NDOT Research Report Report No. 515-13-803 LRFD Resistance Factor Calibration for Axially Loaded Drilled Shafts in the Las Vegas Valley July, 2016 Nevada Department of Transportation 1263 South Stewart Street Carson City, NV 89712 Technical Report Documentation Page Report No. 2. Government Accession No. 5. Report Date 515-13-803 4. Title and Subtitle 3. Recipients Catalog No. LRFD Resistance Factor Calibration for Axially Loaded Drilled Shafts in the Las Vegas Valley July 19, 2016 7. Authors 8. Performing Organization Report No. 6. Performing Organization Code Ramin Motamed, Sherif Elfass, Kevin Stanton 9. Performing Organization Name and Address 10. Work Unit No. University of Nevada Reno 1664 N. Virginia St. Reno, NV 89557-0258 11. Contract or Grant No. P515-13-803 15. Supplementary Notes 16. Abstract Resistance factors for LRFD of axially loaded drilled shafts in the Las Vegas Valley are calibrated using data from 41 field load tests. In addition to the traditional implementation of Monte Carlo (MC) simulations for calibration, a more robust technique is investigated in which nested MC simulations are employed to capture the uncertainty associated with the interpretation of material properties from in-situ test data. Measures are also taken to improve design procedures regarding cemented sandy soils colloquially referred to as caliche. While caliche is common in Las Vegas, its potential contribution to resistance is difficult to predict using typical site investigation data and there is currently no consensus among local engineers regarding how it should be considered in design. Thus, an approach for treating caliche is proposed and compared to three other potentially viable methods. Overall it is found that the proposed design approach produces the most accurate nominal axial capacity predictions and the nested MC simulations yield lower resistance factors than traditional calibration procedures. 17. Key Words 18. Distribution Statement Deep Foundations, Drilled Shafts, Reliability, LRFD 19. Security Classif. (of this report) 20. Security Classif. page) (of this 21. No. of Pages 166 1 22. Price Disclaimer This work was sponsored by the Nevada Department of Transportation. The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of Nevada at the time of publication. This report does not constitute a standard, specification, or regulation. LRFD Resistance Factor Calibration for Axially Loaded Drilled Shafts in the Las Vegas Valley Prepared for The Nevada Department of Transportation Prepared by Ramin Motamed (PI), Ph.D., P.E. Assistant Professor Email: motamed@unr.edu Tel: 775-784-6960, and Sherif Elfass (Co-PI), Ph.D., P.E. Research Associate Professor Email: elfass@unr.edu, and Kevin Stanton Graduate Research Assistant Email: kevinstanton@nevada.unr.edu Department of Civil and Environmental Engineering University of Nevada 1664 N. Virginia St. Reno, NV 89557-0258 July 19, 2016 Contents Notation 5 List of Figures 9 List of Tables 13 Abstract 14 Acknowledgments 14 Disclaimer 15 1 Introduction 1.1 Literature Review . . . . . . . . . . . . . . . . . . . 1.1.1 Other Resistance Factor Calibration Studies 1.1.2 Principles of Resistance Factor Calibration . 1.1.3 Soil Conditions in the Las Vegas Valley . . . 1.1.4 Field Load Testing of Drilled Shafts . . . . . . . . . . 15 16 16 18 25 32 . . . . 36 36 41 41 45 2 Measured Data 2.1 Database . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Sharing . . . . . . . . . . . . . . . . . . 2.2 Interpretation of Bi-Directional Load Test Data 2.3 Scoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Predicted Resistances 49 4 Calibration Procedures 57 5 Results 65 2 Final Report CONTENTS 6 Conclusions and Final Recommendations 6.1 Final Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 71 72 References 73 Appendices 76 A NDFLTD 77 B Collected Load Tests and GI Reports 78 C Interpreted Stratigraphy and Geomaterial Properties 79 D Equivalent Top-Down Load-Settlement Curves 138 E Recommended Procedure for Analysis of Axially Loaded Drilled Shafts in Las Vegas 161 F Example: Recommended LRFD Procedure for a Drilled Shaft in Las Vegas163 3 Notation The following symbols are used in this report: CDF = cumulative distribution function; COV = coefficient of variation; B = shaft diameter; g = margin of safety; L = shaft embedded length; L1 = level 1 approach for LRFD resistance factor calibration; L2 = level 2 approach for LRFD resistance factor calibration; N = number of iterations; NSP T = Standard Penetration Test blow count (blows per foot); PDF = probability distribution function; qu = unconfined compressive strength; Q = axial load at shaft head; R = random variable for axial resistance; Q = random variable for axial load; Rm = measured axial resistance; Rp = predicted nominal axial resistance; RR = factored axial resistance; RQD = Rock Quality Designation; su = undrained shear strength; β = reliability index; φ′ = effective friction angle; φRT = total LRFD resistance factor for the strength limit state; γ = total unit weight; λ = prediction bias (defined as Rm /Rp ); and σv′ = vertical effective stress. 4 List of Figures 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 1.11 1.12 1.13 (a) Interpreted measured and predicted drilled shaft resistances and (b) histogram of bias (Rm /Rp ) from Abu-Farsakh et al. (2012) . . . . . . . . . . . . Interpreted measured and predicted drilled shaft resistances from Garder et al. (2012) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stages of a typical LRFD calibration for deep foundations after Paikowsky (2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example of random variables of a limit state function, µR and µS , in standard normal space and the associated failure surface, G(x) = 0 (GL (x) = 0 is the linearized version), after Paikowsky (2004) and Ayyub et al. (2000) . . . . . Example soil profiles and corrected standard penetration blow counts for the Vegas High Roller project (from Kluzniak et al., 2014) . . . . . . . . . . . . Vs scatter for different soil unit in the Las Vegas Valley (Murvosh et al., 2013) Characteristic Vs profiles for different soil unit in the Las Vegas Valley (Murvosh et al., 2013) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Using seismic methods to detect caliche: example case from the Las Vegas Springs Preserve (Luke et al., 2003) . . . . . . . . . . . . . . . . . . . . . . . Generalized cross-sections for the Las Vegas Valley showing the basin-fill deposits and components of the groundwater flow system under (A) predevelopment conditions and (B) modern conditions (from Thiros et al., 2010) Example of a traditional top-down static load test on a drilled shaft (Brown et al., 2010) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example of a bi-directional (O-Cell) load test setup . . . . . . . . . . . . . . Example of a statnamic loading apparatus (Brown et al., 2010): (a) piston and silencer assembly, (b) load cell and laser target, (c) laser for displacement measurement, (d) schematic diagram . . . . . . . . . . . . . . . . . . . . . . Example dynamic load tests on drilled shafts using a drop weight (left) and pile hammer (right) (Brown et al., 2010) . . . . . . . . . . . . . . . . . . . . 5 17 18 20 23 28 29 30 30 31 32 33 33 34 Final Report LIST OF FIGURES 2.1 2.2 2.3 2.4 2.5 2.6 2.7 Approximate locations of the test shafts included in the study . . . . . . . . Distribution of select characteristics among the data employed for calibration Object components and information mapping in the NDFLTD . . . . . . . . Main search window in the NDFLTD . . . . . . . . . . . . . . . . . . . . . . Example GI report in the NDFLTD . . . . . . . . . . . . . . . . . . . . . . . Example load test report in the NDFLTD . . . . . . . . . . . . . . . . . . . Determination of the rigid equivalent top-down load-settlement curve from bidirectional load test data where Qneti is the net load for movement increment i (modified from original figure provided by Loadtest Inc.) . . . . . . . . . . 2.8 Example patterns of developed side shear stress and corresponding parameters needed to estimate elastic compression above the load cell(s) in a bi-directional load test, after Loadtest Inc. (2000). Note that w′ denotes the buoyant weight when below the water table . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9 Analytical framework for a single shaft section in the t-z method for correcting bi-directional load test data for elastic compression above the load cell, after Loadtest Inc. (2000) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.10 Load test and GI quality scores for the data included in this study . . . . . . 3.1 3.2 3.3 3.4 3.5 3.6 Histogram of unconfined compressive strengths of caliche core samples in Las Vegas reported by Western Technologies Inc. (1994), Arup (2011), and Rinne et al. (1996) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measured and predicted resistances computed with M1 and M1a (trend line and equation pertains only to M1) . . . . . . . . . . . . . . . . . . . . . . . . Measured and predicted resistances computed with M2 . . . . . . . . . . . . Measured and predicted resistances computed with M3 . . . . . . . . . . . . Measured and predicted resistances computed with M4 . . . . . . . . . . . . Relationship between bias and relative amount of caliche along the embedded lengths of the test shafts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CDFs CDFs CDFs CDFs of of of of the the the the bias bias bias bias developed developed developed developed M1 M2 M3 M4 for for for for the the the the L1 L1 L1 L1 and and and and L2 L2 L2 L2 calibrations calibrations calibrations calibrations 43 44 45 52 54 54 55 55 56 PDFs PDFs PDFs PDFs 5.1 L1 and L2 calibration results for different data quality bins and design approaches (M1, M2, M3, and M4) based on AASHTO (2014) . . . . . . . . . 66 Impact of data quality and calibration level on the computed resistance factors 68 6 with with with with 42 4.1 4.2 4.3 4.4 5.2 and and and and 37 37 38 39 39 40 59 60 61 62 Final Report D.1 Equivalent D.2 Equivalent D.3 Equivalent D.4 Equivalent D.5 Equivalent D.6 Equivalent D.7 Equivalent D.8 Equivalent D.9 Equivalent D.10 Equivalent D.11 Equivalent D.12 Equivalent D.13 Equivalent D.14 Equivalent D.15 Equivalent D.16 Equivalent D.17 Equivalent D.18 Equivalent D.19 Equivalent D.20 Equivalent D.21 Equivalent D.22 Equivalent D.23 Equivalent D.24 Equivalent D.25 Equivalent D.26 Equivalent D.27 Equivalent D.28 Equivalent D.29 Equivalent D.30 Equivalent D.31 Equivalent D.32 Equivalent D.33 Equivalent D.34 Equivalent D.35 Equivalent D.36 Equivalent LIST OF FIGURES top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down top-down load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement load-settlement curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve curve 7 for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for data data data data data data data data data data data data data data data data data data data data data data data data data data data data data data data data data data data data number number number number number number number number number number number number number number number number number number number number number number number number number number number number number number number number number number number number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 139 139 140 140 141 141 142 142 143 143 144 144 145 145 146 146 147 148 148 149 149 150 150 151 151 152 152 153 153 154 154 155 155 156 156 Final Report LIST OF FIGURES D.37 Equivalent top-down load-settlement curve for data number 37 D.38 Equivalent top-down load-settlement curve for data number 38 D.39 Equivalent top-down load-settlement curve for data number 39 D.40 Equivalent top-down load-settlement curve for data number 40 D.41 Measured load-settlement curve for data number 41 . . . . . . D.42 Equivalent top-down load-settlement curve for data number 42 D.43 Equivalent top-down load-settlement curve for data number 43 D.44 Equivalent top-down load-settlement curve for data number 44 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 157 158 158 159 159 160 160 List of Tables 1.1 1.2 1.3 . . . . . . . . in the . . . . 22 25 2.1 2.2 Scoring criteria for load test and GI data quality. . . . . . . . . . . . . . . . Summary of test shaft and GI parameters. . . . . . . . . . . . . . . . . . . . 46 47 3.1 3.2 Criteria for describing cementation (after ASTM (2000)). . . . . . . . . . . . Spearman’s ρ statistics to test for dependence between Rp and λ (assumed significance level of 0.05, null = independent). . . . . . . . . . . . . . . . . . 49 Assumed COV values for design parameters in the L2 LRFD calibrations. . . Comparable COV values for design parameters from literature. . . . . . . . . Summary of statistical parameters used to describe the bias for the L1 and L2 calibrations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 63 4.1 4.2 4.3 5.1 5.2 Probability of failure, pf , for different reliability indices, β. . . . . . . Load statistics and factors employed in this study (Paikowsky, 2004). Example classification and drilling/sampling characteristics of caliche Las Vegas Valley (Cibor, 1983). . . . . . . . . . . . . . . . . . . . . . Resistance factors computed with the MC simulations for the L1 and L2 calibration (using the global lognormal bias statistics). . . . . . . . . . . . . Governing LRFD resistance factors for all design approaches and calibration levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.1 Assumed stratigraphy and material properties for data number 1 (water table depth = 85 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.2 Assumed stratigraphy and material properties for data number 2 (water table depth = 101 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.3 Assumed stratigraphy and material properties for data number 3 (water table depth = 101 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.4 Assumed stratigraphy and material properties for data number 4 (water table depth = 10 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 27 51 64 67 67 80 82 83 84 Final Report LIST OF TABLES C.5 Assumed stratigraphy and material properties for data number 5 (water table depth = 7 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.6 Assumed stratigraphy and material properties for data number 6 (water table depth = 7 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.7 Assumed stratigraphy and material properties for data number 7 (water table depth = 12 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.8 Assumed stratigraphy and material properties for data number 8 (water table depth = 22 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.9 Assumed stratigraphy and material properties for data number 9 (water table depth = 20 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.10 Assumed stratigraphy and material properties for data number 10 (water table depth = 20 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.11 Assumed stratigraphy and material properties for data number 11 (water table depth = 24.5 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.12 Assumed stratigraphy and material properties for data number 12 (water table depth = 18 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.12 Assumed stratigraphy and material properties for data number 12 (water table depth = 18 ft). (continued) . . . . . . . . . . . . . . . . . . . . . . . . . . . C.13 Assumed stratigraphy and material properties for data number 13 (water table depth = 15.5 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.13 Assumed stratigraphy and material properties for data number 13 (water table depth = 15.5 ft). (continued) . . . . . . . . . . . . . . . . . . . . . . . . . . C.14 Assumed stratigraphy and material properties for data number 14 (water table depth = 23 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.15 Assumed stratigraphy and material properties for data number 15 (water table depth = 24 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.16 Assumed stratigraphy and material properties for data number 16 (water table depth = 20 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.17 Assumed stratigraphy and material properties for data number 17 (water table depth = 20.5 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.18 Assumed stratigraphy and material properties for data number 18 (water table depth = 15 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.18 Assumed stratigraphy and material properties for data number 18 (water table depth = 15 ft). (continued) . . . . . . . . . . . . . . . . . . . . . . . . . . . C.19 Assumed stratigraphy and material properties for data number 19 (water table depth = 15 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 85 86 87 88 90 91 92 93 94 95 96 97 99 101 102 104 105 106 Final Report LIST OF TABLES C.19 Assumed stratigraphy and material properties for data number 19 (water table depth = 15 ft). (continued) . . . . . . . . . . . . . . . . . . . . . . . . . . . C.20 Assumed stratigraphy and material properties for data number 20 (water table depth = 24 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.21 Assumed stratigraphy and material properties for data number 21 (water table depth = 20 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.22 Assumed stratigraphy and material properties for data number 22 (water table depth = 14 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.23 Assumed stratigraphy and material properties for data number 23 (water table depth = 14 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.24 Assumed stratigraphy and material properties for data number 24 (water table depth = 24 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.25 Assumed stratigraphy and material properties for data number 25 (water table depth = 30 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.26 Assumed stratigraphy and material properties for data number 26 (water table depth = 28 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.27 Assumed stratigraphy and material properties for data number 27 (water table depth = 62 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.28 Assumed stratigraphy and material properties for data number 28 (water table depth = 81.2 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.29 Assumed stratigraphy and material properties for data number 29 (water table depth = 18 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.30 Assumed stratigraphy and material properties for data number 30 (water table depth = 48.1 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.31 Assumed stratigraphy and material properties for data number 31 (water table depth = 5 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.32 Assumed stratigraphy and material properties for data number 32 (water table depth = 19 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.33 Assumed stratigraphy and material properties for data number 33 (water table depth = 20 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.34 Assumed stratigraphy and material properties for data number 34 (water table depth = 20 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.35 Assumed stratigraphy and material properties for data number 35 (water table depth = 15.5 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.36 Assumed stratigraphy and material properties for data number 36 (water table depth = 17.5 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 107 108 109 110 112 114 115 116 117 119 121 122 124 126 128 129 130 131 Final Report LIST OF TABLES C.37 Assumed stratigraphy and material properties for data number 37 (water table depth = 20 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.38 Assumed stratigraphy and material properties for data number 38 (water table depth = 15 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.39 Assumed stratigraphy and material properties for data number 39 (water table depth = 80.5 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.40 Assumed stratigraphy and material properties for data number 40 (water table depth = 16 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.41 Assumed stratigraphy and material properties for data number 41 (water table depth = 16 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 133 134 136 137 F.1 Assumed stratigraphy and material properties for data number 26 (water table depth = 28 ft). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 12 Abstract Resistance factors for LRFD of axially loaded drilled shafts in the Las Vegas Valley are calibrated using data from 41 field load tests. In addition to the traditional implementation of Monte Carlo (MC) simulations for calibration, a more robust technique is investigated in which nested MC simulations are employed to capture the uncertainty associated with the interpretation of material properties from in-situ test data. Measures are also taken to improve design procedures regarding cemented sandy soils colloquially referred to as caliche. While caliche is common in Las Vegas, its potential contribution to resistance is difficult to predict using typical site investigation data and there is currently no consensus among local engineers regarding how it should be considered in design. Thus, an approach for treating caliche is proposed and compared to three other potentially viable methods. Overall it is found that the proposed design approach produces the most accurate nominal axial capacity predictions and the nested MC simulations yield lower resistance factors than traditional calibration procedures. 13 Acknowledgments The authors would like to recognize the Nevada Department of Transportation for funding this project (award number P515-13-803) as well as Loadtest Inc. and Dr. Moses Karakuzian of UNLV for supplying much of the required data. We also extend our thanks to Abbas Bafghi and Brandon Kluzniak for their thoughtful insight and feedback. Disclaimer The opinions, findings, and conclusions expressed in this publication are those of the authors and not necessarily those of the Nevada Department of Transportation or University of Nevada, Reno. Alternative accessible formats of this document will be provided upon request. Persons with disabilities who need an alternative accessible format of this information, or who require some other reasonable accommodation to participate, should contact Dr. Ramin Motamed, Assistant Professor, Department of Civil and Environmental Engineering, University of Nevada, Reno. Tel: (775) 784-6960; Email: motamed@unr.edu. 14 Chapter 1 Introduction The probabilistic framework which forms the basis of modern Load and Resistance Factor Design (LRFD) was introduced by Melchers (1987). After adopting probabilistic loads in the 1980s, the American Association of State Highway and Transportation Officials (AASHTO) formally adopted LRFD in 1994. At the same time, the American Institute of Steel Construction (AISC) also made the switch to LRFD and in 2002, the American Concrete Institute (ACI) did the same. Despite this steady progression into LRFD, however, deep foundation design still does not take full advantage of the probabilistic framework in many parts of the United States due to a lack of area-specific resistance factors derived using reliability theorybased calibrations. Instead, many regions rely on values given in AASHTO (2014) which were developed by fitting to Allowable State Design (ASD) safety factors. It is therefore necessary to develop new resistance factors in areas where sufficient full scale load test data is available. This report aims to contribute to this goal by calibrating resistance factors for LRFD of drilled shafts in the Las Vegas Valley of Southern Nevada. There are a number of examples from past literature which highlight the advantages of properly calibrated resistance factors for LRFD of deep foundations (e.g. Abu-Hejleh et al., 2015; Paikowsky, 2004; Allen et al., 2005). Thus, area-specific calibrations have already been carried out for a number of state Departments of Transportation (DOTs). These include, but are not limited to, Oregon (Smith et al., 2011), Louisiana (Abu-Farsakh et al., 2012), Illinois (Long et al., 2009), Iowa (Roling et al., 2011; Garder et al., 2012), North Carolina (Rahman et al., 2002), and Florida (M. McVay et al., 2005). While such studies represent improvements upon calibrations which fit to ASD, the interpretation of geomaterial properties from in-situ test data remains an unchecked source of epistemic uncertainty. In light of this, the reliability analyses in this study are performed using two approaches, both of which incorporate the Monte Carlo (MC) simulation technique and are validated with the First Oder Reliability Method (FORM). The first approach is intended to represent 15 Final Report CHAPTER 1. INTRODUCTION the current state-of-the-art calibration procedure and the second incorporates a proposed modification which allows uncertainty in estimating geomaterial properties from in-situ test data to be accounted for. The design of drilled shafts in Las Vegas is complicated by the presence of cemented sandy soils, colloquially referred to as caliche. While these materials heavily influence axial response (Stone, 2009), it is difficult to predict their behavior with existing design procedures (Werle & Luke, 2007). Thus, a new practical approach for treating caliche in drilled shaft design is proposed in this report to improve overall predication accuracy. This is compared to design practice based on recommendations from the Nevada Department of Transportation (NDOT) as well as existing models intended for rock and cohesive Intermediate Geo-Material (IGM) described in Brown et al. (2010) and AASHTO (2014). The impact of load test and Geotechnical Investigation (GI) data quality on the outcome of the reliability calibration is also investigated herein through the use of a scoring system modified from Motamed et al. (2016). A similar exercise was conducted by Smith et al. (2011) to calibrate resistance factors for driven piles in Oregon. Analyses are performed using three data subsets which are defined based on the average scores. This helps to ensure that poor data quality does not significantly skew the computed resistance factors in a nonconservative fashion. 1.1 1.1.1 Literature Review Other Resistance Factor Calibration Studies Past resistance factor calibration studies which pertain to drilled shafts include Liang and Li (2009), which does not apply to any specific region, as well as those carried out for Louisiana (Abu-Farsakh et al., 2012), Iowa (Roling et al., 2011; Garder et al., 2012), and Florida (M. C. McVay et al., 1998). All of these except for Liang and Li (2009) also incorporate calibrations for driven piles. Alternatively, studies performed for Oregon (Smith et al., 2011), Illinois/Wisconsin (Long et al., 2009), and North Carolina (Rahman et al., 2002) focus on driven piles exclusively. Abu-Farsakh et al. (2012) utilized 26 drilled shaft load tests (22 of which were bidirectional) to calibrate resistance factors for Louisiana with the MC method and FORM. Figure 1.1 shows the distribution of measured and predicted resistances in the database. This indicates that drilled shaft design in the area typically underestimates nominal axial resistance by a small margin. Consequently, the study found that the local calibration justified an increase in resistance factors compared to the values recommended by AASHTO. They 16 Final Report CHAPTER 1. INTRODUCTION also noted that the local calibration was particularly advantageous due to its applicability regarding local design procedures which are not covered in formal design manuals. Figure 1.1: (a) Interpreted measured and predicted drilled shaft resistances and (b) histogram of bias (Rm /Rp ) from Abu-Farsakh et al. (2012) A relatively small data set consisting of 13 drilled shaft load tests was employed by Garder et al. (2012) as part of a preliminary study to calibrate resistance factors for Iowa. In this case, the predicted nominal axial resistances were slightly higher on average than the interpreted measured values (Figure 1.2). An estimate of the total resistance factor corresponding to a reliability index of 3 was obtained using the First Order Second Moment (FOSM) method. This suggested that 0.66 represented an appropriate value, which is greater than anything within the range recommended by AASHTO at the time (0.40 to 0.60). Liang and Li (2009) compiled a data set of 65 drilled shaft load tests from the NCHRP Project 24-17 database and implemented the MC method to calibrate resistance factors. The data consisted entirely of traditional top-down load tests carried out to failure with soil conditions consisting of clay, sand, mixed soils, and weak rock. Unlike the other studies discussed in this section, the findings supported the suggested values from AASHTO. For additional information regarding deep foundation LRFD calibration analyses, refer to (AbuHejleh et al., 2015) which provides a summary of existing deep foundation load test databases and their uses in research. 17 Final Report CHAPTER 1. INTRODUCTION Figure 1.2: Interpreted measured and predicted drilled shaft resistances from Garder et al. (2012) M. C. McVay et al. (1998) considered five different design methods to calibrate resistance factors for drilled shafts in Florida. Similar to the Las Vegas Valley, cemented soils are somewhat common in Florida. This led to complications/limitations which also pertain to this study. For example, M. C. McVay et al. (1998) did not separate side and tip resistance due to limitations in the load test data. Also, the size of the database employed for calibration purposes was kept relatively small so that only data which did not require extrapolation was considered. Alternatively, as is discussed later in this report, not enough data is available which did not require extrapolation in the Las Vegas Valley to allow for a similar approach to be carried out herein. 1.1.2 Principles of Resistance Factor Calibration There are a number of methods available for conducting LRFD resistance factor calibrations. The simplest method is the Mean Value First Order Second Moment (MVFOSM), which relies on linearizing the limit state function at the mean values of random variables and estimating the mean and standard deviation of random variables using a Taylor series expansion in which only the first order terms are considered. However, such mathematical assumptions are potential sources of error in the MVFOSM and though they are slightly 18 Final Report CHAPTER 1. INTRODUCTION improved in the FOSM method, a more advanced approach is preferred (Paikowsky, 2004). Hence, in this study resistance factors are calibrated using the MC simulation technique and are validated using a First Order Reliability Method (FORM) analysis. These methods consider the true mean, standard deviation, and distribution of the data to carry out the analysis. Equation 1.1 is the design equation for LRFD when a total resistance factor is employed. This can be rearranged to serve as the limit state equation needed for calibration (Allen et al., 2005) as is shown in Equation 1.2. X ηi γi Qni ≤ φRT Rn (1.1) where ηi = factors to account for ductility (ηD ), redundancy (ηR ), and operational importance (ηI ); ηi = ηD ηR ηI < 0.95 γi = load factor applicable to a specific load component Qni = specific nominal load component X γi Qni = total factored load for the given limit state φRT = total LRFD resistance factor Rn = total nominal resistance g = g(X1 , X2 , ..., Xn ) = R − Q (1.2) where g = random variable representing the safety margin R = random variable representing the factored resistance Q = random variable representing the factored load The currently established procedure for performing an LRFD calibration for deep foundations is outlined in Allen (2005) and has been used in other studies including Barker et al. (1991) and Paikowsky (2004). This method, is described in general by a flowchart presented 19 Final Report CHAPTER 1. INTRODUCTION in Paikowsky (2004) which is shown in Figure 1.3. Figure 1.3: Stages of a typical LRFD calibration for deep foundations after Paikowsky (2004) According to Figure 1.3, the first steps in a calibration for drilled shafts should include defining the state of practice, collecting a database, and determining the appropriate the reliability index, β. For the Las Vegas Valley, however, there is no well-established state of practice regarding