 ..5D












ACTUAL Go CMPa)

(C) Gc Calibration


o SEPT 88 CALIBRATION a SEPT 88 Bo NOISE
a NOV a CRLIBRATION NOV 88 On NOISE

Nr- -t


500- y -0.3




Fge 2. fo 0.1 2


/d g
=MO a3 13 1-0.0


oM .--- f -0.1



0 100 200 300 500 amm0
ACTUAL Fs (kPa)


(B) Fe Calibration


Figure 2-28. Calibration for 10-Ton Friction-Cone Penetrometer






36
o BEFORE RECALIBRATION
X( AFTER RECALIBRRTIDN


a Fe NOISE BEFORE
o Fe NOISE AFTER


30 40
ACTUAL Bc (MPa)

(R) Gc Calibration


o BEFORE RECRLIBRRTION


A AFTER RECALIBRATION


00 a 400 an 8am 1000 1200
RCTURL Fa (kPa)


(B) Fe Calibration


Figure 2-29. Calibration for 15-Ton Friction-Cone Penetrometer






37

friction noise readings were poor prior to repair, however, reading as

much as 23 kPa (0.240 tsf) too high. Following repair, the maximum

friction noise was 7 kPa (0.073 tsf). The friction channel read as much

as 14 to 20 kPa too high for the higher friction resistance measurements

prior to the repair. All friction measurements made by the 15-ton cone

penetrometer prior to August 1988 are suspect as a result of the

calibration.

Baseline drift and negative values. The worst problem encountered

in the project was negative friction resistance measurements and

friction baseline drifts, primarily in the 15-ton penetrometer tip.

Physically, negative friction resistance measurements are impossible

since the friction sleeve is free-floating, recording a "true" friction

value only when the sleeve bears on a shoulder of the central core, as

shown in Figure 2-25. Therefore, some type of measurement error must be

present.

Several sources of the problems are possible (13,18-23,41,50).

Regarding the baseline drift problems, the manufacturer defines an

"allowable" drift of 1.0 to 1.5% of the full-scale reading. The 1.5%

limit equates to a drift of 1.5 MPa (15.7 tsf) for the qc channel, 15

kPa (0.157 tsf) for the fs channel, and 0.4 bar (5.8 psi) for the pore

pressure channel. Only the friction channel even approached this limit,

exceeding it on several occasions. While temperature effects on the

strain gauges may account for a small portion of the problem, the

literature suggests the single biggest cause of baseline drift is soil

and water ingress during a sounding. Therefore reasonably rigorous

attention to cleanliness (under field conditions) was exercised

throughout the project. Despite this care, the 15 kPa limit on friction





38
baseline drift was approached fairly regularly, slightly exceeded

occasionally, and on a few occasions was exceeded by a large amount.

All baseline drifts slightly exceeding 15 kPa were flagged in the data

base index (Appendix A), and all clearly unacceptable baselines were

discarded.

The negative friction readings (predominantly on the 15-ton

penetrometer tip) can be partially explained by the unstable baselines.

If the baseline value drifts positively 10 kPa, then a friction reading

that would have read 5 kPa under the original baseline now reads -5 kPa.

The manufacturer also notes that transient voltage surges may

temporarily affect measurement readings, resulting in negative values

(22). A third potential source for error is due to the design of the

subtraction-type electronic friction-cone penetrometer tip (41). The

cone load cell measures the cone resistance, and the friction load cell

measures the resistance on both the cone and the friction sleeve. The

friction resistance is then determined by subtracting the cone load cell

measurement from the friction load cell measurement. While this

particular design is rugged and robust, the calculation of a small

number (fs) by subtracting two large numbers is not good measurement

practice.

Weak soils. Accurate measurements in weak soils are extremely

difficult to obtain. A potential source of error is due to unequal end

areas on the cone and the friction sleeve (41,43,50). Below the water

table, pore pressures bear on the horizontal surfaces at the joints in

the penetrometer tip. For the UF 10-ton tip, these unequal end areas

would increase qc by 0.034 MPa/bar pressure (0.025 tsf/psi), and

increase fs by 1.0 kPa/bar (0.00072 tsf/psi). While the change in qc is





39

virtually negligible over the normal range of pore pressures of -2 to 6

bars (-29 to 87 psi), the change in friction could be significant in

very weak soils, masking any measurements of friction. The unequal end

area calculations for the UF penetrometers are in Appendix B.

In order to account for the pore pressure effects on the

penetrometer tip joints, pore pressures can be monitored during

penetration. Only weak soils are significantly affected by the unequal

end area corrections, which is fortunate since less than 0.3% of the

ECPT soundings in the U.S. monitor pore pressures (36,42).

As a result primarily of problems with baseline drift, compounded

by questions relating to temperature compensation, unequal end area

effects, and measurement design of the subtraction-type penetrometer,

accurate measurements in weak soils are extremely difficult. Even with

careful attention to these problems the errors in measurements may be of

the same magnitude as the properties being measured. The ECPT can

easily identify the soil as weak, but discrimination among various weak

soils is less certain. While the electronic friction-cone penetrometer

is clearly a superior instrument for "average" soils, alternate testing

methods may be required to supplement the ECPT when such discrimination

in weak soil is required.












CHAPTER 3
LOCAL VARIABILITY IN CONE PENETROMETER TEST MEASUREMENTS

Introduction


Variability in soil property measurements can have many sources,

including measurement errors, signal noise, the innate randomness of

soil (on the "micro" scale), and the spatial variability of the soil

property (on the "macro" scale). The term "local variability" has been

adopted to describe the point-to-point variability of a measured soil

property, and encompasses the first three sources mentioned above. This

differentiation is important in spatial variability studies because

local variability could conceivably mask any area trends, producing

inconclusive results. As an example, Baecher notes that typical

measurement error variances for in situ measurements can account for 0

to 70% of the total data scatter (4). Without changes in measuring

equipment and techniques, the local variability in a measured soil

property must be accepted and considered in any design employing the

data.

The purpose of this phase of the research is to quantify the local

variability of cone penetrometer measurements used in the study. The

approach used was to identify pairs of CPT soundings in the data base

that were close to one another, and used the same size penetrometer.

Then using graphical and statistical techniques, the local variance was

described and quantified. Finally, a type of "digital filter" was

devised to reduce the variance while preserving the essence of the data.






41

Local Variability Data Base

The research project data base was searched for pairs of ECPT

soundings that met two criteria: the soundings must be no more than 4.6

meters (15 feet) apart, and the same size cone penetrometer must have

been used in both soundings. The distance criteria was admittedly

somewhat arbitrary, and represented an attempt to include a

representative number of sounding pairs in the analysis, while hopefully

insuring that the penetrometers were sampling the "same" material.

