Load and Resistance Factor Driving Project Study.pdf

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Load and Resistance Factor

Design (LRFD) Pile Driving Project − Phase II Study

Aaron S. Budge, Principal Investigator

Department of Mechanical and Civil EngineeringMinnesota State University, Mankato

April 2014

R h P j


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Technical Report Docu1. Report No. 2. 3. Recipients Accession No.

MN/RC 2014-16

4. Title and Subtitle 5. Report Date

Load and Resistance Factor Design (LRFD) Pile Driving Project – Phase II Study

April 20146.

7. Author(s) 8. Performing Organization Report No.

Samuel G. Paikowsky, Mary Canniff, Seth Robertson, andAaron S. Budge9. Performing Organization Name and Address 10. Project/Task/Work Unit No.

Minnesota State University, MankatoDept. of Mechanical and Civil Engineering205 Trafton Science Center EastMankato, MN 56001

11. Contract (C) or Grant (G) No.

(C) 96272

12. Sponsoring Organization Name and Address 13. Type of Report and Period Covered

Minnesota Department of TransportationResearch Services & Library395 John Ireland Boulevard, MS 330St. Paul, MN 55155

Final Report14. Sponsoring Agency Code

15. Supplementary Notes http://www.lrrb.org/pdf/201416.pdf16. Abstract (Limit: 250 words)

Driven piles are the most common foundation solution used in bridge construction (Paikowskysafe use requires to reliable verification of their capacity and integrity. Dynamic analyses methods attempting to obtain the static capacity of a pile, utilizing its behavior during driving. (aka pile driving formulas) are the earliest and simplest forms of dynamic analyses. The de

examination of such equation tailored for MnDOT demands is presented. In phase I of thePaikowsky et al. (2009, databases were utilized to investigate previous MnDOT (and other) dynuse object oriented programming for linear regression to develop a new formula that was then cmethodology and evaluated for its performance. This report presents the findings of phase II of tcomprehensive investigation of the Phase I findings were conducted. The studies lead to tdynamic formulae suitable for MnDOT foundation practices, its calibrated resistance factors anconcrete and timber piles. Phase II of the study also expanded on related issues associated wanalyses and static load tests, assisting the MnDOT in establishing requirements and specificatio

17. Document Analysis/Descriptors 18. Availability Statement Piles, Dynamics, LRFD, Resistance (mechanics), Bridgefoundations

No restrictions. Document av National Technical InformatAlexandria, VA 22312


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Load and Resistance Factor Design (LRFD) Pile D

Project – Phase II Study

Final Report

Prepared by:

Aaron S. Budge

Department of Mechanical and Civil Engineering

Minnesota State University, Mankato

Samuel G. PaikowskyMary Canniff

Seth Robertson

Geotechnical Engineering Research LabUniversity of Massachusetts Lowell

April 2014

Published by:

Minnesota Department of TransportationResearch Services & Library

395 John Ireland Boulevard, MS 330St. Paul, Minnesota 55155


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AcknowledgmentsThe presented research was supported by Minnesota Department of Transportation

a grant to Minnesota State University, Mankato. The Technical Advisory Panel (TA

acknowledged for its support, interest, and comments. In particular we would like to

Richard Lamb, Gary Person, Dan Mattison and Derrick Dasenbrock of the Foundati

Paul Rowekamp (Technical Liaison), Paul Pilarsky, Dustin Thomas, Dave Dahlberg

Western, Bruce Iwen, and Paul Kivisto of the Bridge Office and Nelson Cruz of the

Services office.

The research presented in this manuscript makes use of a large database specifically

for MnDOT purposes. This database makes use of data originally developed for a F

Highway Administration (FHWA) study by Paikowsky et al. (1994) followed by an

database denoted as PD/LT 2000 presented by Paikowsky and Stenersen (2000), wh

used for the LRFD development for deep foundations (presented in NCHRP Report

Paikowsky et al., 2004). The contributors for those databases are acknowledged for as detailed in the referenced publications. Carl Ealy and Albert DiMillio of the FHW

constructive in support of the original research studies and facilitated data gathering

sources. Significant additional data were added to those databases, most of which w

by six states: Illinois, Iowa, Tennessee, Connecticut, West Virginia, and Missouri. T

obtained from Leo Fontaine of the Connecticut DOT was extremely valuable to enla

MnDOT databases to the robust level presented in this study. In addition, the data pr

Betty Bennet of the Ontario ministry of Transportation were invaluable for developi piles database.

Previous students of the Geotechnical Engineering Research Laboratory at the Univ

Massachusetts Lowell are acknowledged for their contribution to the aforementione

and various studies, namely: John J. McDonell, John E. Regan, Kirk Stenersen, Col

and Jorge Fuentes. Dr. Shailendra Amatya, a post-doctoral fellow at the Geotechnic

Engineering Research Laboratory, is acknowledged for his assistance in employing oriented programming in the code SPlus for the development of the newly proposed

dynamic equation as presented in Phase I of the study. Christopher Jones of GeoDy

is acknowledged for his assistance in developing the timber piles database and cond

WEAP simulations.


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Table of Contents

1 BACKGROUND ..................................................................................................1.1 Resistance Factor for MnDOT’s Pile Driving Formula – Phase I Study ...

1.2 Research Objectives ...................................................................................

1.2.1 Overview .....................................................................................

1.2.2 Concise Objectives......................................................................

1.2.3 Specific Tasks .............................................................................

1.3 Manuscript Outline ....................................................................................

2 REVIEW ALTERNATE FORMULAS AND CONSTRUCTION PILE PRACT

MIDWEST STATES ............................................................................................

2.1 Overview ....................................................................................................

2.2 Illinois Department of Transportation – Long and Maniaci (2000) ..........

2.2.1 Overview .....................................................................................

2.2.2 Databases Summary ....................................................................

2.2.3 Olson and Flaate, 1967 ...............................................................

2.2.4 Conclusions .................................................................................

2.3 Illinois Department of Transportation – Long et al. (2009a) .....................

2.3.1 Overview .....................................................................................

2.3.2 International Database ................................................................

2.3.3 Comprehensive Database ............................................................

2.3.4 Illinois Database..........................................................................

2.3.5 Development of Correction Factors ............................................2.3.6 Resistance Factors and Reliability ..............................................

2.3.7 Summary and Comparison of Results to Phase I of the Mn Stud

(Paikowsky et al., 2009)..............................................................

2.4 Report Submitted to the State of Wisconsin Department of Transportatio

et al., 2009b ................................................................................................

2.4.1 Overview .....................................................................................

2.4.2 Databases ....................................................................................2.4.3 Summary of Findings ..................................................................

2.4.4 Conclusions and Comment .........................................................

2.5 Gates Formulae ..........................................................................................

2.5.1 Gates (1957) ................................................................................


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3.3 Data Match .................................................................................................

3.4 Summary of Data .......................................................................................

3.5 Observations Regarding MnDOT Dynamic Measurements Database ......3.6 Preliminary Evaluation of the Capacity Predictions ..................................

3.7 Conclusions Derived from the Initial Evaluation ......................................

3.7.1 General ........................................................................................

3.7.2 Analysis.......................................................................................

3.8 Final Evaluation of the Capacity Predictions.............................................

3.8.1 Overview .....................................................................................

3.8.2 Performed Analyses ....................................................................

3.8.3 Presentation of Results ................................................................

3.8.4 Conclusions .................................................................................

4 RE-ASSESSMENT OF FORMAT AND LRFD RESISTANCE FACTORS FO

NEW MNDOT PILE DRIVING FORMULA (MPF12) ......................................

4.1 Overview ....................................................................................................

4.2 Examination of the Extreme Over-Prediction Cases in the Database ........

4.2.1 Rationale and Method of Approach ............................................

4.2.2 Examination of the H Pile Cases ................................................

4.2.3 Examination of the Pipe-Pile Cases ............................................

4.2.4 MnDOT Research Panel Evaluation ...........................................

4.3 Re-Evaluation of Dynamic Equations Considering Outliers .....................

4.3.1 Initial Analysis ............................................................................

4.3.2 Evaluation of Outliers .................................................................4.3.3 MnDOT Research Panel Evaluation ...........................................