cemented soils (this is discussed in greater detail hereafter in Section 1.1.3). The database compiled for this study is described in Section 2.1. The reliability index is used to quantify the level of reliability and is defined as the inverse of the Coefficient of Variation (COV) of g from Equation 1.2. For a normally distributed limit state function, β is related to the probability of failure, pf , through the inverse of the normal Cumulative Distribution Function (CDF) as is shown in Equation 1.3. Alternatively, Equation 1.4 can be used to compute β if the distribution of bias (λ = Rm /Rp ) is normal and Equation 1.5 can be used if the distribution is lognormal. Table 1.1 summarizes pf corresponding to various values of β. 20 Final Report CHAPTER 1. INTRODUCTION pf = 1 − Φ(β) (1.3) where β = reliability index (normal distribution) Φ = normal CDF pf = probability of failure β= µ−1 σ (1.4) where β = reliability index (normal distribution) µ = mean of safety margin σ = standard deviation of safety margin βLN µ ln √1+COV 2 √ = ln 1 + COV 2 (1.5) where βLN = reliability index (lognormal distribution) µ = mean of safety margin COV = coefficient of variation of safety margin First Order Reliability Method FORM is a technique developed by Hasofer and Lind (1974) which has the ability to asses the reliability of foundations for specific limit states (i.e. Equation 1.2). The methodology requires only first and second moment information concerning resistance and loads (i.e. 21 Final Report CHAPTER 1. INTRODUCTION Table 1.1: Probability of failure, pf , for different reliability indices, β. β 2.0 2.5 3.0 3.5 4.0 pf 2.2750E-2 6.2907E-3 1.3500E-3 2.3267E-4 3.1686E-5 means and variances) and an assumption of the distribution type. The calibration process using FORM is carried out in this research following the methods from Ayyub et al. (2000) which was also employed by Paikowsky (2004). Figure 1.4, taken from Paikowsky (2004) which was adapted from Ayyub et al. (2000), illustrates a theoretical failure surface (G(x) = 0) and a space of basic random variables. In context, these are represented by Equation 1.2 and R and Q thereof. The reliability index is the distance between the origin of the space of basic random variables and the point on G(x) = 0 at which the joint Probability Distribution Function (PDF) of x is greatest. Hence, the latter, which is often referred to as the design point, is the most probable point on the failure surface. A FORM analysis iteratively solves for the design point and thereby enables the evaluation of reliability. Alternatively, if the target reliability index is known, the associated resistance factor(s) can be back-calculated using FORM. In this study, Microsft Excel Visual Basic for Applications (Excel VBA) was employed to perform FORM analyses to validate the results of the MCSs. The computational steps of this approach are based on the procedure described in Ayyub et al. (2000) and are adapted hereafter to evaluate the resistance factor for a given β. 1. In regular coordinates, assume a design point, x∗i , and obtain its corresponding value in a reduced coordinate system, x′∗ i , using Equation 1.6. With the limit state defined by Equation 1.2, this translates to transforming R and Q into standard normal space (i.e. R′ and Q′ ). The mean of the vector of basic random variables is commonly used as an initial guess for the design point. 2. If the distribution of basic random variables is non-normal, approximate the distribution with an equivalent normal distribution at the design point using Equations 1.7 and 1.8. 3. Set x∗i = αi∗ β, where αi∗ are direction cosines. These can be computed with Equation 1.9. 22 Final Report CHAPTER 1. INTRODUCTION Figure 1.4: Example of random variables of a limit state function, µR and µS , in standard normal space and the associated failure surface, G(x) = 0 (GL (x) = 0 is the linearized version), after Paikowsky (2004) and Ayyub et al. (2000) 4. Given a target β (3.0 in this study), solve Equation 1.10 for the mean resistance, µN R. 5. Use Equation 1.11 to compute the resistance factor associated with the new mean resistance corresponding to the target β. x′∗ i = x∗i − µxi σ xi where µxi = mean value of the basic random variable, Xi σxi = standard deviation of the basic random variable 23 (1.6) Final Report CHAPTER 1. INTRODUCTION ∗ −1 µN (Fx (x∗ )) σxN x = x −Φ σxN = φ (Φ (Fx (x ))) fx (x∗ ) −1 ∗ (1.7) (1.8) where µN x = mean of the equivalent normal distribution σxN = standard deviation of the equivalent normal distribution Fx (x∗ ) = original CDF of Xi evaluated at the design point fx (x∗ ) = original PDF of Xi evaluated at the design point Φ() = CDF of the standard normal distribution φ() = PDF of the standard normal distribution  ∂g ∂x′i  ∗ αi∗ = r Pn  ∂g 2 i=1 ∂x′i   (1.9) ∗ where  g  ∂g ∂x′i  = ∗ ∂g ∂xi σxNi ∗

 ∗ N N ∗ N µN =0 xi − αxi σxi β , ..., µxn − αxn σxn β φRT = Pn i=1 γi µLi µR (1.10) (1.11) It should be noted that since the limit state equation is linear in this case, the selection of the initial design point does not impact the final result if closed form solutions are available to compute the equivalent normal means and standard deviations. Also, the assumed ratio of 24 Final Report CHAPTER 1. INTRODUCTION dead to live load magnitude must be the same as assumed in the MC simulation for the results to be comparable. A ratio of 3 was selected for this research based on recommendations from Allen (2005) and Paikowsky (2004). Monte Carlo Simulation Method The MC method is a broad tool which has been used extensively in science and engineering since the mid 1940s (Lemieux, 2009) and now represents the state of the art in reliability calibrations for deep foundations (Paikowsky, 2004). With a MC simulation, repeated random sampling is used to obtain numerical results. Thus, for deep foundation resistance factor calibration, the LRFD limit state equation (Equation 1.2) is evaluated on each iteration by treating the load and resistances as random variables following specified probability distributions. While resistance statistics must be determined by analyzing field test data, it is appropriate to employ existing load statistics for superstructure analysis. In this report, the values recommended by Paikowsky (2004) are assumed to characterize the random variables for dead and live load. These are shown in Table 1.2. Table 1.2: Load statistics and factors employed in this study (Paikowsky, 2004). Load Type Dead load Live load 1.1.3 Bias λDL = 1.05 λLL = 1.15 COV COVDL = 0.1 COVLL = 0.2 Load Factor γDL = 1.25 γLL = 1.75 Soil Conditions in the Las Vegas Valley Typical soil conditions in the Las Vegas Valley are characterized by inter-bedded layers of silty clay and sand with seams of a hardened sedimentary deposit consisting of calcium carbonate cemented sandy soils, colloquially known as caliche. Caliche is found in arid regions around the world and represents a problematic geomaterial in terms of deep foundation design because of its general absence from design manuals and its uncommonly high strength. In Las Vegas, caliche is most prevalent in the western and central parts of the valley (Wyman et al., 1993). Recent research suggests that the presence of caliche layers at least one shaft width thick can reduce shaft settlement by more than 50% even if only a single such layer exists (Stone, 2009). Caliche can be formed when carbonate basement rock exists beneath carbonate containing soil, the climate is arid to semi-arid, and there is significant capillary activity as well as 25 Final Report CHAPTER 1. INTRODUCTION abundant CO2 in the environment (Atabey et al., 1998). If these conditions exist, the process by which caliche may be formed is as follows: 1. Carbonic acid (H2 CO3 ) is formed when CO2 reacts with water. • CO2 + H2 O = H2 CO3 2. The newly formed carbonic acid dissolves calcium and magnesium bearing rocks resulting in a solution saturated with calcium and magnesium bicarbonate (Ca(HCO3 )2 and M g(HCO3 )2 ). • CaCO3 + H2 CO3 = Ca(HCO3 )2 • CaM g(CO3 )2 + H2 CO3 = M g(HCO3 )2 3. The calcium and magnesium bicarbonate saturated water percolates down through loose soil and mudstone only to rise back towards the surface due to capillary effects and evaporates during dry seasons. 4. Evaporation removes the CO2 and water from the calcium bicarbonate solution, leaving the CaCO3 and M gCO3 to cement the soil in which the solution previously resided. • Ca(HCO3 )2 = CaCO3 + CO2 + H2 O • M g(HCO3 )2 = M gCO3 + CO2 + H2 O Brown et al. (2010) provides a discussion concerning cemented soils and specifically refers to caliche in the western United States. Following this, Table 1.3 shows the recommended classification, drilling, and sampling characteristics of caliche in the Las Vegas Valley. It should be noted that some local practitioners in Las Vegas have called the information regarding drilling rates given in Table 1.3 into question. 26 Final Report CHAPTER 1. INTRODUCTION Table 1.3: Example classification and drilling/sampling characteristics of caliche in the Las Vegas Valley (Cibor, 1983). Nomenclature Cemented Coarsegrained Deposits Cemented Fine-grained Deposits Hardness Classification Drilling Rates1 (min/ft) Without Pulldown With Pulldown Sand and gravel with scattered cementation Decomposed caliche with silt and clay Very dense to slightly hard - - Partially cemented sand and gravel Decomposed caliche Moderately hard ≤5 ≤3 Weathered caliche Hard 6 to 30 3 to 6 Fresh caliche Very hard 700 70 Cemented sand and gravel 1 Using Mayhew 100 drill rig 27 Description of Material and Drill Cuttings Variable matrix of uncemented soil and cemented zones. Samples obtained with split-spoon or thick-walled sampler. Can be crumbled with fingers. Cemented to varying degrees. Fine grained deposits sampled with thick walled sampler; coarse-grained samples cannot be obtained with thick-walled sampler. Drilling produces large, rounded cuttings. Cuttings can be broken with difficulty with hands or easily when hammered. Visible chemical alterations from fresh deposits. Compressive strength similar to fresh deposits. Slight secondary porosity. Samples obtained by coring techniques. Drill cuttings less than inch in diameter. Fragments can be broken with difficulty by hammering. No visible signs of chemical alteration. Non-porous. Resembles metamorphic or sedimentary rock. Drill cuttings less than 1/8 inch in diameter. Samples obtained by coring techniques. Fragments cannot be broken by hammering. Final Report CHAPTER 1. INTRODUCTION A case study of the drilled shaft foundation system for the Vegas High Roller project was conducted by Kluzniak et al. (2014). They found that the nearly all of the load bearing for the drilled shafts was accommodated by a relatively shallow caliche layer and it was also determined that structural capacity of the shafts would likely govern the failure mode. The interpreted stratigraphy, which includes the aforementioned near-surface caliche layer, and corrected standard penetration blow counts for three example boreholes from this project are given in Figure 1.5. Figure 1.5: Example soil profiles and corrected standard penetration blow counts for the Vegas High Roller project (from Kluzniak et al., 2014) Murvosh et al. (2013) employed data from 212 shear wave velocity (Vs ) profiles and 1400 geologic well logs in Las Vegas to develop a three-dimensional model of the sediments in the 28 Final Report CHAPTER 1. INTRODUCTION Las Vegas Valley. This involved building characteristic profiles for four main sediment units: sand, clay, gravel, and mixed. Figure 1.6 shows the scatter in Vs for each of these units with depth which was then used to develop the generalized characteristic profiles (Figure 1.7). Figure 1.6: Vs scatter for different soil unit in the Las Vegas Valley (Murvosh et al., 2013) Luke et al. (2003) provides evidence that the methodology for Spectral Analysis of Surface Waves (SASW) described by Stokoe et al. (1994) can be used to identify cemented material with reasonable accuracy. They compared profiles developed with SASW at the Las Vegas Springs Preserve to boring log information and independent seismic crosshole measurements made across three boreholes, nominally 3 m apart, located at the center of and in line with the SASW array. This is presented in Figure 1.8. While two relatively thin and deep caliche seams could not be resolved with the SASW method, two layers within the upper 10 m were correctly identified and there was a strong general agreement among the different data sets. As noted earlier, groundwater conditions are a critical factor in the formation of cemented material. Thiros et al. (2010) provides a detailed discussion of the hydrological setting in the Las Vegas Valley and highlight the differences between pre-development and modern conditions. This is depicted in Figure 1.9 and suggests that development in the area has led to significant changes in the local hydrology. Thus, the pre-development hydrology is more pertinent to the formation of existing caliche in Las Vegas. 29 Final Report CHAPTER 1. INTRODUCTION Figure 1.7: Characteristic Vs profiles for different soil unit in the Las Vegas Valley (Murvosh et al., 2013) Figure 1.8: Using seismic methods to detect caliche: example case from the Las Vegas Springs Preserve (Luke et al., 2003) 30 Final Report CHAPTER 1. INTRODUCTION Figure 1.9: Generalized cross-sections for the Las Vegas Valley showing the basin-fill deposits and components of the groundwater flow system under (A) pre-development conditions and (B) modern conditions (from Thiros et al., 2010) 31 Final Report 1.1.4 CHAPTER 1. INTRODUCTION Field Load Testing of Drilled Shafts A database of field load test is required to carry out a resistance factor calibration study for drilled shafts. However, full-scale load tests are typically performed to benefit individual projects for two general reasons (Brown et al., 2010): (1) to obtain information concerning load transfer is side and/or base resistance to allow for an improved design and (2) to prove that the constructed test shaft meets the required strength and/or serviceability criteria. Load tests performed with these goals are often referred to as design phase and proof tests, respectively. Another advantage of load testing is cost savings. For example, Vanderpool et al. (2011) performed a case-study on Utah Transit Authority’s Airport Light Rail Trax project and found that load testing saved the owner in excess of $800 thousand dollars. There are many forms of field load tests for drilled shafts. Examples of the most prominent types are given in Figures 1.10 through 1.13. These include: • top-down static load tests conducted with a hydraulic jack and above-ground reaction system, • bi-direction load tests carried out with an embedded bi-directional load cell, • top-down rapid testing using an accelerated reaction mass (i.e. statnamic), and • dynamic testing with a drop weight impact system. Figure 1.10: Example of a traditional top-down static load test on a drilled shaft (Brown et al., 2010) 32 Final Report CHAPTER 1. INTRODUCTION Figure 1.11: Example of a bi-directional (O-Cell) load test setup Figure 1.12: Example of a statnamic loading apparatus (Brown et al., 2010): (a) piston and silencer assembly, (b) load cell and laser target, (c) laser for displacement measurement, (d) schematic diagram 33 Final Report CHAPTER 1. INTRODUCTION Figure 1.13: Example dynamic load tests on drilled shafts using a drop weight (left) and pile hammer (right) (Brown et al., 2010) Despite the inherent advantages of bi-directional load testing, such as relatively low cost and the ability to apply large loads, there are significant challenges associated with its use for resistance factor calibration studies. Mainly, the raw data is not in the form of a traditional top-down test so defining axial capacity according to common limit state criteria is not possible without some level of data processing. This is covered in greater detail in Chapter 2. The most common type of bi-directional load test for drilled shafts was introduced by Dr. Jori Osterberg with development of the O-Cell, the first bi-directional load cell (Schmertmann & Hayes, 1997). While early forms of this technology have been in use since 1984 (Hannigan et al., 1997), alternatives from other sources have only recently entered the market. One example of this is the bi-directional load cell developed by Applied Foundation Testing, Inc. (AFT). The main advantages and disadvantages of using this type of load test for drilled shafts are summarized in Brown et al. (2010) and are reiterated in a general sense hereafter. 