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Investigation of Sediment Erosion Rates of Rock, Sand, and Clay Mixtures using Enhanced Erosion Rate Testing Instruments

Permanent Link: http://ufdc.ufl.edu/UFE0042476/00001

Material Information

Title: Investigation of Sediment Erosion Rates of Rock, Sand, and Clay Mixtures using Enhanced Erosion Rate Testing Instruments
Physical Description: 1 online resource (414 p.)
Language: english
Creator: Crowley, Raphael
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: Civil and Coastal Engineering -- Dissertations, Academic -- UF
Genre: Civil Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: INVESTIGATION OF SEDIMENT EROSION RATES OF ROCK, SAND, AND CLAY MIXTURES USING ENHANCED EROSION RATE TESTING INSTRUMENTS Scour is the primary cause of bridge failures in the United States. Although predicting scour depths for non-cohesive (sandy) bed materials is fairly well understood, much less is known about predicting scour depths when cohesive materials such as clays, sand-clay mixtures, and rock are present. Two semi-empirical methods exist for predicting cohesive scour depths. Both of these methods rely on the input of a sediment transport function or erosion rate as a function of shear stress. Current design guidelines such as HEC-18 recommend measuring sediment transport functions in a laboratory, but there has been some question as to how to do this properly. To answer this question, a series of improvements and enhancements were made to the Sediment Erosion Rate Flume (SERF) at the University of Florida (UF). A laser leveling system, a vortex generator, a shear stress measuring system, computer updates, and a sediment control system were designed. New components to the SERF except for the sediment control system operated as expected. Using the new shear stress system, a series of tests were run to assess the proper way to measure shear stress in a flume-style erosion rate testing device. Results showed that the pressure drop method will not measure shear stress properly, and in the absence of a shear stress sensor, the most effective alternative method for estimating shear stress is to use the Colebrook Equation (which describes the Moody Diagram). A new material was developed for testing in both the SERF and the Rotating Erosion Testing Apparatus (RETA) to serve as a basis of comparison between the two instruments. Results were inconclusive because rock-like erosion described by the Stream Power Model dominated erosion behavior. A database of results from the RETA that has been developed since the RETA s inception in 2002 was used to verify that it is measuring the correct erosion rate vs. shear stress relationships. Results showed that for the special case where particle-like erosion dominates, the RETA appears to produce correct results. Results also indicate that when rock-like erosion is present, it is generally an order of magnitude lower than situations where particle-like erosion dominates. Further analysis of the database showed that there may be a correlation between material strength and erosion rate. Further research was aimed at generalizing erosion rate vs. shear stress relationships for sand-clay mixtures. A series of tests were conducted on a variety of sand-clay mixtures. Results showed sensitivity to the method in which the sand-clay mixtures were prepared. Rock-like erosion and particle-like erosion were present in most sand-clay mixtures even though typical sand-clay mixtures would not typically be described as rock-like materials. Recirculating sediment during sand-clay testing indicated that suspended sediment in the SERF has little effect on bed shear stress.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Raphael Crowley.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Bloomquist, David G.
Local: Co-adviser: Sheppard, Donald M.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042476:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042476/00001

Material Information

Title: Investigation of Sediment Erosion Rates of Rock, Sand, and Clay Mixtures using Enhanced Erosion Rate Testing Instruments
Physical Description: 1 online resource (414 p.)
Language: english
Creator: Crowley, Raphael
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: Civil and Coastal Engineering -- Dissertations, Academic -- UF
Genre: Civil Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: INVESTIGATION OF SEDIMENT EROSION RATES OF ROCK, SAND, AND CLAY MIXTURES USING ENHANCED EROSION RATE TESTING INSTRUMENTS Scour is the primary cause of bridge failures in the United States. Although predicting scour depths for non-cohesive (sandy) bed materials is fairly well understood, much less is known about predicting scour depths when cohesive materials such as clays, sand-clay mixtures, and rock are present. Two semi-empirical methods exist for predicting cohesive scour depths. Both of these methods rely on the input of a sediment transport function or erosion rate as a function of shear stress. Current design guidelines such as HEC-18 recommend measuring sediment transport functions in a laboratory, but there has been some question as to how to do this properly. To answer this question, a series of improvements and enhancements were made to the Sediment Erosion Rate Flume (SERF) at the University of Florida (UF). A laser leveling system, a vortex generator, a shear stress measuring system, computer updates, and a sediment control system were designed. New components to the SERF except for the sediment control system operated as expected. Using the new shear stress system, a series of tests were run to assess the proper way to measure shear stress in a flume-style erosion rate testing device. Results showed that the pressure drop method will not measure shear stress properly, and in the absence of a shear stress sensor, the most effective alternative method for estimating shear stress is to use the Colebrook Equation (which describes the Moody Diagram). A new material was developed for testing in both the SERF and the Rotating Erosion Testing Apparatus (RETA) to serve as a basis of comparison between the two instruments. Results were inconclusive because rock-like erosion described by the Stream Power Model dominated erosion behavior. A database of results from the RETA that has been developed since the RETA s inception in 2002 was used to verify that it is measuring the correct erosion rate vs. shear stress relationships. Results showed that for the special case where particle-like erosion dominates, the RETA appears to produce correct results. Results also indicate that when rock-like erosion is present, it is generally an order of magnitude lower than situations where particle-like erosion dominates. Further analysis of the database showed that there may be a correlation between material strength and erosion rate. Further research was aimed at generalizing erosion rate vs. shear stress relationships for sand-clay mixtures. A series of tests were conducted on a variety of sand-clay mixtures. Results showed sensitivity to the method in which the sand-clay mixtures were prepared. Rock-like erosion and particle-like erosion were present in most sand-clay mixtures even though typical sand-clay mixtures would not typically be described as rock-like materials. Recirculating sediment during sand-clay testing indicated that suspended sediment in the SERF has little effect on bed shear stress.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Raphael Crowley.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Bloomquist, David G.
Local: Co-adviser: Sheppard, Donald M.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042476:00001


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INVESTIGATION OF SEDIMENT EROSION RATES OF ROCK, SAND, AND CLAY MIXTURES FOR PREDICTING SCOUR DEPTH USING ENHANCED EROSION RATE TESTING INSTRUMENTS By RAPHAEL CROWLEY 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 2010

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2 2010 Raphael Crowley

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3 To my sister, Mary

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4 ACKNOWLEDGMENTS First and foremost, I thank my family for their continued support through the years. My moms always been there for me, and she was always someone I could turn to when I needed something. My dads wisdom and guidance through graduate school from coursework, to switching advisors twice through qual ifying exams, and through the dissertation process made it possible for me to complete what I needed to. His perspective as a professor at a 4year institution gave me the insight I always needed to figure out what I was supposed to do. Finally, my sis ter, Mary, for whom this dissertation is dedicated has always been the best little sister on the planet. Shes always willing to listen to me talk about anything school related or otherwise and shes become one of my best friends through my life Tha nks Mar! You Rock! Secondly, I thank the Univers ity of Florida Womens Rowing Team for their support throughout graduate school, and for giving me the opportunity to coach during my time here at UF. Particularly, I thank the UF Crew Novice class of 2010; those women have taught me so much, and have helped k eep me sane through the years. Special shout outs from that class go to (in no particular order) Liz Commins, Courtney Holst, Emily Congdon, Katie Tschopp, Haley Kress, Anna Kantzios, and Laura Thomas. You kids have taught me so much, meant the world to me, and I couldnt have finished grad school without you guys and everyone else from the Womens Team. My only regret has been to cut out on you during your senior year so that I could get this Ph.D. done. Thanks also to Justin Knust for making Florida Crew what it was supposed to be, coaching me for a year, talking me into coaching, and for being a mentor and a friend for me through the years. Third, thanks to anyone who helped me in the lab or administratively during my time working on my Ph.D. Falak Shah, Courtney Holst, Jim Hayne Randall Booker, and Arden Herrin have been great undergrad assistants, and I have loved working with them Chuck

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5 Broward, James JJ Joiner, and Hubert Nard Martin Danny Brown, and Vic Adams have been the best support staff I ever could have hoped for. Thanks also to Debra Hambrick, Lucy Hamm, Carolyn Carpenter Donna Moss, Nancy McIlrath, and Tony Murphy for dealing with the money, computers, paperwork, and everything else administrative that needed to be done. Fourth, thanks to my friends who supported me during the Raf Crowley 2010 plan or the plan for me to spend a solid decade doing college things. It was a big joke in undergrad (modeled after Buckn ells 2010 plan) but it seriously happened, and I cant thank you guys enough for putting up with me through it all. Special thanks to the McCullough family Mr. and Mrs. Tracy McCullough, Aaron and Kim McCullough, and Mikey and Michelle McCullough fo r always having my back and being like a 2nd family to me. Thanks to my undergrad group of friends, Chuck Dickhart, Matt Albrecht, Beau Bryant, Kyle McNeel, Leo Nalini, Bryan Prins, Tom Leary (R.I.P.) Adam Linetty, and the guys at Lambda. Thanks to my t eammates and friends in grad school you taught me what a true team was supposed to be. Particularly thank you Timmy Lucas, Jon Levy, Brian Suggs, Adam Kallin, Bill Griffith Andrew Wellbourne, Ben Michael, Trevor Guynes, Geo ff Scharfenburger, Danny Reach Ben Bolz, Shane Laakso, Andy Selepak, Jen Haas, Neil W. Blackmon, and Derek Snyder Finally, thanks to my friends that have been with me for forever Brandon Phinney, Matt Johnston, Price Taggert, Chris Cooper, and Chris Wickman. Fifth I must thank a few professors in particular who helped to make this Ph.D. possible. Dr. Richard Crago from Bucknell University gave me my start in research and I definitely would not be here today if it wasnt for working with him for two summers while in undergrad. Thanks to my committee members, Dr. David Prevatt and Dr. Toshi Nishida. My cochair, Dr. D. Max Sheppard was a great advisor for my Masters degree, and he was a valuable committee member

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6 during this dissertation I thank him for giving me the opportunit y to go to the TFHRC and work with Kornel Kerenyi that background helped to make my work with the SERF possible. Kornel and Dr. Sheppard were wonderful mentors to me over the years. Finally, thank you so much Dr. Bloomquist for giving me the chance to work with you on this project. You have been the best advisor I ever could have hoped for, and I cannot imagine completing a dissertation with anyone else. You were endlessly patient with me, you were always encouraging, and you were always able to help me when I needed it. To Dr. Bloomquist, my professors, my team, my family, my friends, and everyone else thank you all so much!

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7 TABLE OF CONTENTS page ACKNOWLEDGMENTS ...............................................................................................................4 LIST OF TABLES .........................................................................................................................13 LIST OF FIGURES .......................................................................................................................14 ABSTRACT ...................................................................................................................................26 CHAPTER 1 INTRODUCTION ..................................................................................................................28 1.1 Motivation for Research ..............................................................................................28 1.2 Scour Definitions .........................................................................................................29 1.2.1 General Scour .....................................................................................................30 1.2.2 Aggradation/Degradation ...................................................................................30 1.2.3 Contraction Scour ..............................................................................................30 1.2.4 Lo cal Scour ........................................................................................................31 1.3 Controversy Surrounding HEC 18 ..............................................................................32 1.4 Approach ......................................................................................................................33 1.5 Methodology and Organization ...................................................................................35 2 BACKGROUND AND LITERATURE REVIEW ................................................................39 2.1 Scour Depths in Non Cohesive Soil .................................................................................39 2.2 Predicting Local Scour Hole Depth for Cohesive Soils and Rock ...................................39 2.2.1 Colorado State University Tests (CSU 1991 1996) .............................................40 2.2.1.1 Setup for CSU tests ......................................................................................41 2.2.1.2 Results from CSU tests ................................................................................41 2.2.1.3 Concerns with the CSU correction factor .....................................................42 2.2.2 The EFA SRICOS Method .....................................................................................42 2.2.2.1 HEC 18 version of EFA SRICOS method ...................................................43 2.2.2.2 Complete version of EFA SRICOS method ................................................43 2.2.2.3 EFA SRICOS setup discussion ....................................................................44 2.2.2.4 Discussion of Equation 23 ..........................................................................44 2.2.3 The Miller Sheppard Method .................................................................................47 2.2.4 Scour Depth for More Complex Structures ............................................................51 2.3 Analytical Methods for Determining Erosion Rate Shear Stress Relationships ..............53 2.3.1 Particle Like vs. RockLike Erosion and Shields (1936) .......................................53 2.3.1.1 Einstein (1943) and Christensen (1975) .......................................................55 2.3.1.2 Wiberg and Smith (1987) .............................................................................56 2.3.1.3 Dade and Nowell (1991) ..............................................................................57 2.3.1.4 Dade et al. (1992) .........................................................................................58 2.3.1.5 Mehta and Lee (1994) ..................................................................................58

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8 2.3.1.6 Torfs et al. (2000) .........................................................................................59 2.3.1.7 Sharif (2002) ................................................................................................60 2.3.1.8 Critical shear stress discussion .....................................................................61 2.3.1.9 Partheniades (1962) ......................................................................................61 2.1.1.10 Christensen (1975) .....................................................................................62 2.3.1.11 McLean (1985) ...........................................................................................62 2.3.1.12 Van Prooijen and Winterwerp (2008) ........................................................63 2.3.2 Analytical Methods for Determining Rock Erosion ...............................................64 2.3.2.1 Cornett et al. (1994) and Henderson (1999) .................................................65 2.3.2.2 Stream power method ...................................................................................66 2.4 Empirical Methods for Measuring Scour and Erosion in Cohesive Soils and Rock ........67 2.4.1 Erosion Rate Shear Stress Standards for Rock ......................................................68 2.4.1.1 Geologic, geomorphologic, and geotechnical analyses (Richardson and Davis 2001) ...........................................................................................................68 2.4.1.2 July 1991 FHWA Scourability of Rock Formations (Gordon 1991) ........68 2.4.1.3 Erodibility index method (Annandale et al. 1996) .......................................71 2.4.1.4 Flume tests (Richardson and Davis 1991) ....................................................72 2.4.2 Erosion Rate Testing Devices ................................................................................72 2.4.2.1 Nalluri and Alvarez (1992) ...........................................................................72 2.4.2.2 Mitchener and Torfs (1996) .........................................................................73 2.4.2.3 Panagiotopolous et al. (1997) .......................................................................73 2.4.2.4 SEDFlume (McNeil et al. 1996, Jepsen et al. 1997) ....................................74 2.4.2.5 ASSET (Roberts et al. 2003) ........................................................................77 2.4.2.6 EFA (Briaud et al. 19912004) .....................................................................78 2.4.2.7 RETA (Henderson 1999 Kerr 2001 and Slagle 2006) .................................80 2.4.2.8 Barry et al. (2003) ........................................................................................85 2.4.2.9 SERF (Trammel 2004, Slagle 2006, Kerr 2001) ..........................................85 2.5 Gator Rock ........................................................................................................................89 2.5.1 Gator Rock: A Brief History and Description ........................................................89 2.5.2 Extension of Gator Rock to Flume Tests ...............................................................90 2.5.3 Gator Rock 2.0 ........................................................................................................91 2.5.4 Gator Rock 3.0 ........................................................................................................92 2.5.5 Need for Better Gator Rock ....................................................................................93 3 ENHANCEMENTS AND IMPROVMENTS TO THE SEDIMENT EROSION RATE FLUME .................................................................................................................................117 3.1 Introduction .....................................................................................................................117 3.2 Laser Leveling System ...................................................................................................117 3.3 Temperature Control System and Temperature Patch for SEATEK ..............................123 3.4 New Shear Stress System ...............................................................................................126 3.4.1 Shear Stress Sensor ...............................................................................................126 3.3.2 New Pressure Transducers ...................................................................................129 3.4.3 Paddle wheel Flow meter ......................................................................................130 3.5 Sediment Control System ...............................................................................................131 3.5.1 Filter System .........................................................................................................131 3.5.2 Sand I njector .........................................................................................................135

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9 3.6 Vortex System ................................................................................................................136 3.7 Miscellaneous Other Enhancements to the SERF ..........................................................138 3.8 Summary of SERF Improvements and Brief Discussion ...............................................140 4 ESTIMATIONS AND MEASUREMENTS OF SHEAR STRESSES ON AN ERODING BED MATERIAL IN FLUME STYLE EROSION RATE TESTING DEVI CES .............................................................................................................................151 4.1 Executive Summary ........................................................................................................151 4.2 Review of Relevant Background ....................................................................................151 4.3 Experimental Setup .........................................................................................................153 4.4 Experimental Results and Discussion .............................................................................155 4.4.1 Pressure Drop .......................................................................................................155 4.4.2 Analyt ical Methods ..............................................................................................160 4.4.3 Vortex Conditions ................................................................................................161 4.5 Recommendations and Conclusions ...............................................................................162 4.6 Future Work ....................................................................................................................163 5 DEVELOPMENT AND TESTING WITH A NEARLY UNIFORM, HIGHLY ERODIBLE, SYNTHETIC ROCK LIKE MATERIAL TO BE USED FOR CALIBRATING EROSIONRATE TESTING DEVICES ..................................................178 5.1 Executive Summary ........................................................................................................178 5.2 Review of Relevant Background ....................................................................................178 5.3 Theory Behind Bull Gator Rock .....................................................................................181 5.4 Procedure for Construction of Bull Gator Rock .........................................................182 5.5 First Gator Rock Mix ......................................................................................................184 5.5.1 Mix Compositions ................................................................................................184 5.5.2 Strength Tests .......................................................................................................184 5.5.3 RETA Tests ..........................................................................................................185 5.5.3.1 First round of tests on first batch of Gator Rock in RETA ........................185 5.5.3.2 Second round of tests on first batch of Gator Rock in RETA ....................187 5.5.4 SERF Tests ...........................................................................................................191 5.5.4.1 Shear stress tests .........................................................................................191 5.5.4.2 Erosion tests ...............................................................................................192 5.5.4.3 SERF analysis ............................................................................................197 5.6 Second Gator Rock Mix .................................................................................................198 5.6.1 Tensile and Compressive Strength Tests ..............................................................200 5.6.2 RETA Tests ..........................................................................................................201 5.6.3 Absorption Limits .................................................................................................202 5.7 Disc ussion .......................................................................................................................203 5.8 Summary and Conclusions .............................................................................................205 5.9 Future Work ....................................................................................................................206

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10 6 VERIFICATION OF THE ROTATING EROSION TESTING APPARATUS (RETA) AND USING RETA RESULTS TO PREDICT EROSION RATES AND SHEAR STRESSES OF ERODING BED MATERIALS US ING COHESION ...............................223 6.1 Executive Summary ........................................................................................................223 6.2 Review of Relevant Background ....................................................................................223 6.3 RETA Verification ..........................................................................................................226 6.4 Extending RETA Results Predicting Erosion Rate as a Function of Material Strength .............................................................................................................................228 6.4.1 Approximation of M .............................................................................................228 6.4.2 Dimensional Analysis ...........................................................................................229 6.4.3 Approximating c ..................................................................................................230 6.5 Discussion and Future Work ..........................................................................................232 6.6 Summary and Conclusions .............................................................................................234 7 A STUDY OF EROSION RATES, SHEAR STRESSES AND DENSITY VARIATIONS OF SAND/CLAY MIXTURES ..................................................................242 7.1 Executive Summary ........................................................................................................242 7.2 Review of Relevant Background and Motivation for Research .....................................242 7.3 Materials and Procedure .................................................................................................243 7.3.1 Materials ...............................................................................................................244 7.3.2 Shear Stresses .......................................................................................................244 7.3.3 Mixing Procedure .................................................................................................246 7.3.3.1 Sand mixing procedure ...............................................................................247 7.3.3.2 Sandclay mixing procedure .......................................................................247 7.3.4 SERF Testing ........................................................................................................248 7.3.5 Procedural Variations ...........................................................................................249 7.4.6 Density Profile Tests ............................................................................................250 7.4 Experimental Results and Analysis ................................................................................250 7.4.1 Shear Stress Tests .................................................................................................250 7.4.2 SERF Tests ...........................................................................................................252 7.4.2.1 Zeropercent clay mixture ..........................................................................252 7.4.2.2 Twenty five percent clay samples ..............................................................255 7.4.2.3 Fifty percent clay samples .........................................................................261 7.4.2.4 Seventy five percent clay mixture ..............................................................264 7.4.2.5 One hundred percent clay ..........................................................................265 7.4.3 Density Profile Tests ............................................................................................265 7.4.4 Effects of Sand Concentration on Shear Stresses .................................................268 7.5 Summary and Conclusions .............................................................................................270 7.6 Future and Ongoing Work ..............................................................................................271 8 SUMMARY AND FUTURE WORK ..................................................................................302 8.1 Summary .........................................................................................................................302 8.1.1 Review of Goals for This Study ...........................................................................302 8.1.2 Summary of Work ................................................................................................302

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11 8.2 Conclusions .....................................................................................................................303 8.3 Future Work ....................................................................................................................304 8.3.1 Essential Final Improvements to the SERF ..........................................................304 8.3.2 NonEssential Final Improvements to the SERF ..................................................306 8.3.3 Determine Erosion Patterns for Natural Sand Clay Mixtu res ..............................308 8.3.4 Roughness Number Tests or Improved Testing Apparatus ..................................308 8.3.5 Normal Stress Measurements in SERF ................................................................309 8.3.6 Normal Stress Measurements under Field Conditions .........................................309 8.3.7 Computer Model ...................................................................................................310 8.3.8 Summary of Proposed Future Progression ...........................................................310 APPENDIX A SCOUR DEPTHS IN NONCOHESIVE SOILS .................................................................311 A.1 Introduction ....................................................................................................................311 A.2 HEC 18: Ba sic Principles (Richardson and Davis 2001) ..............................................312 A.2.1 HEC 18: Long Term Aggradation and Degradation (Richardson and Davis 2001) ..........................................................................................................................312 A.2.2 HEC 18: Contraction Scour (Richardson and Davis 2001) .................................313 A.2.3 HEC 18: Local Scour (Richardson 2001) ..........................................................314 A.2.4 HEC 18: Brief Discussion ...................................................................................318 A.3 The Florida DOT Bridge Scour Manual ........................................................................318 A.3.1 Local Scour: Motivation for a Different Computation Algorithm ......................318 A.3.2 FDOTBSM: Local Scour Equations (Florida DOT Bridge Scour Manual 2005) ..........................................................................................................................321 2.2.2.4 FDOTBSM: brief discussion ......................................................................325 B FLORIDA METHOD FOR SERF TESTS ...........................................................................334 B.1 New Operating Procedure for SERF ..............................................................................334 B.2 Preliminary Procedure for Tests ....................................................................................334 B.2.1 Vortex Generator .................................................................................................335 B.2.2 Temperature Control and Filtering ......................................................................335 B.3 StandAlone Shear Stress Test .......................................................................................336 B. 4 Critical Shear Stress Test in SERF ................................................................................340 B.5 Erosion Rate Test ...........................................................................................................342 B.6 Remote SERF Operation ................................................................................................345 B.6.1 Remote Visual Inspection ....................................................................................346 B.6.2 Remote Labview Operation .................................................................................346 B.7 SERF Operation Troubleshooting ..................................................................................347 C SERF COMPUTER CONTROL PROGRAMS ...................................................................365 C.1 Introduction ....................................................................................................................365 C.2 Pump Control .................................................................................................................368 C.2.1 Block Diagram Explanation .................................................................................369

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12 C.2.2 Front Panel Operation ..........................................................................................371 C.3 Motor Mover ..................................................................................................................371 C.3.1 Block Diagram Discussion ..................................................................................371 C.3.2 Front Panel Discussion ........................................................................................372 C.4 SERF Control No Motor ................................................................................................372 C.4.1 Block Diagram Discussion ..................................................................................373 C.4.1.1 SC 2345 channels ......................................................................................373 C.4.1.2 Pump input .................................................................................................374 C.4.1.3 Analog output ............................................................................................374 C.4.2 Front Panel Operation ..........................................................................................375 C.5 SERF Control Full (Temperature Patch) .......................................................................375 C.5.1 SERF Control Full Block Diagram Discussion ...................................................376 C.5.1.1 Pre input sequence .....................................................................................376 C.5.1.2 Pump input .................................................................................................377 C.5.1.3 Anal og output ............................................................................................377 C.5.1.4 Laser input .................................................................................................378 C.5.1.5 SEATEK output .........................................................................................378 C.5.1.6 Motor advancement ...................................................................................382 C.5.1.7 Data writing ...............................................................................................383 C.5.2 Using the Front Panel ..........................................................................................383 LIST OF REFERENCES .............................................................................................................405 BIOGRAPHICAL SKETCH .......................................................................................................413

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13 LIST OF TABLES Table page 51 Niraulas Original Gator Rock Water Cement Ratios .....................................................207 52 Water, Cement, and Limestone Composition for First Round Bull Gator Rock Mix .....207 53 Water, Cement, and Limestone Composition for Second Round Gator Rock Mix .........207 54 Strength Test Results from Round 2 of Gator Rock Testing ...........................................208 55 Actual W/C Ratios for Second Round of Gator Rock .....................................................209 71. Density Profile Results ....................................................................................................274 A 1 Values for k1 .....................................................................................................................326 A 2 Pier Nose Shape Correction Factors ................................................................................326 A 3 Bed Condition Correction Factors ...................................................................................327

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14 LIST OF FIGURES Figure page 11 Photograph of the Schoharie Creek Bridge Collapse ........................................................37 12 Beginning of local scour ....................................................................................................37 13 Local scour after some time has elapsed (Slagle 2006) .....................................................38 21 Results from CSU Pier Scour Study ..................................................................................94 22 Comparison Between Hjorths Results (Top) and Bri auds Computer Model (Bottom). ............................................................................................................................94 23 3D Idealized Scour Hole ...................................................................................................95 24 Definition Sketch from 3 D Ideal Scour Hole ...................................................................95 25 Second Definition Sketch from 3D Ideal Scour Hole ......................................................96 26 Top View of Pile Definition Sketch ..................................................................................96 27 Effective Shear Stress vs. Scour Hole Depth .....................................................................97 28 Extended Shields Diagram .................................................................................................97 29 Ariathurai Partheniades Relationship for Erosion vs. Shear Stress ...................................98 210. Results from Modification of Partheniades Equation ........................................................98 211 Definition Sketch for Rock Fracture ..................................................................................99 212 ERI Definition Sketch ........................................................................................................99 213 Relationship between bulk density and critical shear stress ............................................100 214 Erosion Rate vs. Excess Shear Stress Relationships ........................................................100 215 Critical Shear Stress vs. % Fines Relationships ...............................................................101 216 Original Schematic Diagram of SEDFlume ....................................................................101 217 Flow Rate vs. Shear Stress Relationship ..........................................................................102 218 Example of SEDFlume Results .......................................................................................102 219 Original Schematic of the ASSET ...................................................................................103

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15 220 Results from ASSET Tests ..............................................................................................103 221 Photograph of the EFA ....................................................................................................104 222 Schematic of 1 mm EFA protrusion into flume ...............................................................104 223 An example of a Moody Diagram ...................................................................................105 224. Photograph of the RETA .................................................................................................105 225 RETA Sample Annulus ....................................................................................................106 226 RETA Sample Annulus (Close up) .................................................................................106 227 Schematic Diagram of RETA ..........................................................................................107 228 Photograph of Torque Cell in RETA ...............................................................................107 229 Photograph of Rock Sample in RETA .............................................................................108 230 Top View of RETA ..........................................................................................................108 231 Relat ionship between shear stress and erosion rate for different Jewfish Creek Limestone samples ...........................................................................................................109 232 Relationship between cohesion a nd erosion rate for different shear stresses from Jewfish Creek Limesonte data set ....................................................................................109 233 Definition sketch for Cohesion Derivation ......................................................................110 234 Typical results from Slagles Gator Rock tests. The pink data points are from the SERF and the blue data points are from the RETA .........................................................110 235 Photograph of the SERF (Side View) ..............................................................................111 236 Top View of the SERF (Original Design) .......................................................................111 237 Pumps used to drive water through the SERF .................................................................112 238 Eroding Sample Section of the SERF ..............................................................................112 239 Viewing Window on SERF .............................................................................................113 240 Schematic Drawing of Ultrasonic Ranging System.........................................................113 241 Top View of Ultrasonic Depth Sensor on SERF .............................................................114 243 Comparison Between Roknis Experimental and Numerical Results .............................115

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16 244 Trammels Results for Cohesive Material from Jackson County, FL .............................115 245 Graph of Typical Temperature Rise During Longer SERF Tests ....................................116 246 Eroded Gator Rock 2.0 Sample .......................................................................................116 31 Photograph of Laser Leveling System (the third laser is blocked by the camera angle) .141 32 Photograph of Amplifiers and Control Boxes for the Laser Leveling System ................141 33 Water Chiller ....................................................................................................................142 34 Temperature Drop after Water Chiller Installation ..........................................................142 35 Inner Components of the Shear Stress Sensor .................................................................143 36 Shear Stress Sensor Amplifier .........................................................................................144 37 Modified A/C Unit for Shear Stress Sensor Temperature Control ..................................144 38 Shear Stress Sensor Box with Tube From A/C Unit .......................................................145 39 New Pressure Transducers in SERF ................................................................................145 310 Trammels Original Flow Rate vs. Pump Frequency ......................................................146 311 New Pump Frequency vs. Velocity Curve .......................................................................146 312 Sand Injector Load Cell Mounts ......................................................................................147 313 Sand Injector Expansion Joints ........................................................................................147 314 Completed Sand Injector ..................................................................................................148 315 Gap Ratio Effects on Vortex Development .....................................................................149 316 Graph of Strouhal Number vs. Reynolds Number ...........................................................149 317 Vortex Generator installed in SERF (looking into flume) ...............................................150 41 Spectral analysis of demeaned pressure transducer voltages .........................................165 42 Spectral analysis of demeaned shears stress sensor measurements ................................165 43 The 30 Hz data block from the shear stress sensor showing shear stress difference from t he mean ..................................................................................................................166 44 4 Hz 20 Hz data block from pressure transducer showing voltage difference from the mean ...........................................................................................................................166

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17 45 Spectral analysis of freevibratory flume test voltage measurements .............................167 46 Spectral analysis of freevibratory flume test with sensor on new frame .......................167 47 Average normal stresses in the SERF ..............................................................................168 48 Spectral analysis of normal stresses .................................................................................168 49 Shear stress estimates from pressure differential in SERF ..............................................169 410 Shear stress measurments from the SERFs shear sensor ................................................169 411 Shear Stress vs. Velocity Curves for Flat Flume Bottom ................................................170 412 Shear Stress vs. Velocity Curves for 0.125 mm Sediment ..............................................170 413 Shear Stress vs. Velocity Curves for 0.25 mm Sediment ................................................171 414 Shear Stress vs. Velocity Curves fo r 0.5 mm Sediment ..................................................171 415 Shear Stress vs. Velocity Curves for 1.0 mm Sediment ..................................................172 416 Shear Stress vs. Velocity Curves for 2.0 mm Sediment ..................................................172 417 Combined NonDimensionalized Results From Colebrook Equation, Shear Readings, and SmoothWall Assumption (dashed line is smooth wall) ...........................................173 418 Percent error vs. Reynolds Number between Colebrook Equaiton using a Uniform Roughness and Actual Shear Stress Measurments ..........................................................173 419 Combined Shear Stress Sensor Data Under Vortex Conditions ......................................174 420 Comparison of Pressure Readings for Vortex and NonVortex Conditions for all Sediment Diameters .........................................................................................................174 421 Comparison between Shear Stress Readings for Flat Bottom under Vortex and NonVortex Conditions ............................................................................................................175 422 Comparison between Shear Stress Readings for 0.125 mm Sediment under Vortex and NonVortex Conditions .............................................................................................175 423 Comparison between Shear Stress Readings for 0.25 mm Sediment under Vortex and Non Vortex Conditions ....................................................................................................176 424 Comparison between Shear Stress Readings for 0.5 mm Sediment under Vortex and Non Vortex Conditions ....................................................................................................176 425 Comparison between Shear Stress Readings for 1.0 mm Sediment under Vortex and Non Vortex Conditions ....................................................................................................177

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18 426 Comparison between Shear Stress Readings for 2.0 mm Sediment under Vortex and Non Vortex Conditions ....................................................................................................177 51 Examples of oldstyle Gator Rock after RETA testing ...................................................209 52 Slagles Rotisserie ............................................................................................................210 53 First Round of Bull Gator Rock Grain Size Distributions ...............................................210 54 Strength Test Results from First Round of Bull Gator Rock Mixes. ...............................211 55 Batch 1 Bull Gator Rock after RETA 24 hr. RETA Test ................................................211 56 Batch 2 Bull Gator Rock after RETA 24 hr. Test ...........................................................212 57 Batch 3 Bull Gator Rock after RETA 24 hr. Test ...........................................................212 58 RETA Results From Batch 3, Round 1 ............................................................................213 59 RETA Results from Batch 4, Round 1.............................................................................213 510 RETA Results from Batch 5, Round 1.............................................................................214 511 Cohesion vs. Erosion Relationship for First Round of Gator Rock Samples ..................214 512 RETA Results from Batch 1, Round 2.............................................................................215 513 RETA Results from Batch 2, Round 2.............................................................................215 514 RETA Results from Batch 3, Round 2.............................................................................216 515 RETA Results from Batch 4, Round 2.............................................................................216 516 RETA Results from Batch 5, Round 2.............................................................................217 517 Gator Rock Test Disc Results ..........................................................................................217 518 Time Series of Piston Position During Stand Alone SEATEK Test (Data from Batch 1 test at 50 Pa) ..................................................................................................................218 519 Zoomed in Position vs. Time Graph from Batch 1, 50 Pa Data \ .....................................218 520 Batch 1 Sample Position vs. Time from SERF with BestFit Regression Line ...............219 521 Erosion Rate vs. Shear Stress for Batch 1 in SERF Using First Round Mix ...................219 522 Erosion Rate vs. Shear Stress for Batch 2 from SERF ....................................................220 523 Erosion Rate vs. Shear Stress for Batch 3 from SERF ....................................................220

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19 524 Grain Size Analysis for Round 2 Gator Rock Mix ..........................................................221 525 Non Dimensionalized Water Retained vs. Strength ( WR is the weight of retained water after saturation; WD is the dry sample weight) .......................................................221 526 Batch A RETA Results ....................................................................................................222 527 B atch B RETA Results ....................................................................................................222 61 Example of a RETA Material with Rock Like Erosion Properties .................................235 62 Example of a RETA Material with Particle Like Erosion Properties ..............................235 63 Non Dimensional Erosion Results from RETA Data Set ................................................236 64 Relationship between Erosion R ate Constant and Cohesion ...........................................236 65 Relationship between Critical Shear Stress and Erosion Rate Constant .........................237 66 M from Cohesion based equation vs. M from Measured Data ........................................237 67 Predicted Erosion Rate vs. Measured Erosion Rate Using M Based on Material Strength ............................................................................................................................238 68 Non Dimensional Erosion Constant vs. NonDimensional Material Strength ................238 69 Graph showing computed value for E vs. Measured value for E when the NonDimensional Relationship for M is used ..........................................................................239 610 Non Dimensional Critical Shear Stress vs. Non Dimensional Mate rial Strength ..........239 611 Measured Erosion Rate vs. Predicted Erosion Rate Using the Non Dimensional Expression Developed in Equation 6.44 .........................................................................240 612 Measured vs. Computed Critical Shear Stress .................................................................240 613 Predicted Erosion Rate Using Cohesion Computation vs. Actual Erosion Rate .............241 71 Grain Size Distribution for Sand Used During SandClay Tests .....................................274 72 Grain Size Distribution for EPK Used During SandClay Tests .....................................275 73 Optimum Water Content vs. Clay Content ......................................................................275 74 Shear Stress vs. Velocity for 0% Clay Epoxy Glued Disc ..............................................276 75 Shear Stress vs. Velocity for 12.5% Clay Epoxy Glued Disc .........................................276 76 Shear Stress vs. Velocity for 25% Clay Epoxy Glued Disc ............................................277

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20 77 Shear Stress vs. Velocity for 37% Clay Epoxy Glued Disc ............................................277 78 Shear Stress vs. Velocity for 50% Clay Epoxy Glued Disc ............................................278 79 Shear Stress vs. Velocity for 62.5% Clay Epoxy Glued Disc .........................................278 710 Shear Stress vs. Velocity for 75% Clay Epoxy Glued Disc ............................................279 711 Shear Stress vs. Velocity for 8 7.5% Clay Epoxy Glued Disc .........................................279 712 Shear Stress vs. Velocity for 100% Clay Epoxy Glued Disc ..........................................280 713 Summary Chart Showing Best Fit Lines for Sand Clay Epoxy Glued Discs .................280 714 Shear Stress vs. Velocity for 0% Clay Fiberglass Resin Test Disc .................................281 715 Shear Stress vs. Velocity for 12.5% Clay Fiberglass Resin Test Disc ............................281 716 Shear Stress vs. Velocity for 25% Clay Fiberglass Resin Test Disc ...............................282 717 Shear Stress vs. Velocity for 37.5% Clay Fiberglass Resin Test Disc ............................282 718 Shear Stress vs. Velocity for 50% Clay Fiberglass Resin Test Disc ...............................283 719 Shear Stress vs. Velocity for 62.5% Clay Fiberglass Resin Test Disc ............................283 720 Shear Stress vs. Velo city for 75% Clay Fiberglass Resin Test Disc ...............................284 721 Shear Stress vs. Velocity for 87.5% Clay Fiberglass Resin Test Disc ............................284 722 Shear Stress vs. Velocity for 100% Clay Fiberglass Resin Test Disc .............................285 723 Summary Chart Showing Best Fit Lines for Sand Clay Fiberglass Resin Discs ............285 724 Trammels 2004 Data Overlaid with Data from this Study .............................................286 725 Erosion Rate vs Shear Stress for 100% Sand Sample .....................................................286 726 Non Dimensionalized Erosion Rate vs. Shear Stress for 100% Sand Sample ................287 727 Sample Position vs. Time for 25% Clay Mixture at 13.4 Pa ...........................................287 728 Sample Position vs. Time for 25% Clay Mixture at 40.0 Pa ...........................................288 729 Sample Position vs. Time for 25% Clay Mixture at 3.37 Pa ...........................................288 730 Sample Position vs. Time for 25% Clay Sample at 30.0 Pa ............................................289 731 Sample Position vs. Time for 25% Clay Mixture at 53.2 Pa ...........................................289

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21 732 Erosion Rate vs. Shear Stress for Flat Portions of Sample Position vs. Time Curves .....290 733 Erosion Rate vs. Shear Stress for Rapid Advancement Portions of Sample Position vs. Time Curves ...............................................................................................................290 734 Two lift test at 13.4 Pa .....................................................................................................291 735 Sample Position vs. Time for 25% Clay Mixture Using Double Optimum Water Content 291 736 Sample Position vs. Time for 50% Clay Mixtures ..........................................................292 737 Sample Position vs. Time for 50% Clay Mixture at 27.54 Pa .........................................292 738 Zoom in on First 90 Seconds of Sample Position vs. Time Curves ................................293 739 Sample Position vs. Time for 50% Clay Mixture at Double Optimum Water Content ...293 740 Sample Position vs. Time for 75% Clay Mixture Mixed at Optimum Water Content ....294 741 Sample Position vs. Time for 75% Clay Mixture Mixed at Double the Optim um Water Content ..................................................................................................................294 742 Sample Position vs. Time for 100% Clay Mixed at Optimum Water Content ................295 743 Sample Position vs. Time for 100% Clay Mixed at Double Optimum Water Content ...295 744 Density Profile for Sample I ............................................................................................296 745 Density Profile for Sample II ...........................................................................................296 746 Density Profile for Sample III..........................................................................................297 747 Density Profile for Sample IV .........................................................................................297 748 Density Profile for Sample V ...........................................................................................298 749 Density Profiles for Sample VI ........................................................................................298 750 Density Profiles for Sample VII ......................................................................................299 751 Density Profiles for Sample VIII .....................................................................................299 752 Density Profiles for Sample IX ........................................................................................300 753 Density Profiles for Sample X .........................................................................................300 754 Density Profile for Sample XI .........................................................................................301

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22 755 Shear Stress vs. Velocity for Different Sediment Concentrations ...................................301 A 1 Shields Diagram ...............................................................................................................327 A 2 Fall Velocity vs. Grain Size .............................................................................................328 A 3 Common Pier Shapes .......................................................................................................328 A 4 Diag ram used to determine Khpier .....................................................................................329 A 5 Diagram used to determine a* pc ......................................................................................329 A 6 Illustration sketch for computing scour when a pile cap is above the bed ......................330 A 7 Diagram for computation of correction factor, Km ..........................................................330 A 8 Diagrams used to illustrate computation of equivalent pier width ..................................331 A 9 Diagram used to compute the spacing coefficient, Ksp ....................................................331 A 10 Diagram used to compute the pile group height adjustment factor, Khpg .........................332 A 11 Effective Diameter for Different Pier Shapes ..................................................................332 A 12 Definition Sketch for Scour Around a Complex Pier ......................................................333 B 1 Air Release Valve on Sand Filter .....................................................................................349 B 2 PVC release valve for Chiller Filter System ...................................................................349 B 3 Slide Valve in the Down Position ....................................................................................350 B 4 Pool Pump Switch ............................................................................................................350 B 5 PVC Slide Valve in the Up Position ................................................................................351 B 6 On Switch for Water Chiller ............................................................................................351 B 7 Position of Thermostat on Water Chiller .........................................................................352 B 8 Example of Removable Test Disc (Flat Disc Shown) .....................................................352 B 9 JB Weld Epoxy in its Package .........................................................................................353 B 10 Three Newly Prepared Test Discs (Three Different Aggregate Distributions Shown) ...353 B 11 Shear Stress Sensor Access Hatch ...................................................................................354 B 12 Attachment of Newly Prepared Disk to Shear Sensor (Flat Disc Shown) .......................354

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23 B 13 Knobs on SS Sensor Amplifier ........................................................................................355 B 14 Round Access Hatch on Shear Sensor .............................................................................355 B 15 Schematic of a Slipped Brass Rod Connection................................................................356 B 16 Screw holding brass rod to platform ................................................................................356 B 17 Exposed Electronics in Dry Portion of Sensor .............................................................357 B 18 Shear Stress Test Front Panel in Labview .......................................................................357 B 19 Data Range on the Shear Stress Amplifier ......................................................................358 B 20 Hex Screw to Loosen for Shear Sensor Removal ............................................................358 B 21 Shear Stress Sensor Plug ..................................................................................................359 B 22 Piston Cylinder for SERF ................................................................................................359 B 23 Close up of Ridges on Top of Cylinder ...........................................................................360 B 24 Piston Cylinder with Sample Installed ............................................................................360 B 25 Surge Protector to Turn on Lasers ...................................................................................361 B 26 Front Panel of Motor Mover Program .............................................................................361 B 27 Front Panel of Pump Control Program ............................................................................362 B 28 TeraTerminal Icon (Circled in Red) ................................................................................362 B 29 Flume Control with Motor Front Panel ...........................................................................363 B 30 Remote DVR Panel ..........................................................................................................363 B 31 Windows Remote Desktop Software ...............................................................................364 C 1 SERF Digital Control Display .........................................................................................385 C 2 Pump Control Program Front Panel .................................................................................385 C 3 Block Diagram for Pump Control Program (raf_pump_control.vi) ................................386 C 4 Analog Reader Subvi (raf_DAQ_pump_output.vi) .......................................................387 C 5 View from Top of Flowmeter Showing Data Range. See flowmeters operating manual for directions on how to change it settings ..........................................................387 C 6 Motor Mover Front Panel ................................................................................................388

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24 C 7 Motor Mover Block Diagram (raf_motor_mover.vi) ......................................................388 C 8 SERF Control No Motor Front Panel ..............................................................................389 C 9 Block Diagram for SERF Control No Motor (raf_data_control.vi) ................................390 C 10 Zoom in on SC 2345 Channels .......................................................................................391 C 11 Shear Stress Calibration Sub vi (raf_shear_module.vi) ...................................................391 C 12 Subvi Showing First Portion of Pump Control Program (raf_pump_on.vi) ..................392 C 13 Analog Reader for SERF Control No Motor (raf_DAQ_no_motor.vi) ...........................392 C 14 Five Signal Split in Pump Control No Motor ..................................................................393 C 15 Front Panel for SERF Control With Motor and Temp Patch (raf_control5.vi) ..............394 C 16 Block Diagram for SERF Control With Motor (raf_control5.vi .....................................395 C 17 Analog Input Channels for raf_control5.vi ......................................................................396 C 18 Initial Read Write Sequence Between for SEATEK Serial Port (SEATEK_integrate.vi) ...................................................................................................396 C 19 First Phase of Five Phase Flat Sequence Structure Showing the Pump Input Controller (raf_pump_on.vi) ............................................................................................397 C 20 Second Phase of Five Phase Flat Sequence Structure Showing the Analog Output Module. Subvi is called raf_control_step1 1.vi .............................................................397 C 21 Third Phase of Five Phase Flat Sequence Structure Showing the Laser Output Module (raf_control_step2.vi) .........................................................................................398 C 22 Block Diagram for raf_control_step2.vi ..........................................................................398 C 23 Fourth Phase of Five Phase Flat Sequence Structure Showing the SEATEK Output Module. Subvi is called raf_control_step3.vi .................................................................399 C 24 Block Diagram for raf_control_step3.vi ..........................................................................400 C 25 First Element in Temperature Patch Stacked Sequence Structure ...................................401 C 26 Second Element in Temperature Patch Stacked Sequence Structure ..............................401 C 27 Outside Crystal Depth Algorithm ....................................................................................402 C 28 Example of a Crystal Off Module with Crystal 2 Shown ................................................402

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25 C 29 SEATEK zero checker. Algorithm is the same as Slagles .............................................402 C 30 Fifth Phase of Five Phase Flat Sequen ce Structure Showing the Motor Movement Module 403 C 31 Block Diagram for raf_control_step4.vi ..........................................................................404

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26 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 INVESTIGATION OF SEDIMENT EROSION RATE S OF ROCK, SAND, AND CLAY MIXTURES USING ENHANCED EROSION RATE TESTING INSTRUMENTS By Raphael Crowley December 2010 Chair: David Bloomquist Major: Civil Engineering Scour is the primary cause of bridge failure s in the United States. Although predicting scour depths for non cohesive (sandy) bed materials is fairly well understood, much less is known about predicting scour depths when cohesive materials such as clay s, sandclay mixtures, and rock are present. Tw o semi empirical methods exist for predicting cohesive scour depths. Both of these methods rely on the input of a sediment transport function or erosion rate as a function of shear stress Current design guidelines such as HEC 18 recommend measuring sedi ment transport functions in a laboratory, but there has been some question as to how to do this properly. To answer this question, a series of improvements and enhancements were made to the Sediment Erosion Rate Flume (SER F) at the University of Florida (UF) A laser leveling system, a vortex generator, a shear stress measuring system, computer updates, and a sediment control system were designed. N ew components to the SERF except for th e sediment control system operated as expected. Using the new shear stress system, a series of tests were run to assess the proper way to measure shear stress in a flume style erosion rate testing device. Results showed that the pressure drop method will not measure shear stress properly, and in the

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27 absence of a shear st ress sensor, the most effective alternative method for estimating shear stress is to use the Colebrook Equation (which describes the Moody Diagram) A new material was developed for testing in both the SERF and the Rotating Erosion Testing Apparatus (RETA ) to serve as a basis of comparison between the two instruments. Results were inconclusive because rock like erosion described by the Stream Power Model dominated erosion behavior. A database of results from the RETA that has been developed since the RET As inception in 2002 was used to verify that it is measuring the correct erosion rate vs. shear stress relationships. Results showed that for the special case where particle like erosi on dominates, the RETA appears to produce correct results. Results al so indicate that when rock like erosion is present, it is generally an order of magnitude lower than situations where particle like erosion dominates Further analysis of the database showed that there may be a correlation between material strength and er osion rate. Further research was aimed at generalizing erosion rate vs. shear stress relationships for sandclay mixtures. A series of test s were conducted on a variety of sand clay mixtures. Results showed sensitivity to the method in which the sandcl ay mixtures were prepared. Rocklike erosion and particle like erosion were present in most sandclay mixtures even though typical sand clay mixtures would not typically be described as rock like materials. Recirculating sediment during sand clay testing indicated that suspe nded sediment in the SERF has little effect on bed shear stress.

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28 CHAPTER 1 INTRODUCTION 1.1 Motivation for Research In 1987, The Schoharie Creek Bridge on the I 90 Thruwa y corridor in upstate New York coll apsed resulting in the loss of four cars, a truck, and most im portantly 10 lives (Figure 1 1). The collapse of this bridge led to an investigation by the Federal Highway Administration (FHWA), where they found that the bridge collapsed because of the loss of support capacity of the bridges footings due to scour (Trammel 2004) In 1989, largely as a result of the Schoharie Creek Bridge failure, the United States Geological Survey (USGS) and the Federal Highway Administration (FHWA) launched a cooperative study to monitor and asses s the scour problem on bridges in the United States (Placzek and Haeni 1995). This study found that scour was a much greater issue than anyone had previously realized. The results of this study were two fold: first, engineers for the first time began to realize how large of an issue scour was in the United States; secondly, the first edition of the Hydraulic Engineering Circular No. 18 (HEC 18) Evaluating Scour at Bridges was published in 1991. This document was the first reliable method that engineers in the United States had for designing for bridge scour. Shortly after the Schoharie Creek Bridge collapse in 1987, Murillo determined that from 1961 to 1976, 48 of 86 major bridge failures in the United States, or 56%, were the result of scour near the bridge piers (Murillo 1987). Other studies were conducted after Murillos research to give even more credence to the scour problem, and some of these studies are cited in the latest edition, the 4th edition, of HEC 18. According to HEC 18, the most common cause of bridge failure is from scour (Richardson and Davis 2001) Although the Schoharie Creek Bridge collapse was one of the most publicized bridge failure of the 1980s, FHWA determined that

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29 during the floods of 1985 and 1987, 16 other brid ges in New York and New England also failed because of scour. In 1985, 73 bridges were destroyed by floods in Pennsylvania, West Virginia, and Virginia. The 1993 floods in the Mississippi Basin caused 23 bridges to fail, and the total cost of these failu res was estimated to be $15 million. Of these 23 bridges, over 80% of them failed because of scour. In 1994, flooding from Tropical Storm Alberto in Georgia caused scour damage to over 150 bridges resulting in damage costs of approximately $130 million ( Jones et al. 1995). In 2004, Briaud launched another study to quantify the scour problem. He determined that there are 600,000 bridges in the United States and of these 600,000 bridges, one third of them are scour critical. Over 1,000 bridges have susta ined significant damage due to scour, and it costs approximately $50 million per year on average to keep up with this issue (Briaud 2004). Because engineers had no meth od for scour design for so long, it is not surprising that the scour problem is so severe with regard to existing bridges. Now that HEC 18 exists, engineers are starting to get a better handle on the scour problem, and based on the data just presented, this is essential. The main downside though to HEC 18 is that several engineers some engineers at the FDOT for example believe that some of its guidelines for designing new bridges are overly conservative (Slagle 2006) T he complaint of many engineers is that HEC 18 takes a reactionary approach with regard to design specifications for t he scour problem (Trammel 2004) The goal of this dissertation is to investigate some of these design specifications presented in HEC 18. Before getting into specifics however, the general concepts of scour as presented in HEC 18 will be discussed. 1.2 Sco ur Definitions HEC 18 divides scour into four subcategories, general scour, aggradation/degradation, contraction scour, and local scour. When designing a structure over a waterway, an engineer is

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30 instructed to compute the amount of scour caused by each of these four components and add their total effects together to get the total net scour depth. 1.2.1 General Scour General scour describes channel migrations, tidal inlet instability, or river meanders. It is different from other types of scour because it may not produce a net reduction in sediment at the bridge section. However, the bed elevation at a particular locus can be raised or lowered because of the channel migration. Manmade disruptions such as water redirection structures may contribute to general channel migration (Slagle 2006). General scour occurs at a much slower rate than other types of scour (cm per year vs. cm per storm event) it is generally better understood than other scour mechanisms, and it is not the focus of this dissertation. 1.2.2 Aggr adation/Degradation Aggradation and degradation refer to long term elevation changes due to natural or unnatural changes in the sediment system. Aggradation refers to deposition of sediment previously eroded from an upstream location while degradation ref ers to erosion of sediment due to a deficit of upstream sediment supply (Slagle 2006) These processes are also better understood than the remaining two scour mechanisms and are not discussed further in this dissertation. 1.2.3 Contraction Scour Contraction s cour is a decrease in bed elevation in a channel caused by a reduction in cross sectional area of the channel. The cross sectional area of the channel may be reduced by either the presence of a structure such as a bridge pier or a natural obstruction such as a block of ice or debris. Flow rate is given as Q = VA where V is the average flow velocity and A is the cross sectional area of the channel. Because of continuity, Q must be constant upstream and downstream from any obstruction within the channel. Therefore, when an obstruction is present,

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31 velocity must increase and the water moving through the channel must accelerate past the obstruction. This increase in water velocity results in higher forces along the bed, and these higher forces result in grea ter bed shear stresses. Greater bed shear stresses in turn cause greater erosion rates, or scour, in the vicinity of the structure. Scour will continue under these conditions until a depth is reached where the bed shear stress reverts to subcritical lev els or the sediment deposition rate equals the sediment erosion rate (Richardson and Davis 2001) Work completed in this dissertation can potentially be used to improve understanding of the contraction scour problem. 1.2.4 Local Scour Local scour is the most complicated scour mechanism, because it is caused by a series of events that occur nearly simultaneously. Any obstruction in a waterway will cause flow dynamics in the direct vicinity of the structure to change. A pier will be used as a simple example to illustrate how these hydrodynamic changes affect the bed material in the vicinity of the structure. A protruding pier in a free stream will cause a pile up effect of water on its upstream face, which in turn will cause a downflow along this face. When water from the downflow reaches the bed, it spawns secondary flows, or horseshoe vortices, along the bed. The velocity of water within these vortices is often fast enough to exceed the critical velocity, or velocity for incipient motion of sediment particles, of the bed. Subsequently, a scour hole forms around the base of the pier. Scour will continue until the vortices weaken sufficiently such that either deposition rate equals erosion rate or the vortex velocity is less than the critical velocity of the bed material. Figure 1 2 and Figure 1 3. illustrate the process of local scour (Slagle 2006).

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32 1.3 Controversy Surrounding HEC 18 As mentioned in Section 1.1, w hen HEC 18 was introduced in 1991 it was the first document like it in the United States. Its importance cannot be overlooked or understated for the first time, engineers had a reliable set of guidelines to use for designing a structure to withstand mechanisms associated with scour. Despite its benefits, HEC 18 has been somewhat controversia l due to certain design guidelines that may be overly conservative. The scour problem is already so large so problematic, and so expensive, that avoiding an overly conservative approach is necessary. If scour can be better understood, and if more accura te equations can be developed, overestimation of scour depths may be avoided. This result could be remarkable millions of dollars could be saved every year in construction costs, or better yet, this money could be allocated to mitigate existing bridges that already have well documented scour problems. Much of the controversy surrounding HEC 18 stems from its approach with regard to computing scour for cohesive bed materials. Although cohesionless sediments such as sands will erode much faster than cohesive soils and rock HEC 18 assumes that even bed mater ials that are resistant to scour will erode to the same depths as sands given enough time. The 4th edition of HEC 18 does cite the Briaud 2004 EFA SRICOS method as an alternative for designing foundations on cohesive bed material, but according the HEC 18, Briaud found that clays erode to the same depth as sands eventually, and the EFA SRICOS method should only be used under certain conditions. HEC 18 acknowledges that this finding may be overly conservative. S pecifically it says: The equations and methodologies presented in this manual, which predict the maximum scour depth in noncohesive soil, may, in some circumstance be too conservative The pier

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33 scour equation represents an envelope curve of the deepest scour observed during the various laborat ory studies and fiel d data(Richardson and Davis 2001) With regard to rock scour, HEC 18 gives Annandales Erodibility Index Method (Annandale 2005) for predicting scour depths, but acknowledges that this method needs further research. In response to the se controversies, Florida Department of Transportation (FDOT) has been sponsoring research in the area of cohesive scour and rock scour in the past ten years. FDOT has worked in conjunction with the University of Florida (UF) to develop experimental mechanisms for determining erosion rates of bed materials as a function of other bulk material properties. Results from some of this research have been used in recent FDOT bridge designs. Although progress has been made in this area, more research needs to be done. 1.4 Approach As previously mentioned, HEC 18 cites the EFA SCRIOS method as an alternative option when designing bridge foundations that rest on cohesive and rocklike beds. Integral to implementation of the EFA SRICOS method is the ability to accurate ly obtain a relationship between erosion rate and shear stress. In 2003, Miller and Sheppard developed an analytical model for predicting scour hole depth for a noncohesive soil. Although cohesive scour holes are shaped differently than noncohesive sco ur holes (Ting et al. 2001), the EFA SRICOS method, which is empirically based seems to agree in principle with the overall theme of the Miller Sheppard approach namely that erosion rate, and in turn scour depth, should be a function of bed shear stress under particle like erosion conditions. This agreement between the two methods, which were developed independently from one another, appears to be a positive sign and serves as a rudimentary indication that an erosion rate vs. shear stress relationship appears to be physically valid and useful.

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34 In nature, t his correlation of shear stress to erosion rate is important because there is a definitive relationship between stream velocity. In general, faster flow velocities produce larger bed shear stresses. Using modern computer models (for example, the EFA SRICOS method uses a k approach), bed shear stresses can be computed fairly accurately for a situation where water flows past a protruding pile or bridge pier. A k model is a commonly used turbulence closure model that employs the use of a turbulent kinetic energy equation (the k component) and a turbulent dissipation equation (the component). This relationship between stream velocity and shear stress is critical for implementation of either the EFA method or the Miller Sheppard approach because if a designer has a stream hydrograph, a predictive time series of stream velocities and corresponding shear stresses can be approximated. Given this time series of stream velocities, a corresponding time series of shear stresses can be determined using the shear stress velocity relationship under natural conditions With this shear stress information, final local scour depths can be computed assuming the relationship between erosion rate and shear stress is known for the bed material in question. This final step is where the aforementioned semi empirical methods tend to struggle and where the crux of research for this dissertation rests. To properly implement either alternative design approach, t here needs to be a way to accurately measure shear stresses and corresponding erosion rates for an eroding bed material. Although this sounds simple, it is very complicated. The purpose s of this dissertation then are to: 1. Develop test equipment to measure erosion rates and shear stresses for a wide range of eroding bed materials. 2. Use these equipment upgrades and other analytical tech niques to comment quantitatively on older methods for measuring these parameters by running a series of test s with the new equipment.