4.4 Re-Evaluation of the New MnDOT Dynamic Equation for EOD and B

and High Blow Count ................................................................................

4.4.1 Summary Tables .........................................................................

4.4.2 Intermediate Conclusions............................................................

4.5 Evaluation of MPF12 and Resistance Factors Development .....................

4.5.1 Graphical Presentation ................................................................4.5.2 Summary Tables .........................................................................

4.5.3 Driving Resistance Bearing Graphs ............................................

4.5.4 Calculated Resistance Factors .....................................................

4.6 MPF12 Final Format and Recommended Resistance Factors ...................


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5.6 Recommendations ......................................................................................

6 EVALUATION OF MPF12 FOR TIMBER PILES.............................................

6.1 Overview ....................................................................................................6.2 Timber Pile Database .................................................................................

6.2.1 Summary .....................................................................................

6.2.2 Pile Capacity Evaluation .............................................................

6.2.3 Extrapolated Non-Failed Load Tests ..........................................

6.3 Statistical Analysis .....................................................................................

6.3.1 Equation Modification ................................................................

6.3.2 Performance and Graphical Presentation ....................................

6.3.3 Control Cases ..............................................................................

6.4 Resistance Factors ......................................................................................


7 MINNESOTA LOAD TESTING PROGRAM ....................................................

7.1 Overview ....................................................................................................

7.2 TP1 Load Test at Victoria, MN .................................................................

7.2.1 General ........................................................................................

7.2.2 Static Capacity Evaluation ..........................................................

7.2.3 Load and Resistance ...................................................................

7.2.4 Dynamic Observations, Predictions, and Static Load Test Resu

7.3 TP2 Load Test at Arden Hills (Old Snelling) ............................................

7.3.1 General ........................................................................................

7.3.2 Static Load Test ..........................................................................7.3.3 Dynamic Observations and Predictions ......................................

7.4 Comparisons Between Statically Measured and Dynamically Predicted C

7.4.1 Nominal Resistance ....................................................................

7.4.2 Factored Resistance ....................................................................

8 WEAP ANALYSIS (WAVE EQUATION ANALYSIS PROGRAM) ...............

8.1 Overview ....................................................................................................

8.2 Design and Construction Process of Deep Foundations ............................8.3 The Accuracy of WEAP for Capacity Evaluation .....................................

8.4 WEAP Analyses for Typical MnDOT Pile Driving Conditions ................

8.4.1 Overview .....................................................................................

8.4.2 Typical WEAP Analyses for MnDOT Conditions .....................


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9.6.1 Background .................................................................................

9.6.2 Paikowsky and Tolosko (1999) Extrapolation Method ..............

9.6.3 Evaluation of the Extrapolation Method .....................................9.6.4 Extrapolation Implementation in MnDOT Load Test Database

Program .......................................................................................

9.7 Recommended Load Test Procedures and Submittals for the MnDOT ....

9.7.1 Recommended Load Test Procedures and Interpretation ...........

9.7.2 Pre Load-Test Submittal Requirements ......................................

9.7.3 Load Test Reporting ...................................................................

9.7.4 Database/Data Requirements for MnDOT Load Testing Progra

10 SUMMARY CONCLUSIONS AND RECOMMANDATIONS .........................

10.1 Summary ....................................................................................................

10.2 Conclusions ................................................................................................


REFERENCES ............................................................................................................

APPENDIX A Summary Tables

APPENDIX B Hammer Specifications

APPENDIX C Load Testing Report – TP1 in Victoria, MN, July 20

Construction, Inc.

APPENDIX D Load Testing Report – TP2 in Aren Hills, MN, January 20

Engineering Testing, Inc., Prepared for Lunda Construction C


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List of Figures

Figure 1. Graphic representation of predicted vs measured capacity ..........................Figure 2. Predicted versus measured capacity from Flaate 1964. ................................

Figure 3. Predicted versus measured capacity from Fragaszy 1988, 1989. .................

Figure 4. Predicted versus measured capacity from Paikowsky et al., 1994 ...............

Figure 5. Predicted versus measured capacity for Florida DOT. .................................

Figure 6. Predicted versus measured capacity from Eslami, 1996 ..............................

Figure 7. Predicted versus measured capacity for FHWA database using EOD data)

Figure 8. Predicted versus measured capacity for FHWA database using BOR data .

Figure 9. International database selected predictions of dynamic equations vs. SLT d

Figure 10. Comprehensive database selected dynamic and static methods vs. SLT. ..

Figure 11. FHWA-Gates vs. static methods ................................................................

Figure 12. Dynamic vs. static capacities for the Illinois database ...............................

Figure 13. Cumulative distribution of FHWA-Gates/IDOT static data, Illinois datab

Figure 14. Dynamic vs. corrected static capacities for the Corrected Illinois database

Figure 15. Dynamic vs. corrected static capacities for the comprehensive database ..

Figure 16. Resistance factors vs. reliability index for different predictive methods us

......................................................................................................................................

Figure 17. Resistance factors vs. reliability index for different predictive methods us

......................................................................................................................................

Figure 18. Graphic solutions of formula ......................................................................

Figure 19. Probability distribution and cumulative distribution function for energy trdriving Pipe-Piles with diesel hammers in the MnDOT dynamic measurements data

Figure 20. CAPWAP vs. Energy Approach prediction method all CIP piles..............

Figure 21. CAPWAP vs. Energy Approach prediction 16" x 5/16" CIP piles ............

Figure 22. CAPWAP vs. Energy Approach prediction 16" x 1/4" CIP piles ..............

Figure 23. CAPWAP vs. Energy Approach prediction 12.75" x 1/4" CIP piles .........

Figure 24. CAPWAP vs. Energy Approach prediction 12" x 1/4" CIP piles ..............

Figure 25. CAPWAP vs. Energy Approach prediction 12.75" x 5/16" CIP piles .......Figure 26. CAPWAP vs. Energy Approach prediction 12" x 5/16" CIP piles ............

Figure 27. CAPWAP vs. Gates formula all CIP piles .................................................

Figure 28. CAPWAP vs. Gates formula 16” x 5/16” CIP piles ...................................

Figure 29. CAPWAP vs. Gates formula 16” x 1/4” CIP piles .....................................


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Figure 37. CAPWAP vs. Gates formula 12.75” x 1/4” CIP piles ................................


Figure 39. CAPWAP vs. Gates formula 12.75” x 5/16” CIP piles ..............................Figure 40. CAPWAP vs. Gates formula 12” x 5/16” CIP piles ...................................

Figure 41. CAPWAP vs. Gates formula all CIP piles .................................................



Figure 44. CAPWAP vs. Gates formula 12.75” x 1/4” CIP ples .................................


Figure 46. CAPWAP vs. Gates formula 12.75” x 5/16” CIP piles ..............................


Figure 48. CAPWAP vs. WSDOT formula all CIP piles ............................................

Figure 49. CAPWAP vs. WSDOT formula 16” x 5/16” CIP piles .............................

Figure 50. CAPWAP vs. WSDOT formula 16” x 1/4” CIP piles ...............................

Figure 51. CAPWAP vs. WSDOT formula 12.75” x 1/4” CIP piles ..........................

Figure 52. CAPWAP vs. WSDOT formula 12” x 1/4” CIP piles ...............................

Figure 53. CAPWAP vs. WSDOT formula 12.75” x 5/16” CIP piles ........................

Figure 54. CAPWAP vs. WSDOT formula 12” x 5/16” CIP piles .............................

Figure 55. CAPWAP vs. current MnDOT formula all CIP piles ................................

Figure 56. CAPWAP vs. current MnDOT formula 16” x 5/16” CIP piles ..................

Figure 57. CAPWAP vs. current MnDOT formula 16” x 1/4” CIP piles ....................

Figure 58. CAPWAP vs. current MnDOT formula 12.75” x 1/4” CIP piles ...............