34 Final Report CHAPTER 1. INTRODUCTION Advantages of bi-directional load testing: • Larger loads can be achieved than with any other type of load test, enabling testing of production-size shaft in many cases. • The side and base resistances may be isolated if multiple load cells are used or proper strain gauge instrumentation is installed. This may also allow for the side resistance in specific layers to be determined in some cases. • Static loading can be maintained so that creep behavior can be observed. Disadvantages of bi-directional load testing: • The test shaft must be predetermined because load cells cannot be installed after construction takes place. • If only a single load cell is used, the weaker portion of the shaft (i.e. above or below) will dictate the maximum load which can be achieved. This often prevents tests from reaching loads corresponding to the geotechnical strength limit state requirements. • Elastic compression above the load cell must be accounted for theoretically in order to construct the equivalent top-down load-settlement curve. • Current procedures for estimating side resistance in rock sockets are likely to be conservative. 35 Chapter 2 Measured Data 2.1 Database The load test data employed for this study consists of 41 tests selected from the Nevada Deep Foundation Load Test Database (NDFLTD) (Motamed et al., 2016). These represent the tests that passed an initial screening of 45 tests to ensure that an associated GI report was obtainable and that enough movement was achieved to enable an estimate of the axial resistance corresponding to the geotechnical strength limit state. In the final data set, shaft diameters range from 2 to 8 ft with embedded lengths from 31.6 to 128.0 ft and all but one of the selected load tests involved a bi-directional load cell to induce movement (as opposed to a traditional top-down test). While some of the test shafts were constructed under dry conditions, the number of such cases was insufficient to separate the LRFD calibrations based on construction methods. Also, inspection of the measured responses reveals that there is no statistical basis for treating the dry constructed test shafts as outliers from the rest of the data. Figure 2.1 shows the approximate locations of the test shafts and histograms of shaft diameter, embedded length, and relative fractions of different soil types along shafts are given in Figure 2.2. 36 Final Report CHAPTER 2. MEASURED DATA Figure 2.1: Approximate locations of the test shafts included in the study Figure 2.2 Distribution of select characteristics among the data employed for calibration 37 Final Report CHAPTER 2. MEASURED DATA Microsoft Access and Visual Basic for Applications (VBA) is employed to develop the interface for the NDFLTD. Figure 2.3 depicts the finalized object components and information mapping. With this framework, the user can query foundation load test data and associated GI information. Drop down menus are also included to enable constrained searches based on the type of foundation, a specified range of diameters, and/or the presence of designated soil units. A screen-shot of the main search window is given in Figure 2.4. Figure 2.3: Object components and information mapping in the NDFLTD Once a selection is made in the main search window, the user can choose to view the associated GI data, load test data, or plot up to 10 points at once on an interactive map. Examples of the GI and load test data report formats are given in Figures 2.5 and 2.6, respectively. All pages which display data include an active link to the original source of the information being presented. Additionally, the data may be exported to Microsoft Excel or to a Personal Data Form (PDF). The processed GI data, such as in the example from Figure 2.5, include the minimum parameters required for design against axial loading for the assumed units in each layer. Hence, additional information is sometimes available in the original reports which are linked on the same page. 38 Final Report CHAPTER 2. MEASURED DATA Figure 2.4: Main search window in the NDFLTD Figure 2.5: Example GI report in the NDFLTD 39 Final Report CHAPTER 2. MEASURED DATA Figure 2.6: Example load test report in the NDFLTD 40 Final Report 2.1.1 CHAPTER 2. MEASURED DATA Sharing The NDFLTD is available as an electronic supplement linked in Appendix A. Note that usage guidelines and a stand-alone ReadMe text file are accessible from within the database interface. Additionally, all of the interpreted soil boring logs are given in Appendix C and the original load test and associated geotechnical reports are available as an electronic supplement in Appendix B. 2.2 Interpretation of Bi-Directional Load Test Data Bi-directional load test data consists of two components of load and displacement, one for movement above the load cell and one below. An example of this is shown in Figure 2.7 (upper plot). To estimate the measured capacity from this type of test, additional details about the test shaft must first be known so that the equivalent top-down curve can be constructed in a fashion that accounts for the elastic settlement which would have occurred in the upper portion of the shaft in a traditional top-down test. Namely, this information includes the buoyant weight of the test shaft above the load cell, the unit stiffness above the load cell, and the shaft geometry. Construction of an equivalent top-down curve typically involves to phases: (1) computing the equivalent rigid top load-settlement and (2) correcting for the effects of elastic compression. Phase 1 requires summing the loads associated with equal movement above and below the load cell, as is shown in Figure 2.7. Two methods of correcting for elastic compression above the load cell are considered in this study. It is important to note that the elastic compression below the load cell is not taken into account in either method because it is theoretically the same for both a bidirection and top-down load test. The first method, hereafter referred to as the approximate method, accounts for the estimated pattern of developed side shear stress through coefficients known as centroid factors. This approach, described by Figure 2.8 and Equation 2.1, is often advantageous because it allows for any available data to be used in estimating the pattern of developed side shear. Kishida et al. (1992) and Ogura and Kishida (1996) offer validation for the approximate method. 41 Final Report CHAPTER 2. MEASURED DATA Figure 2.7: Determination of the rigid equivalent top-down load-settlement curve from bi-directional load test data where Qneti is the net load for movement increment i (modified from original figure provided by Loadtest Inc.) δup Q′upA (L1 + L2 ) = C1 AE (2.1) where δup = Elastic compression for a single stage bi-directional load test above load cell A C1 = Centroid factor (see Figure 2.8) Q′upA = Net upward load applied by the load cell (i.e. QupA − wL′ 0 +L1 +L2 ) A = Cross-sectional area of the test shaft E = Elastic modulus of the test shaft 42 Final Report CHAPTER 2. MEASURED DATA Figure 2.8: Example patterns of developed side shear stress and corresponding parameters needed to estimate elastic compression above the load cell(s) in a bi-directional load test, after Loadtest Inc. (2000). Note that w′ denotes the buoyant weight when below the water table If good quality and complete strain gauge data is available, then the second method of correcting for elastic compression may be applicable. This is hereafter referred to as the load-transfer method and is based on procedures and validations from Lee and Park (2008) and Meyer et al. (1975). The procedure is described by Figure 2.9 and Equations 2.2 through 2.4, which must be solved for in an iterative fashion for each section, i, until the computed displacements and loads match the measured values. Note that the load transfer in endbearing (i.e. the q-z curve) must also be considered in the same way as the side load transfer in the above shaft sections. 43 Final Report CHAPTER 2. MEASURED DATA Figure 2.9: Analytical framework for a single shaft section in the t-z method for correcting bi-directional load test data for elastic compression above the load cell, after Loadtest Inc. (2000) δi = Qi+1 (Qi + Qi+1 ) Li 2Ai Ei ∆i+1 = ∆i + δi   ∆i + ∆i+1 Ai = Qi + t 2 (2.2) (2.3) (2.4) where δi = Elastic compression assoicated with section i Qi & Qi+1 = Load at the bottom and top of the section, respectively ∆i & ∆i+1 = Displacement at the bottom and top of the section, respectively Ai = Cross-sectional area of the section Ei = Elastic modulus of the section In this study, the load-transfer method is preferred over the approximate method for constructing equivalent top-down load curves from bi-directional test data because it directly considers the accumulated strains to account for the pattern of developed side shear (as opposed to approximating it with the centroid factor). Once this step is complete, measured resistances are interpreted as the lesser of axial loads corresponding to: (a) settlement equal 44 Final Report CHAPTER 2. MEASURED DATA to 5% of the shaft diameter or (b) the onset of plunging failure. Plunging is defined when movement occurs without the application of additional load. Also, the method from Chin (1970) is employed when extrapolation is required to reach the governing failure criteria. The finalized equivalent top-down load-settlement curves for all of the load tests in this study are presented in Appendix D. 2.3 Scoring System A scoring system proposed by Motamed et al. (2016) is employed herein to quantify the quality of each load test and associated GI. This enables separate reliability calibrations to be carried out for three data quality bins: (a) all data, (b) mean score > 2, and (c) mean score ≥ 3. The scoring criteria, which is presented in Table 2.1, is setup such that a higher score indicates higher quality and considers factors such as the amount of extrapolation required to approximate the axial load at failure from the load test data, the thoroughness of the GI, and the distance from the GI to the test shaft. The precise distance from the borehole to the test shaft is known for 22 of 41 data points and an approximate distance is known for an additional 11 points. Figure 2.10 portrays the distribution of scores for the data included in this report and Table 2.2 tabulates this information along with basic test shaft properties and measured resistances. Note that the names of the load test projects are not included in order maintain confidentiality with the owners of some of the data. Figure 2.10: Load test and GI quality scores for the data included in this study 45 Final Report CHAPTER 2. MEASURED DATA Table 2.1: Scoring criteria for load test and GI data quality. Score 1 (worst) 2 3 4 (best) Scoring Criteria Load Test Data Geotechnical Investigation Data Extrapolation > 2% of the shaft diIncomplete boring logs with little to no ameter is required for both compoSPT data or proper visual-manual classifinents of bi-directional movement or cations. No lab data. > 3% is required for a top-down test. Extrapolation > 2% of the shaft diameter is required for one compoBoring logs with minimal SPT data (i.e. nent of bi-directional movement (secmissing for some geologic units) and useful ond component may require < 2%) or visual-manual classifications. No lab data. > 2.5% but ≤ 3% is required for a top-down test. Extrapolation < 2% of the shaft di- Boring logs are complete with SPT data, ameter is required for both compo- visual-manual classifications and possibly nents of bi-directional movement or torvane or pocket pen data. Limited lab > 2% but ≤ 2.5% is required for a data and/or additional in situ data is availtop-down test. able. Either no extrapolation is needed or Complete boring logs with detailed mateextrapolation ≤ 2% of the shaft di- rial classifications, SPT data and possibly ameter is required for only one compo- other data such as CPT or shear wave venent of load-cell movement or in total locity measurements. Thorough lab data for a top-down test. covering soil strengths is available. Note: If a test shaft is not fully instrumented, the load test data score is reduced by 1. For every 150 ft a borehole is spaced from the test shaft, or if the distance is unknown, the GI data score is reduced by 1. If quality control is lacking or significant problems/irregularities are present in the constructed shaft, the load test data score is reduced by 2. Figure 2.10 shows that there is a higher concentration of low quality load test data than GI data in the current data set. When combined, the mean data quality scores are predominately in the range of 2.0 to 2.5. From the data provided in Table 2.2, it can be determined that the average distance between the test shaft and GI is approximately 90 ft with a maximum distance of nearly 350 ft. Also, the load test data indicates plunging failure occurred before the shaft head settlement reached 5%B in nearly half of load tests. 46 Final Report CHAPTER 2. MEASURED DATA Table 2.2: Summary of test shaft and GI parameters. Data Load Test GI Quality Const. B L Rm Failure Shaft to GI Dist. (ft) Number Quality Score Score Method (ft) (ft) (kip) Criteriaa 1 2 2 Wet 4.00 103.00 10707 1 < 100 2 4 4 Dry 5.00 39.97 3423 2 90 3 1 4 Dry 7.67 74.43 13989 2 87 4 3 4 Dry 8.00 32.00 7905 2 12 5 3 4 Dry 2.00 31.60 1125 2 3 6 1 2 Dry 2.00 82.50 3812 1 5 7 1 4 Dry 2.00 43.00 1426 2 22 8 1 2 Wet 4.00 106.00 19299 1 < 165 9 2 2 Wet 4.00 105.00 12641 1 NA 10 2 2 Wet 4.00 116.80 10940 1 < 165 11 2 2 Dry 4.00 112.50 12699 1 < 165 12 2 3 Dry 4.00 123.00 20937 1 0c 13 2 3 Wet 4.00 122.50 20109 1 0c 14 1 3 Wet 3.00 102.00 5260 1 79 15 1 4 Wet 4.00 100.00 10616 1 85 16 1 4 Wet 4.00 101.00 11848 1 91 17 3 4 Wet 6.00 122.00 13215 2 0c 18 2 2 Wet 4.00 121.70 8112 1 < 100 19 2 2 Wet 4.00 121.80 15935 1 < 100 20 1 3 Wet 3.50 90.70 22110 1 14 21 1 3 Wet 3.50 105.50 20669 1 < 150 22 1 2 Wet 4.00 128.00 15964 1 68 23 2 2 Wet 4.00 117.00 13286 2 347 24 1 3 Wet 3.50 100.00 12185 2 100 25 1 4 Wet 4.00 82.00 7142 2 < 150 26 4 4 Wet 4.00 90.50 3682 2 < 150 27 4 3 Wet 5.00 95.50 9965 2 NA 28 4 3 Wet 5.00 96.00 10822 2 NA 29 2 1 Wet 4.00 62.00 6611 2 10 30 1 3 Wet 4.00 101.60 8876 2 NA 31 1 3 Wet 6.00 112.70 18519 1 NA 32 2 2 Wet 3.75 104.33 15268 1 < 150 33 4 3 Dry 3.50 70.00 7923 2 < 165 34 2 2 Wet 3.50 70.00 10943 1 220 35 4 4 Wet 3.50 75.00 7712 1 0c 47 Final Report CHAPTER 2. MEASURED DATA 36 2 3 Wet 3.50 105.50 16945 1 120 37 4 1 Wet 3.50 112.00 9918 2 NA 38 4 2 Wet 5.00 101.00 10276 1 NA 39 1 3 Wet 4.00 106.20 11001 2 NA 40 4 4 Wet 4.00 84.00 3376 2 83 41b 4 4 Wet 3.00 83.00 2204 2 55 a 1=shaft head settlement equivalent to 5%B; 2=plunging b Top-down load test c Borehole is known to have been drilled prior to shaft installation in the same location 48 Chapter 3 Predicted Resistances The design methodology described in AASHTO (2014) with 2015 and 2016 interim revisions, which is largely based on Brown et al. (2010), is employed to estimate the geotechnical strength limit state of the test shafts included in this study. However, there is currently no consensus among local practitioners or NDOT engineers regarding the proper treatment of cemented materials in Las Vegas. Thus, four different approaches regarding the treatment of cemented local geomaterials are evaluated hereafter. In the context of this report, cemented materials are defined in accordance with the language from ASTM D 2488 (ASTM, 2000). This is presented in Table 3.1. According to the experience of NDOT engineers, soil with weak cementation (defined by ASTM D 1586) can be sampled during a Standard Penetration Test (SPT) and a meaningful value for NSP T may be obtained. Soil with moderate cementation (ASTM D 1586) can only be sampled partially during SPT (e.g. 50 blows per 4 inches) and a meaningful value of NSP T cannot be achieved. Alternatively, soil with strong