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35 3. Use these new measurements and older results to determine if erosion rate can be related to any other existing common geotechnical parameters. 4. Use the new equipment to develop a series of erosion rate shear stress curves for sand clay mixtur es. Under natural conditions, it is rare to find a bed material that is purely cohesive or purely noncohesive. Instead, usually sand is interspersed with clay particles or vice versa. Previous research has looked mostly to classify erosion rate shear s tress curves under conditions where a uniform material is present but because this is rarely the case, erosion properties of mixtures is investigated. 1.5 Methodology and Organization To meet these goals the following methodology was used: 1. An existing pi ece of equipment the Sediment Erosion Rate Flume (SERF) was enhanced so that it could more accurately measure erosion rates and shear stresses for eroding bed materials. 2. A nearly uniform material, Gator Rock, was developed so that it could be tested in the SERF and the Rotating Erosion Testing Apparatus (RETA). The original goal with the development of Gator Rock was to use it to directly compare the two devices Although this direct comparison between the RETA and SERF did not go as planned the deve lopment of Gator Rock is still significant. 3. A series of test s was conducted in the SERF where shear stress was measured for samples containing var ious uniform roughnesses. The r esults were compared with analytical shear stress estimates used by other fl ume style devices similar to the SERF such as the erosion function apparatus (EFA), and Sediment Erosion and Depth Flume (SEDFlume). 4. Extensive data analysis was conducted on an existing database where RETA results are contained The goal was first to ve rify that the RETA was properly measuring erosion rate and shear stresses under particlelike erosion conditions. Once this was shown, the second goal was to find another geotechnical parameter that could be used to predict erosion rate. 5. A series of test s was conducted in the RETA on the new Gator Rock samples. Similar style test s were concurrently conducted in the SERF. As discussed in (3), the direct comparison between the two devices did not go as planned. Still, results from this dataset are interesting, and they should be discussed. Additionally, tensile and compressive strength test s were run on the Gator Rock samples to determine the materials strength characteristics. 6. A series of test s was conducted in the SERF on sandclay mixtures. Previously, the SERF was not usable when clay was present in the bed material because the old sample leveling devices a series of ultrasonic probes would penetrate into the sample. Because of the enhancements to the SERF discussed in (1), clay testing is now possible. Tensile strength, compressive strength, and density profile test s were also conducted on the sandclay mixtures to try to explain (and eventually quantify) differential erosion rates that were observed both within the samples and from a sample to sample standpoint.

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36 This dissertation is organized in the following manner : 1. Chapter 2 presents an in depth background discussion and a literature review. 2. Chapter 3 presents a summary and a discussion of the SERF including improvements a nd enhancements to the device completed as part of this dissertation. 3. Chapter 4 presents a discussion on the shear stress test s that were conducted in the SERF. 4. Chapter 5 presents a discussion on the development of Gator Rock and preliminary test s with it. 5. Chapter 6 presents a discuss ion on RETA database analysis. 6. Chapter 7 provides a discussion on sandclay test s in the SERF. 7. Chapter 8 provides a brief discussion regarding conclusions and recommendations for future work. 8. The appendices chronicl e the state of the art in sand scour, the newly developed Florida Method for SERF testing, and a discussion on associated computer programs that were rewritten for the SERF as part of this study.

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37 Figure 1 1. Photograph of the Schoharie Creek Bridge C ollapse [Adapted from McGuire, Mark. Drafts of Capital regions severe weather history. Legacy of Change, http://web.timesunion.com/specialreports/tu150/stories/weather.asp Nov. 11, 2010.] Figure 1 2. Beginning of local scour [Adapted from Slagle, P. M. (2006). Correlations of erosion rate shear stress relationships with geot echnical properties of rock and cohesive sediments. M.S. thesis, University of Florida, Gainesville, Florida.] Pier Velocity Profile Bed Water Level Horseshoe Vortices Downflow

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38 Figure 1 3. Local scour after some time has elapsed (Slagle 2006) [Adapted from Slagle, P. M. (2006). Correlations of erosion rate shear stress relationships with geotechnical properties of rock and cohesive sediments. M.S. thesis, University of Florida, Gainesville, Florida.] Velocity Profile Pier Water Level Bed Horseshoe Vortices Scour Hole Surface Roller (Bow Wave) Downflow

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CHAPTER 2 BACKGROUND AND LITERATURE REVIEW 2.1 Scour Depths in Non Cohesive Soil When design guidelines such as HEC 18 were being developed for scour, the noncohesive problem was addressed first for two reasons. First, predicting scour or erosion is less complex under a noncohesive condition than it when cohesive sediments are present because cohesive forces do not need to be taken into account. Secondly, if noncohesive assump tions are used for cohesive soils, the result will be conservative because cohesive forces will only serve to slow erosion and decrease the equilibrium local scour depth. Therefore, even if noncohesive materials are not well understood, noncohesive equa tions can be used for design, and the structures foundation will not fail. Because of these reasons, when the scour problem was first being tackled, most research for predicting local scour focused on noncohesive conditions. From this research, HEC 18 was developed ( Richardson and Davis 2001) Using guidelines from these manuals, engineers can compute equilibrium scour depths for a variety of complex bridge piers under stream flow from any attack angle. Specifics for scour design from these manuals are presented in Appendix A. 2.2 Predicting Local Scour Hole Depth for Cohesive Soils and Rock In Chapter 1, four different scour modes were briefly outlined aggradation/degradation, general scour, contraction scour, and local scour. Local scour is the most complicated, most interesting, and often the most significant source of scour for bridge f oundations. M ost of HEC 18 involves predicting local scour depths. If the design guidelines presented in HEC 18 are to be modified for cohesive conditions, it is logical to start with methods for predicting local scour depths.

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40 There are two known methods available for computing local scour depths for cohesive soils the scour reduction method and a semi empirical approach. Under the scour reduction method, scour depth is computed as if a foundations bed material is noncohesive. Then, a scour reduct ion factor is applied, and the final equilibrium scour depth is computed. Under the semi empirical methods (of which there are also two), a more sophisticated approach is used where scour depth is estimated as a function of bulk material erosion rate. T o compute scour depth for rock HEC 18 becomes somewhat muddled. HEC 18 briefly mentions that the erodibility index method (ERI) may be usable, but this method is not discussed in depth. Some qualitative techniques are presented for determining whether or not a rock may or may not erode, but as far as predicting an actual local scour depth, the only reliable met hod that is given is a permutation of the cohesive soil semiempirical approaches where again bulk material erosion rate ultimately determines scour depth. 2.2.1 Colorado State University Test s (CSU 1991 1996) From 1991 1996, tests were conducted at Colorado State University (CSU) to test the effects of sediment gradation and cohesion on scour development. During these test s, 20non cohesive sediments and 10 cohesive sediment mixtures were tested in five flumes of varying sizes with nine different cy lindrical pier sizes and seven differe nt abutment protrusion lengths. Flumes for these tests were open channel and filled with sediment such that as flow eroded the sediment, a scour hole could be measured. Molinas synthesis report divides the tests int o four sections (Molinas 2003): 1. Effects of gradation and coarse material fraction from pier scour tests 2. Effects of gradation and coarse material fraction on abutment scour tests 3. Effects of cohesion on pier scour tests 4. Effects of cohesion on abutment scour tests Of particular interest are the portions of these test s pertaining to cohesion effects on pier scour.

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41 2.2.1.1 Setup for CSU t ests These test s were conducted in three flumes at CSU: the first flume was 2.4 m wide by 60 m long; the second flume was 5 m wide by 30 m long; and the third flume was 1.2 m wide by 12 m long. C ircular piers of 0.15 m diameter were used with a constant approach depth of 0.24 m. Velocity was measured in these flumes via a magnetic flow meter, and approach velocity was found from depth and width integrated averages of vertical velocity profiles. Approach depth was determined from width and lengthaveraged values of water elevation. Scour depth was measured during and at the end of each test by measuring the difference betw een minimum bottom elevation in the scour hole and maximum elevation away from the structure. Investigators at CSU used a combination of Montmorillonite clays and sand that had a median diam eter of 0.55 mm and a gradation of 2.43 (Molinas 2003). 2.2.1.2 Results from CSU t est s Results from the CSU test s which were run for 16 hours, are presented in Figure 2 1. The best fit trend line through this data was: 9 011 1 1 CC KCC (2 1) where KCC is a scour reduction factor to be applied to the sandscou r equations in HEC 18 and CC is clay content. Sand clay mixtures with clay contents higher than 11% were found to be dominated by initial water content and consolidation. Because these factors were not controlled in this round of test s, they were not included in finding the correction factor for clay content (Molinas 2003).

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42 2.2.1.3 Concerns with the CSU c orrection f actor In his 2006 Masters Thesis, Slagle argues that the grain size standard deviation of 2.43 is probably too large for accurate testing. Grain size standard deviation was computed using E quation 22: 16 50 50 842 1 D D D Dg (2 2) where D s are the grain size (mm) at the particu lar percent passing This relatively large grain size standard deviation is a concern because of the potential for armoring effects. Larger sand grain particles require stronger forces to incite incipient motion than smaller particles. At lower flow rates then, fine grained material is transported while larger particles are left in place. Eventually, these large particles settle into voids created by the smaller particles, and thus, an armoring layer develops along the top of the bed. When velocities inc rease in the channel, t he armor layer will scour away. This will expose the smaller diameter particles and cause a faster erosion rate. The question then, is if the scouring resistance was truly due to cohesive forces or if it was a result of these artif icially created armoring effects (Slagle 2006)? The second area of concern with the CSU equation is with regard to recirculating flumes. Two of CSUs flumes allow sediment to erode and remain in the flow Th e presence of sediments in the water column cha nges clear water con ditions to live bed conditions. This may change the scour rate. These areas of concern gave credence to the notion that further investigation needed to be conducted to determine scour rates for sand clay mixtures. 2.2.2 The EFA SRICOS Method Recall from Chapter 1 that HEC 18 does offer one alternative design approach for designing local scour depths for foundations built on cohesive soils and rock the EFA SRICOS

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43 method. As explained in HEC 18, the EFA SRICOS method is not written in its entirety; rather, HEC 18 offers an elementary version of this method. 2.2.2.1 HEC 18 version of EFA SRICOS m ethod According to HEC 18, the EFA SRICOS method may be used as an alternative approach for scour design when either rock like or cohesive bed material is present. Under the HEC 18 version of the method, first statistical storm data is used to determine a hydrograph of stream flow conditions during the lifespan of the structure. Then, based on this hydrograph of flow velocity, Equation 23 is used to determine the shear stress on the stream bed: 10 1 Re log 1 0094 02 maxV (2 3) where is the density of water, V is the fluid velocity, and Re is the Reynolds Number. Next, an engineer takes a soil sample of sediment at the foundation site. Then, using an erosion rate testing device (which will be discussed in Section 2.4.2), a relationship must be found between erosion rate and shear stress. Once erosion rate shear s tress relationship is known and the stream hydrograph is known, the final scour depths can be computed based on these two relationships. 2.2.2.2 Complete version of EFA SRICOS m ethod The HEC 18 version of the EFA SRICOS method is rather elementary, and i n his papers, Briaud offers a more complete version of the method. The first step and second steps from both HEC 18 and Briauds papers are the same. The engineer must compute a hydrograph of stream data and use E quation 23 to find shear stress as a function of time over the bridges life cycle. The next step in the complete EFA SRICOS method however is different. Instead of simply using the erosion rate shear stress relationship to find equilibrium scour depth, Briauds

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44 full version of t he method say s that Equation 24 should be used to find the upper limit cap on scour hole depth: 635 0 maxRe 18 0 z (2 4) Equation 24 (which is in mm), is an empirical formula found from 43 different scaled model flume tests (Briaud et al. 1999). According t o Briaud, this equation appears to be valid for both sands and cohesive materials. Briaud also says that these equations are limited to conditions where a uniform soil is present, there is a constant velocity hydrograph, and the bridge pier is in deep wat er. Once zmax has been computed, equilibrium scour depth is still found using the bed materials erosion rate, although in the complete version of the method, zmax is a parameter in the final scour depth expression: max1 z t z t z (2 5) 2.2.2.3 EFA SRICOS s etup d iscussion The presumptions behind the EFA SRICOS method are two fold. First, the EFA SRICOS method presumes that an erosion rate testing device can accurately measure the erosion rate and estimate shear stress for an eroding sample. Al though this sounds simple, it is not particularly because shear stress is difficult to accurately determine. Different erosion rate testing devices and methods will be discussed in Section 2.24. Secondly, Equation 23 must be a valid means of relating site specific shear stress to site specific freestream velocity. 2.2.2.4 Discussion of Equation 23 Equation 23 was developed in the late 1990s using a computer model. In 1996, Chen et al. developed the CHIMERA RANS model, and in 1997, Wei et al. applied it to a situation

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45 where water was flowing past a single cylindrical bridge pier over a flat bottom. The CHIMERA RANS model is a Reynolds Averaged Navier Stokes (RANS) numerical model (Briaud 2004). Briauds 2004 Transportation Research Board (TRB) report describes the details of the model: .First, the docompositional domain was divided into a number of smaller grid blocks to allow complex configuration and flow conditions to be modeled efficiently through the judicious selection of different block topology, flow solvers, and boundary conditions. The chimera domain decomposition technique was used to connect the overlapped grids together by interpolating information across the block boundaries. The Reynolds stresses were evaluated using the two lay er turbulence model of Chen and Patel. The mean flow and turbulence quantities were calculated using the finiteanalytical method of Chen, Patel, and Ju. The SIMPLER/PISO pressure velocity coupling approached of Chen and Patel and Chen and Korpus was use d to solve for the pressure field(Briaud et al. 2004a ). The m odel that generates Equation 23 requires the input of initial conditions. Specifically, the initial Reynolds Number and Froude Number must be specified because they affect how the computational grid is generated. Additionally, the boundary conditions on surfaces and must be specified. From these initial conditions, the computer model computes an initial velocity profile based on the geometry and the mean velocity. From this it appears as though the CHIMERA RANS model is an adequate method for estimating the maximum shear stress on a flat bottom if the initial conditions are correct. In Briauds 2004 report, he indicated that a boundary layer of 0.06m was used for computer model runs, and that this was consistent with a 1997 study by Gudavalli (Briaud et al. 1999). To calibrate his computer model, Briaud used a dataset from a 1975 study by Hjorth (Figure 22).

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46 The most notable source of error with Briauds model is that he is assuming a flat bottom a nd ignoring friction. H is model uses a k approach with a homogeneous eddy viscosity to model the Reynolds stresses (Wei et al. 1997). Because he is using a two equation kmodel, roughness is taken into account in the model with regard to the interface between the water column and the bed material, but roughness is only taken into account with regard to calibrating the model to Hjorths results. Hjorths test used a hot fil m probe that was flush to a flat flume bottom to measure the shear stress around the cylinder (Hjorth 1975). Since Hjorths test used a flat flume bottom with a flush hot film probe, and this was the dataset used to calibrate Briauds model, it can be in ferred that in a sense, roughness of bed material was not taken into account when E quation 23 was estimated. In other words, if shear stress was measured directly in a situation where there a circular cylinder surrounded by sand, it is possible that the stresses could be different. R esearch from Papanicolau et al. (2001), Roberts and Yaras (2005), Suntoyo and Tanaka (2008), and several others indicates that surface roughness can affect boundary layer propagation and development. Discussion regarding su rface roughness effects on boundary development is not the focus of this dissertation however, and will not be discussed extensively Instead, for the purposes of this paper, it must be understood that Equation 23 is a good approximation for maximum bott om shear stress based on a flat bottomed kmodel, but the usage of a model such as this may warrant some criticism, and it may be possible to improve this model. It is important to understand that this model is based on the hydraulic conditions under which the circular pile is subjected; it is theoretical in nature; and it may be limited because it was calibrated against a dataset that used a flat bottom

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47 A misconception regarding this argument is that if Equation 23 was developed using a flat bottom, then shear stress within an erosion rate testing apparatus should also be computed or measured under flat conditions. This is incorrect because the shear stress vs. erosion rate curve developed using the EFA SRICOS method is material dependent whereas th e maxi mum shear stress computed from E quation 23 is material independent. It is irrelevant whether or not E quation 23 is used with cobbles, sand, silt, or clay as the bed material; the equation will give the same maximum shear stress based on hydrodynam ics. Conversely, the erosion rate vs. shear stress curves that are generated during implementation of an erosion rate testing device are generated for a specific material. Cobbles will have a different erosion rate vs. shear stress curve than coarse sand ; coarse sand will have a different erosion rate vs. shear stress curve than fine sand; etc. Measuring both the erosion rate and the shear stress accurately for the specific material is essential in implementation of the EFA SRICOS method. 2.2.3 The Mille r Sheppard Method In 2003, Dr. D. Max Sheppard of UF and Dr. William Miller (Sheppards former Ph.D. student) developed their own semi empirical method similar to the EFA SRICOS method (Miller 2003). Millers method started with an idealized scour hole as shown in Figure 23, Figure 24, and Figure 25. Miller used these definition sketches to develop a volumetric expression where the depth of scour hole was found per unit time such that the avalanched volume of material back into the scour hole was take n into account. In other words, as a scour hole develops, some material will fall back into the hole (VA1), while at the same time, some material is eroded directly from the bottom of the scour hole (VA2). Both of these sediment quantities need to be taken into account to properly predict scour hole depth at each time step. Miller a nalysis resulted in Equation 26:

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48 2 2tan tan 1 2 1 1 s s s xd d n D n nD dt d d p dt dV (2 6) where p is the bed materials porosity, and other terms a re defined in the definition sketches. The net volume rate of sediment transport is described by E quation 27 where w is the widths of area over which the sediment transport function acts. w q Q dt dVout out s (2 7) Next, Miller looked a top view of the pile shown in the previous definition sketches (Figure 2 6). Miller used Figure 2 6 to incorporate pile width as a function of flow separation angle, e. Then, he nondimensionalized scour depth such that y = ds/dse where dse is the total scour depth: y K q K dt dyout D (2 8) e Dn D K1 2 (2 9) 2 2tan tan 1 2 1 1 y d y d n D n nD d p y Kse se se (2 10) These equations assume that no sediment is introduced into the scour hole from upstream (clear water conditio ns). To generalize Equation 28 (make it usable under live bed conditions), Miller introduced an input sediment transport term: w q q K y K dt y din out D (2 11) Miller assumes that input bed load sediment falls into the scour hole and becomes part of the avalanched material while susp ended material remains in suspension and passes over the scour hole. This results in the following equations:

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49 y K q y L q K dt dyin out D (2 12) y L Ly L1 0 (2 13) n D L2 10 (2 14) tan 21 sed L (2 15) Equation 28 and Equation 212 show that both under clear water and live bed conditions, scour depth should be a function of a materials sediment transport function. Although Miller verified his model using noncohesive results, from this derivation, it is clear that this model should also be valid when cohesive bed materials are present. To apply his model, Miller relied on analytically based stochastic formulations from Meyer Peter and Mueller (1948), Einstein (1950), Englund and Hansen (1972), Neilsen (1992), and Van Rijn (1992). Note that these sediment transport models use an average sediment diameter to determine sediment transport function ( d50). For a nonuniform material, these methods either need to be modified, or alternatively, the erosion rate shear stress relationship can be measured directly. Miller notes that the commonality among these different sediment transport models is that they are of the form q=Cf() or alternatively some constant times a function of shear stress. Miller realized that finding the effective shear stress in a scour hole is not easy nor is it always measurable, but he assumed that the sediment transport constant would remain the same as the scour hole developed. The refore, he expressed Equation 212 in terms of the sediment trans port constant: y K q y L CfK dt dyin D (2 16)

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50 Then, he used a scour depth vs. time fit function from his results to determine dy/dt when t = 0 and from Sheppards 2002 work, he found the initial bed shear stress required for scour to initiate. From this, he computed C and based on this constant value of C he was able to develop a time series of effective shear stress over the development of the scour hole (Figure 2 7). These curves show that as a scour hole develops, shear stress reaches a peak at yp and a break at yb. Miller used his results to develop a table where effective shear stress, the break point, and the peak point were determined as a function of velocity, sediment size, and pile diameter. Millers model is depe ndent on noncohesive s ediment because of his assumed scour hole shape. Work from Ting et al. (2001) indicates that for a cohesive soil, the scour hole generally will begin to form adjacent to the front edge of a protruding pile and extend some distance downstream from the pile face. The front edge of the idealized Miller scour hole, or the avalanched volume as labeled in Figure 2 3 will not develop because stiffer cohesive soils will resist falling into a developing hole. Still, according to Briaud and his equation for zmax, (Equation 24) ultimate scour depth should be the same regardless of sediment size. In other words, although the front portion of the scour hole may not avalanche into the hole, the maximum depth of the hole, which should occur at a point nearly adjacent to the protruding pile, should be similar. According to this reasoning then, the shape of Millers scour hole in Figure 23 should be a semi circle rather than a circle, yet the depth at the front fact of the semi circle should be same as indicated thr ough Millers derivation. Although work in this dissertation does not attempt to use Tings work to develop a cohesive analytical solution similar to Millers, in principle Millers

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51 expression for scour hole development (Equation 216) should still hold t rue in the sense that scour hole development should be a function of the bed materials erosion rate. 2.2.4 Scour Depth for More Complex Structures The preceding EFA SRICOS method and the Miller Sheppard approach for predicting scour depth are somewhat limited in that they are only valid for a single pile situation. When a bridge pier or foundation is more complex, methods presented in Section 2.2.2 and Section 2.2.3 need to be modified to fit these more complicated scenarios. Although Miller Sheppard follow up with more complex pier geometries could not be found, in the late 1990s and early 2000s, Briaud did perform more research. Briaud developed an empirical dataset so that his EFA SRICOS method could be used for more complicated foundations. Br iaud used Equation 24 and E quation 25 as a starting point, and his hypothesis was that correction factors could be developed where max and zmax from these two equations could be modified for more complex pier geometries. This approach assumes that eff ect of one parameter (for example, attack angle) is independent from the effects from another parameter (for example, pier shape). This approach is common; it is the same approach used in the sandscour equations in HEC 18. Briauds equations for complex piers are given in E quation 216 and E quation 217: 10 1 log 1 094 02 max VB V k k k ksh sp w (2 16) 635 0 max' 018 0VB K K K zsh sp w (2 17) Shallow water correction factors are given in E quation 218 and E quation 219:

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52 62 1 / 62 1 / 1 85 034 0 B H B H B H Kw (2 18) B H deep we k4 ) max( max16 1 (2 19) where H is the height of the water and B is the width of the piers. For the case of closely spaced piers, the spacing correction factors are given by Equation 220 and Equation 221: nB W W Ksp 1 1 (2 20) B S spe k1 1 ) single max( max5 1 (2 21) where W1 is the width of the channel without the piers n is the number of piers, and S is the spacing between piers. For the case of different shaped piers, the shear stress correction factor is given by E quation 222 where L is the width of the pier. The scour depth correction factor is given by HEC 18. Briauds test s were on square nosed piers, so he used a Kh value of 1.1. For piers of other shapes, Briaud recommends using the same Kh factors prescribed in HEC 18. B L she k47 15 1 (2 22) Finally, for the case of flow from a different angle, Briaud used the pier projected width, B (the same B as in HEC 18) to evaluate this condition. Even with B defined however, introduction of an attack angle coefficient, k was still necessary for shear stress: cos sin cos sin B L B B L B (2 23) 57 0 degree) (0 max max90 5. 1 1 k (2 24)

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53 2.3 Analytical Methods for Determining Erosion Rate Shear Stress Relationships Based on the preceding discussion on the aforementioned semi empirical metho ds and Slagles 2006 argument regarding the CSU equations, in recent years research has looked not to define the scour hole proper as a function of bed material, but rather use either a method similar to the Miller Sheppard approach or the EFA SRICOS approach as a standard for overall scour depth and fit one of these two models to a generalized scour scenario. At their core, these two models require the same input parameter an erosion rate vs. shear stress relationship. Logically then, the goal of resea rch should not be to define scour depth, but it instead should be to define an erosion rate shear stress relationship and if possible, generalize this relationship to other geotechnical parameters. There are two methods to determine this erosion rate she ar stress relationship. On one hand, as discussed in the last section, engineers can take Briauds approach and try to fit analytical sediment transport models to the problem. There is a significant advantage to this approach. These models are based on physics and analytical reasoning, and as such, results from these models can be used in a generalized scour situation for any bed material in which the models assumptions are valid. In this section, a brief analysis will follow of several analytical mode ls, while in Section 2.4 analysis of the other method for determining an erosion rate shear stress relationship will be discussed measuring this parameter directly using an erosion rateshear stress testing device. 2.3.1 Particle Like vs. Rock Like Ero sion and Shields (1936) In 1936, Shields argued that for a coarse sediment, there should be a shear stress, c that would represent the shear stress that caused incipient motion of bed material. This shear stress could be drawn as a function of bed roughness, or the roughness Reynolds Number, Re* such that:

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54 *Ren s cf gd (2 25) d u* *Re (2 26) cu (2 27) Although Shields original data only corresponded to sands, over the years (following Mantz 1977), his data points have been extended to cover the fine sediment regime (Figure 2 8). The essential component of Shields work is his critical shear stress assumption. Shields assumed that during an erosion event, erosion would only be caused by a s mall tug or pull on a few particles on a material lattice. It can be shown that if this is the case, erosion must correspond only to the Shields parameter given in Equation 225. This erosion mode will be defined as particlelike erosion. For noncohes ive sediments, this is true, but for cohesive sediments and rock another erosion mode exists. For cohesive material such as sand clay mixtures and stiff rock like materials, this slight tug or pull on the exterior rock like face sometimes does little in terms of net overall erosion. Rather, erosion under these conditions is instead governed by the fluctuating normal force component on the materials face and localized density/roughness/cracking in the rock or sand clay lattice. When Miller used analyti cal sediment transport equations to calibrate his model, he only considered the particle like erosion mode. Under rocklike conditions, it may not be possible to find erosion rate as a strict function of shear stress. U nder this scenario, Equation 212 i s still valid, while Equation 216 is not because it would not be possible to define a sediment transport constant when the governing component of sediment transport was a normal forcing parameter.

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55 Still, for certain sand clay mixtures, certain cohesive s ediments, and some rocklike materials, particle erosion is present, and methods for finding both critical shear stress and sediment transport functions (erosion rate shear stress relationships) should be discussed in some detail. First, critical shear stress will be discussed, then methods for determining erosion rate shear stress curves will be examined. 2.3.1.1 Einstein (1943) and Christensen (1975) In 1943, Einstein developed an expression for critical shear stress, c of a cohesive bed material based on a stochastic method (Einstein 1943): e x cr cd (2 28) where de is the effective grain size, s is the specific weight of the sediment, is the specific weight of water, and cr is the Shields parameter, or parameter for incipient motion. Although Einstein developed his own Shields parameter, in 1975, Christensen modified Einsteins equation and developed Equation 229 based on a probability distribution for erosion: 2 2 2 2 11 1 4 10 1 ln 556. 0 cot 1u u crnS r S (2 29) where r is the roughness/grain size ratio defined as the equivalent sand roughness, ks divided by de, Su is the dimensionless value of the standard deviation of the velocity fluctuation, defined by u/ubar, ubar is the average veloci ty, n is the normalized velocity fluctuation u/su, u is the velocity fluctuation component, and 1 and 2 are shape factors. Christenson found that Su = 0.164 and n = 3.09 which corresponded to the probability of erosion Pr = 0.001 which was related to the Gaussian distribution of u (Mehta 2007).

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56 2.3.1.2 Wiberg and Smith (1987) Wiberg and Smith developed their own relationship for critical shear stress in the late 1980s. Their expression centered around the argument that when dealing with a mixed bed, there are two length scales that need to be dealt with the diameter of particle size that will be eroded and the roughness of the surrounding bed. In other words, when dealing with a heterogeneous bed, a smaller particle may erode first, but the flow velocity is dependent on the size of the larger particles surrounding the smaller particle. To grapple with this scaling problem, Wiberg and Smith introduced a factor into their equations because they argu e that the particle failure angle changes for a mixed bed. In other words, it would be easier for a large particle to roll over a bed that consisted of smaller particles than it would be for a small particle to roll over a bed that consisted of larger par ticles (Barry 2003). An expression from Miller and Byrne (1966) was used to quantify Wiberg and Smiths critical velocity equation and equation are given by: tan 1 sin cos tan 1 20 2 3 D L b b D crF F z z f C (2 30) 1 / / cos0 1 s sk d z k d (2 31) Where a3 is a parameter for grain geometry, f2(z/z0) is derived from the expressions for drag and lift coefficient ( E quation 232 and E quation 233), z is the height above the bed, z0 is the bottom roughness parameter, qb is the bed slope, d is the particle diameter, and ks is the length scale for bed roughness. A u C FD D 22 1 (2 32)

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57 A u C FL L 22 1 (2 33) In these expressions, A is the cross sectional area of the particle, CL is the lift coefficient, CD is the drag coefficient, and is the density of water. To find the bottom roughness parameter, an expression for the velocity profile is necessary. Wiberg and Smith used Reichardts 1951 expression which gives a transition between the viscous sub layer and the logarithmic flow above (Schlichting 1979, Barry 2003). z ze z e c z u z u33 0 6 11 *6. 11 1 1 ln 1 (2 34) xk z R z u z* 0 (2 35) sk z R z u z0 0 0 (2 36) sk u R* (2 37) where is the kinematic viscosity of water and R* is the roughness Reynolds Number. 2.3.1.3 Dade and Nowell (1991) Dade and Nowell sought to develop a relationship for critical shear stress for a cohesive or mixed bed that was independent of inter particle cohesion forces (Barry 2003). Their expression for critical shear stress i s given in the Equation 238 : 1 1 cos 1 3 1 240 tan tan 402 2 3 2 2d gd g ds b s cr (2 38) where b is the mud interfacial mud shear stress, g is the acceleration due to gravity, was found to be 65o, and previous terms have already been defined.

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58 2.3.1.4 Dade et al. (1992) The purpose of this investigation was to find an e xpression for critical shear stress that included the effects of cohesion. This approach was similar to Weiberg and Smiths approach except that Dade et al. included a cohesion term. The resulting expression for the Shields parameter is given in the following equations: 21 1 3 4 tan 5 1 X X Xcr (2 39) 2 3 d gs (2 40) 3 1tan 4800 1 b W F Xb A (2 41) where b1 is a shape factor, Z is the Yalin (1972) parameter FA is the magnitude of the inter particle force, and Wb is the immersed weight of the particle grain. The ratio of immersed weight to inter particle force is obtained from E quation 242 (Barry 2003): d g b W Fs y b A cos 1 32 (2 42) 2.3.1.5 Mehta and Lee (1994) In 1994, Mehta and Lee developed their own relationship for critical shear stress by using a balance between angle of repose, drag force, buoyant weight, and lift force as their starting point rather than using the F factor that was employed by Christensen and Dade et al. Their starting point, equation 2.319 led to the development of E quation 243 for critical shear stress: ` L c b DF F W F tan (2 43)

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59 3 2 1 2 1 3tan tan tantan d g F d gs c s c (2 44) ac a x 4 (2 45) Equation 245 was developed from an evaluation of previous laboratory test s where 4 and are sediment specific constants. The advantage of this approach is that the Shields parameter is easily recognizable on the left hand side of the equation. 2.3.1.6 Torfs et al. (2000) Torfs took equation 2.319 from Mehta and Lee and noticed that it was only applicable for erosion of a bed material of single sized particle grains. Torfs sought to characterize entrainment within a fine coarse sediment mixture by size, dm and density, s. Everything else remains the same as it did in equation 2.319, except that the cohesive force, Fc must be replaced with Fm, or the force representing the effects of both cohesion and the influence of inter bedded grains. The result is: m s m s vc v cg cg cg cg cg cd g d g K tan tan2 1 3 (2 46) where vc is the fine solids volume fraction, v is the threshold value of vc below which the bed becomes fluid like, and are coefficients that depend on bed composition and levels of consolidation, and a3 is the shape factor related to dm. dm is the chara cterization diameter, analogous to the effective diameter, D* from the FDOTBSM except that dm refers to particle size. The critical aspect of this equation is the inclusion of K which is included to qua ntify increases or decreases in critical shear stresses due to the presence of fine materials. If K = 0, it implies that there are no fine materials; an increasing K signifies a greater presence of fine materials and a corresponding higher critical shear stress.

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60 2.3.1.7 Sharif (2002) In 2002, Sharif developed his own model for critical shear stresses of cohesive soils. Sharif concluded that developing an all inclusive model was not practical and instead, in his work three different models were created. The first model was for pure clays, the second model was for mixed beds with low clay content and the third model was for mixed beds with high clay content. For the case of a mixed bed with high clay content, E quation 247 provides an expression for critical shear stress. In this equation, sn is the density of the non cohesive particle, sc is the density of cohesive particles, dnc is the diameter of noncohesive particles fc is the weight fraction of cohesive particles, d is the bulk density of the bed, is the magnitude of the force between cohesive and noncohesive particles, is the Shields parameter, dc is the average diameter of cohesive particles, vc and vn are shape factors for cohesive particles, and sn is an area shape factor for non cohesive particles. 3 2 2 218 d n sn sn d c sc c n vn vc sn n sn cnf f d d d g (2 47) Equation 247 was developed by setting up a force balance between a single non cohesive particle and a cohesive sediment bed. The equation for critical shear stress of a pure clay bed, E quation 248, was developed by creating a force balance for separation of aggregates from the bed itself. In this equation ag is the density of aggregates, is the average force between cohesive particles, is the Shields parameter, and k1 a nd k2 are coefficients based on floc diameter. sc c vc ag k ag cad g k 2 1 1182 (2 48)

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61 Equation 249, the model for mixed beds with low clay content, was developed under the assumption that critical shear stress under these conditions would be dominated by noncohesive particles and aggregates. Then, Sharif assumed that each component of critical shear stress the aggregate component and the noncohesive component would govern critical shear stress based on the average area along the failure plane covered by each of these materials. a bb ab ca b nb cn chA A (2 49) where Anb is the area of non cohesiv e particles,, Aab is the area of cohesive aggregate, and bn and ba are experimentally determined coefficients. 2.3.1.8 Critical s hear s tress d iscussion The previous section was a brief summary of a number of expressions that can define the critical shear stress for a bed material analytically. This critical shear stress parameter is useless as means of estimating scour depths if it cannot somehow be correlated to erosion rate. This advance forward began in the 1960s with Partheniades and has been improved since then. 2.3.1.9 Partheniades (1962) In the early 1960s Partheniades was conducting test s on mud from San Francisco Bay, and he noticed that cohesive sediment eroded much differently from noncohesive sediment. Partheniades discovered that unlike sand, equilibrium scour depths are not reached when livebed conditions develop. In other words, with sand, eventually a scour hole gets to the point where rate of deposition equals rate of erosion as sand particles are eroded and redeposited within the domain of the hole. Mud on the other hand appears to harden until eventually the floc shear strength can withstand the shear stress that is applied to it.

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62 Using this rationale, Partheniades took the stochastic approach used by Einstein to develop a rel ationship for critical shear stress. The resulting equation, E quation 250 gives erosion rate as a function of bed shear stress (Figure 2 9 ). 2 2 12 22 1 erf 2 1 1b bk kk E (2 51) where erf is the error function, and k1 and k2 are experimentally determined coefficients that equal 0.036 and 1.61 respectively (Mehta 2007). In 1974, Ariathurai used empirical data to develop E quation 251, which is a straight line approximation of the trend shown by the Partheniades equation (Fig ure 2 9) c bM E (2 51) This equation is extraordinarily significant because of its overall form. Equation 251 shows that for a cohesive bed material under particle like erosion conditions, erosion rate should be a function of the de ficit between critical shear stress and bed shear stress or the same form that Miller saw during his analysis. 2.1.1.10 Christensen (1975 ) In parallel with his work on critical shear stresses for cohesive beds, Christensen also developed an expression for average shear stress on a sediment bed: 2 21 2 u u Cu D b (2 52) 2.3.1.11 McLean (1985) In 1985, McLean developed an energy balance between potential energy of an entrained particle and bed shear stress. This resulted in an expression for erosion rate of eroding bed particle, or Equation 253 :

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63 c c b eM E (2 53) According to Mehta, this relationship was actually first proposed by Kandiah (1974) based solely on empirical results. This equation also resembles the Partheniades equation and the Ariathurai equation, and serves as further evidence that quantifying erosion rates as a function of shear stress should be a valid method for predicting scour depths under particle like conditions. 2.3.1.12 Va n Prooijen and Winterwerp ( 2008) In 2008, Van Prooijen and Winterwerp argued that knowledge regarding the stochastic nature of the turbulent boundary layer has increased. Therefore, the probability density function (PDF) is now known more accurately than before when Einstein, Chris tensen, and others were conducting their research. Using the same approach as Hofland and Battjes (2006) and data from Obi et al. (1996), a new PDF was developed for bed shear stress: *2 1 exp 2 2 ) ( T T T p (2 54) where T is given by the dimensionless pa rameters: u bu (2 55) u uu (2 56) | |2 u bT (2 57) |2 T T (2 58) and is the dimensionless near bed velocity, is the dimensionless mean velocity, and T is the dimensionless shear stress. P arameters are scaled with u or the standard deviation of near bed

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64 velocity. The factors and were fitted to match Obis 1996 data such that = 1.75, = 0.83, and = 3.1. The result of this is that the Gaussian distribution assumed by Einstein in 1950 and used by Partheniades in 1962 became skewed slightly to the left. When applied to the Ariathurai expression, a formula for erosion rate is given: 2 2 erf 21 2 2 exp 2 2 1* 2 2 2 c c c c c c uT T T T T T M E (2 59) Results from E quation 259 are presented in Figure 2 10 and compared with Ariathurais relationship. Because E quation 259 is not convenient for use in numerical models, a three piece third order polynomial was developed: 7 1 if 1 204 0 823 0 904 0 144 0 52 0 if 02 3 c b c b c b c b c b c b cM E (2 61) Where M is Ariathurais coefficient. Note that even here, erosion rate is parameterized via critical shear stress bed shear stress function. 2.3.2 Analytical Methods for Determining Rock Erosion At present, engineers do not have a comprehensive quantitative understanding of what occurs during rocklike erosion events where the fluctuating normal force component on the rock face is a more significant erosion source than the shear stress tug or pull that acts against the rock surface. As mentioned in the preceding section, during particle like erosion events, a critical shear stress can usually be defined where below this threshold no erosion occurs. With

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65 rock and materials that obey rock like erosion characteristics, this shearing threshold does not appear to exist. There has been limited analytical work completed in this area, and most of it is still unable to accurately predict erosion rates. 2.3.2.1 Cornett et al. (1994) and Henderson (1999) According to Cornett et al. (1994) and Henderson ( 1999), erosion of rock may not be directly related to shear stress because erosion occurs along a rocks internal fracture plane, and this may be driven by hydrodynamic pressures wi thin the fracture. Figure 211 was proposed by Cornett, Henderson, and Ker r (2001) as a possible explanation for erosion of rock In this sketch, the crack, which is a stagnation point, of length l and height t is subjected to a steady flow of velocity u. Bernoullis equation can be applied to this scenario such that the press ure difference between the crack and the external flow can be expressed: 2 2 22 1 2 2 2 1u gt u u P (2 62) This pressure differential is the velocity head that acts to open the crack, and fracture occurs when the pressure within the crack exceeds rock strengt h. The bending moment, M in the rock above the crack base can also be written (Equation 263), and the maximum axial stress to carry the moment can be expressed (Equation 264 ) (Henderson 1999). 4 22 1 2 2u l P l M (2 63) 2 1 max5 1 t lu (2 64) Equation 264 implies that to failure resistance is proportional to the square of free flowing fluid velocity. Note that this expression deviates from the shear based expressions presented in Section 2.3.1.

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66 2.3.2.2 Stream p ower m ethod In 2003, Bo llaert developed the only known quantitative analytical method for predicting erosion for a jointed rock mass. Bollaert argues that rock masses are jointed, and as such erosion is most likely to occur along the joints or cracks in the material lattice. B ollaert set up a force balance between the pulsating normal forces along the rock face that result from flow along the rock surface: t t s s g down upv m dt F F W F F 0 2 1 (2 65) where Fup and Fdown are the total upward and downward impulses causes by fluctuating pressures on top of a rock block, Wg is the submerged weight of the rock block, and Fs1 and Fs2 are the resistive shear forces (friction forces) that develop along the rock blocks side faces as it is being lifted up out of the material matrix. Bollaert solved this force balance for the case where a jet of water pours into a plunge pool. Based on analytical reasoning, the height at which the rock block will be lifted up is given by: 2 2 2 2 2 2 4 22 2 1 2 2 sh b b s b j I x b b b b upF z x x g V C z gx c z x h (2 66) where xb is the width of the rock block, zb is the height of the rock block, c is the pressure wave celerity of water, g is the acceleration due to gravity, s is the density of the rock block, Vj is the jet velocity into the plunge pool, s = sg, g =g where is the density of water, Fsh is the sum of shear forces acting on the rock block, and CI is the dynamic impulsion coefficient. Based on experimental results for a jet entering a plunge pool, the dynamic impulsion coefficient was found as a function of pool depth ( Y ) and jet diameter ( Dj):

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67 2 1 119 0 0035 02 j j ID Y D Y C (2 67) Based on these equations, one can compute the height to which an eroding rock mass will move from its matrix. If the ratio between hup and zb is greater than 1.0, the rock segment will likely erode. If this ratio is betwee n 0.5 and 1.0, the rock may erode or it may not depending on ambient flow conditions. If this ratio is between 0.1 and 0.5, the rock block will probably vibrate but remain in place and not erode. If this ratio is less than 0.1, the rock block will remain in place. Bollaert intended this model to be used under conditions similar to a dam spillway. However, it may be possible to develop a similar model for use under local scour conditions. When local scour is present, the force balance shown in equation 2.340 is the same as it would be when water is entering a plunge pool. The vertical forces are caused by the horseshoe vortices instead of the jet velocity while the resistive forces remain the same. If this is the case, then it appears as though the fluctuating normal stress impulse along a rocks surface during a local scour event should play a role in determining the rocks erosion rate. 2.4 Empirical Methods for Measuring Scour and Erosion in Cohesive Soils and Rock Most of these aforementioned analytical expressions for erosion in cohesive soils and rock contain a high number of governing parameters. Trammel (2004) argues that it is unrealistic to develop accurate p redictive correlations based on these variables. T rammel also cites Mehta who says that the cost of evaluating this high number of parameters is often prohibitively high. Because of this, Trammel argues that pure empirical methods for predicting erosion rate shear stress relationships should be explored. Slagle (2006) agrees with Mehtas and Trammels assessments, and he also advocates the use of pure empirical erosion rate shear stress

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68 relationships for use in the EFA SRICOS or a method similar to the Miller Sheppard scour models. 2.4.1 Erosion Rate She ar Stress Standards for Rock HEC 18, the state of the art in rock erosion and rock scour is empirically based, but even still most results in this design manual are strictly qualitative. A series of simple guidelines are presented where an engineer can ps eudodetermine whether or not a given rock or rock like material will erode. Specifically, four categories of analysis are recommended to qualitatively determine how a rock face will react to changing flow conditions resulting from a foundation (Richardso n and Davis 2001): 1. Geologic, geomorphologic, and geotechnical analyses 2. The July, 1991 memorandum from the FHWA titled Scourability of Rock Formations 3. Flume tests to determine the resistance of rock to scour 4. Erodibility Index procedure 2.4.1.1 Geologic, g eomorphologic, and geotechnical analyses (Richardson and Davis 2001) To determine the geologic parameters used for design, HEC 18 says that extensive rock coring should be used. The cores should be subjected to standard field classification and soil mechanics tests. The geologic formation on which the bridge is to be constructed needs to be determined and mapped. The geomorphology of the site needs to be determined and related to the erodibility of the foundation material. The long term stability of the stream or waterway needs to be estimated, studied, and related to these geomorphological parameters. Additionally, erosion should be made if the erodibility or scourablity of the rock is unknown. 2.4.1.2 July 1991 FHWA Scourability of Rock Formations (Gordon 1991) According to Trammel, because numerous bridge foundation failures have occurred as a result of rock or rock like scour, in 1991, the FHWA developed an interim guidance document to assess rock scourability by using empirical methods and testin g procedures. Trammel says that

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69 these procedures are provided as a guideline until results from ongoing research permit more accurate evaluation procedures for estimating a design scour depth (Gordon, 1991 and Trammel 2004). According to the memorandum, no single rock index property will properly predict whether or not a rock will scour. Rather, different rock scour s for different reasons. For example, a rock may have a high bearing capacity and be very hard, but when water passes over it, it may erode rather quickly. The memorandum encourages designers to use a combination of the following seven methods to assess scourability until more qualitative procedures become available (Gordon 1991): 1. Subsurface Investigation 2. Geologic Formation/Discontinuities 3. Rock Quality Designation (RQD) 4. Unconfined Compressive Strength, qu 5. Slake Durability Index (SDI) 6. Soundness 7. Abrasion Subsurface i nvestigation The subsurface investigation portion of the memo simply provides guidelines for the type of borehole pattern to be used when preparing to build a bridge foundation. At minimum, a 3.3 m core length of material below the footing should be obtained and subjected to the remaining six examinations (Gordon 1991). Geologic f ormation/ d iscontinuities In general, the 1991 memo says that rock cores with one or fewer fractures per foot should indicate that the rock is of good quality and may resist scour. High rock fracture rates (five or six fractures per foot) qualitatively appear to indicate a poor quality rock that may be scourable (Gordon 1991). Rock q uality d esignation (RQD) RQD is a modified computation of percent rock core recovery that reflects the relative discontinuity of the rock. The 1991 memo says that rock cores

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70 with an RQD less than 50 should be considered to be soil like and therefore at a high risk of scour (Gordon 1991). Unconfined c ompression s trength According to the 1991 memo, qualitatively as compressive strength, qu, increases, bearing capacity increases and scourability decreases. The memo say s that there is only a generalized correlation between scourability and compressive strength. Slake d urability i ndex (SDI) The SDI test is a test used on metamorphic rock and sedimentary rock like slate and shale. In general, a low SDI number indicates a highly erodible material. The 1991 memo says that an SDI value lower than 90 indicates that the rock could be highly scourable (Gordon 1991). Soundness S oundness is measured by soaking the rock in a magnesium or sodium sulfate solution for twelve hours. Generally, the less sound the rock, the more scourable it will be, but specifically, the 1991 memo says nothing regarding what less sound means relative to more sound (Gordon 1991). Abr asion The 1991 memo cites the Los Angeles Abrasion Test as the method to use for measuring this parameter. Qualitatively, the less a material abrades, the less it will scour, and materials with loss percentages greater than 40 should be considered highl y erodible (Gordon 1991). Discussion of 1991 Memorandum The above mentioned memorandum is highly qualitative; there is no definitive point where an engineer can say that a material will or will not erode based on the information given here. Further, t his memo was released in 1991; it is nearly twenty years old and there is not yet a better, more quantitative method for estimating or establishing an erosion rate of a rock or rocklike bed material that can be used in design.

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71 2.4.1.3 Erodibility i ndex m e thod (Annandale et al. 1996) The Erodibility Index Method (ERI) was developed by Annandale, and it identical to Kirstens Excavatability Index (Richardson and Davis 2001). It is used to quantify the relative ability of a non uniform earth material to resi st erosion. Figure 212 provides a definition sketch f or the erodibility index method. Water along the bed experiences turbulence, and Annandale suggests that this turbulence causes a pressure gradient that progressively jacks rock material from its in itial position. Once removed, the material then becomes dislodged and displaced. In the ERI, a materials ability to withstand erosion, Kh, which is identical to Kirstens ripability index, is defined: s d b s hJ K K M K (2 68) such that Ms is the mass strength number, Kb is the particle or block size number, Kd is the discontinuity or inter particle bond strength, and Js is the relative ground structure number. The mass strength number is determined from the material strength of an intact sample of rock without regard to geologic heterogeneity. The particle or block size number is a factor that represents the rock mass quality. The bond strength factor represents the relative strength of discontinuities and it is made by visual observations. The ground structure number relates the shape of material particles to the direction of free stream flow (Henderson 1999) Test s were conducted at the Turner Fairbank Highway Research Centers (TFHRC) Hydraulics Laboratory to find a relationship betwee n stream power and scour depth so that a practical application of the ERI could be used for scour prediction. HEC 18 says that based on these test s, although a relationship between ERI and stream power appears feasible, more research is sti ll needed (Rich ardson and Davis 2001). T his evaluation was made before development of Bollaerts Stream Power Model.

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72 2.4.1.4 Flume t ests (Richardson and Davis 1991) Appendix M of HEC 18 says that samples should be subjected to flume tests to see if they erode. This conclusion is interesting, as it is the same conclusion that is drawn from Appendix L for cohesive sediments. Under cohesive scour conditions, the semi empirical approaches discussed in Section 2.3 may be applied, and in principle, these methods should be usable when rock is present as well. This ultimately means that for either cohesive sediments or rock the same method flume tests (or similar erosion rate testing devices) are the most effective solution for determining a materials erosion rate she ar stress curve. 2.4.2 Erosion Rate Testing Devices In principle, the idea of using a flume or a similar erosion rate testing devices appears as though it should be straight forward. However, like most variables pertaining to the scour problem, implementa tion of a properly working erosion rate testing device is not as easy as it sounds. The following is a summary of a number of erosion rate testing test s that have been conducted in recent years. Although not every device mentioned w as designed to capture erosion rate shear stress relationships specifically, this discussion highlights the evolution of erosion rate testing devices. 2.4.2.1 Nalluri and Alvarez (1992) In 1992, Nalluri and A lvarez ran a study to identify the influence of cohesive sediment on erosion of a mixed bed. Tests were conducted in a 154 mm wide open channel and a 302 mm diameter pipe. Their study led to three important conclusions. First, even a low level of cohesive material (and subsequently cohesion) can increase the critical s hear stress (and critical velocity required to move a noncohesive particle). Secondly, sand size has no effect on the critical shear stress of a cohesive sediment. Third, for a given sand, an optimum sandclay mixture could be achieved where critical sh ear stress was maximized.

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73 2.4.2.2 Mitchener and Torfs (1996) In 1996, Mitchener and Torfs synthesized a series of laboratory data from a variety of different types of erosion tests to try to quantify the erosion rates of sand mud mixtures. After analysi s of these data sets, a number of interesting conclusions were drawn: 1. The addition of even a little bit of mud to sand affects the critical shear stress of the mixture. 2. If enough mud is added to sand, the entire mixture behaves as if it was a mud, and these results agree with earlier results by Dade and Nowell. 3. The addition of mud to clays also decreases the erosion rate of sand/clay mixtures compared to what the erosion rate would have been had the mud not been present. Inversely, the addition of sa nd to mud increases the erosion rate of the mixture. 4. A recommendation was made to further investigate the relationships between erosion parameters (critical shear stress and erosion rates) and bed roughness from 0% sand to 100% sand. Mitchener and Torf s tried to develop a relationship between erosion rate and bulk density and critical shear stress as well. This relationship is presented in Figure 21 3, while relationships between erosion rate, shear stress, and critical shear stress are presented in Fi gure 2 14 and Figure 2 15. 2.4.2.3 Panagiotopolous et al. (1997) Panagiotopolous sought to further study the effects of clay content on a noncohesive bed. His results suggest that critical shear stresses increase with increased clay content while erodi bility decreases with increased clay content. He identified two specific regimes: 1. For sand/clay mixtures where there was less than 30% clay present, there is a slight increase in the critical shear stress for the bed material. 2. For sand/clay mixtures whe re there was more than 30% clay present, erosion rate decreases. According to Barry (2003), t hese results agree with Dyer (1986) and Raudkivi (1990).

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74 Additionally, this study found that if the critical stress was not exceeded for a sand/clay mixture, or if u was less than uc, and then the velocity was to be slowly increased, critical shear stress would also increase as a result of this pre critical flow. This was expected because even though the critical velocity was not met at these lower speeds, grai ns or aggregates that did not satisfy the threshold criterion adjusted to more stable positions within the bed. The implication here is that when test s are run in the EFA or the SERF, velocities need to be selected randomly when erosion rates are measured If velocities are not selected randomly, the erosionrate testing apparatus will under predict the erosion rate for a given shear stress. 2.4.2.4 SEDFlume (McNeil et al. 1996, Jepsen et al. 1997) In 1996, McNeil wanted to find the erosion rates and associated shear stresses of fine grained bed materials. His motivation was environmental, not structural; his goal was to see if there was there potential for contaminants at the bottom of rivers to re suspend themselves during largescale storm events. To investigate this issue McNeil developed the Sediment Erosion at Depth Flume or SEDFlume. At the time, SEDFlume was unique because it allowed for in situ testing of bed materials. According to McNeil, previous laboratory designs by their nature disturbed the sediment that th ey tested; McNeil cites Fukuda and Lick (1980), Mehta, et al. (1982), Tsai and Lick (1988), and MacIntyre et al. (1990) as examples of laboratory flumes that disturb the samples that they are testing (McNeil et al. 1996). A sketch of the original SEDFlume schema tic is presented in Figure 2 16. An undisturbed sample is loaded into the SEDFlume a nd a piston is placed below the sample. Then, water was pumped through the 2 cm by 10 cm flume from a 120 gallon reservoir tank. As the water passed over the sample, the sample eroded, and as the sample eroded, the piston was raised so that the sample was level with the bottom of the bed (McNeil et al. 1996). The mechanism for raising

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75 the piston (and subsequently the sample) into the flume was operator dependent. The research technician was charged with watching the sample erode and advancing the sample into the flume with a 1/3 hp motor. One had to have experience using the device to know when to turn on the motor, how fast to raise the piston, etc. Shear stresses in the SEDFlume were estimated using Prandtls Universal Law of Friction. For a rectang ular duct with smooth surfaces, Equation 269 can be derived: 8 0 log 0 2 1 UD (2 69) w h hw D 2 (2 70) 28 U (2 71) where is the friction factor defined by the Darcy Weisbach equation given in 271, U is the frees tream velocity, D is the hydraulic di ameter defined by equation 270, is the kinematic viscosity of water, h is the height of the duct, w is the width of the duct, and is the shear stress on the four walls of the duct. These three equations can be combined to find shear stress as an implicit function of freestream velocity. To measure freestream velocity, a paddlewheel flowmeter was used (McNeil et al. 1996). As show n here then, the SEDFlume did not measure shear stresses directly; rather, shear stresses were implied as a function of freestream velocity. The assumption behind the usage of these equations then is that the shear stress on the walls of the flume approxi mates the shear stresses seen by the eroding sample and that the SEDFlume is hydraulically smooth. I n his paper, McNeil says that he uses a relationship between shear stress and flow rate for a hydraul ically smooth flow (Figure 217).

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76 Measurements of er osion rates were conducted on a series of samples from the Trenton Channel of the Detroit River from 1993 1994. McNeil organized erosion rates as a function of both core depth and shear stress. McNeils results (Figure 2 18) show that as shear stress i ncreases, erosion rate also increases, and they imply another interesting phenomenon. As depth increases for a given shear stress, it appears that erosion rates level off. Consider that McNeil was dealing with cohesive sediments. As depth increases for a cohesive sediment, it is likely that consolidation of these sediments increases. As consolidation increases, it is likely that material strength, or cohesive strength, of the sediments increases. If this is true, then his results imply that material strength also plays a role in determining the erosion rate of his bed materials. In 1997, Jepsen, Roberts, and Lick extended McNeils work. They ran a series of test s to determine erosion rate and critical shear stress as a function of bulk density for be d particles from the Detroit River in Michigan, the Fox River in Wisconsin, and a slough near Santa Barbara, California. Their results show that for a given shear stress, erosion rate is a unique function of bulk density and erosion rate decreases as bulk density increases (Jepsen et al. 1997). This appears to imply a further dependence between erosion rate and material strength because as density increases, materials become more compacted. As materials become more compacted, their cohesive strength should inc rease. Shear stress estimation still utilized a flatbottom Darcy Weisbach frictional coefficient. In 2003, the SEDFlume was enhanced so that it could produce oscillatory flows. The new device was named the SEAWOLF and it was used to measure eros ion rates of quartz sand under these oscillating conditions. To predict shear stress under oscillating conditions, an effective shear stress for wave motion is estimated. Then, this effective shear stress is transferred to

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77 erosion rate shear stress relat ionships from unidirectional flow to yield a prediction for erosion rate under wave conditions (Jepsen et al. 2003). 2.4.2.5 ASSET (Roberts et al. 2003) In 2003, Roberts et al. developed the second generation of the SEDFlume. The motivation behind this research was that although the SEDFlume was useful for measuring sediment erosion rate properties, it only measured bulk erosion rates. The SEDFlume gave no information regarding the transport mode of eroded materials i.e. bedload vs. suspended load. T he Adjustable Shear Stress Erosion and Transport (ASSET) Flume was designed to determine the bedload fraction of eroding sands. The ASSET was similar to the SEDFlume, but its dimensions were slightly larger. While the SEDFlume was 2 cm tall by 10 cm wid e, the ASSET was 5cm high by 10.5 cm high. The ASSET was designed with a bedload trap positioned 1 m downstream from the eroding sample (Fig ure 2 19). Sand caug ht in the bedload trap during a test was the bedload fraction from that run (Roberts et al. 2003). The ASSET used the same method as the SEDFlume for estimating shear stresses on an eroding sample equation 2.42, equation 2.43, and equation 2.44. Results from the ASSET show that bedload fraction is a function of both particle siz e and shear stress (Figure 2 20). Because the focus of this study was on determining bedload, no information was given regarding total erosion rate as a function of shear stress. Roberts mentions that some qualitative work was done in the ASSET with cohesive sedime nts. Although no hard data is given in his paper, he notes that cohesive sediments often do not erode as individual particles; rather, cohesive sediments erode as aggregates, or flocs (Roberts et al. 2003).