Figure 59. CAPWAP vs. current MnDOT formula 12” x 1/4” CIP piles ....................Figure 60. CAPWAP vs. current MnDOT formula 12.75” x 5/16” CIP piles .............

Figure 61. CAPWAP vs. current MnDOT formula 12” x 5/16” CIP piles ..................

Figure 62. CAPWAP vs. New MnDOT formula (35) All CIP piles ...........................

Figure 63. CAPWAP vs. New MnDOT formula (35) 16” x 5/16” CIP piles ..............

Figure 64. CAPWAP vs. New MnDOT formula (35) 16” x 1/4” CIP piles ................

Figure 65. CAPWAP vs. New MnDOT formula (35) 12.75” x 1/4” CIP piles ...........

Figure 66. CAPWAP vs. New MnDOT formula (35) 12” x 1/4” CIP piles ................Figure 67. CAPWAP vs. New MnDOT formula (35) 12.75” x 5/16” CIP piles .........


Figure 69. CAPWAP vs. New MnDOT formula (30) All CIP piles ...........................



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Figure 77. CAPWAP vs. Modified Gates formula (W*H) all CIP piles .....................

Figure 78. CAPWAP vs. Modified Gates formula (75%*En) all CIP piles ................

Figure 79. CAPWAP vs. Modified Gates formula (En) all CIP piles .........................Figure 80. CAPWAP vs. WSDOT formula (E=Wr *h) all CIP piles ...........................

Figure 81. CAPWAP vs. current MnDOT all CIP piles ..............................................

Figure 82. CAPWAP vs. New MnDOT formula (35) (E=En) all CIP piles ................

Figure 83. CAPWAP vs. New MnDOT formula (35) (E=Wr *h) all CIP piles ...........

Figure 84. CAPWAP vs. New MnDOT formula (30) (E=En) all CIP piles ................

Figure 85. CAPWAP vs. New MnDOT formula (30) (E=Wr *h) all CIP piles ...........

Figure 86. Bias vs. blow count for Pipe Pile EOD cases only (low and high outliers

the New MnDOT Equation (coefficient 40, 75% energy); (a) linear scale, and (b) lo

Figure 87. Bias vs. blow count for Pipe Pile EOD cases only (low and high outliers

the New MnDOT Equation (coefficient 40, 75% energy) where the blow count is lim

15bpi maximum; (a) linear scale, and (b) average and standard deviation per 2bpi se

Figure 88. Bias vs. blow count for Pipe Pile BOR cases only. ....................................

Figure 89. Bias vs. blow count for Pipe Pile BOR cases only. ....................................

Figure 90. Bias vs. blow count for H Pile EOD cases only .........................................

Figure 91. Bias vs. blow count for H Pile EOD cases only. ........................................

Figure 92. Bias vs. blow count for H Pile BOR cases only. ........................................

Figure 93. Bias vs. blow count for H Pile BOR cases only . .......................................

Figure 94. Average and standard deviation of the bias vs. blow count for H Pile case

Figure 95. New MnDOT equation vs. blow count for set energy values. ...................

Figure 96. Measured static capacity vs. New MnDOT dynamic equation prediction fcases. ............................................................................................................................

Figure 97. Measured static capacity vs. New MnDOT dynamic equation prediction f

cases (B.C. ≥ 2 bpi). .....................................................................................................


cases. ............................................................................................................................


cases (B.C. ≥ 2 bpi). .....................................................................................................Figure 100. Bias vs. blow count for PSC EOD cases for the New MnDOT Equation

Figure 101. Bias vs. blow count for PSC BOR cases for the New MnDOT Equation

Figure 102. Bias versus PSC pile size (in) for (a) EOD cases and (b) BOR cases. .....

Figure 103. Davisson’s criteria predicted static capacity versus CAPWAP prediction


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Figure 106. CAPWAP predicted static capacity versus New MnDOT dynamic equa

prediction for 106 BOR cases. .....................................................................................

Figure 107. Shape of Curve versus Davisson’s Criteria for determining the static captimber piles...................................................................................................................

Figure 108. Extrapolated load-displacement curve for TS #13 Pile #1. ......................

Figure 109. Extrapolated load-displacement curve for TS #13 Pile #2. ......................

Figure 110. Extrapolated load-displacement curve for TS #13 Pile #12. ....................

Figure 111. Extrapolated load-displacement curve for TS #13 Pile #14. ....................

Figure 112. Measured static capacity vs. New MnDOT dynamic equation prediction

timber pile cases. ..........................................................................................................

Figure 113. Bias vs. blow count for timber pile cases for the (0.50)*MPF12 (75% en

Figure 114. 500 ton MnDOT load frame being used at Bridge 10003 in Victoria, Mi

Figure 115. Static pile capacity predictions for Bridge 10003 in Victoria, Minnesota

Figure 116. Additional load vs. displacement of pile top – static load test results for

10003 in Victoria, Minnesota ......................................................................................

Figure 117. Load versus pile head movement for each of the pile’s top LVDT senso

Pile 2 (per Report No. 22-01025 submitted to MnDOT by American Engineering Te

......................................................................................................................................

Figure 118. Load-displacement relations for TP2 based on top displacement of LVD

displacement based on LVDT 6. ..................................................................................

Figure 119. Load-displacement relations for TP2 based on top displacement of LVD

displacement based on LVDT 6. ..................................................................................

Figure 120. Measured and extrapolated load-displacement relations for TP2 based o – 4.................................................................................................................................

Figure 121. Measured and extrapolated load-displacement relations for TP2 based o

– 2.................................................................................................................................

Figure 122. Nominal resistance static capacity (Rs) vs. predicted dynamic capacity (

EOD and (b) restrikes ..................................................................................................

Figure 123. Factored resistance of the nominal static capacity (Rs) vs. the factored p

dynamic capacity (Ru): (a) EOD and (b) restrikes ......................................................Figure 124. Design and construction process for deep foundations (Paikowsky et al.

Figure 125. WEAP utilization in the design and construction process of driven piles

Figure 126. WEAP analysis results presenting static capacity vs. driving resistances

12x53 piles driven by a D-12 hammer .........................................................................


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12in x 0.25in piles driven by a D-19 hammer ............................................................

Figure 131. WEAP analysis results presenting static capacity vs. driving resistances12in x 0.25in piles driven by a D-25 hammer .............................................................


16in x 0.3125in piles driven by a D-30 hammer .........................................................

Figure 133. Victoria, Minnesota Bridge 10003 longitudinal cross-section of subsurf

conditions based on drilled SPT boring (T05) .............................................................

Figure 134. Victoria, Minnesota Bridge 10003 longitudinal cross-section of subsurf

conditions based on drilled CPT boring (T05) ............................................................

Figure 135. Boring T05................................................................................................

Figure 136. Resistance distribution input file – Victoria L.T. design stage ................

Figure 137. Input file for drivability analysis and resistance distribution along the pi

the input table presented in Figure 136. .......................................................................

Figure 138. Drivability analysis results .......................................................................

Figure 139. Driving log of TP1 Victoria static load test..............................................

Figure 140. Abutment W input file ..............................................................................

Figure 141. Abutment W driving resistance graph ......................................................

Figure 142. Input file for GeoDynamica modified WEAP analysis using CAPWAP

......................................................................................................................................

Figure 143. GeoDynamica modified WEAP driving resistance graph ........................

Figure 144. Driving resistance summary of Geodynamica modified WEAP, MPF12

load test results .............................................................................................................Figure 145. Sample pile driving equipment data form ................................................

Figure 146. Typical load testing values for Pile A acceleration vs. (a) relative wavel

(b) force duration .........................................................................................................

Figure 147. Typical loading durations for various tests performed on Test Pile #3 by

Geotechnical Engineering Research Laboratory of the University of Massachusetts

Newbury test site..........................................................................................................

Figure 148. Static pile load testing procedures according to ASTM ...........................Figure 149. Static pile load testing procedures according to MassDOT .....................