cementation cannot be penetrated under normal SPT hammer blows. Table 3.1: Criteria for describing cementation (after ASTM (2000)). Description Criteria Weak Crumbles or breaks with handling or little finger pressure Moderate Crumbles or breaks with considerable finger pressure Strong Does not crumble or break with finger pressure Based on Table 3.1 and in consideration of the available evidence regarding the behavior of cemented material in Las Vegas, caliche is defined as strongly cemented material which also yields refusal in a standard penetration test (i.e. no penetration). In addition, it is not recommended to derive any strength from caliche in design if coring has not been performed to verify the composition and competency of the layer in question. If only weak to moderate 49 Final Report CHAPTER 3. PREDICTED RESISTANCES cementation is evident, but not enough to warrant classification of caliche, it is recommended that the behavior of the parent material (i.e. very dense sand or hard clay) be considered for design purposes. While a few techniques for dealing with moderately cemented material have been proposed in the past (e.g. Rinne et al., 1996), preliminary analyses revealed that simply treating such material the same as the parent material is generally acceptable. That being said, in the proposed approach presented hereafter, a slightly modified model for moderately cemented material with NSP T ≥ 50 is found to be necessary to ensure that there is no dependence between the Rp and λ (which is required for a valid calibration of the resistance factor). It is emphasized that special treatment of non-caliche materials is for calibration purposes only and is not recommended for design. The four potential design approaches investigated herein are summarized as follows: • Current practice (caliche as very dense sand) - Method (M1) This is the approach is meant to represent typical design outcomes in Las Vegas for cases in which full scale load test data is not available. Caliche is treated as very dense sand with γ = 140 pcf, φ′ = 40◦ , and NSP T = 50. This is mainly based on discussions with local practitioners. • Caliche as cohesive IGM - Method (M2) Caliche is treated according to procedures from Brown et al. (2010) for cohesive IGM. Unless site-specific data suggests otherwise, it is assumed that qu = 100 ksf for caliche layers based on the maximum suggested by Brown et al. (2010) for a material classified as cohesive IGM. • Caliche as rock - Method (M3) Caliche is treated according to codified procedures from Brown et al. (2010) for rock with an assumed qu = 729 ksf and RQD = 70%, unless site-specific data suggests otherwise. Note that the value of qu is assumed for calibration purposes only. • Proposed approach - Method (M4) Side resistance in caliche is computed with Equation 3.1. For the purpose of calibration only, it is assumed that qu = 729 ksf for layers not associated with sitespecific unconfined compression test data. Base resistance is taken as the value from the rock model or 100 ksf, whichever is lower. For calibration purposes only, material which is not strongly cemented with NSP T ≥ 50 is modeled with fSN = 6 ksf. Otherwise, if NSP T < 50, moderately cemented material is treated the same as the parent material. 50 Final Report CHAPTER 3. PREDICTED RESISTANCES Base resistance in moderately cemented material is always treated the same as the parent material. For each design approach, a non-parametric correlation test (Spearman’s ρ) was conducted to ensure that there is no dependence between the predicted resistance and bias for a level of significance of 0.05. The p-values associated with the null hypothesis that Rp and λ are independent are provided in Table 3.2. Thus, if the reported p-value is greater than 0.05, the null is accepted and independence is assumed (as is the case for all approaches). Table 3.2: Spearman’s ρ statistics to test for dependence between Rp and λ (assumed significance level of 0.05, null = independent). Caliche Model Spearman’s ρ p-value M1 -0.2061 0.0962 M2 -0.1537 0.1656 M3 -0.0944 0.2752 M4 -0.2024 0.1002 The default qu of caliche assumed for calibration of the resistance for M3 and M4 is based on the results of laboratory tests performed on 60 caliche core samples for projects in Las Vegas (Western Technologies Inc., 1994; Arup, 2011; Rinne et al., 1996). This data conforms to a lognormal distribution with a geometric mean of 729 ksf and COV of 0.59 (see Figure 3.1). In general, this supports the findings from Cibor (1983) which suggest that qu of competent caliche in Las Vegas ranges between 576 and 1440 ksf. Thus, the data collected is accepted and the default qu of caliche is set to 729 ksf for the analyses conducted hereafter. Any value much lower than this would prevent a meaningful calibration as the resistance factor would need to be uncommonly high to match the target reliability index, even for the least conservative design approaches considered herein. It is important to clarify that the default value of qu = 729 ksf is intended to serve as an upper bound for practitioners only after laboratory testing has been carried out to determine the actual unconfined compressive strengths of the caliche layers at a given site. Also, the estimated concrete compressive strength should also be considered as a limiting factor for assigning qu . If for some reason such testing is not carried out or is inconclusive, the authors recommend a much more conservative value of qu = 100 ksf which, following Equation 3.1, produces a nominal side resistance in caliche of approximately 12 ksf. Given the data in this study as well as the results of discussions with local practitioners, this assumption appears to represent a conservative path forward for engineers who are tasked with carrying out design in cemented soils in Las Vegas despite a lack of proper GI data. As Figure 3.1 shows, there is significant variation in caliche strength properties so assuming fSN > 12 ksf without proper justification would not be acceptable. 51 Final Report CHAPTER 3. PREDICTED RESISTANCES Figure 3.1: Histogram of unconfined compressive strengths of caliche core samples in Las Vegas reported by Western Technologies Inc. (1994), Arup (2011), and Rinne et al. (1996) Inspection of the bias, λ, reveals that treating caliche as cohesive IGM or rock, produces generally low estimates of Rp . Given this, a modification to the codified procedure for rock is proposed herein to increase the likelihood that meaningful resistance factors will be obtained (i.e. φRT < 0.70). The proposed approach (M4) employs Equation 3.1 to estimate skin resistance. Equation 3.1 is identical to Equation 10.8.3.5.4b-1 from AASHTO (2014) except that the coefficient, C, is set to 0.85 instead of 1.0. The value of C = 1 suggested for design of ”normal” rock sockets in AASHTO (2014) is based on the results of regression analyses performed by Kulhawy et al. (2005). However, according to the Spearman’s Rho test for the strength of association between two variables, a value of 0.85 is necessary to assume independence between Rp and λ at a significance level of 0.05 (which is assumed to be adequate for this study). A detailed description of a similar application of this nonparametric statistical test is provided in Bathurst et al. (2008). 52 Final Report CHAPTER 3. PREDICTED RESISTANCES fSN = 0.85 pa r qu ≤ 15.8 pa (3.1) where fSN = Unit side resistance in caliche pa = Atmosphereic pressure qu = Unconfined compressive strength of caliche The limit placed on Equation 3.1 is to ensure that designers do not exceed upper bound of the unit side resistances considered for the resistance factor calibrations for M4 within this study. This corresponds to fSN = 33.4 ksf based on the default assumptions for caliche strength. This also falls within the range suggested by Brown et al. (2010) that unit side resistance in Las Vegas caliche, based on bi-directional load test data, is typically between 30 to 55 ksf. It is important to convey that the assumptions regarding caliche strength are employed for the purpose of calibration only and should not, under any circumstances, be applied in practice. In the context of calibration, it is conservative to assign the highest feasible predictions that might be generated in design so that the computed resistance factor encompasses all potential design outcomes. Again, the intent is that designers will always rely on appropriate site-specific data to validate the strength parameters required for the approximation of side resistance in caliche or any other geomaterial. Regarding unit end-bearing in M4, a value equal to the lesser of 100 ksf or the value from M3 is assumed. Trial and error with a number of potential options reveals that this maximizes prediction accuracy for the cases in which the test shafts were tipped into caliche. This is also supported by the findings from Stone (2009). To further improve prediction accuracy of the proposed design approach, moderately cemented material with NSP T ≥ 50 is modeled with fSN = 6 ksf and is treated the same as the parent material otherwise (including base resistance). This was found to reduce the dependence between Rp and λ whereas the application of the recommendations from Rinne et al. (1996) had the opposite effect. Note that this approach is employed for calibration purposes only since assuming the behavior of the parent material is more conservative. It should be noted that NDOT has also recognized a simplified alternative to M1 that some engineers use in Las Vegas to obtain a rough estimate of expected resistance without 53 Final Report CHAPTER 3. PREDICTED RESISTANCES conducting a GI. In this approach, which is hereafter referred to as M1a, all soil along the embedded length of the shaft is modeled with a unit side resistance of 4 ksf. Despite such a sweeping assumption, however, Figure 3.2 shows that M1a does produce results of similar, if not slightly improved, accuracy to M1. Nevertheless, M1a is not considered in the LRFD calibrations herein because it undermines all codified design procedures and introduces unnecessary epistemic uncertainty. Figures 3.2 through 3.5 show the distributions of measured and predicted resistances developed with M1 through M4, respectively. From these plots, it appears that M4 is the most accurate on average and that the level of conservatism decreases from M1 to M4. Figure 3.2: Measured and predicted resistances computed with M1 and M1a (trend line and equation pertains only to M1) Figure 3.3: Measured and predicted resistances computed with M2 54 Final Report CHAPTER 3. PREDICTED RESISTANCES Figure 3.4: Measured and predicted resistances computed with M3 Figure 3.5: Measured and predicted resistances computed with M4 Figure 3.6 portrays the relationship between prediction bias and the relative amount of caliche present along the embedded lengths of the test shafts (caliche fraction) for each design approach, including M1a. The relatively steep positive slopes of the trend lines for M1 and M1a suggest that these methods underestimate the resistance in cemented soils compared to the other methods. M2, M3, and M4 all maintain relatively consistent bias for different levels of caliche fraction, although M4 is closest to λ = 1.0 on average. The distribution of bias is discussed in greater detail in the next chapter. 55 Final Report CHAPTER 3. PREDICTED RESISTANCES Figure 3.6: Relationship between bias and relative amount of caliche along the embedded lengths of the test shafts Overall, Figures 3.2 to 3.6 suggest that M1 and M1a drastically underestimate Rp , especially for greater caliche fractions. M2 and M3 represent improvements over M1 and M1a because they produce consist bias for different amounts of caliche but both models still underestimate resistance in general. 56 Chapter 4 Calibration Procedures Bi-directional load cells for test shafts in Las Vegas are often installed at a significant distance above the shaft base to promote equal movement above and below the load cell. This leaves a significant portion of the shaft below the load cell to contribute to skin friction (the average is 15.25 m for the shafts in this study) and makes it difficult to accurately differentiate between tip and side resistance when interpreting the measured data. While it is sometimes possible to use strain gauge readings to approximate the relative contribution of side and tip resistance below the load cell, in many cases such information is unavailable or not reliable enough to be used for that purpose. As a result, the LRFD calibrations in this study only produce total resistance factors, φRT , as opposed to separate factors for side and tip resistance. Two approaches for implementing the MC method to calibrate φRT are carried out: L1 and L2. In L1, which may be considered the current state-of-the-art, the statistical characterization of the random variable representing resistance is based on ”best-estimate” geomaterial properties. Alternatively, L2 employs nested MC simulations to capture the uncertainty associated with the interpretation of geomaterial properties from Standard Penetration Test (SPT) data and other information made available in Geotechnical Investivation (GI) reports such as boring logs and laboratory test results. Compared to L1, L2 is more robust because it considers an additional source of epistemic uncertainty. That being said, L2 is also more computationally demanding because dedicated MC simulations must be performed on many individual design parameters for each data point. The generalized procedure for L1 is as follows: 1. Collect load test data and associated information required to estimate the resistance of each test foundation. 2. Determine the mean (λR ), COV (COVR ), and distribution type for the resistance bias 57 Final Report CHAPTER 4. CALIBRATION PROCEDURES using best-estimate geomaterial properties. 3. Assume a mean (λLL and λDL ), COV (COVLL and COVDL ), and distribution type for the dead and live load bias. • These values are taken from Paikowsky (2004) and are shown in Table 1.2. 4. For a given resistance factor, compute the limit state equation (g = R − Q) with values for R and Q assigned randomly following the statistical characterizations from steps 2 and 3. • This requires a dead to live load ratio be assumed (QDL /QLL = 3 herein). 5. Repeat step 4 until increasing the number of iterations no longer impacts the results. 6. Compute the probability of failure and reliability index, β for the given resistance factor. 7. Repeat steps 4 through 6 for a range of resistance factors to find the value which corresponds to the desired level of reliability. 