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78 2.4.2.6 EFA (Briaud et al. 19912004) The EFA SRICOS method has been discussed at length in terms of the initial requirements necessary for its implementation. In preceding discussion regarding the EFA SRICOS method, it was assumed that it would be possible to determine a shear stress erosion rate relationship. The principle behind the EFA is similar to principles behind both the ASSET and the SEDFlume. In fact, the EFA and the ASSET/SEDFlume/SEAWOLF apparatuses were developed independently from one another. A sample is loaded into the flume through the bottom, and as water passes over the sample, the sample is advanced into the flume. Through this mechanism, erosion rate is measured directly. Similar to the ASSET and the SEDFlume, the advancement mec hanism of the EFA (Figure 221) was also conducted via visual inspection. A ccording to the EFA testing procedure, the sample is to be loaded into the flume such that it protrudes into the flume 1 mm (Figure 2 22 ); presumably this protrusion into the flume is recommended so that visual inspection is easier. As of the date of the last kn own EFA publication (2010), the EFA estimates shear stress by us ing a Moody chart (Figure 2 23). Shear stress is approximated by finding a friction factor from the Moody diagram and then using the friction factor to approximate shear stress with the following equation: 28 1 f (2 72) where is the shear stress, f is the friction factor, is the density of water, and is the kinematic viscosity of the water. Like other erosion rate testing devic es, the EFA does have drawbacks and erosion rate shear stress data from it has been criticized in the past. First, the EFAs method for shear stress estimation has been questioned (for example in Annandale 2005). Friction factor estimation

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79 from the Mood y Diagram depends on selection of the correct roughness factor or roughness height. If the relative roughness of an eroding sediment can be properly estimated, the Moody Diagram method may work properly, but selecting the correct relative roughness for a bed material is not straight forward. In his work, Briaud does not indicate what value for relative roughness he is using during EFA shear stress estimates, nor does he indicate how any roughness coefficient is calibrated in his flume. Further, the Moody diagram was developed from test s with pipes of uniform roughnesses. T he EFA does not have a uniform roughness. The side walls and top of the EFAs rectangular duct are smooth while the false bottom in which the sample projects itself into the flume is r ough. Annandale (2005) says that because of this, using a uniform roughness coefficient from a Moody diagram is incorrect. The second possible oversight with regard to the EFA is that the sample protrudes into the flume 1 mm. If the sample is protrudin g into the flume, it will be subjected to both normal stresses and shear stresses. Because of the addition of a normal stress, the sample will erode faster than it would if it was subjected to only a shear stress. It appears then that for a given shear s tress as estimated upstream using a Moody Diagram (or more explicitly a given velocity, as velocity is the only quantity that is actually measured during the test ), erosion rates would be overestimated. This overestimation of erosion rates for a given shear stress would lead to conservative equations. Conservative equations are better than nonconservative equations, but if the erosion rate data from the EFA could be improved, it may be possible to avoid over design s The third possible issue associated with the EFA SRICOS method is the same discussed with both the ASSET and the SEDFlume in that these devices rely on visual inspection to

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80 advance the sample into the flume. They are also operator dependent, and reproduction of results may be difficult. 2.4.2.7 R ETA (Henderson 1999 Kerr 2001 and Slagle 2006) P receding erosion rate testing devices that were used to estimate shear stresses and measure erosion rates of cohesive material were designed similarly: a flume was used to flow water over an advancing sample, an d a theoretical relationship used to estimate the shear stress on it. In 2001, The Rotating Erosion Testing Apparatus (RETA) was designed to measure erosion rates and shear stresses of a intact bed material as an alternative design. The RETA (Figure 2 24 ) approaches the problem differently than traditional bottom loaded flume designs. Instead of flowing water over a sample, in the RETA a sample is loaded into an annulus fi lled with water (Figure 2 25 and Figure 2 26) The annulus spins around the sample which creates a shear stress, which in turn causes th e sample to erode (Figure 2 27). Before the sample is loaded, a hole is drilled through it, a rod is attached through the center of the sample, and the sample rod is attached to a torque cell (Figure 2 28 ). Rather than inferring shear stress from theoretical equations that describe shear stress on the flat wal ls of a flume, the torque cell measures the shear stresses directly (Kerr 2001). The torque cell measures torque on the entire sample as the rotating cylinder spins around it. To convert the torque measurement to shear stress, E quation 273 is used. To find the total erosion rate, E quation 274 is applied. In these equations, R is the radius of the sample, L is the leng th of the sample, T is the torque measured from the torque cell, E is the erosion rate, m is the amount of mass removed during the test and D is the duration of the test. Standard RETA tests are specified to be run f or 72 hour increments (Sheppard et al 2006). L R T22 Stress Shear Average (2 73)

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81 RLD m E2 (2 74) The most obvious advantage to RETA testing is that shear stress is measured directly, but this technique is not without criticism. Because the gap between the sample and the annulus walls is small, the size of turbulence induced vortices in the flow regime between the sample and the annulus wall may be different for the same average shear stress. In other words, under rock like erosion conditions where the normal force impulse to the rock face is the dominant erosion mode, the fluctuating normal stress component to the material face will be diff erent than it would be in nature. There are other pitfalls in using average shear stress along a vertical plane to represent the shear stress on the sample. The RETA presumes uniform erosion; in re ality, when rock is subjected to RETA testing, sometimes chunks of the material often erode at one time. This chunking and pitting mechanism can produce localized shear or normal stresses along the sample face that are higher than the uniform component. This is in turn can produce more chunking, which in t urn can produce even greater localized shear stresses, etc. These localized stresses cause localized higher erosion rates, yet a value for average erosion rate is the goal. Because erosion rate is measured by simply measuring the mass of the sample at th e end of the test and subtracting this mass from the total mass at the beginning of the test these localized variations can cause inaccurate average erosion rate results. This pitting or chunking mechanism implies the presence of rocklike erosion, and while shear stress is measured directly in the RETA, normal stress is not. As shown in Section 2.3, when rock like erosion dominates, erosion rate is probably not a strict function of shear stress. The third criticism with the RETA is that because the f luid around the sample is being spun, the sample is subjected to torque or moment action. Kerr (2001) and Slagle (2006) both

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82 argue that this behavior would lead to slightly more conservative equations and is therefore acceptable. Critics, particularly th ose who would label the equations in HEC 18 as overly conservative would argue that because there is no quantitative date to support the claim that this moment action leads to only slightly conservative results, one cannot be sure how overly conservati ve the RET A is (Kerr 2001). Because of these concerns, there has been a goal in recent years to correlate RETA results to results from another erosionrate testing apparatus. RETA Results Test s in the RETA were conducted by both Kerr in 2001 and Slagle in 2006. Kerrs test s were preliminary; the focus of his work was development and calibration of the instrument. Slagles w ork on the other hand was more comprehensive. First, Slagle conducted a series of RETA test s on limestone taken from the Jewfish Creek Bridge on US 1 in near Key Largo, FL. Like Briaud, Slagle developed curves for erosion rate vs. shear stress (Figure 2 31). However, Slagle, afte r discussion with Bloomquist, hypothesized that erosion rate of a rock may also be a function of materi al strength. He presented one graph of pa rticular interest (Figure 2 32) in his Masters Thesis that shows Jewfish Creek limestone behaving with some dependence on cohesion. Cohesion may be derived from two Mohrs Circle using simple geometry (Figure 2 33). If one examines triangle zqy the cosine of the angle, and the sin of the angle can be defined: 1 2 1 2cos R R RR (2 75) 1 2 2 12 sin R R R R (2 76) If a perpendicular line is then drawn from point x perpendicular to the x axis, a new triangle will be apparent, and the length of the perpendicular distance from point x to the x axis

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83 can be shown to be R2sin. Furthermore, the slope of line wx must be the same as the slope of line zq and it must equal x cot. Next, coordinates are transferred such that the new y axis now sits at the center of C ircle 2. From above, the equation of line wx through the new coordinate system must be: cot sin '2x R y (2 77) Cohesion is defined as the point where l ine wx the line tangent to both the Mohrs Circle for axial strength (circle 1) and the Mohrs Circle for compressive strength (circle 2) crosses the x axis. Setting x in E quation 277 equal to R2 and using E quation 275 and E quation 276 yields the following expression: 2 1 1 2' R R R R y (2 78) Substituting qu and qt, or the compressive and axial strength definitions respectively, yields: t uq q C y 2 1 (2 79) where C is the cohesion. Figure 2 32 appears to indicate that erosion rate may be a function of shear stress and cohesion, but Slagles results are limited. This figure is the only graph that he presents that shows this relationship. More work needs to be done to determine if this dependence on cohesion is simply a coincidence that was found for Jewfish Creek Limestone or if this dependence on cohesion can be extrapolated to other materials. Work by Briaud et al. (1999) suggests that erosion rate is not a function of any geotechnical properties and therefore he recommends his site specific EFA SRICOS method. In a 2002 discussion piece, Hanson and Simon agree with Briauds assessment (Hanson and Simon 2002).

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84 On the other hand, if one examines the definition of a Mohrs circle, the vertical axis represents shear stress while the horizontal axis represents norma l stress. If erosion is caused by a simple shearing action, then the normal stress or value along the Mohrs circle x axis should be close to zero. This would lead directly to the y intercept, or the c ohesion point. If Slagles apparent relationship between erosion rate and cohesion can be proven, it may contradict Briauds assessment and provide an alternative means for predicting erosion rates of bed materials. Slagles relationship implies that und er the conditions that he studied, shear stress was dominant. Slagles other results from the RETA focused at finding erosion rates of Gator Rock (Section 2.5). For now, it is sufficient to say that Gator Rock is a materia l that is designed to be unifor m in its properties Slagle did this run of test s presumably to compare RETA results with SERF (Section 2.4.2.9) results, but instead of running the same shear stresses in each instrument, he used the RETA to measure results with a low shear stress and the SERF to measure results with a high shear stress. Slagle argued that because his erosion rate curves followed the same trends when he com bined instrumental results, it proved that both instruments were measuring erosion rate and shear stresses correctly and that the RETA had been verified. This argument is questionable because there are significant gaps between the data s ets. For example (Figure 2 34), there are no readings between approximately 40 Pa and approximately 70 Pa. This data gap is typica l for most of Slagles tests. Slagle only ran one test where there was an overlapping shear stress data point. Although this data point did match in both the RETA and the SERF, one point is not enough to say that results from the RETA are proven to be the same as they would have been using a more traditional erosion rate testing device. Slagle also

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85 discussed issues with Gator Rock nonuniformity in his thesis. Although a graph of Gator Rock erosion is presented here, there no data could be found that verified near uniform erosion in the RETA. As discussed, a fundamental assumption behind the RETA is that samples will erode uniformly; if they do not, it is difficult to rely on the results, and hard to say what is being measured. 2.4.2.8 Barry et al. (2003) In 2003, Barry ran a series of test s to determine how critical shear stress of a sand clay mixture varied based on clay content. Testing was conducted in a recirculat ing flume, a Schultz ring tester, and a RETA (although as of 2003, the RETA was called the Simulator of Erosion Rate Function, or SERF). Barry concluded that as clay particles were added to the mixture, critical shear stress first decreased to some valu e, min, then steadily increased beyond this min threshold. Barry developed a shear resistance model for clay lubrication, to explain this behavior of min but it performed poorly when compared to experimental results. Barry blamed the analytical model s failure on the fact that the model predicted a clay layer that was much smaller than the asperities in the sand. Although this round of test s is excellent for providing critical shear stresses as a function of clay content, it did not measure erosion ra tes of these materials. 2.4.2.9 SERF (Trammel 2004, Slagle 2006, Kerr 2001) The SERF (Figure 2 35 and Figure 2 36 ) was designed over a number of years at the University of Florida in parallel with developmen t of the RETA. The goal during design was to c onstruct a traditional flume designed erosionrate testing device. Although i t was to be similar to the devic e s developed and used by Professor Wilber Lick at the University of California at Santa Barbara (the ASSET, SEDFlume and SEAWOLF ) it would contain significant

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86 improvements. It should be emphasized that these devices were all developed independently from the EFA. According to Braiud, development of the EFA began in the 1990s, while Licks devices were starting to be developed duri ng the 1980s (McNeil et al 1996, Briaud et al. 2010). The SERF is a rectangular recirculating flume that is driven by two 1000 gpm pumps (Figure 2 37). The flumes cross sectional dimensions are 1.75 inches by 8.00 inches Along its length, the flume is designed so that a 4 inch Sch.80 pipe feeds into the rectangula r section through a tapered 1 foot transition region. Beyond the transition region is a 1 foot section of rectangular flume that is equipped with a flow straighte ner. Total length of the rectangular section of the flume is 10 f eet Flow rates in the flume can reach approximately 2000 gpm. An eroding sample section is located in the center of the rectangular por tion of the flume (Figure 238). The first goal of the design of the SERF was to remove operator dependency from testing Recall that during an EFA test the sample is to protrude into the flume 1 mm, the sample is to be eroded, and the n the sample is advanced again. A dvancement procedures with the EFA (and previous devices) were conducted manually. With the SERF, its designers hoped to take the operator out of the equation; instead a computer would decide when a sample was to be advanced into the flume. Although the SERF i s equipped with a viewing port (Fi gure 2 39), unlike the EFA whose port is used by the operator on when to advance the sample. Instead, the SERFs port was installed so that a video camera can keep a record of erosion testing. Trammel, Bloomquist, Sheppard, Kerr, and several others designed the SERF so that s ample advancement would be automated. On the top of the flume an array of ultrasonic depth sensors was mounted so that it could consistently monitor the distance between the top of the flume and the top of the sample (Figure 240 and Figure 2 41). The s ample was placed in a

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87 cylinder with a piston on its bottom, and the piston was advanced via an electric stepper motor. Both the stepper motor and the ultrasonic depth sensor array were programmed into a feedback loop with one another. If the samples pos ition deviated by more than 0.5 cm from where it should have been, it was advanced (or retracted). This feedback mechanism marks a breakthrough in erosionrate testing devices because this was the first time that sample advancement had been automated. A s originally designed, the SERF, like the EFA, was not capable of measuring shear stresses directly on a sample. However, instead of using a strictly theoretical approach where velocity is correlated to shear stress, the SERF used a pressure drop measurem ent to infer the shear stresses on the sample. From continuity, given a control volume of fluid in a closed flume, the shear stress along the flume walls should balance the pressure gradient within the flume. Put another way: plw L w l 2 2 (2 80) where is the shear stress along the flume walls, l is the cross sectional width of the flume, w is the cross sectional height of the flume, and p is the difference in pressure along an arbitrary flume length, L. Rearranging (Trammel 2004): L w l plw 2 2 (2 81) This expression assumes that shear stress along the flume walls is representative of the shear stress across the face of the sample. It also assumes that the sample is large enough to increase the average pressure drop relative t o what the pressure drop would have been under smooth conditions. The SERF was specifically designed to maximize the likelihood of achieving a uniform velocity profile over the eroding test section. B ecause velocity profiles cannot currently be

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88 measured, it is important to understand the flow dynamics associated with flow in a rectangular duct so that the likely distribution of the velocity profile can be better understood. Therefore, an investigation on flow in rectangular ducts was conducted. In 1998, Rokni et al. developed a numerical model to study flow in a rectangular duct, and Results were verified in an experiment (Figure 2 42 and Figure 243). Several other numerical and experimental studies were conducted in rectangular ducts including C okljat et al. (1996), Gessner and Emery (1980), Naimi and Gessner (1993), and Melling and Whitelaw (1976). These analyses were also studied to determine flow characteristics in rectangular ducts although their results are not presented in this dissertatio n. In 2004, Trammel ran a series of test s in the SERF, and he developed curves that correlated shear stress to erosion for a sand and a clay material that was obtained from Jackson County, FL. In 2006, Slagles test s (as previously mentioned) were used to extrapolate RETA results to higher values for shear stress. Typical results from Slagle have already been presented; typical results from Tramme l are presented in Figure 2 44 Although in principle, the SERF is well designed, there were several issue s in its operation. First, the ultrasonic depth array, the centerpiece of the devices design, was prone to errors. During test s with cohesive materials, the ultrasonic pulses from the depth array were found to penetrate into the samples and return incorrect bottom measurements (Slagle 2006). These instrument errors caused the stepper motor (and sample) to advance (or retract) uncontrollably. The net result was a ruined test. Secondly, during longer tests the water temperature within the closed SERF system would rise qu ickly because the pumps generate significant amount s of heat (Figure 2 45). This rise in water temperature was problematic for two reasons. First, the speed of sound is dependent on

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89 temperature; a temperature rise caus es sound waves to speed up. When the temperature rose, the ultrasonic depth array was calibrated for the old temperature, and as time went on, it produced incorrect results and caused the sample to uncontrollably advance (or retract). Sec ondly, there w as a fear that high temperatures could break the depth sensor and the pressure transducers. These issue s aside, in nature, a temperature flux of ~+2oC per hour are not seen. The final issue that was seen during SERF testing has already been discussed: t he inference of shear stress on the sample from the press ure drop on the flumes walls. Because no verification could be provided from these pressure drop readings, one could argue that this was the weakest element of the SERF tests A dire ct measurement of shear stress would be better. Despite these issue s, at the time of its construction the SERF was one of the a valuable upgrade when compared with other erosion rate testing devices Although the ultrasonic depth array wasnt without its limitations w hen it was working properly it did provide reproducible results. These limita tions are readily apparent however, and hence can be improved. 2.5 Gator Rock Gator Rock has been mentioned briefly at several points throughout this background section. A discussion of Gator Rock will follow in the subsequent section, while a discussion of a new method of creating Gator Rock will be presented in Chapter 5. 2.5.1 Gator Rock: A Brief History and Description Gator Rock was originally developed at the University of Florida by Niraula (2004) for use in centrifuge testing. Niraulas motivation for research was to develop new T Z curves for the FB Pier bridge modeling system, and he needed a uniform, homogeneous, material that could be subjected to centrifuge testing A T Z curve is a non linear spring used in FB Pier that transfers load from a foundations pile/shaft to the soil/rock on which the pile rests. Niraula argued that Florida limestone is subject to high variability, and variability could not be tolerated in his study

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90 (Niraula 2004). However, he needed a material that would behave similarly to Florida limestone in terms of strength therefore, Ga tor Rock was invented. To create Gator Rock, Niraula dried crushed limestone for 72 hours, and then he mixed water, and Portland cement with the limestone aggregate. Three water contents were mixed, and axial and compressive strength tests were run on t wo samples from each of the three batches. According to Niraula, these batches showed good repeatability during these strength tests Niraula indicates that tests were repeated three to five times, although he does not indicate the standard deviation among tests nor does he indicate the percent difference between the tests. 2.5.2 Extension of Gator Rock to Flume Tests As previously discussed, since the inception of the RETA, there has been a desire to verify that experimental results from it are the same as they would have been in another erosionrate testing apparatus. There are two levels of verification possible in the RETA: 1. Does the RETA reproduce traditional flume style (devices similar to the SERF) results for a material that is homogeneous ? 2. Does this change for a material that has been developed in layers like a sedimentary rock? Before the second question can be answered, the first one must be addressed because if the RETA cannot even produce similar results for a completely uniform material, c hances are small that it will be possible to reproduce results for an nonhomogeneous material. To answer this question via a direct comparison, a material needed to be either located or engineered that was hearty enough to withstand the moment forces associated with rotating RETA water and maintain its structural integrity on its own. However, the material could not be so resistant to erosion that it completely resisted eroding forces. The material had to erode uniformly topto bottom; in other words, it had to be nearly homogeneous

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91 2.5.3 Gator Rock 2.0 Slagle (2006) and his advisor Bloomquist proposed using a material like Niraulas Gator Rock because from Niraulas experience, Gator Rock exhibited nearly uniform characteristics. Although Niraulas Gat or Rock was constructed using a relatively high water to cement ratio, Slagle and Bloomquist reasoned that they could increase this ratio and thereby make a weaker material such that this newer version of Gator Rock, Gator Rock 2.0, was easily erodible. T he original version of Gator Rock would not erode in the RETA. Slagle attempted mixing batches of this new version of Gator Rock at various water to cement ratios. He used Niraulas specifications during the mixing procedure, but he increased his water content. When Slagles samples had cured, they visually appeared to be uniform, but when he subjected them to RETA testing, he saw that Gator Rock 2.0 did not erode uniformly. Instead, the tops of the samples eroded much faster than the bottom of the sam ples (Figure 2 46) Slagle and Bloomquist attributed this differential erosion rate to capillary processes during the mixing procedure. The Gator Rock slurry was mixed while it was wet and allowed to sit and dry for 28 days. While it was drying, Bloomquist reasoned that capillary forces in the water caused water from the bottom of the Gator Rock mold to move up through the sample as it was curing. Meanwhile, the heavier materials particularly the crushed limestone sank to the bottom of the molds. In effect, after 28 days what happened was that there was more water at the top of the mold and the curing sample than there was at the bottom. Conversely, there was more crushed limestone and Portland cement in the bottom of the sample than there was in the top. The result was the formation of Gator Rock that was nonhomogeneous in erosion, and the top of each sample was much weaker and easily erodible than the bottom of the sample. Erosion results from this batch of Gator Rock are what is presented in Slagles thesis.

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92 Gator Rock 2.0 did visually appear to be uniform, and if it had been subjected to strength testing, it may have exhibited near uniform break patterns like Niraulas. Recall from the discussion on rock scour (Section 2.3) that strength properties of rock do not necessarily correlate to erosion properties of rock. The behavior of Gator Rock 2.0 is an example where a material appeared to be homogeneous but under erosion it did not behave as such. It would have been interesting to see axial and compressive strength results from this batch of Gator Rock, but none exist. 2.5.4 Gator Rock 3.0 Bloomquist and Slagles response to this phenomenon was to design a new curing procedure for the Gator Rock. Instead of allowing the Gator Rock to sit while it was curing, they proposed using a rotisserie to slowly spin the sample as it was drying. The hope that was by spinning the sample slowly this capillary forcing mechanism could be averted and a uniform sample would be created. The rotisserie me thod for curing Gator Rock did prevent capillary action from occurring during curing, but it still did not produce a uniform Gator Rock sample. Because the sample was spinning during curing, the centrifugal forces associated with this caused separation be tween the Portland cement and the crushed limestone. The lighter particles were sucked to the outside of the mold while the heavier particles remained in the center of the mold. Although descriptions of RETA tests from this batch of Gator Rock are non e xistent, one can easily speculate as to what would have happened. During the beginning of a RETA test erosion would have been fast relative to erosion rates at the end of the RETA test On average, an erosion rate could be computed over the entire domai n, but a time series of erosion would have showed a continuously decreasing trend. Meanwhile, during a SERF test the sides of the sample would have eroded much faster than the center of the sample. The resulting eroded

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93 sample would have resembled a smal l hump. The middle of the sample would have hardly eroded at all, while the sides would have eroded. The endproduct would have resembled an upside down V shape or a cone. These results would not have been an acceptable means of calibrating the RETA a nd SERF devices either. 2.5.5 Need for Better Gator Rock Based on the above discussion, it is clear that there was still a need for a material that is strong enough to withstand RETA testing, soft enough to erode, and homogeneous enough to erode uniforml y. Chapter 5 will discuss a new version of Gator Rock, Bull Gator Rock (or Gator Rock 4.0) that meets these criteria.

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94 Figure 2 1. Results from CSU Pier Scour Study [Adapted from Molinas, A (2003). Bridge scour in nonuniform sediment mixtures and in cohesive materials: synthesis report. FHWA Report Number FHWA RD 03083, Washington, D.C.] Figure 2 2. Comparison Between Hjorths Results (Top) and Briauds Computer Model (Bottom). [Adapted from Briaud, J. L., Ting, F. C. K., Chen, H. C., Gudavalli, R., Perugu, S., and Wei, G. (1999). SRICOS: Prediction of scour rate in cohesive soils at bridge piers. Journal of Geotechnical and Geoenvironmental Engineering, 125(4), 237 246.]

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95 Figure 2 3. 3D Idealized Scour Hole [Adapted from Miller, William Jr. (2003). Model for the time rate of local sediment scour at a cylindrical structure. Ph.D. dissertation, University of Florida, Gainesville, Florida.] Figure 2 4. Definition Sketch from 3D Ideal Scour Hole [Adapted from Miller, William Jr. (2003). Model for the time rate of local sediment scour at a cylindrical structure. Ph.D. dissertation, University of Florida, Gainesville, Florida.]

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96 Figure 2 5. Second Definition Sketch from 3D Ideal Scour Hole [Adapted from Miller, William Jr. (2003). Model for the time rate of local sediment scour at a cylindrical structure. Ph.D. dissertation, University of Florida, Gainesville, Florida.] Figure 2 6. TopView of Pile Definition Sketch [Adapted from Miller, William Jr. (2003). Model for the time rate of local sediment scour at a cylindrical structure. Ph.D. dissertation, University of Florida, Gainesville, Florida.]

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97 Figure 2 7. Effective Shear Stress vs. Scour Hole Depth [Adapted from Miller, William Jr. (2003). Model for the time rate of local sediment scour at a cylindrical structure. Ph.D. dissertation, University of Florida, Gainesville, Florida.] Figure 2 8. Extended Shields Diagram [Adapted from Mehta, A. J. (2007). An introduction to hydraulics of fine sediment transport. OCP 6297 class notes draft University of Florida, Gainesville, Florida.]

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98 Figure 2 9. Ariathurai Partheniades Relationship for Erosion vs. Shear Stress [Adapted from Mehta, A. J. (2007). A n introduction to hydraulics of fine sediment transport. OCP 6297 class notes draft University of Florida, Gainesville, Florida.] Figure 2 10. Results from Modification of Partheniades Equation [Adapted from Van Prooijen, B. C. and Winterwerp, J. C. (2008). A stochastic formulation for erosion of cohesive sediments. Journal of Geophysical Research, Draft Copy, December 9, 2008.]

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99 Figure 2 11. Definition Sketch for Rock Fracture [Adapted from Henderson, M. R. (1999). A laboratory method t o evaluate the rates of water erosion of natural rock materials. M.S. Thesis, University of Florida, Gainesville FL.] Figure 2 12. ERI Definition Sketch [Adapted from Henderson, M. R. (1999). A laboratory method to evaluate the rates of water erosi on of natural rock materials. M.S. Thesis, University of Florida, Gainesville FL.]

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100 Figure 2 13. Relationship between bulk density and critical shear stress [Adapted from Mitchener, H. and Torfs, H. (1996). Erosion of mud/sand mixtures. Coastal En gineering, 29, 1 25.] Figure 2 14. Erosion Rate vs. Excess Shear Stress Relationships [Adapted from Mitchener, H. and Torfs, H. (1996). Erosion of mud/sand mixtures. Coastal Engineering, 29, 1 25.]

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101 Figure 2 15. Critical Shear Stress vs. % Fines Relationships [Adapted from Mitchener, H. and Torfs, H. (1996). Erosion of mud/sand mixtures. Coastal Engineering, 29, 1 25.] Figure 2 16. Original Schematic Diagram of SEDFlume [Adapted from McNeil, J., Taylor C., and Lick, W. (1996). Measurements of erosion of undisturbed bottom sediments with depth. Journal of Hydraulic Engineering, 122(6), 316 324.]

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102 Figure 2 17. Flow Rate vs. Shear Stress Relationship [Adapted from McNeil, J., Taylor, C., and Lic k, W. (1996). Measurements of erosion of undisturbed bottom sediments with depth. Journal of Hydraulic Engineering, 122(6), 316 324.] Figure 2 18. Example of SEDFlume Results [Adapted from McNeil, J., Taylor, C., and Lick, W. (1996). Measurem ents of erosion of undisturbed bottom sediments with depth. Journal of Hydraulic Engineering, 122(6), 316 324.]

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103 Figure 2 19. Original Schematic of the ASSET [Adapted from Roberts, S. K., and Yaras, M. I. (2006). Effects of surface roughness geometry on separation bubble transition. Journal of Turbomachinery (128)349, 349 356.] Figure 2 20. Results from ASSET Tests [Adapted from Roberts, S. K., and Yaras, M. I. (2006). Effects of surface roughness geometry on separation bubble transition. Journal of Turbomachinery (128)349, 349 356.]

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104 Figure 2 21. Photograph of the EFA [Adapted from Briaud, J. L., Chen, H. C. and Ting, F. (2010). The, EFA, erosion function appa ratus: an overview. http://tti.tamu.edu/conferences/scour/sample_paper.pdf, March 23, 2010.] Figure 2 22. Schematic of 1 mm EFA protrusion into flume [Adapted from Briaud, J. L., Chen, H. C. and Ting, F. (2010). The, EFA, erosion function apparat us: an overview. http://tti.tamu.edu/conferences/scour/sample_paper.pdf, March 23, 2010.]

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105 Figure 2 23. An example of a Moody Diagram [Adapted from Davis, Tom. Moody Diagram. Matlab Central, http://www.mathworks.com/matlabcentral/fileexchange/7747 moody diagram November 11, 2010.] Figure 2 24. Photograph of the RETA [Adapted from Sheppard, D. M., Bloomquist, D. and Slagle, P. M. (2006). Rate of erosion properties of rock and clay (corr elation of erosion rate shear stress relationships with geotechnical properties of rock and cohesive sediments). FDOT Rep ort for Project Number BD 545, RPWO #3. ]

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106 Figure 2 25. RETA Sample Annulus [Adapted from Sheppard, D. M., Bloomquist, D. and Slagl e, P. M. (2006). Rate of erosion properties of rock and clay (correlation of erosion rate shear stress relationships with geotechnical properties of rock and cohesive sediments). FDOT Rep ort for Project Number BD 545, RPWO #3. ] Figure 2 26. RETA S ample Annulus (Close up) [Adapted from Sheppard, D. M., Bloomquist, D. and Slagle, P. M. (2006). Rate of erosion properties of rock and clay (correlation of erosion rate shear stress relationships with geotechnical properties of rock and cohesive sedime nts). FDOT Rep ort for Project Number BD 545, RPWO #3. ]

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107 Figure 2 27. Schematic Diagram of RETA [Adapted from Sheppard, D. M., Bloomquist, D. and Slagle, P. M. (2006). Rate of erosion properties of rock and clay (correlation of erosion rate shear s tress relationships with geotechnical properties of rock and cohesive sediments). FDOT Rep ort for Project Number BD 545, RPWO #3. ] Figure 2 28. Photograph of Torque Cell in RETA [Adapted from Sheppard, D. M., Bloomquist, D. and Slagle, P. M. (2006) Rate of erosion properties of rock and clay (correlation of erosion rate shear stress relationships with geotechnical properties of rock and cohesive sediments). FDOT Rep ort for Project Number BD 545, RPWO #3. ]

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108 Figure 2 29. Photograph of Rock Sam ple in RETA [Adapted from Sheppard, D. M., Bloomquist, D. and Slagle, P. M. (2006). Rate of erosion properties of rock and clay (correlation of erosion rateshear stress relationships with geotechnical properties of rock and cohesive sediments). FDOT Rep ort for Project Number BD 545, RPWO #3. ] Figure 2 30. Top View of RETA [Adapted from Sheppard, D. M., Bloomquist, D. and Slagle, P. M. (2006). Rate of erosion properties of rock and clay (correlation of erosion rateshear stress relationships with geotechnical properties of rock and cohesive sediments). FDOT Rep ort for Project Number BD 545, RPWO #3. ]

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109 Figure 2 31. Relationship between shear stress and erosion rate for different Jewfish Creek Limestone samples [Adapted from Slagle, P. M. (2006). Correlations of erosion rateshear stress relationships with geotechnical properties of rock and cohesive sediments. M.S. thesis, University of Florida, Gainesville, Florida.] Figure 2 32. Relationship between cohesion and erosion rate for different shear stresses from Jewfish Creek Limesonte data set [Adapted from Slagle, P. M. (2006). Correlations of erosion rate shea r stress relationships with geotechnical properties of rock and cohesive sediments. M.S. thesis, University of Florida, Gainesville, Florida.] Erosion Rate vs Cohesive Strength 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 0.5 1 1.5 2 2.5 3 3.5 4 Millions Cohesive Strength (Pa) Erosion Rate (cm/yr) 30 Pa 35 Pa 40 Pa 45 Pa 50 Pa 55 Pa 60 Pa 65 Pa 70 Pa 75 Pa 80 Pa

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110 Figure 2 33. Definition sketch for Cohesion Derivation Figure 2 34. Typical results from Slagle s Gator Rock tests. The pink data points are from the SERF and the blue data points are from the RETA [Adapted from Slagle, P. M. (2006). Correlations of erosion rate shear stress relationships with geotechnical properties of rock and cohesive sediments. M.S. thesis, University of Florida, Gainesville, Florida.]

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111 Figure 2 35. Photograph of the SERF (Side View) [Adapted from Trammel, M. A. (2004). Laboratory apparatus and methodology for evaluating water erosion rates of erodible rock and cohesive se diments. M.S. thesis, University of Florida, Gainesville, Florida.] Figure 2 36. Top View of the SERF (Original Design) [Adapted from Trammel, M. A. (2004). Laboratory apparatus and methodology for evaluating water erosion rates of erodible rock and cohesive sediments. M.S. thesis, University of Florida, Gainesville, Florida.]

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112 Figure 2 37. Pumps used to drive water through the SERF [Adapted from Trammel, M. A. (2004). Laboratory apparatus and methodology for evaluating water erosion rates of ero dible rock and cohesive sediments. M.S. thesis, University of Florida, Gainesville, Florida.] Figure 2 38. Eroding Sample Section of the SERF [Adapted from Trammel, M. A. (2004). Laboratory apparatus and methodology for evaluating water erosion rates of erodible rock and cohesive sediments. M.S. thesis, University of Florida, Gainesville, Florida.]

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113 Figure 2 39. Viewing Window on SERF [Adapted from Trammel, M. A. (2004). Laboratory apparatus and methodology for evaluating water erosion rates of erodible rock and cohesive sediments. M.S. thesis, University of Florida, Gainesville, Florida.] Figure 2 40. Schem atic Drawing of Ultrasonic Ranging System [Adapted from Trammel, M. A. (2004). Laboratory apparatus and methodology for evaluating water erosion rates of erodible rock and cohesive sediments. M.S. thesis, University of Florida, Gainesville, Florida.]

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114 Figure 2 41. Top View of Ultrasonic Depth Sensor on SERF [Adapted from Trammel, M. A. (2004). Laboratory apparatus and methodology for evaluating water erosion rates of erodible rock and cohesive sediments. M.S. thesis, University of Florida, Gainesvi lle, Florida.] Figure 2 42. Roknis Results [Adapted from Rokni, M., Olsson, C., and Sunden, B. (1998). Numerical and experimental investigation of turbulent flow in a rectangular duct International Journal for Numerical Methods in Fluids 28, 225 242.]

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115 Figure 2 43. Comparison Between Roknis Experimental and Numerical Results [Adapted from Rokni, M., Olsson, C., and Sunden, B. (1998). Numerical and experimental investigation of turbulent flow in a rectangular duct International Journal for Numerical Methods in Fluids 28, 225 242.] Figure 2 44. Trammels Results for Cohesive Material from Jackson County, FL [Adapted from Trammel, M. A. (2004). Laboratory apparatus and methodology for evaluating water erosion rates of erodible ro ck and cohesive sediments. M.S. thesis, University of Florida, Gainesville, Florida.]

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116 Figure 2 45. Graph of Typical Temperature Rise During Longer SERF Tests Figure 2 46. Eroded Gator Rock 2.0 Sample.

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117 CHAPTER 3 ENHANCEMENTS AND IMPROVMENTS TO THE S EDIMENT EROSION RATE FLUME 3.1 Introduction As discussed in Chapter 2, when originally built the Sediment Erosion Rate Flume (SERF) wa s designed to be one of the most advanced erosion rate testing devices in the world. Still, the issu e s with the device outlined in Chapter 2, prevented the device from being as accurate as one might need when developing shear stress erosion rate relationships for use in the SheppardMiller method or the EFA SRICO S method. The specific issues with the fl ume have already been described in detail, but to summarize, testing was limited because of the following factors: 1. When cohesive sediments were used the ultrasonic pulses from the ultrasonic ranging system (SEATEK) often penetrated into the samples. 2. Wat er temperature in the flume increased over longer durations tests. 3. Shear stress was not measured directly; rather it was inferred via a pressure drop. In addition to fixing these issue s with the SERF, another goal of this study was to make the flume operable for two more sets of experimental conditions: 1. The first goal was to strictly control recirculating sediment as it passes through the flume so that clear water vs. live bed conditions could be more precisely regulated and isolated from one another. 2. The second goal was to study the effects of upstream artificially induced vortices on both shear stress and erosion rate development. This chapter discusses the improvements, enhancements, and additions made to the SERF to solve the original is s ues asso ciated with testing and meet the new experimental goals for this project. 3.2 Laser Leveling System The ultra sonic pulse sample penetration problem was introduced in Chapter 2. To combat this issue in the past, researchers like Slagle, who was actuall y the only previous researcher to use cohesive sediment in the SERF only had one option to input an artificial depth value in the

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118 motor feedback loop program. The depth from the top of the flume to the top of the sample should be 4.92 cm. When Slagle tested cohesive material with the SEATEK (ultra sonic ranging system) and the SEATEK returned depth values that deviated from the correct 4.92 cm reading, his solution was to tell the computer to use a different comparative depth. So, for example if on av erage the sand clay mixture was returning a level value of 5.20 cm, this was used as the basis for comparison. There were a several issues associated with this procedure. I f Slagle was going to use the user defined input parameter to level his samples, he would have standardized his testing procedure. Slagles recommendation when experimenting on a sample that is experiencing ultrasonic penetration is essentially to run the SEATEK while the flume is running, and observe what the SEATEK return values should have been. A better method would have been to run the SEATEK for a specified time length while the sample was level with the flume bottom and pump speed was low (to prevent erosion). Then, SEATEK data would be recorded, an average would be taken, and this quantity would be used as the user defined input value. Slagle does not explain why he did not use this method, but further analysis shows that e ven doing this would have not worked. Experience with the SERF shows that at higher flow rates, return pulse timing yields a greater depth than it would have yielded at a slower flow rate. At first, this result appeared puzzling since the SEATEK should work independently from flow speed. This is because at higher flow rates, erosion rate s are higher as w ell. Although a filter was added to the device (to be discussed in Section 3.5), the filter was unable to keep up with rapid cohesive erosion. The filter was also incapable of removing fine material from suspension such that when clayey samples eroded, t here was often tiny clay flocs present in the water column.

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119 When the ultrasonic pulses hit a sample (and potentially penetrate it), the presumption is that they are traveling in a straight line. As the pulses get deflected or diffracted, the time that it takes the pulses to return to their source increases. When more material is in suspension, as it is at higher flow rates because of recirculating material, the pulses will get deflected or diffracted more frequently. This causes a slightly slower pul se return time, which in turn translates into an apparent deeper SEATEK reading. Combine this with penetration effects, and its obvious that a user defined solution cannot work. To set up this condition quantitatively, one would have to run the SERF at a certain flow speed and continuously manually level off the sample with the flume bottom. Then, the SEATEK return values would be recorded, averaged, and the correct user defined depth would be known. Lastly, the test could be repeated such that the feedback mechanism, not user control, would govern sample advancement. W ith this procedure, one of the SERFs advantages is eliminated because development of the flow speed vs. user defined depth correlations would be dependent on operator input. Under the se conditions then, the SERF would in effect be the same as the EFA, the ASSET, or the SEDFlume. Secondly, this procedure would double the amount of time it takes to obtain a dataset. The manual advancement step in the previous procedure would have produced its own erosion rate shear stress curve. It is also likely that for a given flow speed, user defined depth would have changed dynamically with time. In other words, as a sample erodes, more of the eroded portion of the sample recirculates back throu gh the SERF. As this occurs, sediment concentration in the water column increases, which in turn causes a greater scatter frequency over the course of the test In other words, if at the beginning of the test the SEATEK output a depth of 5.10 cm, by the end of

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120 the test due to the increase in particle concentration, the SEATEK would output a higher depth such as 5.30 cm. Even if this could be accounted for, this ignores the fact that different sand clay compositions yield different penetration depths. F or example, penetration into a 25% sand sample is different from penetration into a 75% sample. To calibrate this procedure would require the development of an increasingly complicated set of curves and correction factors, and even with this, would likely not work as expected One goal of this project was to make the SERF capable of testing sand cla y materials Because the SEATEK cannot be used as a stand alone system when cohesive sediment is used, something else needed to be invented and installed. There are two ways to measure whether or not a sample is level with the flume bottom. The SEATEK approach involves a topdown design where measurements from a fixed point above the sample determine whether or not the sample needs to be advanced. The second approach a flushness approach had not yet been tried. The simple analogy to a flushness approach is the safety for a garage door opener. The safety is designed such that at one end of th e garage door, a laser is aimed at a corresponding photoelectric sensor at the other side of the door. If the photoelectric sensor can see the laser, it must mean that the path for the garage door is clear, and the door is allowed to operate. If the sensor cannot see the laser, then some thing is blocking the lasers path. Under these conditions, a voltage is sent back through a feedback loop with the garage door openers motor, and the motor is not allowed to operate unti l this voltage switch is on or until the laser is no longer blocked. A similar design to this was in stalled in the SERF. Using miniature fiber optics, a series of three lasers and three corresponding photoelectric sensors were installed along the top of the

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121 flumes bottom wall (Figure 31). Diameter of the lasers and photoelectric sensors were 1.50 mm. Signals from the photoelectric sensors are amplified so that a light signal can be read even when the flume is full of water ( Figure 3 2). Output from the amplifiers is sent through a control box, into the computer s SCC 2345 analog signal processor, to the computers PCI 6310 card, and to the Labview control program. Using the same principles as a garage door opener, if a laser photoelectric sensor pair is blocked, it must be blocked by the sample. Under these conditions then, the motor will not move. When the laser photoelectric sensor pair becomes uncovered, it must mean that the sample needs to be advanced. Qualitative testing with the laser photoelectric sensor pairs before SERF installation showed two thing s: 1. This system would work under cloudy water conditions that would be seen during sand clay erosion testing. 2. This system would be able to provide a recess resolution of approximately half the laser diameter or approximately 0.75 mm. The second point is discussed in some detail. When the lasers are operating, the motor will not move until all of them (if AND logic is used) or some of them (if OR logic is used) are uncovered. Only when the correct combination of lasers is uncovered will a sample adva nce. The advancement minimum then 0.75 mm becomes the associated error with this design. In other words, when the lasers are engaged, the sample cannot advance unless at least 0.75 mm of material has eroded from it. The SEATEK was slightly more prec ise as it would cause advancement/retraction with a depth deviation of 0.5 mm. This design error is of the same order of magnitude as the SEATEK, and smaller waterproof lasers could not be found. Although three lasers were used during this study due to budget limitations, it is easy to see how to idealize this design. Often, samples in the SERF erode nonuniformly. For example, certain sand clay mixtures exhibit erosion characteristics where their front face erodes much

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122 slower than their back edge. R ather than using three lasers spaced at a certain interval, the most effective design for a laser leveling system would be to install a continuous laser array and corresponding photoelectric sensors. This would eliminate the AND logic vs. OR logic question and instead allow a certain percentage of eroding sample length to determine sample advancement. Implementation of this complete system would not have fit within this projects budget constraints however, and therefore was economically not feasible. Wh en implemented, the laser system as installed worked as it was designed. Due to differential erosion rates, the lasers were programmed such that OR logic was used. Therefore, if two of the three lasers became uncovered, the sample advanced. Although the primary purpose of the laser leveling system was to allow for cohesive sediment testing another valuable aspect of the system is that when noncohesive sediments are used in the SERF, it can be used as a redundant check in conjunction with the SEATEK. Although not as common as with cohesive sediments, when sands are tested at high flow rates sometimes sand particles can be seen recirculating back through the flume. As with clays, recirculation causes SEATEK errors, but with the implementation of the lasers, t hese errors can be reduced. During non cohesive testing then, instead of using the SEATEK or the lasers as independent systems, the following feedback algorithm was programmed: 1. Check to see if the middle laser and either the front or back laser is uncovered. 2. Read depth from SEATEK. 3. If the correct laser sequence is uncovered and the SEATEK says that top depth is greater than 0.5 mm, record the SEATEK depth deviation. 4. Move sample required amount. 5. Go back to (1)

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123 This laser leveling system is the only known system like it in an erosion rate testing device, it has helped to reduce the number of SEATEK errors when noncohesive sediments are present, and it has allowed the SERF to be used for testing cohesive materials. The introduction of the laser leveling system is the most significant upgrade made to the SERF during this project. 3.3 Temperature Control System and Temperature Patch for SEATEK Besides the SERFs initial inability to test cohe sive materials, the next issue with the device w as the temperature rise during longer tests discussed in Chapter 2. Because of the speed of sound s dependence on water temperature, the temperature rise caused the SEATEK to malfunction and could have caused damage to the SEATEK. The SEATEK gets hot as it sends ultrasonic pulses and it is dependent on a steady str eam of water to cool itself If the water gets too hot, the SEATEK will be damaged Also, a temperature rise during tests is not typical in nature and should be avoided. To combat against te mperature rises during tests a temperature control device was installed There were several designs that were considered when designing the temperature controlling apparatus. For example, there is a chilled water line that runs through Reed Lab (where t he SERF was built), and there was talk at one point about running a cooling coils of that line into the reservoir tank. This design was ultimately rejected because there was a less expensive, more efficient option. Eventually, the final cooling method w as developed using a water chiller There were two options for water chiller design The first option involved sending flow through the SERF (2,000 gpm during max capacity) through the chiller so that it could be cooled. However, a chiller with 2,000 gpm capacity is prohibitively expensive, and it was not feasible with the budget constraints of the project.

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124 The second chiller option was to install a cooling device in parallel with the SERF pumps. Although the temperature rise in the SERF is large over a 24 hour time scale, over a one hour time span it is actually relatively small. Temperature only rises approximately 2oC per hour during maximum capacity flow conditions. Because of this it was possible to de sign a chiller that was just large enough to keep up with that degree of temperature r ise at a much lower flow rate. A 6 ton chiller was installed in parallel with the primary pumps and powered by an i nternal 30hp pump (Figure 33). The RTS 603 Rite Temp Water Chiller runs at a flow rate of approximately 30 gpm, and it is capable of removing 72,000 BTU/hr. of heat. Because the temperature in the SERF reservoir tank rose at approximately 2oC/hr., the following calculation was conducted to make sure that the 6ton chiller could keep up wi th the temperature rise: d. req' BTU/hr. ,000 0 5 ~ rise) (temp 3.6 (S.F.) 1.5 9,177 gal 1 ft 1337 0 ft lb 62.4 gal 1100o 3F As shown here, the 6ton chiller is oversized by approximately 30% even with a safety factor of 2.0 built into the calculation. Although a 5ton chiller could have done the job (max capacity of 60,000 BTU/hr.) the chiller was oversized for two reasons. First, the 6ton chiller wasnt much more expensive than the 5ton chiller. Secondly, the 6 ton chiller is capable of cooling water in the tank more quickly. This means that the 6ton chillers compressor wil l shut off more often and it will not have to run constantly. Because the chiller is not continually running, it will save electricity and it will lessen the chance that the chiller could break. To confirm that the chiller was the proper size, a cooling test was conducted in the SE RF reservoir tank (Figure 3 4). As demonstrated, the chiller is capable of producing a temperature drop of approximately 3.0oC/hr. and temperature in the tank appears to respond to the relationship given by Equation 31:

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125 i fT t T 05 0 (3 1) where t is the time in minutes and Ti is the initial temperature. Once the water chiller had been installed, the SERF was capable of running longer duration tests This was essential for testing of rock like materials because these tests often take 24hours or more. Once the temperature had been regulated, the SEATEK feedback loop was studied one more time. In the past, getting the SEATEK up and running was cumbersome from a computing standpoint. Previously, SERF users were required to open a serial connection with HyperTerminal or TeraTermainal, input a water temperature, and start a data run. Then, the experimenter would close HyperTerminal, start the control program that was written in Labv iew, and every time the Labview program ran through another loop, it would read output data from the SEATEK on the serial port. The obvious problem with this method is that water temperature is only being fed to the SEATEK once at the beginning of the te st The temperature control system can effectively regulate temperature to +/2oC, yet even within this range, the SEATEK is supposed to be accurate within 0.5 mm. Because the speed of sound in water is so dependent on water temperature, if the SEATEK do es not know the exact water temperature precisely, it will output a depth value that is incorrect. A thermocouple had been installed in the SERF, but had not been incorporated into a feedback loop with the SEATEK. The presence of the thermocouple makes it possible to program the actual temperature to the SEATEK in real time. A patch was written in Labview for the control program so that before the SEATEK takes a depth reading, it first is given a temperature reading from the thermocouple so that it know s what the correct ultrasonic pulse

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126 return time should be at that temperature. Although a quantitative analysis of error reduction was not conducted after installation of this temperature patch, qualitatively it appears that when the SEATEK is used as a s tand alone system now, particularly at low flow speeds, the sample appears to say more level with the flumes bottom. SERF computer programs including the new control system with the temperature patch are presented in Appendix C. 3.4 New Shear Stress Sys tem As discussed in Chapter 2, other flume style erosionrate testing devices such as the EFA, the ASSET, the SEDFlume, and the original SERF did not measure shear stress on an eroding sample directly. Rather, shear stress was inferred in a few different ways. The EFA inferred shear stress from a Moody diagram; the ASSET and the SEDFlume inferred shear stress from a relationship obtained from the Darcy Weisbach equation; and the SERF used a pressure drop relationship to infer shear stress. When these ot her flumes were developed, the technology did not exist to measure shear stress directly. While some of these approximating methods are accurate, and some of them are not, there was no way to quantify which were better than the others. To definitively answer how to properly measure shear stress in a flume style erosion rate testing device, three sets of improvements were made to the SERF. 3.4.1 Shear Stress Sensor The issue with measuring shear stress in devices like the SERF in the past stemmed from th e inability to accurately measure shear stresses of small magnitudes. In the SERF, at high flow rates, a relatively high the shear stress is 100 Pa (0.015 psi) while typically at lower flow rates shear stresses are around 10 Pa (0.0015 psi). These stress values are much too small to be picked up accurately by most commercial strain gauges or pressure sensors.

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127 From 2003 through 2007, researchers at the Turner Fairbank Highway Research Center were conducting research on the pressure flow scour problem, and they sought to correlate scour depth below a submerged bridge deck with bed shear stress. Kornel Kerenyi from the TFHRC worked with Hans Prechtl of Elektat in Austria to develop a device that was sensitive enough to measure small scale forcing associated with wall shear stresses in an open flume. During their research from 2003 through 2007, researchers at the TFHRC and Prechtl developed three generations of sensors that could measure wall shear stresses. Each new generation was an improvement over the previous design, and by 2007, their open channel design was excellent. When talk of improving the SERF began, it was logical to go to Prechtl and develop a device that was capable of measuring wall shear stresses in a closed flume or rectangular duct. A new shear stress sensor was designed and developed that works in rectangular closed ducts (Figure 3 5). In the new shear stress sensor, a 50 mm disk (25) is attached to the top of a movable plate with three springs (24 and 26). The movable plate is attached to the base of the sensor using two bronze leaf springs (28 and 34). Because bronze is so elastic, there is little friction with regard to platform movement. As water passes over the disk, the disk deflects a small amount, and in turn, the platf orm (36) will deflect. On the underside of the platform, a magnet (29) is mounted above a Hall Sensor (33). On the upstream and downstream face of the underside of the sensor, a brass rod is mounted and attached to two more magnets. Around these upstrea m and downstream magnets, an electrical solenoid is installed. When the platform deflects, the Hall sensor reads the deflection reading and sends a signal to the upstream solenoid (35) so that the solenoid magnetizes enough to pull the platform back to i ts resting position. This feedback loop runs constantly, and an output voltage is sent from the

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128 deflection solenoid, through an amplifier (Figure 36), and to the SERFs analog signal control box based on the amount that the solenoids had to magnetize to move the disk back to equilibrium. To calibrate the device, a signal can be sent from the control box, through the amplifier, and to the downstream solenoid (27) so that it magnetizes a specified amount. In response to this small deflection, the Hall se nsor upstream solenoid feedback loop will react, and voltage can be correlated to shear stress accurately. The shear stress sensor is capable of measuring stress differences on the order of 0.02 Pa (3x106 psi). The TFHRC sensors only used a Hall sensor to measure the initial deflection of the disk; there was no solenoid to move the disk back to equilibrium. Because of this, any presence of grit, bubbles, or turbulence in the sensor caused errors with the shear stress reading. The dual solenoid design allows deflections of only 5 m, and because the parts in the sensor move such a small amount, the potential for errors is lower. This design is a significant improvement over the TFHRC sensors, and at present, it is believed that this is the only sensor like it in the world. This sensor was installed in the SERF just upstream from the erosion test section. When using their shear stress sensor, researchers at the TFHRC often had issue s with data drift associated with temperature and humidity increases. They constructed a box around their sensor where cold air from an air conditioning unit could be pumped in and around their sensors to avoid these data collection errors. A similar design was used with the SERF (Figure 37 and Figure 3 8 ) Although there was talk of installing and removing the sensor in concert with test specimens, FDOT wanted it installed upstream. It is not believed that its upstream placement will affect the readings from the sensor since it was positioned so that it was only 1 ft. ups tream

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129 from the test section. However, until velocity profiles can accurately be measured in the SERF, there is no way to be sure of this. 3.3.2 New Pressure Transducers The second shear stress system enhancement that was made to the SERF was the installation of a set of new pressure transducers. When the flume was originally designed (Trammel 2004), pressure difference upstream and downstream from the sample was measured with a transducer with a range of +/ 2.5psi (14,000 Pa). The accuracy of this device was 0.02% times its range which equals 5.2 Pa. When one examines the formula for converting pressure difference along a flumes walls to shear stress: L w l plw 2 2 (3 2) and uses an order of magnitude argument, it becomes obvious that measuring a pressure differential with 5.2 Pa a ccuracy could be better. In this equation, l = 4.44 cm, w = 20.32 cm, and L = 10.16 m. Substituting into equation 3.31, is accurate to +/ 0.50 Pa, which is well below the accuracy of the shear stress sensor. In 2006, Slagle tried to improve the accuracy of the device by installing a differential pressure transducer that had a range of +/ 10 inches of water (+/ 2450 Pa). This improved the accuracy of shear stress estimations from the pressure drop to +/ 0.0891 Pa. Although Slagles improvement gave more accurate shear stress data, the drawback was that the new sensors were ultrasensitive to over pressurizing and they often broke due to operat ional error. Often, a hose would be connected that was too long, and as a result, the membrane inside the sensor would snap or pop causing the sensor to break. This mistake happened often, and every time it happened, a new se nsor would need to be purchased.

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130 Slagle had the right idea when improving the accuracy of his pressure sensors, he chose s ensors from Sensotec that had little over pressurization tolerance. During this project, the most effective aspects from Slagle and Trammels design were combi ned. A new sensor system was purchased from Omega Engineering, and these sensors were capable of withstanding over pressurization forces of approximately 50 psi., while their range was accurate to +/ 0.02% of 0.5 psi (3447 Pa). Although slightly less acc urate than Slagles sensor, they do not break as often, and they are close to the accuracy of Slagles. Two sensors and four pressure ports were ins talled in the SERF (Figure 3 9). The first set of taps was installed upstream and downstream from the sam ple section while the second set of taps were positioned upstream and downstream from the shear stress sensor. The motivation behind this setup originally was to use the sensors as a check of the shear stress sensor, but as Chapter 4 will discus s, this may not be the most effective solution because inferring shear stress from a pressure drop may not be correct. 3.4.3 Paddlewheel Flow meter The third shear stress system improvement that was made was the installation of a paddlewheel flow meter. In 2004, Trammel measured SERF velocity using an ultrasonic velocity flow meter (Figure 310) but ultrasonic flow meters are not accurate enough Trammels velocity correlation appeared incorrect physically it was logarithmically distributed which implies tha t for a low pump frequency, a negative velocity should ensue so operators knew that it was questionable. To properly evaluate the Briaud Moody Diagram method and the Flat Wall method for estimating shear stress, an accurate velocity vs. pump speed distr ibution was required. When the first round of tests were conducted, i nvestigators tried to use Trammels velocity correlation in the Colebrook Equation (which describes the Moody Diagram to be discussed in Chapter 4) and Flat Wall equations, but results w ere such were even under flat

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131 conditions, the equations pre dicted shear stresses that were 10 times lower than actual readings from the shear stress sensor. These results indicated that the velocities appeared to be incorrect so an Omega FBP151 paddlewheel flow was installed to measure velocities accurately. This flow meter outputs a 4 20 mA analog signal, and has a m aximum flow velocity of 30 fps. According to its manufacturer, it is accurate to +/ 1% F.S. The flow meter has a digital display along its top, and different flow ranges can be selected to improve its accuracy. With the installation of the new flow meter, a new velocity pump frequency correlation was developed and used on existing data to compute the velocity in the SERF (Figure 311). 3.5 Sediment Control System As discussed in the introduction to this chapter, another goal of this project was to design a series of devices for the SERF such that suspended sediment in the water column could be strictly controlled. When a clear water test was to be conducted in the flume previously eroded sediment must be removed from the device. Conversely, sometimes investigators would want to run a test that simulated live bed conditions. If live bed conditions were to be analyzed, another device was to be designed to inject s and into the device. The sand concentrations coming from the sand injector would be highly regulated so that the sediment concentration variable could be isolated from other variables in the erosion problem. Two devices were designed to be used in this s ediment control system, but unfortunately the failure of one of these devices precludes final installation of the other. 3.5.1 Filter System Like the temperature rise problem, the recirculating eroded sediment (both sand and cohesive material) issue was examined from two approaches On one hand, water could be pumped through a filter at its maximum 2,000 gpm flow rate to insure that suspended sediment

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132 was removed from it. On the other hand, an independent filtering mechanism could be installed in series with the water chiller to act as a secondary system similar to the way in which a swimming pool or a hot tub works. Because of budget constraints, the second option was chosen. While the independent system design worked well with the water chiller, it did not work well with the filter. During a day of testing particularly with sand clay mixtures, visual observation showed that as testing continued, water became cloudier. Although quantitative measurements of sediment concentration were not conducte d, qualitatively, by the end of a full day of testing, the camera could not see the eroding sample from its viewing port. This means that there was less than 10 cm of visibility in the flume. At first, it was assumed that this cloudiness was the result of suspended clay flocs in the water column. Eventually, after a day of testing the flume was emptied as it was after every day of testing, and as water exited the flume 5 gallon bucket samples of water were taken. Material was allowed to settle out of s uspension, and as the eroded material settled, the resulting residue showed that sand and clay had stuck together to form sandclay flocs. These sand clay flocs were suspended in the water column during testing and they were the causing some of the cloudiness in the water column. This is not to say that the filter d id nothing to help After each round of testing, the reservoir tank was emptied, refilled with clean water, the filter was backwashed, and then the tank was filled again. Backwashing the fi lter revealed even cloudier (qualitatively) water than the dirty water that drained from the tank after a day of testing. It appears that the filter was doing some work in terms of removing suspended sediment from the water column, but after a day of exte nsive SERF testing ( approximately 4 samples were tested per day), the filter simply

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133 could not keep up with the sediment influx rate. Whereas the smaller water chiller could keep up with a 2oC temperature rise, the filter could not keep up with particle concentrations rising to 0.50g/L over the course of 5 hours of testing ([~500 g of solid material per sample*4 tests per day ] divided by1,100 gallons[ 4,164 L] = 0.48 g/L). Or, put another way, the sand filter could not keep up with sediment concentration increases of 0.1 g/L per hr. The pump that drives the filter runs at approximately 30 gpm. Therefore, assuming uniform mixing, it would take the filter approximately 37minutes to filter the water in the reservoir tank. If this were the case, then break s between tests to change the sample, reset the computer program, analyze data, etc. should have given the filter enough time to clean the tank s water. Because visually it was not happening, either: 1. The filter cannot filter fine enough particles out of the water. 2. Water in the reservoir tank is not being uniformly mixed. It is difficult to isolate which of these two possibilities presented the largest problem with filtration, although because the installed filter was a sand filter, it is likely that it was unable to remove the small diameter particles from suspension. Still, even if a finer filter was used, it is unclear whether or not it would have any effect as much of the particulate that drained from the reservoir tank after testing was sandy. Because of the filters failure during sand clay tests results from the latest round of sandclay tests in the SERF may be subject to the same criticism as results from the CSU tests This was a disappointing development during testing, and it was not anticipated when the filter was installed. The second issue that is caused by the sand filters failure is that the highly regulated sand injection system could not be installed in the S ERF. Adding another source of sand to the device without proper filtering would allow even more sediment to cycle back through the flume. Even

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134 without the introduction of another sediment source, it is impossible to say how sediment concentration is chan ging with time. Within the context of a flume run suspended sediment concentration is continually changing. Adding another source of sediment would further skew the results and not produce valid data because it would be impossible to isolate sand concen tration as a variable. The third concern from the filter systems failure is that if the SERF is allowed to run with high sediment concentrations in the water, the pu mps will eventually be compromised The centrifugal pumps require a clean water source. If sand is moving through them at high flow speeds, the net effect is that the pumps impellers are in effect, being sand blasted. I ntroduction of a sand injector would only serve to exacerbate the issue Although the SERF has been operable since Tra mmel started his work in 2002, this latest round of tests is the only time when the device was used extensively under high erosion conditions. Analysis of Slagles work shows that his data points with the SERF are limited. Most of Slagles work involved RETA testing. When Slagle did use the SERF, erosion rates were low and the device could not be used for long periods of time (because of the temperature rise). Therefore, during Slagles tenure at UF, the SERF frequently sat idle. Even with this downtime, when this project began, one of the flumes primary pumps was already damaged presumably because of recirculating sediment. Fortunately this damage was only to the pumps seal, and rep air was relatively inexpensive. If an impeller had to be replace d though, the cost could be prohibitively expensive and shut the SERF down for the foreseeable future. Because of this, a decision was made to limit the dataset as much as possible. The necessity to do this though was disappointing and unexpected both because it precluded the

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135 installation of the sand injector and because it meant that the datasets generated would not be as extensive as they might have been. 3.5.2 Sand Injector This project was conducted such that additions and improvements were designe d and built before testing began. This was done to streamline the project and make things more efficient. Thus, if the sand injector, laser system, filter system, and chiller system could be designed and built at the same time in parallel with one anothe r, then installation could happen simultaneously and testing could follow. However, since the filter perform ed so poorly, work with on the aforementioned sand injector had been completed before testing began. The limitation is that it cannot be installed because it currently cannot be used. However, when a proper filter is added to the SERF, the sand injector is r eady to be put into operation. The sand injector must operate in a continuous stream under clear conditions To do this, a 4 inch diameter PVC feedscrew was designed and built by Carleton Helical Technologies. The feedscrew was installed inside a 10 inch PVC reservoir and a 4 inch PVC pipe was extended down the screw length. The 10 inch PVC reservoir is filled with sand, and as the feedscre w turns, it feeds sand up the 4 inch PVC pipe into the PVC section of the SERF. Although initially attempts were made to use a larger reservoir (a 55 gallon drum for example), these larger vessels were unable to with stand water pressure during operation. According to Sferraz z a and Williams (2002), above a threshold pressure of approximately 14 psi, a 55 gallon drum will likely fail. Because it would be filled with water, this failure would not be explosive. Instead, a seal would break, and water would leak from it. The SERF, at higher flow rates produces up to 20 psi, and the goal was for the sand injector to be usable at any flow rate. Therefore, eventually the 55gallon drum was abandoned in favor of the smaller 10 inch PVC pipe reservoir.