The laboratory-type requirement that the material be the same for a

comparative analysis is virtually impossible to achieve in the field,

making criticism a certainty. If the soundings are too close, then

stress relief and other cross-hole interference may result. If the

soundings are too far apart, then "different" soils may be tested due to

spatial variability. The minimum spacing was determined to be 36 cm (14

inches), based on Robertson and Campanella's recommendation of 10 hole

diameters from open boreholes and excavations, to allow for potential

radial stress relief effects (41). As a check on the maximum selected

spacing of 4.6 meters, the sounding pairs were graphically overlaid and

evaluated as to the likelihood that the material was approximately the

same. If reasonable doubt existed, the sounding was discarded from

further analysis. A typical comparison is shown in Figure 3-1.

The resulting data base used in the local variability study is

summarized in Table 3-1, and the actual soundings are identified in

Appendix A and Knox (25). Note that separation distances varied between

1.8 and 4.6 m (6 and 15 ft), and all three University of Florida

penetrometer tips are represented. At the Fort Myers site, the 5-ton

penetrometer tip was paired with the 10-ton tip, both of which are the






42























2-












10








0 2 4 6 8 10 12 14 16 18 20 22

CONE RESISTANCE (MPa)




SITE = FT MYERS

Figure 3-1. Typical Matched Soundings for Local Variability Study







standard 35.6 mm (1.4 inches) in diameter. A check of the results

showed that the Fort Myers data fell well within scatter for all

penetrometer pairs, so this pairing was judged acceptable. All other

pairings involved one cone penetrometer only. For the instances where

the friction baseline readings were unacceptable (as discussed in

Chapter 2), only the cone resistance data were used. The designation of

"Site #1" and "Site #2" was strictly arbitrary; hence any perceived

skewness in the plots favoring one sounding or another could easily be

reversed by simply switching the designations.

Table 3-1. Data Base for Local Variability Study


Location (ID)

Archer Landfill (ALFa)

Archer Landfill (ALFb)

Fort Myers (FMYER)

Sarasota Condo (SCNDO)

Sarasota Garage (SGARa)

Sarasota Garage (SGARb)

Sarasota Garage (SGARc)


Site Site
#1 #2

C029A C029B

C029C C029D

C010D C010E

C008A C008B

C006C C006D

C007A COO07B

C007C C007D


Distance Penetrometer
m (ft) (tons) Comments

3.7 (12.0) 10

4.6 (15.0) 10

2.9 (9.5) 5/10 qc only

2.4 (8.0) 15

1.8 (6.0) 15

2.1 (7.0) 15

2.6 (8.5) 15 qc only


Figure 3-2, representing 1287 observations, shows the cone

resistance data plotted about the expected 1:1 line. Most of the data

are relatively well-behaved about the line. Figure 3-3 shows a similar

plot for the friction resistance data, representing 809 observations.

Data Filter


As can be observed in Figure 3-1, many of the large-magnitude
"errors" between Soundings #1 and #2 are due to mismatches in the high-












++4 44-

t ^A t


10 20

SITE #2 CONE RESISTANCE (MPa)


SITE + + + ALFa
0 0 0 SGARa


XX X ALFb
A A A SGARb


* * FMYER
: # # SGARc


0 0 0 SCNDO


Figure 3-2. Cone Resistance Data for Local Variability Study

300- D

C-
C+
S 0 + +0 +



100 + O+6 X


U-
a


0 20 40 60 80 100 120 140 160 180 200

SITE #2 FRICTION RESISTANCE (kPa)


SITE + + + ALFa
0 0 0 SGARa


XXX ALFb c FMYER 0 C] 0 SCNDO
A A A SGARb 4 4 # SGARc


Figure 3-3. Friction Resistance Data for Local Variability Study


T 1 1 T





45

frequency (and often high-amplitude) peaks characteristic of some soils,

especially stiffer ones. These mismatches result in some of the large

magnitude scatter observed in Figures 3-2 and 3-3. To reduce the

influence of this high-frequency "noise" in the spatial variability

study, a digital filter was sought.

Several typical digital filters were tested on sample data sets,

including moving average and nonrecursive filters employing parabolic

fits (24). However, either inadequate smoothing of the data occurred,

or sudden shifts in the data were anticipated too early. The adopted

filter used a simple average method. The data were divided into 0.5-

meter (1.6-foot) increments, the average value of the increment

determined, and this value assigned to the midpoint of the increment.

This filter was able to smooth out the high-frequency noise in a

sounding, while preserving the essence of the sounding. Figure 3-4

shows one of the soundings from Figure 3-1 before and after filtering.

Figures 3-5 and 3-6 are identical to Figures 3-2 and 3-3, except

that the data have now been filtered. Note that the scatter has been

reduced. The number of data points has also been reduced by a factor of

10 as a result of filtering. In computer-intensive applications where

the point-to-point soil properties are not critical, such a filter can

greatly reduce computer processing time and storage requirements, while,

to a point, still reflect the influence of the entire (unfiltered) data

set.

Evaluation of Data Scatter


To evaluate the data scatter, regression analysis using the REG

procedure of the SAS system was used. The models used in the analysis

were

































LJ
LI


















12-

0 2 4 6 a 10 12 14 16 16 20 22


CONE RESISTANCE (MPa)


Solid = Unfiltered Dashed = Filtered

Figure 3-4. Effect of Average-Value Data Filter











+x



+a


30

20


, 20

I-
on
LCJ
10




0
I-
0A


SITE + + + ALFa XXX ALFb
0 0 0 SGARa A A A SGARb


* * FMYER
4 4 4 SGARc


D 0 0 SCNDO


Figure 3-5. Cone Resistance Data After Data Filtering


+ x
+ + -


0 20 40 60 80 100 120 140 150


SITE #2 FRICTION RESISTANCE (kPa)


SITE + + + ALFa XXX ALFb
0 0 0 SGARa A A A SGARb


* * FMYER CE [ SCNDO
4 4 4 SGARc


Figure 3-6. Friction Resistance Data After Data Filtering


0 10 20


SITE #2 CONE RESISTANCE (MPa)


-? 300
C-

LU
C-,
200
20
ow
LLJ
a:

1-
CL-


4S 0
LU
I-
CA





48

(qc)l = bo + b (qc)2 ................................. ............ (3-1)

(fs)1 = bo + b1(fs)2 ............................................. (3-2)

Besides calculating a slope and intercept using the ordinary least

squares approach, the REG procedure also calculates the root mean square

error of the model, or RMSE:


E(Z Z )2
RMSE = A P .......................................... (3-3)
n 2

in which n is the number of observations, Z is the soil property being

measured (either qc or fs), and the subscripts A and P refer to actual

and predicted values of the soil property, respectively. This RMSE is

an unbiased estimate of the standard deviation of the errors about the

regression line (9,16).