Figure 150. Load-settlement curve with (a) a non-specific scale for Pile Case No. 5,

the elastic compression line inclined at 20 degrees ....................................................

Figure 151. Load-settlement curve for Pile Case No. 5 of the PD/LT data set with th


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Figure 184. Test Pile #3 short duration test & static cyclic comparison of load distri

Figure 185. Histogram and frequency distributions of K SD for 186 PD/LT2000 pile c

types of soils ................................................................................................................Figure 186. Histogram and frequency distributions of K SD and K SLD for 30 and 20 P

pile-cases, respectively, in all types of soils ................................................................

Figure 187. K SLD values vs. pile diameter for all large diameter PD/LT2000 pile-cas

of soils ..........................................................................................................................

Figure 188. Comparison between pile capacity based on davisson’s criterion for slow

load tests and static cyclic load test capacity for 75 piles ............................................

Figure 189. Displacement over load versus displacement for Pile Case No. 5 accordfailure determination method .......................................................................................

Figure 190. Actual and extrapolated load-settlement relations for the re-analysis of P

14 using ranges of available data .................................................................................

Figure 191 actual and extrapolated load-settlement relations for the analysis of Pile

using ranges of the designated static capacity .............................................................


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List of Tables

Table 1. Construction pile evaluation practices Midwest states (May 2010 with updaTable 2. Statistical parameters for Q p/Qm values for all load test data from Flaate 19

Table 3. Statistical parameters for Q p/Qm values for all load test data from Fragaszy

......................................................................................................................................

Table 4. Statistical parameters for Q p/Qm values for all load test data from Paikowsk

1994..............................................................................................................................

Table 5. Statistical parameters for Q p/Qm values for all load test data from Davidson

Townsend, 1996. (Long and Maniaci, 2000) ...............................................................Table 6. Statistical parameters for Q p/Qm values for data from Eslami, 1996. (Long

2000) ............................................................................................................................

Table 7. Statistical parameters for Q p/Qm for FHWA database (Long and Maniaci, 2

Table 8. International database selected dynamic/SLT statistics for all piles (Long e

......................................................................................................................................

Table 9. Comprehensive database dynamic and static methods vs. SLT statistics (Lo

2009a) ..........................................................................................................................

Table 10. Dynamic vs. static method statistics (Long et al., 2009a) ...........................

Table 11. Capacity ratio statistics for all piles in the Illinois database ........................

Table 12. Capacity ratio statistics for H-Piles in the Illinois database ........................

Table 13. Capacity ratio statistics for Pipe-Piles in the Illinois database ....................

Table 14. Capacity ratio statistics for piles in sand in the Illinois database ................

Table 15. Capacity ratio statistics for piles in clay in the Illinois database .................


Table 17. Capacity ratio statistics for H-Piles in clay in the Illinois database .............


Table 19. Capacity ratio statistics for Pipe Piles in clay in the Illinois database .........

Table 20. Correction factors ........................................................................................

Table 21. Statistics for dynamic vs. corrected static methods, corrected Illinois datab

al., 2009a).....................................................................................................................Table 22. Statistics for dynamic vs. corrected static methods, comprehensive databa

al., 2009a).....................................................................................................................

Table 23. Databases best agreement methods summary ..............................................

Table 24. Parameter Feff to be used in Equation 9 for hammer and pile type combina


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Table 30. Summary of resistance factors developed using FORM and at a target reli

2.33) (Long et al., 2009b). ...........................................................................................

Table 31. Summary of resistance factors using FORM and βT = 2.33, based on distrmatching the extreme cases (Long et al., 2009b).........................................................

Table 32. Gates 1957 dynamic equation prediction for H and Pipe Piles (summary o

Paikowsky et al., 2009) ................................................................................................

Table 33. FHWA (1988) Modified Gates dynamic equation prediction for H and Pip

(summary of results from Paikowsky et al., 2009) ......................................................

Table 34. Hammers with different rated energy ..........................................................

Table 35. Summary of driving data statistics for MnDOT database ...........................

Table 36. Summary of total pile length .......................................................................

Table 37. Summary of pile cases categorized based on soil conditions ......................

Table 38. Summary of driving criteria – pile performance .........................................

Table 39. Equipment summary ....................................................................................

Table 40. Investigated equations..................................................................................

Table 41. Summary of available cases for CAPWAP and Energy Approach based on

measurements ...............................................................................................................

Table 42. Summary of statistical analysis and best fit line correlation (page 1/2) ......

Table 43. Updated investigated equations ...................................................................

Table 44. Summary of statistical analysis and best fit line correlation .......................

Table 45. Summary of statistical analysis and best fit line correlation .......................

Table 46. Summary of the extreme data cases related to MnDOT study ....................

Table 47. Dynamic equation predictions for H-Piles EOD condition only .................Table 48. Dynamic equation predictions for Pipe Piles EOD condition only .............

Table 49. Summary of selective outlier references for H Piles ...................................

Table 50. Summary of selective outliers references for Pipe Piles ..............................

Table 51. Details of additional low end outliers encountered in the analyses of the P

database. .......................................................................................................................

Table 52. Dynamic equation predictions for Pipe Piles EOD condition only .............

Table 53. Dynamic equation predictions for Pipe Piles EOD condition only blow comax 15BPI (20 cases) ..................................................................................................

Table 54. Dynamic equation predictions for Pipe Piles BOR condition only .............

Table 55. Dynamic equation predictions for Pipe Piles BOR condition only blow co

max 15 BPI (17 cases) .................................................................................................


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EXECUTIVE SUMMARY

Driven piles are the most common foundation solution used in bridge construction aU.S. (Paikowsky et al., 2004). The major problem associated with the use of deep fo

the ability to reliably verify the capacity and the integrity of the installed element in

Dynamic analyses of driven piles are methods attempting to obtain the static capacit

utilizing its behavior during driving. The dynamic analyses are based on the premis

each hammer blow, as the pile penetrates into the ground, a quick pile load test is be

out. Dynamic equations (aka pile driving formulas) are the earliest and simplest form

dynamic analyses. MnDOT used its own pile driving formula; however, its validity has never been thoroughly evaluated. With the implementation of Load Resistance F

(LRFD) in Minnesota in 2005, and its mandated use by the Federal Highway Admin

(FHWA) in 2007, the resistance factor associated with the use of the MnDOT drivin

needed to be calibrated and established.

Systematic probabilistic-based evaluation of a resistance factor requires quantifying

uncertainty of the investigated method. As the investigated analysis method (the molarge uncertainty itself (in addition to the parameters used for the calculation), doing

(i) Knowledge of the conditions in which the method is being applied, and

(ii) A database of case histories allowing comparison between the calculated val

measured.

The first phase of the research addressed these needs via:

(i) Establishing the MnDOT state of practice in pile design and construction, an

(ii) Compilation of a database of driven pile case histories (including field meas

static load tests to failure) relevant to Minnesota design and construction pra

The first phase of the study was presented in a research report by Paikowsky et al. (

I concentrated on establishing MnDOT practices, developing databases related to th

and examining different dynamic equations as well as developing new equations. Thresistance factors developed in Phase I for the driving of pipe piles were assessed to

conservative in light of the MnDOT traditional design and construction practices, an

Phase II of the research was initiated.

Ph II f th t d t bli h d t


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4. Examine the recommended formula in use with timber and prestressed preca

piles,

5. Examine WEAP analyses procedures for MN conditions and recommend pr be implemented, and

6. Examine load test procedures, interpretations and submittals.

The operative findings in Phase II relevant to the dynamic pile formula to be used b

MnDOT are the following:

1. The final formulation known as M

P

=

F12 (

20 Minne

×

sota Pile Formula 2012) that

for use:

× 10 where R n = nominal resistance (tons), H = s

1,t

0r

0oke

0 (

log

heig

ht

of fall) (ft), W = w

(lbs), s = set (pile permanent displacement per blow) (inch). The value of the

(W⋅H) used in the dynamic formula shall not exceed 85% of the manufactur

maximum rated energy for the hammer used considering the settings used du

2. The MPF12 is to be used with the follow

i

=

ng r

e

×

sis

tance factors (RF) in order

factored resistance:

For pipe and concrete piles, φ = 0.50, 2 < BC ≤ 15BPI

For H piles, φ = 0.60, 2 < BC ≤ 15BPI

3. The MPF12 recommended for timber piles is:

× 10 Where φ = 0.60

= 10 1,000 × log

4. Although the equations were developed for hammers with Eh ≤ 165 kip-ft, i

be applicable for hammers with higher energies but this was not verified dirstudy.