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136 The sand injectors feedscrew is connected to a variable speed motor control. Although the motor control is not currently hooked up to the SERF control computer (this step was skipped since t he sand injector was not installed), in the future this upgrade should be made so that the motor can be computerized. To regulate sand entering the SE RF, the sand injector rests on four, 250lb capacity load cells (Figure 3 12). B efore a test is conduc ted, the weight of the sand injector will be known. During testing, the sand injector weight will decrease, and after testing, the final weight will be known. By measuring t he amount of mass lost researchers will precisely know the quantity of sand that was injected. Because the load cells need to slightly deflect so that they can output a weight signal, two expansion joints were installed at either end of the sand injector (Figure 3 13 ). These expansion joints allow for 0.5 in of deflection, which is less than the deflection of the load cell mounts. A photograph of the completed sand injec tor is presented in Figure 3 14. Because the sand injector was not installed, it could not be calibrated. However when a proper filter is added to the SERF, it ca n be easily installed and calibrated. 3.6 Vortex System Besides sediment control, there was another new scenario under which the SERF was to be run the scenario where vortices are generated in the flume upstream from the eroding sample or shear stress se nsor. These vortices do not mimic the horseshoe vortex. The vortex generator is instead designed such that the presence of an upstream obstruction generates a turbulent wake region (or vortex region) behind the obstruction. This newly formed turbulent f low regime may affect erosion rate. To create this vortex obstruction, a circular cylinder was selected because flow past circular cylinders has been studied extensively, and flow patterns in the cylinders wake region

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137 are fairly well understood. From experience at the TFHRC, in flume tests blockage should less than 20% of the flumes cross sectional area (Crowley 2007). Because the goal was to generate the largest wake region possible, the maximum cylinder size was c hosen. The SERF height is 1.75 in., so the maximum horizontal cylinder that could be used was approximately 3/8 inch to ensure that blockage requirements were met. To design the height of the cylinder properly so that full intact vortices were produced research was conducted on flow fields around circular cylinders near a wall. Because the eroding test section is loaded into the flume from the bottom, the goal was to generate vortices as close to the bed as possible. According to Sumer and Fredsoe (2006 ), when a cylinder is placed near a wall, the flow around the cylinder changes compared to how it would look if the wall was not present (Figure 3 15) Among the changes to the flow patterns around a cylinder, the most important for the SERF is that vo rtex shedding becomes suppressed for gap ratio values smaller than approximately e/D = 0.3, where e is the gap between the bottom of the cylinder and the wall and D is the cylinders diameter. Because the diameter of the cylinder is fixed at 0.375 inches due to blockage requirements, the minimum gap that could be used to achieve a gap ratio of 0.3 is 0.104 inches. Because this is a nonstandard size and because generally speaking, a larger gap means a larger wake region, this value was slightly expanded so that the design gap for the vortex generator would be 0.125 inches This results in a gap ratio of 0.33 which is acceptable. Once the cylinder and th e gap had been properly sized, the Strouhal Number was used to predict the number of vortex oscillations that will occur. Past tests in the SERF have shown that erosion does not occur below a Reynolds Number of ~3x102, and based on a 2,000 gpm max volumetric flow rate limit, the largest Reynolds Number that will ever be pre sent in the SERF is

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138 ~4.5x104. Within these ranges, the Strouhal number (Equation 33 and Figure 316) should stay fixed at approximately 0.2. U D f Stv (3 3) In E quation 33, fv is the vortex shedding frequency, D is the cylinders diamete r, and U is the water velocity. Therefore, at low flow rates, vortices should be produced at approximately 0.7 Hz, at high flow rates, approximately 70 Hz, and in between this upper and lower limit, a Strouhal analysis can be conducted to predict the vort ex shedding frequencies at each velocity. Analysis was conducted to determine the relative expected size of the vortices induced by the cylinder when they reach the test section and the sample section, but little research has been done in terms of charac terizing vortex size specifically as a function of distance downstream from the cylinder. Rather, the bulk of research in flow past a circular cylinder has involved qualitative measurements for the characteristics of the vortex street with quantitative wo rk focusing on drag and lift forces on the protruding structure. The vortex generator was installed so that the protruding cylinder was easily removable. A photograph of the newly installed vortex section is shown in Figure 317. Water velocities in th e SERF were first estimated using Trammels curves, but when the flow meter was installed, the calculations were updated to account for the more accurate velocity measurements. 3.7 Miscellaneous Other Enhancements to the SERF Other than the major additions, enhancements, and improvem ents to the SERF, several ot her minor changes were made to the device. First, to accommodate the new components, the Labview computer program that runs the SERF was rewritten. When this project was started in 2007, the late st version of the computer program had numerous bugs, not intuitive and slower than it should have been. Although its original designers did an adequate job of integrating the

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139 device with Labview, more could have been done. Because of this, when the new s ystems were integrated it made more sense to simply rewrite large sections of Labview code rather than create patches to piece together the existing program. A full discussion of the command modules of the SERF is presented in Appendix C, but in brief, t he new version of the program allows the user to turn on or turn off any of the twelve crystals in the SEATEK, the entire SEATEK array, the laser system, the shear sensor, the pressure transducers (one or both of them), and the Servomotor. The pump, whic h had been controlled with a digital display device that was independent from the computer, has been integrated with the GUI. Because of the advanced computer integration of the SERF, a system was set up where the device could be controlled remotely. To operate the flume now, a user needs to load a sample, set up the instrument, and then periodically check on it from time to time to make sure everything is operating properly If something malfunctions the pump, the motor, and the entire system can be stopped remotely. This capability is advantageous during longer testing (24 hours or more) of rock specimens. Even with this capability though, computers were used to create soft limit switches for the motor so that if something were to go malfunction t he SERF should shut down autonomously Along with the need to rewrite the entire SERF computer system came a need to revamp the method in which the device captures and records video. The original method involved the use of a VCR or a Hi 8 recorder. The Hi 8 device was converted to a DVR with networking capabilities so that the operator can now operate the computer, see inside the flume, record video, and review video remotely.

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140 3.8 Summary of SERF Improvements and Brief Discussion In summary, the following components improvements and enhancements were made to the SERF: Laser l eveling s ystem installed Temperature c ontrol s ystem o Water c hiller installed o Temperature c ontrol p atch written Shear stress s ystem o Shear s tress s ensor installed o Pressure t ransducer s installed o Paddlewheel f lowmeter installed Suspended s ediment c ontrol s ystem o Sand f ilter built and installed o Sand i njector built Vortex i nduction s ystem Miscellaneous i mprovements o Computer code updated and rewritten o Computer operating system updated o Video capture system updated Because of the improvements and enhancements made to the SERF, the testing procedures had to be changed. The last version of instructions for SERF testing appeared in Trammels (2004) thesis, and is out of date. New testing procedures can be found in Appendix B. Also included in Appendix B are a series of steps that can be used for troubleshooting malfunctions in the device. Appendix C provides a discussion on the new computer programs for SERF control. These improvements and enhancements to the SERF are significant and they make the SERF unique when compared with other erosionrate testing devices. The device was already unique because of its ultrasonic depth sensor stepper motor feedback loop, but the addition of these components have move the SERF even further ahead of these other devices in terms of precision and sophistication. There are no other known devices like it in the world that have these computerized capabilities.

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141 Figure 3 1. Photograph of Laser Leveling System (the third laser is blocked by the camera angle) Figure 3 2. Photograph of Amplifiers and Control Boxes for the Laser Leveling System

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142 Figure 3 3. Water Chiller Figure 3 4. Temperature Drop after Water Chiller Installation 0 20 40 60 80 100 120 15 16 17 18 19 20 21 22 23 24 25 Time (Min) Temperature ( oC ) y(x) = a x + b a = -0.045949 b = 24.038 R = 0.99509 (lin) y(x) = a x + b a = -0.05016 b = 21.547 R = 0.99877 (lin)

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143 Figure 35. Inner Components of the Shear Stress Sensor

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144 Figure 3 6. Shear Stress Sensor Amplifier Figure 3 7. Modified A/C Unit for Shear Stress Sensor Temperature Control

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145 Figure 3 8. Shear Stress Sensor Box with Tube From A/C Unit Figure 3 9. New Pressu re Transducers in SERF

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146 Figure 3 10. Trammels Original Flow Rate vs. Pump Frequency [Adapted from Trammel, M. A. (2004). Laboratory apparatus and methodology for evaluating water erosion rates of erodible rock and cohesive sediments. M.S. thesis, Uni versity of Florida, Gainesville, Florida.] Figure 3 11. New Pump Frequency vs. Velocity Curve 0 10 20 30 40 50 60 0 1 2 3 4 5 6 7 Pump Frequency (Hz)Velocity (m/s) y(x) = a x a = 0.10119 R = 0.99945 (lin)

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147 Figure 3 12. Sand Injector Load Cell Mounts Figure 3 13. Sand Injector Expansion Joints

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148 \ Figure 3 14. Completed Sand Injector

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149 Figure 3 15. Gap Ratio Effects on Vortex Development [Adapted from Sumer, M. and Fredsoe, J (2006). Hydrodynamics around cylindrical structures World Scientific Publishing, Hackensack, NJ.] Figure 3 16. Graph of Strouhal Number vs. Reynolds Number [Adapted from S umer, M. and Fredsoe, J (2006). Hydrodynamics around cylindrical structures World Scientific Publishing, Hackensack, NJ.]

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150 Figure 3 17. Vortex Generator installed in SERF (looking into flume)

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151 CHAPTER 4 ESTIMATIONS AND MEAS UREMENTS OF SHEAR ST RESSES O N AN ERODING BED MATERIAL IN FLUME STYLE EROSION RATE TESTING DEVICES 4.1 Executive Summary With the installation of the new shear stress system as described in Chapter 3, a method was finally available for measuring shear stress directly in a closed rect angular duct and comparing results with previous methods for estimating shear stresses in a flume. Tests were run in the SERF for varying bed roughnesses (grain sizes) and at a range of velocities to determine the accuracy of shear stress estimates using a pressure drop. Tests showed that large particles can cause higher shear stresses than the returned computed value using a pressure drop. As particle sizes become smaller, the pressure drop method for estimating shear stress becomes increasingly more accurate. Relationships were developed for shear stress as a function of particle size and compared with flatwalled results from the Darcy Weisbach equation and results obtained using the Colebrook Equation (Moody Diagram) as specified in the EFA. For lar ge grain sizes, the flat walled assumption provides poor results. The EFAs method for estimating shear stress works better than the flat wall assumption and the pressure drop if the roughness height is taken as half the diameter of the eroding particle. When upstream vortices are artificially induced in the flow field, bed shear stress decreases sharply for a flat bottom, while shear stress for rough bottoms remains similar to shear stresses under non vortex conditions. 4.2 Review of Relevant Backgroun d Recall from Chapter 2 that the method for estimating shear stresses in rectangular closed flumes like the SERF, the EFA, the ASSET, and the SEDFlume relied on estimation techniques. Shear stress in the ASSSET, the SEDFlume, and the SEAWOLF used the Dar cy Weisbach Equation and a hydraulically smooth assumption to estimate shear stress. In the EFA, friction

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152 factor was obtained from a Moody Diagram, the Darcy Weisbach Equation was used, and shear stress was estimated. Originally in the SERF, a pressure d rop was measured and a force balance was used to solve for shear stress as a function of head differential. Recall from Chapter 2 that the ASSET, SEDFlume, and SEAWOLF use Equations 4 1, 42, and 43 to estimate wall shear stress. T hese devices make a flat walled assumption, and consequently, the friction factor can be solved implicitly. 8 0 log 0 2 1 UD (4 1) w h hw D 2 (4 2) 28 U (4 3) In the EFA, Briaud et al. estimate shear stress using E quation 43, but their friction factor, is not solved for using Equation 41. Instead, a Moody Diagram is used to estimate this parameter. The Moody Diagram can be described by the Colebrook Equation: Re 51 2 7 3 / log 0 2 1 D e (4 4) where e is the roughness height, D is the hydraulic radius of the rectangular duct, and Re is the Reynolds Number with respect to hydraulic radius. Reynolds Number is given in Equation 45while hydraulic diameter is given in Equation 46. w h hw D 2 (4 5) VD Re (4 6)

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153 where h is the height of the duct, w is the width, and is the kinematic viscosity of water. When examining a cross section of any erosion rate test flume, it becomes clear that the walls roughnesses are not constant. On three sides of the flume, smooth walls are present, but along the bottom of the flume, a sample with a different roughness is present. Because the Colebrook Equation and Moody Diagram solve for average friction factor, and these methods rely on su rface roughness, the question becomes which surface height to use when doing these calculations. Although Briaud does not say what value for ks (or e these two parameters are used interchangeably) in his calculations, he implies that this parameter must be a function of grain size. I f one considers how much of a grain will be exposed to surface flow when it is eroding, it makes sense to equate thi s parameter to precisely one half of the total average grain size. Nikraudese verified that this relationship should be correct in rough pipes with a uniform roughness, but no one has yet to determine if this assumption would be valid for a hydraulic radi us that was mostly smooth with one rough section. Under conditions in flume style erosion devices like the SERF, the question became whether or not the Moody Diagram would still be valid. Once this question can be answered under steady flow conditions, the next question is how the presence of vortices affects bed imposed shear stress (and particle like erosion since E = f() ). To study the effects of bed shear stress under vortex conditions, the vortex generator was installed upstream from the shear stres s sensor and shear stress measurements were made under vortex conditions using both the pressure drop and shear stress sensor method. 4.3 Experimental Setup Appendix B provides indepth details of how to run a shear stress test in the SERF, but in brief, the procedure that was used for this study proceeded as follows:

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154 1. Experimental discs were prepared for the SERF using the synthetic disc preparation method. Crushed limestone was sieved so that particles of 5 diameters 2.0 mm, 1.0 mm, 0.5 mm, 0.25 m m, and 0.125 mm were isolated from one another. Then, a 50 mm diameter acrylic disc was coated with epoxy and particles from one of the five batches were attached to the front face of the disc. A flat disc was also prepared. 2. An experimental disc was lo aded into the shear stress sensor and leveled with the flume bottom. 3. The shear stress sensor was calibrated such that there was a linear relationship between output voltage and shear stress. For example, an output voltage of 10 V indicates a shear stres s of 100 Pa; an output voltage of 3 V indicates a shear stress of 30 Pa. 4. Water was run through the SERF to pressurize it, and the pressure transducers were bled to remove any bubbles from their hoses or nozzles. Once the transducers were bled, a 10 seco nd, 1 kHz pressure reading burst was taken at no flow so that a zero point for data analysis could be established. 5. Water was run through the SERF at varying flow rates. Flow rates ranged from 0 m/s to 6 m/s. Data was recorded from the pressure transd ucers, the shear stress sensor, and the paddlewheel flowmeter at a 1 kHz sampling frequency. 6. Pressure readings were converted to shear stresses using the following relationship: L w l plw 2 2 (4 7) which is based on a force balance between wall shear stress and an upstream and downstream pressure gradient. Velocity readings were used to compute s hear stresses using Equation 41 through Equation 46. 7. Steps 2 6 were repeated for each experimental dis c and the flat disc. The result was the development of a relationship between velocity and shear stress for a pressure drop reading, a velocity reading, and a shear stress sensor reading for a given sediment size. Each test was repeated at least four tim es. 8. A gage pressure transducer was attached to one of the SERFs pressure ports. Axial stress readings were measured at the specified flow rates 9. S hear stress tests were repeated with the vortex generator installed. Since the paddlewheel flowmeter wa s installed upstream from the vortex generator, average velocity readings did not change. Therefore, only the pressure drop and shear stress sensor readings were compared with one another.

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155 4.4 Experimental Results and Discussion 4.4.1 Pressure Drop Test s were first conducted on a flat disc to compare the pressure drop method for estimating shear stress with actual measurements. The first issue with testing was that significant noise was present in the pressure transducer signal. While small pressure di fferential voltage fluctuations were expected (103 V) relatively high pressure differential pressure voltage fluctuations were observed (101 V). Spectral analysis was conducted on the demeaned signal from the pressure transducers ( Figure 4 1) to determine why thes e fluctuations were occurring. T here are several noticeable peak s in pressure readings between 5 Hz and 20 Hz. The 65 Hz spike is electrical noise (120 VAC will produce this) and the 80 Hz and 90 Hz spike were only present on a few data runs. These are probably due to bubble development at higher flow rates and effectively can be ignored. The focus then became determining what the 5 Hz 20 Hz spike was. Because shear stress readings were availabe from the sensor, it was possible to find what the oscillating frequency of the shear stress should have been based on the sensor data by performing another spectral analysis of shear measurements (Figure 4 2). T he shear stress sensor shows a large spike at approximately 30 Hz. This is most likley the fundamental vibratory mode of the sensor itself. Recall that the shear stress sensor oscillate s back and forth on its leaf springs. When the sensor is knocked, or water flows over it, it will oscillate at its own fundamental frequency, and this is probabaly what is causing the 30 Hz spike. If the true frequency of shear stress readings was at 30 Hz, a similar peak should be seen in Figure 4 1 because frequency should correspond to pressure. Although the correct pressure differential may not necessarily produce the correct shear stress, one would expect pressure fluctations and shear stress fluctuations to be similar. This relationship does not exist as demonstrated in these two

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156 figures. Therefore, t he true data from the shear stress sensor is most likely the block of data seen in the Figure 4 2 spectograph that lies below 10 Hz This can be confirmed by isolating the 30 Hz signal using a bandpass filter and plotting a time series of filtered data (Figure 4 3). The same technique used to isolate the 30 Hz data block from the shear stress sensor was used to look at the 5 20 Hz spike Specifically, a 5 Hz 20 Hz 4th order Butterworth bandpass filter was designed where this data block would be isolated, and another time series was plotte d with transdcuer filtered data (Figure 4 4). T he 5 Hz 20 Hz data block oscillates at approximately 0 V. This data block then is not the actual signal that needs to be captured. Rather, it is some vibratory mode that is aliasing the signal. The next thought was that flume vibration may be causing this fluctuation. Two tests were conducted to rule this out. First, a test was designed where the pressure transducers were turned on, wired, and hooked up to the SERF. Then, the control valves from the flume tubing to the transducer were closed so that water could not cause a pressure differential on the transducers membrane. The signal from the pressure transducers then would be the vibratory mode of the flume (Figure 4 5). As shown, there is no peak between 5 Hz and 20 Hz. Therefore, the 5 Hz to 20 Hz signal must not be caused by flume or pump vibrations. The ~45 Hz spike only occurs in one dataset and is not repeated. This could be due to a number of factors a n air conditioning unit turning on, a fan blowing, someone using another piece of electrical equipment in the lab, etc. Because it is not repeated and it is not present under flow conditions, it was ignored. Again, the 65 Hz spike represents electrical noise. To further confirm that the 5 Hz to 20 Hz vibration must be a real signal and not caused by pump or vibrational noise, the pressure transducer was removed from the SERF frame and attached to the external frame that holds the sample into place. Doing this eliminated most vibrations from the transducer. Data was taken at three flow rates because this data was taken

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157 concurrently with Gator Rock tests and a spectral anal ysis was conducted (Figure 4 6 ). Although only three flow rates are used for this analysis, these results are exp ected to be typical The combination of Figure 44, Figure 4 5, and Figure 46 show two things : 1. Flume vibrations are not the cause of the 5 Hz 20 Hz spike in pressure transducer data. 2. The 5 Hz 20 Hz signal is a real measurement. Because the gage pressure in the SERF is several orders of magnitude higher than the pressure difference, it was thought that the normal stresses were causing this fluctuation in pressure difference. A gage sensor was set up to measure normal forces (Figure 4 7 ) and a s pectral analysis o f these readings was conducted ( Figure 4 8). Figure 4 7 confirms that normal stresses in the SERF are four orders of magnitude higher than pressure differences while Figure 4 8 shows that normal pressure in the flume does not oscillate at 5 Hz 20 Hz There is a small frequency spike between 5 and 20 Hz, but it is several orders of magnitude smaller than the frequency spike seen with the pressure differential readings. Pressure transducers are accurate up to 0.25% FS. For measuring normal stress in the SERF, a 30 psi transducer was selected because of the high pressures associated with high flow rates, and this sensor is only accurate to 0.075 psi. Conversely, the differential pressure transducers have a range of 0.5 psi, and they a re accurate to 0.00125 psi. The 5 20 Hz signal from the differential transducers has a maximum magnitude of +/0.1 V from the mean. This corresponds to a pressure difference of 0.02 psi, which is much lower than the pressure fluctuation that can be ac curately picked up with the gage pressure sensor. The pressure differential readings are then real and they are small too small to be detected by a gage pressure sensor. If a gage sens or were attached, it would fail because the pressure is too high. These fluctuations must be attributed to normal stress fluctuations because they cannot be attributed to anything else. Relative to the normal stress scale, O (104), these fluctuations are small, but relative to the scale required for shear stress, O (100), they are large.

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158 Data was collected over a 10 s time interval at a frequency of 1 kHz, and then averaged over the time domain. Because the 5 Hz 20 Hz frequency band averages to zero, for the purposes of averaging, i t could be ignored. If one wanted to eliminate this noise band it would be possible using a longer flume length, but modifications would be required. Pressure could be measured higher upstream and further downstream than it currently is measured. Curre ntly the pressure difference is only measured +/ 2 inches from the sample because the goal is to isolate the samples effect on pressure difference. With the flumes present setup, if the pressure ports were spaced further apart, a larger portion of the p ressure difference would be caused by the flume walls, while a smaller relative portion of the pressure differential would be caused by the eroding sample roughness. On average then, a higher portion of the overall average shear stress would be computed f rom walls thereby theoretically throwing the calculation for shear stress across the face of the sample off even further. The word theoretically is used in the preceding paragraph because the rationale behind the original spacing of the SERF pressure ports was to space them close to the sample so that the samples effects on pressure differential would be maximized. In other words, pressure ports were spaced just upstream and just downstream from the sample because the sample is rougher than the flum e walls. The rougher sample should produce an overall higher pressure differential effect on its respective flume portion. As pressure ports are spaced further from the sample locus, there will be more flat flume area relative to the amount of rough samp le area (which must remain constant). This increase in relative smooth area should cause the pressure differential to behave more like a smooth pressure differential and less like a rough pressure differential. Unfortunately, even with close pressur e port spacing, the samples effect on pressure differential (and subsequently shear stress) was minimal. To illustrate this, a series of discs with

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159 different uniform grain sizes 2 mm, 1 mm, 0.5 mm, 0.25 mm, and 0.125 mm were prepared by sieving crushed limestone through several ASTM standard sieves and gluing these particles to 50 mm shear stress sensor discs. P refabricated 50 mm discs acrylic discs with a predrilled hole through the center were purchased, attached to the shear stress sensor and the SERF run at varying flow rates. S pectral analyses for these rough discs were similar to spectral analyses presented for flat discs and will not be repeated. Namely, there are zero mean pressure fluctuations in the device that are probably caused by a smallscale axial stress fluctuation. Comprehensive results of different diameter tests will be presented in Section 4.4.2 so that they can be compared with analytical methods for estimating shear stress, but with respect to estimating shear stress via the p ressure transducer, Figure 4 9 is an illustration of what is happening in the SERF. As roughness of the shear stress sensors disc changed from smooth under flat conditions to rough under large sediment conditions, the shear stress estimate from the pres sure transducers did not change. It appears as though the sample area relative to the flat flume wall area is too small to see a noticable effect on average pressure difference in the flume. Figure 4 9 can be constrasted with Figure 4 10 to show the actual shear stress measurements across the face of the sample from the s hear stress sensor. Figure 410 shows that as roughness (sediment size) increases, shear stress increases signficinatly. Therefore, using a pressure drop to estimate shear stress for a rough sample would underestimate the actual shear stress conditions because on average, the pressure drop is influenced more by the contributing area of the flat wall cross sectional area than it is by the rough sediment disc cross sectional area. If one wanted to use a pressure differential to estimate shear stress, it would be possible if conditions in the SERF were different. The Moody diagram was developed by measuring

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160 pressure differences in uniformly rough pipes. A similar technique could be used to predict shear stresses in a flume like the SERF wall inserts would need to be constructed where different size diameter sediments were used. This would provide accurate pressure drop readings. However, in its present setup, because the SERF has flat walls and because the area of the test section (and shear sensor) is so small relative to the area of the flat walls, estimating shear stress from a pressure drop will not produce accurate results. The implications of this for previous SERF tests are interesting Several erosion rateshear stress curves have already been developed for materials that should not be considered flat (Gator Rock in particular). Because of this, the shear stresses used i n development of these curves must be too small, and should be modified. 4.4.2 Analytical Methods Once the question of shear stress estimation using a pressure drop had been assessed, the other methods for estimating shear stress were investigated (Figur e 4 11 through Figure 417). Figure 4 18 is a nondimensionalized summary of data, and probably provides the most concise method for analyzing results. Even though the Moody Diagram in the closest alternative technique for estimating shear stress, it is s till produces some error. Figure 418 is a diagram that shows % Error vs. Reynolds Number. Results show three things: 1. Estimating shear stress from a pressure drop will over predict shear stress under flat conditions. 2. Estimating shear stress from a flatw alled approach will underest imate shear stress as grain size increases. 3. Estimating shear stress with the Colebrook Equation is the most accurate alternative technique if effective roughness is assumed to be one half the sediment diameter. At low Reynol ds Numbers, the percent error is high, but the shear stress magnitude is low. Therefore, this should not be a major concern. The largest errors occur for the smoothest test

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161 discs. The 1.37x108 value for ks for a smooth disc was obtained from the litera ture, so it is not reflective of the experimental technique. The next largest source of error was for the 0.125 mm grain size. Since errors approach zero for this test disc as Reynolds Number increases (and shear stress magnitude increases), the high ove restimation of friction factor should not be a huge concern either. For larger diameter grain sizes, error is low; as Reynolds Number becomes greater than 1x105, error is less than 20% when grain size is 0.25 mm or greater. Because the larger diameter sp ecimens show the highest shear stress magnitudes, presumably these estimations are the most important. 4.4.3 Vortex Conditions Another goal of this study was to determine how the introduction of vortices influences erosion rate shear stress curves. As d iscussed in the literature review, for particlelike erosion there is both a theoretical basis for quantifying erosion rate as a function of shear stress and a history of empirical relationships between these two parameters. When investigating the effects of vortices, preliminarily, it appears as though the vortex itself should not be studied, especially since at present a velocity profile cannot be measured in the SERF. Rather, the effect of the vortex on average shear stress should be studied, and because erosion rate is a function of shear stress, erosion rate effects of vortices would follow. P receding tests on uniform diameter experimental discs were repeated with the SERFs vortex generator installed. Results are presented from Figure 4 19 through Figure 4 26. I n general, the induction of upstream vortices in the SERF appears to produce on average lower shear stresses for a given velocity. These effects appear to become magnified as particle size decreases. For larger particles (1 mm and 2 mm), the effects of vortices are nearly negligible. Qualitatively, this particle dependency on vortex effects makes sense. Large particles are going to produce their own vortices, and introducing a large upstream vortex (wake region)

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162 should have a minimal eff ect when compared with the effects of the vortices caused by the roughness of the sample. Conversely, small particles will not produce large vortices on their own. Therefore, they are more affected by the shearing action of water against their surface an d less affected by vortex particle interaction. As such, when a large upstream vortex is induced in the SERF, its net effect from the perspective of a small particle is simply to retard the flow in the direct vicinity of a small particle, thereby decreasi ng the net overall shearing action against it. Large particles on the other hand are more affected by local vortex affects, and a small net retardation of the flow will produce little shear stress effect. Because in general vortex induction lowers shear stress, and because particle like erosion rate is intrinsically tied to shear stress, under vortex conditions, particle like erosion rate should decrease proportionally with shear stress This could be useful under field conditions where a large obstruction is directly upstream from a bridge pier (a large pipe for example). The design shear stress may be able to be reduced by a factor proportional to particle size. To be conservative h owever, even under vortex conditions, nonvortex shear stress (and erosion rates) should be used until field verification of this can be made. 4.5 Recommendations and Conclusions In summary, from this it appears as though the shear stress question in the SERF has been solved. The following are recommendations and conclusions from this study: 1. For a rough sample, a pressure drop may not be used to estimate shear stress because it will under predict actual shear stress conditions 2. For a flat sample, the met hod used in the ASSET, SEDFlume, and SEAWOLF may be used to predict shear stress. The EFA method may also be used with a small roughness height. 3. For a rough sample, the ASSET/SEDFlume/SEAWOLF method should not be used because it will under predict shear s tress.

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163 4. For a rough sample, the EFA method may be used, but the roughness height should be one half the particle diameter. 5. If the correct roughness is not known (for example with a rock or nonuniform sediment), an estimation based on largest particle diameter should be used. 6. Vortices appear to have a small overall effect on average shear stress development. 7. For large particles, upstream vortices have a minimal effect. 8. For small particles, upstream vortices have a larger effect, and they generally reduce net overall average shear stress. 4.6 Future Work The equivalent roughness concept may help future flume style shear stress testing This can be viewed similarly to equivalent diameters for bridge piers as presented in the FDOT bridge scour manual. In other words, even though a rock core or sand clay mixture may not have a specific grain size associated with it from a bulk sense (as would be common with natural samples), it may be possible to measure its surface roughness and assign an equivalent grain size that can be used to predict the shear stress from a Moody Diagram. Although not in the scope of this project, there is a devic e at UF called AIMS that can measure the roughness of a series of concrete cores using high resolution cameras. The AIMS can be modified so that it can measure the roughness of rock cores or sand clay samples As demonstrated in this chapter, roughness s hould be equivalent to one half the grai n size, and in the case of rock and nonuniform mixtures, it appears as though this relationship should hold true as well. This chapter has shown that producing the correct shear stress in a flume style erosion rat e testing device is critical in measuring the correct erosion rate for an erosion rate shear stress curve. Shear stress is inherently tied to grain size, or roughness, and without proper stress estimation in the SERF, improper erosion rates will be comput ed. When dealing with an intact

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164 rock core or natural sand clay sample, it is essential to know what shear stress is actually being applied to it so that the correct erosion rate relationship can be developed. Future AIMS testing appears to be a possibl e solution as a link between intact rock core roughness and future SERF testing

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165 Figure 4 1. Spectral analysis of demeaned pressure transducer voltages Figure 4 2. Spectral analysis of demeaned shears stress sensor measurements. 0 10 20 30 40 50 60 70 80 90 100 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Frequency (Hz)Spectral DensitySpectral Analysis of Pressure Transducer Signals 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 Frequency (Hz)Spectral DensitySpectral Analysis of Shear Sensor Signals

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166 Figure 4 3. The 30 Hz data block from the shear stress sensor showing shear stress difference from the mean Figure 4 4. 4 Hz 20 Hz data block from pressure transducer showing voltage difference from the mean 0.2 0.4 0.6 0.8 1 1.2 1.4 -0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 Time (s)Shear Stress (Pa) 5.5 6 6.5 7 7.5 8 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 Time (s)Pressure Voltage (V)

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167 Figure 4 5. Spectral analysis of freevibratory flume test voltage measurements Figure 4 6. Spectral analysis of freevibratory flume test with sensor on new frame 0 10 20 30 40 50 60 70 80 90 100 0 0.005 0.01 0.015 0.02 0.025 Frequency (Hz)Spectral DensitySpectral Analysis of Pressure Transducer Signals 0 5 10 15 20 25 30 35 40 45 50 0 0.005 0.01 0.015 0.02 0.025 0.03 Frequency (Hz)Spectral DensitySpectral Analysis of Pressure Transducer Signals

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168 Figure 4 7. Average normal stresses in the SERF Figure 4 8. Spectral analysis of normal stresses 1 2 3 4 5 6 0 0.5 1 1.5 2 2.5 3 3.5 4 x 104 Normal Stress (Pa)V (m/sec) 10 20 30 40 50 60 70 80 0.5 1 1.5 2 2.5 3 3.5 4 4.5 x 10-3 Frequency (Hz)Spectral DensitySpectral Analysis of Pressure Transducer Signals

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169 Figure 4 9. Shear stress estimates from pressure differential in SERF Figure 4 10. Shear stress measurments from the SERFs shear sensor 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/sec)Shear Stress (Pa) Flat 0.125mm 0.25mm 0.5mm 1mm 2mm 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/sec)Shear Stress (Pa) Flat 0.125mm 0.25mm 0.5mm 1mm 2mm

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170 Figure 4 11. Shear Stress vs. Velocity Curves for Flat Flume Bottom Figure 4 12. Shear Stress vs. Velocity Curves for 0.125 mm Sediment 0 1 2 3 4 5 6 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) Smooth Wall Colebrook Equation (ks = 0.001e-3) Shear Stress Sensor Pressure Drop 0 1 2 3 4 5 6 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) Smooth Wall Colebrook Equation (ks = 0.0625e-3) Shear Stress Sensor Pressure Drop

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171 Figure 4 13. Sh ear Stress vs. Velocity Curves for 0.25 mm Sediment Figure 4 14. Shear Stress vs. Velocity Curves for 0.5 mm Sediment 0 1 2 3 4 5 6 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) Smooth Wall Colebrook Equation (ks = 0.0625e-3) Shear Stress Sensor Pressure Drop 0 1 2 3 4 5 6 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) Smooth Wall Colebrook Equation (ks = 0.25e-3) Shear Stress Sensor Pressure Drop

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172 Figure 4 15. Shear Stress vs. Velocity Curves for 1.0 mm Sediment Figure 4 16. Shear Stress vs. Velocity Curves for 2.0 mm Sediment 0 1 2 3 4 5 6 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) Smooth Wall Colebrook Equation (ks = 0.5e-3) Shear Stress Sensor Pressure Drop 0 1 2 3 4 5 6 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) Smooth Wall Colebrook Equation (ks = 1e-3) Shear Stress Sensor Pressure Drop

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173 Figure 4 17. Combined NonDimensionalized Results From Colebrook Equation, Shear Readings, and SmoothWall Assumption (dashed line is smooth wall). Figure 4 18. Percent error vs. Reynolds Number between Colebrook Equaiton using a Uniform Roughness and Actual Shear Stress Measurments 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 x 105 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 Ref e/D=6.85e-9 e/D=8.55e-4 e/D=1.72e-4 e/D=1.92e-3 e/D=6.85e-3 e/D=1.37e-2 Smooth Bottom 0.5 1 1.5 2 2.5 3 3.5 4 4.5 x 105 0 20 40 60 80 100 120 RePercent Difference e/D=1.37e-8 e/D=1.71e-3 e/D=3.43e-3 e/D=6.85e-3 e/D=1.37e-2 e/D=2.74e-2

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174 Figure 4 19. Combined Shear Stress Sensor Data Under Vortex Conditions Figure 420. Comparison of Pressure Readings for Vortex and NonVortex Conditions for all Sediment Diameters 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/sec)Shear Stress (Pa) Flat 0.125mm 0.25mm 0.5mm 1mm 2mm 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s) Shear Stress (Pa) With Vortices Without Vortices y(x) = a x^n a = 1.0005 n = 2.3806 R = 0.92757 (lin) y(x) = a x^n a = 2.9275 n = 1.8773 R = 0.99666 (lin)

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175 Figure 4 21. Comparison between Shear Stress Readings for Flat Bottom under Vortex and Non Vortex Conditions Figure 4 22. Comparison between Shear Stress Readings for 0.125 mm Sediment under Vortex and NonVortex Conditions 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) With Vortices Without Vortices 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) With Vortices Without Vortices

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176 Figure 4 23. Comparison between Shear Stress Readings for 0.25 mm Sediment under Vortex and NonVortex Conditions Figure 4 24. Comparison between Shear Stress Readings for 0.5 mm Sediment under Vortex and NonVortex Conditions 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) With Vortices Without Vortices 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) With Vortices Without Vortices

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177 Figure 4 25. Comparison between Shear Stress Readings for 1.0 mm Sediment under Vor tex and NonVortex Conditions Figure 4 26. Comparison between Shear Stress Readings for 2.0 mm Sediment under Vortex and NonVortex Conditions 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) With Vortices Without Vortices 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) With Vortices Without Vortices

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178 CHAPTER 5 DEVELOPMENT AND TESTING WITH A NEARLY UNIFOR M, HIGHLY ERODIBLE, SYNTHETIC ROCKLIKE MATERIAL TO BE U SED FOR CALIBRATING EROSIONRATE TESTING DEVICES 5.1 Executive Summary Because of questions in comparing Rotating Erosion Test Apparatus (RETA) results with more traditional flume style erosion rate test devices results, attempts were made to develop a nearly uniform material to serve as a direct comparison medium between these different instruments. A new material, Bull Gator Rock, was developed using similar principles to older Gator Rock designs. While older versions of Gator Rock were mixed with water Bull Gator Rock was mixed dry and water was added to the mix later through capillary action. Two different rounds of Bull Gator Rock mix were developed. The first round showed excellent compressive and tensile strength test results. Erosion results in the RETA and the Sedimentary Erosion Rate Flume (SERF) were mixed. Cement content and water content were increased, and a second round of Bull Gator Rock was produced. Because of aggregate variability, strength tests were not repeatable. Erosion tests showed the presence of rock like erosion, although a promising result from one round of testing showed the potential for a particle dominated erosion scenario. Eventually, researchers realized that for a direct RETA to flume style device comparison, an ex tensive dataset was required. 5.2 Review of Relevant Background Because the RETA measures erosion on a rock core or Shelby tubes sides instead of on its top surface, people have questioned whether or not results from these erosion tests were the same as they would have been had a more traditional flume style device been used instead. When this project began, the first thought was that the most effective way to verify that the RETA was to directly compare RETA results to results from a flume style device. The Sediment Erosion Rate

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179 Flume (SERF) was used as the flumestyle device in this study. To perform this comparison, a suitable material was needed for testing in both instruments. Finding a naturally occurring material that is erodible in both the S ERF and the RETA is not as easy as it sounds. Any material to be tested in both devices needs to have enough internal strength to withstand moment forces associated with the RETA. Materials like soft clay and compacted sands will often crumble during RET A testing, and these results are useless when trying to compare the two devices. Despite this internal strength criterion, the material to be tested in both devices also needs to be highly erodible. Any material to be tested in both devices must be nearl y uniform. A nonuniform sample will become obvious during a RETA test as one localized section of the sample will erode faster than another section. The material tested in both devices must also be uniform from another standpoint from sample to sample. Repeatability of erosion and strength test data is important because if one can differentiate one sample from another, any results coming from a comparison will be meaningless. Because of these stringent criteria for testing a material in the RETA an d the SERF, work to compare results from the two devices in the past has focused on the use of synthetic materials. Niraulas (200 4 ) Gator Rock was used as a starting point because previous researchers, particularly Slagle (2006) indicated that he believed Gator Rock could serve as a proper material for testing in both instruments. Although the original version of Gator Rock was too strong, Slagle believed that by raising the water content, he could create a nearly uniform material that was strong enough to withstand RETA testing while still eroding. As discussed in Chapter 2, Slagles original attempt to create Gator Rock was problematic. He mixed water, crushed limestone, and cement in the appropriate proportions, and allowed the samples to cure for 28 days. Then, the samples were subjected to RETA testing.

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180 Slagle noticed that erosion was defin itively nonuniform (Figure 5 1). Instead of uniform erosion, he observed that the tops of his samples eroded much more quickly than the bottoms. This phenomenon can be explained rather easily. The crushed limestone water cement slurry that was used to make his Gator Rock was mixed wet and water content was high. During curing, the samples were placed on the ground right side up, and while this happened, b ecause of the high water content, water worked its way up through the limestone cement matrix because of capillarity. Over the 28 day curing time, a higher amount of water was present in the tops of the samples compared to the amount of water in the bottoms of the samples. This higher water content caused the tops of the samples to be much weaker than the bottoms of the samples, and as a result, differential erosion occurred. As discussed briefly in Chapter 2, as a result of this water differential, Sla gle tried another technique to make Gator Rock (Gator Rock 3.0 from Chapter 2). He next developed a rotisserie (Figure 5 2) so that the samples would spin while they were curing. Spinning the sample during curing did stop capillary action from occurring during curing, but it may have created another issue During curing, particles in the slurry should separate due to the spinning rotisseries centrifugal forces such that smaller pieces of aggregate would be spun to the outside of the molds while larger a ggregate should remain concentrated in the samples center. When looking at the sample from the top, the middle of the sample had a higher concentration of large aggregate than the outside of the sample. Although from topto bottom the sample may have be en uniform, from side to side the sample was not. Topto bottom uniformity is essential for RETA testing, but side to side uniformity is vital for SERF testing. Because of the different types of heterogeneity, these Gator Rock samples could not be used e ither. In his 2006 thesis, Slagle discusses the issue s with Gator Rock testing and he cites nonuniformity as a constant source of

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181 concern. He does not specifically say how his samples were heterogeneous, but from the mechanisms just described, one can infer how it must have happened. As of 2006, no known material had been developed that was appropriate for a SERF vs.RETA comparison. In this study, another attempt was made to create a new version of Gator Rock that would meet the stringent material c riteria for SERF RETA tests and that could be easily and inexpensively mass produced. 5.3 Theory Behind Bull Gator Rock In this study, a new tactic was used to construct Gator Rock. The rotisserie idea was abandoned, and instead, researchers looked to take advantage of the waters capillarity. Because capillary action occurs slowly, researchers hypothesized that cement and crushed limestone could be mixed dry, the dry mix could be placed in a specific amount of water, and over time, the water would work its way up through the sample much like it did during the first attempt to make Gator Rock. As the water worked its way through the sample slowly, a new, nearly uniform Gator Rock specimen should be created. In pri nciple this mixing technique had the potential to have the opposite effect as the original Gator Rock recipe. Because the Gator Rock sample was mixed dry, and water was added to it from the bottom, one could argue that the bottom of the sample would entra in more water than the top of the sample. If capillary forcing of the water was not higher than the force of gravity, indeed, this argument would make sense. A smaller quantity of water could be transferred to the top of the sample while a larger quantity of water would remain at the samples bottom. However, during the original attempt to make Gator Rock, the opposite was seen. The tops of the samples, not the bottoms were locally the weakest. Therefore, it was known that capillary forces in the crushed limestone cement matrix were relatively high, and researchers thought that this dry mix technique would produce a nearly uniform sample.

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182 5.4 Procedure for Construction of Bull Gator Rock The following procedure for development of Bull Gator Rock was developed by Crowley, Bloomquist, and Shah; it was originally presented in Shahs Honors Thesis in 2009, but since then, some modifications have been made to the process. The finalized process in presented below: 1. Crushed limestone was obtained from the Whitehurst Companys mine in Newberry, FL (FDOT Reference No. 34 104), and oven dried for 72 hours. 2. Dried material was sieved through a standard ASTM No. 10 sieve to eliminate cobbles and larger pieces of aggregate. 3. 4 inch molds were cut from standard PVC pipe. For RETA testing completed Gator Rock samples must have a final diameter of 2.40 inches while for SERF tests samples must have a final diameter of 2.30 inches Therefore, when samples were prepared for use in the RETA, standard 2.5 inches SC H 40 pipe was used; when samples were prepared for use in the SERF, standard 2.5 inches SCH 80 PVC pipe was used. 4. Micro filter paper was glued to the molds bottoms and the molds sides were coated with prestress mold release to prevent the samples from sticking to the molds sides. 5. Water cement ratios were chosen for the Bull Gator Rock batches. Then the appropriate amounts of water, cement, and water were determined using the following formulas: Total LimestoneM C LS LS M % % % (5 1) Total CementM C LS C M % % % (5 2) Cement Limestone WaterM M W W M % 1 %/ (5 3) In these equations, %LS is the percentage limestone per batch, %C is the percentage cement per batch, and %W is the percentage water per batch. To estimate MTotal or the total mass of the entire Gator Rock batch, a m old was filled with crushed limestone, rodded, and weighed to determine the approximate total mass of substance that could be fit into a mold. Then, this weight was multiplied by the number of samples to be made. For example, if 40 samples were to be mad e, and the mass of crushed limestone that would fit inside of a mold was approximately 475 g (which was typical for a SCH 40 mold), then MTotal = (40*475 g) = 19 kg.

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183 6. A cement mixer was cleaned and dried. Limestone and cement were added to the mixer in the appropriate quantities. To prevent fines from escaping during mixing, a plastic bag was used to seal the cement mixer. The cement mixer was turned on, and mixing l asted for 10 minutes. 7. Molds were prepared. 8. First, an empty mold was weighed. 9. A collar was secured over the mold, and the dry mix was poured into the mold. 10. When the cement limestone mix was approximately 2.5 inches from the top of the collar, pouring ceased, and the sample was gently leveled. 11. A plastic disc was placed in the 2.5 inches void to seal the contents of the mold. The disc was included so that during vibrating, the fines would not escape. 12. Samples were placed on a shake table with a 3.6 kg mass resting over each samples plastic disc. The shake table was set to a speed of 2 and allowed to run for 8 minutes. This insured a densely packed dry mix. 13. The collar was removed, and pressure was applied to the plastic disc. Excess material was scraped from the top of the mold using a screed. The sample (with the mold) was weighed. 14. During SERF tests it is useful to have an anchor to hold the sample to the piston. During high flow rates in the flume the pressure differential caused by high velocities in the flume causes the sample to be sucked to the flumes top. For samples to be used in the SERF then, a 2.5 inch x 0.25 inch diameter bolt was inserted into the dry mix. A 1.0 inch pilot hole was first manually drilled, the bolt was inse rted, and the void was back filled and tampered by hand. 15. Because RETA testing requires the presence of a 0.25 inch hole through the sample, for samples that were to be used for RETA testing, a 0.25 in ch aluminum bar was coated with Prestress Release and driven through the samples. This step was added because drilling through a Gator Rock specimen with a drill press may cause the sample to crack. A similar technique to the technique used in (10) was used to insert these rods into the samples 16. Samples were placed in buckets to allow for curing. The appropriate amount of distilled water was weighed and placed into each curing container. Each bucket was sealed to prevent evaporation during curing. Originally, plastic wrap and Duct Tape we re used to seal the buckets, but later, buckets were replaced with Ziploc containers with air tight lids. 17. Samples were allowed to cure for 28 days.

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184 5.5 First Gator Rock Mix 5.5.1 Mix Compositions During this project, two different versions of Gator R ock were created. The first version of Gator Rock was based on Niraulas 2002 original mixing procedure. According to Niraula, when he changed water cement ratios, he fixed his water content at 20%, and he varie d the cement content (Table 5 1). Slagle did not indicate which water cement ratios he used during his tests Because Niraula was the only other available reference for Gator Rock, the same thing was done, a nd water content was fix ed at 20%. To make samples weaker than Niraulas, cement content was reduced while Limestone content was increased (Table 52). Before mixing, four random limestone aggregate samples were taken and sieved so that a grain size distribution could be produced. If random samples showed repeatability and regular uniformit y, it was thought that there would be a better chance to insure that results from one sample could be compared with results from another sample. As shown (Figure 53) the four random grain size distributions were close to one another. 5.5.2 Strength Tests After curing had been completed, a random set of Bull Gator Rock samples were selected for tensile and compressive strength testing. Two samples from each batch were used in each test Results from tensile and compressive strength tests were used to com pute cohesion for each batch of Gator Rock according to the formula derived in Chapter 2 (Figure 5 4) : t uq q C 2 1 (5 4) where qu is the materials compressive strength and qt is the materials tensile strength. As shown in Figure 5 3, repeatability from sample to sample of the same batch was excellent. Average percent difference between one sample and another of the same batch during

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185 tensile strength testing was 5.86%. Maximum difference from one sample to another during tensile str ength testing was 12.86% (Batch 2); the minimum difference was 2.00% (Batch 1). The standard deviation of percentage difference between samples of the same batch was 4.37%. During compressive strength, the average percent difference between samples of t he same batch was 5.25%. The maximum difference from one sample to another of the same batch during compressive testing was 9.09% (Batch 2); the minimum difference was 0.22% (Batch 4). The standard deviation of percent difference between samples of the s ame batch was 3.95%. 5.5.3 RETA Tests RETA tests were conducted at the FDOT State Materials Office (SMO). Testing followed the s tandard RETA testing procedure The only difference between RETA tests conducted during this study and typical RETA tests wa s that because the Gator Rock samples were artificially created and allowed to cure in molds, their side walls were abnormally smooth. Therefore, before each test the Gator Rock samples were slightly machined so that approximately 0.1 inch es of material was removed from their smooth surface. This artificial procedure roughened the surfaces slightly. Overall, RETA tests produced mixed results. 5.5.3.1 First r ound of t ests on f irst b atch of Gator Rock in RETA The first round of samples that were tested produced encouraging results. Dan Pitocchi (2010), the SMOs lab technician who oversees FDOT RETA tests indicated that these Bull Gator Rock samples were the best Gator Rock samples that he had ever worked with because from top to bottom, they appeared to erode uniformly. Unlik e Slagles samples (Figure 5 3) there were not obvious weaker sections on either the tops or the bott oms of the samples (Figure 55, Figure 5 6, and Figur e 5 7). During the first round of testing, quantitative results showed wha t was expected in terms of an erosion rate shear stress relationship. Generally, as shear stress increased, eros ion rate

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186 increased (Figure 5 8, Figure 59, and Figure 510 ). A best fit line was fit to each dataset using linear regr ession. As shown in Figure 5 8, the 10 Pa data point is higher than most other data point s Often, when a RETA test begins, RETA operators run the lowest shear stress first and increase the stress every specified time period (24 hours for these tests ). Recall that as part o f preconditioning, these samples were slightly machined. The standard RETA testing procedure calls for a quick wash of the material before data is taken. During the wash a high shear stress is used to remove any loose particle or debris from the sample. Then, RETAs annulus is emptied, cleaned, and the data run begins. This washing technique does remove some loose particles from the sample. However, if erosion is rocklike, which for Gator Rock it may be, then this washing technique also serves to loosen or weaken some grains within the sample matrix. During the samples first prolonged exposure to stresses normal or shear these loosened pieces of material will also break off over time. Therefore, during a 24 hour test one should expect that some of the looser, exterior particles that comprise the sample will fall from it. In other words, although the wash removes some loose pieces of the material, it loosens some others weaker particle bonds, and over t he course of 24 hours of cyclical loading, it is likely that these relatively loose materials also fall from the samples surface. This is important because it explains why the first data point in Batch 3 is so high. During the first portion of the erosion test, most relatively loose material is removed from the face of the Gator Rock. Because the RETA does not continually measure erosion as a function of time, it is impossible to say how to define the first portion of the test but the argument is t hat after a certain amount of cyclical loading on the surface of the rock face, most of the relatively loose material will eventually fall from it. What is left then is a more uniform material

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187 in terms of the particle pack density. Because of this rationale, when fitting a line to the Batch 3 data, the first point was ignored. Another encouraging thing regarding this first round of RETA tests is that they showed some qualitative relationship between material strength and erosion rate. Intuitively it a ppears that stronger materials should erode more slowly. HEC 18 makes this generalization, but does not give any quantitative method for correlating material strength to erosion properties. The reason that Batch 1 and Batch 2 data are not shown in this dataset is that Batch 1 and Batch 2 both failed before the end of the test The Batch 1 sample failed at a 10 Pa shear stress while the Batch 2 sample failed at a 20 Pa shear stress. Batch 3, Batch 4, and Batch 5 stood up to the entire gambit of tests If erosion rate was a function of material strength, as material strength increases, dE/d should decrease. While generally this is the case, this is probably not enough to draw any conclusive correlations. Even with this disclaimer, limited analysis wa s conducted to try to draw a correlation between cohesion and erosion rate for these Gator Rock sample (Figure 5 11) Interestingly, this figure shows that there may be some correlation between these two parameters, and most interestingly, erosion rate ap pears to level off near zero above a cohesion value of 500 kPa. Because this graph shows cohesion vs. erosion rate, it was possible to use the Batch 1 and Batch 2 failed samples in its computation. 5.5.3.2 Second r ound of t ests on f irst b atch of Gator R ock in RETA After the initial promising tests with Bull Gator Rock, a second series of tests was conducted on different samples from the same corresponding Bull Gator Rock batches. The goal of this series of tests was to produce some repeatability between the first dataset and a new dataset. If erosion results from both sets of data were similar, one could conclude that from a

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188 sample to sample perspective, the Bull Gator Rock was behaving as designed. If this second round of tests was able to produce the expected results, it would mean that Bull Gator Rock responded predictably to erosion and that because of this it could be used as a basis of comparison with the SERF. D uring the second round of tests, samples behaved much differently than they behaved during the first data run (Figure 5 12 through Figure 516). W hile during the first round of tests the sample from Batch 1 and the sample from Batch 2 failed at increasingly higher shear stresses, none of the samples from the second round of tests failed. While during the first round of tests, samples that made it through every test exhibited a pattern where increasing shear stress meant increasing erosion rate, during the second round of tests only samples from Batch 1 and Batch 5 resembled this trend. Even these results needed to be modified to produce the expected erosion rate shear stress results. During Batch 5 data analysis, the final 50 Pa erosion point was eliminated because it showed zero erosion rate; during Batch 1 data analysis, the ~ 40 Pa data point was eliminated for the sake of fitting a best fit line to the data (although this data point is still shown in Figure 5 16 ). The question became why the disparaging results between the data sets? To explain this phenomenon, multiple di scussions between Crowley of UF and Pitocchi of FDOT were conducted. Pitocchis exact words when describing the second data run was, we had problems (Pitocchi 2010). Pitocchi described how during tests several RETA control boxes broken. On one occas ion a RETA was accidentally turned off. On a few others, the bottom plate that holds the sample to the RETA annulus kept coming loose, etc Essentially, he claimed that the entire dataset was not very good and should be repeated with new samples. Pitoc chi blamed the failure of the second run of Ga tor Rock samples on two factors: 1. Bull Gator Rock was still too resistant to erosion to give accurate RETA results. 2. Bull Gator Rock samples were slightly the incorrect diameter.