Table 3-2. Results of Local Variability Study

Parameter
(units) Filter b0 b1 RMSE

Cone Resistance No 1.77 0.80 3.44
(MPa)
Yes 1.41 0.83 2.91
Friction Resistance No 3.79 1.03 31.1
(kPa)
Yes 0.46 1.10 23.9

From Table 3-2 one can see that using the average-value data filter

reduced the root mean square error by approximately 15% for qc, and over

23% for fs. Thus the use of the filter appears desirable, especially

when one is primarily interested in the most likely value of the soil


property in question.





49

Based on the results of this study as summarized in Table 3-2,

reasonably conservative values for the local standard deviation of

friction-cone penetrometer measurements are estimated to be 3.0 MPa for

qc, and 24 kPa for fs. Figures 3-7 and 3-8 plot the residuals from the
regression analysis (Actual minus Predicted) as a function of the

independent variable for qc and fs, respectively. Only the lower-

magnitude values of the data are shown in the figures for amplification.

Note that at very low values of qc and fs the variability is lower,

increasing with increasing values of the soil property. It is proposed

that the following standard deviation be adopted for the spatial

variability study, as shown on Figures 3-7 and 3-8:


local standard deviation (qc) = 0.5(qc) for qc < 6.0 MPa (62.7 tsf)

= 3.0 MPa (31.4 tsf) for qc > 6.0 MPa


local standard deviation (fs) = 0.5(fs) for f.s 48 kPa (0.50 tsf)

= 24 kPa (0.25 tsf) for fs > 48 kPa


The local standard deviation can be interpreted as the minimum

precision one can expect from the cone penetrometer measurements used in

the spatial variability study. It may be argued that the variability

measured in the local variability study was in reality true spatial

variability. However this author contends that any variability measured

over a horizontal span of less than 4.6 meters (15 feet) in what appear

to be nearly identical soils is for most practical applications a

"local" phenomenon, and can be treated as such.










+ X


5 i0 15

SITE #2 CONE RESISTANCE (MPa)


SITE + + + ALFa XXX ALFb * FMYER
0 0 0 SGARa AA A SGARb tt a SGARc


7 0 0 SCNDO


Figure 3-7. Residual Analysis and Proposed Standard Deviation for qc








90g-

80-

70-
+
60-

50-

40-

30-

20- A





0 -10- X -0 i0 A
++,

-20- O + + A __

-30-

-40 A

-50-

-60-

-70-

-60-

-90-
-30-









0 20. 40 60 80 100 120

SITE #2 FRICTION RESISTANCE (kPa)



SITE + + + ALFa X X X ALFb * FMYER E C 0 SCNDO
0 0 0 SGARa A A A SGARb 4 # = SGARc



Figure 3-8. Residual Analysis and Proposed Standard Deviation for fs












CHAPTER 4
DESCRIBING THE SPATIAL VARIABILITY OF SOILS


Introduction


Because of the way it is formed, even nominally homogeneous soil

layers can exhibit considerable variation in properties from one point

to another. This variation is termed spatial variability. Depending on

the factors involved in soil formation (source material, transport

mechanisms, etc.) and their fluctuations over both time and space, the

spatial variability may be large or small. Lumb notes this variability

in soil properties tends to be random, although general trends may exist

both vertically and horizontally (30).

The evaluation of soil variability is important because soil

properties must be estimated from a limited number of in situ and

laboratory tests. When soil properties are estimated at an unobserved

location, the engineer needs to have confidence that his estimates are

likely to be representative of the actual soil properties at that

location, or at least be able to quantify his confidence in the

estimates.

In evaluating soil variability, modern statistics and data analysis

offer several tools to help achieve these goals. The purpose of this

phase of the research is to evaluate these tools, and to develop a

field-usable methodology for describing the spatial variability of

Florida soils. A word of caution is in order, however. In applying

these tools one is reminded of Ralph Peck's admonition that subsurface

52





53

engineering is an art--"...every interpretation of the results of a test

boring and every interpolation between two borings is an exercise in

geology. If carried out without regard to geologic principles the

results may be erroneous or even ridiculous" (37, p.62). Fortunately

most of Florida's soils are depositional due to their marine origin,

somewhat simplifying the geology and aiding interpolation.


Descriptive Statistics for Spatial Variability


Summarizing a Data Set


Traditionally, a deterministic, or single-valued approach is used

in describing soil properties. The most commonly used approach to

quantify a measured property, x, of a nominally homogeneous soil layer

is to use the average or mean value, R, of the property:


n
Sx(4-1)
x = i=l i .................... .. ............................ (4-1)
n


in which xi is the measured value of the property at point i, and n is

the total number of measurements. This estimator is the best choice for

summarizing data if the data are normally distributed. However, this

measure is sensitive to nonnormal distributions and to outliers, which

are unusually high or low data points that stand out from the rest due

to mistakes or other reasons.

An alternative to the mean for describing the center of a

distribution is the median, defined as the middle value of a data set

ordered from smallest to largest value. The median is robust against





54

outliers, and can do a better job of summarizing nonnormal

distributions.

Siegel (54) offers a compromise between the mean and median for

describing a set of data, called the trimmed average. This statistic

removes the extremes from a distribution, and averages the remaining

data. For example, a 10% trimmed average would remove 10% of the

highest values, and 10% of the lowest (rounding down when the sample

size is not evenly divisible by 10), and then take the mean of the

remaining 80% of the data.


Describing Variability

The uncertainty in the mean of a data set is described by its

variance, V, or the square root of the variance, termed the standard

deviation, s:


V= Z(x x)
S= ................................................ (4-2)

s = / .......................................................... (4-3)


For normally distributed data, approximately 68% of the data should lie

within one standard deviation of the mean, and 95% within two standard

deviations. As is true of the mean, the variance and standard deviation

are sensitive to outliers and nonnormal distributions.