The independent examination of the developed equations against an independent da

measurements in MN and load tests conducted by the MnDOT, affirmed the effectiv

f th d ti F th it i d lib ti i


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1 BACKGROUND

1.1 Resistance Factor for MnDOT’s Pile Driving Formula – Phase I Stu

Driven piles are the most common foundation solution used in bridge construction a

U.S. (Paikowsky et al., 2004). The major problem associated with the use of deep fo

the ability to reliably verify the capacity and the integrity of the installed element in

Dynamic analyses of driven piles are methods attempting to obtain the static capacit

utilizing its behavior during driving. The dynamic analyses are based on the premiseach hammer blow, as the pile penetrates into the ground; a quick pile load test is be

out. Dynamic equations (aka pile driving formulas) are the earliest and simplest form

dynamic analyses. MnDOT uses its own pile driving formula; however, its validity

has never been thoroughly evaluated. With the implementation of Load Resistance F

(LRFD) in Minnesota in 2005, and its mandated use by the Federal Highway Admin

(FHWA) in 2007, the resistance factor associated with the use of the MnDOT drivin

needed to be calibrated and established.

Systematic probabilistic-based evaluation of a resistance factor requires quantifying

uncertainty of the investigated method. As the investigated analysis method contain

uncertainty itself (in addition to the parameters used for the calculation), doing so re

Knowledge of the conditions in which the method is being applied, and (ii) A datab

histories allowing comparison between calculated and measured values.

Phase I of the research addressed these needs via: (i) Establishing the MnDOT state

pile design and construction, and (ii) Compilation of a database of driven pile case h

(including field measurements and static load tests to failure) relevant to Minnesota

construction practices.

Establishing the MnDOT state of practice in pile design and construction was achiev

conducting: review of previously completed questionnaires, review of the MnDOT b

construction manual, compilation and analysis of construction records of 28 bridges

interviews with contractors, designers, and DOT personnel.

Compilation of a database of driven pile case histories included field measurements

load tests to failure relevant to Minnesota design and construction practices. Based o


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by diesel hammers ranging in energy from 42 to 75 kip/ft. with 90% of the piles dri

beyond 4 Blows Per Inch (BPI) and 50% of the piles driven to or beyond 8 BPI. Up

statistics developed in Phase II of the study (presented in Chapter 3) refers to projecMinneapolis where the most common piles were CEP 16″ × 0.3125 and 16″ × 0.25

34% and 21% of the total number of CEP piles compiled in that database. These val

somehow different from the statistics presented in Phase I report, which is more app

entire state.

Large data sets were assembled, answering to the above practices. As no data of sta

were available from MnDOT, the databases were obtained from the following: (i) Rhistories from the dataset PD/LT 2000 used for the American Association of State H

Transportation Officials (AASHTO) specification LRFD calibration (Paikowsky an

2000, Paikowsky et al., 2004) (ii) Collection of new relevant case histories from DO

sources.

In total, 166 H pile and 104 pipe pile case histories were assembled in the MnDOT

database. All cases contain static load test results as well as driving system and, drivresistance details. Fifty three percent (53%) of the H piles and 60% of the pipe piles

MnDOT LT 2008 were driven by diesel hammers.

The static capacity of the piles was determined by Davisson’s failure criterion, estab

measured resistance. The calculated capacities were obtained using different dynam

namely, Engineers News Record (ENR), Gates, Modified Gates, WSDOT, and MnD

statistical performance of each method was evaluated via the bias of each case, expr

ratio of the measured capacity over the calculated capacity. The mean, standard dev

coefficient of variation of the bias established the distribution of each method’s resi

The distribution of the resistance along with the distribution of the load and establis

reliability (by Paikowsky et al. 2004 for the calibration of the AASHTO specificatio

utilized to calculate the resistance factor associated with the calibration method und

condition. Two methods of calibration were used: MCS (Monte Carlo Simulation),

iterative numerical process, and FOSM (First Order Second Moment), using a close

solution.

The MnDOT equation generally tends to over-predict the measured capacity with a

The performance of the equation was examined by detailed subset databases for eac


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obtained resistance distribution using numerical method (Goodness of Fit tests) and

comparisons of the data vs. the theoretical distributions.

Due to the MnDOT dynamic equation over-prediction and large scatter, the obtained

factors were consistently low and a resistance factor of φ = 0.25 was recommended

with the original equation, for both H and pipe piles. The reduction in the resistance

φ = 0.40 currently in use, to φ = 0.25, reflects a significant economic loss for a gain

consistent level of reliability. Alternatively, one can explore the use of other pile fie

evaluation methods that perform better than the currently used MnDOT dynamic eq

allowing for higher efficiency and cost reduction.

Two approaches for remediation were presented. In one, a subset containing dynam

measurements during driving was analyzed, demonstrating the increase in reliability

dynamic measurements along with a simplified field method known as the Energy A

Such a method requires field measurements that can be accomplished in several way

An additional approach was taken by developing independently a dynamic equation

MnDOT practices. A linear regression analysis of the data was performed using a co

software product featuring object oriented programming. The simple obtained equat

structure) was calibrated and examined. A separate control dataset was used to exam

equations, demonstrating the capabilities of the proposed new MnDOT equation. In

database containing dynamic measurements was used for detailed statistical evaluat

existing and proposed MnDOT dynamic equations, allowing comparison on the sam

field measurement-based methods and the dynamic equations.

Finally, an example was constructed based on typical piles and hammers used by M

example demonstrated that the use of the proposed new equation may result at time

savings and at others with additional cost, when compared to the existing resistance

currently used by the MnDOT. The proposed new equation resulted in consistent sa

compared to the MnDOT current equation used with the recommended resistance fa

developed in this study for its use (φ = 0.25).

1.2 Research Objectives

1.2.1 Overview


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8

Table 1. Construction pile evaluation practices Midwest states (May 2010 with updates

State Contact Person Material Findings

I l l i n o i s

Bill [email protected]

1. Report R27-24“Evaluation/Modification ofIDOT Foundation Piling

Design and ConstructionPolicy”, Long, Hendrix andBaratta, Jan 2007 to March2009, 204pp.

2. Bridge Manual directions.3. Proposal for new research4. Various inspection and

relations for evaluation.

1. H pile (~60%), Pipe pile (~38%)2. ENR until 2006

3. Mod. Gates with φ=0.50 (arbitrary)

2007-20094. Long study tried to develop

independent equation but it was notworking well (B.K. believes the budget was limited). They thereforedecided to use WS-DOT but juststarted to check its performance withthe new study (see comments) with purchasing a PDA.

1. Original2. New res

Design P

and Statmainly P$300,00

3. Third ph(update

I o w a

Robert [email protected]

Ahamad [email protected]

1. Design Charts based on loadtest interpretations (300carried out over 20 years ago).

2. Possible data from Iowa stateresearch that is planned to becompleted by year end.

3. Bridge Manual.

1. H pile (80-90%), All others includingdrilled shafts 10-20%, do not use Pipe piles. Drive with diesel hammers.

2. Did not start to implement LRFD yet.3. Pile design is based on the charts.

Construction runs WEAP on everyhammer submittal using that WEAP inthe field.

4. PDA used by construction (own one)whenever needed but not routinely.

1. Engageda researcdesign c

conduct 2. Research

as part oframe avTotal bu

3. Update Sritharadatabase

W i s c

o n s i n

Robert [email protected]

1. “Comparison of Five DifferentMethods for Determining PileBearing Capacities”, report byLong, Hendrix and Jaromin,

February 2009, 160pp.2. Collaboration in checking the

equations developed for MN.