8. Validate with an alternative procedure (e.g. FORM) L2 is essentially the same as L1 except for the key feature that the bias computed in step 2 treats the ”best-estimate” values of γ, qu , φ′ , su , and NSP T as the means of random variables with COVs given in Table 4.1. These values were determined based on the data collected for this study and, while they are relied upon hereafter, they are also compared to similar values from published literature in Table 4.2. Thus, for L2, an additional MC simulation take place every time one of the aforementioned parameters enters the design. The number of iterations (N ) required for the nested simulations is determined by increasing N until the mean Rp for each test shaft converges. For this study, N = 500000 is found to be sufficient. Hence, the bias statistics for the final MC simulation of L2 encompass 2.05 × 107 potential design outcomes (41 × 500000) as opposed to just 41 in L1. The nested MC simulations for L2 are carried out with Microsoft Excel Visual Basic for Applications (Excel VBA) since the design of axially loaded drilled shafts can be readily performed within a spreadsheet environment. Alternatively, the final MC simulations for L1 and L2 are implemented in Matlab (MATLAB, 2014). FORM validations are performed with Matlab for each final MC simulation using the procedure from Ayyub et al. (2000). Both L1 and L2 rely on statistical characterizations of the bias summarized in Table 4.3. Inspection of various distribution types fit to cumuative distributions of the bias as 58 Final Report CHAPTER 4. CALIBRATION PROCEDURES well as in consideration of the goodness of fit statistics (Chi-Squared), a global lognormal distribution is determined to be the most appropriate for characterizing the data for all of the prediction methods. This is because it is globally accurate and maintains conservatism in the lower tail regions of the Cumulative Distribution Functions (CDFs). The normal distribution is also considered as well as the best fit to the lower tail, which is defined as described in Allen et al. (2005) and represents the region on the CDF where Rm < Rp . However, using the statistics from the best fit to tail is found to be nonconservative. The Probability Distribution Functions (PDFs) and CDFs of the bias for M1 through M4 are given in Figures 4.1 through 4.4, respectively. (a) L1 (b) L2 Figure 4.1: PDFs and CDFs of the bias developed with M1 for the L1 and L2 calibrations 59 Final Report CHAPTER 4. CALIBRATION PROCEDURES (a) L1 (b) L2 Figure 4.2: PDFs and CDFs of the bias developed with M2 for the L1 and L2 calibrations 60 Final Report CHAPTER 4. CALIBRATION PROCEDURES (a) L1 (b) L2 Figure 4.3: PDFs and CDFs of the bias developed with M3 for the L1 and L2 calibrations 61 Final Report CHAPTER 4. CALIBRATION PROCEDURES (a) L1 (b) L2 Figure 4.4: PDFs and CDFs of the bias developed with M4 for the L1 and L2 calibrations 62 Final Report CHAPTER 4. CALIBRATION PROCEDURES Table 4.1: Assumed COV values for design parameters in the L2 LRFD calibrations. Parameter γ φ′ su γ φ′ su NSP T qu 1 2 Measured (M) or Interpreted (I) I I I M M1 M M M2 Number of Samples COV (%) 898 362 412 208 8 51 1105 60 9.2 14.5 44.9 8.6 6.5 34.1 37.4 59.0 COV obtained using Bayesian equivalent sampling Not all data is pertinent to the test shafts in this study Table 4.2: Comparable COV values for design parameters from literature. Measured or Interpreted Parameter Mean COV (%) Source γ 5.0 Duncan (2000) φ′ 7.5 Duncan (2000) su 26.5 Duncan (2000) NSP T 30.0 Duncan (2000) Notes cited values range from 3 to 7% cited values range from 2 to 13% cited values range from 13 to 40% cited values range from 15 to 45% Inspection of Figures 4.1 through 4.4 reveals that a lognormal distribution provides the most accurate characterization of the bias data in all cases. Thus, the values of the random variables for resistance are generated on each iteration within the MC simulations accordingly. This is accomplished through the use of random number generation functions available in Matlab and Excel VBA which are designed to output values following a lognormal distribution. To further validate the final resistance factors, the inverse transformation method (e.g. Au & Wang, 2014) was also employed to generate values for the MC simulations directly from the empirical CDFs. However, this approach consistently produced greater resistance factors (i.e. less conservative) than those from the fitted lognormal distributions. Thus, the resistance factors from the lognormal fitting approach are reported hereafter. 63 Final Report CHAPTER 4. CALIBRATION PROCEDURES Table 4.3: Summary of statistical parameters used to describe the bias for the L1 and L2 calibrations. All Data Mean Score > 2 Mean Score ≥ 3 Calibration Caliche Mean λ COV Mean λ COV Mean λ COV Level Model M1 3.57 (1.48) 0.47 (0.07) 3.27 (1.61) 0.55 (0.13) 3.27 (1.61) 0.55 (0.13) M2 1.63 (1.00) 0.29 (0.03) 1.63 (1.05) 0.28 (0.04) 1.63 (1.05) 0.28 (0.04) L1 M3 1.81 (1.07) 0.30 (0.01) 1.78 (1.11) 0.29 (0.09) 1.78 (1.11) 0.29 (0.09) M4 1.43 (0.94) 0.29 (0.06) 1.45 (0.95) 0.28 (0.02) 1.29 (0.95) 0.26 (0.02) M1 3.76 (1.19) 0.47 (0.06) 3.45 (1.16) 0.53 (0.06) 2.50 (1.16) 0.36 (0.06) M2 1.71 (0.98) 0.30 (0.01) 1.70 (0.98) 0.28 (0.01) 1.46 (0.98) 0.28 (0.01) L2 M3 1.91 (0.98) 0.31 (0.02) 1.89 (0.98) 0.31 (0.02) 1.59 (0.98) 0.30 (0.02) M4 1.52 (0.92) 0.32 (0.07) 1.55 (0.93) 0.31 (0.07) 1.33 (0.93) 0.29 (0.07) - Values in parenthesis represent the best fit to tail. 64 Chapter 5 Results The results of the MC simulations for L1 and L2 are shown in Figure 5.1 and are summarized in Table 5.1. Additionally, the governing resistance factors for each design approach and calibration level are given in Table 5.2. These represent the lowest computed values of φRT among the three data quality bins (including both L1 and L2). Figure 5.2 shows the impact of data quality and calibration level on the computed resistance factors. For M2, M3, and M4, the resistance factors from L1 are slightly greater than those from L2. However, the opposite is generally true for M1 and M2. In light of this, it is important to note that AASHTO (2014) and Brown et al. (2010) allow most design parameters to affect the outcome only if they fall within a certain range. For example, su is limited to 2.5pa in the Alpha Method for cohesive soils Brown et al. (2010). Hence, if most of the assumed material properties happen to fall close to the effective upper limit, then the L2 calibration is likely to produce a greater φRT than L1 because any randomly generated values that fall above this threshold would not actually increase the predicted resistance. Alternatively, considering that qu of caliche in M2, M3, and M4 has the potential to impact design outcomes for any value greater than zero, it is reasonable for L2 to produce lower resistance factors than L1 for these approaches. 65 Final Report CHAPTER 5. RESULTS Figure 5.1: L1 and L2 calibration results for different data quality bins and design approaches (M1, M2, M3, and M4) based on AASHTO (2014) 66 Final Report CHAPTER 5. RESULTS Table 5.1: Resistance factors computed with the MC simulations for the L1 and L2 calibration (using the global lognormal bias statistics). φRT at β = 3 Design Caliche All data Mean Score > 2 Mean Score ≥ 3 Method Model M1 1.05 (1.09) 0.78 (NR) 0.79 (NR) M2 0.81 (0.98) 0.85 (0.93) 0.85 (0.93) L1 M3 0.90 (1.01) 0.91 (0.91) 0.91 (0.91) M4 0.73 (0.87) 0.77 (0.89) 0.72 (0.88) M1 1.09 (NR) 0.86 (NR) 1.02 (NR) M2 0.84 (0.96) 0.87 (0.96) 0.76 (0.96) L2 M3 0.90 (0.95) 0.91 (0.95) 0.77 (0.96) M4 0.71 (0.84) 0.74 (0.85) 0.66 (0.85) - Values in parenthesis represent the best fit to tail. - NR = No result Table 5.2: Governing LRFD resistance factors for all design approaches and calibration levels Calibration Level L1 L2 Governing φRT at M1 M2 M3 0.78 0.81 0.90 0.86 0.76 0.77 67 β=3 M4 0.72 0.66 Final Report CHAPTER 5. RESULTS Figure 5.2: Impact of data quality and calibration level on the computed resistance factors Inspection of Figure 5.2 suggests that the impact of data quality is dependent on the design method of choice. For example, the resistance factors from M2, M3, and M4 developed with L2 are governed by the data bin with a mean score ≥ 3 but those for M1 are governed by the data bin with a mean score > 2. Additionally, while the governing resistance factors developed with all data qualities only vary slightly between L1 and L2, the difference is much greater for the other data quality bins. Overall, the resistance factors computed with the MC method are in close agreement with those from FORM at β = 3. Both approaches also suggest that the resistance factor at the target level of reliability is not governed by the best fit to tail for any case. Thus, the results of the MC simulations are considered valid. 68 Chapter 6 Conclusions and Final Recommendations A database (the NDFLTD) has been developed in Microsoft Access and employed to calibrate LRFD resistance factors for axially loaded drilled shafts in the Las Vegas Valley. Collected data includes 41 load tests and associated GI information. The framework of the NDFLTD will allow NDOT engineers to add to the current data in the future for the benefit of engineers and researchers involved with deep foundation design in state of Nevada. The impact of data quality, assessed through the ranking system from Table 2.1, is found to be non-trivial. In fact, all of the governing resistance factors in this study were obtained by using one of the top two data quality bins (mean score > 2 or ≥ 3). Since it is impossible to tell which subset of the data is truly the most appropriate representation of the conditions in Las Vegas, it can be concluded that the final recommendations might have been non-conservative if no attempt had been made to address the impact of data quality. The four design approaches considered in this report are summarized below. The results suggest that only the proposed approach (M4) and the associated resistance factor (0.66) is potentially adequate for design purposes. • Current practice (caliche as very dense sand) - Method (M1) This is the approach is meant to represent typical design outcomes in Las Vegas for cases in which full scale load test data is not available. Caliche is treated as dense sand with γ = 140 pcf, φ′ = 40◦ , and NSP T = 50. • Caliche as cohesive IGM - Method (M2) Caliche is treated according to procedures from Brown et al. (2010) for cohesive IGM. Unless site-specific data suggests otherwise, it is assumed that qu = 100 ksf for 69 Final Report CHAPTER 6. CONCLUSIONS AND FINAL RECOMMENDATIONS caliche layers based on the maximum suggested by Brown et al. (2010) for a material classified as cohesive IGM. • Caliche as rock - Method (M3) Caliche is treated according to codified procedures from Brown et al. (2010) for rock with an assumed qu = 729 ksf and RQD = 70%, unless site-specific data suggests otherwise. Note that the value of qu is assumed for calibration purposes only. • Proposed approach - Method (M4) Side resistance in caliche is computed with Equation 3.1. For the purpose of calibration only, it is assumed that qu = 729 ksf for layers not associated with sitespecific unconfined compression test data. Base resistance is taken as the value from the rock model or 100 ksf, whichever is lower. Two calibration techniques were used to develop total resistance factors: L1 and L2 (see Chapter 4). The results demonstrate that the application of a nested MC simulation to capture the uncertainty associated with the interpretation of material properties (i.e. L2) has the potential to produce lower resistance factors than more typical calibration procedures (i.e. L1). Also, of the four methods for treating problematic local geomaterials investigated herein, M1 is the only case for which the governing resistance factor came from L1. This suggests that the L2 style calibration should be considered in future studies of this nature. The governing resistance factor for M4 is the only value which can be recommended for design purposes (φRT = 0.66). This is because all of the other design approaches require a resistance factor greater than 0.70 to achieve the target reliability index of 3, in all cases. Allen (2005), which is cited in AASHTO (2014), suggests that the maximum value for drilled shaft design, even if field load testing has been conducted, should not exceed 0.70. Thus, treating caliche as dense sand (M1), cohesive IGM (M2), or rock (M3) does not appear to be compatible with accepted LRFD guidelines for drilled shafts in Las Vegas. That being said, the proposed approach is still relatively conservative and was only shown to require a resistance factor < 0.70 when the highest data quality bin was considered within the L3 framework. Overall, the evidence presented herein suggests that the prevalence of caliche in the subsurface is one of the most influential factors regarding deep foundation performance in Las Vegas. Consequently, it is necessary to exercise extreme care so that such layers are accurately identified and conservatism is maintained. The authors therefore recommend that site investigations in the area be extended to prove the lateral extent and competence of cemented layers before considering them in design. Also, the unconfined compressive 70 Final Report CHAPTER 6. CONCLUSIONS AND FINAL RECOMMENDATIONS strength of caliche layers must be verified using laboratory testing before employing Equation 3.1 for design. 6.1 Final Recommendations Given the findings of this investigation, it is recommended that LRFD of axially loaded drilled shafts in the Las Vegas Valley, with respect to the strength limit state, be carried out using M4 with a total LRFD resistance factor, φRT , of 0.66. In the M4 design methodology, all subsurface materials except for caliche must be modeled according to the AASHTO (2014) design guidelines. Side resistance in caliche should be computed with Equation 3.1 after laboratory testing on cored samples is employed to determine the unconfined compressive strength, qu , and the final qu employed for design should not exceed 729 ksf under any circumstances. Also, the estimated concrete compressive strength should also be considered as a limiting factor for assigning qu . If caliche layers cannot be associated with site-specific test data, a value of qu = 100 ksf should be assumed to account for the exceptionally high variability in caliche strength. Base resistance in caliche should be evaluated using the AASHTO (2014) design methodology for rock. All material which is not strongly cemented is to be treated according to the codified guidelines for the parent material. It is important to clarify that if any amount of caliche is to be considered in design, the proposed resistance factor should be applied to the sum of nominal base resistance and all of the nominal side resistances computed for the different soil layers along the embedded shaft length. Additionally, base resistance may be neglected if the pertinent properties of the tipping material are inconclusive based on the results of the GI. This would match current common practice and represents a conservative assumption. Due to the nature of the data employed for the calibrations in this report, there are no direct comparisons which can be made between the resistance factors recommended in AASHTO (2014) the total resistance factor of 0.66 suggested herein. This is because separate resistance factors could not be determined herein for side and tip resistance or for different individual material types (due to a lack of necessary data). That being said, engineers may choose to use the AASHTO (2014) LRFD methodology and associated resistance factors if no caliche is considered in design. The recommended LRFD procedure for drilled shafts in Las Vegas is described concisely in Appendix E and in an example provided in Appendix F. 71 Final Report 6.2 CHAPTER 6. CONCLUSIONS AND FINAL RECOMMENDATIONS Future Work The analyses carried out for this report should be repeated in the future as additional load test and GI data becomes available in the Las Vegas Valley. With a large enough sample size, it may also be possible to separate the calibration procedures to provide resistance factors which are more ideal for different foundation geometries and construction methods. This could be accomplished, for example, by binning the data according shaft diameter before carrying out the calibrations to produce a table of appropriate resistance factors for various shaft diameters. New data may also enable more robust empirical relationships to be developed between site investigation data and caliche strength parameters. This might require creating spatially dependent models for different areas within Las Vegas, which was not feasible herein due to limited data barring statistical significance. Furthermore, promoting more non-invasive exploration techniques to allow engineers to make use of Vs measurements in design may also prove beneficial in this regard. It would also be advantageous for the database to be published online. This would allow more practitioners to benefit from it and may also speed up the accumulation of additional data since more potential sources will be aware of its existence. There is a need for load tests to be performed for research purposes only. This is because the goals of field load tests conducted for industry are very different from those of researchers attempting to solve broader problems concerning foundation design methods. For example, simply proving that a test shaft provides a specified level of axial resistance does not necessarily require the load test to be carried out to failure. Researchers are also often forced to contend with limited GI data which is usually collected by a third party. Thus, it would be useful for at least a few tests to be associated with a GI which was designed to be as thorough as possible for research purposes. 