If the variance is comprised of contributions from different,

uncorrelated sources (such as from spatial variability, measurement

error, signal noise, etc.), then the total variance is equal to the sum

of the individual variances (3,26,57,63):

VT = VI + V2 + ... + Vn .......................................... (4-4)





55

A more robust measure of variability, related to the median, is the

interquartile range. If the data are ordered from smallest to largest,

the lower quartile is the 25% value (one-fourth of the data is less that

or equal to the lower quartile), the median is the 50% value, and the

upper quartile is the 75% value. Therefore


interquartile range = upper quartile lower quartile ............(4-5)


Using tables for the area beneath a normal distribution, for normally

distributed data the standard deviation and interquartile range can be

related by

interquartile range = 1.46 s ..................................... (4-6)


If we have a random sample from a normally distributed population,

we can determine a confidence interval on the mean of the sample using

the following:

t s
interval = x n-1 ........................................ (4-7)



in which tn-1 is called the t-value. Given the desired confidence level

and the number of degrees of freedom (equal to n-1), the t-value can be

obtained from standard statistical tables. Figure 4-1 shows the t-value

for selected two-sided confidence intervals.


Measuring Association


If Z is a function of two variables x and y, then the strength of

association between the two variables is usually measured by their

correlation coefficient, r:













1.2



1.0



0.8



0.8



0.4



0.2



0.0
0.0


N 95Z CONFIDENCE


O- -902 CONFIDENCE
CONFIDENCE


0.2 0.4 0.8 0.8 1.0 1.2 1.4 1.8 1.8 2.0
LOG (SAMPLE SIZE)


Critical t-Values for Two-Sided Confidence Intervals



1.0











0.4



0.2


0.4 0.B 0.8 1.0 1.2 1.4 1.8 1.8 2.0

LOG SIMPLEE SIZE)

Source: Siegel f1988) p.430



Figure 4-2. Critical Values for Testing Significance of
Correlation Coefficient


Figure 4-1.







LU
W
_j
t-J
z
W

Z
W




0








S[(x x)(y y)]
r(Z) = r(x,y) = i i .................. (4-8)
/[E(x -_ )2[E(y 7)2]'
V i i1

The correlation coefficient ranges between +1 and -1, with +1 indicating

perfect 1:1 correlation, 0 indicating no correlation, and -1 indicating

perfect inverse correlation (i.e., as one variable increases, the other

decreases proportionally). For interpreting other values of the

correlation coefficient, Smith (55) suggests the following guide:


I|rl 0.8 Strong correlation, assume complete dependence
0.8 > Irl 0.2 Moderate correlation

0.2 Irl Weak correlation, assume complete independence

Siegel (54) suggests minimum values of the correlation coefficient for

testing that a significant association exists, given the sample size and

level of confidence desired. The data must represent a random sample of the

population and must be bivariate normal, meaning the two variables come

from normal distributions and plot linearly (x versus y) except for

randomness. These requirements rule out outliers and nonlinear data

sets. Figure 4-2 is a plot of the critical r values for a 90%

confidence level.

The association between the uncertainty of two variables, x and y,

is usually described by their covariance, C:



C(x,y) =- [(x x)(y y)] ............... .... (49)
n 1 i= i i

Note that when x=y, then the covariance equals the variance (i.e., the




W


58

diagonal terms in a covariance matrix are the variances, V). It can

also be shown that the covariance, correlation coefficient, and standard

deviation are related by


r(x,y) = s s ............................................... (4-10)
x y


Estimation Models

Traditional Choices


When faced with the need for determining a soil property for input

into a design process, the conventional or deterministic approach is to

assume a homogeneous soil (or soil layer), described by some "average"

value for the soil property. This single-value approach is appealing

due to the simpler mathematics involved. If a measure of the soil's

variability is also desired, the standard deviation of the measured

property and perhaps a confidence interval are the usual choices.

Often, however, the variability of measured soil properties is so

great that a simple "average" could result in needlessly conservative or

dangerously nonconservative design. Thus explicit consideration of the

spatial variability of soil is required. A model is needed which can

predict a soil property at a point i, based on measurements of the

property at n other points.

Some of the most commonly used estimation techniques seek to

interpolate between measured points by fitting linear and higher order

regression models to the data using the well-known least squares curve

fitting techniques (17,26,46,58). Distance weighting functions, such as

a/d and a/d2 (where d is the distance between the measured point and the





59

point to -be estimated, and a is a suitably chosen parameter) are also

often used to estimate soil parameters. Regarding the use of these

models for estimating properties used in the mining industry, Rutledge

criticizes these procedures as being "quite arbitrary and without a

sound theoretical basis. The so-called 'principle of gradual change'

and the 'rule of nearest points' are an appeal to mysticism, not

science" (46, p.300). Rutledge's objections notwithstanding, these

methods have been successfully used for many years in designing and

constructing innumerable civil structures.

Random Field Models


In response to the need for an estimation model based at least in

part on theoretical principles, numerous researchers have acknowledged

the stochastic nature of soil by employing random field models to

estimate soil properties (3,5,10,12,26,27,28,30,46,58,59,60,63).

Generally, these models are two-part models consisting of a

nonstationary and a stationary portion. The nonstationary, or trending

portion of the model is generally described by conventional regression

analysis. The random field models are used for the stationary, or

stochastic portion (i.e., the residuals from the regression analysis).

The stationary portion of the model attempts to improve the soil

property prediction from the regression analysis (the nonstationary

portion) by considering any correlation structure within the residuals.

This correlation structure (more properly termed autocorrelation)

results from the fact that nearby soil volumes tend to have similar

residuals from the regression analysis (i.e., adjacent soil volumes

would both tend to be above or below the prediction from regression,





60

whereas more distant soil volumes would more likely follow the expected

random variation about the regression prediction).
Figure 4-3 describes the Random Field Model concept. While the

straight line (determined from regression analysis) predicts the general

trend of the data, knowledge of Points #1 and #2--which are correlated

with one another--would permit a better prediction of Point #3, thus

enhancing the prediction from the regression model.





/' I













Figure 4-3. Random Field Model Concept

Regression analysis. In using the ordinary least squares (OLS)

approach to regression analysis, the model used is typically

[Z] = [X][b] + [e] ............. ............ . ........... (4-11)

in which

[Z] = (n x 1) column vector of n observations of the dependent variable Z
[X] = (n x p) matrix comprised of I's in the first column to represent the

intercept term b1 (i.e., X1 = 1), and of the n observations on

(p 1) independent variables X2,...,Xp





61

[b] = (p x 1) column vector of unknown weights to be determined: b1 (the
intercept term), b2 ,...,bp

[e] is an (n x 1) column vector of n residuals, ei

Several key assumptions are made relative to the residual terms in

applying the OLS method to regression analysis, namely that they have

zero mean, are uncorrelated, have constant variance, and are normally

distributed (14). These assumptions are often represented by

e = N(0,V) ..................................................... (4-12a)

E[C(ei,ej)] = 0 for i / j .................................... (4-12b)

in which E[ ] is the expected value of [ ].