1. H piles (75%) 10×42, 12×53; Pipe piles – closed ended (25%) 10¾ –12¾

2. D – 12 to D – 30

3. Mod. Gates with φ=0.50 withoutdistinction between H to Pipe.

1. The reseaware ofand Pipean offsh

2. ListenedFebruary plans to for MN.

mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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To estimate the ultimate capacity, Qu, of a pile under axial load, the sum of the pile

Q p, and the shaft capacity, Qs, was used by Long and Maniaci in the format tradition

presented:

Qu = Q p + Qs

Equation (1) can be further broken down as follow:

Qu = (q p *A p-W) + ∑ siii=1 Where q p = bearing capacity at the pile’s tip, A p = area of pile tip, W = weight of pil

f si=ultimate skin resistance per unit area of pile shaft segment i, C i, = perimeter of p

li= length of pile segment i, and n number of pile segments

In order to evaluate the ultimate pile capacity, the magnitude f s for each pile segmen

tip resistance q p must be estimated. Most of this information is based on empirical m

derived from correlations of measured pile capacity with soil data.

The dynamic formulae are an energy balance equations which relate the energy deli pile hammer to the work produced during pile penetration. The dynamic formulae a

in equations of the following form:

eWH = Rs

Where e= efficiency of hammer system, W = ram weight, H = ram stroke, R= pile res

s= pile set (pile displacement per hammer blow). The pile resistance, R, is assumed

directly to the ultimate capacity, Qu.

Dynamic formulae provide a simple method to estimate pile capacity; however, ther

shortcomings associated with their simplified approach (FHWA, 1995):

• Dynamic formulae focus only on the kinetic energy of driving, not on the dr

• Dynamic formulae assume constant soil resistance rather than a velocity dep

resistance, and• The length and axial stiffness of the pile are ignored.

Two techniques were used to identify how well predicted pile capacity agreed with

pile capacity. The first is a graphic representation of predicted capacity versus the m


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distortion of the match between measured to calculated values and the viewer needs

of that when examining such presentation.

The second technique uses statistical methods to quantify the degree of agreement b

predicted and measured capacity for a specific method. The statistical analysis used

Maniaci was done with the relationship between predicted values versus measured v

(Q p/Qm). Bias and precision, were used as two simple statistical parameters for defin

method’s ability to predict capacity. Bias is the systematic error between the averag

Q p/Qm and the ideal ratio of Q p/Qm (which is unity). Statistically, the bias can be est

sample mean. Precision is the scatter or “variability of a large group of individual te

obtained under similar conditions” (ASTM C670-90a, 1990). Statistically variability

estimated with a sample standard deviation. The distribution Q p/Qm is log-normal (C

1969). A log-normal distribution means that the values of ln (Q p/Qm) are normally d

Accordingly Long and Maniaci (2000) estimated the mean and standard deviation fo

(Q p/Qm) for the predictive measures as a method to assess the bias and precision.

It should be noted that Long and Maniaci (2000) presented the bias ratio as that of p

value over measured value, opposite to the common way, and hence, required at the

report to re-evaluate the reversed ratio (correct bias) to be used in their calculations.

detailed at a later stage (section 2.3.7), this reversed ratio affected our ability to com

used for analyzing Mn DOT Phase I study to that presented by Long and Maniaci (2

Long et al. 2009a,b.

The analyses of the load test databases were investigated separately and together to

effects of using EOD data versus BOR data to estimate capacity. The results from c

penetration methods were compared with those obtained using driving data. The Ga

was investigated further and modifications to improve the equation were provided.

2.2.2 Databases Summary

2.2.2.1

Flaate, 1964

The pile load test data used by Flaate represents pile types and installation methods period of time that pre-dates 1964. The Flaate (1964) database includes, pile types a

installation methods that are no longer common in today’s practice, hence, these dat

result of the general database (Long and Maniaci, 2000). For example, several of th

tests in the Flaate database were conducted on timber piles. Timber piles are not use


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Figure 1. Graphic representation of predicted vs measured capacity (Long an

2000)


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Table 2. Statistical parameters for Qp /Qm values for all load test data from F

(Long and Maniaci, 2000)

Method Hammer Type n µ σlnEN

All hammer types

116 1.23 0.790

Hiley 116 0.82 0.499

Janbu 116 1.03 0.307

Gates 116 0.78 0.429

EN

All hammer types except gravity

54 2.45 0.523

Hiley 54 0.74 0.614

Janbu 54 1.08 0.397

Gates 54 0.85 0.459

EN

Gravity hammer only

62 0.68 0.393Hiley 62 0.91 0.346

Janbu 62 0.98 0.192

Gates 62 0.73 0.391


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Figure 3. Predicted versus measured capacity from Fragaszy 1988, 1989 (L

Maniaci, 2000)

Table 3. Statistical parameters for Qp /Qm values for all load test data from Fra

1989 (Long and Maniaci, 2000)

Methodµln µ σln

EN 0.950 2.58 0.610

Hiley 0.045 1.05 0.438

Janbu -0.060 0.94 0.437

Gates -0.459 0.63 0.307

2.2.3.2

Paikowsky et al., 1994Two large datasets were collected and interpreted in this study. One set (labled PD/L

dynamic measurements on 120 piles tested statically to failure. The other set (labled

contained 403 piles monitored during driving without static load tests. The measure

predicted capacities reported by Paikowsky et al., 1994, are plotted in Figure 4. The


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This dataset provides insight into possible errors associated with the dynamic formu

investigated previously. For example, a very similar formula to the EN formula was

ME approach, but the correlations are improved. A possible reason for improved ac

be because pile dynamic monitoring results in more reliable estimates for energy de

pile and the quake developed by the pile as compared to the rough estimates of ham

(Long and Maniaci, 2000). A detailed analysis and explanations are provided by Pai

(1994) and the aforementioned references (not detailed by Long and Maniaci, 2000)

2.2.3.3 Davidson and Townsend, 1996

The measured and predicted capacities reported by Davidson and Townsend, 1996,

Figure 5, and statistical results are presented in Table 5. All the data presented are r

concrete piles driven in Florida. The predicted capacities were obtained using PDA

analysis, CAPWAP analyses, and static method evaluation using SPT94. There are

of capacity using the PDA (the Case Method) and CAPWAP under both driving con

EOD and BOR.


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Table 5. Statistical parameters for Qp /Qm values for all load test data fro

and Townsend, 1996 (Long and Maniaci, 2000)

Method µln µ σlnPDA-EOD -0.171 0.84 0.298

PDA-BOR 0.070 1.07 0.266

CAPWAP-EOD -0.356 0.70 0.375

CAPWAP-BOR -0.052 0.95 0.317

SPT94 -0.626 0.53 0.734

2.2.3.4 Eslami, 1996

Eslami only considered methods that use results of cone penetration test to predict t

capacity of piles. He investigated six methods and the graphical presentation of the p

versus measured capacities are provided in Figure 6. The statistical parameters for Q

are presented in Table 6. All methods provide a very narrow range and relatively sm

considering the static capacity prediction methods are very different from one metho

The predictions and the statistical parameters identify much better agreement betwe

and measured capacity using cone methods than using other static methods such as method reported by Davidson et al. (1994).


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Table 7. Statistical parameters for Qp /Qm for FHWA database (Long and Ma

Method n µ σln

E n d o f

D r i v i n g

E O D

EN 123 2.60 6.75Gates 123 0.53 0.410

WEAP 88 0.64 0.501

ME 73 0.93 0.462

PDA 77 0.71 0.454

CAPWAP 75 0.58 0.591

B e g i n

n i n g o f

R e s t r i k e

B

O R

EN 116 4.62 0.514

Gates 116 0.72 0.392

WEAP 114 1.11 0.385

ME 92 1.41 0.363PDA 85 0.91 0.319

CAPWAP 112 0.86 0.269

Static Formula 112 1.15 0.556

*Note: All load tests are included in which a methodcould be used to compute capacity.