72 References AASHTO. (2014). AASTHO LRFD Bridge Design Specifications, U.S. Customary Units with 2015 and 2016 Interim Revisions (7th Edition). American Association of State Highway and Transportation Officials, Washington DC . Abu-Farsakh, M., Yu, X., & Zhang, Z. (2012). Calibration of side, tip, and total resistance factors for load and resistance factor design of drilled shafts. Transportation Research Record: Journal of the Transportation Research Board , 2310 , 38–48. Abu-Hejleh, N., Abu-Farsakh, M., Suleiman, M. T., & Tsai, C. (2015). State of Practices in Databases for Deep Foundation Load Tests. In Ifcee 2015 (pp. 237–246). San Antonio, TX. Allen, T. M. (2005). Development of geotechnical resistance factors and downdrag load factors for lrfd foundation strength limit state design: Reference manual. US Department of Transportation, Federal Highway Administration, National Highway Institute. Allen, T. M., Nowak, A. S., & Bathurst, R. J. (2005). Calibration to determine load and resistance factors for geotechnical and structural design. Transportation Research ECircular (E-C079). Arup. (2011). Vegas high roller - geotechnical interprative report (Tech. Rep. No. 210193). Prepared for Caesar’s Entertainment. ASTM. (2000). ASTM D 2488, Standard Practice for Description and Identification of Soil (Visual-Manual Procedure) (Tech. Rep.). ASTM, West Conshohocken. Atabey, E., Atabey, N., & Kara, H. (1998). Sedimentology of caliche (calcrete) occurrences of the kirşehir region. Bulletin of the Mineral Research and Exploration, 120 , 6980. Au, S.-K., & Wang, Y. (2014). Engineering risk assessment with subset simulation. John Wiley & Sons. Ayyub, B., Assakkaf, I., & Atua, K. (2000). Reliability-Based Load and Resistance Factor Design (LRFD) of Hull Girders for Surface Ships. Naval Engineers Journal , 112 (4), 279–296. Barker, R., Duncan, J., Rojiani, K., Ooi, P., Tan, C., & Kim, S. (1991). Manuals for the design of bridge foundations. NCHRP Report, 343 . 73 Final Report References Bathurst, R. J., Allen, T. M., & Nowak, A. S. (2008). Calibration concepts for load and resistance factor design (lrfd) of reinforced soil walls. Canadian Geotechnical Journal , 45 (10), 1377–1392. Brown, D., Turner, J., & Castelli, R. (2010). Drilled Shafts: Construction Procedures and LRFD Design Methods (Tech. Rep. No. FHWA NHI-10-016). Federal Highway Administration. Chin, F. (1970). Estimation of the ultimate load of piles not carried to failure. In Proceedings of the 2nd southeast asian conference on soil engineering (pp. 81–90). Cibor, J. M. (1983). Geotechnical considerations of las vegas valley. In Geological environmental and soil properties (pp. 351–373). Duncan, J. M. (2000). Factors of safety and reliability in geotechnical engineering. Journal of geotechnical and geoenvironmental engineering, 126 (4), 307–316. Garder, J. A., Ng, K. W., Sritharan, S., & Roling, M. J. (2012). Development of a Database for Drilled SHAft Foundation Testing (DSHAFT) (Tech. Rep. No. InTrans 10-366). Hannigan, P. J., Goble, G. G., Thendean, G., Likins, G. E., & Rausche, F. (1997). Design and construction of driven pile foundations-volume ii (Tech. Rep.). Hasofer, A. M., & Lind, N. C. (1974). Exact and invariant second-moment code format. Journal of the Engineering Mechanics division, 100 (1), 111–121. Kishida, H., Tsubakihara, Y., & Ogura, T. (1992). Pile loading tests at osaka amenity park project. preprint by Mitsubishi Co. Kluzniak, B., Karakouzian, M., & Afsharhasani, R. (2014). Case History: Drilled Shaft Foundation System in Cemented Soils, Las Vegas, Nevada. In 39th annual conference on deep foundations (pp. 365–372). Atlanta, GA. Kulhawy, F. H., Prakoso, W. A., & Akbas, S. O. (2005). Evaluation of capacity of rock foundation sockets. In Proceedings of the 40th united states symposium on rock mechanics. Lee, J.-S., & Park, Y.-H. (2008). Equivalent pile load–head settlement curve using a bidirectional pile load test. Computers and Geotechnics, 35 (2), 124–133. Lemieux, C. (2009). Monte Carlo and Quasi-Monte Carlo Sampling. Springer Science & Business Media. Liang, R., & Li, J. (2009). Resistance Factors Calibrated from FHWA Drilled Shafts Static Top-down Test Database. In Proc., international foundation congress and equipment expo: Contemporary topics in in situ testing, analysis, and reliability of foundations (pp. 15–19). Loadtest Inc. (2000). Construction of the equivalent top-loaded load-settlement curve from the results of an o-cell load test (Tech. Rep.). 74 Final Report References Long, J., Hendrix, J., & Baratta, A. (2009). Evaluation/modification of IDOT foundation piling design and construction policy (Tech. Rep. No. ICT-09-037). Illinois Center for Transportation (ICT). Luke, B., Calderón-Macı́as, C., Huynh, M., Culligan, P., Einstein, H., & Whittle, A. (2003). Inverting Surface Wave Data from a Site Containing Cemented Inclusions. In (pp. 157–162). Verlag Glückauf GMBH, Essen. MATLAB. (2014). Version 8.3.0.532 (R2014a). Natick, Massachusetts: The MathWorks Inc. McVay, M., Hoit, M., Hughes, E., Nguyen, T., & Lai, P. (2005). Development of a web based design, and construction bridge substructure database. In Transportation research board 84th annual meeting (pp. 9–13). McVay, M. C., Kuo, C. L., & Singletary, W. A. (1998). Calibrating Resistance Factors in the Load and Resistance Factor Design for Florida Foundations (Tech. Rep. No. FHWA Report No. WPI0510772). Raleigh, North Carolina: University of Florida, Florida Department of Transportation. Melchers, R. (1987). Structural reliability: Analysis and prediction. UK: Ellis Horwood Limited. Meyer, P. L., Holmquist, D. V., Matlock, H. L., et al. (1975). Computer predictions for axially-loaded piles with nonlinear supports. In Proceedings of the 7th offshore technology conference. Houston, TX. Motamed, R., Stanton, K., Elfass, S., & Kluzniak, B. (2016). Development of the Nevada Deep Foundations Load Test Database: Drilled Shaft Component. In Transportation research board 95th annual meeting (p. 12). Washington D.C.. Murvosh, H., Luke, B., Taylor, W. J., & Wagoner, J. (2013). Three-dimensional shallow shear-wave velocity model for the las vegas valley. Environmental & Engineering Geoscience, 19 (2), 115–134. Ogura, S., & Kishida, Y. (1996). Application of the pile toe test to cast-in-place and precast piles. Translated by Karkee, Foundation Drilling Magazine. Paikowsky, S. G. (2004). Load and resistance factor design (LRFD) for deep foundations (Tech. Rep. No. 507). Transportation Research Board. Rahman, M. S., Gabr, M., Sarica, R., & Hossain, M. (2002). Load and Resistance Factor Design (LRFD) for analysis/design of piles axial capacity (Tech. Rep. No. FHWA/NC/2005-08). Raleigh, North Carolina: North Carolina State University. Rinne, E., Thompson, J., & Vanderpool, W. (1996). I-15/US 95 Load Test Program, Las Vegas, Nevada (Tech. Rep. No. 31-215903-07A). Las Vegas, Nevada: Parsons Brinckerhoff. 75 References Roling, M. J., Sritharan, S., & Suleiman, M. T. (2011). Introduction to PILOT database and establishment of LRFD resistance factors for the construction control of driven steel H-piles. Journal of Bridge Engineering, 16 (6), 728–738. Schmertmann, J. H., & Hayes, J. A. (1997). The osterberg cell and bored pile testing–a symbiosis. In Proceedings: 3rd international geotechnical engineering conference, cairo university, cairo, egypt (pp. 3–12). Smith, T., Banas, A., Gummer, M., & Jin, J. (2011). Recalibration of the GRLWEAP LRFD Resistance Factor for Oregon DOT (Tech. Rep. No. OR-RD-98-00). Salem, Oregon: Oregon Department of Transportation, Research Unit. Stokoe, K. H., Wright, S., Bay, J., Roesset, J., et al. (1994). Characterization of geotechnical sites by sasw method. Geophysical Characterization of Sites, 15–25. Stone, R. C. (2009). Analysis of a Caliche Stiffened Pile Foundation (Ph.D. Dissertation). University of Nevada, Las Vegas. Thiros, S., Bexfield, L., Anning, D., & Huntington, J. (2010). Conceptual Understanding and Groundwater Quality of Selected Basin-Fill Aquifers in the Southwestern United States (Tech. Rep. No. 1781). National Water Quality Assessment Program, USGS. Vanderpool, W. E., Chesnut, R. L., & McGettigan, M. E. (2011). Geotechnical exploration phase drilled shaft load testing. DFI Journal-The Journal of the Deep Foundations Institute, 5 (2), 16–22. Werle, J., & Luke, B. (2007). Engineering with heavily cemented soils in Las Vegas, Nevada. Geo-Denver , 1–9. Western Technologies Inc. (1994). Geotechnical Exploration, Fremont Street Experience (Tech. Rep. No. Project No. 4123JZ254). Las Vegas, Nevada. Wyman, R. V., Karakouzian, M., Bax-Valentine, V., Slemmons, D. B., Peterson, L., & Palmer, S. (1993). Geology of las vegas, nevada, united states of america. Bulletin of the Association of Engineering Geologists, 30 (1), 33–78. 76 Appendix A NDFLTD The NDFLTD may be opened from this link if the appropriate files have been saved to the same path as the current document. The link will not work if this report file was obtained as a stand alone document. For further assistance, please contact the Nevada Department of Transportation. 77 Appendix B Collected Load Tests and GI Reports The original load test and GI reports may be found at this link if the appropriate files have been saved to the same path as the current document. The link will not work if this report file was obtained as a stand alone document. For further assistance, please contact the Nevada Department of Transportation. 78 Appendix C Interpreted Stratigraphy and Geomaterial Properties Tables C.1 through C.41 describe the assumed stratigraphy and material properties employed for the estimation of nominal axial capacity and load-settlement behavior of the test shafts in the NDFLTD. Note that the unconfined compressive strength of caliche, which is not given in the following tables, was set to 729 ksf for all cases for calibration purposes. Also, the superscript PCM indicates that a given layer was noted in the boring log as being partially cemented (i.e. not caliche but exhibiting some level of cementation). 79 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.1: Assumed stratigraphy and material properties for data number 1 (water table depth = 85 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 2 4 8 11.5 13.5 16.5 18 19 22 27 30 36.5 42 47 57 60 62 64.5 65 68 69.5 74.5 79.5 84.5 86 87.5 90 94 97 99 100.5 Cohesionless (GP) Cohesionless (SM) Cohesive Cohesionless (GP) CohesivePCM Caliche Cohesive Caliche Caliche Cohesionless (SM) Cohesionless (SM)PCM Caliche Cohesive Cohesionless (SM) Cohesionless (SM) CohesivePCM Caliche Cohesionless (SM) Cohesive Cohesionless (SM) Caliche Cohesive Cohesive Cohesive Cohesive Cohesionless (SM) Cohesionless (SM) Cohesive Cohesive Cohesionless (SM) Cohesionless (SM) 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 98 98 111 110 111 140 111 140 140 101 117 140 119 115 114 131 140 119 131 119 140 131 131 129 131 108 108 126 131 108 102 43 43 43 40 40 40 40 44 40 39 38 40 41 41 40 36 36 36 35 25 25 19 39 45 45 21 50 6 16 15 50 30 24 30 19 19 18 40 11 11 15 25 11 8 2992 5453 5112 718 4806 2225 1671 1630 1507 3301 1209 1987 - 80 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES 122 Cohesionless (SM) 4 81 116 39 25 - APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.2: Assumed stratigraphy and material properties for data number 2 (water table depth = 101 ft). Bottom Layer Depth (ft) 45.03 53 58 60 67 77 84 85 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesive Cohesive Cohesive Cohesionless (SM) 5 5 5 5 5 5 5 5 131 111 125 114 114 125 112 121 44 43 42 50 30 50 40 20 50 22 50 5800 7830 6590 7800 4850 - 82 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.3: Assumed stratigraphy and material properties for data number 3 (water table depth = 101 ft). Bottom Layer Depth (ft) 10.47 20.67 34 44 69 79 84.9 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SM) Cohesionless (SM) Cohesionless (SM)PCM Cohesionless (SM) Cohesionless (SM) Cohesionless (SM) Cohesionless (SM) 7.67 7.67 6 6 6 6 6 140 131 115 122 115 120 123 45 44 40 33 33 44 42 50 50 50 50 50 50 50 - 83 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.4: Assumed stratigraphy and material properties for data number 4 (water table depth = 10 ft). Bottom Layer Depth (ft) 3.5 9 10 12 13.5 21 32 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SM) Cohesive Cohesive Cohesive Cohesionless (SM)PCM Caliche Cohesionless (SM)PCM 8 8 8 8 8 8 8 91 114 125 125 115 140 136 28 39 40 45 19 25 13 13 13 50 4200 1770 1675 - 84 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.5: Assumed stratigraphy and material properties for data number 5 (water table depth = 7 ft). Bottom Layer Depth (ft) 0.5 7 8.5 14 17 18.5 22 26 29 30.5 31.2 31.6 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SM) Cohesive Cohesive Cohesionless (SM) Caliche Cohesionless (SM) Cohesive Cohesive Cohesionless (SM) Cohesive Caliche Cohesive 2 2 2 2 2 2 2 2 2 2 2 2 97 111 132 130 140 119 112 125 126 131 140 131 38 41 40 41 44 40 - 8 10 18 18 20 24 38 45 11 50 1794 1428 820 582 1706 7952 85 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.6: Assumed stratigraphy and material properties for data number 6 (water table depth = 7 ft). Bottom Layer Depth (ft) 0.5 7 8.5 14 17 18.5 22 26 29 30.5 31.2 31.6 34 34.5 37.5 40 43 44 82.5 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SM) Cohesive Cohesive Cohesionless (SM) Caliche Cohesionless (SM) Cohesive Cohesive Cohesionless (SM) Cohesive Caliche Cohesive Caliche Cohesionless (SM) Caliche Cohesive Cohesive Caliche Cohesive 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 97 111 132 130 140 119 112 125 126 131 140 131 140 107 140 149 109 140 107 38 41 40 41 45 40 40 45 40 40 33 8 10 18 18 20 24 38 45 11 50 50 30 8 27 1794 1428 820 582 1706 7952 1407 608 3252 86 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.7: Assumed stratigraphy and material properties for data number 7 (water table depth = 12 ft). Bottom Layer Depth (ft) 1 3.5 7 9.5 12 12.5 13 20 21 22 26.5 30 34 35.5 37 40.5 43 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SM) Cohesionless (GP) Cohesionless (SM) Cohesive Cohesionless (SM) Cohesionless (SM) Caliche Cohesionless (SM) Cohesive Caliche Cohesionless (GP) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 105 120 99 105 105 125 140 130 115 140 139 115 135 137 137 119 132 42 43 40 42 42 40 42 40 43 44 44 41 19 25 13 11 31 31 24 46 32 12 45 50 50 16 22 1237 504 551 1319 1084 - 87 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.8: Assumed stratigraphy and material properties for data number 8 (water table depth = 22 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 4 8 12 18 20 21 24 32.5 38.5 42 47 48.5 50.5 52.5 53 53.5 57.5 62.5 63.5 65 67 70.5 78 79 83.5 84.5 88 88.5 92.5 99 101 108.5 Cohesionless (SM) Cohesionless (SM) Cohesionless (GP) Cohesionless (GP) Caliche Caliche Cohesive Caliche Cohesionless (GP) Caliche Cohesive Caliche Cohesionless (GP) Caliche Cohesive Caliche Cohesionless (SM) Cohesive Caliche Cohesive Cohesionless (GP) Cohesive Cohesionless (SM) Caliche Cohesive Caliche Cohesive Caliche Cohesive Cohesionless (GP) Caliche Cohesive 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 106 140 111 126 140 140 131 140 116 140 124 140 133 140 131 140 149 131 140 131 129 131 123 140 134 140 124 140 132 133 140 126 42 45 43 44 40 40 40 44 40 40 43 40 30 40 43 40 43 40 40 40 40 42 40 - 22 50 43 50 40 50 16 50 50 50 38 30 50 43 28 50 50 34 50 40 5349 1899 5563 3996 3070 4287 4529 4561 2755 3361 88 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES 114.5 117 126 Caliche Cohesive Caliche 4 4 4 89 140 121 140 40 40 15 - 1206 - APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.9: Assumed stratigraphy and material properties for data number 9 (water table depth = 20 ft). Bottom Layer Depth (ft) 2.5 5 7.5 16 20 25 29 35 36 43 47 53.5 56 65 69 85 85.5 87 100 110 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesive Cohesionless (SM) Cohesionless (SM) Cohesive Cohesive Cohesionless (GP) Caliche Cohesionless (SM) Caliche Cohesionless (GP) Caliche Cohesive Caliche Cohesive Caliche Cohesionless (SM) Caliche Cohesive Cohesionless (SM) Caliche 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 124 110 110 116 133 140 140 132 140 