As introduced above, though soil is typically considered a random

media, soil properties for neighboring soil volumes tend to be more

correlated than the properties for more distant volumes, causing the

covariance assumption (Equation 4-12b) to be violated for some i j.

This feature is termed autocorrelation. The random field models attempt

to improve soil property estimation by accounting for the

autocorrelation structure of the residuals.

Autocorrelation structure. Autocorrelation structure is often

described by a semi-variogram (Figure 4-4), which is a graph showing the

degree of continuity of a soil property (26,33,40). By graphing the

semi-variogram function,Y (r), against separation or lag distance, r,

the semi-variogram provides information on how far data may be spatially

extrapolated (4). The theoretical semi-variogram function is equal to



y(r) = 0.5 V [Z(x + r) Z(x)] ..............................(4-13)





62




+ NORMRLIZEO RF DOTR --- FITTED THEORETICAL CURVE
1.8

1.4

Iz ++ ++





// 2 + ,++
M 0.8 !


0.4 /




LAG DISTANCE Cr)



Figure 4-4. Typical Experimental Semi-Variogram of Normalized Data


in which Z(x) is the value of property Z at point x and V[] is the

variance. For random residuals this function will level off to a

constant value (the variance of the data set) at r greater than a

distance termed the range of the variogram. For finite data sets,

Equation 4-13 is estimated by


1 N(r)
Y(r) Z [Z(x + r) Z(x )]2 .................... (4-14)
2N(r) i=l i i


in which N(r) is the number of observation pairs whose separation

distance is r. In working with real data spaced at less than uniform

intervals, a band or tolerance is often applied to the separation

distance (i.e., r = 50 feet + 10 feet). Tang (59) cautions that the

error in the estimated variogram can be substantial if r varies








significantly from the discretized average distance. Also, the

reliability of the estimate for Y(r) decreases with increasing r, so

usually only separation distance values up to one-fourth to one-half the

total distance spanned are used in the analysis (26,28).

Vanmarcke (63) notes that statistical analysis of actual soil data

can often be handled easier if the soil is normalized to be

"statistically homogeneous," producing what Lumb calls a "grossly

uniform soil" (31). Data can be normalized to have a mean of zero and a

standard deviation (and variance) of unity by the following

transformation:


x = x x ..................................................... (4-15)
n s
x
where xn is the normalized data corresponding to x. For normalized

random data, the semi-variogram function should level off to a value of

one.

Given a data set of normalized residuals, the autocorrelation

function, P(r), is complementary to the semi-variogram function, and can

be determined by


p(r ) = 1 y (r ) ............................................... (4-16)

Similar to the correlation coefficient, p(r) can vary between 1 (perfect

continuity of the soil property) and 0 (completely random variation).

However, as a measure of association between data pairs, the correlation

function may seem more familiar to engineers than the semi-variogram

function, whose origin lies in mining geology. Figure 4-5 shows the

correlation function corresponding to Figure 4-4, and fitted by an






64




+ NORMALIZED RAW DATA --- FITTED CRVE
1.0


w \

0.4 \







-0.8


LAG DISTANCE Cr)



Figure 4-5. Typical Experimental Autocorrelation Function


exponential expression from Vanmarcke (63). Vanmarcke presents four

analytical expressions of many in the literature describing the

correlation function, each characterized by a single parameter. He

notes that all of the formulas are merely curve fitting expressions with

no theoretical basis; hence, they all "work" about equally well--a

practical point of view echoed by Tang (59).

The correlation function (or the related semi-variogram) is used to

account for autocorrelation structure in regression analysis, as

described below. It is also a powerful device for estimating the

maximum spacing between samples. In order to characterize the

autocorrelation structure of a site (and, hence, the spatial variability

of the measured soil property), Peters (40) states the maximum spacing

between samples is the range of the variogram (or correlation function),







with a recommended spacing of two-thirds to three-quarters of the range.

A larger spacing would likely miss the correlation structure, and a

smaller spacing would be unnecessarily expensive. Naturally, closely-

spaced trial samples in the area would be initially required to

establish the correlation structure of the soil property. Kulatilake

and Miller note that if the purpose of a site investigation is to

generally characterize the site while avoiding redundancy (i.e., to

describe the general trend of the site), then sample spacing should be

greater than the range (27).

Incorporation into model. If the nonstationary portion of the

regression model is designated Z and the stationary portion Z then

the complete model is

Z(xi) = Z*(xi) + Z**(xi) ........................................ (4-17)


in which Z(xi) is the estimated value of the soil property, Z, at point

xi. The nonstationary portion is estimated using conventional

regression techniques. The stationary portion is estimated using a

method presented by Kulatilake and Ghosh (26).

One of the difficulties in applying a random field model is testing

for stationarity of the data. Normally replicate testing techniques can

be used to insure that the residuals are N(O,V) beyond the range of the

semi-variogram; but with destructive tests such as the CPT and SPT,

alternate methods are required. Kulatilake and Ghosh proposed examining

the form of the semi-variogram at large lag distances. If the

normalized semi-variogram function levels off to 1 (or the complementary

autocorrelation function to 0), then stationarity can be assumed.

However, if leveling-off behavior is not exhibited, then a trend






66

component is apparently remaining in the residuals, and a higher order

regression model should be used. They recommend using the lowest order

trend (nonstationary) model that results in a satisfactory semi-

variogram.

In quantifying the stationary portion of the estimation model,

Kulatilake and Ghosh employed an approach related to the geostatistical

procedure called kriging. Briefly, kriging is a computer-intensive

process used to estimate the value of an unknown, autocorrelated

property using a linear weighting function. The weights are chosen

subject two conditions: the sum of the weights must equal 1, and the

sampling variance should be minimized (4,6,10,12,33). Z**(xi) was thus

estimated by

** q
Z (x ) = (s ) Z a h ....................................... (4-17)
i e k=l ki k


a = ki ; j = ,2 ... q .............................(4-18)
ki
ji
in which

q = the number of measurements within the correlated region

around xi

se = the standard deviation of the residuals from lowest order

regression model resulting in stationary autocorrelation

function

hk = the normalized residual at location k within the correlated

region about point i

aki = a suitable weighting coefficient, and






67

mn = value of the correlation function for a separation distance

corresponding to the distance between points m and n














CHAPTER 5
EVALUATION OF THE SPATIAL VARIABILITY MODELS

Application of Estimation Models

Five general models for predicting soil properties influenced by

spatial variability were evaluated at three sites, as discussed below.