The conclusions obtained by Long and Maniaci (2000) from the study are presented

prediction method analyzed (See Figures 7 and 8 for the performance of the various

a. EN Formula. This method appears to over-predict capacity and exhibits poor

The method requires calibration to address the issue of over-prediction. The

the EN method is poor and is slightly improved when BOR conditions are u

the improvement is not enough to make this a precise method for predicting

capacity.

b. Gates Formula. The Gates formula under-predicts capacity and exhibits goo

The method requires calibration to address the issue of under-prediction. Th

for the Gates method is good – to – fair and the use of data from BOR condi

improve the precision of the method.

c. WEAP. WEAP under-predicts capacity for EOD conditions and slightly ove

capacity when using BOR. Precision was good when using BOR conditions,

when using EOD information. It appears that WEAP predictions benefits sig

from using BOR data.

d. Measured Energy (ME) Method (Energy Approach). The ME approach pred

well for EOD conditions and over-predicts capacity when using data from B

conditions. Precision is good when using EOD and BOR information. It app


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2.2.4

Conclusions

1. The purpose of the report prepared by Long and Maniaci (2000) was to evalu

effectiveness of the current practices of IDOT when predicting driven pile ca

the time Long and Maniaci report was written, LRFD was being developed i

States but was not yet adopted for practice by IDOT.

2. Long and Maniaci used different databases from the literature that did not m

pile types, soil characteristics, and driving conditions of the current practice

of Illinois. These conditions are, by and large, similar to those found in Minn

way of driven steel piles though most piles driven in Illinois are H piles and piles.

3. Several dynamic methods were analyzed to estimate their accuracy and valid

Gates, WEAP, PDA, EM, and CAPWAP. The first three methods estimate p

based on field observations of driving resistance, like hammer energy based

pile type, soil type, and with this information develop a relationship between

driving resistance. The last three methods, required dynamic measurements

variation of force and velocity with respect to time during driving. The meth by Long and Maniaci, utilized pile behavior at the end of driving (EOD) or b

restrike (BOR).

4. Each of the databases utilized by Long and Maniaci used pile capacity (failu

a different static load test interpretation method, which presents just a variat

capacity (typically 3.5 - 5%, see Paikowsky et al., 2004). The database prese

Flaate (1964), and the one presented by Paikowsky et al. (1994), used Davis

criteria to evaluate the pile’s capacity. Fragaszy (1988, 1989) used Q-D overand Olson tangent intersection method.

5. The statistical analyses was conducted based on the relationship between pre

versus measured values (Q p/Qm), which are the inverse of the bias definition

and the one used by Paikowsky et al. (2004) to develop the LRFD parameter

approach indicates that for mean values above unity, the method is over-pred

capacity and if it is under unity, the method under-predicts pile capacity. Th

was found to be log-normal.

6. Long and Maniaci (2000) analyzed the databases separately and together to

effects using EOD versus BOR data for estimated capacity. The results are p

graphically and statistically. No relationships were investigated regarding so


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The information obtained with this database was sufficient to allow the developmen

resistance factors for dynamic formulae, also for the development of an optimized m

improve the agreement between design and field values.

This database compiles the data from several smaller load test databases. The databa

those developed by Flaate (1964), Olson and Flaate (1967), Fragaszy et al. (1988), F

(Rausche et al., 1996), Allen (2007), and Paikowsky et al. (2004). A total of 132 loa

collected for this database. Sufficient information is available for each pile so that th

capacity based on any of the dynamic formulae evaluated can be determined. Suffic

information is not available so that the pile capacity can be estimated based on static

The results of a static load test are available for each pile.

Based on the piles in the International Database, the FHWA-UI formula predicts cap

the most accuracy and precision. This formula is followed by the WSDOT, then FH

formulae in degree of accuracy and precision, see Figure 9 and Table 8 presenting a

summarizing the obtained results. Based on the analysis of the data, Long et al. (200

the FHWA-Gates, FHWA-UI, and WSDOT formulae into one category of predictio

performs fairly well and group WEAP and the EN-IDOT formula into a category of prediction that does not perform as well.

Table 8. International database selected dynamic/SLT statistics for all piles (L

2009a)

WSDOTFHWA-

GatesFHWA-UI

vs.

SLT

Mean 1.14 1.22 1.02

Std. Dev. 0.51 0.59 0.41

COV 0.45 0.49 0.41

r 2 0.35 0.31 0.42

n 132 132 132


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Figure 9. International database selected predictions of dynamic equations

dynamic (Long et al. 2009a)

2.3.3

Comprehensive Database

2.3.3.1

Extent and Data

The Comprehensive database comprised of the following information:


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T bl 9 C h i d t b d i d t ti th d SLT t ti t


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Table 9. Comprehensive database dynamic and static methods vs. SLT statist

al., 2009a)

WSDOT/SLT FHWA-Gates/SLT FHWA-UI/SLT IDOT-S/SLT K-IDOT/SLT

Mean 1.02 1.02 0.97 1.30 2.00 Std. Dev. 0.29 0.31 0.42 0.88 1.37

COV 0.29 0.31 0.43 0.67 0.68 r 2 0.52 0.73 0.52 0.36 0.42 n 26 23 23 26 26

2 3 3 4 A t b t t ti th d d d i f l


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2.3.3.4 Agreement between static methods and dynamic formulae

Some general trends appear when comparing the Dynamic vs. Static capacity predic

piles in clay the general trend is for the dynamic formula to predict higher capacity

method, with the exception of the WSDOT/ICP data (Figure 11). In sand, the K-IDO

methods predict higher capacities than the dynamic formulae. The IDOT static meth

opposite, predicting a smaller capacity in sand than dynamic formulae. In pipe piles

formula will generally predict a higher capacity than the IDOT-S and K-IDOT meth

opposite occurs with the ICP method, it predicts a higher capacity than the dynamic

H piles, the K-IDOT and ICP methods tend to predict higher capacity than the dyna

formulae. The dynamic formulae tend to predict higher capacities overall when com

static analyses. The dynamic formulae tend to predict higher capacities in H-piles th

IDOT-S method.

The dynamic/static analyses provide information only for the agreement between a


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The dynamic/static analyses provide information only for the agreement between a

static method; they do not indicate how accurately either method predicts the actual

capacity. A considerable amount of scatter can be seen in all the Dynamic/Static ana

indicates it would not be uncommon for any single pile to produce capacity ratios ap

different than the average capacity ratios (Long et al. 2009a).

Based on the comprehensive database, the WSDOT and the FHWA-Gates formulae

to be the most precise dynamic formulae, while the EN-IDOT formula is the least pr

WEAP and the FHWA-UI formulae have intermediate precisions between the two a

categories.

Table 10 presents the statistical analysis for the relationship between a dynamic met

static method. Note that the good agreement between any two methods does not ind

either of the methods accurately predicts the capacity of a pile, but rather indicates t

compared methods agree well with each other.

The ICP method appears to offer the best agreement with dynamic formulae (Table

WSDOT, FHWA-Gates, and FHWA-UI/Static average capacity ratios display the lowith the ICP method, followed by the IDOT Static method, and then the K-IDOT m

lowest COV was displayed by the WSDOT formulae, combined with the IDOT, K-I

ICP methods.

Based on the comprehensive database, the WSDOT and the FHWA-Gates formulae

to be the most precise dynamic formulae, while the EN-IDOT formula is the least pr

WEAP and the FHWA-UI formulae have intermediate precisions between the two a

categories.