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189 First, the latter point will be addressed. As noted in S ection 5.4, when the first round of Bull Gator Rock samples was produced, it was designed using the SERFs sediment diameter. Because originally the goal was to make SERF and RETA samples that were as similar as possible, the smaller 2.30 inch diameter was chosen. Any d eviation from a precise 2.40 inch diameter sample will force the RETAs motor to run harder than it normally would to produce a speci fied shear stress. At aggressive shear stresses such as 40 Pa or 50 Pa th e motor was forced to work hard, and this led to some of the mechanical issue s seen during the second round of tests To address this problem, Pitocchi recommended creating a new Gator Rock batch with a slightly larger diameter. The former point the G ator Rocks resistance to erosion also concerned Pitocchi. Pitocchi indicated that even if the diameter was modified, based on his experience he still doubted whether or not Bull Gator Rock could be expected to yield reproducible experimental results. Pitocchi said that Gator Rock, like most rocklike material exhibited a moderate to high degree of blocking. In this paper, blocking is synonymous with previous words such as chunking or pitting in that it is a rudimentary description of rock like erosion. Materials that exhibit even a moderate degree of rock like erosion properties may not produce a regularly expected erosion rate vs. shear stress curve, especially when a relatively small amount of particle like erosion is present. When erosion is small, the blocking mechanism becomes dominant, and it does not appear to occur at regularly expected shear stress intervals. Rather, it is the function of another property perhaps normal stress and therefore cannot be quantified using a RETA. Du ring both rounds of Gator Rock tests a relatively small amount of material eroded from the Gator Rock surface. The most significant erosion rate was seen during Batch 2, Run 2,

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190 where 18 g of material were removed from the sample. This extreme erosion rate can be attributed to blocking for several reasons. First, this erosion rate is abnormally high when compared with erosion rates of similar materials at similar shear stresses. Secondly, this erosion rate was produced during testing of a weak batch Ba tch 2. At times, Batch 1 and Batch 2 exhibited characteristics like a compacted sand. For example, during SERF testing (to be discussed in Section 5.5.4) it was often impossible to secure the samples to the piston because as the samples screws were driv en into the plastic piston, the screws often broke loose while chunks of loose sand fell from the rock core. In other words, there was little glue or Portland cement physically holding the samples together as evidenced in Table 5.3.2. As a result, whe n the screw offered any resistance, it would break free and spin within the sample. A similar lack of glue mechanism is probably to blame for the Bach 2, Round 2 data point. If the Batch 2, Round 2 data point is taken as a sort of loose sand outlier, the next most significant erosion rate is found during the tail end of Batch 5 testing during both rounds of tests where approximately 3 mm/yr. of erosion was seen. Extrapolating this back to a 24 hour test implies that during the test, only .008 mm of mate rial was removed from the Gator Rock surface. Or, in terms of density, this means that only approximately 0.1 g of material eroded during the entire test This is an incredibly small amount of material to accurately measure. Erosion quantities this small can easily be muddled due to simple effects such as not properly cleaning the RETA annulus. Further, this amount of material is extremely small when compared with the any block material erosion that could occur. For example, if during an erosion test 0.05 g of material block were to break due to pulsing action on the sample face (in a m anner similar to Bollaerts 2003 mechanism), it would only take two of these block failures to equal the total amount of erosion resulting from the 3 mm/yr. presumed shear stress failure. And, making

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191 matters even more confusing, with a RETA test it is impossible to differentiate between the two modes of erosion failure because erosion rates so small and erosion as not measured continuously. If higher erosion rates c ould be produced with the Gator Rock, it was thought that it could be possible to isolate the particle like erosion from the blocklike mechanism. Rather than relying on pulsating impulse action, a steady tugging mechanism should force more, weaker, loose r material to erode from the Gator Rock surface. This would allow for a better basis of comparison between the RETA and the SERF. Before this new Gator Rock mix is discussed however, testing on Round 1 Gator Rock should be analyzed. 5.5.4 SERF Tests SERF tests were conducted concurrently with RETA tests R esults confirmed that a new Gator Rock mix was required. 5.5.4.1 Shear s tress t ests The first question during Gator Rock SERF testing was how to accurately estimate the shear stress on the Gator R ocks surface at a given flow rate. Based on the Chapter 4 discussion, shear stress is dependent on roughness, and shear stress cannot be accurately estimated using a flat walled assumption. An assumption was made where regardless of water/cement ratio, each Gator Rock batch was assumed to have approximately the same roughness. Intuitively, this is logical because for a given Gator Rock sample, the only thing that changes is the ratio between the limestone and the Portland cement. The determining factor in finding a samples roughness should be dominated by the crushed limestones grain size distribution, not the cement content. Procedures for running a shear stress test are discussed in detail in Appendix B, but a brief synopsis will be given here. T o produce a test disc for use in the shear sensor, leftover Gator Rock aggregate was spread out on a counter. Four test discs were coated with epoxy and pressed against the Gator Rock aggregate surface. The epoxied discs were allowed to dry overnight.

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192 T he result (Figure 5 17) was that four test discs were created such that random samples of the nonuniform Gator Rock aggregate were stuck to the discs surfaces. These test discs were subjected to three rounds of shear stress testing each. If the discs w ere a true random sampling of Gator Rock roughness conditions, shear stress values at the same flow rate should be close to one another from both an intra disc and inter disc perspective. Figure 5 17 shows data points combined with one another; data time series were similar to results shown in Chapter 4. Because of the tight pack between data points, it appeared as though the true roughness of the Gator Rock surface had been captured. This shear stress vs. pump frequency curve was then used to define shear stress at varying flow rates in the SERF. For example, from Figure 5.5.14, a pump frequency of approximately 20 Hz corresponds to a Gator Rock shear stress of approximately 19 Pa. Values from this curve were used to match shear stress values approx imately 10 Pa, 20 Pa, 30 Pa, 40 Pa, and 50 Pa that were used during RETA testing because the goal was to reproduce RETA data points as closely as possible using the SERF. 5.5.4.2 Erosion t ests During SERF erosion tests two experimental procedures were used. At first, the SEATEK ultrasonic ranging system was used in conjunction with the laser leveling system as a redundant mechanism. The programs were manipulated such that for a sample to advance, two of the three lasers needed to be uncovered and the SEATEK needed to determine that the sample was no longer level with the flume bottom. SERF testing began with the mid point sample, or Batch 3. During the Batch 3 test no erosion was shown. While the SEATEK, which is more prone to errors than the las ers, would sometimes try to initiate the motor motion sequence, the lasers would block this from happening. After five days without sample advancement, the Batch 3 sample was removed from the device

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193 and replaced with a Batch 2 sample. Batch 2 also showed no sample advancement after five days, and eventually it was replaced with a Batch 1 sample. The Batch 1 sample also showed no advancement. In other words, the SERF was indicating that the sample was not eroding. Visual observation of the samples pa rticularly the Batch 1 specimen showed that a no erosion result must be incorrect. The Batch 1 sample visually showed some erosion along its front face and some pit like localized erosion zones along its top surface. While the top of the sample hadn t eroded uniformly enough to warrant sample advancement as per the stringent programming restrictions utilized in the SERF control programs some material had been removed from it. The problem with the laser design is that it assumes the entire surface from photoelectric sensor to light source will erode. If edges erode faster than a samples mid section, the sample cannot advance until the midsection also erodes. E ven a small block or chunk of material blocking the lasers light will preclude advancement. Small chunks blocking the lasers light were not observed often; rather, advancement was usually prevented because on average there was not enough erosion to warrant a full steps worth (a step equaling 1.0 mm) of advancement. Still, to say no erosion occ urred would be incorrect. Two options existed for finding a better method to measure erosion. The first option was to use a method similar to the RETA. Before and after testing the sample could be weighed, and the weight difference should equal the e rosion rate. There were issue s with this method however. Similar to problems in the RETA, using this weight method would not give a topto bottom erosion time series. The second issue with weighing the sample is that because of t he nature of the SERF testing it is unclear and unlikely whether or not the correct results could be obtained. T wo possibilities are present. One is that the sample fits snugly into the test cylinde r. When the

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194 sample fits snugly advancement often causes some erosion along the samples edge due to friction between the sample and the test cylinders side walls. Although normally during higher erosion rates this erosion quantity is small when compared with the erosion of the samples topface, with Gator Rock, topface erosio n quantities are extremely small. The relative magnitude of side wall sample advancement friction is nearly the same or greater than friction due to fluid flow. Any weight measurement would take both erosion sources into account and not be an accurate representative of actual erosion conditions. Even if sample advancement was precluded completely as it was when the lasers were used insertion of the sample into the testcylinder produces some friction. Then, once the sample is placed in the cylinde r, the piston must be attached to the SERVO motor, and the SERVO motor must be stepped forward so that the sample can be level with the flumes bottom. T his causes friction, and the fear was that this frictional component to material lost during a SERF tes t would be on the same order of magnitude as actual fluidflow erosion. The second possibility is that a gap can be engineered between the sample and the test cylinder. T his carries with it three underlying issue s as well. First, if the gap between the sample and the sample cylinder is large enough, the sample itself will oscillate or rock back and forth as water passes over it. This cyclical knocking or rocking action will serve as another source of unnatural material loss, which again will be as high as or greater than the erosion rate due to fluid flow. The second issue with creating a gap between the sample and the test cylinder is that when small particles erode as part of the samples bed load, they would fall into the gap. When the sample was weighed after the test the total mass of the sample plus piston plus cylinder would be the same, yet particles would be removed from the samples surface. Unlike the RETA, extractin g a SERF sample is not easy, and doing so will cause friction which will

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195 result in material loss so this is not an option either. Third, a gap will modify any fully developed flow pattern in the flume which will also affect erosion rate. The SERF is designed to produce a fully devel oped velocity profile. Deviation from this velocity profile, especially at the sample interface, will change fluid conditions which will in turn produce nonnatural erosion results. The other solution w as to take advantage of the fact that the SEATEK p roduces more errors than the lasers. The lasers were turned off and a test was run with just the SEATEK for 24 hours. O n average the SEATEK readings were accu rate. As typical with ultrasonic sensors such as the SEATEK, every ten readings or so, a value is returned that forces motor advancement/retraction, especially when there is little erosion. When this happens, the motor slightly advances/retracts. This repeats itself several times during a test Over the course of a 24hour test, if one looks at a time series of motor position, an oscillating graph will be generated (Figure 5 18 and Figure 5 19). One of the advantages to the SERF compared to the RETA is that the SERF measures erosion in real time. Although one way to measure erosion rate is to t ake the difference between start position and end position, this method does not give real time erosion as a function of sample position. Using this method will give no information on a situation where the top of a sample erodes faster than the bottom or viceversa. In the past, the final minus the initial position method was used exclusively by Trammel and Slagle during SERF analysis. Expressed mathematically, Trammel and Slagles method for finding erosion rate was: t y E (5 5) wher e y is the total sample position change and t is the total length of the SERF test The method for computing erosion rate is crude; a better method is easily available. Instead of

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196 finding the average erosion rate using Equation 55, a better method for finding erosion rate is to take the differential limit and compute erosion rate as: dt dy E (5 6) In other words, sample position and time should be plotted against one another. If a best fit curve is plotted through the data set, then instantaneous erosion rate must (and average erosion rate if the curve is linear) be equal to the slope of this line. If the best fit function through these data points is not linear, then it must mean that the sample is nonuniform from topto bottom because a portion of the sample must have eroded more quickly (or more slowly) than another portion. With Gator Rock, using this method can provide an erosion rate for even small values of material loss even values smaller than those that would normally warr ant a step. On average, the positive errors should cancel out with the negative errors. The overall slope of the best fit line through the data points then should on average be representative of the sample slowly working its way upward throughout the tes t As implied in Figure 5 18 tests were repeated so that the SEATEK stand alone method could be tried, and best fit lines were produced using least squares regression. Sometimes, the sample slowly worked its way down during a test Under these conditions, the implication is that negative erosion occurred, which is not possible. Whenever this happened, erosion rate was defined as a no erosion condition. Tests using the SEATEK standalong method were conducted on two samples from Batch 1, one sample from Batch 2, and one sample from Batch 3 at 10 Pa, 20 Pa, 30 Pa, 40 Pa, and 50 Pa, and when possible, best fit lines were computed thr ough the data points. Figure 520 is shown as a typical sample position vs. time signal with the b est fit line while erosion rate vs. shear stress results are shown from Figure 521 through Figure 5 23. Since generally Batch 3 showed no erosion and Batch 4 and Batch 5 were shown to

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197 be stronger from the tensile and compressive strength tests SERF test s were stopped after the Batch 3 data set was taken. 5.5.4.3 SERF analysis Batch 2 and Batch 3 produced poor SERF results. In these tests an increase in shear stress did not show a corresponding increase in erosion rate. This poor performance is attri buted to the same mechanism discussed already with the RETA tests rock like erosion. Visual observation of tests showed that generally it was common for small chunks of particulate to be removed from the materials surface. It was rare to find an insta nce where small particles eroded because a materials true critical shear stress had been achieved and incipient motion caused movement of several particles within the material matrix. The Batch 1 results were similar in that rock like erosion was present during testing, but the interesting thing concerning Batch 1 is the shear stress vs. time implication. During the first Batch 1 data run, erosion rate appeared to be relatively uniform throughout the course of the test at a given shear stress. During t he second data run however, this was not the case. Figure 5.5.18 was generated by analyzing the Batch 1 data set in parts. During the first part of each Batch 1, Run 2 shear stress (generally approximately the first six hours), erosion rates were relatively high. As the Batch 1 sample eroded, erosion rate level off and becomes much closer to the erosion rate seen during the first data run. This mechanism can be attributed to rocklike erosion. If one zooms in on a Gator Rock sample surface, there are lo calized sections that are bonded together more securely than others. In other words, there are certain particles within the Gator Rock matrix that are more likely to come off than others. A quick qualitative test that can be done to illustrate this is to scratch the Gator Rocks surface with a fingernail a few times. Scratching will show that certain particles are removed easily from the surface while other particles or chunks are more difficult to remove.

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198 Under an erosion rate test water is used to e rode the material instead of a fingernail. Still, the erosion mechanism will be similar; sometimes it will be easy to remove chunks of material while other times chunks will remain for a given erosive effort. This effect must mean that some of the partic les within the Gator Rock matrix are more securely bonded together than other particles. Under a rock like erosion mechanism, cyclical impulse forcing along the materials surface will weaken and eventually break the bonds holding the particles together. This cyclical forcing mechanism must act such that the looser bonds are broken first, and they are broken more quickly than the relatively strong localized particle bonds. For a given flow rate and consequently a given normal force these weaker parti cles will be vibrated out of position early on in the erosion test The strong particles will remain because the normal forcing is not strong enough to break these relatively strong bonds. In other words, under a rocklike erosion event, the Gator Rock r esults from Batch 1 imply that whatever can erode for a given flow condition will erode relatively quickly. After this erosion has occurred, nothing else will happen at that specified flow rate. This result was thoroughly unexpecte d, and it deviates from particle like erosion theory. Under particle like erosion, a critical shear stress induces incipient motion, and then over time, erosion occurs as a function of shear stress vs. critical shear stress deficit. Batch 1 results defy this theory and show that another mechanism must be present, and interestingly this mechanism is not seen during the first data run indicating different behavior for two different samples. 5.6 Second Gator Rock Mix If rock like erosion is indeed present with Gator Roc k, attemp ts of verifying the RETA by using a direct comparison with the SERF may not be possible. Based on this reasoning, and the rationale presented in Section 5.5.3, it was logical to try to design a material that would respond in a more particle erosion friendl y manner.

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199 To induce particle like erosion, the goal must be to uniformly weaken the bonds holding the Gator Rock matrix together. Portland cement, or the glue that is used for Gator Rock, derives its strength from the amount of water used during mixing Simply put, more water means less bond strength; less water means more bond strength. Meanwhile, the glue holding the Gator Rock together must be more homogeneous so to reduce the number of chunks that come from the material. Preparation of the first round of Gator Rock mix was performed using low cement ratios so much so that as described, Batch 1 at times behaved more like compacted sand than a coherent rock matrix (this may explain the different behaviors seen during SERF tests ). The theory behind the second Round of Bull Gator Rock mix was to use more, weaker glue to hold the material together and see if that would help to induce particlelike erosion in the two instruments. Using the same mixing procedures described in Section 5.3.4, a second round of samples was d esigned and prepared (Table 5 3). During this mixing procedure, the cement content was held steady at 4% while the water content and limestone content were varied. Because the time between mixing the first round of Bull Gator Rock and the second round of Bull Gator Rock was over a year, the source of crushed limestone had changed. A similar sieve analysis was conducted on the new crushed limestone to be used in the Round 2 mixing procedure (Figure 5 24). As before, the grain size analysis was conducted four times. Each grain size sieving run showed excellent agreement with one another. Maximum percent deviation between data points was 9.44% (seen with the No. 200 sieve). This grain size analysis shows that crushed limestone part icles used for the second round of Gator Rock mixing are on average slightly larger than the particles used for the first round of tests ( D50s of 0.5 vs. 0.25 respectively).

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200 5.6.1 Tensile and Compressive Strength Tests As with the first round of Gator Rock tests the first step in verifying sample to sample regularity was to conduct tensile and compressive strength tests Tests were conducted on three randomly chosen samples from each of the four batches of Gator Rock (Table 5 4). T here was a high amount of variability among samples of the same batches during strength tes ting. T he only batch that performed reasonably well was the Batch 2 specimen. There are two possible explanations that are related to one another that can be blamed for this poor strength testing behavior. The first i s given in Figure 5 24. Explicitly, aggregate for the second round of Gator Rock mixing was larger and more nonuniform than aggregate for the first round of Gator Rock tests This is significant because of Gator Rock mixing procedure. During Gator Rock mixing, aggregate for samples are chosen at random, packed, and vibrated. If aggregate is more nonuniform, it stands to reason that each individual sample would likewise be more nonuniform. More nonuniformity within a sample implies more jagged edges between particles or more precisely more voids in the Bull Gator Rock matrix. More nonuniformity also increases the chance that from a sampleto sample perspective, one Bull Gator Rock sample from the same batch will be different from another sample from the same batch of aggregate/cement. Bull Gator Rock is dependent on waters ability to work its way up through a sample. More, larger voids hinder capillary action. Even a sma ll increase or decrease in voids size or frequency from a sample to sample standpoint could severely inhibit water transport up through the material thereby creating slightly stronger or weaker samples. In other words, a variable void ratio from a sampl e to sample perspective affects strength test data. This effect was even greater than the water to cement ratio change that occurred from batch to batch. There was some empirical evidence of this effect even before strength testing occurred, although at the time it was ignored and quantitative measurements were not obtained. When the

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201 Round 2 Gator Rock samples were done curing after 28 days, some of the samples had failed to absorb their water properly. Although the observation was qualitative, there did not appear to be a corresponding relationship between cement content and water absorption. Although measurements were not conducted post curing, measurements were conducted pre break testing. Before each break test, Gator Rock samples were saturated. As Gator Rock was saturated, different samples from the same batch took different water quantities. This indicates different void ratios for different samples from the same batch (same cement content). It also implies different water absorption rates d uring curing for different samples from the same batch. The sample with the most uniform water absorption rate post curing was Batch 2. It is not a coincidence that this round of tests was also the most regular from a sample to sample standpoint. To il lustrate this phenomenon, a plot was made for Gator Rock batches created during the second round of tests (Figure 5 25). This graph shows a trend whereby an increase in absorbed water during saturation yields a lower strength value during the break test. This relationship appears to supersede any relationship between cement content and strength. It also implies that different void ratios must be present from a sample to sample perspective, which again should be caused by more variability in limestone agg regate. 5.6.2 RETA Tests The preceding discussion marked the first time in the history of Gator Rock that a series of Gator Rock samples failed to display regularity during compressive and tensile strength tests Given the variability of strength data, it appeared unlikely that obtaining erosion data (which overall is generally even more v ariable than strength data) would be repeatable. Since there is only one SERF, testing time in it is at a premium. Before spending more time trying to compare

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202 somethi ng that may be incomparable, with a SERF test, two sets of RETA tests were completed on two batches of material from the Round 2 Bull Gator Rock mix (Figure 526 and Figure 527). Of these four tests only one showed a regular erosion rate shear stress cu rve sample B 2. This is not to say that if these tests were to be repeated several times, it would not be possible to generalize RETA results for one of these samples. After several repetitions, it may be possible to generalize a scenario where erosion rate increases with shear stress. Given these results though, the dataset is limited. These two graphs indicate that rock like erosion is present, and it is of a similar order of significance as the particle like erosion mode seen in Sample B 2. Under ideal conditions, several tests (twenty or more) could be repeated with the Batch B mixing configuration. This would be the best case scenario because Batch B showed the least strength test variability and it showed the ability to produce a regular erosi on rate vs. shear stress relationship. If these tests were conducted repeatedly, they may show that on average shear stress and erosion rate can be correlated. Conversely, they may also show that rocklike erosion is dominant and that a regular erosion r ateshear stress curve cannot be developed for this material given current measurement limitations. 5.6.3 Absorption Limits Once RETA tests and strength tests confirmed that repeatability for the second round of Gator Rock testing would not be achieved, investigation looked to further quantify why this could have occurred and determine whether or not the Bull Gator Rock was functioning as designed. During the second round of Gator Rock curing, investigators noticed that some water had not been absorbed b y the dry limestone Portland cement mixture. At higher water contents in particular, there were often significant amounts of water remaining in the samples curing containers. The amount of remaining water for each sample batch, which was measured by mas s and is presented as a function of the intended water content (Table 5 5).

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203 B eyond approximately 20% water, the dry Bull Gator Rock mixture does not appear to be capable of absorbing any more fluid. In other words, by mimicking Niraulas (2002) original 20% water content during the first iteration of Gator Rock mixes, investigators had inadvertently stumbled upon the absorption limit for the bottom up method of creating Gator Rock. The scatter seen then in Table 5 4 and in the RETA results is actually a representation of Gator Rock samples with similar water contents. The implications of this are two fold. First, it does not appear that creating a bottom up version of Gator Rock with a higher water content than 20% is possible. Because of the rock like erosion discusses in Section 5.6.2, this means that creating a weaker Gator Rock where particle like erosion may be induced appears to require another method. Likewise, perhaps it would be better in further tests to use a different material; a sand c lay mixture for example. Secondly, if the distinctions between batches can be eliminated, the implication of this round of tests is that Gator Rock will often produce rocklike erosion at a 20% water content. As evidenced by Figure 5 26 and Figure 527, only once was a direct erosion rate vs. shear stress relationship developed. 5.7 Discussion The problem with large scale replication of this round of tests and in general with RETA tests is time. Large scale production of Bull Gator Rock is time consum ing. Obtaining material, meticulously measuring out samples, mixing the samples, shaking the samples, measuring water, etc. is labor intensive. Mixing a large scale batch takes between fifty and sixty man hours. Then, the samples need to cure for 28 day s, and even then theres no guarantee theyre going to work ; in fact, according to Section 5.6, it appears likely that particle like erosion induction will fail Finally, they must occupy a RETA for often weeks at a time. Although the first round of Bull Gator Rock testing in the RETA was completed using a 24 hour testing timeframe, the

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204 Round 2 mixes were tested with a 72 hour timeframe per shear stress because this is the standard in FDOTs RETA Florida Method. This translates to at least nine days of t esting per dataset. FDOT has a limited number of RETAs and taxing one (or several) of these instruments for weeks on end increases the probability for breakage which right now is nearly a certainty after a few weeks of testing. As discussed, control boxes often break, machines malfunc tion, equipment turns off, etc. T hese issue s mean that from start to finish, development of one erosion rate vs. shear stress curve takes approximately a month. At RETA testings present rate of success, developing an ext ensive dataset could take years. E ven with the most successful outcome possible after a string of RETA tests a particlelike dominated erosion rate vs. shear stress curve there is an additional need to compare results with SERF data. The SERF has its own set of issue s, particularly with longer duration erosion tests. Although the improvements chronicled in Chapter 3 now make longer duration erosion rate tests possible, a SERF test t oo is labor intensive. Loading the sample into the SERF often makes samples break, samples often get knocked loose from the screw holding the sample to the piston, motors, which are a constant problem, get burned out after several hours of testing etc. In short, although the device is much improved, and its present setup will allow eventually for a full scale dataset to be taken, this in effect is a massive project in and unto itself. D ata discussed in this chapter was taken over the course of six mont hs with little equipment downtime. During this timeframe, two SERF motors burned out (this is common for stepper motors), and three RETAs broke. Add to this mix time, preparation time, false starts emanating from failed procedures and tests, implication s from the absorption limit study, and it was fortuitous that it was even possible to obtain this limited dataset.

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205 With the present setup, the RETA and the SERF can only measure shear stresses and erosion rate Whenever erosion becomes dominated by rock like erosion and a regular erosion rate vs. shear stress graph cannot be generated, the test is effectively useless as a basis for comparison between the two instruments. Realizing these limitations, after the second Batch 2 Round 2 dataset was taken, particularly with respect to the absorption limit problem, the effort to verify RETA results via a direct comparison was effectively abandoned. SERF testing was not conducted on these materials because of variability seen during strength testing and rock like erosion seen during RETA testing Instead, the SERF was reserved for sand clay tests (Chapter 7). It appeared unlikely that any dataset could be produced that would even closely mimic data coming from the B 2 RETA sample, and as per an FDOT proposal sandclay data needed to be taken. Still, the effort to verify particle like RETA results was not abandoned completely. As will be discussed in Chapter 6, there is another pseudoanalytical method that is available for both verifying RETA results some what and correlating RETA results to cohesion. 5.8 Summary and Conclusions A summary of work completed in this chapter and a list of conclusions from work presented in this chapter is presented below: 1. A new method was invented for the creation of Gator Rock (Bull Gator Rock) where limestone and cement were mixed dry and water was slowly added to the limestone cement mixture through capillarity. 2. Initial strength and erosion testing for Gator Rock was encouraging. The first Gator Rock mix showed little variability in strength tests. Erosion tests showed erosion rate vs. shear stress curves that indicated possible particle like erosion. 3. Secondar y testing for Bull Gator Rock showed issue s with its initial design. Erosion tests showed rock like characteristics. R epeatability was not shown between the first and second dataset indicating that although particle like erosion may have been present the first time around, it may have been fortuitous.

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206 4. Attempts were made to improve Gator Rock design. Improvements showed sensitivity to void ratio which led to variability in both strength and erosion testing. 5. The improved Gator Rock batch appeared to indicate that obtaining more than ~20% water content using the bottom up water absorption method may not be possible. Therefore, it appears unlikely that using this material as a standard of comparison between the RETA and the SERF will be successful because of the presence of rock like erosion. 6. Variability in the strength and erosion results revealed that a comprehensive dataset was necessary for a direct RETA to SERF comparison. 5.9 Future Work A direct comparison should be attempted between the RETA and the SERF, but the time frame for production of such a dataset must be extensive (at least three years). During this time, there must be an investigator who is committed to producing a material to test in both devices and using the SERF exclusively for to produce an extensive dataset for this material. There must also be a RETA dedicated to production of a corresponding dataset. Because of the current limited amount of laboratory equipment, this is the only way to insure that enough data points are pr oduced. Based on lessons from the second round of the Bull Gator Rock mix, in the future a more uniform aggregate should be used when mixing Bull Gator Rock, or a new material/method for Gator Rock production should be developed. As shown during the sec ond round of tests, obtaining a water content higher than 20% may not be obtainable using this method. Although work for this chapter began as a method to provide definitive SERF to RETA answers, in the end, it had the net opposite effect, as more questi ons were generated: 1. Can Gator Rock be designed to induce particlelike erosion? 2. Might a different material be more appropriate? 3. Can either Gator Rock or a similar material be used to induce particle like erosion? 4. Can either Gator Rock or a new mater ial be designed to generalize SERF or RETA data?

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207 Table 5 1. Niraulas Original Gator Rock Water Cement Ratios Batch Number % Limestone % Cement % Water I 75 5 20 II 72.5 7.5 20 III 70 10 20 [Adapted from Niraula, L. D. (2004). Development of modified T Z curves for large diameter piles/drilled shafts in limestone for FB Pier. M.E. Thesis, University of Florida, Gainesville, FL.] Table 5 2. Water, Cement, and Limestone Composition for First Round Bull Gator Rock Mix Batc h Number % Limestone % Cement % Water 1 77 3 20 2 76 4 20 3 75 5 20 4 76 6 20 5 77 7 20 [Adapted from Shah, F. D. (2009). Development and testing of uniform synthetic limestone samples for the calibration of equipment used to determine erosion rates. Honors thesis, University of Florida, Gainesville, FL.] Table 5 3. Water, Cement, and Limestone Composition for Second Round Gator Rock Mix Batch Number % Limestone % Cement % Water A 78 4 18 B 76 4 20 C 74 4 22 D 72 4 24

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208 Table 5 4. Strength Test Results from Round 2 of Gator Rock Testing SAMPLE SAMP. w DRY MAX. TENSILE COMP. STRAIN TARE WET DRY NO. UNIT WT LOAD STRENGTH STRENGTH @ FAIL. WT. WT. WT. NAME (%) (pcf) (lbs) (kPa) (kPa) (in) (%) (g) (g) (g) UC-A 1 16.31 105.1 677.5 978.0 0.0461 1.14 76.4 684.0 598.8 2 14.82 103.7 1234.8 1781.8 0.0534 1.30 75.7 674.6 597.3 3 13.21 102.0 382.8 552.4 0.0301 0.74 75.0 646.6 579.9ST-A4A 12.46 105.6 256.0 232.2 0.0196 74.3 365.0 332.8 4B 12.43 102.8 214.3 194.8 0.0180 77.3 358.7 327.6 5 14.33 105.8 321.3 237.7 0.0143 75.4 438.4 392.9 6 13.38 106.9 362.1 269.4 0.0254 76.2 437.2 394.6UC-B11 15.96 103.9 603.3 866.8 0.0319 0.78 75.7 679.5 596.4 12 15.07 103.9 578.1 830.0 0.0577 1.41 76.7 675.5 597.1 13 14.81 103.6 608.7 870.1 0.0518 1.27 74.6 670.9 594.0ST-B14 15.80 106.6 277.0 201.7 0.0160 76.5 451.1 400.0 15 9.62 104.0 345.4 260.5 0.0147 78.6 414.7 385.2 16 14.56 104.2 222.3 165.6 0.0147 75.6 429.7 384.7UC-C21 10.83 101.1 850.4 376.8 1216.3 0.0354 0.87 77.5 633.2 578.9 22 12.88 101.6 735.3 1058.2 0.0268 0.66 72.5 641.3 576.4 23 10.96 102.4 922.2 1317.2 0.0338 0.83 76.9 642.9 587.0ST-C24 10.54 99.4 251.7 182.6 0.0161 81.1 416.8 384.8 25 9.91 103.4 288.9 212.1 0.0163 75.1 416.7 385.9 26 10.78 103.5 316.1 230.3 0.0164 75.1 423.4 389.5UC-D31 15.58 100.4 494.7 220.0 705.2 0.0313 0.78 77.1 651.3 573.9 32 14.41 104.2 764.4 1087.1 0.0319 0.79 76.3 668.5 593.9 33 13.48 102.0 698.5 995.5 0.0343 0.85 76.7 651.8 583.5ST-D34 14.52 103.6 250.2 188.2 0.0190 77.3 425.1 381.0 35 12.62 105.8 325.6 243.7 0.0192 76.1 428.5 389.0 36 14.79 102.6 240.1 177.2 0.0190 77.7 430.1 384.7 DISPL. @ FAIL.

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209 Table 5 5. Actual W/C Ratios for Second Round of Gator Rock Water Remaining (g) Water Absorbed (g) Cement (g) Actual W/C Ratio 6.59 154.52 32.22 20.85% 29.14 148.08 32.22 21.76% 43.15 150.18 32.22 21.46% Figure 5 1. Examples of oldstyle Gator Rock after RETA testing [Adapted from Shah, F. D. (2009). Development and testing of uniform synthetic limestone samples for the calibration of equipment used to determine erosion rates. Honors thesis, University of Florida Gainesville, FL.]

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210 Figure 5 2. Slagles Rotisserie [Adapted from Slagle, P. M. (2006). Correlations of erosion rateshear stress relationships with geotechnical properties of rock and cohesive sediments. M.S. thesis, University of Florida, Gainesville Florida.] Figure 5 3. First Round of Bull Gator Rock Grain Size Distributions [Adapted from Shah, F. D. (2009). Development and testing of uniform synthetic limestone samples for the calibration of equipment used to determine erosion rates. Honor s thesis, University of Florida, Gainesville, FL.]

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211 Figure 5 4. Strength Test Results from First Round of Bull Gator Rock Mixes. The orange line is cohesion; the green line is from compressive strength; the blue line is tensile strength. Figure 5 5. Batch 1 Bull Gator Rock after RETA 24 hr. RETA Test [Adapted from Shah, F. D. (2009). Development and testing of uniform synthetic limestone samples for the calibration of equipment used to determine erosion rates. Honors thesis, University of Florida Gainesville, FL.] 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 0 500 1000 1500 2000 2500 3000 3500 4000 W/C RatioStrength (kPa) y(x) = a x^n + c a = 5164.4 c = 95.343 n = -1.9641 R = 0.99318 (lin) y(x) = a x^n + c a = 1.3561e+006 c = 1483.3 n = -6.1676 R = 0.98966 (lin) y(x) = a x + b a = -137.83 b = 1009.2 R = 0.9735 (lin)

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212 Figure 5 6. Batch 2 Bull Gator Rock after RETA 24 hr. Test [Adapted from Shah, F. D. (2009). Development and testing of uniform synthetic limestone samples for the calibration of equipment used to determine erosion rates. Honors thesis, University of Florida, Gainesville, FL.] Figure 5 7. Batch 3 Bull Gator Rock after RETA 24 hr. Test [Adapted from Shah, F. D. (2009). Development and testing of uniform synthetic limestone samples for the calibration of equipment used to determ ine erosion rates. Honors thesis, University of Florida, Gainesville, FL.]

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213 Figure 5 8. RETA Results From Batch 3, Round 1 Figure 5 9. RETA Results from Batch 4, Round 1 5 10 15 20 25 30 35 40 45 50 0 0.5 1 1.5 2 2.5 3 3.5 x 10-3 Shear Stress (Pa)Erosion Rate (m/yr) y(x) = a x + b a = 9.4546e-005 b = -0.0015958 R = 0.9307 (lin) 5 10 15 20 25 30 35 40 0.5 1 1.5 2 2.5 x 10-3 Shear Stress (Pa)Erosion Rate (m/yr) y(x) = a x + b a = 3.7659e-005 b = 0.00060709 R = 0.57723 (lin)

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214 Figure 5 10. RETA Results from Batch 5, Round 1 Figure 5 11. Cohesion vs. Er osion Relationship for First Round of Gator Rock Samples 5 10 15 20 25 30 35 40 45 50 0 0.5 1 1.5 2 2.5 3 x 10-3 Shear Stress (Pa)Erosion Rate (m/yr) y(x) = a x + b a = 6.217e-005 b = -0.00047792 R = 0.78981 (lin) 0 100 200 300 400 500 600 700 800 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 Cohesion (kPa)Erosion Rate (m/yr.) 10 Pa 20 Pa 30 Pa 40 Pa 50 Pa y(x) = a x^n a = 2.6343e+008 n = -4.1431 R = 0.97353 (lin) y(x) = a x^n a = 18649 n = -2.6386 R = 0.98937 (lin) y(x) = a x^n a = 7.4657e+010 n = -5.1579 R = 0.98557 (lin) y(x) = a x + b a = -1.8911e-006 b = 0.002777 R = 0.42705 (lin) y(x) = a x + b a = -1.1824e-007 b = 0.0030585 R = 1 (lin)

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215 Figure 5 12. RETA Results from Batch 1, Round 2 Figure 5 13. RETA Results from Batch 2, Round 2 5 10 15 20 25 30 35 40 45 50 55 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 Shear Stress (Pa)Erosion Rate (m/yr) 5 10 15 20 25 30 35 40 45 50 55 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 Shear Stress (Pa)Erosion Rate (m/yr)

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216 Figure 5 14. RETA Results from Batch 3, Round 2 Figure 5 15. RETA Results from Batch 4, Round 2 5 10 15 20 25 30 35 40 45 0 0.5 1 1.5 2 2.5 3 x 10-3 Shear Stress (Pa)Erosion Rate (m/yr) 5 10 15 20 25 30 35 40 45 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 x 10-3 Shear Stress (Pa)Erosion Rate (m/yr)

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217 Figure 5 16. RETA Results from Batch 5, Round 2 Figure 5 17. Gator Rock Test Disc Results 10 15 20 25 30 35 40 45 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 x 10-3 Shear Stress (Pa)Erosion Rate (m/yr) y(x) = a x + b a = 8.9172e-005 b = -0.00098252 R = 0.90819 (lin)

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218 Figure 5 18. Time Series of Piston Position During Stand Alone SEATEK Test (Data from Batch 1 test at 50 Pa). Figure 5 19. Zoomed in Position vs. Ti me Graph from Batch 1, 50 Pa Data \ 0 5 10 15 20 25 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 Time (hr)Position (cm) 7.4 7.5 7.6 7.7 7.8 7.9 8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 Time (hr)Position (cm)

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219 Figure 5 20. Batch 1 Sample Position vs. Time from SERF with BestFit Regression Line Figure 5 21. Erosion Rate vs. Shear Stress for Batch 1 in SERF Using First Round Mix 0 5 10 15 20 25 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 Time (hr) Position (cm) y(x) = a x + b a = 0.00058337 b = -0.05572 R = 0.024816 (lin) 0 5 10 15 20 25 30 35 40 45 50 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Shear Stress (Pa)Erosion Rate (m/yr) Batch 1 Run 1 Batch 1 Run 2 (hours 0 6) Batch 1 Run 2 (hours 10 end) y(x) = a x + b a = 0.0014212 b = -0.018113 R = 0.82502 (lin) y(x) = a x + b a = 0.014552 b = -0.088048 R = 0.70218 (lin) y(x) = a x + b a = 0.002355 b = -0.010199 R = 0.31723 (lin)

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220 Figure 5 22. Erosion Rate vs. Shear Stress for Batch 2 from SERF Figure 5 23. Erosion Rate vs. Shear Stress for Batch 3 from SERF 10 15 20 25 30 35 40 45 50 0 0.5 1 1.5 2 2.5 3 3.5 x 10-3 Shear Stress (Pa)Erosion Rate (m/yr) 10 15 20 25 30 35 40 45 50 0 1 2 3 4 5 6 7 x 10-3 Shear Stress (Pa)Erosion Rate (m/yr)

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221 Figure 5 24. Grain Size Analysis for Round 2 Gator Rock Mix Figure 5 25. Non Dimensionalized Water Retained vs. Strength ( WR is the weight of retained water after saturation; WD is the dry sample weight). 0 10 20 30 40 50 60 70 80 90 100 0.01 0.1 1 10Percent PassingGrain Size (mm) Round One Gator Rock Mix Round 2 Gator Rock Mix 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16 0 200 400 600 800 1000 1200 1400 Wr/Wd Strength(kPa) Compressive Strength Tensile Strength y(x) = a x + b a = -953.8 b = 303 R = 0.57464 (lin) y(x) = a x + b a = -8993.2 b = 2091.4 R = 0.83405 (lin)

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222 Figure 5 26. Batch A RETA Results Figure 5 27. Batch B RETA Results 6 8 10 12 14 16 18 20 22 24 26 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 Shear Stress (Pa)Erosion Rate (m/yr) Sample A-1 Sample A-2 6 8 10 12 14 16 18 20 22 24 26 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 Shear Stress (Pa)Erosion Rate (m/yr) Sample B-1 Sample B-2

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223 CHAPTER 6 VERIFICATION OF THE ROTATING EROSION TES TING APPARATUS (RETA) AND USING RETA RESULTS T O PREDICT EROSION RATES AND SHEAR STRESS ES OF ERODING BED MATERIALS USING COHE SION 6.1 Executive Summary In an effort to verify results from the Rotating Erosion Test Apparatus (RETA), a semi analytical approach was used. Results f rom the RETA were filtered so that only data that exhibited a direct erosion rate vs. shear stress relationship was considered. Then, this dataset was fit to a shear stress deficit erosion rate formula. Results showed agreement between the formula and RE TA results. Dimensional analysis was used to develop relationships for erosion rate constant and critical shear stress based on other geotechnical parameters. Based on Mohrs Circle reasoning, a cohesion term was added to nondimensional parameters. Cohesion based nondimensional correlations were used to estimate erosion rate vs. shear stress curves and compared with RETA results. Data showed agreement between the empirical formulae and actual results, although the R2 value when comparing these two par ameters was low. Because the cohesive dataset was limited, it is thou ght that this is what led to these low R2 values 6.2 Review of Relevant Background Development of an extensive RETA dataset is labo r intensive The same is true for a SERF dataset or any dataset in a flume style device. Even if a comprehensive dataset could be generated for both instruments, there is no way to verify that the material tested in both machines is the same from one test to another. The first version of Bull Gator Roc k was the most uniform, regular, and homogeneous manmade material found to date that would stand up to testing in both devices, and even Gator Rock tests in the RETA showed variability. Although the RETAs dataset was quite limited, for a comparison to be made between the RETA and the SERF, the testing materials results should be repeatable even with a small dataset. Faced with these

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224 difficulties for making a direct comparison between the RETA the SERF, another method was investigated to determine wh ether or not the RETA was providing accurate erosion rate and shear stress relationships Since its inception, the SERF has been used limitedly because of issues associated with its operation. The RETA on the other hand has been used extensively for over fi ve years. The FDOT SMO has a laboratory with several RETAs, and these machines are run often with a variety of soil, rock, and hybrid samples. Every time a test is run its results are added to FDOTs comprehensive RETA database. As of 2010, FDOT had conducted RETA testing on 83 different materials. Materials from both Florida and from out of state were tested S ome materials were manmade, while other materials were natural. During the SMO tests at least three shear stresses were run during each test. Each test lasted for 72 hours. This database represents 6,000 hours of RETA testing, and it is the most extensive rotating erosion rate testing device database known to exist. It took years to g enerate this database, and it would take several more years to generate anything similar. As it is set up, the RETA is designed to measure erosion rates and corresponding shear stress for particle like erosion only. As discussed at several points throughout this dissertation, particle like erosion assumes that a gentle pull or tug removes a few outside particles from a sediment sample. This pull or tug is different than rocklike erosion in that during rocklike erosion, a pulsating impulse force removes chunks or blocks of material from the sediment sample. The overall goal of this project is to use erosion rate testing devices to measure the sediment transport function that is used in computing scour depth. Recall from Chapter 2 and Chapter 3 that part icle like erosion is generally defined by functions taking the following form: f C E (6 1)

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225 where E is the erosion rate, C is an erosion rate constant, and f() is a function of shear stress. Although more complicated computations for t his function have been presented (for example Van Prooijen and Winterwerp 2008), they can be approximated by this generalized equation. Typically, following shields, the shear stress function is instead expressed as a deficit between bed shear stress and critical shear stress: c bM E (6 2) where b is the bed shear stress and M is the material specific erosion rate constant. As discussed in Chapter 2, Equation 6.616 was discovered in the 1970s for particle like cohesive erosion rates by Kandiah (1974) and Ariathurai (1974) This equation confirmed analytical results from Einstein (1950), Partheniades (1965), and others. Equations of this form have been shown to work using a variety of erosion rate testing equipment. For example, Partheniades verified his equation for erosion rate by measuring sediment concentrations. Kandiah on the other hand used a device similar to the RETA to develop his expression. In general, equations similar to Equation 6.617 have shown global relevancy for the particlelike cohesive erosion problem. If an equation of this form is valid and the RETA is measuring erosion rate vs. shear stress relationships correctly, RETA data should fit this form as well. Although a direct comparison between the RETA and the SERF proved difficult, a direct comparison between the RETA and equations of this form is possible because of the existence of the RETA database. The technique used for this analysis was to take the RETA database and try to fit data from it to Equati on 6.62. This meant that instead of just comparing the RETA to one other dataset using one other erosion rate testing device, the RETA is compared with every other cohesive erosion study.

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226 6.3 RETA Verification The first step of RETA data analysis was to filter the RETA dataset so that only particle like results were analyzed. For each material in the RETA database, erosion rate and shear stress were plotted against one another, and best fit least squares regression line was fit to the data points. I f the line had a positive slope (Figure 6 2) the material for which the curve was generated was used for overall analysis; if the line had a negative slope (or no slope), the material was ignored and rock like erosion conditions were presumed to have domi nated that particular RETA test (Figure 6 1) Filtering cut the number of samples used for analysis. Whereas the dataset started with plots relating erosion rate to shear stress for 83 materials, after filtering, only 32 materials fit the stringent eros ion rate shear stress curve criteria ( approximately 40%). Although this looks like a great deal of data elimination, for a material to survive a RE TA test it must be resilient. Generally resilient materials are harder and stiffer than materials that cru mble under the moment forcing associated with RETA testing. In other words materials that are able to withstand a RETA test are more rock like, which implies that they are more likely to respond to erosion via a rock like mechanism. In this context then, a 40% data recovery rate is actually quite precise. Once filtering was completed, a brief analysis was conducted to compare average rock like erosion quantities with implied particle like erosion quantities. In general, materials that exhibit direct shear stress to erosion rate relationship erode an order of magnitude faster than materials that do not exhibit a direct shear stress to erosion rate relationship. This fits with the overall rock like erosion argument. Once filtering had been completed, the next step in fitting a shear stress deficit equation to the RETA dataset was to find the critical shear stresses associated with each material. By definition, erosion rate below the critical shear stress point should equal zero. Therefore, the

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227 critical shear stress should be defined as the x intercept corresponding to a materials erosion rate vs. shear stress line. For each case where a direct erosion rate vs. shear stress relationship was observed, the critical sh ear stresses were estimated using the corresponding best fit least squares regression equation. Bed shear stress and erosion rate were measured directly during each RETA test With critical shear stress estimated from the bestfit regression lines, the o nly unknown in Equation 61 was M the erosion rate constant. For each erosion rate and shear stress point, the value of M was computed. As a basis of comparison with the Equation 6.62 linear erosion approximation, the most recent analytical expression for erosion rate was also used (Van Prooijen and Winterwerp 2008). Van Prooijen explains that his expression for erosion rate can be approximated using a three piece function that also utilizes a shear stress deficit approach and an erosion rate constant Erosion rate constant was als o computed using Van Prooijen and Winterwerps set of equations. 7 1 if 1 204 0 823 0 904 0 144 0 52 0 if 02 3 c b c b c b c b c b c b cM E (6 3) These two approaches for solving for M differ only slightly only by 0.984%. Results were nondimensionalized, and a best fit regression line was fit through the dataset (Figure 63). Based on Equation 61, both the slope and the x intercept of the regression line should have been 1.0. As shown, with a high R2 value (0.8279), the slope and the intercept of the regression line are extremely close to where they should be. The x intercept shows an error of 0.93% and the slope shows an error of 11.78%. Generally, the Equation 61 appears to be a somewhat conservative estimate of erosion rate. The Van Prooijen and Winterwerp Equation

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228 appears to capture true erosion rates at lower shear stresses somewhat better than the linear approximation particularly when bed shear stress approaches critical shear stress. Overall, from this analysis, data from the RETA appears to fit corr ectly. This implies that when a direct shear stress vs. erosion rate curve is developed, the RETA is providing accurate results. 6.4 Extending RETA Results Predicting Erosion Rate as a Function of Material Strength Based on the relatively small error associated with the preceding analysis, there was confidence behind RETA results. The next step in this study involved trying to find a way to correlate erosion rate constant and critical shear stress to another geotechnical property of rock like bed mate rials. The goal of this phase of research was to eliminate or reduce the need for extensive RETA testing If the need for RETA tests could be cut down to a situation where a user simply had to run a RETA test to confirm or disprove a direct erosion rat e vs. shear stress relationship this would be invaluable. Based on the Mohrs Circle analysis in Chapter 2, if erosion rate is to be a function solely of shear stress, then the y intercept of the Mohrs Circle failure line, or cohesion, should be correl ated to erosion rate. This Section discusses attempts to use cohesion to approximate a materials erosion rate constant and critical shear stress. 6.4.1 Approximation of M The first goal was to approximate M as a function of a bulk mate rial property. Intuitively, as material gains strength, erosion rate should decrease. Likewise, as the material gains strength, its critical shear stress should increase, and therefore overall, the erosion rate constant, M should decrease. A correlation was developed between erosion constant, M and material cohesion, or C (Figure 6 4). As shown, there appears to be a relatively strong correlation between these two parameters. Previously, for clay like materials, attempts have been made to correlate M to critical shea r stress, but for the materials tested in the RETA, no direct relationships could be computed between critical shear stress, erosion rate constant, or m aterial strength (Figure 6 5).

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229 The relationship given in Figure 6 4 for M was compared with computed M values from erosion rat e data. Although in Figure 64, the relationship between M and cohesion appears to be excellent, when comparin g the equation from Figure 64s trend line with measured data (Figure 66), results start to deviate. Using the predictiv e equation shown in Figure 64, the R value between Mmeasured and Mcalculated drops from 0.92 to 0.69 although even with this low er R value, the equation of the line is nearly where it should be (along y=x Figure 6 6). If the relati onship developed in Figure 6 4 is used to predict erosion rate and compared with measured data, results are still reasonable, even though M appears sen sitive to cohesion (Figure 67). The best fit line plotted for measured vs. predicted values of erosion rate lies within 10% of where it should be (along y = x). The magnitudes of measured erosion rate vs. predicted erosion rate are excellent, although the predicted erosion rate is on average lower than the measured value. Still, overall, when dealing with erosion rates on t he order of magnitude of mm/year, this analysis should provide the correct order of magnitude for erosion. 6.4.2 Dimensional Analysis Because it looked as though it was possible to predict M from material strength, the next step was to nondimensionalize this parameter. Based on dimensional analysis, it was hypothesized that erosion rate constant should be nondimensionally depended on the ratio between material strength and critical shear stress such that: C f u MCc* (6 4) In this express ion, C is cohesion, c is critical shear stress, M has been converted to m2s/kg by dividing by material density, and u* is the friction velocity associated with the critical shear stress such that:

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230 cu (6 5) According to this argument then, as the ratio between material strength and critical shear stress increases, erosion rate constant should decrease. Intuitively, this appears to fit what Gordons 1991 memo already stated stronger materials tend to erode less quickl y than weaker ones (Figure 68) T he R value for this relationship is 0.86. M ore data is needed to shore up this relationship between material strength and erosion rate constant. This is emphasized when one uses the logari thmic expression in Figure 6 8 to estimate erosion rate direct ly. In contrast to Figure 6 7, erosion rate is not as accurate as it was when using the dimensional relationship between material strength and ero sion rate constant (Figure 6 9). As shown, the R value drops to 0.86 when the nondimensional expression is used, and the best fit line through the data points under predicts the erosion rate by approximately 30% on average. As before, the same order of magnitude is recovered. 6.4.3 Approximating c With a correlation d eveloped between material strength and cohesion, the only remaining unknown in the Equation 6.62 (and the non dimensionalized cohesion relationship) was critical shear stress. Development of a relationship between material strength and critical shear str ess has been done before for clay like particles. Migniot (1986) developed an expression that showed a relationship between upper Bingham yield stress and critical shear stress such that: Pa 6 1 ; 289 0 Pa 6 1 256 0y y y y c (6 6) Migniot was dealing with much softer and weaker sediments than the sediments found in the RETA database. Migniots critical shear stresses were four orders of magnitude higher than

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231 typical RETA critical shear stresses ( O(101) vs. O(103)). Based on this large difference for critical shear s tress, it is not surprising that his correlations did not work for RETA materials. Following the development for the nondimensional expression for M as a function of the critical shear stress cohesion ratio, another nondimensional group was developed via dimensional analysis such that: C f u Mc s (6 7) where s is the bulk material density, M has once again been converted to m2s/kg by dividing by the density, and previous terms have been already defined. A power law regression li ne was fit to data (Figure 6 10). This predicted value for M was used to approx imate erosion rate (Figure 611), and results show that although this relationship also under predicts erosion rate, the relation ship developed in Equation 64 appears to be more accurat e than the relationship developed in Equation 67 because on average, it only under predicts erosion rate by approximately 25%. With the development of Figure 63, Figure 68, and Figure 610, a nondimensional method was finally available for estimating erosion for a given shear stress without development of an erosion rate shear stress curve. Under field conditions using the Van Prooijen and Winterwerp expression, bed shear stress, or b, i s known from a hydr ograph, and between Figure 68 and Figure 610, there are two expressions presented in terms of one another with two unknowns: M and c. These two expressions, which are implicit in one another, can be combined and solved together to fi nd erosion rate solely as a function of cohesion, or an interpolative solution can be found using the diagrams provided. Combining the expressions from these two diagrams, an equation was developed for c as a function of only cohesion such that:

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232 b C a C u C u Ac k c s ln* (6 8) where A = 2.17x107, a = 8.29x106, b = 7.12x105, and k = 0.545. Solving this equation for c and comparing with measured values for c le d to development of Figure 612. T he errors associated with the best fit curves from t he two nondimensional groups appear to have compounded leading to a large error between actual and estimated critical shear stress estimates. This was expected since the sum of the errors of the two non dimensional groups was on the order of 60%. Using the predicted values for critical shear stress, new values of M were computed, and they in turn were used with the computed critical shear str esses to solve for erosion rate (Figure 6 13). 6.5 Discussion and Future Work The most significant result from this study is that data from the RETA database has been shown to fit a shear stress deficit equation. Several assumptions with the cohesion estimating method have been alluded to, but should be discussed explicitly. As shown in most of the cohesion plots only eight cohesion data points are shown. Until 2006, no one had realized that cohesion could possibly be related to erosion rate. When RETA tests were conducted then, a separate cohesion test was not conducted on material before 06. As a result, the n umber of cohesion points is limited. Combine the time frame limits with initial filtering requirements, and the cohesion dataset is small compared with the overall usable RETA dataset. More research needs to be done to determine whether or not the cor relations developed in Section 6.4 hold up when more data points are used to develop these curves. The fundamental assumption in using cohesion as a correlating parameter with the RETA dataset is that the materials on which cohesion was measured are simi lar to the materials on which erosion was measured. Quantifying a materials erosion rate vs. shear stress relationship

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233 and a materials cohesion value requires three sediment samples. During each test tensile strength, compressive strength, and RETA the sediment sample is destroyed. Using cohesion as the correlating factor assumes that unlike the Round 2 Bull Gator Rock samples, there is little variability among the three sediment cores. This assumption may or may not be true; a method for verifyi ng sample uniformity should be developed if a method similar to this is to be used for design. If the dataset used to construct these correlations can be extended though, and if uniformity between samples can be established, results from this study are pr omising especially when examining Figure 6.4.10. Although the R value (which again is probably caused by a lack of data points) is poor (0.764), the slope of the best fit line through the dataset is within 5.5% of where it should be. This indicates tha t, this method for predicting erosion rate based only on material strength has the potential to provide an accurate estimate of actual erosion rate. If the data set becomes more populated, more accurate critical shear stress estimates and more accurate er osion rate constant estimates would follow. This method does predict the order of magnitude of erosion properly. On average, it also provides a slightly conservative estimate of erosion rate. Although the % error between this method and the actual erosi on rate is relatively high, in terms of actual magnitudes of erosion rates, it is fairly low. On average, this method for predicting erosion rate over predicts erosion by 0.52 mm per year; the maximum error is 3.86 mm of erosion per year; and the minimum error is an under prediction of 1.28 mm of erosion per year. Although not perfect (as evidenced by the lower R value in Figure 6.4.10), this method will provide erosion rates with 5 mm of the actual conditions, and it can potentially lead to a reduction i n the am ount of SERF or RETA tests If

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234 this method is to be used, a safety factor should be employed because of the uncertainty among the nondimensional material strength relationships. This method may be able to reduce the number of RETA testing Eve n though this method was created using filtered data that represented a direct erosion rate vs. shear stress relationship, it was also found that data that did not exhibit a direct relationship was an order of magnitude higher than data that did exhibit a direct relationship. If then, this method is used and it turns out that the material in question exhibits an indirect erosion rate vs. shear stress relationship o r no shear stress vs. erosion rate relationship, it can be inferred that it must erode less than it would if it were to obey a direct distribution. Using cohesion to estimate erosion rate would be considered conservative. However, substantially more data are required to validate this theory. 6.6 Summary and Conclusions A summary of work and a list of conclusions are presented below: 1. FDOTs RETA database was filtered so that only direct erosion rate vs. shear stress relationship data was considered. 2. Filtered data was used in development of erosion rate vs. shear stress best fit regression lines. Regression lines were used to imply critical shear stress for the eroding material. 3. Implied critical shear stresses were used with erosion rate and actual shear stress data to solve for erosion rate constant. 4. Results were non dimensionalized and plotted. RETA data showed excellent agreement with shear stress deficit analytical formulas. 5. Cohesion was used to develop two nondimensional groups to estimate ero sion rate constant and critical shear stress. 6. Using cohesion as a predictive erosion parameter shows some promise, although the cohesion dataset for RETA samples is quite limited. 7. More research should be conducted before any of these curves are used fo r design purposes. Specifically, the erosion rate vs. shear stress vs. cohesion database should be expanded.