In all cases the approach taken was to attempt to predict a sounding.

(whether it be an SPT or CPT sounding) by suppressing that sounding from

the data base, and using the remaining soundings for the prediction.

The three sites selected were Choctawhatchee Bay (CPTs), Apalachicola

River (SPTs), and Archer Landfill (CPTs).

Evaluation Criteria

The root mean square error, RMSE, was used as a criterion to

evaluate the accuracy of the various models. The model to predict a

soil property, Z, which minimizes the RMSE can likely be judged the best

of the evaluated models:


RMSE = j=1 A P j ......................................... (5-1)
n


in which n is the number of observations, and the subscripts A and P

refer to actual and predicted values of the soil property, respectively.

The RMSE is an estimate of the standard deviation of the errors about

the prediction; however it is not a true unbiased estimate (as was used






69

in Chapter 3 to evaluate the local variability of cone penetration test

measurements) because the denominator equals the total number of

observations, not the number of independent observations. This slightly

revised definition of the root mean square error is deliberate to permit

true comparison between all of the prediction methods--the affect on the

value of the RMSE will be negligible due to the large number of

observations involved.

In addition to the RMSE criterion, the predictions were graphically

overlaid onto the actual soundings and subjectively compared. This was

an important check on the root mean square error to insure that the best

RMSE did indeed reflect the best prediction.


Data Manipulation


For the Choctawhatchee Bay and Apalachicola River sites, the five

general models were applied to both transformed and nontransformed

variables. Only transformed variables were used at the Archer Landfill

site. The transformation used was logarithmic (base 10), which has the

effect of spreading out small values of the variable while bringing in

large values. This was judged potentially beneficial for the Florida

data sets used in this analysis because of the relatively large

percentage of small values of the variables (whether they be qc, fs, or

N), and the large-valued spikes in some of the soundings. It was felt

that such a transformation may emphasize the smaller values of the

variables, giving a somewhat more conservative estimate. Another

potential advantage of the logarithmic transformation was the

elimination of any negative predictions, an occasional problem with the

regression models. Figure 5-1 compares a typical frequency distribution










GC MIDPOINT
0.0
1.5
3.0
4.5
6.0
7.5
9.0
10.5
12.0
13.S
15.0
16.5
18.0
19.5
21.0
22.5
24.0
25.5
27.0
28.5
30.0
31.5
33.0
34.5
36.0
37.5 I


0 100 200 300 400 500 600 700 600 900 1000

FREQUENCY


GC MIDPOINT
-0.65
-0.556
-0.45
-0.35
-0.25
-0.15
-0.05
0.05
0.15
0.25
0.35
0.45
0.55
0.65
0.75
0.85
0.95
1.05
1.15
1.25
1.35


(a) No Transformation


100 200 300

FREQUENCY

(b) Logarithmic Transformation


Figure 5-1. Effect of Data Transformation on Cone Resistance Data
at Choctawhatchee Bay Site


CUM.
PCT. PCT.
i4.46 14.46
20.90 35.36
13.85 49.21
11.79 61.01
98.5 69.59
6.67 76.26
5.23 81.49
3.61 95.10
2.20 87.30
1.62 86.92
2.01 90.93
1.69 92.62
1.32 94.14
1.01 95.15
0.99 98.14
0.73 96.87
0.76 97.63
0.71 98.34
0.34 98.66
0.40 99.08
0.17 99.24
0.27 99.52
0.15 99.66
0.19 99.85
0.06 99.92
0.06 100.0




CUM.
PCT. PCT.

0.04 0.04
0.17 0.21
1.15 1.39
3.29 4.66
5.71 10.4
5.15 15.5
3.865 19.4
4.83 24.2
4.95 29.2
3.59 32.8
4.81 37.6
6.38 43.9
7.22 51.2
8.12 59.3
7.91 67.2
7.66 74.9
7.41 92.3
4.81 87.1
4.01 91.1
3.80 94.9
2.92 97.8
1.59 98.4
0.61 100







of a variable with its associated transformation. While neither

distribution is statistically "normal," the transformed variable is much

more symmetrical, suggesting that deterministic estimates of the

distribution (i.e., the mean and median) may be more representative of

the entire data set.

In addition to the logarithmic transformations, the cone

penetration test data were filtered using the average value over a 0.5

meter increment. As discussed in Chapter 3, this digital filter

smoothes out the high-frequency noise seen in many CPT soundings, while

preserving the true character of the sounding. As a result the RMSE

will be reduced, and will better reflect the standard deviation in the

average value of the estimate.


Autocorrelation Function


As suggested by Anderson et al., the autocorrelation function was

estimated for each site by considering the measured soil property values

along lines of constant elevation, and pooling vertically (3). The

lowest order regression model which demonstrated stationary residuals

(using Kulatilake and Ghosh's approach) was used to remove the trend

component. Then an equation was fitted to the autocorrelation function

exhibited by the residuals. Appendix E contains two BASIC programs for

calculating the autocorrelation function: one assuming the soundings are

equally spaced, the other assuming irregular spacing.

As mentioned above, the autocorrelation function permits rational

evaluation of the spacing of soundings during a geotechnical site

investigation. Also, two of the estimation models employed in this

study make use of the information obtained from the autocorrelation







72

function; specifically the range or correlated distance, and the fitted

autocorrelation function. As noted earlier, since the exact form of

this equation is of little real significance and has no theoretical

basis, a simple exponential form was used for each site:

-r/ib
p(r) = e .................................................... (5-2)


in which r is the lag or separation distance, and Bis the constant

which causes the function to best fit the actual data. The range of the

autocorrelation function is the distance at which the data become

uncorrelated. For the purposes of this research project, data were

assumed uncorrelated when the correlation coefficient was approximately

0.1 or less.


Model Types


Deterministic. A constant value was used to represent the soil for

the whole depth. Three deterministic models were evaluated: the mean,

the median, and the 10% trimmed average of the entire data set.

Distance Weighting. Two distance weighting functions were applied

to soundings within the range of the sounding to be predicted. The

first used al/d for the weighting function, whereas the second function

used a2/d2, where d is the horizontal distance from the sounding in the

data base to the sounding to be predicted. The "an" terms were

determined so that the sum of the weighting functions equaled 1:


n
E d
a = i=l i ..........................................(5-3)
1 n n
Z [ T d /(d )]
i=l i=1 i i










n
1 d2
a = i=l i ........................................ (5-4)
2 n n
E [ H d2/(d2)]
i=1 i=1 i i
in which di is the distance to sounding i of n total soundings within

the range about the sounding to be predicted. If individual

observations of a particular sounding were missing for some reason, then

the weights were recalculated using the remaining soundings within the

correlated region.