Table 10. Dynamic vs. static method statistics (Long et al., 2009a)

WSDOT FHWA-Gates FHWA-UI

vs. IDOT Static

Mean 1.16 1.11 1.12

Std. Dev. 0.86 0.88 0.97

COV 0.74 0.80 0.87

r 0.37 0.29 0.29

n 26 23 23

vs. Kinematic

IDOT

Mean 0.79 0.74 0.77Std. Dev. 0.66 0.64 0.81

COV 0.84 0.87 1.050 27 0 30 0 18


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Figure 12. Dynamic vs. static capacities for the Illinois Database (Long et a

The Illinois database offered information that was very useful as it consists of data g

from the state of Illinois; however static load tests were not performed on any of the

such, no firm conclusions can be drawn about the accuracy of any given method. In

conclusions can be drawn about how well a given dynamic formula agrees with a gi

method. This information is useful, as a low COV between Dynamic/Static data ind

length of pile estimated to be necessary using a static method will be similar to the l

Table 11. Capacity ratio statistics for all piles in the Illinois

database

Table 12. Capacity ratio statistics for H

database


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33

Table 13. Capacity ratio statistics for Pipe-Piles in the Illinoisdatabase

Table 14. Capacity ratio statistics for pIllinois database

Adapt


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Table 19. Capacity ratio statistics for Pipe Piles in clay in the Illinois dat


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The cumulative distribution of the FHWA-Gates/IDOT Static data is plotted in Figu

axis of the figure indicates the probability that the FHWA-Gates/IDOT Static capac

be less than or equal to the capacity ratio on the x-axis. The solid line represents the

distribution for a log normal distribution data.

2.3.5

Development of Correction Factors


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2.3.5.1 Overview

Based upon the results of the analyses carried out on the above reviewed three databmethods were defined as most useful utilizing terms of actual accuracy and precisio

agreement between static and dynamic methods. The IDOT Static, K-IDOT, and IC

were found to be the most promising static methods, while the FHWA-Gates, FHW

WSDOT formulae were found to be the most promising dynamic methods. To furth

these results, correction factors were applied to each static method. These correction

based on pile type and soil type, and are unique to each method. Table 20 presents t

correction factors for each Dynamic/Static combination. Based on these correction fcorrected statistics were determined for the Dynamic/Static data for both; the Illinoi

Comprehensive Databases. The International Database was not analyzed due to insu

information to determine the pile capacity using static methods.

Table 20. Correction factors

Pipe, Clay Pipe, Sand H, Clay H, Sand

WSDOT/ICP 1.067 0.730 1.277 0.353

FHWA-Gates/ICP 1.178 0.924 1.438 0.461

FHWA-UI/ICP 1.355 0.677 1.226 0.300

WSDOT/IDOT-S 1.174 0.758 1.500 0.724

FHWA-Gates/IDOT-

S

1.284 0.955 1.500 1.073

FHWA-UI/IDOT-S 1.500 0.711 1.500 0.500

WSDOT/K-IDOT 1.174 0.758 1.500 0.300

FHWA-Gates/K-

IDOT

1.284 0.955 1.500 0.387

FHWA-UI/K-IDOT 1.500 0.711 1.353 0.300

2.3.5.2 Corrected Illinois Database

Figure 14 and Table 21 present the corrected analyses for Illinois database using the

correction factors with the range in average (for each group) between 1.09 to 1.22, i

the correction factors brought the average capacity ratios to a value close to unity. Fcorrected Illinois database, the K-IDOT method appears to be the static method that

with the dynamic formulae, both with respect to the COV. All the dynamic formula

well when analyzed using the K-IDOT method, the range in Dynamic/K-IDOT COV

misleading. For example, all the graphs presented in Figure 14 show very large scat


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being presented on an unreasonably large scale) that is associated with a coefficient

determination r 2 of a very low value (see Table 21). This coefficient suggests practic

correlation between the two values. Paikowsky et al. (1994) defined ranges of a coe

determination for geotechnical data to be r 2 < 0.60 poor, 0.60 ≤ r 2 < 0.80 moderate,

having a good correlation. The ‘corrected’ data presented in Table 21 shows r 2 < 0.1

average means smaller than 1.22, hence of very poor value. One should also note th

of the averages reported in Table 21 is in the range of 0.43 to 0.53, again suggesting

as observed in Figure 14.

Table 21. Statistics for dynamic vs. corrected static methods, corrected Illino

(Long et al 2009a)


61/513

(Long et al., 2009a)


vs. Corrected

IDOT Static

Mean 1.19 1.16 1.22

Std. Dev. 0.62 0.54 0.67

COV 0.52 0.46 0.55

r 0.05 0.01 0.08

n 92 92 92

vs. Corrected

Kinematic IDOT

Mean 1.11 1.12 1.10Std. Dev. 0.54 0.48 0.53

COV 0.49 0.43 0.49

r 2

0.07 0.05 0.08n 92 92 92

vs. Corrected ICP

Mean 1.16 1.14 1.13Std. Dev. 0.67 0.62 0.63

COV 0.58 0.55 0.56r 2 0.09 0.11 0.15n 92 92 92

2.3.5.3

Corrected Comprehensive DatabasePredicted static capacities in the Comprehensive Database were corrected using the

factors determined for the Illinois database (Table 20). The statistical results for the

Comprehensive database are summarized in Table 22, and presented graphically in

The corrected K-IDOT method best agrees with the WSDOT and FHWA-Gates form

the sub-categories of the WSDOT/Corrected K-IDOT data, there appears to be little

pipe piles and H-piles; however, there is a stronger tendency to over-predict capacit piles. The degree of scatter is smaller for H-piles than Pipe-piles.

The sub-categories of the FHWA-Gates/Corrected K-IDOT data suggested that ther

tendency to under-predict capacity for both H-piles and pipe-piles. There is a tenden

predict capacity in clay with a higher degree of scatter.

For the statistical evaluation of these results, refer to the commentary provided in th

section, noting that the COV values in Table 22 range between 0.56 to 0.78 (!) and r

cases is smaller than 0.37.

Table 22. Statistics for dynamic vs. corrected static methods, comprehensive

(Long et al 2009a)


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(Long et al., 2009a)


vs. Corrected

IDOT Static

Mean 1.20 0.94 1.17

Std. Dev. 0.86 0.72 0.91

COV 0.71 0.77 0.78

r 0.37 0.29 -

n 26 23 23

vs. Corrected

Kinematic IDOT

Mean 1.09 0.90 0.96Std. Dev. 0.63 0.56 0.62

COV 0.58 0.62 0.65

r 2

0.27 0.30 -n 26 23 23

vs. Corrected ICP

Mean 0.94 0.78 0.84Std. Dev. 0.60 0.53 0.56

COV 0.64 0.67 0.66r 2 0.00 0.00 -n 26 23 23

2.3.5.4 Recommended Methods

Table 23 summarizes the best agreement between the different methods of pile capa


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Table 23 summarizes the best agreement between the different methods of pile capa

and the databases utilized. Based on their research, Long et al. (2009a) proposed tha

WSDOT formula replaces the FHWA Gates formula as the standard method of desiconstruction and verification for pile capacity, and the Modified IDOT static metho

used to develop the Structure Geotechnical Report (SGR) pile design tables. The eq

used, therefore, is:

= 6.6(10) Where F eff is provided in Table 24.

Table 23. Databases best agreement methods summary

DYNAMIC METHOD STATIC METHO

International DatabaseUI-FHWA,WSDOT,FHWA-

GATESK-IDOT

Comprehensive DatabaseWSDOT,FHWA-GATES,UI-

FHWA*K-IDOT

Illinois Database

H-PILES IN SAND:

• FHWA-GATESH-PILES IN CLAY:

• UI-FHWAALL H-PILES:

• FHWA-GATESPP IN SAND:

• FHWA-GATESPP IN CLAY:

• FHWA-GATESALL PP:

• WSDOTALL PILES IN SAND:

• FHWA-GATESALL PILES IN CLAY:

• FHWA-GATESALL PILES:

• FHWA-GATES

H-PILES IN SAND:

• IDOTH-PILES IN CLAY:

• IDOTALL H-PILES:

• IDOTPP IN SAND:

• IDOTPP IN CLAY:

• DrivenALL PP:

• DrivenALL PILES IN SAN

• IDOTALL PILES IN CLA

• K-IDOTALL PILES:

• IDOT

*The corrected K-IDOT provided the best agreement with the prediction of capacity fdynamic formula a

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