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235 Figure 6 1. Example of a RETA Material with Rock Like Erosion Properties Figure 6 2. Example of a RETA Material with Particle Like Erosion Properties 0 10 20 30 40 50 60 70 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01 Shear Stress (Pa)Erosion Rate (m/yr) y(x) = a x + b a = -2.1795e-005 b = 0.0020288 R = 0.86548 (lin) 0 10 20 30 40 50 60 70 0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.02 Shear Stress (Pa)Erosion Rate (m/yr) y(x) = a x + b a = 0.00039548 b = -0.013047 R = 0.85269 (lin)

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236 Figure 6 3. Non Dimensional Erosion Results from RETA Data Set Figure 6 4. Relationship between Erosion Rate Constant and Cohesion 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 0 0.5 1 1.5 2 2.5 3 taub/taucE/(M*tauc) Data Points Using M from Ariathurai Equation Data Points Using M from Van Prooijen Equation Ariathurai Equation Van Prooijen Equation y(x) = a x + b a = 0.9844 b = -1.0188 R = 0.90323 (lin) 0 500 1000 1500 2000 2500 3000 3500 4000 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x 10-5 C (kPa)M (g/(N-s)) y(x) = a x^n a = 0.0060567 n = -1.0402 R = 0.91887 (lin)

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237 Figure 6 5. Relationship between Critical Shear Stress and Erosion Rate Constant Figure 6 6. M fro m Cohesion based equation vs. M from Measured Data 0 10 20 30 40 50 60 0 0.2 0.4 0.6 0.8 1 1.2 x 10-5 tauc (Pa)M (g/(N-s)) 0 1 2 3 4 5 6 7 8 x 10-6 0 1 2 3 4 5 6 7 8 x 10-6 M from Data (g/(N-s))M Calculated (g/(N-s)) Data y=x y(x) = a x a = 0.91425 R = 0.68863 (lin)

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238 Figure 6 7. Predicted Erosion Rate vs. Measured Erosion Rate Using M Based on Material Strength Figure 6 8. Non Dimensional Erosion Constant vs. NonDimensional Material Strength 0 1 2 3 4 5 6 x 10-3 0 1 2 3 4 5 6 x 10-3 E Measured (m/yr)E Computed (m/yr) Data y=x y(x) = a x a = 0.88059 R = 0.85327 (lin) 10-6 10-5 10-4 10-5 tauc/CMC/u* Data y(x) = a log(x) + b a = -8.2903e-006 b = -7.1191e-005 R = 0.85549 (log)

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239 Figure 6 9. Graph showing computed value for E vs. Measured value for E when the NonDimensional Relationship for M is used. Figure 6 10. Non Dimensional Critical Shear Stress vs. Non Dimensional Material Strength 0 1 2 3 4 5 6 x 10-3 0 1 2 3 4 5 6 x 10-3 E Measured (m/yr)E Computed (m/yr) Data y=x y(x) = a x a = 0.65287 R = 0.80726 (lin) 10-6 10-5 10-4 10-10 10-9 10-8 tauc/CM*rho*u* y(x) = a x^n a = 2.1694e-007 n = 0.54457 R = 0.94294 (log)

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240 Figure 6 11. Measured Erosion Rate vs. Predicted Erosion Rate Using the Non Dimensional Expression Developed in Equation 6.44. Figure 6 12. Measured vs. Computed Critical Shear Stress 0 1 2 3 4 5 6 x 10-3 0 1 2 3 4 5 6 x 10-3 E Measured (m/yr)E Computed (m/yr) Data y=x y(x) = a x a = 0.75974 R = 0.86614 (lin) 0 10 20 30 40 50 60 0 10 20 30 40 50 60 Measured Critical Shear Stress (Pa)Computed Critical Shear Stress (Pa) Measured vs. Estimated Critical Shear Stress y=x y(x) = a x a = 0.40308 R = 0.61037 (lin)

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241 Figure 6 13. Predicted Erosion Rate Using Cohesion Computation vs. Actual Erosion Rate 0 1 2 3 4 5 6 x 10-3 0 1 2 3 4 5 6 x 10-3 E Measured (m/yr)E Computed (m/yr) Data y=x y(x) = a x a = 1.0458 R = 0.76412 (lin)

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242 CHAPTER 7 A STUDY OF EROSION RATES, SHEAR STRESSES AND DENSITY VARIATIONS OF SAND/CLAY MIXTURES 7.1 Executive Summary In an effort to generalize sediment transport functions for sandclay mixtures, a series of tests was conducted in the Sediment Erosion Rate Flume (SERF). R esults from the tests show that shear stress on sand clay mixtures corresponds to roughness such that as roughness (average sediment diameter) increases, shear stress increases. Work in this study provides some data for the question of suspended particle e ffects on shear stress. This study showed that for a smooth wall, as suspended particle concentration increases, shear stress does not necessarily correspondingly increase. Erosion rate testing was inconclusive. Generally, erosion rate testing showed t hat sand clay mixtures erosion response is sensitive to how the sandclay mixtures are initially created Changes in initial water content and compaction methods affect erosion rate as much as or more so than sandclay ratio. More research, particularly on natural samples, is needed to generalize the erosive behavior of sand clay mixtures. 7.2 Review of Relevant Background and Motivation for Research As discussed in Chapter 2, the EFA SRICOS method for predicting scour depth presumes that a relationship exists between erosion rate and shear stress for an eroding bed material. S ome particle like erosion models have been proposed over the years for cohe sive bed materials. (Einstein 1950, Mehta and Lee 1992, Van Prooijen and Winterwerp 2008, etc.). Most of these erosion models presume a uniform sediment diameter. Some models have been developed for determining the critical shear stress of cohesive bed materials that are composed of mixed elements (Barry 2003, Sharif 2002), but sediment transport models for mixedbed sediments have yet to be developed. If a direct erosion rate vs. shear stress relationship exists for mixed bed materials, then it may be possible to determine critical shear stress and use a particlelike

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243 stochastic erosion model. If a direct erosion rate vs. shear stress relationship does not exist for these materials however, application of a particlelike erosion model may be inappropri ate. The Rotating Erosion Test Apparatus (RETA) database described in Chapter 6 was obtained by running tests on materials that conform to the stringent guidelines associated with RETA testing. For a material to b e tested in the RETA, it must erode near ly uniformly from topto bottom it must be strong enough to stand up on its own, and it must be strong enough to withstand the forces associated with the RETAs spinning annulus. Generally speaking, materials that meet these requirements are stiff, hard, strong, rocklike earth materials. The Bull Gator Rock described in Chapter 5 was engineered to mimic the characteristics of hard, stiff, strong, rocklike materials that constitute successful RETA tests The distinction between rocklike cohesive sedi ments and weaker cohesive sediments is important. RETA database analysis showed a split between rocklike and particle like erosive behavior for samples that are strong enough to withstand a test For weaker cohesive bed material such as sand clay mixtur es, there is limited available data. The goals then of this phase of research are then two fold. First, is to determine how mixed bed earth materials behave. Do they obey a direct erosion rate vs. shear stress relationship or do they exhibit more rocklike erosion qualities? If mixed bed earth materials respond to a direct erosion vs. shear stress pattern, is it possible to generalize this behavior? For example, may it be possible to claim that an increase in clay content decreases the erosion rate? Barry and Sharif indicate that varying clay contents in a sand clay mixture affect critical shear stress, so it may be possible that varying clay contents also affects erosion rate. 7.3 Materials and Procedure T he Sediment Erosion Rate Flume (SERF) was use d exclusively to obtain erosion rates and shear stresses for a range of sand clay mixtures. Although originally the plan was to conduct

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244 tests at 12.5% clay intervals from 0% clay to 100% clay, unexpected testing results forced investigators to limit to th e SERF dataset to 0% clay, 25% clay, 50% clay, 75% clay, and 100% clay. 7.3.1 Materials Edgar Plastic Kaolinite (EPK) and industrial sand were obtained from Edgar Minerals, Inc. of Edgar, FL. Grain size distributions for these ma terials were obtained (F igure 7 1 and Figure 72). 7.3.2 Shear Stresses Shear stresses needed to be determined for the bed materials to be used during the test Recall from Chapter 4 that shears stress is dependent on roughness. Because of this, a similar series of shear st ress tests was conducted on sandclay mixtures using the shear stress sensor. Sensor discs were prepared at 12.5% clay increments from 0% clay to 100% clay so that a relationship between flow speed and sample roughness could be determined. This 12.5% dis tribution corresponds to the original testing plan. Two different test disc preparation procedures were used dur ing shear stress testing The first round of disc preparation used the method described in Chapter 4. Sand and clay were weighed in the appr opriate proportions and mixed as dry materials in a small Tupperware container. Plastic test discs were coated with JB Weld epoxy. Then, the sand clay mixtures were spread out over a table and the acrylic test discs were pressed onto each of the sand cla y mixtures. At least three test discs were made for each sand clay mixture to insure that a true representative roughness was captured from each mixture batch. When epoxy is spread on the plastic test discs, the goal is to spread it evenly, but despite best efforts, sometimes small ripples (thickness less than the diameter of the pinhead) will be formed as the epoxy is spread. When sand content is relatively hig h, these ripples are masked

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245 because the diameter of a sand grain is much larger than the diameter of a ripple. When sand content is low however, the diameters of clay particles are on the same order of magnitude as the size of the ripples. As a result, w hen the clay particles stick to the plastic test disc, small epoxy ripples are exposed thereby slightly falsely increasing the testdiscs roughness. To combat this issue a second disc preparation method was invented. Fibergla s resin was poured into 50 mm molds. Then, the sandclay mixtures were sprinkled onto the fiberglass before it dried Because the planed fiberglass should be self leveling, it was thought that pouring a sandclay mixture onto the fiberglass before it dried would create a ripple free sample. This fiberglass method worked well when sand content was high especially under 100% sand conditions. Unfortunately, as sand content was reduced and clay content increased, this method became much less successful. Under higher clay conten t conditions (anything above 12.5%), the presence of clay particles interfered with the fiberglass hardener. The result was that the test discs either did not dry properly, or the discs dried at differential rates. When differential drying occurred, the discs edges dried much faster than the discs mid sections so that when the discs were removed from the molds, some of the midsection would stick to the molds top, and a concave surface was created along the discs front face. When put into the shea r sensor, this concave disc surface could not be fully leveled with the flume bottom. If the bottom of the concave surface was leveled, the side walls would protrude into the flume such that when water was run through the flume, the shear sensor would als o measure a normal component of force on the test disc. If the side walls were leveled with the flume bottom, the bottom would not be level, and the correct shear stress would not be measured either. Tests were conducted on the discs to see how much of a n effect this concave

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246 surface had on overall results compared to the effect of epox y ripples. In either fiberglass resin tests or epoxy glued disc tests testing on each t est disc was repeated at least three times. 7.3.3 Mixing Procedure Because the goal of this study was to determine sand clay erosion behavior and attempt to generalize erosion behavior as a function of clay content, samples for testing had to be relatively uniform and repeatable. There was some question as to how to properly mix sand a nd clay together to produce uniform, repeatable samples. Previous research with sandclay materials is limited. One reference (Barry 2002) indicates that when he conducted his tests he made his samples by adding tap water and working by hand. He does not indicate how much tap water, but he does describe his bulk density after the sample has been prepared. Rather than use a target density procedure which would be difficult to duplicate, especially when making a SERF pistoncylinder sample, different pr ocedures were developed. First, investigators tried to mix sand and clay dry and slowly add water to the samples from the bottom up (as with Bull Gator Rock). The result was a souplike mixture with little apparent bonding between sand grains and fine se diment. Eventually, investigators realized that if water was added to the sandclay mixture as it was mixed, sand and clay began to flocculate. The obvious question became how much water to add during mixing? Rather than pick an arbitrary amount of wate r, investors settled on using an already established parameter the optimum water content based on a Proctor Test (Figure 73). Because the 100% sand sample does not have any cohesive material in it, optimum water content could not be obtained. Because of this, sand mixing procedure was slightly different from sand clay mixing procedure. The sand mixing procedure and sandclay mixing procedure are given below:

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247 7.3.3.1 Sand m ixing p rocedure The sand mixing procedure is as follows: 1. A SERF piston cyli nder was thoroughly cleaned to eliminate contamination from other sand or clay particles. The piston was inserted into the cylinder and its O rings were thoroughly greased. 2. The weight of the pistoncylinder was recorded. 3. The distance to top of sample was recorded and divided by 4. Then, the cylinder was filled of the way to its top. 4. A 2.5 lb. compaction hammer was used to deliver 17 blows to the sample. This blow count and hammer weight was based on the compactive effort delivered during Proctor tests. Because the SERF sample is smaller than the sample for a Proctor mold, calculations were developed to ensure that the same compactive effort was used under both conditions. 5. Once the first sample lift had been completed, a 2nd, 3rd, and 4t h lift w ere added to the sample and compacted with the same blow count used in (3). 6. The 4th lift was planed with the sample cylinder surface 7. Water was slowly added to the sand clay sample using a burette, a tube, and a small plastic pipe fitting. Water flow r ate was less than 0.1 mL/sec. 8. Once the sample had been fully saturated (as evidenced by water coming out of the top of the sample), the sample was weighed so that its density was known. 7.3.3.2 Sand c lay m ixing p rocedure The sand clay mixing procedur e is as follows: 1. The volume of the pistoncylinder was computed and multiplied by an assumed worst case total density of 2.65 g/cm3. This number was multiplied by the percent clay to get the required mass of clay and it was multiplied by percent sand to g et the required mass of sand. 2. The appropriate mass of sand and clay were weighed using an electronic balance. 3. Sand and clay were evenly and slowly poured into a mixing bowl and the mixer was turned on low for approximately 5 min. Halfway through mixin g, the mixer was stopped and the sides of the bowl were scraped so that any material on the sides would be moved toward the center of the bowl. 4. The mixer was turned on again, and half of the water required to achieve optimum water content was measured wi th a graduated cylinder and added to the mixing bowl.

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248 5. Once the mixer had run for two minutes, it was stopped, scraped, and restarted. Then, the of the remaining water was added to the bowl. 6. After two minutes, the mixer was stopped and scraped again. 7. The mixer was restarted, set to high, and the rest of the water was added. The mixer was allowed to blend for another 2 minutes. 8. The sand clay mixture was added to the pistoncylinder until the pistoncylinder was full. 9. Using the modified 2.5 lb. Proctor hammer, 17 blows in a circular pattern were applied to the sample. 10. Steps (9) and (10) were repeated for lifts of full and full. For the final lift, a collar was placed over the tube, and the tube was overfilled such that material extended approximately above the lip. Again, 17 blows were applied to the sample. 11. A metal screed was used to level the sample with the top of the piston cylinder. 12. A small piece of remaining material was placed in a water content canister, and the canister was placed in an oven at 125oF so that water content could be measured. 13. The sample was attached to a burettevalve device and filled from the bottom up so that until it was saturated. Saturation w as defined as water flowing out from the top of the sample. 14. The final mass of the sample was recorded. 15. The sample was placed in the SERF. 7.3.4 SERF Testing The procedure for SERF testing is outlined in Appendix B, but in brief, the following procedure was used during tests : 1. The sample was loaded into the SERF such that the two 1.0 mm cutouts along the top of the sample cylinder lined up with the lasers. 2. The motor and motor platform were slowly moved into position. 3. The SERF was filled with water. 4. Once the flume had been pressurized, the sample was allowed to slightly protrude into the flume. 5. A high flow rate was used to re level the sample with the flumes bottom.

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249 6. The flume pumps were started at a low flow rate. Flow speed was incrementally increased until incipient motion was detected. 7. Because tests were run with sandclay, the lasers needed to be used exclusively during SERF tests Therefore, the lasers were turned on, and the test began. 8. At the end of the SERF test, once the sample wa s empty, a new sample was prepared, and steps 1 7 were repeated. 7.3.5 Procedural Variations In the preceding SERF discussion, Step 7 is intentionally described vaguely. At first, the plan was to vary flow rates randomly from 0 Pa to a shear stress wher e erosion rate was approximately 1.0 mm/s. The 1.0 mm/s erosion rate criterion was chosen because the laser motor system cannot keep up with erosion rates higher than this. At each flow rate and shear stress, erosion rate would be recorded, then the flow rate would be changed, and the procedure would repeat. This procedure assumes that a sample will erode uniformly from topto bottom. As will be described in Section 7.4, this did not happen. Instead, erosion rates varied as a function of sample depth. Because of localized hard patches where bulk density increased, which corresponded to depths at which the modified Proctor hammer was used, erosion was nonuniform. To capture this behavior, eventually instead of using several shear stresses on one sample, one shear stress was tested per sample. Because of these differential erosion rates, investigators tried to develop samples that would respond more uniformly. During mixing, water contents were modified so that double the optimum water content was used to determine if mixed water content affects erosion behavior. For some clay contents, a third batch of samples were made where the number of lifts during mixing was reduced from 4 lifts to 2, while the blow count delivered per lift was doubled to determine if lift height affected sample erosion o r uniformity.

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250 7.4.6 Density Profile Tests Because of the differential erosion rates, a series of samples were prepared where density profiles were measured. To run these tests the samples were mixed according to the procedure outlined in Section 7.3.3. Then, samples were extracted from their molds and cut into 1.5 cm segments using a modified coping saw. Instead of a typical saw blade, the modified coping saw used a 3 strand braided 28gauge wire. The sample segments were weighed, dried, and reweighed to give both dry and wet densities as a function of sample depth. Results were compared with lift depths recorded during sample preparation. 7.4 Experimental Results and Analysis Experimental results are divided into three segments: Results from shear stress tests Results from SERF tests Results from Density Profile Tests 7.4.1 Shear Stress Tests Shear stress experimental results are divided into results from the epoxy glued discs (Figure 7 4 through Figure 713) and results from the fiberglass resin discs (Figure 7 14 through Figure 7 23) Although Figure 723 shows that the 100% clay mixture does indeed have the lowest shear stress associated with it, this figure also shows that certain sand clay percentages may produce higher shear stresses than the shear stresses seen under 100% sand conditions. This may be true under special circumstances for example, when floc erosion instead of particle erosion is an important erosion mode but as of yet, there is no known way to predict when this will occ ur and the purpose of this test was not to predict the floc size that would erode from a sample, but rather, to find the relative shear stress magnitudes along a sample face resulting from simple particle effects. Figure 7 23 shows that when clay content is at 87.5% and when clay content is

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251 at 62.5%, shear stress is higher than it would be when there is no clay in the sample. Based on a particle based roughness rationale, this must be impossible. These higher shear stress readings then can be attributed to the concave nature of the test discs. Because an average leveling height was used even though the disc was not level, the discs protruded into the flume somewhat (~1 mm 2 mm). Even this slight protrusion caused a large increase in the force on the disc because of the added normal component. During the epoxy testing, the discs were precisely level with the flume bottom, and there were no normal forces acting on them. Figure 723 shows no discernable pattern that would indicate a decrease in shear stress that corresponds to a decrease in sand content. This appears to contradict the data shown in Chapter 4 which indicates that as roughness decreases (for a given flow rate), shear stress should also decrease especially at higher flow rates. Becau se of this, the data in Figure 7 23 must be rejected in favor of the shear stresses corresponding to the data shown in Figure 713. This conclusion has interesting implications regarding EFA tes ting because in the EFA procedure, a protrusion of 1 mm into the flume is specified. This data indicates that shear stresses are sensitive to any protrusion into a flume. Because the EFA uses a Moody Diagram to estimate shear stresses on its samples (Chapter 4 verified that this was an accurate method), the shear stress should not be underestimated using this device. But, because a 1 mm protrusion could cause a much higher actual stress on the sample compared with the stress than would be caused with a level sample, the actual stress on a sample would be greater b ecause of the normal component. This in turn would produce a higher erosion rate than usual, and this could lead to an overly conservative erosion rate shear stress curve. For example, the protrusion caused by the wave like 50 mm test disc under 87.5% cl ay conditions shows over a 100% increase in stress at a moderate velocity of 4 m/s. Because

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252 erosion rate shear stress curves have thus far proven to be mostly linear, this should correspond to an over prediction of erosion rate by over 100%. 7.4.2 SERF Tests When investigation into erosion rates of sandclay mixtures began, the hypothesis was that a repeatable mixing procedure modeled after the Proctor test would lead to samples that eroded uniformly from topto bottom. As testing moved from the 0% clay to the 25% clay sample, it became clear that samples were not responding this way. Instead, as clay was introduced, localized portions of samples erode d more slowly than other localized sample sections. Instead of attempting to generalize erosion rate as a function of shear stress and clay content then, research looked instead to explain these localized erosion variations. As a result of this, the same gambit of tests was not necessarily conducted for each sand clay ratio. The following is a summary of results, some rationale behind the tests that were conducted, and some analysis to explain why results were found as shown. 7.4.2.1 Zerop ercent c lay m ixture Shear Stresses The first goal during sand clay testing was to verify that the SERF was providing the correct experimental results based on historically accepted data the Shields Diagram. Shields Diagram calculations showed that incipient motion should occur at a shear stress of approximately 0.25 Pa. Three criti cal shear stress tests were conducted on 100% sand samples using the procedure outlined in Appendix B. Average actual critical shear stress from tests was 0.43 Pa. At first, this looked like a discrepancy. Analysis of the shear stress sensor shows why it may over predict critical shear stress values. Experience has shown the sensor to be accurate within 0.2 Pa of accuracy (+/ 2% F.S.). At lower flow rates, the shear sensor appears to be less accurate than it is at higher flow rates T he strength of t he shear sensor lies in its ability to accurately measure shear stresses that are

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253 still low at much higher flow speeds. In other words, if a creeping flow is moving past the sensor, it wont pick up this small deflection in the platform test disc leaf spr ing Servo magnet system. This has to do with the mass of these moving parts which relative to the small flow speed and the small associated shear stress is large. Therefore, with the current setup it would be impossible to with 100% accuracy to measu re shear stresses at such a low range. When dealing with a shear stress less than 1 Pa, accuracy within 2% is actually quite precise. The next dataset to consider was Trammels 2004 data. Here he attempted to verify the pressure drop system within 5% of accuracy by using a Shields Diagram. Chapter 4 shows that especially at higher flow rates, the pressure drop method does not accurately estimate shear stresses in the SERF. Because shear stress can be correlated to a power law distribution with respect t o velocity, at low flow speeds, the pressure drop readings do converge to a curve that appears to be the same. Trammel verified the pressure drop method (again at low speeds) by measuring the pressure drop on samples that were 1.4 mm, 0.921 mm, 0.696 mm, and 0.508 mm in diameter. In terms of the tests run during this round of testing the 0.921 mm data and the 0.508 mm data are of interest because they should closely correspond to 1.0 mm data and 0.50 mm data respectively as presen ted in Chapter 4. Figure 7 24 shows Trammels data overl aid with data from this study. This diagram shows the readings from these two sets of tests are close on average within 8.63% of one another. The Shields calculations however are extremely sensitive to shear stress val ues; any deviation even an 8% deviation at such low shear stress magnitudes will cause a large deviation in the shear stress entrainment function. This in turn causes a relatively large difference in the computed shear stress which in turn causes a large deviation in overall results.

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254 Trammels collected his pressure readings with a pressure transducer with +/ 2.5psi range at 0.1% F.S. error. This means that Trammel was capturing pressure accurately within +/ 0.0025psi or +/ 17.24 Pa. Shear stress is related to pressure drop using the following equation: L w l plw 2 2 (7 1) In Trammels case, he had pressure sensors spaced further apart than the current setup (4 ft. or 1.22 m). If one was to ignore the fact that with this setup most of the flume wall along this spacing length was smooth and therefore most of the pressure drop was be caused by smooth rather than rough conditions, the following accuracy calculation would follow: m m m m m PaERROR2032 0 2 0445 0 2 22 1 2032 0 0445 0 24 17 (7 2) PaERROR258 0 (7 3) This calculation shows that Trammels measurements could only have been accurate within 0.26 Pa which for some of his me asurement shown in Figure 7 24 is less than the shear s tress value than he was measuring. Chapter 4 showed how difficult it is to get accurate readings from the pressure drop method, and no spectral analysis or filtering of his data except for the installation of a hard wired 4th order low pass Butterworth filter was employed. T he fact that because of his pressure port spacing, most of his pressure drop would have been influenced by smooth flume bottom and not the roughened sample portion of the flume. On the other hand, results from the shear stress sensor allowed investigators to conclude that shear stress results were accurat e enough to proceed to erosion rate tests Erosion Rates Based on the preceding analysis, an erosion rate vs. shear stress curve was developed based on the shear stress vs. flume velocity curve presented in Section 7.4.1

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255 (Figure 7 25). Rather than use a simple final position minus initial position divided by time calculation to find erosion rate (as was used by Trammel and Slagle), a best fit least squares regression line was fit to a sample position vs. time plot. Results in Figure 7 25 are developed from the slopes of these trend li nes, and they were nondimensionalized in the same manner in which RETA database results were non dimensional ized in Chapter 6 (Figure 726). A 100% sand sample was allowed to run at a moderate shear stress ( 3.0 Pa) to verify that erosion rate was the same from top to bottom throughout the sample. Since erosion rate was constant throughout the sample, 100% erosion data was taken such that multiple shear stresses were used on one sample. In total, erosion rate t ests were conducted on five 100% sand samples. 7.4.2.2 Twenty f ive p ercent c lay s amples At first, when the sample mixing procedure presented in Section 7.3.3.2 was introduced, an additional step was added after Step 16. The sample was put into a bucket for 24 hours to insure saturation. However, by mistake, one time samples were prepared at the end of the day on a Friday and tested on a Monday. Over the weekend, it appeared as though consolidation had taken place, as erosion rates were much smaller than the erosion rates shown in previous tests. To determine if allowing the samples to sit upright for 24 hours or more played a role in erosion rate, a sample was prepared and inserted immediately into the SERF. This sample showed a much higher erosion ra te than the erosion rates seen with previous samples that had been allowed to soak. Because of this then, a standard needed to be developed, and since investigating consolidation effects is not within the scope of this project, it was decided that zero consolidation would be used as the standard for future testing. In other words, for samples with clay, samples would be prepared and immediately put into the SERF. The 25% sandclay mixture was the first time that the lasers described in Chapter 3 were forc ed to perform sample advancement on their own. Because erosion rates were moderately

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256 quick, the issues that were found in using the lasers with the Gator Rock were nearly nonexistent with the clays. The lasers worked as designed. The only downside to t he lasers was that at higher flow rates, the motor could not keep up with the analog blips coming from the lasers. Therefore, sometimes when erosion rate was high, there would be a backlog of advancement that should have happened but could not. Once eros ion rate leveled out, this would cause sample penetration into the flume which in turn would cause the sample to erode even faster and exacerbate the problem. However, this was only an issue at high flow speeds (relative to the erosion rate), and if one extrapolates these erosion rates over a year, the result returns kilometers of erosion per year, which is not possible. For the 25% clay data run, procedures remained the same as they had for the 0% clay data run as far as testing the effects of multiple shear stresses per sample. However, during testing, investigators noticed that with the 25% clay sample, as more of the sample eroded from the pistoncylinder, the sample appeared to exhibit hard layers. Whenever these hard layers were encountered, qua litatively it was observed that erosion rate slowed down. Eventually, these hard layers would erode, albeit through a more undercutting, normal stress induced, rocklike erosion mechanism, and once the layer had been removed from the sample, erosion would continue on as normal. Because of this layered effect, at first results were confusing because at first, before the layered effect was discovered, it appeared as though sometimes lower shear stresses caused higher erosion rates. It was evident that th e layers probably corresponded to the 4lifts that were used in sample preparation by analyzing a timeseries of erosion data and allowing the same shear stress to be run over the entire length of a sample (Figure 7 27). The 4steps shown in this diagram should not be a coincidence, especially because this pattern was seen on the 25% clay samples. Of note

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257 though is that this 4step effect is only visible during moderately paced erosion rates. When erosion rate is high (Figure 728) or erosion rate is low (Figure 7 29), the sample responds much more uniformly. The interesting thing regarding this pattern is that the bestfit line method for determining erosion rate, which was an effective indicator of erosion rate beforehand, is not necessarily relevant u nder these conditions even though the R2 value is close to 1.0. Figure 7 27 shows two different erosion rates one slow and one fast. The slow erosion rate can be explained by the initial portion of a rock like erosion mode. During the first part of rock like erosion, little actual particle movement is seen. Rather, water flowing past the sample surface serves to weaken the top layer (which can be viewed as a large chunk of material) by loosening small localized portions of it. Eventually, channels developed along certain parts of the sample face, and once these channels developed, flow was concentrated into the sample through them. These channels are analogous to cracks or fractures that form on a rock face. Eventually, the undercutting flow caused by the channels causes a large chunk of material (or sometimes most of the sample surface) to erode, and once this happened, the rapid advancement of the sample into the flume would occur. During rapid advancement, additional chunks and blocks of materia l would be removed from the material surface as quickly as the material advanced. This would continue until another hard layer was exposed. Then, erosion rate slowed down and the beginning stages of rock like erosion occurred again. The rapid sample adv ancement portion of erosion is similar to a particle like scenario. The critical shear stress for this relatively loose, relatively weaker material has already been exceeded, and because of this rapid erosion of large sections of material is seen. This back and forth particle like vs. rock like erosion pattern continued until the sample was gone. In addition to the 3.37 Pa, 13.4

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258 Pa, and 40.0 Pa results already presented using the first mixing procedures, experimental results were obtained at 30.0 Pa and 53.2 Pa (Figure 730 and Figure 731). If the hardened layers were caused by the initial stages of rock like erosion, and the hardened layers were localized, the conservative solution to the problem should be to eliminate the hardened sections from the p iston position curve and use the rapid advancement portions of the curve to define an erosion rate. This should give a generalized erosion rate vs. shear stress relationship. Figure 7 32 was developed by analyzing the flat sections of the five erosion ra te shear vs. shear stress curves. Figure 7 33 was developed by analyzing the rapid advancement portions of the curves. In both cases, data was cut from the datasets into alternating flat and rapid advancement sections. Then, a best fit line was fit to e ach data string. Since the best fit line corresponds to y = mx + b the average slope coefficient, m was found for each flat case and each rapid advancement case at each shear stress. Analysis of Figure 7 32 and Figure 733 show that the beginning stage of rock like erosion hypothesis appears to be correct. In Figure 733, there appears to be strong erosion rate vs. shear stress relationship for the rapid advancement erosion sections of each of the five samples. Although there appears to be some relati onship between erosion rate and shear stress for the flat portions of erosion as well, the correlation is not nearly as strong. This corresponds to the notion that rock like erosion may have something to do with flow speed, but flow speed is not the only parameter that affects the normal stresses acting along the sample face. Comparison of these two figures also confirms what was hypothesized with the RETA database in terms of rocklike vs. particle like orders of magnitude. Recall from the RETA database that generally, materials that did not respond to a direct erosion rate vs. shear stress relationship had erosion rates that were an

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259 order of magnitude lower than materials that did have a direct relationship between erosion rate and shear stress. The q uestion then became, how does one define the correct erosion rate? Should the erosion rate be defined conservatively by the rapid erosion portions of the signal, or is the hardened layer portion of erosion more valid? Or, are both of these erosion rate measurements bad estimates because of the way in which the sample was prepared? According to a Westergard soil stress distribution, some sandclay mixtures are assumed to be comprised of an infinite number of alternating layers of sandy soil and fine gr ained soil. In soils such as these, a vertical surface load leads to a lower vertical stress within the soil column than the stresses that would be seen using a Boussinesq approximation which assumes a uniform material. Presumably these materials should also exhibit differential erosion rates as observed in Figure 7 27. However, investigators had to verify that the 4layers erosion sequence did indeed respond to the sample preparation method. To do this, a test was run at 13.4 Pa where the sample was pr epared using two lifts (Figure 7 34). Results appear to indicate that sample preparation procedure is the same for the differential erosion rate behavior. After observing this behavior a hypothesis was made that lift to lift cohesive forces would be str onger if the top of the first lift is rougher. Essentially what was happening using an optimal water content approach was that doing this was like trying to glue two flat, slick, and smooth surfaces together using relatively small cohesive forces. Instead, what should have happened if samples were to be nearly uniform was that the compactive effort applied to the top lift should have been high enough to cause lift to lift particle interaction. This could only be achieved if the sample was allowed to slum p more. In other words, allowing for a more easily deformable material would induce more particleto particle bonding per blow, which in turn would create a

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260 more uniformly sticky material that although weaker, should exhibit lower erosion qualities. No te that this notion contradicts the erosion as a function of cohesion theory as discussed in Chapter 6 because this method says that creating a weaker material will actually provide a lower average erosion rate due to the increase in vertical cohesive forces within the material matrix. This does not necessarily mean the discussion in Chapter 6 is incorrect; rather perhaps it only applies to stiffer materials. When unhardened sandclay samples are present, another mechanism may be dominant. Two more samp les were prepared using double the optimum water content, and a time series erosion test was run on them (Figure 7 35). As shown, the steps that were easily visible using the first sample preparation method disappear. However, Figure 7 35 shows that on a verage, erosion rates are slower than they would be using the preparation method outlined originally. Note that the samples that were prepared to create Figure 7 35 were mixed with the original 4 lift mixing procedure. Development of Figure 7 35 shows that a change in the mixing water content changes erosion rate. This figure shows that weaker samples can erode more slowly than stronger samples. This was unexpected. If Figure 7 33 is a conservative estimate of erosion rate vs. shear stress for a 25% c lay sample when the sample is mixed at the optimum water content, Figure 735 shows that the estimate is only accurate under the special optimum water content case. An interesting study would be to determine where the tipping point between the two curves is from a mix perspective. In other words, if optimum water content produces a certain particle like erosion rate, and an increase in water content produces a slower erosion rate, at what point is erosion rate the maximum? And, is this maximum valid for any layered sediment? Or, does the amount of layers in the sediment strata play a role? Could layer thickness preclude the development of a

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261 particle like erosion mode from taking place at all? Another interesting question was at what clay content does t his layered effect become relevant? Although investigation started with the 25% clay sample, it may be possible that below a certain clay threshold, layering no longer has an effect. At this point, it was obvious that substantial tests would be required to provide one data point for a 25% clay mixture. Even if the 25% clay mixture problem could be solved, it does not necessarily apply to other sandclay ratios. Because the purpose of this project was to bound the sand clay erosion problem, investigation into erosion rates for the 25% sandclay mixture was stopped. The above analysis shows that a variety of erosion behaviors can be engineered by varying the initial water contents and liftheights, but the real question is what do natural earth materials do? Both the slower layered rocklike erosion and the faster rapid advancement particle like erosion are valid erosion modes. 7.4.2.3 Fifty p ercent c lay s amples Analysis of the 25% clay samples shows that an increase in the water content may eliminate the stepping behavior seen under optimum water content mix conditions. When investigation into the 50% clay samples began, investigators first goal was to determin e if the 50% sandclay ratio behaved similarly. As with the 25% mixture, a series of erosion rate tests was conducted at a variety of shear stresses for the 50% mixture using the initial optimum water co ntent mix approach (Figure 736). Figure 7 36 sho ws that the step like erosion behavior seen for 25% clay mixtures is not as apparent with the 50% samples. As flow rate (and subsequently shear stress) increases, the step like erosion behavior becomes more apparent, but for lower flow rates and shear str esses, step like erosion is nearly non existent. Although perhaps not as obvious from Figure 736, when one

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262 zooms in on an erosion rate curve at a higher flow rate, the steplike erosion behavior is obvious (Figure 7 37). Figure 7 37 also shows another interesting phenomenon. The top layer of each sample erode s much more quickly than other sample layer (Figure 7 38). As testing time and subsequently sample depth increases, erosion rates become much slower. This implies that the bottoms of the 50% samples are more erosion resistant than the tops of the samples. This behavior was not seen with the 25% sandclay mixtures. Water content was doubled to determine if a change in initial water content would affect erosion rate behavior. As with the 25% sandclay mixture, an increase in water content made a much weaker sample that tended to slump much more easily. This weaker sample showed much greater resistance to erosion than the optimum water content mixture so much so that to observe any erosion, she ar stresses had to be increased from the values used for the optimum water content tests (Figure 7 39). Figure 7 35 and Figure7 39 show an interesting phenomenon for samples mixed at double the optimum water content. In Figure 7 39, a ~7 Pa increase in shear stress appears to cause a high increase in erosion rate. Likewise, Figure 7 35 shows that a 14 Pa increase causes a similar rise in the magnitude of erosion rate. Beyond this tipping point, erosion rate appears to increase relatively slowly; below this tipping point erosion rate behaves similarly. This behavior appears to suggest that for a certain shear stress, erosion rate will transform from a regular slow particle like erosion pattern to a more chaotic floc like erosion sce nario. Visual observation during the erosion rate test showed that the behavior seen below this t ipping point is much different than erosion rate behavior above this tipping point. When erosion rate is relatively small (as it is a t 30 Hz or 35 Hz in Fig ure 7 39 ), an observer can see individual

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263 flocs breaking from the samples surface and moving downstream. Floc erosion is nearly uniform across the sample face, although the middle of the sample does erode somewhat more slowly than the samples outsides ( probably due to where most of the compaction is applied for a given sample). As erosion rate increases, erosion becomes much more irregular, and an observer can see large chunks of material removed from the sample. W hen the flow rate becomes high enough, erosion transforms to an advanced stage of rocklike erosion. With the 4lift optimum water content samples the beginning stages of rocklike erosion were seen with the hardened layers and subsequent rapid advancement once the layer was broken. When w ater content is doubled, layering does not have as much of an effect, yet beyond a certain point, instead of steady flocflow, large scale chunking is observed. If samples with higher water content s are more cohesive, this phenomenon appear s to make sens e. Whereas for the optimum water content, individual flocs struggle to stick together, when water content is increased, the cohesive bonds holding flocs together have more of an effect. This inhibits the creation of hard layers. At the same time, once particle and floc vertical movement is initiated by the normal forces associated with rock like erosion this increase in cohesion causes larger chunks of material to be removed. Again, the question is how does one define a proper erosion rate for any sa ndclay mixture? Once this behavior was observed for the 50% sample, the goal for the 75% sample and the 100% samples became to bound this tipping point between rapid erosion and slow erosion at higher optimum water contents. Additionally, the original 4layered mix would be studied to verify that layering was an important erosion rate effect at certain initial water contents. T his is done with the unde rstanding that until tests are run on a series of natural samples, developing a generalized erosion rate vs. shear stress relationship for any sand clay mixtures is premature

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264 7.4.2.4 Seventy f ive p ercent c lay m ixture As with the 25% mixture and the 50% mixture, the 75% mixture was studied using a 4lift mixing procedure. Both optimum water content and double the optimum water content were used during mixing. As before, samples were saturated before they were subjected to SERF testing (Figure 7 40 and Figure 7 41). The 75% sandclay mixture shows a tendency that was slight with the 50% sa mple. As shown in Figure 7 40, the relationship between sample position vs. time is not linear. The bottoms of the samples erode much more slowly than the samples tops. As such, it would be inappropriate to fit an erosion rate line to the data. This tendency was shown with the 50% sample, although this nonlinear behavior is much more apparent with the 75% mixture. The 75% sample at optimum water content is similar to the 50% sample at optimum water content in that the step like erosion behavior does not exist below a certain shear stress. However, above a certain flow rate/shear stress threshold, step like erosion still occurs. When water content is doubled, the bottom sample layer becomes much harder than the top sample layers (Figure 7 41). Rapid erosion is seen until this point, and beyond this point erosion is slow. This bottom layer approximately corresponds to the bottom sample lift. In other words, for the first time at any sand clay ratio, there appears to be some relationship between lift height and eros ion behavior at the higher water content. Below this slow erosion zone, erosion rate is approximately linear for the higher water content case. Erosion rate cannot be compared with the lower water content case since erosion at the lower water content is nonlinear. Overall, the 75% sample is similar to the other sand clay mixtures in that erosion is hypersensitive to the manner in which the sample was prepared.

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265 7.4.2.5 One h undred p ercent c lay As with the mixtures, two batches of 100% clay were prepare d using both the optimum and double the optimum water contents. By the time testing reached this point, a pattern emerged When the water content was doubled, the erosion rate decreased. At the higher water content, there was a tipping point between regular particle like erosion and chaotic advanced rock like erosion. At the optimum water content, erosion appeared to be steplike and/or nonlinear. Therefore, fitting an erosion rate curve to a sample position vs. time graph is inappropriate because for a non linear sample, erosion rate is more dependent on localized material properties. The 100% clay tests were run simply to confirm this pattern of behavior (Figure 7 42 and Figure 743). The double optimum water content samples show n here appear to exhibit the pattern from highly regular particle like erosion behavior to more chaotic behavior somewhere between 17.76 Pa and 19.41 Pa. Interestingly, the 100% clay sample at optimum water content also appear s to show a tipping point to a more chaotic erosion behavior somewhere between 19.41 Pa and 22.84 Pa. Although this is the case, erosion rate appear s to be highly nonlinear as it was for the 75% sample. As more of the sample erodes, the bottom portion of the sample provides more er osion resistance. 7.4.3 Density Profile Tests The goal of the density profile tests was to quantify density variability in the sandclay mixtures and to compare localized density variations with hardened erosion rates observed during SERF testing. Resul ts from the density profile tests are presented in Table 7 1 and Figure 744 through Figure 754 Where appropriate, the black lines represent lift interfaces measured during sample preparation. Since the most extreme erosion fluctuation with density var iability

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266 occurred with the 25% sandclay mixtures ( from a stepped erosion perspective), most density profiles concentrated on this mixture concentration. Generally speaking, lift intervals does not necessarily align themselves with localized density fluc tuations. Sometimes a lift interface will line up with a higher density, while other times it will align itself with a lower density. Still, other times, lift interfaces do not align with any maxima or minima density measurements. This is not to say tha t the lift interfaces are not causing density variations Whenever a lift is poured on top of a previous lift and compacted, some of the compactive effort on the new lift helps to densify the previous lifts interface further. This means that lift heights may not necessarily match up with density variations. Interestingly, the 75% sample shows the best alignment between lift height and density increases (which is what would be expected). This implies that under the 75% clay content scenario, the initial compactive effort per lift is sufficient to bury it to its ultimate depth. The 25% clay sample showed generally, average bulk density ranged from ~1.50 ~1.70 g/cm3 with standard deviations on the order of 0.2 g/cm3. In other words, the amount of ave rage variability in the sample corresponds to the expected density range. Or the density variation is within the range of averaged densities. This implies that the intra sample density fluctuations are relatively low when compared with overall density. The 8 lift sample implies that using more lifts may help to reduce the density variability, but it is equally possible that a second run of an 8lift sample will yield similar results to the 1 lift sample. During one 1 lift sample, density variability wa s low but during another one, it was a little bit higher. This argument also ignores the fact that density variability is relatively low to average density regardless of what lift method is used.

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267 This is not to say that larger localized maximum or minimum density changes do not exist. The density profiles were taken by cutting the samples into 1.5 cm sections. Visual observation of the samples after they were extracted from their molds showed that the lift interfaces could be plainly seen with the nake d eye. Observation also showed that the lift interface was relatively small (1 mm 2 mm) compared to the 1 cm sections that were used to make the density profile. Because of limits with available tools, investigators could not cut less than a 1.5 cm sec tion of sample at a time. Cutting precise smaller sections would increase the resolution of the density profile, and this might reveal the expected localized density variability between lift sections. Unfortunately, an accurate method for cutting strips of sample that were thin enough to do this could not be found. Analysis of the SERF results back up the notion that density fluctuations smaller than those picked up using the 1.5 cm strip method may exist. Generally, data shows that the localized hard patches seen during steplike erosion behavior are only 1 mm 3 mm thick. If this is the case, then it implies the need to refine the density profile method. It was hypothesized that it may be possible that density fluctuations are not the deciding factor. When the compaction hammer is applied to the top of the lift, it does two things: 1. It compacts the sample. 2. It presses protruding flocs into the top of the sample thereby smoothing out the surface. It was thought that it may be possible to visually see the lift interfaces because the top of the lift is smooth. If the top of the lift is smooth, the lifts top section may have trouble bonding to another lifts bottom section because of a decrease in r elative surface area. To test this hypothesis, a 100% clay sample was prepared with 6 lifts. In between these lifts, the surfaces were roughened before the new lift was poured. Under these conditions, density variability was similar when compared with previous density profiles (standard deviation was 0.11). In addition

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268 to this quantitative test a qualitative test was done where a sample was prepared by roughening the surfaces between lifts and extracted. Visually, lift interfaces were still apparent, and this method was abandoned. The most plausible hypothesis right now is that sample preparation causes localized density variability along a small length scale. This localized variability in turn causes localized changes in erosion rate. When densit y increases, eventually it induces a hardened surface that behaves similar to the way a rock face behaves. 7.4.4 Effects of Sand Concentration on Shear Stresses When this project was originally proposed, one of the goals of it was to determine the effect of suspended sediment on bed shear stress. While the sand injector could not be implemented (Chapter 3), another method for estimating bed shear stress became apparent during sand clay tests When the 100% sand samples were run through the SERF, investigators noticed that some sand particles were cycled back through the flume during operation. The filter system, which was primarily designed to protect the water chiller, did not work fast enough to filter out eroded material during an erosion test When clay was added to the sand during tests conditions in the flume became even worse. Clay particles and mudsand flocs were held in solution and suspension through the flume water. The resu lt was that during some tests operators could visibly see sand and sand clay flocs being cycled bac k through the device. A t times there was so much suspended material in the water column that the cameras could not see through it any longer. This corresponds to less than 3 inches of visibility. A series of qualitative tests was conducted to determine whether or not recirculating sand would could affect erosion rate shear stress development A flat disc was installed in the shear stress sensor, and an e rosion test was conducted on a 50% sandclay mixture. The 50 50 ratio

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269 was used because it was a logical midpoint. Once the erosion test was conducted, a shear stress test was run. Once this had been completed, an erosion test was run on another 5050 sa ndclay mixture, and then another shear stress test was run. This was repeated a third time so that by the end of the series of tests, the SERF water was cloudy. Investigators hesitated to run a fourth series of tests because they did not want to damage the primary pumps. What resulted were shear stress readings for four conditions clear water, after one data run, after two data runs, and after three data runs. Although sediment concentrations could not be measured directly, they can be implied roughly by dividing the sample volume by the volume of the reservoir tank. If the results showed that shear stress varied from one sediment concentration to another, then it would call into question any result from SERF tests because as the test is run it woul d mean that shear stress is changing dynamically with time. The result though varied from Sheppards conclusions in a 20 06 study. In it he was scour hole depths under field conditions and one day due to snow melt, the amount of suspended material in his water was abnormally high. This corresponded to abnormally low shear stress readings. Sheppard reasoned that suspended material should weaken turbulent eddies, which in turn should decrease bed shear stress (Sheppard and Miller 2006) Results from this round of tests show something else (Figure 7 55). According to this figure, as sediment concentration changes, there does not appear to be a correlation between concentration and shear stress. Instead, similar shear stresses are recovered (+/ 4 Pa) com pared with shear stresses for a smooth wall. Sheppard (2010) speculated that at higher flow velocities (velocities greater than 1 m/s) as seen in the SERF, flow conditions would become turbulencedominant. In this case then, shear stress reduction infect s from an increase in sediment concentration may be overridden by turbulence. It is also possible that during

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270 Sheppard and Millers work, the decrease in erosion rate that was seen during his tests was in fact the manifestation of an increase in sediment deposition. During Sheppards tests, shear stress was not measured directly; rather scour depth was measured It is possible that because Sheppard et al. measured depth, they were in fact measuring sediment aggradation. Both explanations are plausible, and since many of the SERF results were obtained at higher flow rates (velocities greater than 1 m/s), they do not necessarily contradict one another. During this series of tests investigators did not want to overdo it, and therefore, they avoided running a comprehensive series of shear stress tests because they did not want to ri sk further damage to the pumps. Still, both explanations should be investigated. 7.5 Summary and Conclusions The purpose of this study was to generalize sandclay erosion base d on the sandclay ratio. What was found was that this may not be possible without testing natural materials. Investigators expected to see regular, linear sample position vs. time signals from the SERF. Instead, what was found was for lower clay contents, a step like behavior that corresponded to liftheights existed. As clay content increased, the steplike behavior disappeared in some cases, but it was replaced with nonlinear erosion behavior. A modification of water content to the samples helped to eliminate some of the step like behavior, but it also induced a flow rate and/or shear stress dependent tipping point between regular particle like erosion and more chaotic rocklike erosion. And, changes in water content changed average erosion rate. In summation, sandclay mixture response is sensitive to the manner in which they were created. Their behavior appear s to result directly from the following factors: Initial water content Initial layer depth Initial compaction

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271 These variables appear to supersede variables that would be expected to govern the sandclay erosion problem: the sandclay ratio. The conclusion of this study is that it should be possible to engineer sand clay materials to respond in a variety of different ways. The question is which one of these response mechanisms most effectively corresponds to natural conditions? Until this que stion is answered, further testing is not justified. Other conclusions from this study are as follows: 1. Generally, it appear s as though an incr ease in initial water content decreases erosion rate. It would be interesting to see if there is some threshold i .e. does an increase in initial water content decrease erosion rate to a certain maximum and then does erosion rate increase again? 2. Likewise is there a lower threshold limit? Below a certain initial water content, can a minimum erosion rate vs. shear stress relationship be found for sandclay mixtures such that erosion rate is maximized for a given shear stress. 3. Solving (1) and (2) above may provide designers with an erosion rate range for a given shear stress based on sand clay content. This could be useful for design purposes. 4. Although unintended, work during this study did provide information regarding the e ffects of suspended sediment on shear stresses. S hear stresses are not necessarily attenuated by the presence of suspended sediment. This may contradict earlier work by Sheppard et al. (2006b), and this discrepancy should be investigated. 5. Shear stresses appear to correspond to the theory that increasing roughness causes an increase in shear stress. This confirms work in Chapter 4 and also shows that without a shear stress sensor, the Moody Diagram is the most effective method for estimating shear stres s in a flume style erosion rate testing device. 7.6 Future and Ongoing Work One phase of study that should be investigated further is the question of the suspended sediment effects on bed shear stress. Results from this study contradict earlier work, and this discrepancy needs to be addressed. This could be due to lab scaling effects, the fact that a flat wall instead of a roughened surface was used during this study, or other unknown factors. Currently, FDOT is running a series of triaxial tests on several sand clay mixtures prepared in the methods outlined in this Chapter. Work from this s tudy will be presented in a students

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272 future Honors Thesis. It will be interesting to see if preparation method affects the behavior of sand clay mixtures when using a more traditional test Generally, triaxial test should be repeatable for samples that are similar. If this is the case, it may be possible to use modified triaxial data to engineer samples so that they respond like natural material from a triaxial test perspective. If triaxial behavior can be matched, it may hold the key to matching erosion behavior to natural materials. T he most important thing moving forward is to determine how actual sandclay samples behave. Samples during this study appear t o correspond to Westergard theory where individual layers are important. Do actual field samples behave similarly? The RETA cannot answer this question, but future SERF tests can. If actual sand clay mixtures show a dependence on layering, using the RET A for development of sandclay sediment transport functions is inappropriate. Another effect that was ignored during this study was the effect of consolidation. T his needs to be examined as well. Does consolidation play any role in either suppressing o r enhancing the layered like behavior seen here? If it suppresses layered like behavior, and it can do so rather quickly, perhaps the layered behavior is a moot point. For example, if 24 hours of consolidation reduces layering by a certain factor, this m ay show that there is no need to take layering into account. T his ignores the initial water content dependence seen during this study, but perhaps that is not that important either. U nder natural conditions, sediment is saturated as it is deposited (under a stream erosion scenario). Perhaps, it would be beneficial to prepare samples as naturally as possible. This means, mix sand and clay in water, let it sit for a while (30 days or more), and form a sample under its own self weight. Then, extract this sample, and test the

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273 erosion rate on it. This may prove to be the most effective way to mimic field behavior in the future.

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274 Table 7 1. Density Profile Results Sample Designation Water Content Times Optimum Clay Content (%) Number of Lifts Average Dry Denisty (g/cm3) Average Wet Density (gm/cm3) Dry Density Standard Deviation (g/cm3) Wet Density Standard Deviation (g/cm3) I 1x 25 1 1.70 1.91 0.070 0.077 II 1x 25 1 1.54 1.77 0.111 0.117 III 1x 25 2 1.53 1.76 0.125 0.133 IV 1x 25 2 1.53 1.84 0.213 0.234 V 1x 25 4 1.52 1.75 0.166 0.789 VI 1x 25 4 1.52 1.75 0.144 0.160 VII 2x 25 4 1.70 2.07 0.277 0.339 VIII 1x 25 8 1.58 1.81 0.074 0.089 IX 1x 50 4 1.44 1.83 0.162 0.175 X 1x 50 4 1.39 1.75 0.127 0.154 XI 1x 75 4 1.20 1.57 0.276 0.313 Figure 7 1. Grain Size Distribution for Sand Used During SandClay Tests 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% 0.01 0.10 1.00 10.00Percent PassingGrain Size (mm)

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275 Figure 7 2. Grain Size Distribution for EPK Used During SandClay Tests Figure 7 3. Optimum Water Content vs. Clay Content 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% 0.01 0.10 1.00 10.00 100.00Percent PassingGrain Size ( m) y = 0.0019x + 0.0652 R = 0.925 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 0 20 40 60 80 100Optimum Water Content (%)Clay Content (%)

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276 Figure 7 4. Shear Stress vs. Velocity for 0% Cla y Epoxy Glued Disc Figure 7 5. Shear Stress vs. Velocity for 12.5% Clay Epoxy Glued Disc 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 3.6357 n = 1.8813 R = 0.98686 (lin) 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 0 10 20 30 40 50 60 70 80 90 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 2.934 n = 1.9836 R = 0.99727 (lin)

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277 Figure 7 6. Shear Stress vs. Velocity for 25% Clay Epoxy Glued Disc Figure 7 7. Shear Stress vs. Velocity for 37% Clay Epoxy Glued Disc 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 0 10 20 30 40 50 60 70 80 90 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 2.7442 n = 1.9906 R = 0.9964 (lin) 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 0 10 20 30 40 50 60 70 80 90 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 2.9093 n = 1.9438 R = 0.99568 (lin)

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278 Figure 7 8. Shear Stress vs. Velocity for 50% Clay Epoxy Glued Disc Figure 7 9. Shear Stress vs. Velocity for 62.5% Clay Epoxy Glued Disc 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 5.1915 n = 1.3883 R = 0.88425 (lin) -1 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 2.3712 n = 1.8262 R = 0.99586 (lin)

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279 Figure 7 10. Shear Stress vs. Velocity for 75% Clay Epoxy Glued Disc Figure 7 11. Shear Stress vs. Velocity for 87.5% Clay Epoxy G lued Disc -1 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 1.9533 n = 1.7323 R = 0.9819 (lin) -1 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 1.4779 n = 1.6812 R = 0.85282 (lin)

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280 Figure 7 12. Shear Stress vs. Velocity for 100% Clay Epoxy Glued Disc Figure 7 13. Summary Chart Showing Best Fit Lines for Sand Clay Epoxy Glued Discs 0 2 4 6 8 10 12 14 0 10 20 30 40 50 60 70 80 90 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 2.4385 n = 1.3798 R = 0.92919 (lin) 0 1 2 3 4 5 6 7 8 9 10 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) 0% Clay 12.5% Clay 25% Clay 37.5% Clay 50% Clay 62.5% Clay 75% Clay 87.5% Clay 100% Clay

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281 Figure 7 14. Shear Stress vs. Velocity for 0% Clay Fiberglass Resin Test Disc Figu re 7 15. Shear Stress vs. Velocity for 12.5% Clay Fiberglass Resin Test Disc -1 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 3.6319 n = 1.723 R = 0.98538 (lin) -1 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 4.1463 n = 1.7097 R = 0.98693 (lin)

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282 Figure 7 16. Shear Stress vs. Velocity for 25% Clay Fiberglass Resin Test Disc Figure 7 17. Shear Stress vs. Velocity for 37.5% Clay Fiberglass Resin Test Disc -1 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 2.5273 n = 1.9055 R = 0.99127 (lin) -1 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 3.2161 n = 1.848 R = 0.98056 (lin)

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283 Figure 7 18. Shear Stress vs. Velocity for 50% Clay Fiberglass Resin Test Disc Figure 7 19. Shear Stress vs. Velocity for 62.5% Clay Fiberglass Resin Test Disc -1 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 2.8309 n = 1.7545 R = 0.94202 (lin) -1 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 2.8231 n = 1.8371 R = 0.99484 (lin)

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284 Figure 7 20. Shear Stress vs. Velocity for 75% Clay Fiberglass Resin Test Disc Figure 7 21. Shear Stress vs. Velocity for 87.5% Clay Fiberglass Resin Test Disc -1 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 1.6917 n = 1.8772 R = 0.95905 (lin) -1 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 2.8901 n = 1.8322 R = 0.97378 (lin)

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285 Figure 7 22. Shear Stress vs. Velocity for 100% Clay Fiberglass Resin Test Disc Figure 7 23. Summary Chart Showing Best Fit Lines for Sand Clay Fiberglass Resin Discs -1 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 Velocity (m/s)Shear Stress (Pa) y(x) = a x^n a = 2.8901 n = 1.8322 R = 0.97378 (lin) y(x) = a x^n a = 2.0237 n = 1.6828 R = 0.96004 (lin) 0 1 2 3 4 5 6 7 8 9 10 0 10 20 30 40 50 60 70 80 90 100 Velocity (m/s)Shear Stress (Pa) 0% Clay 12.5% Clay 25% Clay 37.5% Clay 50% Clay 62.5% Clay 75% Clay 87.5% Clay 100% Clay

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286 Figure 7 24. Trammels 2004 Data Overlaid with Data from this Study Figure 7 25. Erosion Rate vs. Shear Stress for 100% Sand Sample 0 0.5 1 1.5 2 2.5 3 3.5 4 0 0.1 0.2 0.3 0.4 0.5 0.6 Pump Frequency (Hz)Shear Stress (Pa) Trammel 0.921 mm Data Trammel 0.508 mm Data Crowley 1.0 mm Data Crowley 0.50 mm Data 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 Shear Stress (Pa)Erosion Rate (m/yr) y(x) = a0 + a1 x + a2 x^2 a0 = -0.0095099 a1 = 0.0080322 a2 = 0.0075811 R = 0.92172 (lin)

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287 Figure 7 26. Non Dimensionalized Erosion Rate vs. Shear Stress for 100% Sand Sample Figure 7 27. Sample Position vs. Time for 25% C lay Mixture at 13.4 Pa 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 taub/taucE/(M*tauc) y(x) = a0 + a1 x + a2 x^2 a0 = -0.50807 a1 = 0.18345 a2 = 0.073791 R = 0.92172 (lin) 0 50 100 150 200 250 300 350 400 0 2 4 6 8 10 12 Time (s)Sample Position (cm)

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288 Figure 7 28. Sample Position vs. Time for 25% Clay Mixture at 40.0 Pa Figure 7 29. Sample Position vs. Time for 25% Clay Mixture at 3.37 Pa 0 20 40 60 80 100 120 140 0 1 2 3 4 5 6 7 8 9 Time (s)Sample Position (cm) 0 100 200 300 400 500 600 0 1 2 3 4 5 6 7 8 Time (s)Sample Position (cm)

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289 Figure 7 30. Sample Position vs. Time for 25% Clay Sample at 30.0 Pa Figure 7 31. Sa mple Position vs. Time for 25% Clay Mixture at 53.2 Pa 0 50 100 150 200 250 300 350 400 0 2 4 6 8 10 12 Time (s)Sample Position (cm) 0 20 40 60 80 100 120 140 160 180 200 0 2 4 6 8 10 12 14 Time (s)Sample Position (cm)

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290 Figure 7 32. Erosion Rate vs. Shear Stress for Flat Portions of Sample Position vs. Time Curves Figure 7 33. Erosion Rate vs. Shear Stress for Rapid Advancement Portions of Sample Position vs. Time Curves 0 5 10 15 20 25 30 35 40 45 50 55 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 Shear Stress (Pa)Erosion Rate (cm/s) y(x) = a x + b a = 0.00038459 b = 0.0015323 R = 0.73314 (lin) 0 5 10 15 20 25 30 35 40 45 50 55 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 Shear Stress (Pa)Erosion Rate (cm/s) y(x) = a x + b a = 0.0032632 b = -0.00024212 R = 0.9927 (lin)

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291 Figure 7 34. Two lift test at 13.4 Pa. Figure 7 35. Sample Position vs. Time for 25% Clay Mixture Using Double Optimum Water Content 0 200 400 600 800 1000 1200 1400 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 x 104 Time (s)Sample Position (cm) 0 1000 2000 3000 4000 5000 6000 0 2 4 6 8 10 12 Time (s)Position (cm) 25.0 Pa 11.5 Pa y(x) = a x + b a = 0.023397 b = -1.184 R = 0.98784 (lin) y(x) = a x + b a = 0.0010716 b = 0.17626 R = 0.9913 (lin)

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292 Figure 7 36. Sample Position vs. Time for 50% Clay Mixtures Figure 7 37. Sample Position vs. Time for 50% Clay Mixture at 27.54 Pa 0 100 200 300 400 500 600 700 800 900 1000 0 2 4 6 8 10 12 Time (s)Sample Position (cm) 2.94 Pa 4.40 Pa 5.99 Pa 5.99 Pa 15.69 Pa 27.54 Pa 41.07 Pa 0 10 20 30 40 50 60 70 80 90 100 0 1 2 3 4 5 6 7 8 Time (s)Sample Position (cm)

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293 Figure 7 38. Zoom in on First 90 Seconds of Sample Position vs. Time Curves Figure 7 39. Sample Position vs. Time for 50% Clay Mixture at Double Optimum Water Content 10 20 30 40 50 60 70 80 90 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Time (s)Sample Position (cm) 2.94 Pa 4.40 Pa 5.99 Pa 5.99 Pa 15.69 Pa 27.54 Pa 41.07 Pa 0 200 400 600 800 1000 1200 1400 1600 1800 2000 -2 0 2 4 6 8 10 12 Time (s)Sample Position (cm) 27.54 Pa 34.11 Pa 41.07 Pa 55.98 Pa

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294 Figure 7 40. Sample Position vs. Time for 75% Clay Mixtur e Mixed at Optimum Water Content Figure 7 41. Sample Position vs. Time for 75% Clay Mixture Mixed at Double the Optimum Water Content 0 200 400 600 800 1000 1200 1400 1600 1800 0 2 4 6 8 10 12 Time (s)Sample Position (cm) 2.34 Pa 4.72 Pa 7.76 Pa 15.67 Pa 25.80 Pa 0 1000 2000 3000 4000 5000 6000 0 2 4 6 8 10 12 14 Time (s)Sample Position (cm) 7.76 Pa 11.42 Pa 15.67 Pa

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295 Figure 7 42. Sample Position vs. Time for 100% Clay Mixed at Optimum Water Content Figure 7 43. Sample Position vs. Time for 100% Clay Mixed at Double Optimum Water Content 0 200 400 600 800 1000 1200 1400 1600 1800 2000 0 2 4 6 8 10 12 100% Clay Time (s)Piston Positiion (cm) 19.41 Pa 22.84 Pa 26.41 Pa 0 100 200 300 400 500 600 700 800 0 2 4 6 8 10 12 Time (s)Sample Position (cm) 17.76 Pa 19.41 Pa 26.41 Pa

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296 Figure 7 44. Density Profile for Sample I Figure 7 45. Density Profile for Sample II 1 1.5 2 2.5 3 0 2 4 6 8 10 12 14 Density(g/cm3Height (cm) Wet Density Dry Density 1 1.5 2 2.5 3 0 2 4 6 8 10 12 14 Density(g/cm3Height (cm) Wet Density Dry Density

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297 Figure 7 46. Density Profile for Sample III Figure 7 47. Density Profile for Sample IV 1 1.5 2 2.5 3 0 2 4 6 8 10 12 14 Density(g/cm3Height (cm) Wet Density Dry Density 1 1.5 2 2.5 3 0 2 4 6 8 10 12 14 Density(g/cm3Height (cm) Wet Density Dry Density

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298 Figure 7 48. Density Profile for Sample V Figure 7 49. Density Profiles for Sample VI 1 1.5 2 2.5 3 0 2 4 6 8 10 12 14 Density(g/cm3Height (cm) Wet Density Dry Density 1 1.5 2 2.5 3 0 2 4 6 8 10 12 14 Density(g/cm3Height (cm) Wet Density Dry Density

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299 Figure 7 50. Density Profiles for Sample VII Figure 7 51. Density Profiles for Sample VIII 1 1.5 2 2.5 3 0 2 4 6 8 10 12 14 Density(g/cm3Height (cm) Wet Density Dry Density 1 1.5 2 2.5 3 0 2 4 6 8 10 12 14 Density(g/cm3Height (cm) Wet Density Dry Density

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300 Figure 7 52. Density Profiles for Sample IX Figure 7 53. Density Profiles for Samp le X 1 1.5 2 2.5 3 0 2 4 6 8 10 12 14 Density(g/cm3Height (cm) Wet Density Dry Density 1 1.5 2 2.5 3 0 2 4 6 8 10 12 14 Density(g/cm3Height (cm) Wet Density Dry Density

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301 Figure 7 54. Density Profile for Sample XI Figure 7 55. Shear Stress vs. Velocity for Different Sediment Concentrations 1 1.5 2 2.5 3 0 2 4 6 8 10 12 14 Density(g/cm3Height (cm) Wet Density Dry Density 0 5 10 15 20 25 30 35 40 45 50 -5 -4 -3 -2 -1 0 1 2 3 4 Smooth Wall Shear Stress (Pa)Shear Stress Difference (Pa) After 1 Run After 2 Runs After 3 Runs

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302 CHAPTER 8 SUMMARY AND FUTURE W ORK 8.1 Summary 8.1.1 Review of Goals for This Study The purpose of this research was to aid in the development of erosion rate functions for use in a similar style Miller Sheppard approach or EFA SRICOS approach for predicting local scour depths. The specific goals for this were to: 1. Develop equipment to improve accuracy of erosion and shear stress measurements for a wide range of eroding bed materials. 2. Use these equipment upgrades and other analytical techniques to comment quantitatively on older methods for measuring these parameters by running a series of tests with the new equipment. 3. Use these new measurements and older results to determine if erosion rate can be related to any other existing common geotechnical parameters. 4. Use the new equipment to develop a series of erosion rate shear stress curves for sand clay mixtures. Under na tural conditions, it is rare to find a bed material that is purely cohesive or purely noncohesive. Instead, usually sand is interspersed with clay particles or vice versa. Previous research has looked only to classify erosion rate shear stress curves un der conditions where a uniform material is present, but because this is rarely the case, erosion properties of mixtures are investigated 8.1.2 Summary of Work To achieve these goals, the following was accomplished during this project: 1. The Sediment Ero sion Rate Flume (SERF) was enhanced and improved. Significant upgrades to the device include a. A laser system b. A new shear stress measurement system c. A vortex generation system d. Computer upgrades e. Software upgrades

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303 f. Additionally, a sand injection/control system was designed. Although funding for this system does not yet exist, when funding becomes available, installation of this system should be relatively straight forward. 2. A series of tests were conducted with the new shear stress sensor in the SERF to deter mine the apparent most effective method for estimating shear stress in a flume style erosion rate testing device. 3. A synthetic material, Bull Gator Rock, was designed so that it could be tested in both the SERF and the Rotating Erosion Testing Apparatus (RE TA). 4. Bull Gator Rock testing revealed the presence of rock like erosion, which is difficult to analyze with present equipment that is only designed to measure erosion rate and shear stress. Therefore, the entire RETA database was filtered and used in co njunction with analytical equations to verify whether or not the device was working properly. 5. A series of tests was run on a variety of sandclay mixtures in an attempt to generalize sand clay mixture erosion behavior as a function of sandclay ratio. 8.2 Conclusions The following is a list of conclusions from this project: 1. The laser leveling system is an effective means of maintaining a sample level during an erosion rate test in a flume style device, especially during clay and sand clay tests. 2. The temperature control system in the SERF was effective at holding temperature within +/ 2oC. 3. T he sediment control system was unable to keep up with rapid erosion rates. 4. Tests with the new shear sensor showed that as sample roughness increases for a given flow rate, corresponding shear stress also appears to increase. 5. In the absence of a shear stress sensor, the most effective alternative means for estimating shear stress on an eroding sample in a flume style testing device is to use a Moody Diagram or C olebrook Equation. The roughness factor in the Colebrook Equation should be equal to one half the median sediment diameter. Flatwall assumptions for predicting shear stress under predict shear stress, especially at higher roughness and flow velocities. 6. Bull Gator Rock may be a promising material for use in comparing results between different erosion rate testing devices if somehow water content can be increased. Currently, it appears that the bottom up approach will produce a nearly uniform material i f limestone aggregate is similarly nearly uniform. However, Gator Rock tests also showed that obtaining a water content greater than ~20% using a bottom up technique may not be possible.