Regression Analysis. The third general model evaluated was

regression analysis, which fits the "best" curve through the given data

by minimizing the squared distance between the curve and the data points

using the method of least squares. The adequacy of the model fit is

usually summarized using the squared multiple correlation coefficient,

R2:



2E(Z Z )(5-5)
R( Z )- ....1...................... .............. (5-5)
R2 = I A P
(z z)2
A

in which the subscripts A and P refer to actual and predicted soil

property values. The R2 value represents the proportion of the total

variability in the dependent variable that can be explained by the

regression model, and can vary between 0 (no fit) to 1 (perfect fit).

As a rule of thumb, Brook and Arnold recommend an R2 of at least 0.5 in

order to have much confidence in the model (9).

Several levels of regression analysis were used. The lowest level,

termed Model 1, was a simple first order (linear) model:






74

P = b0 + blX + b2Y + b3Z + e ..................................... (5-6)

in which P is the predicted value, X and Y are perpendicular horizontal

distances, and Z is the vertical depth from some selected reference

point. Model 2 was similar to Model 1, except that a second order depth

term was added:

P = bo + biX + b2Y + b3Z + b4Z2 + e .............................. (5-7)

The remaining two levels of regression analysis are termed "Low

Term Regression" and "High Term Regression," terminology which requires

some explanation. To better describe observed trends in the data set,

higher order variables are often required. However, part of the

difficulty in applying regression analysis to a problem is determining

which variables are important and significant in describing the trends.

A stepwise variable selection technique, contained in the SAS procedure

STEPWISE, was employed for selection of significant higher-order

regression variables.

The stepwise technique is a well-regarded variable selection

method, the details of which can be found in many texts on regression or

multivariate analysis (14,16,48,56). Briefly, the stepwise procedure

enters and removes predictors one by one until some "best" regression

equation is found. The method starts out by entering the variable most

highly correlated with the dependent variable (i.e., the predictor

having the largest squared correlation coefficient--squared to allow for

significant negative correlations). Succeeding variables are added at

each step according to the largest F-value, a statistic which measures

whether a variable's contribution to the model was significant, or could

be explained by chance. A significance level of 0.15 was used to admit








predictor variables to the model (meaning there was at most a 15% chance

that the variable's contribution was due to chance). After a variable

is admitted to the model, all previously admitted variables are then

checked for possible removal by calculating their F-values, assuming

that they were the last variable admitted to the model. This test

eliminates predictors that may be highly correlated with subsequently

entered predictors. A significance level of 0.15 was also used to

remove variables. The stepwise procedure continues until all variables

meeting the required F-value are entered into the model.

Lumb (30) and Tabba and Yong (58) note that horizontal trends can

generally be described using first or second order variables, whereas

depth variables often must be of much higher order. Therefore the

variables selected for evaluation by the STEPWISE procedure were depth

up to order 8, horizontal distance up to order 2, and depth-distance

interaction terms up to order 5 for depth and order 2 for distance.

After the STEPWISE procedure completed its analysis, the "High Term

Regression Model" was the final step in the procedure, and represented

the best model (as measured by the R2 statistic) containing all

predictor variables significant at the 0.15 level. The "Low Term

Regression Model" was a model from one of the earlier steps in the

STEPWISE procedure with an R2 statistic nearly as large as the High Term

Model (i.e., subsequent steps reflected the Law of Diminishing Returns

in improvement of the model fit).

Random Field. The fourth type of model evaluated for predicting

soil properties is the random field model. The nonstationary, or trend

portion of the model is the lowest order regression equation exhibiting

stationary residuals, as determined during evaluation of the






76

autocorrelation function. The stationary, or random portion employs

Equations 4-17 and 4-18, using the equation for the fitted

autocorrelation function determined above. A BASIC program for

calculating the stationary portion of the model is contained in Appendix

E.

Linear Interpolation. The final general model evaluated in this

study was a simple linear interpolation model. For this model, the

sounding to be predicted was linearly interpolated from the immediately

adjacent soundings, based on separation distance. This model provides

an important comparison for the more "sophisticated" attempts to improve

on a single-value deterministicc) estimate, because it is the method

most likely to be employed by an engineer.


Sites Investigated

Choctawhatchee Bay. The first site evaluated was a portion of a

replacement bridge being built by the Florida Department of

Transportation (FDOT) across Choctawhatchee Bay in the Florida

panhandle. Twelve friction-cone penetrometer soundings were used,

running generally south to north between Stations 110+88 (Sounding A)

and 119+47 (Sounding L) on the causeway south of the main channel, a

distance of 859 feet. Figure 5-2 shows a plan view of the site. For

purposes of evaluating their spatial variability, the twelve soundings

were assumed on a straight line (reducing the problem to a two-

dimensional problem), except that the autocorrelation function was

calculated based on true separation distances. Three soundings were
"predicted," located at Stations 114+78 (Sounding E), 117+00 (Sounding

H), and 119+00 (Sounding J).








a ECPT TO BE PREDICTED


STATION (I STATION 30.5 m)


Figure 5-2. Spatial Variability Soundings at Choctawhatchee Bay

1.0,



0.4 o o.----
0.-8 '2

0. /




-0.4 0o
u I
-0.6
-O 2



a Fs
-0.8 2

-1.0 '-
0 100 200 300 400
LAG DISTANCE (FEET)

Figure 5-3. Autocorrelation Function for Normalized Raw Data at
Choctawhatchee Bay


0 ECPT








The spatial variability analysis was based on the upper 20 meters

of soil. The surface elevation was nearly level at approximately 1.8 m

MSL (6.0 ft MSL), with a range of 1.6 m to 2.1 m (5.4 ft to 7.0 ft).

The soil is predominantly fine sand and silty sand, with some sandy clay

layers.

The ECPT soundings show the site, in general, to have low to

moderate qc values to a depth of 5-7 meters (16-23 feet), followed by

very low qc's. Between 11 and 14 meters (36-46 feet) the cone

resistance increases somewhat, becoming moderate to high at depths

ranging from 13 to 17 meters (43-56 feet). The friction resistance

values remained low throughout the soundings, increasing modestly when

the stiffer sand layer was encountered. A subjective evaluation of the

site would describe it as reasonably uniform, sounding to sounding.