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304 7. The RETA database showed that under special conditions where erosi on conditions correspond to a direct erosion rate vs. shear stress relationship, results from the RETA correspond to results from equations that fit the Ariathurai equation form accurately. 8. RETA database analysis showed that there is an approximately even split between materials that behave with a direct erosion rate vs. shear stress relationship and materials that do not. For materials that do not obey a direct relationship, rocklike erosion described by The Stream Power Model is likely the culprit. W hen rocklike erosion is present, current equipment limitations do not allow for prediction or analysis of this erosion mode. This is significant because generally, rock like erosion appear s to occur an order of magnitude slower than particle like erosion. 9. RETA database analysis showed that for the special case of particle like erosion, correlations may exist between erosion rate constant, critical shear stress, and material cohesive strength. 10. Tests on sandclay mixtures showed that sand clay mixture e rosion behavior is dependent on initial water content when the sample was formed, compactive effort as the sample was formed, and layering thickness as the sample was formed. 11. Sand clay mixtures exhibit a combination of rock like and particle like erosion qualities even though sandclay mixtures would usually not be described as rocklike materials 12. Sand clay mixtures tended to exhibit the opposite effect as stiffer RETA materials in terms of erosion rates for stronger materials. With sand clay mixtures when initial water content increased, samples were created that were qualitatively weaker in compression and tension. These samples exhibited more resistance to erosion than samples that were stiffer and stronger in compression and tension. 13. Recirculat ing sand in suspension in the SERF does not appear to affect shear stresses on a smooth plate compared to what shear stresses would have been under clear water conditions. 8.3 Future Work The following is a proposed progression of future work Proposed work discussed here will help to further improve the SERF, aid in developing erosion rate and shear stress relationships, and help to predict when (or if) particle like or rock like erosion will occur. 8.3.1 Essential Final Improvements to t he SERF Before the rock like vs. particle like erosion question can be answered, essential final improvements need to be made to the SERF. As discussed, w hen an erosion rate test is run, the

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305 eroded material recirculates back through the flume; this issue is common with recirculating flumes with a relatively small (1,100 gal.) reservoir tank. This recirculating particle issue is troublesome for three reasons. First, due to the number of variables associated with the erosion rate problem, researchers need to isolate them from one another. The second and more important reason to add a highcapacity filter is that without it, the SERF will probably be damaged T he pumps will be damaged because these sandclay flocs are essentially sand blasting the centrif u gal pumps impellers. S ometimes when the reservoir tank is drained, rust particles are often seen interspersed with the sand clay flocs. Since the flume is made of aluminum, the rust particles must be from the cast iron pumps. This proves that damage i s already occurring. Towards the end of this project, one of the pumps mechanical seals began leaking which appears to indicate that damage due to recirculating sediment has occurred The third reason to add a highcapacity filter is so that the sand injector can be installed, tested, and used during tests Installation of the sand injector will allow investigators to follow up on tests shown in Section 7.4.4 so that definitive results can be obtained for the effects of suspended sediment on shear st ress. If the filter is installed in the SERF, it makes sense to extend the device. Given the present setup, the sand injector cannot be installed anywhere else other than where the paddlewheel flowmeter is currently located. Presumably, a filter would have the same problem the PVC run up to the rectangular portion of the flume is not long enough to accommodate more than one device. Therefore, PVC pipes leading into the rectangular portion of the device should be extended in conjunction with the filte r installation so that as water passes through the flume it

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306 passes through the pumps, through the filter, past the flowmeter, through the sand injector, and finally into the rectangular portion of the flume. 8.3.2 Non Essential Final Improvements to the SERF Although the following improvements to the SERF are listed as nonessential their inclusion is beneficial because these improvements will impro ve the accuracy of the device. From 20032006, three of the twelve crystals in the SEATEK array broke; it is unclear why this happened, but what is clear is that the array should be replaced or fixed so that the crystals work properly. It may be advantageous to replace the array completely so that a faster mechanism can be installed. Computer technology has advanced in the past ten years and the current ultrasonic array is only capable of sending an output signal once every second. A faster digital signal will allow for less time in between steps, which in turn will allow for a more level sample at the bot tom of the SERF. Secondly, the three laser leveling system should be replaced with a lightsheet design. When certain materials erode under rock like conditions, often differential erosion rates are present such that the back face of the sample erodes f aster than the front or vice versa. When the lasers are used as a stand alone mechanism for measuring sample flushness (as is necessary with clays), a simple array of three lasers is not enough to account for this. A better design would be to illuminate one side of the sample with a light sheet and install corresponding photoelectric sensors on the other side. Then, when a certain percentage of the light is seen (such as 50%), the sample would advance. This would provide a much more accurate method of l eveling sand clay mixtures with the flumes bottom. The third improvement is quite simple a new computer should be added to the SERF control room. The present computer system is old, and sometimes it cannot keep up with a

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307 rapidly eroding sample. A ne w computer running on a 64 bit operating system with more memory is needed. The fourth improvement that should be made to the SERF is that the stepper motor assembly needs to be redesigned. The current design uses a stepper motor assembly where the maximum force against the piston is approximately 400 lb. Over time, due to normal wear and tear, the amount of for ce that can be applied to the piston decreases. Eventually, the motor cannot provide enough force to overcome the friction between the pistons O rings and the side walls of the piston casing. What makes matters worse is that during an erosion test smal l pieces of sediment get stuck in the tiny gap between the piston and its housing. Although these pieces of sediment are tiny, they are large enough to increase the friction factor between the piston and its housing to the point where the piston can no longer move. When this happens, the motors gears begin to grind against one another, and eventually the motor burns out. During longer duration testing (as seen with rocklike material and G ator Rock), this is especially an issue because as this is happening the motor heats up rapidly To prevent this, the motor needs to be replaced by a heavy duty linear actuator. These devices ar e designed for repeated loading, and will be better able to apply a higher force to the piston. Since 2006, three motors ha ve burned out during SERF testing due to normal motor usage combined with harsh experimenta l conditions. Rather than spend money on new motors every 18 months, an impr ovement should be implemented. The fifth improvement that should be made to the SERF is to install a n ultra sonic Doppler radar (UDV) system for measuring velocity profiles in the device. This system will allow researchers to verify that flow conditions in the SERF are fully developed.

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308 8.3.3 Determine Erosion Patterns for Natural Sand Cla y Mixtures Once final improvements and enhancements have been made, the next step is to determine how natural sand clay mixtures behave when subjected to SERF testing. As discussed, sensitivity exists between erosion rates and sample preparation methods. Rather than engineer a series of materials to behave differently, it makes more sense to mimic natural erosion patterns during future tests Therefore, natural materials need to be obtained and tested in the flume to see how they react to erosion rate testing. These materials should be subjected to a series of tests under clear water and simulated livebed conditions. Because the filter will have been installed, these test s will now be possible. These samples also should be subjected to tensile and com pressive strength testing ( if strong enough) to determine if the relationships developed for erosion rate as a function of cohesion presented in Chapter 6 is still valid 8.3.4 Roughness Number Tests or Improved Testing Apparatus As discussed in Chapter 3, shear stress is sensitive to sample roughness. Because it is not yet possible to measure shear stress at the same time as erosion rate, the question as to how to properly measure erosion rates and shear stresses of natural samples is difficult to answ er. If it continues to be impossible to measure erosion rate and shear stress at the same time, then the most effective alternative is to measure a samples roughness and use that to approximate the shear stress on it. Curves have already been develo ped for shear stress as a function of flow rate and sediment diameter. Using high resolution photography (UF has a device that can do this), it should be possible to measure the roughness associated with these samples. Then, given a natural sample, the came ras can be used to approximate its roughness and existing shear stress curves can be used to determine shear stresses associated with given flow rates.

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309 Alternatively, using the principles from the shear stress sensor, it may be possible to engineer a dev ice that can measure erosion rate and shear stress at the same time. Both approaches should be investigated. 8.3.5 Normal Stress Measurements in SERF P receding discussion assumes particlelike erosion, but as discussed several times for cohesive material, a strict particle like erosion mode is hardly ever seen. The next research step hinges on how natural sandclay bed materials behave. Hence, first one needs to measure the normal stresses on different sandclay mixtures to determine under what conditions rocklike erosion dominates. Presumably, if rocklike erosion were to dominate, the normal stresses on a sample should be larger than they would be under particle like conditions. If this is not the case, then it may be possible to find a sediment property that would indicate whether or not rocklike erosion is likely. For example, it should be possible to prepare a series of sediments at the same sand clay ratio, same liftheights, and different water contents. Based on result s presented in Chapter 7, eventually at a certain initial water content, rocklike erosion should become important. Likewise beyond this water content, rocklike erosion may become less important. Similarly, there may be a threshold clay content where ro ck like erosion also begins to take an effect. Right now, it is obvious that rocklike rock erosion does not and cannot occur for a 100% sand bed. At what clay content does it begin to become an issue? Finding these threshold values specifically (this dissertation simply defined this as an important variable and helped to bound the problem) may hold the key to explaining when rock like erosion will occur for a sandclay mixture. 8.3.6 Normal Stress Measurements under Field Conditions Because normal stress is caused by the fluctuating velocity component, and the turbulent ed dy mixing length is much shorter in the SERF than it is in nature, normal stress measurements

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310 must be compared with normal stress measurements under field conditions to test their validity. A field device or devices should be designed to measure both normal and shear stresses in an actual streambed to answer these questions. Additionally, an upstream Acoustic Doppler Current Profiler (ADCP ) probe should be installed in this stream so that conditions can be monitored continuously. 8.3.7 Computer Model If researchers find that normal stresses a re significant then these field results should be use d to calibrate a computer model similar to Briauds model. The difference between this mo del and Briauds is that instead of just returning a maximum shear stress value, this new model may be able to return a normal stress value as well. 8.3.8 Summary of Proposed Future Progression The preceding discussion is not a comprehensive discus sion regarding the cohesive erosion rate problem. Predicting erosion rate for coh esive sediments is complicated and the discussion serves as a logical series of steps based on research presented in this dissertation.

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311 APPENDIX A SCOUR DEPTHS IN NONCOHESI VE SOILS A.1 Introduction As discussed in Chapter 2, erosion of noncohesive materials is a mode of particle erosion. Erosion processes such as these may be analyzed using a simple force balance relationship. When a noncohesive particle is resting on th e bed and water passes over top of it, the particle is subjected to a drag force, FD and a lift force FL where the magnitude of these forces is a function of the particles geometry. An expression for drag forces and lift forces has been developed such th at: 25 0 u A C Fyz D D (A 1) 25 0 u AC Fxy L L (A 2) where Axy is the planform area of the particle, Ayz is the cross sectional area of the particle, is the density of water, u is the fluid velocity, and CL and CD are experimentally determined drag and lift coefficients. Under nonerosion conditions, these drag forces and lift forces, plus the buoyant weights of the particle are balanced by bed friction and the force of gravity. Under erosion conditions, the flo w velocity, u, must become high enough to cause the lift force, FL to overcome the force of gravity; or u must become high enough for the drag force on the particle, FD to overcome the force of static friction between the particle and the bed. When the li ft force becomes larger than the drag force, the particle is said to go into suspension whereas when the drag force overcomes the friction force, the particle is said to be moving as part of the bedload. In 1936, Shields studied the minimum velocity req uired to initiate incipient motion of bed particles of various sizes, and he developed a diagram similar to Figure A 1 where below the

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312 purple line, there is no sediment motion and above the line, sediment will erode. The green line (Developed by Bagnold in 1966) divides the bed load from the suspended load. Although this analytical approach to solving the erosion problem is elegant, the hydrodynamics in the vicinity of a scouring structure are complicated, and to date, a comprehensive theoretical soluti on to the scour problem could not be found. To solve analytically, flow conditions in the vicinity of a complex pier would need to be solved so that one could determine when flow velocity exceeded the critical velocity of the bed material. Because of th e difficulties associated with solving for these hydrodynamic conditions, an empirical approach has been used when solving for particle scour. The following is an analysis of the existing empirical standards for estimating scour depths due to noncohesive sediment transport. A.2 HEC 18: Basic Principles (Richardson and Davis 2001) HEC 18 instructs an engineer to compute the scour effects from aggradation/degradation, general scour, contraction scour, and local scour. Then, the effects from each of these parameters are added and the total scour depth is computed. A.2.1 HEC 18: Long Term Aggradation and Degradation (Richardson and Davis 2001) Long term aggradation and degradation are computed by using a three level fluvial system approach. First, a qual itative determination regarding long term stream stability is established using general geomorphic and river mechanics relationships. Then, engineering analyses follow where the probable behavior of the stream system is estimated. Finally, physical model s or computer models are to be used to predict quantitative changes to the streambed elevation. According to HEC 18, acceptable computer models include Bridge Stream Tube Model for Alluvial River Simulation (BRI STARS) and USACE HEC 6, Scour and Deposition in Rivers and Reservoirs computer model (Richardson and Davis 2001).

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313 A.2.2 HEC 18: Contraction Scour (Richardson and Davis 2001) To find the design contraction scour depth, first an engineer must determine if flow upstream from a bridge or an obstructi on is already transporting bed material. Therefore the critical velocity for incipient motion, Vc, needs to be computed based on the upstream bed materials mean diameter, D50. According to HEC 18, critical velocity is computed using the following equati on: 3 / 1 6 / 1D y K Vu c (A 3) where Ku is a constant that is dependent on English or SI units (6.19 for SI units, 11.17 English units), y is the average flow depth upstream from the bridge or obstruction, and D is the average particle size. Once critical velocity is known, the engineer can compute determine whether clear water ( V < Vc and there are no particles in suspension) or live bed ( V > Vc and there are particles in suspension) conditions are present. Fo r clear water conditions, contraction scour is computed using Equation A 4: 7 / 1 2 3 / 2 2 2 W D Q K ym u (A 4) where y2 is the equilibrium scour depth after contraction scour, Q is the discharge through the obstruction, Dm is the diameter of the smallest nontra nsportable particle in the bed material (1.25D50) in the contracted section, W is the bottom width of the contracted section minus the pier widths, and Ku again is a constant that is unit dependent (0.025 for SI units and 0.0077 for English units). For l ive bed conditions, contraction scour is computed using Equation A 5: 12 1 7 / 6 1 2 1 2 kW W Q Q y y (A 5)

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314 where y1 is the average depth in the upstream channel, y2 is the average depth in the contracted section, Q1 is the flow in upstream channel, Q2 is the flow in the contracted channel, W1 is the width of the upstream channel, W2 is the width of the contracted section, and k1 is an exponent based on shear velocity in the upstream section and fall velocity of the bed material. k1 can be determined v ia table A 1 and E quation A 6: 2 / 1 1 2/ 1 0* S gy V (A 6) where V* is the shear velocity, g is the acceleration due to gravity, 0 is the shear stress on the bed, is the density of water, is the fall velocity as determined from Figure A 2, and S1 is the slope of the energy grade line of the main channel. A.2.3 HEC 18: Local Scour (Richardson 2001) Local scour is computed with the underlying assumption that each parameter that affects the final depth of the scour hole is independent of the other parameters. In other words, the effects of angle of attack, shape of the pier, spacing between pier piles, bed conditions, and armoring are computed separately. Then, these parameters are fit together with the following equation: 43 0 1 35 0 1 4 3 2 1Fr a y K K K K a ys (A 7) where K1, is the correction factor due to pier shape, K2 is the correction factor for angle of attack K3 is the correction factor for the bed conditions (live bed or clear water) and K4 is the correction factor for armoring; ys is the depth of the scour hole, a is the pier width and Fr1 is the Froude Number defined by 2 / 1 1 1 1 gy V Fr (A 8)

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315 where V1 is the velocity upstream from the pier and y1 is the depth upstream from the pier (Richardson and Davis 2001). The correction factor, K1 is calculated using Figure A 3 and Table A 2. The correction factor K2 is computed using Equation A 9: 65 0 2sin cos a LK (A 9) where is the attack angle, and L and a were previously defined as the length and width of the pier respectively. The correction factor K3 is found using Table A 3. T he correction factor K4 is computed using a series of equations: 15 0 44 0RV K (A 10) 095 50 501 icD cD icD RV V V V V (A 11) x xcD x icDV a D V053 0645 0 (A 12) 3 / 1 6 / 1 1 x u cDD y K Vx (A 13) In these equations, VicDx is the approach velocity required to initiate scour at the pier for a given grain size, Dx, a nd VcDx is the critical velocity for incipient motion for the grain size, Dx; y1 is the depth of flow just upstream of the pier; V1 is th e velocity of approach flow upstream of the pier, and Ku us a constant that is unit dependent (6.19 for SI and 11.17 for English). HEC 18 recommends a minimum value of K4 of 0.4. The preceding equations assume a narrow pier. For wide piers, or piers that have multiple piles or pile caps, the equations become somewhat more complicated. To compute scour for a wide pier, an additional correction factor, Kw i s introduced and Equation A 7is modified: 43 0 1 35 0 1 4 3 2 1Fr a y K K K K K a yw s (A 14)

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316 1 for 58 265 0 34 0 c wV V Fr a y K (A 15) 1 for 0 125 0 13 0 c wV V Fr a y K (A 16) When computing scour when there are multiple piles, a pile cap, and/or a pier involved, scour is computed by adding the effects of scour due to these three components: spg spc spier sy y y y (A 17) where yspier is the scour component from the pier stem, yspc is the scour component due to the pile cap and yspg is the scour component due to the pile group. The equation for computing yspier is similar to E quation A 18: 43 0 1 1 65 0 1 4 3 2 1 10 2 gy V y a K K K K Ky ypier hpier spier (A 18) The difference between E quation A 18 and E quation A 14 is the introduction Khpier, which is the coefficient used to account for both the height of the pier stem above the bed and the shielding effect by the pile cap overhanging distance f in front of the pier stem. Khpier is found by using Figur e A 4. Scour from the pile cap is divided into two scenarios. On one hand, the bottom of the pile cap may be above the bed and in the flow path. On the other hand the pile cap may be located on or below the bed. When the pile cap is above the bed, the technique for computing scour is to reduce the pile cap width to an equivalent pier depth and width, a* pc, using Figure A 5. Then, scour for the pile cap component is computed using E quation A 19: 43 0 2 2 65 0 2 4 3 2 1 20 2 gy V y a K K K K K y ypc w spc (A 19)

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317 Under the scenario where the pile cap is on or below the bed, a slightly different series of equations, E quation A 20 E quation A 21, and E quation A 22 are used instead: 43 0 65 0 2 4 3 2 1 20 2 f f pc w spcgy V y a K K K KK y y (A 20) 1 93 10 ln 1 93 .10 ln2 2s s f fK y K y V V (A 21) 21 xpier fy h y (A 22) In these equations, Vf is the average velocity in the flow zone below the top of the footing and yf is the distance from the bed to the top of the footing. Figure A 6 is an illustration of thes e parameters. When the footing is on or above the bed, there is no need to compute the scour effect from the pile group. Therefore, under this scenario, Equation A 22 can be modified: spc spier sy y y (A 23) The strategy for finding the scour com ponent that results from a pile group is to represent the pile group by an equivalent solid pier width, a* pg, which is found using Equation A 24: m sp proj pgK K a a (A 24) Km is the number of rows factor, and it is equivalent to 1.0 for the general case of skewed or staggered rows of piles. When piles are aligned, Km mus t be determined from Figure A 7. To illustrate how to compute the projected pile group equivalent pi er wid th, Figure A 8 is useful. The coefficient of spacing, Ksp is found using Figure A 9. T he scour component from the pile group is computed using E quation A 25, E quation A 26, and E quation A 27 The pile group high factor, Khpg is given in Figure A 10.

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318 2 21 3 spc spiery y y y (A 25) 3 1 1 3y y V V (A 26) 2 20 3 spc spiery y h h 43 0 3 3 65 0 3 4 3 1 30 2 gy V y a K K K K y ypg hpg spg (A 27) A.2.4 HEC 18: Brief Discussion The local scour equations from HEC 18 are based on a series of tests at CSU, and they are empirical. Because these equations are empirical, after they were introduced, there was some concern that they may need to be slightly adjusted. This will be discussed in the subsequent section. A.3 The Florida DOT Bridge Scour Manu al The FDOT Bridge Scour Manual (FDOTBSM) was published in 2005, and it is another source that can be used to compute design scour depth. This document is similar to HEC 18 in many respects; in both documents the scour component associated with long term aggradation/degradation, contraction scour, general scour, and local scour are computed independently and added together. To compute total scour, the FDOTBSM uses the same equations and recommendations as HEC 18 for scour components except for local scour A.3.1 Local Scour: Motivation for a Different Computation Algorithm Computation of local scour is different in the FDOTBSM than it is in HEC 18. The FDOTBSM argues that because the CSU equation s (HEC 18 equations) are empirical and based

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319 on small scal e laboratory data, they may yield different results when they are applied to prototype scale structures. The hydrodynamics surrounding the scour problem would appear to indicate that this is a valid concern, and an order of magnitude analysis can be used to illustrate this point: 1.) Under field conditions: assume a typical bridge pier width of 1.0m and a typical grain size of 1x104m; the ratio of grain size to pier width is on the order of 1x104m. 2.) Under laboratory conditions: grain size of sand cannot chang e because if the sand were smaller, it would no longer be a sand. Instead, it would be a cohesive material like a silt, and it would be affected by associated cohesive forces. However laboratory pier width may be on the order of 1 cm. Therefore the rati o of grain size to pier width is two orders of magnitude smaller 1x102. In 1988, Melville proposed a design method that allowed the designer to follow flow charts to calculate the limiting armor velocity and the local scour depth. According to this s tudy, the maximum scour depth that can occur equals 2.4 times the pier diameter. However, when the designer is dealing with shallow water, larger sediment grain sizes, and clear water conditions, Melville proposed that this 2.4 factor should be reduced b ased on dimensionless flow velocity, V/Vc; the ratio of flow depth to pier diameter, y0/D50; the ratio of pier diameter to sediment grain size, b/D50; and shape and alignment factors (Melville and Sutherland1988). In the early 2000s, Dr. Sheppard of UF conducted three series of prototype scale tests under both clear water and live bed conditions (Sheppard, et al. 2004) The first series of tests which were conducted in Turners Falls, MA, tested scour depths of three pier sizes and three sediment sizes over a range of depths and velocities under clear water conditions. An interesting phenomenon was noticed during this study. The water supply for this flume study was taken

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320 from a hydroelectric power plant reservoir, and there was no control over the amount of suspended sediment in it. During testing Sheppard noticed sudden increases in shear stresses that were attributed to reduced levels of turbulence from increased suspended sediment load during high runoff events such as snow melt. Sheppard reasone d that these suspended sediments slowed or stalled the formation of equilibrium scour depths because the sediment helped to dampen the turbulence. The implication here is that under live bed conditions the equilibrium scour depth may be lower than it woul d be under clear water conditions (Sheppard et al. 2004). In 2004, Sheppard launched a second study concerning an additional pressure gradient factor affecting the formation of local scour (Sheppard 2004) According to Sheppard, the pressure field adjacent to the bed is determined by the pressure field in the main flow. When a pier or another structure interrupts the flow, pressure gradients near the structure will impose forces on sediment particles of a greater magnitude than drag forces because of the water flowing around the sediment particles. Sheppard mathematically explored these pressure gradient forces and their dependence on the D/D50; and he found that the magnitude of these forces caused by t his pressure gradients decreased as this ratio increased (Sheppard 2004). This observation agreed with his previous experimental work (Slagle 2006). Sheppards third study was conducted at the University of Auckland and its purpose was to investigate live bed local pier scour for a circular pile and compare calculated equilibrium scour depths with those that are physically measured. Sheppard observed a decreased dependence of normalized equilibrium scour on D/D50 at higher values of V/Vc where D is the pier width, D50 is the median sediment grain size, V is the upstream flow velocity, and Vc is the sediment critical velocity (Sheppard 2006 b).

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321 The combination of these tests helped in development of the equations in the FDOTBSM for local scour. According to Slagle, these equations are the most successful at predicting the local scour depth. Slagle says that the average percent difference in results between computed scour depth and measured scour depth was 16.6%; the standard deviation was 18.2% (Slagle 2006). A.3.2 FDOTBSM: Local Scour Equations (Florida DOT Bridge Scour Manual 2005) For computing the design scour depth of a pier under clear water conditions, pier, E quation A 28 is used: 13 0 50 5 0 50 50 2 4 0 0 */ 6 10 / 4 0 / ln 75 1 1 tanh 5 2 D D D D D D V V D y D yc x (A 28) Equation A 28 is only appr opriate when (0.47 < V/Vc < 1). Under live bed conditions ( 1
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322 velocity is computed using Equation A 31 through Equation A.335; live bed scour peak velocity is computed using E quati on A 36 through E quat ion A 39. 00 *72 2 ln 5 .2 z y u Vc c (A 31) 50 *1 elocity friction v critical gD sg uc c c (A 32) 150 for 0575 0 150 3 for 005 0 / 23 2 ln 000378 0 0023 0 3 01 0 for 1 0 25 0* * *d d d d d d d dc (A 33) 3 / 1 2 50 */ 1g sg D d (A 34) 70 Re for 30 / 70 Re 5 for Re / 111 Re 002 0 Re ln Re 58 0 Re 85 2 6 10 5 Re 0 for 9 /2 3 0 c x c c c c c c s c ck k u z (A 35) mm D Dmm D D ks6 0 for 5 6 0 for 5 25050 50 50 (A 36) 0 18 0 gy V (A 37) 50 0 2/ 4 log 31 29 D y u Vc (A 38) 2 1 2 2 1 1 if if V V V V V V Vlp (A 39) To compute the design scour depth for a complex pier, the FDOTBSM uses a similar approach to HEC 18: the effects of scour of the pier, the pile group, and the pile cap are computed separately and added together. This assumes that these three components of scour act separately and do not interact. The difference between HEC 18 and the FDOTBSM is that the FDOT Manual looks for an effective pier diameter for each of these three shapes and then it us es

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323 this diameter to compute the scour depth. Conversely, HEC 18 applies a correction factor, Kx to the scour parameter instead of the effective diameter parameter. Explicitly, the total scour of a complex pier in the FDOTBSM is computed by: * pg pc colD D D D (A 40) To compute the eff ective column (pier) diameter, E quation A 41 through Equation A 46 are used: 1 y H for 0 1 0 for 2476 0 3617 0 1162 00(max) col (max) 0 (max) 0 2 (max) 0 y H y H y H b K K K Dcol col col col f s col (A 41) col col colb y y b y b y 5 for 5 for 50 0 0 (max) 0 (A 42) columns r rectangula for 4 180 97 0 86 0 columns circular for 14 o sK (A 43) col col colb l b K sin cos (A 44) o of f f f f 45 for 4 3 45 for 4 31 2 2 1 (A 45) 3 for 0 3 b f for 1 03 0 12 0 .col 2 col col col fb f b f b f K (A 46)

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324 To illustrate bcol, the column width, and f the side overhangs, a definition sketch has been provided (Figure A 12 ). T o compute the effective pier diameter of the pile cap, E quation A 47 is used: 5 0 (max) 0 (max) 0 *695 1 exp 77 1 04 1 exp y T y H b K K Dpc pc s pc (A 47) where Ka is the same as it was when it was computed for a column except that the pile caps b is used instead of the columns b; Ks is the same as for a column; y0(max) is computed using Equation A 48: 7 25 2 0 0 7 2 5 2 0 7 2 5 2 (max) 064 1 for 64 1 for 64 1pc x pc x pcxb K T yy b K T y b K T y (A 48) To compute the effective pier diameter for the pile group, Equation A 49 through E quation A 55 are used: p s m h sp pgW K K K K D (A 49) ) ( ) ( ) ( ) ( ) (9 10 9pilegroup s pile s pile s pilegroup s pile s sK K K b sK K K (A 50) groups pile r rectangula or piles square for 4 180 97 0 86 0 arrays group pile or piles circular for 14 group) pile or pile ( sK (A 51) 6 01 1 1 3 4 1pi p pi spw s W w K (A 52)

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325 5 1 5 & 5 19 1 5 & 5 96 0 045 0 m m m Km (A 53) m sp p s m sp p s m sp p sK K W K y K K W K K K W K y y y 2 2 2 ,0 0 0 (max) 0 (A 54) 1 0 for 1 0 0 for 0 1 0 for 8 0 tanh 5 1(max) 0 (max) 0 (max) 0 (max) 0y H y H y H y H Kpg pg pg pg h (A 55) 2.2.2.4 FDOTBSM: b rief d iscussion As briefly mentioned in Section A.3.1, these series of equations have been the most successful equations for predicting bridge pier scour depths (Figure A 13). The equations in the FDOT Bridge Scour Manual are considered by many to be the state of the art with the regard to predicting scour depths for a cohesionless sediment. They appear to provide excellent results, and although t hey are somewhat complicated, they are easily programmed into a computerized algorithm. Unfortunately, the FDOTBSM does not give any recommendations as far as designing scour depths for rock or cohesive sediments. Therefore, an engineer has no choice but to refer back to HEC 18 for guidelines when designing for these bed materials.

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326 Table A 1. Values for k1 V*/ k 1 Mode of Bed Transport <0.50 0.59 Mostly contact bed material discharge 0.50 to 2.0 0.64 Some suspended bed material discharge >2.0 0.69 Mostly suspended bed material discharge Adapted from Richardson, E. V. and Davis, S. R. (2001). Evaluating scour at bridges fourth edition. Publication No. FHWA NHI 01001 hydraulic engineering circular no. 18. http://isddc.dot.gov/OLPFiles/FHWA/010590.pdf, Sept. 2, 2008. Table A 2. Pier Nose Shape Correction Factors Correction Factor K 1 For Pier Nose Shape Shape of Pier Nose K 1 (a) Square nose 1.1 (b) Round nose 1.0 (c) Circular nose 1.0 (d) Group of cylinders 1.0 (e) Sharp nose 0.9 Adapted from Richardson, E. V. and Davis, S. R. (2001). Evaluating scour at bridges fourth edition. Publication No. FHWA NHI 01001 hydraulic engineering circular no. 18. http://isddc.dot.gov/OLPFiles/FHWA/010590.pdf, Sept. 2, 2008.

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327 Table A 3. Bed Condition Correction Factors Increase in Equilibrium Pier Scour Depths, K 3 for Bed Condition Bed Condition Dune Height (m) K 3 Clear water scour N/A 1.1 Plane bed and antidune flow N/A 1.1 Small dunes 3 > H 0.6 1.1 Medium dunes 9 > H 3 1.2 to 1.1 Large dunes H 9 1.3 Adapted from Richardson, E. V. and Davis, S. R. (2001). Evaluating scour at bridges fourth edition. Publication No. FHWA NHI 01001 hydraulic engineering circular no. 18. http://isddc.dot.gov/OLPFiles/FHWA/010590.pdf, Sept. 2, 2008. Figure A 1. Shields Diagram [Adapted from Lemke, K. A. (2010). Stream sediment. University of WisconsinStevens Point, http://www.uwsp.edu/geo/faculty/lemke/ geomorphology/lecture_outlines/03_stream_sediment.html, Sept. 14, 2010.]

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328 Figure A 2. Fall Velocity vs. Grain Size [Adapted from Richardson, E. V. and Davis, S. R. (2001). Evaluating scour at bridges fourth edition. Publication No. FHWA NHI 01001 hydraulic engineering circular no. 18. http://isddc.dot.gov/OLPFiles/ FHWA/010590.pdf, Sept. 2, 2008.] Figure A 3. Common Pier Shapes [Adapted from Richardson, E. V. and Davis, S. R. (2001). Evaluating scour at bridges fourth edition. Publication No. FHWA NHI 01001 hydraulic engineering circular no. 18. http://isddc.dot.gov/OLPFiles/ FHWA/010590.pdf, Sept. 2, 2008.]

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329 Figure A 4. Diagram used to determine Khpier [Adapted from Richardson, E. V. and Davis, S. R. (2001). Evaluating scour at bridges fourth edition. Publication No. FHWA NHI 01 001 hydraulic engineering circular no. 18. http://isddc.dot.gov/OLPFiles/ FHWA/010590.pdf, Sept. 2, 2008.] Figure A 5. Diagram used to determine a* pc [Adapted from Richardson, E. V. and Davis, S. R. (2001). Evaluating scour at bridges fourth edition. Publication No. FHWA NHI 01001 hydraulic engineering circular no. 18. http://isddc.dot.gov/OLPFiles/ FHWA/010590.pdf, Sept. 2, 2008.]

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330 Figure A 6. Illustration sketch for computing scour when a pile cap is above the bed [Adapted from Richardson, E. V. and Davis, S. R. (2001). Evaluating scour at bridges fourth edition. Publication No. FHWA NHI 01 001 hydraulic engineering circular no. 18. http://isddc.dot.gov/OLPFiles/ FHWA/010590.pdf, Sept. 2, 2008.] Figure A 7. Diagram for computation of correction factor, Km [Adapted from Richardson, E. V. and Davis, S. R. (2001). Evaluating scour at bridges fourth edition. Publication No. FHWA NHI 01001 hydraulic engineering circular no. 18. http://isddc.dot.gov/OLPFiles/ FHWA/010590.pdf, Sept. 2, 2008.]

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331 Figure A 8. Diagrams used to illustrate computation of equivalent pier width [Adapted from Richardson, E. V. and Davis, S. R. (2001). Evaluating scour at bridges fourth edition. Publication No. FHWA NHI 01001 hydraulic engineering circular no. 18. http://isddc.dot.gov/OLPFiles/ FHWA/010590.pdf, Sept. 2, 2008.] Figure A 9. Diagram used to compute the spacing coefficient, Ksp [Adapted from Richardson, E. V. and Davis, S. R. (2001). Evaluating scour at bridges fourth edition. Publication No. FHWA NHI 01001 hydraulic engineering circular no. 18. http://isddc.dot.gov/OLPFiles/ FHWA/010590.pdf, Sept. 2, 2008.]

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332 Figure A 10. Diagram used to compute the pile group height adjustment factor, Khpg [Adapted from Richardson, E. V. and Davis, S. R. (2001). Evaluating scour at bridges fourth edition. Publication No. FHWA NHI 01 001 hydraulic engineering circular no. 18. http://is ddc.dot.gov/OLPFiles/ FHWA/010590.pdf, Sept. 2, 2008.] Figure A 11. Effective Diameter for Different Pier Shapes [Adapted from Richardson, E. V. and Davis, S. R. (2001). Evaluating scour at bridges fourth edition. Publication No. FHWA NHI 01 001 hydr aulic engineering circular no. 18. http://isddc.dot.gov /OLPFiles/ FHWA/010590.pdf, Sept. 2, 2008.]

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333 Figure A 12. Definition Sketch for Scour Around a Complex Pier [Adapted from Richardson, E. V. and Davis, S. R. (2001). Evaluating scour at bridges fourth edition. Publication No. FHWA NHI 01001 hydraulic engineering circular no. 18. http://isddc.dot.gov /OLPFiles/ FHWA/010590.pdf, Sept. 2, 2008.] Figure A 13. Measured vs. computed scour depths [Adapted from Florida department of transportation br idge scour manual (2005). , Sept. 2, 2008.]

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334 APPENDIX B FLORIDA METHOD FOR S ERF TESTS B.1 New Operating Procedure for SERF Because of the overhaul of the SERF a new operating procedure for tests needed to be developed. When conduc ting a test there are three possible things that can be tested, and each of these three tests has its own procedure: Shear stress for a specified sample Critical shea r stress for a specified sample Erosio n rate for a specified sample Please note that although it is possible to measure shear stresses and erosion rates at the same time (the computer code has been developed), it may be inaccurate. Shear stresses should only be measu red concurrently with erosion rates when a flat disk is installed in the shear stress sensor or when a sample is smooth enough to be considered flat. Generally, as per the discussion in Chapter 4, if median size of eroding elements is expected to be gre ater than 0.5 mm, an eroding section is not considered flat and it probably will affect the velocity profile downstream from it. Because of the positioning of the shear sensor, this disturbance in velocity profile will probably affect the erosion rate of the sample. If/when it becomes possible to measure velocity profiles in the SERF (Chapter 8), it may be possible to justify measuring shear stress and erosion rates at the same time, but until that happens, the eroding material should not be considered to be flat. Procedure for the testing erosion rate includes a section on concurrently measuring shear stress, but keep this disclaimer in mind when running future tests B.2 Preliminary Procedure for Tests Before any of the three categories of tests a re set up, a few preliminary steps need to be completed. First, the SERF operator needs to decide what condit ions are going to be run. Because of sand recirculating issue s, the sand injector has not been installed. When installed, the

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335 sand injector will warrant its own set up procedure, but currently the only decision for an operator is whether or not to install the vortex generator. B.2.1 Vortex Generator To install/remove the vortex generator, the following procedure needs to be conducted: 1. If vortex bar is already installed and it needs to be removed, push the bar through the flume and install the vortex generator plugs. 2. If the vortex bar needs to be installed, remove the vortex generator plugs from the SERF, and slide the bar into the flume. Make sure the O rings remain on (they prevent leakage). B.2.2 Temperature Control and Filtering To insure temperature control during tests the temperature control filter system needs to be turned on, but first it needs to be primed. To do this: 1. Open the ai r release valve on the filter (Figure B 1). 2. Open the PVC release valve in the filter chiller system (Figure B 2). 3. Push the plunger on the PVC slide valve down (Figure B 3). This causes the filter to run in reverse and fills it with water. 4. Turn on t he pool pump using the white switch ( circled in Figure B 4 ). When water begins to shoot out of the air release valve, close the air release valve. 5. Turn the pool pump off. 6. Pull the slide valve up to the filter position (Figure B 5). Turn to lock it into place. 7. Turn the pool pump back on. The filter is now primed and running. Pressure in the filter should be approximately 25 psi. If pressure is much higher than 25 psi, the filter needs to be backwashed. To backwash the filter, fill the reservoir tank with water and run the filter in reverse until approximately 700 gallons of water remain in the reservoir tank. Make sure the PVC valve in (2) is open. If it remains closed, a pipe will burst because pressure inside the filter chiller system will build. This may irreparably damage the water chiller. Once drainage water is clear, the filter has been properly backwashed. Then, refill the reservoir tank and resume with instructions below. 8. Turn on the water chiller (Figure B 6) Pressure in the system should drop to approximately 20 psi. Now, water is being cooled and filtered (somewhat as discussed in Chapter 7 and Chapter 8).

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336 9. Turn the thermostat on the back side of the water chiller to the desired temperature (Figure B 7). 10. The temperature control filter system is now working. Leave this system on during tests B.3 Stand Alone Shear Stress Test As discussed in Chapter 4, during a shear stress test the goal is to find how the roughness of a soil or rock sample affects t he wall shear stresses along its face. Please note that a shear stress test on an existing sample will disturb it; this is a disadvantage to the testing method, but at present, there is no way to accurately measure shear stress and avoid this. If multipl e Shelby tubes are taken, it may be advantageous to dry one, sieve it, mix thoroughly (assuming uniformity) and use the procedures for conducting a synthetic test If gradation is present, each graded section could be dried and mixed separately to produce multiple test discs using the synthetic method. T hese procedures assume a uniform sample. The procedure for a shear stress test is as follows: 1. Prepare the test disk. Because the shear sensor has a removable circu lar disk (Figure B 8 ), this disk can be replaced with a disk of the appropriate roughness for use in shear stress testing. a. For a synthetic sample, disk preparation is easy. i. A uniformly, evenly mixed batch of aggregate used to make the sample synthetic sample should be set aside. ii. A 50 mm a crylic disk should be coated with epoxy. Experience has shown that JB Weld twopar t epoxy (Figure B 9 ) works the most effectively Be sure to spread the epoxy evenly and level. iii. Press the epoxy coated disk onto a random portion of the aggregate so that the disk coats with sediment particles. iv. Multiple disks should be prepared to ensure that a true random sampling form the aggregate is achieved (Figure 3 10) b. For a non synthetic nonrock sample, disk preparation is similar i. A 50 mm acrylic disk should be coated with epoxy.

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337 ii. Press the epoxy coated disk onto the top of the Shelby tube so that aggregate from the Shelby tube sample coats the disk. iii. If multiple Shelby tubes are collected, multiple disks should be prepared. c. For a rock core, disk preparation is the most complicated. i. A sliver of the top of the rock core needs to be cut from it. The sliver must remain intact and it must be no more than 1cm thick. The sliver must be flat on both its top and the bottom. ii. The rock core sliver needs to be car efully epoxied to a 50mm plastic disk. iii. A hole needs to be carefully drilled through its center, and the entire rock core sliver disc apparatus needs to be milled to 50 mm in diameter. 2. Open the shear stress sen sors access hatch (Figure B 11 ) and remove the existing plastic disc. Attach the newly prepared disk using the screw through the center of the disc (Figure B 12 ). 3. Close the SERF access hatch by using an Allen wrench to tighten the 8 hex screws on top of the flume Make sure the black rubber gasket is between the access hatch cap and the top of the flume. DO NOT OVER TIGHTEN. The screw threads that attach the access hatch to the flume are drilled directly into the aluminum and if they are over tightened, they w ill strip. 4. Check the shear sensor access window for pollution. Often, particles get stuck in the wet section of the shear sensor, and they can block the space between the Servo magnet and the Hall Sensor. 5. If the wet section of the shear sensor is poll uted, remove the Plexiglass window and clean it. If not, proceed to step 6. 6. Calibrate the shear stress sensor. a. Zero the sensor using the knobs (1) and (2) as shown in Figure B 13. The coarse adjustment knob is knob (2) and the find adjustment knob is knob (1). When the sensor is properly zeroed, the ammeter on the front of the amplifier should read 0. b. Press and hold the CAL button on the shear stress sensor (4). This sends a voltage to the calibration solenoid based on the position of knob (5), whi ch causes the deflection solenoid to react. Turn knob (3) such that the ammeter on the amplifier deflects to the corresponding correct stress reading. For example, if knob (5) is turned to 50, the corresponding stress read on the ammeter should be 50 Pa. Release the CAL button (4). Repeat for several values of (5) to make sure the sensor is working. Note that the shear sensor runs at 4 different ranges: 10 Pa, 20 Pa, 50 Pa, and 100 Pa. To set the range of the sensor, turn knob (6). Keep in mind that if the range is set to anything less than 100 Pa, a calibration voltage of

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338 100 Pa will rail the instrument. Likewise, if the range is 20 Pa a calibration voltage of 50 Pa will do the same thing. When the range goes below 100 Pa, the scale on the ammeter adjusts accordingly. Therefore, a calibration voltage of 50 Pa when the range is on 50 P a will still deflect the ammeter to full deflection (this is correct and it is how the instrument was designed). 7. If the shear sensor does not calibrate, there must b e something incorrect with the mechanical setup of the sensor. Mechanical issue s with the shear sensor are common, and they must be addressed before a shear test can commence. a. The most common mechanical issue with the shear sensor is pollution (see step 5). First, recheck to make sure the gap between the Servo magnet and the Hall Sensor is present. If not, re clean and recalibrate. b. If the gap between magnet and sensor is present, the next most common mechanical issue is the connection between the def lection magnet and the platform. Open the round upstream access hatch on the left side of the sensor as the sensor is observed from the middle of Reed Lab (Figure B 14 ) c. When looking into the round hole in the sensor, there should be a small gap between t he magnet battery and the solenoid. When the connection slips, an illus tration as shown in Figure B 15 is what will be seen instead. d. If it looks as though the magnet battery is rubbing against the deflection solenoid as shown in this figure, loosen the screw shown in Figure B 16 and reposition the brass rod so that there is an air gap present between the deflection solenoid and the magnet battery. Try to recalibrate the sensor. If it still does not work, remove the brass rod and magnet battery complet ely and check to make sure the connection between the magnet and the brass rod is not broken (it often breaks). Reattach using a twopart plastic super glue. DO NOT USE METAL EPOXY SUCH AS JB WELD. IT WILL CAUSE AN ERROR IN THE SOLENOID BECAUSE JB WELD IS MAGNETIC. e. If the sensor will still not calibrate, repeat steps i. v. with the calibration solenoid (although this breaks much less often). f. This should fix the sensor. The only other possible explanation could be that the leaf springs have become permanently deformed and they need to be replaced (contact Raf Crowley for procedure on how to do that) or water has leaked into the dry portion of the sensor. g. If leakage is suspected at any time when using the shear sensor, open the panel exposing the electronics (Figure B 17 ), UNPLUG THE SENSOR, and assess the damage. The sensor may need to be resealed and/or ICs may need to be replaced. Usually though, if the sensor is given time to dry out, it will begin working again.

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339 h. If these troubleshooting proc edures do not work, contact Raf Crowley ( raf.crowley@gmail.com ) or Hans Prechtel ( elekt@aon .at ). If these procedures have made the sensor work properly, the SERF is now ready for shear stress testing. 8. Turn on the computer in the SERF control room and st art the vi labeled Flume Control No Motor. The shortcut for this vi is located on the desktop in a folder called Flume Shortcuts If the correct vi has been opened, its front pane l should look like Figure B 18. 9. Make sure that the data range in the Labview program matches the data range on the shear stress sensor amplifier (Figure B 19 ) 10. Turn the pump on and make sure that the digital display reads remote. Press forward on the digital pump readout (this initializes the pump). 11. Press start in Labvi ew, and the Labview program should ask where to save the data file. Files are saved as .dat files to allow for easy Matlab data analysis, but this can be changed to .xls format if the operator feels more comfortable using Excel. Directions on how to do t his are in Appendix C. 12. Press record on the DVR. 13. Adjust the pump speed to approximately 20 Hz. This will start a flow of water through the flume and pressurize it. Anything less than 20 Hz is not high enough to overcome the head differential between t he flume and the reservoir tank. Allow the flume to run for a few seconds until bubbles in the flume are eliminated. 14. If pressure transducers are to be used during the shear test next they need to be bled. To bleed a pressure transducer, unplug both tubes from the flume to the transducer. Make sure to unplug the downstream tube first. The transducers are designed to tolerate over pressurization on the positive side only. Because the flume should be running at approximately 20 Hz, when the tubes are unplugged, water should be going into them. Lightly tap on the tubes to eliminate bubbles from them. When bubbles have been eliminated, plug the tubes back into the sensor. Plug the upstream side in first this time, then the downstream side. 15. While bl eeding the sensors, real time graphs should have begun to be generated in the Labview program. As far as data collection goes, these graphs are meaningless. They were included in the program for the user to get a broad idea of shear stress/pressure condi tions, but they are sampled at a much lower frequency than data collection. 16. Now, everything is ready for data collection. Bring the pump to the desired flow speed. Give the system a few seconds to stabilize. Stabilization is achieved when the shear se nsor begins to display a somewhat consistent shear stress from the graphs. 17. When conditions have stabilized, press the RUN button in Labview. This will stop the generation of the graphs (but this is OK) for a length of time that is dependent on the Sam pling Rate Parameter (default is 1 kHz) and the Number of Samples Parameter

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340 (default is 10,000). Both these values can be changed as the Labview program runs to allow for different sampling frequencies and different sampling lengths. A long sampling leng th though means lots of computer memory. Anything over 15,000 data points should not be used because the computer does not have enough RAM to support this (the computer only has 3.5 GB). 18. When the graphs start moving again, data collection has been completed. Bring the pump to the next desired flow rate, wait for it to stabilize, hit the run button to collect data, repeat. 19. Repeat for flow rates that are to be investigated. 20. When testing is done, gradually bring the pump speed down to zero. For example, if a pump frequency of 30 Hz was used for the test bring the pump down to 25 Hz, let it run for 5 seconds, then bring it to 20 Hz, let it run for 5 seconds, etc. the way to 0 Hz pump fre quency. This gradual velocity reduction allows the shear sensor to catch up with changing flow conditions and can help prevent breakage to the instrument. 21. The test may now be repeated, or a new sample disk may be installed. It is recommended to repeat each test at least three times and to recalibrate the shear sensor after every test Before removing the access hatch to install a new sample, make sure the valves on the pressure transducer are closed. When the access hatch is removed, water rushes fr om the flume into the reservoir because of the pressure differential created by exposing the flume to air. If the pressure transducer valves are open, there is a significant risk of breaking one or both of the transducers. 22. Go to the saved .dat files and perform data analysis. The most effective method for data analysis is Matlab, and there are several Matlab algorithms written in the G: \ analysis folder that average samples over the entire sampling domain, perform spectral analyses, plot graphs of the re sults, etc. Excel can be used, but every data run has 10,000+ data points per flow rate and an Excel analysis will be arduous. SERF operators are encouraged to learn Matlab. B.4 Critical Shear Stress Test in SERF Shear stress tests should be conducted first on any sample batch so that a correlation can be developed between flow rate and shear stress. Once a shear stress analysis has been completed, a critical shear stress test may be performed: 1. Make sure that a flat disk has been installed in the shear stress sensor. Alternatively, the entire shear sensor may be removed and the shear sensor plug (Figure B 20 ) can be installed in its place. 2. To remove the shear sensor, loosen ONLY the hex screw on the sensor fla nge (Figure B 20).

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341 3. Once the hex screw loosened, carefully pull and twist the sensor to release the shear sensor from its blue Loctite bond. 4. Co at the sensor plug (Figure B 21) with blue Loctite, and slide it into the sensor hole. Make sure the plug is level with the flume bottom. 5. Retighten the hex screw. 6. If the sensor plug is not installed, the shear sensor still needs to be turned on during shear stress tests The magnets in the sensor help to prevent the sensor disc from moving, and turning the sensor on will help to prevent damage to the sensor and help to prevent flow disturbances in the SERF. 7. Load the test section of rock core, Shelby tube, or synthetic material into the pistoncylinder apparatus (Figure B 22 ). Grease the piston cylinders O rings with a silicon grease. 8. Attach the piston to the Servo motors feedscrew using a screw. 9. Raise the Servo motor (which is now attached to the piston and the cylinder) into the SERFs sample hole. Make sure to put an O ring around the top of the cylinder to prevent leakage. If using an opaque piston, also make sure that the ridges on the top of the cylinder provide a clear line of si ght for the lasers (Figure B 23 ). 10. Attach the four threaded rods around the cylinder to the bottom of the SERF. 11. Tighten the compression coupling around the bottom of the test cylinder through the threaded rods. DO NOT OVER TIGHTEN AS IT WILL CAUSE THE TEST CYLINDER TO CRACK. If leaks develop, the compression screws can be retightened. When installed, the apparat us should look like Figure B 24. 12. If lasers are to be used, turn on the lasers by clicking the on button on the surge protector shown in Figure B 25. 13. Open the program labeled Motor Mover. The front panel is shown in Figure B 26. 14. Flip the input switch on the VGA BNC adap ter so that the right monitor of the control program is looking into the flume. Tune to channel 2 on the DVR to see the eroding test section. 15. Turn on the motor by flipping the switch on the black box below the laser amplifiers and start the motor mover program. Click start on the Motor Mover program. 16. Move the motor up in small increments until the control boxed from the lasers indicate that they are blocked. The control boxes will click when they are blocked and the lights on the amplifiers will sw itch off. A small increment is approximately 500 steps in the motor mover program. DO NOT MOVE THE MOTOR MORE THAN 5,000 STEPS AT A TIME. It will cause the motors gears to grind and may cause the motor to break.

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342 17. Once the control boxes have clicked and the lasers indicate that the sample is level with the flume bottom, verify that the flume is level visually by looking at the right monitor on the SERF computer. If the sample is level, proceed. If not, re level. 18. Open the program labeled Pump Control from the Flume Control Shortcuts folder on the desktop Press the start button. The front panel is shown in Figure B 27. 19. Click Start the pump at approximately 20 Hz to pressurize the flume. If dealing with a highly erodible material, this can be rather tricky. The flume will need to be pressurized, then the motor will need to re level the sample using the same procedure outlined in (11). 20. Bring the pump down to a slow flow speed ( approximately 5 Hz). 21. Press record on the DVR. 22. Slowly incre ase the pump speed in small increments ( approximately 0.25 Hz) and watch the right monitor. Look for any particle motion along the bed. 23. When incipient motion is detected, stop the pump and record the flow rate. The critical velocity for the material ju st tested in now known. 24. Use correlations developed in shear stress testing to convert critical flume velocity to critical flume shear stress. Now, the critical shear stress for the material is known and the critical shear stress test has been completed. 25. Repeat the criti cal shear stress test at least three times for each material. B.5 Erosion Rate Test The erosion rate test is the most complicated test that can be conducted in the SERF. To reiterate, until velocity profiles can be measured in the flu me shear stress tests and erosion rate tests should be conducted independently. Once velocity profiles can be measured, it may be possible to justify running both sets of tests concurrently, but until then, it is believed that placing a rough sample upst ream from an eroding sample will affect the hydrodynamics downstream from the rough sample and alter the measured erosion rate of the bed material. To conduct the erosion rate test : 1. Load a sample into the pistoncylinder using the same steps for a critical shear test 2. Install the flat disc into the shear stress sensor, or install the shear sensor plug. If the sensor is to remain in the flume, follow plug installation steps from the critical shear stress test

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343 3. Turn on the motor. 4. Turn on the lasers and level the sample using the same techniques used for an erosion rate test Note that by default the lasers use and logic to advance the sample. If an operator feels that or logic is more appropriate, refer to Appendix C for guidelines on how to chan ge this. 5. Open the pump control program. 6. Fill and pressurize the flume. If pressure drops are to be measured, bleed the pressure transducers using the same method used for a critical shear stress test 7. Stop the pump. If shear stresses are to be measured, calibrate the shear sensor using the same method used for a shear stress test 8. Turn the pump on a low flow rate (~5 Hz). This will help to push bubbles away from the SEATEK during SEATEK calibration. If the SEATEK is not to be used, skip steps 915 and go straight to step 16. 9. Open TeraTerminal. TeraTerminal is sort of like a Windows 7 version of HyperTerminal (which is what Slagle and Trammel used to control the SEATEK when it was first turned on). The TeraTerminal shortcut is on the Windows 7 control bar at the bottom of the screen, next to the Lab view 2009 shortcut (Figure B 28). 10. In TeraTerminal, click the option to read from the Serial Port. 11. Turn on the SEATEK. In TeraTerminal, output should read MTAS V3.12S Input Command [? For help] 12. Pressing ? will give a list of SEATEK commands. The important parameters to change in the SEATEK are temperature (TE), maximum transducer depth (MA), threshold voltage (V), and number of sample runs (N). 13. First, change the temperature to the correct temperature. For example, to change the temperature to 21oC, type TE 21 and Enter. 14. Next, press P to ping the SEATEK sensors. If the depth readings that are returned are around 4.92 cm, the SEATEK is working properly. If not, play with the MA a nd V commands until the proper depths are being returned. There are 12 crystals in the SEATEK array 8 within the samples area and 4 along its outside. The crystals that are most important are the first 8 as they measure the sample position. The out side 4 crystals measure the depth to the solid flume bottom (4.92 cm). Please note that when this project began, crystal #2, crystal #5, crystal #6, and crystal #10 were damaged; as of 2007, they were not working properly (this will be addressed in Labvie w). 15. Keep pinging the crystals after each MA and V command to determine if they are working properly.

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344 16. Once it the crystals are working properly, set up a data run to confirm that the crystals can read multiple resu lts correctly. Take at least twenty readings by executing the following commands: a. N 20; Enter b. D; Enter 17. Exit TeraTerminal. The serial port on any computer can only communicate with one program at a time, and if TeraTerminal is left open, Labview will not read from the Serial port beca use TeraTerminal already is doing so. 18. Open the Labview vi entitled Flume Control With Motor (With Temperature Patch). Its front panel should look like Figure B 29. 19. If the SEATEK is not to be used, turn it off using the switch on the control panel. 20. If the lasers are not to be used, turn them off using the switch on the control panel. 21. Assuming the SEATEK is to be used, from the TeraTerminal procedures, turn off the crystals that are not working properly by clicking on the green LED lights on the La bview front panel. Note that crystals 8 12 are not listed. The malfunctioning crystal, crystal #10 is automatically eliminated from consideration during data processing (Appendix C). 22. Make sure the motor is turned on in the front panel. 23. Press start on the Labview program. Two output files will be generated one for analog signals (shear stress, pressure, temperature, flume speed, time) and the other for digital data (time, number of steps, motor position). Save these files in the appropriate direc tory. 24. Move the pump control to the desired flow speed. 25. Press record on the DVR. 26. The test should now be running. Keep an eye on it for approximately 20 minutes to make sure everything is working properly. If a crystal begins to malfunction, it can be turned off in the middle of a data run. If the SEATEK overall or the lasers begin to malfunction or give nonsensical results (for example, the SEATEK when some clays are used), they can be turned off as well. 27. If two pumps are to be used during the tes t turn on the 2nd Pump switch. This will insure that the correct velocities are recorded in the analog output file. 28. If analog signals are to be recorded (shear stress and pressure drop), the program will run much like it did for shear stress tests Water will flow through the flume, graphs will generate on the front panel, and every specified time step (the default is 10 minutes, but this can be changed see Appendix C), the graphs will freeze and analog output files will be written at the specified frequency.

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345 29. If analog signals are not to be recorded, skip step 29. Motor position will be recorded every time the program goes through its loop (see Appendix C) in the digital output file. 30. Allow the SERF to run for the specified length of time. It s hould be monitored during operation to insure that nothing malfunctions, but once it has been set up, it should work properly. B.6 Remote SERF Operation In any SERF test it is now possible to control the flume remotely if required hard switches have bee n turned on. This is especially useful in monitoring erosionrate tests because erosion rate tests often need to run for 24 hours or more. Tests do not need to be constantly monitored, but they do require someone to look at them from time to time to make sure they are running correctly. Remote operation also useful for changing flow parameters during a test If one of these scheduled modifications needs to be made late at night or early in the morning, remote operation is convenient. The worst thing that can happen during a SERF test is the motor can malfunction due to bad readings from the SEATEK or lasers. Sometimes, the motor may retract such that the piston pulls out of the test cylinder. If this happens, the lab can flood. Other times, the mot or may advance uncontrollably and cause the sample to be pushed up to the top of the flume. If either of these things happens, the test needs to be stopped, the source of error needs to be assessed and addressed so that nothing malfunctions again. Softwa re limit switches have been installed in MAX (Measurement and Automation Explorer), and the lab has yet to flood, but there is no hard wired method installed to prevent uncontrolled advancement/retraction. Therefore, operators need to be conscious of this risk. Although most bugs from operation have been worked out, operators are still encouraged to monitor testing as much as possible.

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346 B.6.1 Remote Visual Inspection The easiest way to monitor a SERF test is visually. Two cameras look at the important parts of the flume (the eroding test section and the shear stress sensor), and visual data from these cameras is useful at determining whether or not a test is running correctly. A third camera is set up to monitor the entire lab from above so that a remot e operator can have even more information with how a test is going in the device Visual inspection is conducted by using the DVR because the DVR has networking capabilities. To see what is being recorded in the DVR and to control the DVR remotely, the fo llowing procedure should be followed: 1. Initiate a Virtual Private Network (VPN) connection with the UF network. The DVR and SERF can only be accessed through a VPN because when remote operation was set up, there were concerns about providing security for it. The VPN allows for security options that are not possible through a normal network connection. To set up a VPN connection for the first time, follow the UF guidelines for VPN. They can be accessed at http://netservices.ufl.edu/provided_services/vpn/vpninstall.html. 2. Open Microsoft Int ernet Explorer. Remote operation of the DVR will not work with Mozilla Firefox. This technique has not been tested with Apple Safari or Google Chrome, but it may work. 3. In the address bar of Internet Explorer, type the static IP address for the DVR ht tp://10.5.72.138. Press Enter. 4. A popup will appear that asks for a username and password. The username is admin and the password is also admin. 5. A window should appear that looks like Figure B 30. If this window is generated, remote operation has been initialized properly and the DVR can be viewed and operated from Internet Explorer. B.6.2 Remote Labview Operation If after visual inspection, an operator realizes that a test needs to be shut down or if an operator wishes to monitor data collectio n and Labview operation remotely, the SERF computer

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347 has also been configured to do this. The following procedure is used for remote control of the computer: 1. Initialize a VPN connection to the UF network. 2. Open Windows Remote Desktop Software. This capability is standard in Windows XP, Windows Vista, and Windows 7. Once open, the remote desktop software looks like Figure B 31. 3. Go back to the remote desktop connection software. Next to Computer, type the name of the computer. The computers name is KEYWEST. 4. Press connect. 5. The computer will ask for a username and password. Type the correct username and password for the computer. 6. Remote desktop connection is now complete. The computer is now being controlled remotely. While remote control is taking place, the computer will lock itself. B.7 SERF Operation Troubleshooting Although most bugs have been worked out, there are some common errors associated with SERF operation. Problems with the shear stress sensor are the most common and they have already been discussed. The next largest issue with operation is the development of leaks around the test cylinder. Leaks tend to emanate from two sources from the piston and from the cylinder flume connection. Fixing the latter is easy. If water is leaking from the cylinder flume connection, the compression screws on the bottom of the cylinder need to be tightened until the leak stops. Fixing leaks around the piston are much more difficult. Piston leaks develop because the pistons O rings were no t properly cleaned when the sample was loaded. Often, pieces of debris, sediment, or grit will get stuck in between the space between the O ring and the inner cylinder wall. Eventually, a leak will develop and it needs to be addressed. Unfortunately, the only way to address these leaks is to stop the test remove the cylinder, remove the piston,

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348 remove the O rings, clean the O rings, re grease them, and start the test again. As seen then, it is vitally important to make sure that O rings are properly cl eaned before initializing a test Leaks due to motor retraction were already discussed in the erosion rate test section of this appendix. The most effective method for preventing these leaks is to monitor the test as much as possible (remote access makes this easier). Visual data analysis, even from afar, will show if the gears are grinding. If SEATEK data is coming back badly or if a many negative steps are being recorded in the Labview program, a remote operator can be sure that gears are grinding. Visual inspection too will show that the sample is not level with the flume bottom. This can only mean that the piston/sample is too low and that the motor has retracted too much. Other problems in the SERF are mu ch less common, and they will not be discussed further. If a problem comes up that a novice operator cannot solve, Raf Crowley should be contacted ( raf.crowley@gmail.com ) so that he can help to troubleshoot

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349 Figure B 1. Air Release Valve on Sand Fil ter Figure B 2. PVC release valve for Chiller Filter System

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350 Figure B 3. Slide Valve in the Down Position Figure B 4. Pool Pump Switch

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351 Figure B 5. PVC Slide Valve in the Up Position Figure B 6. On Switch for Water Chiller

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352 Figure B 7. Position of Thermostat on Water Chiller Figure B 8. Example of Removable Test Disc (Flat Disc Shown)

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353 Figure B 9. JB Weld Epoxy in its Package Figure B 10. Three Newly Prepared Test Discs (Three Different Aggregate Distributions Shown)

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354 Figure B 11. Shear Stress Sensor Access Hatch Figure B 12. Attachment of Newly Prepared Disk to Shear Sensor (Flat Disc Shown)

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355 Figure B 13. Knobs on SS Sensor Amplifier Figure B 14. Round Access Hatch on Shear Sensor ( 1 ) (2) (3) (5) (6) (4)

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356 Figure B 15. Schematic of a Slipped Brass Rod Connection Figure B 16. Screw holding brass rod to platform. Magnet Battery Deflection Solenoid

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357 Figure B 17. Exposed Elec tronics in Dry Portion of Sensor Figure B 18. Shear Stress Test Front Panel in Labview

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358 Figure B 19. Data Range on the Shear Stress Amplifier Figure B 20. Hex Screw to Loosen for Shear Sensor Removal

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359 Figure B 21. Shear Stress Sensor Plug Figure B 22. Piston Cylinder for SERF

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360 Figure B 23. Close up of Ridges on Top of Cylinder. Figure B 24. Piston Cylinder with Sample Installed (Note, Figure B 21 shows a PVC Cylinder while this Figure Shows a Clear Acrylic Cylinder).

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361 Figure B 25. Surge Protector to Turn on Lasers Figure B 26. Front Panel of Motor Mover Program

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362 Figure B 27. Front Panel of Pump Control Program Figure B 28. TeraTerminal Icon (Circled in Red)

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363 Figure B 29. Flume Control with Motor Front Panel Figure B 30. Remote D VR Panel

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364 Figure B 31. Windows Remote Desktop Software

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365 APPENDIX C SERF COMPUTER CONTRO L PROGRAMS C.1 Introduction Because of the enhancements, improvements, and additions to the SERF, most computer control programs for the device had to be rewritten. Some components from the original programs were still usable, but the overall structure of the programs changed. Before discussing in detail, s ome background should be given. P rograms are written in Labview; before modifying any of these programs, futu re operators should learn Labview. If changes or modifications are to be made to any of the programs, the programs should first be backed up so that the current working programs are not overwritten. SERF programs have gone through a three generation progression first with Trammel, then with Slagle, and finally Crowleys third generation programs. The basic purposes of two of these programs have not changed much over the years. Generally, a motor mover and a primary control exist. The motor mover has served as a stand alone program to move the stepper motor up and down when loading a sample into the device. Secondarily, the motor mover is used as a subvi component in the primary control program. The primary control program is to be used to run erosi onrate tests From Trammel to Slagle, the basic principle of the SERF control programs did not change, nor did their structure. The algorithm for the control programs was: 1. Read SEATEK signals 2. Read analog (pressure transducer and thermocouple) signals 3. M ove the motor 4. Write SEATEK and averaged analog data to a file In Trammels original program, SEATEK data, which takes a second or two to read, was recorded in the computer memory. Then, analog data, whose timing is based on sampling frequency and sampling time, was saved into the computer memory. Next, analog data was

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366 averaged over the sampling time domain, and an average analog signal was recorded along with a massaged SEATEK signal (Slagle and Trammel used different methods for this). At the end of a l oop through the program, a user was left with a depth reading, a sample position reading, an average pressure transducer reading, and an average temperature reading. The problem with this algorithm is that it is slow and CPU intensive. Allowing the comp uter to keep high amounts of analog and digital information in memory, perform computations on it, etc. caused a loop to take several seconds. When erosion rates were high this meant that by the time the motor should have moved again, its updated position was already out of date. The other issue was that at no point in the algorithm was a raw analog signal ever captured. Rather, it was averaged over the sampling time domain. This meant that any spectral analysis from an analog signal was impossible. S lagle recognized the issue s with the original SERF programming setup, and he tried to address them. Slagles solution was first to try to speed up Trammels original programs and to reduce their errors. Trammel originally eliminated zeros from the SEATEK number string (which are caused by false depth readings) by using an eliminate zeros from string command. Slagle changed this to an eliminate zeros from array command, which is faster. Slagle tried to improve the SEATEK depth average by programming a rather rudimentary module where individual crystals from the 8 crystal inner SEATEK array could be turned off. Slagle also added a module in his program that eliminated the maximum and minimum SEATEK reading from the depth average so that a more representative average depth was computed. During longer duration tests Slagle realized that erosion rates were slow. This gave Slagle the opportunity to add a second SEATEK average depth component to his program. Slagle differentiated between rock erosion programs and normal erosion programs via this

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367 additional SEATEK depth module. During a rock test the SEATEK took a secondary depth reading before the motor was allowed to move. If secondary SEATEK reading did not correspond to the first reading, he assumed that one of the readings must have been in error. The motor was only allowed to move when both back to back SEATEK readings indicated that movement was necessary. To speed up his programs further, Slagle realized that reading shear stress from a pressure difference was difficult. His other analog signal a temperature reading from a thermocouple was not a critical component in the overall system. Therefore, Slagle designed two programs called slim programs where analog signal capturing was eliminated. As a result of this, the SEATEK motor loop sped up a little bit more and the used a little bit less CPU power. When Slagle was done his modifications, he was left with five programs the motor mover, a rock program, a slim rock program, a normal program, and a slim normal program. Slagles programs were a welcome improvement to SERF control, but parts of them were still rather elementary, and although some timing elements were incorporated in them, there was more work that could be done. With the addition of the lasers and the computerization of the pump analog components that could not be ignored using a slim design the opportunity was perfect for a program overhaul. Because of the addition of the lasers, in the new computer progra ms, Slagles dual layer SEATEK control was eliminated. The lasers are a precise enough check for SEATEK data without the dual layered system. This eliminated the need for Slagles rock programs. To allow for slimmish designs, buttons and case structur es were added to the overall normal program so that different components could be turned on or off. Slagles slim concept was reversed such

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368 that a program was written to capture only analog signals; this program is to be used for shear stress tests An other program was written to execute pump control. Currently, there are four updated computer programs that can be used to control the SERF: Pump Control Motor Mover SERF Control No Motor SERF Control Full P rograms are found in the G: \ SERF_control_new directory on the SERF computer. Generally, programs begin with the three letter extension raf because they were written by Raf Crowley and starting programs with the same three letter extension was an easy way to differentiate them from other programs. A program in Labview is also generally called a vi (pronounced vee eye). In this discussion, the terms program and vi will be used interchangeably. C.2 Pump Control The pump control program is a brandnew SERF control program. Previously, pump c ontrols were coordinated with the digital display drive in the control room (Figure C 1). A logical step in SERF development was to computerize the primary SERF pump. Although when running on two pumps the SERFs second pump still needs to be run manuall y, computerizing the variable speed pump gives more computer control than before. When Trammel set up the SERF, he hardwired the pump so that it could be controlled by a computer. Over the years, this design characteristic was forgotten, and nobody connected the wires to the analog control box. To connect the pumps to the computer, a set of wires simply needed to be added from the pump control box to the computers SC 2345 analog reader/writer. One common improvement that was made to the Slagle/Tramme l generation of SERF programs was the incorporation of timing using flat sequence structures. This theme will be

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369 discussed throughout the SERF computer discussion. Slagle and Trammel used tickcount timers to coordinate analog vs. digital signal readings instead of flat sequence structures. The addition of the pump forced analog signals to be read and written at different times during each loop through the program. As such, flat sequence structures were appropriate. The pump control program is simply a two phase analog input/output program. During the first phase of the program, the correct pump voltage is written to the pump. During the second phase of the program, the return voltage from the pump is read which shows actual pump frequency. Additiona lly, an analog signal is read from the paddwheel flowmeter. Flow speed in recorded in m/s. The pump controls front panel is shown in Figure C 2 while the pump controls block diagram is shown in Figure C 3. C.2.1 Block Diagram Explanation Explanation of this program is fairly straight forward. First, the rate at which velocity and actual pump frequency are to be read is specified. Since this program is designed to read, then write, then repeat, there is no need to take more than one analog reading at a time. As such, the sampling rate does not matter; default is set to 100 Hz, although any number greater than or equal to 1 will work. Next, the pump output channel and velocity output channel from the SCC 2345 are specified. As wired, actual pump fre quency is read from \ SCC1Mod2/ai0 and the flowmeter velocity is read from SCC1Mod5/ai1. Next, the program moves into the first phase of the twophase flat sequence structure. During the first phase of the program, a check is run to see if the STOP button has been pressed; if it has, the True case sends a 10V signal to the pump. This stops water flow, and it also kills the program. If the False case is read, then the appropriate user specified input voltage is sent to the pump via the three DAQ mx commands. The DAQ mx sequence is as follows: 1. SC 2345 channel where data is to be written is specified.

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370 2. Voltage is output from SC 2345 to the pump. 3. DAQmx task is cleared. Voltage is calibrated such that the user inputs frequency in the front panel. If someone wanted to change this, it would be easy to change the calibration to allow for a velocity or volumetric flow rate instead. SERF operators were moving from a using the manual digital display control which only has a frequency output. Familiar ity with using frequency for the pump input is why another parameter was not used instead. During the second phase of the flat sequence structure, the computer is told which channels on the SC 2345 are to be read. One similarity between the current gen eration of SERF programs and the Slagle/Trammel programs is the use of a sub vi to initiate the DAQ mx data reading sequence ([a] in Figur e C 3). The block diagram for the pump subvi (raf_DAQ_pump_output.vi) is shown in Figure C 4. The structure of this subvi is the same as Trammel and Slagles. The algorithm is as follows: 1. Create the DAQ mx task 2. Specify the first channel to read. 3. Specify the second channel to read (more channels can be added if necessary). 4. Use a sample clock to input the correct sampling rate. Output from the subvi is sent back to the pump control block diagram. Then, a new DAQmx task is started to read everything from the channels that were specified in the sub vi. Finally, the signal is split between so that the velocity signal and the actual pump frequency signal are isolated from one another. Lastly, the signals are calibrated so that instead of a voltage output, a vel ocity or pump frequency output is given. The wait timer, [b] is a new addition to SERF control programs. Slagle and Trammel did not realize that this simple addition frees up comput er memory Generally, addition of a wait timer in a Labview program in between each loop is considered good programming practice.

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371 This component should not be removed from any of the new SERF computer programs. Once the while loop waits the specified number of seconds (10 ms in this case), the loop repeats. The block diagrams loop will continue to repeat until the user pushes the large stop button on the front panel. C.2.2 Front Panel Operation To operate the pump control, press the Labview start button on the top of the program. Then, press FWD on the digi tal pump display to initialize the pump. Failure to press FWD will not allow the pump to run, and this is a common error. Move the slider on the front panel to the desired pump frequency or alternatively input the pump frequency in the number box. The pump should start moving water through the flume and real time frequency and velocity graphs should be generated. The max flow rate button should not be changed from 1000 gpm unless the setting on the flowmeter is also changed. The max flow rate in this box corresponds to the data range coming from the flowmeter (Figure C 5). C.3 Motor Mover The motor mover is the one SERF component that has hardly changed over the years. In 2010, Labview components were updated from Labview 7.1 to Labview 2009. Some of the old motor mover commands have changed since the 7.1 version of Labview, but overall the program remains the same. The changes result from slightly different syntax requirements between 2009 Labview and Labview 7.1. A screenshot of the Motor Mover s f ront panel is shown in Figure C 6 while a screenshot of its block diagram is shown in Figure C 7. C.3.1 Block Diagram Discussion The algorithm for the motor mover is as follows: 1. Specify the movement axis. For the stepper motor, only one axis, Axis 1, moves.

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372 2. Specify the Board ID. Board ID corresponds to the slot in which the motion controller (a National Instruments PCI 7330 Card) is plugged in the control computer. With the current SERF setup, the PCI 7330 is plugged into Slot #1. 3. Specify the velocity, acceleration, deceleration, and jerk quantities. None of these should be changed. 4. Read the current motor position. The default is for the motor to start at 0 when the Motor Mover program is first run. If the user wants to reset its overall position, press the reset switch on the front panel and press start. 5. Read relative vs. absolute position. The current setting as shown is relative position. This should not be changed. 6. Set the target position. 7. Initiate movement. 8. Continually move t he motor and monitor movement for errors. 9. Stop movement when target position is reached. C.3.2 Front Panel Discussion First, input the target position in steps. One centimeter equals 7874 steps. Then, press start. The motor should move. Press the r eset switch and press start to move the computer position back to zero. The End Position Indicator should return the motors position every time the program is run. The program will stop automatically when the final position is reached. C.4 SERF Contro l No Motor As discussed in Appendix B, shear stress tests should be run independently from erosion rate tests until a velocity profiler can be installed in the SERF. The Full Version of the control program can be used to conduct a shear stress test by tur ning off several of the buttons, but another standalone program was written as well to be used for shear stress tests The standalong program is the same as the first few steps of the full version of the control program, but there is much less clutter o n its front panel thereby making it a little bit easier to operate. A

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373 screenshot of the front panel for SERF Control No Motor is shown in Figure C 8 while a screenshot of its block diagram is presented in Figure C 9. C.4.1 Block Diagram Discussion The b lock diagram for SERF Control No Motor is similar to the pump control program. Both vis are a two phase flat sequence structure. C.4.1.1 SC 2345 c hannels First, SC 2345 channels are specified. Figure C 10 zooms in on this portion of the algorithm. Fo llowing Figure C 10 is a list of the way everything is currently wired in the SC 2345. t hermocouple ( t emperature c hannel): SCC1Mod1/ai0 f lowmeter: SCC1Mod5/ai1 pressure t ransducer: SCC1Mod5/ai0 a ctual pump f requency ( pump box): SCC1Mod2/ai0 To explain the shear stress sensors wi ring, the sub vi in Figure C 10 needs to be explained. A screenshot of this subvi is shown in Figure C 11. On the front panel for the SERF Control No Motor Program, one of the inputs is for the shear stress sensors range. R ecall from Chapter 4 that range can be adjusted on the shear stress sensors amplifier so that more accurate readings can be obtained at lower shear stresses. Like the flowmeter with the pump control program, the range from the instrument must equal the r ange in the Labview program. Four wires come from the shear stress sensor; each wire corresponds to a different output range. The subvi shown in Figure C 11 takes the user input range and compares it with the possibilities from the from the shear stress sensor 10 Pa, 20 Pa, 50 Pa, and 100 Pa. Then, an array is built with three zero values. The last value in this array corresponds to an actual channel in the SC 2345 based on the results from the four case structures shown in the figure. The array max /min

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374 command returns the maximum value from this array so that the computer knows which channel is the correct channel from which to read during shear stress tests In addition to specifying which SC modules are to be read, the vi also asks where the use r wants to save data. In general programs were written so that .dat files were generated. This makes Matlab analysis the easiest. The .dat extension can be changed to .xls for an Excel file or .txt for a text file. C.4.1.2 Pump i nput The next step in this program uses the first part of the pump control program as a subvi. The user specifies a pump frequency, the pump program is run, and the pump begins. A screenshot of this subvi is shown in Figure C 12. C .4.1.3 Analog output The last step in the SERF Control No Motor is also the same as it is with the pump control program except that five analog signals are taken instead of two. The only thing that changes between these two programs is that the analog reader in the second half of the case struct ure now has five channels. In the base program, the analog signal is now split five ti mes instead of two (Figure C 13 and Figure C 14). The case structure surrounding the analog read portion of the base vi is to allow for manual data acquisition. Durin g a shear stress test a SERF operator specifies a flow rate, waits for flow to stabilize, and collects data. Then, he or she presses Run in the base vi and data is collected. The true case for the case structure corresponds to when the Run button is pressed while the false case corresponds to when no button is pressed. As the base vi runs, real time graphs are generated because of this truefalse sequence. During false execution, analog signals are sampled one time through the loop, and the results are plotted immediately. During true execution, raw data is sampled at the specified sampling

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375 rate according to the Samples Per Channel input parameter. Then, this data sequence is written to the appropriate .dat file. This portion of the prog ram is much different than Slagles or Trammels. Slagle and Trammel both captured an analog block of data every time through the Labview loop, averaged it, and wrote the average to the output file. The acquisition of raw data allows for more advanced da ta analysis. C.4.2 Front Panel Operation To operate raf_data_control.vi: 1. Specify the shear stress range using the drop down menu. 2. Specify the maximum flow rate being used in the flowmeter. 3. Specify the number of samples per analog channel. 4. Specify t he sampling rate in Hz. 5. Press the Labview Run button 6. Initialize the Pump by pressing FWD 7. Bring the pump to the desired flow speed using either the computerized pump dial or the number input below the pump dial. 8. When flow speed and shear stress have leveled out, press Run to record a burst of raw data. Data time can be computed by t = N/f where N is the number of samples per channel and f is the sampling frequency. 9. When data is taken, the real time graphs on the front panel will freeze. When data is done being taken, the graphs will resume moving. If this happens, it means the program is working as designed. C.5 SERF Control Full (Temperature Patch) The latest ed ition of the full version of the SERF control program incorporates a feedback loop between the thermocouple and the SEATEK, or a temperature patch (hence the programs name). The full version of the program is the most complicated program written for the device and it uses portions of the other programs. Figure C 15 shows a screenshot of front panel for the

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376 full version of the SERF control computer program. Figure C 16 shows a screenshot of the programs block diagram. C.5.1 SERF Control Full Block Diagram Discussion Like the other SERF control programs, the full control program is a multi phased flat sequence structure. The following is a discussion of its components. C.5.1.1 Pre i nput s equence As with other analog SERF programs, the first step in the full version of the control program is to specify input channels for analog devices. This step is the same as it was for the control program without the motor except that the lasers input channels also must be specified. As wired right now, the laser s are run through SCC1Mod6/ai0 and SCC1Mod6/ai1 (Figure C 17) Although there are three lasers installed in the device, they are hard wired in an or logic sequence. This will be discussed in Section C.5.1.6, but for now, it is sufficient to say that there are only two sets of wires coming from the laser system. The unique pre input element to the full version of the control program is a serial port r ead write sequence (Figure C 18). The subvi in Figure C 18 was taken verbatim from the NI examples fo lder and should not be changed. The subvi sends a N 1; Enter command to the SEATEK, waits for 200 ms, and then reveals an optional output that shows that the number of samples to be taken per SEATEK burst is indeed one. Previous SERF testing procedure s called for a user to open a HyperTerminal session, specify the number of samples to be taken during a test, and start a data run. When a SEATEK data run is started, the SEATEK pings each of its crystals at 5 MHz, takes an average over 1 s, and then outputs its result. The SEATEK N command corresponds to the number of times this pinging averaging is to repeat itself. Once a SEATEK data run is started, output data will continue to be generated until the number of bursts from the SEATEK equals N. When a data run is started, new input parameters cannot be added to the SEATEK until N is met. For the

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377 temperature patch to function properly, the SEATEKs input temperature needs to be updated continuously. Put another way, after each 5 MHz SEATEK burst aver age, the SEATEK needs to know that it should stop taking data and wait for a new temperature command. Inputting the N 1 command as the SEATEK is initialized guarantees that this will occur properly. C.5.1.2 Pump i nput Like the SERF control program wit hout the motor, the next step is to specify the correct flow rate (frequency) to the primary control pump. As before, raf_pump_on.vi is used (Figure C 19). C.5.1.3 Analog output Once the correct input frequency is given to the pump, the next step as bef ore is to read analog signals coming from the shear stress sensor, the thermocouple, the pressure transducer, the pump frequency indicator, and the flowmeter (Figure C 20 ). This step is similar to the no motor SERF control program. The difference between this step and the similar step in the no motor control program is that during the full version of the program, two timers have been added. Recall that with the Slagle/Trammel vis, analog signals were read and averaged every time the motor made a step. The problem with this is that a complete raw signal was not recovered. A better method is to set up a timed analog burst at a specified interval (top timer). Based on the users capture time an analog burst will be taken every time the timer hits the capture time interval. The second timer keeps track of total time; it corresponds to motor position readings so that a user can get a motor position vs. time output signal. If an operator wanted to go back to the old way of capturing data, the capture time, sampling rate, and samples per channel could be adjusted accordingly. For example, if a user wanted to capture data every time the program made its way through its loop (as it did before) at 1 kHz, number of samples would be set to 1000 and capture time would be set to 0.1667.

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378 The Manual Analog Override button functions the same way as the Run button in the version of this program without the motor. If the user turns off the motor, the lasers, and the SEATEK buttons on the front panel, the f ull version of the program will skip the last three phases of the five phase flat sequence structure, and the program will effectively run the same as the version of the program without the motor. The subvi shown in Figure C 20 is similar enough to the S ERF Control No Motor code that it does not need to be shown here. C.5.1.4 Laser i nput The third phase of the five phase flat sequence structure is to read the voltage sign al from the lasers (Figure C 21 ). Although the laser output signal is also just another analog signal, there should not be a need to capture or sustain an analog burst. Output from the lasers is simply a +10 V reading if the lasers photoelectric sensor picks up a light signal or a 0 V reading if the sensors do not pick up a light sign al. Because there was no need to pick up a raw laser signal, this element from the program was conducted separately. This phase was also programmed separately so that the lasers could be turned off easily using the front panel. The subvi in this phase of the program is shown in Figure C 22. It should be obvious from its structure that this subvi functions like the second phase of the pump control program. Data from lasers is picked up, split, and output to the base program. C.5.1.5 SEATEK output On ce data from the lasers is read, data must next be tak en from the SEATEK (Figure C 23). Unique to this SEATEK output module compared with previous versions of the control program is a series of buttons for each of the eight inner SEATEK crystals. In his thesis, Slagle set up the groundwork for a system where individual crystals could be turned off, but using his program was not intuitive and required blockdiagram input. T he buttons shown in Figure C 23

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379 simply require a true/false input; if the button is pressed, the crystal runs and if not, the crystal is turned off. The SEATEK subvi, raf_control_st ep3.vi is shown in Figure C 24. Much of raf_control_step3.vi is the same as Slagle and Trammels code. The difference between this vi and the Trammel/Sla gle generation of programs is that this vi has a temperature patch. The patch reads writes temperature data to the SEATEK just before every SEATEK depth reading. When raf_control_step3 is run, first the SEATEK channel is specified. The SEATEK channel co rresponds to the control computers serial port labeled SerialPort in MAX (National Instruments Measurement and Automation Explorer). Once the computer knows the correct serial port, signals are sent from the Labview program to the Serial Port. The Inp ut Temperature control in raf_control_step3.vi comes from the analog signals read during the third phase of the flat sequence structure. Once the input temperature is known, two more block diagrams are executed (as a Stacked Sequence Structure, Figure C 25 and Figure C 26) This stacked sequence performs the following algorithm: 1. Input temperature is converted to a string so that it can be written to the SEATEK 2. The command TE XX Enter is input to the serial port where XX corresponds to the input temper ature in degrees Celsius. 3. The Serial Port waits for 200 ms. 4. The command D Enter is written to the serial port. This tells the SEATEK to begin a data run. Because the data run was capped at one analog burst from the previous N 1 command, one set of numbers will be returned from the SEATEK. 5. The program waits 3000 ms. This is approximately the amount of time it takes the SEATEK to initialize a data run. 6. After 3000 ms, Labview reads the SEATEK return data from the serial port. The disadvantage to this algorithm is the 3000 ms wait time. Before, Slagle and Trammel would begin a data run corresponding to the amount of time for an erosion test. Then, they would read SEATEK data as it was written to the serial port. As soon as th is happened, the

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380 depth data was recorded. The temperature patch, because of its 3200 ms wait time (which is required and should not be changed) sacrifices a certain amount of speed for accuracy. Some quick mathematics can be used to show why the temperature patch should be effective. The speed of sound in water is temperature dependent such that a change of one degree Celsius corresponds to approximately a change in sound speed of approximately 3.1 m/s. Although the water chiller can hold water tempera ture steady with approximately 2 degrees Celsius accuracy, this uncertainty could correspond to as much as 12.4 m/s in speed of sound uncertainty. As originally conceived, devices like the SEATEK are generally used under field conditions where temperatu res do not vary drastically and where +/ a few centimeters of depth accuracy is acceptable. Because of the SERFs sensitivity requirements for motor advancement (0.5 mm), a few centimeters of depth accuracy is unacceptable. Slagle realized this; hence h is inclusion of the user defined depth parameter in his generation of programs. Although quantitatively tests were not conducted to determine average SEATEK error with and without the temperature patch, qualitatively, it was clear that as soon as the temp erature patch was designed the user defined depth parameter was no longer necessary. Therefore, the SEATEK program went back to using the four outside crystals to define the fixed distance from the top to bottom of the flume (Figure C 27 or the bottom por tion of the full block diagram). O ne of the outside crystals was damaged when this project began and was not working. The malfunctioning crystal consistently returns a low reading. For example, if bottom depth should be 4.92 cm, actual depth reading fr om the malfunctioning crystal holds steady at approximately 3.30 cm. To combat this problem, this crystal is automatically eliminated by eliminating the minimum value from the array of readings from the SEATEK. If somehow the entire SEATEK reading gets b otched, a case structure is included with a user defined bottom

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381 depth parameter so that this value will be used instead. A botched reading is defined when one of the outside crystals returns a value of 0.00. Readings of 0.00 from the SEATEK are given whe n there is an error with the device. After the temperature patch, the top half of raf_control_step3.vi is similar to Slagles version of the program. Once SEATEK data is read and converted to an array, there is an option to turn any of the inner crystal s on or off. When a crystal is turned off, the number in the corresponding 1x8 array is replaced with a zero Slagles method was to simply remove the element completely, but zero replacement is easier to understand, and after the crystal checking algorithm, a zero checker is included anyway. The purpose of the zerochecker originally was to check for SEATEK errors. Now, not only does it check for errors, but it also checks for zeroes corresponding to crystals that were turne d off by the user (Fig ure C 29 ). After the zero checker, a Slaglestyle maximum and minimum procedure is used. The max/min eliminator only functions if an array size is long enough. For example, if two crystals are malfunctioning, the maximum array that can be expected from the SEATEK is a 1x6 matrix. Suppose three SEATEK readings were in error and they returned a 0.00 value. Under these conditions, the maximum array to be expected would now be 1x3. If the Max SEATEK Array Size for max/min elim. parameter is set to a nu mber greater than 3, the max/min elimination will be skipped altogether. The user specifies the minimum array size under which a max/min elimination is considered acceptable. Lastly, results are averaged and compared with results from the outside SEATEK crystals. The STOP vi is a safety where if the SEATEK returns a string of zeros, (this happens rarely), the whole program will stop. This is important because if this safety did not exist, the motor could advance uncontrollably. This would push the sam ple against the top of the flume and cause the

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382 sample to rub against the SEATEK crystals. This would scratch the SEATEKs crystals. This mechanism is probably why so many of the SEATEK crystals currently do not work. The raf_control_step3.vi subvi retu rns a bottom offset, outside crystal depth, a raw SEATEK string, and SEATEK data after data manipulation to the base program. C.5.1.6 Motor advancement The motor advancement phase is the last portion of the SE RF control program (Figure C 30). In the bas e portion of the program, SEATEK depth and laser output are fed into a subvi called raf _control_step4.vi (Figure C 31 ). The output from this subvi is sample position in steps. There are three case structures programmed in this sequence shown in Figure C.5.17: 1. The f ar left case structure corresponds to when the flume is run with the lasers only. 2. The m iddle case structure corresponds to when the flume is run with the SEATEK only. 3. The f ar right case structure corresponds to when the flume is run with the SEATEK and lasers in a redundant system. In the case where the lasers are used exclusively, the lasers return a positive voltage only if their corresponding photoelectric sensor picks up light. When the photoelectric sensor picks up light, a voltage of ~+16 V is returned; in the program a comparison voltage of + 10 V is used, although this number is arbitrary. Recall that two lasers are wired to one control box such that they respond via or logic i.e. a voltage will be returned if either of their photoelectric sensors picks up light. Experience with the SERF has shown that generally samples erode backto front or front to back. It is rare to see a sample stay intact in its center while exhibiting high erosion rates in the back and the front. This why the outside lasers were chosen for the or logic, while the middle laser was wired by itself. When either of these outside lasers plus the middle laser are uncovered, the sample advances. Although it would have been better to wire lasers

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383 independent ly with one another, this would have been more expensive, and funding was not available for it. This solution was the most effective alternative. In the case where the SEATEK is used by itself, depth data from the base program is written to raf_control_step4.vi. If the bottom offset distance is greater than 0.5 mm, the sample advances. If the bottom offset distance is less than 0.5 mm, the sample retracts. The retraction allows for a constant depthcheck. If the sample has advanced too much (which happens sometimes), the retraction allows for a correction to be made. In the case where the SEATEK and the lasers are used together, the retraction algorithm does not change from the case where the SEATEK is used by itself. The weakness of the lasers is that they do not provide any retraction information should the sample advance too far into the flume. In the advancement case, an and logic sequence is programmed where laser output plus SEATEK output are both needed to trigger movement. In every case the subvi that controls movement is the same raf_motor_mover.vi describe in Section C.5.3. C.5.1.7 Data w riting Data writing is somewhat different in this program than it was in the Slagle and Trammel versions. Previously, data was written to one da ta file. Now, because analog bursts are taken to recover the original analog signal, two output files need to be generated one for analog data and the other for piston position data. The analog burst data is written during the analog phase of the flat sequence structure while the depth data is written after the motor advances. C.5.2 Using the Front Panel The front panel improvements to the full version of the SERF control program are significant because for the first time ever, a user has touch of the finger control on any of the devices components. The front panel is much more user friendly than it was in the past.

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384 Although using the front panel should be self explanatory, the following is an example of how to use it: 1. Specify shear stress range ( if shear stress readings are to be taken), sampling frequency, number of samples per channel, capture time, and the minimum SEATEK size to be used for max/min elimination. 2. Turn off any malfunctioning SEATEK crystals (if the SEATEK is to be used). 3. Decide what kind of data run is to be used. Options include: a. Analog capture only using Manual Analog Override b. Erosion test c. With Lasers only d. With SEATEK only e. With both lasers are SEATEK 4. Specify flow rate. 5. Press FWD on the pumps digital control. 6. Press the RUN button. 7. The program will ask where to save the two .dat files. Specify the directory. 8. The SERF should run until the STOP button is pressed.

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385 Figure C 1. SERF Digital Control Display Figure C 2. Pump Control Program Front Panel

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386 Figure C 3. Block Diagram for Pump Control Program (raf_pump_control.vi) [a] [b]

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387 Figure C 4. Analog Reader Subvi (raf_DAQ_pump_output.vi) Figure C 5. View from Top of Flowmeter Showing Data Range. See flowmeters operating manual for directions on how to change it settings.

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388 Figure C 6. Motor Mover Front Panel Figure C 7. Motor Mover Block Diagram (raf_motor_mover.vi)

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389 Figure C 8. SERF Control No Motor Front Panel

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390 Figure C 9. Block Diagram for SERF Control No Motor (raf_data_control.vi)

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391 Figure C 10. Zoom in on SC 2345 Channels. Figure C 11. Shear Stress Calibration Sub vi (raf_shear_module.vi)

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392 Figure C 12. Subvi Showing First Portion of Pump Control Program (raf_pump_on.vi) Figure C 13. Analog Reader for SERF Control No Motor (raf_DAQ_no_motor.vi)

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393 Figure C 14. Five Signal Split in Pump Control No Motor

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394 Figure C 15. Front Panel for SERF Control With Motor and Temp Patch (raf_control5.vi)

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395 Figure C 16. Block Diagram for SERF Control With Motor (raf_control5.vi

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396 Figure C 17. Analog Input Channels for raf_control5.vi Figure C 18. Initial Read Write Sequence Between for SEATEK Serial Port (SEATEK_integrate.vi)

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397 Figure C 19. First Phase of Five Phase Flat Sequence Structure Showing the Pump Input Controller (raf_pump_on.vi) Figure C 20. Second Phase of Five Phase Flat Sequence Structure Showing the Analog Output Module. Subvi is called raf_control_step1 1.vi.

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398 Figure C 21. Third Phase of Five P hase Flat Sequence Structure Showing the Laser Output Module (raf_control_step2.vi) Figure C 22. Block Diagram for raf_control_step2.vi

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399 Figure C 23. Fourth Phase of Five Phase Flat Sequence Structure Showing the SEATEK Output Module. Subvi is called raf_control_step3.vi.

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400 Figure C 24. Block Diagram for raf_control_step3.vi

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401 Figure C 25. First Element in Temperature Patch Stacked Sequence Structure Figure C 26. Second Element in Temperature Patch Stacked Sequence Structure

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402 Figure C 27. Outside Crystal Depth Algorithm Figure C 28. Example of a Crystal Off Module with Crystal 2 Shown Figure C 29. SEATEK zero checker. Algorithm is the same as Slagles.

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403 Figure C 30. Fifth Phase of Five Phase Flat Sequence Structure Sho wing the Motor Movement Module

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404 Figure C 31. Block Diagram for raf_control_step4.vi

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405 LIST OF REFERENCES Annandale, G. W., Smith, S.P., Nairn, R. and Jones, J.S. (1996). Scour power Journal of Civil Engineering, July, 5860, cited in Slagle, P.M. (2006). Annandale, G.W. (2005). Scour technology McGraw Hill, New York. Ariathurai, R. (1974). A finite element model for sediment transport in estuaries. Ph.D. dissertation, University of California, Davis CA. Bagnold, R. A. (1956). An approach to the sediment transport problem from general physical. Professional Paper, No. 4421, USGS, Washington, D.C. Barry, K. M. (2003). The effect of clay particles in pore water on the critical shear stress of sand. Ph.D. dissertation, University of Florida, Gainesville, Florida. Barry, K. M., Thieke, R. J., and Mehta, A. J.(2005). Quasi hydrodynamic lubrication effect of clay particles on sand grain erosion. Estuarine and Coastal Shelf Science 67, 161 169. Bloomquist, D (2010). Personal conversations and correspondence. Associate Professor, University of Florida, Gainesville, FL. Bollaert, E. and Schleiss, A. (2003). Scour of rock due to the impact of plunging high velocity jets p art I: a state of the art review. Journal of Hydraulics Research, 00(0), 1 14. Bollaert, E. and Schleiss, A. (2003). Scour of rock due to the impact of plunging high velocity jets part II: experimental results of dynamic pressures at pool botto ms in one and twodimensional closed end rock joints. Journal of Hydraulics Research, 00(0), 1 16. Briaud J. L. (2004). Scour#1 killer of bridges. Geostrata, The Geotechnical Institute of the American Society of Civil Engineers, Fall. Briaud J. L., Chen, H. C., Li, Y. and Nurtjahyo, P. (2004a). The SRICOS EFA method for complex piers in fine grained soils. Journal of Geotechnical and Geoenvironmental Engineering, 130(11), 1180 1191. Briaud, J.L., Chen, H. C., Li, Y., Nurtjahyo, P ., and Wang, J. (2004b). Pier and contraction scour in cohesive soils. NCHRP Report 516, Transportation Research Board, Washington, D.C. Briaud, J. L., Chen, H. C. and Ting, F. (2010). The, EFA, erosion function apparatus: an overview. http:/ /tti.tamu.edu/conferences/scour/sample_paper.pdf, March 23, 2010. Briaud, J. L., Ting, F. C. K., Chen, H. C., Gudavalli, R., Perugu, S., and Wei, G. (1999). SRICOS: Prediction of scour rate in cohesive soils at bridge piers. Journal of Geotechnical and Geoenvironmental Engineering 125(4), 237 246.

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406 Christensen, B. A. (1972). Incipient motion on cohesionless channel banks. Symposium to Honor Professor H. A. Einstein, edited by H. W. Shen, Colorado State University, Fort Collins, CO, 1 22. Christensen, B. A. (1975). On the stochastic nature of scour initiation. Proceedings of the 16th International Association for Hydraulic Research Congress Sao Paulo, Brazil (2), 65 72. Cokljat, D., Carter, J. G. and Eme rson, D. R. (1996). Calculation of flow in transition duct using second order closure and wall functions. American Instittue of Aeronautics and Astronautics 34(11), 2437 39. Cornett, A., Sigouin, N., and Davies, M. (1994). Erosive response of Northumberland straight till and sedimentary rock to fluid flow. National Research Council of Canada, Institute for Marine Dynamics TR 199422, September, Ottowa, Canada, 1 15, 26 27. Crowley, R.W. (2008). Drag forces on pile groups. M.S. Thesis, University of Florida, Gainesville, FL. Dade, W. B. and Nowell, A. R. M. (1991). Moving muds in the marine environment. Proceedings of the 1991 Conference on Coastal Sediments ASCE, New York, NY, 54 71. Dade, W. B., Nowell, A R. M., and Jumars, P. A. (1992). Predicting erosion resistance of muds. Marine Geology 105, 285 97. Dyer, K. R. (1986). Coastal and estuarine sediment dynamics Wiley Interscience, Chichester, United Kingdom. Einstein, H. A. (1943). Fl ow on a movable bed. Proceedings from the Second Hydraulics Conference, Iowa City, 27, 332 46. Einstein, H.A. (1950). The bedload function for sediment transportation in open channel flows. US Department of Agricultural Technologies 1026, Wa shington. Englund F. and Hansen, E. (1972). A monograph on sediment transport in alluvial streams Technial Press, Copenhagen, Denmark. Florida department of transportation bridge scour manual (2005). , Sept. 2, 2008. Fukada, M. and Lick, W. J. (1980). The entrainment of cohesive sediments in fresh water. Journal of Geophysical Researc h, 85(C5) 2813 2824, cited in McNeil, J. (1996). Gessne r, F. B., and Emery, A. F. (1980). The numerical prediction of developing turbulent flow in rectangular ducts. Journal of Fluids Engineering, 103, 445 455.

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407 Gordon, S. (1991). Scourability of rock formations. Federal Highway Administration Memorandum, July 19, Washington, D.C. Hanson, G. J. and Simon, A. (2002). Discussion of erosion function apparatus for scour rate predictions by J.L. Briaud, F. C. Ting, H. C. Chen, Y. Cao, S. W. Han, and K. W. Kwak. Journal of Geotechnical and Ge oenvironmental Engineering, 627 628. Henderson, M. R. (1999). A laboratory method to evaluate the rates of water erosion of natural rock materials. M.S. Thesis, University of Florida, Gainesville FL. Hofland, B. and Battjes, J. A. (2006). P robability density function of instantaneous drag forces and shear stresses on a bed. Journal of Hydraulic Engineering, 132, (11), 1169 1175. Hjorth, P (1975). Studies on nature of local scour. Bulletin Series A, No. 46, Department of Water Resources Engineering, Lund Institute of Technology University of Lund, Sweden. Jepsen, R., Roberts, J. and Lick, W. (1997). Effects of bulk density on sediment erosion rates. Water, Air, and Soil Pollution, 99, 21 31. Jepsen, R., Roberts, J D. and Gailini, J. (2004). Erosion measurements in linear, oscillatory, and combined oscillatory and linear flow regimes. Journal of Coastal Research, 20(4), 1096 1106. Jones, J. S., Alqalam, Kamel, Gratton, Buddy, Summers, Brian (1995). Ef fect of the 1994 southeast flooding on the highway system in Georgia Handout from presentation at the 1995 American Society of Civil Engineers Water Resources Engineering Conference San Antonio, TX., cited in Mueller, J., Field based research on ch annel scour at bridges, , March 22, 2010. Judson, S., Kaufman, M. E. and Leet, L. D. (1987). Physical geology PrenticeHall, Englewood Cliffs, NJ. Kandiah, A. (1974). Fundamental aspects of surface erosion of cohesive soils. Ph.D. dissertation, University of California, Davis, CA. Kerenyi, K. (2010). Personal conversations. Lead researcher, FHWA Hydraulics Laboratory, McLean, VA. Kerr, K. (2001). A laboratory apparatus and methodology for testing water erosion in rock materials. M.E. thesis, University of Florida, Gainesville, Florida. Laufer, J. (1954). The structure of turbulence in fully developed pipe flow Report no. 1174, National Advisory Committee fo r Aeronautics Washington, D.C.

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408 Lemke, K. A. (2010). Stream sediment. University of WisconsinStevens Point, http://www. uwsp.edu/geo/faculty/lemke/geomorphology/lecture_outlines/03_stream_sediment.html, Sept. 14, 2010. MacIntyre S., Lick, W. and Tsai, C. H. (1990). Variability of entrainment of cohesive sediments in freshwater. Biogeochemistry ,9, 187 209, cited in McNeil, J. (1996). Mantz, P. A. (1977). Incipient transport of fine grains and flakes by fluids. Jo urnal of Hydraulics Division of ASCE 103(6), 601 615. McGuire, Mark. Drafts of Capital regions severe weather history. Legacy of Change, http://web.timesunion.com/specialreports/tu150/stories/weather.asp Nov. 11, 2010. McLean, S.R. (1975). Theoretical modeling of deep sediment transport. Marine Geology 66, 243 265. McNeil, J., Taylor, C., and Lick, W. (1996). Measurements of erosion of undisturbed bottom sediments with depth. Journal of Hydraulic Enginee ring, 122(6), 316 324. Mehta, A. J. (2007). An introduction to hydraulics of fine sediment transport. OCP 6297 class notes draft University of Florida, Gainesville, Florida. Mehta, A. J. and Lee, S. C. (1994). Problems in linking the thres hold condition for the transport of cohesionless and cohesive sediment grain. Journal of Coastal Research, 10(1), 170 177. Mehta, A. J., Parchure, T. M., Dixit, J. G., and Ariathuri, R. (1982). Resuspension potential of deposited cohesive be ds. Estuarine c omparisons edited by V.S. Kennedy, Academic Press, New York, N.Y, cited in McNeil, J. (1996). Mehta, A. J. and Parachure, T. M. (2000). Surface erosion of fine grained sediment revisited. Muddy coast dynamics and resource m anagement edited by: Belmming, B. W., Delfontaine, M. T. and Liebezeit, G., Elsevier, Amsterdam, The Netherlands, 55 74. Melling, A. and Whitelaw, J. H. (1976). Turbulent flow in a rectangular duct. Journal of Fluid Mechanics 27(2), 289 315. M elville, B. W. and Sutherland, A. J. (1988). Design method for local scour at bridge piers. Journal of Hydraulic Engineering, ASCE, 125(1), 59 65, cited in Slagle, P.M. (2006). Melville, B. W. Scour at bridge sites. Civil engineering practices v ol. 2, edited by Cheremisinoff, N. P., Technomic Publishing, Lancaster, PA, cited in Slagle, P. M. (2006). Meyer Peter E. and Mueller, R. Formulations for bedload transport. Proceedings for the 2nd Meeting International Hydraulics Research. Stockholm, 39 64.

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409 Migniot, C. (1989). Tassement et rheologie des vases, lere partie. La Houille Blanche 1, 11 29, cited in Mehta, A. J. (2007). Miller, R. L. and Byrne, R. J. (1966). The angle of repose for a single grain on a fixed rough bed. Sedimentology 6, 303 314. Miller, William Jr. (2003). Model for the time rate of local sediment scour at a cylindrical structure. Ph.D. di ssertation, University of Florida, Gainesville, Florida. Mitchener, H. and Torfs, H. (1996). Erosion of mud/sand mixtures. Coastal Engineering, 29, 1 25. Molinas, A (2003). Bridge scour in nonuniform sediment mixtures and in cohesive material s: synthesis report. FHWA Report Number FHWA RD 03083, Washington, D.C. Murillo, J.A. (1987). The scourge of scour. Civil Engineering American Society of Civil Engineers, 57(7). Naimi, M. and Gessner, F. B. (1993). A calculation method f or developing turbulent flow in rectangular ducts of arbitrary aspect ratio. Journal of Fluids Engineering, 117, 249 258. Nalluri, C. and Alvarez, E. M. (1992). The influence of cohesion on sediment behavior. Water Science and Technology 25(8), 151 164. Neilsen, P. (1992). Coastal bottom boundary layer and sediment transport. World Scientific Publishing, River Edge, NJ. Niraula, L. D. (2004). Development of modified T Z curves for large diameter piles/drilled shafts in limes tone for FB Pier. M.E. Thesis, University of Florida, Gainesville, FL. Obi, K., Inoue, T. S., Furukawa, T., and Masuda, S. (1996). Experimental study on the statistics of wall shear stress in turbulent channel flows International Journal of Heat and Fluid Flow 17(3), 187 192. Papanicolaou, A. N., Diplas, P., Dancey, C. L., and Balakrishnan, M. (2001). Surface roughness effects in near bed turbulence: implications to sediment entrainment. Journal of Engineering Mechanics 127(3), 211 218. Panagiotopoulos, I., Voulgaris, G., and Collins, M. B. (1997). The influence of clay on the threshold of movement of fine sandy beds. Coastal Engineering, 32, 19 43. Partheniades, E. (1965). Erosion and deposition of cohesive soi ls. Journal of Hydraulics Division of ASCE 91(1), 105 138.

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BIOGRAPHICAL SKETCH Raphael Crowley was b orn in Syracuse, NY in 1982, and grew up on Skaneateles Lake just outside of Syracuse. H e has always had an interest in engineering and r esearch. In 2004, Crowley graduated from Bucknell University with a double major in Civil Engineering and History. While at Bucknell, Crowley began his career in research under the tutelage of Dr. Richard Crago. Crago and Crowley coauthored two papers for publication during their two summer s of research. In 2004, The Journal of Hydrology published Complementary Relationships for Near Instantaneous Evaporation and in 2005, Water Resources Research published A Complementary Evaporation Approach to the Scalar Roughness Length. Although this research was in fluid mechanics and hydrology, Crowley still maintained a strong interest in structures as well. After graduation in 2004, Crowley spent a year working in West Nyack, NY, a suburb of New York City, as a Marine Engineer for M.G. McLaren, P.C. This job allow ed Crowley to combine his interests in fluids with his interests in structures. As Crowley learned more about the field, he realized that he wanted to return to academia and learn as much as possible about Marine and Coastal Engineering. In the fall of 2 005, Crowley enrolled at the University of Florida to pursue his post graduate work. Upon matriculation at the University of Florida, Crowley began working on his m aster s degree under the tutelage of Dr. D. Max Sheppard. His research was conducted at t he Turner Fairbank Highway Research Center (TFHRC) in McLean, VA under the supervision of Dr. Kornel Kerenyi. While in VA, Crowley ran smallscale model tests on bridge decks and cylinders. The goal was to quantify lift and drag forces on the bridge decks, drag forces on the

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cylinders, and scour depths under pressure flow scenarios. Crowleys Masters Thesis, Drag Forces on Pile Groups was completed in May, 2008. After completion of his Master Degree, Crowley began working with Dr. David Bloomquist on erosion rates of rock like and sand/mud mixtures. Bloomquist hoped to use the experience that Crowley had gained as a lab technician and research assistant at the TFHRC to revitalize and improve UFs SERF. Improvements to the SERF we re completed in December of 2009, and tests involving sand/mud mixtures were completed in August of 2010. Improvements to the SERF include a state of the art shear stress measurement system, a vortex generator, and a water chilling system to a llow for multiday tests Exper iments and data analysis have helped to answer how to properly measure erosion rates in erosion rate testing devices. The purpose of this work is to use these results in with methods for predicting local scour depths that require the input of a sediment t ransport function. Crowleys future plans include converting this dissertation to a series of publishable papers. Once that is completed, Crowley looks forward to finding employment as an Assistant Professor or Po st Doc at a four year College or Univers ity so that he can pursue his life long dream of following a career in academia.