Figure 5-3 is the autocorrelation function for the raw data (normalized

using equation 4-15). Autocorrelation was assumed to be a circular

function in the horizontal plane (i.e., autocorrelation in the x-

direction = autocorrelation in the y-direction). Figure 5-3 supports

the subjective description of "reasonably uniform" since it generally

leveled off to an average correlation coefficient of around 0.5 to 0.6

for at least 122 meters (400 feet) laterally.

Several of the soundings recorded negative friction values in very

weak soils, a problem discussed in Chapter 3. Friction resistance

values less than -10 kPa (-105 tsf) were deleted from the data base; all

other negative friction values were forced to zero (Note: These values

were forced to 1 kPa for the transformed fs).

Apalachicola River. The second site evaluated for spatial

variability was another FDOOT bridge project across the Apalachicola






79

River. Thirteen standard penetration test (SPT) soundings were used,

running on a line east to west between Stations 105+00 (Boring 10) and

124+00 (Boring 22) within the boundaries of the Apalachicola River.

Figure 5-4 shows a plan view of the site. Three soundings were

"predicted," located at Stations 106+00 (Boring 11), 114+00 (Boring 9),

and 118+00 (Boring 13).

The spatial variability analysis was based on the SPT soundings

between elevation -9.1 and -27.4 meters (-30 and -90 feet) MSL. To

facilitate the analysis, the individual soundings were slightly adjusted

up or down so that the SPT N values (with units of blows per foot)

occurred at the same elevation for all soundings. The decision to limit

the analysis to elevations between -9.1 and -27.4 meters was due to


1. The SPT measurements display nearly perfect uniformity from the
mud line to elevation -9.1 m (with an N=1-2), and hence show
virtually no detectable spatial variability; and

2. Data are sparse below elevation -27.4 m.


To minimize any undue effect of individual large data values on the

analysis, all N values in excess of 150 (such as 50 blows per 3 inches,

equivalent to 200 blows per foot) were truncated to 150 blows per foot.

This was the only filtering performed on the Apalachicola River data

set.

The soil profile is typically loose clayey sand overlying stiff

clay, which overlies dense sand. The SPT soundings show the site, in

general, to have low N values between elevation -9.1 and -27.4 meters (-

30 and -13.7 feet). Between elevation -13.7 and -27.4 meters (-45 and -

90 feet), however, the N values range widely. Adjacent soundings tended

to have somewhat similar profiles, but large differences were not














o SPTs
* PREDICTED SPTs


112

, I


105 : /
I I
I -ji
LU

a:


BORING NUMBERS

14 18 18
0 0 0 = *


22


1 15

STATIONS

(I STATION = 30.5 m)


Figure 5-4. Spatial Variability Soundings at Apalachicola River

S1.0

0.8 -\
I-






S0.0
S-0.2

S-0.4
C3- o SPT
-0.68

-0.8
-1.0
0 100 200 300 400 500 800 700 800 900 1000
LAG DISTANCE (FEET)
Figure 5-5. Autocorrelation Function for Normalized Raw Data at
Apalachicola River






81

uncommon. Figure 5-5 shows the autocorrelation function for the raw SPT

data. Note the generally decreasing correlation coefficient up to a lag

distance of 500 feet (152 m). The correlation seems to improve slightly

beyond 500 feet, but since autocorrelation functions are known to be

less reliable at larger lag distances, this improvement is thought to be

an artifact of the particular data set.

Archer Landfill. The final site evaluated for spatial variability

was a future landfill located west of Archer, Florida. Ten electronic

cone penetrometer soundings were used, spread out over approximately 0.7

hectares (1.7 acres). Figure 5-6 shows a plan view of the site. For

this analysis, the data were located three-dimensionally since the

soundings were not in a relatively straight line. Soundings #4, #5, and

#8 were "predicted." The source of the data was a University of Florida

Master's degree thesis by Basnett (7).

The spatial variability analysis was based on the ECPT soundings

between elevations 20 and 30 meters (66-98 feet) (data were sparse below

elevation 20 meters). The surface elevation averaged 31.85 meters MSL

(104.5 ft MSL), with a range of 30.60 to 32.85 meters (100.4 to 107.8

ft). The soil is described as medium to fine-grained quartz sand. No

water table was encountered.

The ECPT soundings show the site to have cone resistance and

friction resistance values that generally increase with depth. The site

is remarkably uniform, although measured stresses are somewhat more

variable for the lower five meters (16 ft) of the sounding. Figure 5-7

shows the autocorrelation function for the raw SPT data.

Autocorrelation was assumed to be a circular function in the horizontal

plane. The uniformity of the site is reflected by the leveling off of























1 4

n 2
n2
u3


I 8
6n1
7


0 ECPT

I PREDICTED ECPTs



0 100 FEET

0 30.5 METERS


COORDINATES C(FT1
1. (95.204)
2. (122.159)
9 3. (124.149)
0 4. (153.195)
10 5. (382.99)
0 6. (404.5B)
7. (408.45)
8. (430.71)
9. (533,100)
10. (580.50)


Figure 5-6. Spatial Variability Soundings


I 0.

LL 0.

o.



-0.
LJ
-0.

S-0.

-0.


at Archer Landfill


50 100 150 200
LAG DISTANCE (FEET)


250 300


Autocorrelation Function for Normalized Raw Data at
Archer Landfill


84- ',








SOc

4 c


I Fs


I*.I


0


Figure 5-7.


no-


-}





83

the correlation coefficients to values generally over 0.6 as far as 300

feet (91.4 m) apart.


Results and Discussion


Choctawhatchee Bay Site


Autocorrelation Function. Since the autocorrelation function for

the normalized raw data did not level off to zero (Figure 5-3), a

nonstationary component was assumed to be present. Following Kulatilake

and Ghosh's recommended technique (26), a first order regression model

(Model 1) was used to try to describe the trend. However, the

autocorrelation function for the residuals from the regression analysis

showed little change from Figure 5-3. Again increasing the order of the

regression model one step (Model 2), the autocorrelation function began

to approach the expected leveling-off behavior. In order to better

describe the trend component, the STEPWISE model generator in the SAS

system was employed. A four-term model was selected for both qc and fs:



qc = bo + biD + b2D2 + b3D8 + b4D5X R2 = 0.55 ..............(5-8)
fs = bo + blD2 + b2X2 + b3DX + b4D5X R2 = 0.65 ..............(5-9)

in which 0 is the depth in meters, and X is the distance from Sounding A

in.feet. This model produced the autocorrelation functions used in the

analysis (Figure 5-8):

By trial and error an exponential curve corresponding to equation

5-2 was fitted to both the cone resistance and friction resistance data

of Figure 5-8 (since the two curves were very similar). A constant (S)

of 20, and a range of 50 feet were estimated. The fact that the range