132 140 136 140 131 140 130 140 128 129 140 40 40 43 40 42 40 41 40 40 40 41 40 37 40 16 23 23 4 17 42 42 44 35 40 40 16 16 - 3016 534 2060 3920 4175 1450 - 90 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.10: Assumed stratigraphy and material properties for data number 10 (water table depth = 20 ft). Bottom Layer Depth (ft) 5.2 6 9 10.5 16.5 19 20 25 32.5 36 44 50.5 59.5 69 71 90 98 100.5 122 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SP) Cohesionless (SP) Cohesionless (GP) Cohesionless (SP) Cohesionless (SP) Caliche Cohesionless (SM) Cohesionless (SM) Cohesionless (SM) Caliche Cohesive Cohesionless (SP) Cohesive Cohesive Cohesive Cohesive Cohesive Cohesionless (SP) Cohesive 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 112 111 101 135 140 140 103 125 126 140 131 140 123 131 131 131 131 118 120 42 41 41 44 44 40 41 42 37 40 44 36 - 19 19 19 50 50 30 30 11 18 50 9 50 50 37 50 11 9 2286 1095 5218 5039 3447 4452 676 91 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.11: Assumed stratigraphy and material properties for data number 11 (water table depth = 24.5 ft). Bottom Layer Depth (ft) 9 10 14 19.5 26 29.5 33 38 45 54 56 66 67 69.5 78 78.5 92 100 110 121.5 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SM) Cohesionless (SM) Cohesive Caliche Cohesive Cohesionless (SM) Cohesive Cohesionless (GP)PCM Cohesive Cohesive Caliche Cohesive Caliche Cohesive Cohesive Caliche Cohesive Cohesive Cohesive CohesivePCM 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 99 97 111 140 131 140 131 140 131 124 140 131 140 131 96 140 114 122 122 131 39 38 40 44 44 40 40 40 - 11 11 50 50 31 50 41 50 42 9 31 50 4 4 10 10 50 7064 4410 5764 4736 1061 3384 5172 339 388 928 805 4275 92 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.12: Assumed stratigraphy and material properties for data number 12 (water table depth = 18 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 1.5 8 12 12.5 13.5 15 18.5 20 22 29 29.5 33 38.5 39 40.5 41.5 43 45 48 49 54.5 57 58.5 59.5 61 63 64 66 69.5 73 79 80 Cohesionless (SP) Cohesive Cohesionless (SP) Cohesive Cohesive Cohesionless (GP) Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (GP) Cohesive Cohesionless (GP) Caliche Cohesionless (GP) Caliche Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (GP) Cohesive Caliche Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 117 111 109 111 111 112 140 131 140 131 131 131 140 131 140 140 129 140 138 131 126 131 127 130 140 131 126 131 130 131 129 131 43 40 43 44 44 43 44 44 40 43 40 44 42 42 40 42 43 43 - 25 50 21 40 40 40 50 40 48 32 40 30 50 40 50 40 50 35 37 24 40 24 30 40 45 50 50 50 45 9362 4702 5173 4993 4509 4346 5291 4205 2730 2668 3261 4797 5126 4405 93 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.12: Assumed stratigraphy and material properties for data number 12 (water table depth = 18 ft). (continued) Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 84 87 89.5 94 97.5 98.5 100 103 Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesive Cohesionless (GP) Cohesive Caliche 4 4 4 4 4 4 4 4 128 130 120 131 131 122 131 140 43 41 42 40 50 28 33 38 23 40 23 - 2656 3500 2078 2046 - 107 109 112 113.5 117 118.5 120 125 129.5 132 133 134 135.5 Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (GP) Cohesive Caliche CohesivePCM Cohesive Caliche Cohesive Caliche 4 4 4 4 4 4 4 4 4 4 4 4 4 131 126 131 119 129 116 130 140 131 131 140 131 140 42 41 39 40 40 40 50 50 50 35 18 25 30 50 50 30 - 4327 4231 1496 2457 3979 3922 2326 - 94 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.13: Assumed stratigraphy and material properties for data number 13 (water table depth = 15.5 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 9.5 13 15 17 24 30.5 34.5 35.5 36.5 38.5 39.5 41.5 43.5 45 50.5 54.5 57.5 59.5 60.5 61 63 68.5 73 77.5 82 88 88.5 89.5 90 93 93.5 94.5 Cohesionless (SP) Cohesive Cohesionless (SM) Cohesionless (SM) Cohesive Cohesive Cohesive Cohesionless (SM) Cohesive Caliche Cohesive Caliche Cohesive Cohesionless (SM) Cohesionless (GP) Caliche Cohesionless (SM) Cohesionless (GP) Caliche Cohesionless (SM) Caliche Cohesive Cohesive Cohesive Cohesionless (SM) Cohesive Caliche Cohesive Caliche Cohesive Caliche Cohesionless (SM) 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 140 110 124 139 131 131 131 113 131 140 131 140 131 129 136 140 107 116 140 128 140 131 131 130 122 131 140 131 140 126 140 120 45 44 44 38 40 40 43 44 40 36 39 40 43 40 41 40 40 40 41 50 14 50 50 47 34 50 10 50 35 35 40 50 8 17 45 50 50 27 36 50 45 14 35 1556 6736 4689 6835 6609 4481 4330 4896 5215 5059 2662 4696 4141 1273 - 95 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.13: Assumed stratigraphy and material properties for data number 13 (water table depth = 15.5 ft). (continued) Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 96.5 102 102.5 108.5 113.5 119 123.5 126.5 Cohesive Cohesive Caliche Cohesive Cohesive Cohesive Cohesive Caliche 4 4 4 4 4 4 4 4 131 131 140 126 130 127 131 140 40 40 50 27 15 29 16 45 - 4468 2371 1290 2427 1313 3625 - 127 127.5 129.5 130.5 132 Cohesive Caliche Cohesive Cohesionless (SM) Cohesive 4 4 4 4 4 131 140 131 114 131 40 38 - 45 40 20 44 3547 3133 3416 96 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.14: Assumed stratigraphy and material properties for data number 14 (water table depth = 23 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 2.5 8.5 13.5 18 20.5 23 30 31 34 35 35.5 37.5 40 45.5 48 55 59.5 60.5 68.5 72 75 78 81.5 86.5 88.5 96.5 99.5 105.5 107.5 108 118 129 Cohesionless (GP) Cohesionless (SM) Cohesionless (SM) Cohesionless (SM) Cohesionless (SM) Cohesive Cohesive Cohesive Caliche Cohesive Caliche Cohesive Caliche Cohesive Cohesionless (SM) Cohesionless (SM) Cohesionless (GP) Caliche Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesionless (GP)PCM Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Caliche Cohesionless (SM)PCM Cohesive 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 104 99 102 101 108 111 131 131 140 131 140 131 140 131 127 135 132 140 131 127 131 118 128 131 127 131 126 130 126 140 125 131 42 40 41 41 43 40 40 40 43 44 43 40 43 40 43 43 42 42 40 42 - 20 12 22 21 32 48 50 50 45 45 27 38 50 50 39 44 39 27 50 50 50 50 50 50 50 50 50 7076 6814 7182 6150 6017 3417 4135 3918 4283 3573 3927 4024 97 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES 132 Cohesive 3 98 131 - 50 3709 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.15: Assumed stratigraphy and material properties for data number 15 (water table depth = 24 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 2 6 8 19 21.5 22.5 24 27 36 37 40 41.5 47 48.5 53 57 59.5 63 68.5 72 81 82 83 93.5 95 96 99.5 106.5 111 120 124 127 Cohesionless (GP) Cohesionless (SM) Cohesive Cohesionless (GP) Cohesionless (SM) Caliche Cohesive Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (GP)PCM Caliche Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (GP) Cohesive Cohesionless (SM) Caliche 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 105 113 111 140 135 140 111 131 140 131 123 123 131 124 131 131 140 131 123 131 128 131 123 131 126 131 125 130 125 131 118 140 42 43 44 44 40 44 42 42 42 43 40 28 43 42 42 42 42 40 40 20 30 30 50 50 50 50 50 50 32 32 34 35 39 50 50 37 50 50 50 40 46 50 49 49 30 50 35 34 - 4679 6367 6177 6089 3906 4278 5114 4877 4610 2127 4241 2518 2804 - 99 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES 135 140 Cohesionless (SM) Cohesive 4 4 100 123 131 42 - 50 50 3730 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.16: Assumed stratigraphy and material properties for data number 16 (water table depth = 20 ft). Bottom Layer Depth (ft) 10 13 20 30 36 44.5 48.5 54.5 57.5 60 62.5 68 69.5 75 78.5 83 89 93 98 102 104 106 113 117.5 122 126 131 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (GP) Cohesionless (SM) Cohesionless (GP) Cohesive Caliche Cohesionless (SM) Cohesive Cohesionless (SM) Caliche Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesive Cohesive Cohesive Cohesionless (SM) Cohesive Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesive 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 140 111 116 131 140 131 131 127 140 131 130 131 129 131 111 101 131 124 131 131 116 131 114 130 135 131 131 45 43 44 40 43 43 40 43 43 41 40 38 37 - 50 50 50 50 50 45 50 50 50 50 50 50 50 50 32 50 34 38 39 24 25 39 18 40 50 50 50 6473 5208 5319 4596 4906 3073 4738 3153 3464 2089 3329 3297 3989 4239 101 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.17: Assumed stratigraphy and material properties for data number 17 (water table depth = 20.5 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 10 13.5 19 20.5 22 30 34 37 39.5 42 43 50 55 59.5 64 68 72.5 75 79 82 86 86.5 89.5 94.5 98 101 104 111.5 112 114 115 120 Cohesionless (SM) Cohesive Cohesive Cohesionless (SM) Cohesionless (SM) Cohesionless (SM) Cohesive Caliche Cohesionless (GP)PCM Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Caliche Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 127 125 125 115 140 140 131 140 139 110 131 134 130 131 140 102 128 131 128 131 122 122 122 131 123 126 131 125 131 119 131 122 44 44 44 44 40 44 37 44 43 40 43 43 42 42 42 42 42 41 42 38 41 50 47 47 50 50 50 9 35 50 22 50 48 49 40 50 50 41 41 41 20 43 50 45 50 50 35 45 45 5815 6149 230000 6570 4186 2432 4866 3902 4711 3764 1790 3849 4130 3682 - 102 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES 126 132 136.5 138.5 148.5 152 Cohesive Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive 6 6 6 6 6 6 103 131 128 117 131 123 130 40 42 - 50 18 30 45 50 36 3973 1401 3414 2636 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.18: Assumed stratigraphy and material properties for data number 18 (water table depth = 15 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 45 47.5 50 50.5 51.25 52 53.5 54 57 57.75 59.5 62.5 62.75 67.5 72.25 75 80.5 81 87 91 91.2 95 95.25 96.75 97 98 98.2 100 103 105 106 106.5 Cohesionless (SM) Cohesive Cohesive Caliche Cohesionless (SM) Caliche Cohesive Caliche Cohesive Caliche Cohesive Cohesionless (SM) Caliche Cohesive Cohesionless (SM) Cohesionless (SM) Cohesive Caliche Cohesive Cohesive Caliche Cohesive Caliche Cohesive Caliche Cohesionless (SM) Caliche Cohesive Cohesive Cohesionless (GP)PCM Cohesive Caliche 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 140 131 131 140 132 140 131 140 131 140 131 121 140 127 120 125 119 140 131 121 140 131 140 131 140 117 140 123 127 117 131 140 45 40 43 40 40 40 41 40 41 42 40 40 40 40 40 40 40 40 50 40 44 45 45 45 50 30 12 30 40 7 41 9 50 45 25 11 15 27 25 - 5155 5539 5461 5334 5778 1327 720 4076 872 4744 4206 1014 1368 2242 - 104 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.18: Assumed stratigraphy and material properties for data number 18 (water table depth = 15 ft). (continued) Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 107 107.25 109 110 111.5 111.75 113 113.25 Cohesive Caliche Cohesive Caliche Cohesive Caliche Cohesive Caliche 4 4 4 4 4 4 4 4 131 140 131 140 131 140 131 140 40 40 40 40 35 50 45 45 - 3120 4429 3937 3908 - 116 118 120 120.5 122 122.25 123 123.25 125 125.25 127 127.25 130 131 132.5 132.75 136 136.75 137.5 138.25 166.7 Cohesive Caliche Cohesionless (SM) Caliche Cohesive Caliche Cohesionless (SM) Caliche Cohesionless (SM) Caliche Cohesive Caliche Cohesive Caliche Cohesive Caliche Cohesive Caliche Cohesive Caliche Cohesive 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 131 140 124 140 131 140 115 140 115 140 131 140 131 140 130 140 126 140 122 140 120 40 42 40 40 39 40 39 40 40 40 40 40 40 - 50 46 45 20 20 23 27 20 16 12 10 4300 3763 1890 2197 1608 1274 947 754 105 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.19: Assumed stratigraphy and material properties for data number 19 (water table depth = 15 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 54.2 55 56 57 59 59.5 60 67 68.25 72 73 76 78 83.5 92 92.5 95 95.5 97 97.25 98.5 99 100 100.2 104 105 105.8 106 107 107.25 110.5 111.25 Cohesionless (SP) Caliche Cohesive Caliche Cohesive Caliche Cohesive Cohesionless (SM) Caliche Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesive Caliche Cohesive Caliche Cohesive Caliche Cohesionless (SM) Caliche Cohesionless (GP) Caliche Cohesive Cohesionless (SM) Cohesive Caliche Cohesive Caliche Cohesive Caliche 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 140 140 131 140 131 140 131 121 140 120 118 123 124 131 131 140 131 140 131 140 113 140 111 140 131 116 131 140 131 140 127 140 45 40 40 40 41 40 40 42 40 40 40 38 40 37 40 39 40 40 40 50 45 50 45 30 7 25 10 39 39 21 21 21 15 13 20 21 20 20 15 - 5289 5767 5111 746 1040 3915 2027 1964 1939 1800 1775 1766 1311 - 106 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.19: Assumed stratigraphy and material properties for data number 19 (water table depth = 15 ft). (continued) Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 112.5 112.75 114.5 115.5 116.5 117 117.75 119 Cohesive Caliche Cohesive Cohesionless (SM) Caliche Cohesive Caliche Cohesive 4 4 4 4 4 4 4 4 121 140 120 105 140 121 140 131 40 35 40 40 - 10 9 9 10 45 863 771 847 3787 119.45 120 120.5 122 122.5 123 123.25 124 124.5 125 125.25 127 127.25 128 128.25 132 137 176 Caliche Cohesive Caliche Cohesive Caliche Cohesionless (SM) Caliche Cohesive Caliche Cohesive Caliche Cohesive Caliche Cohesive Caliche Cohesive Cohesionless (SM) Cohesionless (SM) 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 140 131 140 131 140 125 140 131 140 131 140 131 140 131 140 121 124 100 40 40 40 42 40 40 40 40 40 42 34 45 45 50 45 45 45 45 10 50 8 3765 3741 3706 3688 3668 3647 804 - 107 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.20: Assumed stratigraphy and material properties for data number 20 (water table depth = 24 ft). Bottom Layer Depth (ft) 10 18.5 24 29.5 30 34 36 52 53 53.5 60 62.5 69.5 71 77.5 82.5 87.5 92.5 96 100.7 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SM) Cohesionless (SM) Caliche Caliche Cohesionless (SM) Caliche Cohesionless (SM) Cohesionless (SP) Cohesionless (SM) Caliche Cohesive Cohesive Cohesionless (SM) Caliche Cohesive Cohesionless (GP) Cohesionless (SM) Cohesive Caliche Cohesive 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 110 140 140 140 136 140 140 140 131 140 134 122 134 140 120 127 124 124 140 118 42 46 40 40 44 40 44 43 43 40 43 40 43 40 40 - 22 50 50 50 50 50 47 13 50 27 50 29 39 14 4880 1314 2544 3433 1190 108 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.21: Assumed stratigraphy and material properties for data number 21 (water table depth = 20 ft). Bottom Layer Depth (ft) 10 12.5 19 20 30 33.5 34.5 35.5 39 42.5 47.5 52.5 56 59 72.5 76 77.5 85 86 87 94 95.5 99 101 104.5 115.5 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SP) Cohesive Cohesionless (SM) Cohesionless (SP) Caliche Cohesionless (SM) Caliche Cohesive Caliche Cohesive Cohesionless (SM) Cohesionless (SM) Cohesionless (SM) Caliche Cohesive Cohesionless (SM) Caliche Cohesionless (SM) Cohesionless (SM) Caliche Cohesive Caliche Cohesive Caliche Cohesive Caliche 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 131 126 135 120 140 148 140 131 140 131 136 137 146 140 124 131 140 126 131 140 114 140 131 140 131 140 39 43 43 40 44 40 40 38 40 43 40 43 40 40 42 40 40 40 40 12 13 41 50 50 50 45 14 24 50 11 50 24 40 31 40 40 - 1472 6244 5330 1093 2739 3441 3359 - 109 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.22: Assumed stratigraphy and material properties for data number 22 (water table depth = 14 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 2 6 10 12 14 16 18.25 20 29.5 31 36 36.5 40 45 51 51.5 59 61 62 63 71 72 75 76 90 93 95.5 100 108 109 111.5 115 Cohesionless (GP) Cohesionless (SM) Caliche Cohesionless (SM)PCM Caliche Cohesionless (SM)PCM Caliche Cohesionless (SM) Cohesionless (SM)PCM Cohesive Cohesionless (SM)PCM Caliche Cohesionless (SM)PCM Caliche CohesivePCM Caliche Cohesive Cohesionless (SM) CohesivePCM Caliche CohesivePCM Caliche Cohesive Caliche Cohesive Cohesionless (SM)PCM Caliche Cohesionless (SM)PCM Cohesive Cohesionless (SM)PCM Caliche Cohesive 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 98 114 140 116 140 140 140 137 117 131 140 140 140 140 131 140 131 100 131 140 131 140 131 140 113 119 140 118 130 112 140 126 39 44 40 44 40 44 40 44 39 44 40 44 40 40 43 40 40 40 41 40 41 38 40 - 10 31 50 50 50 50 45 50 50 50 45 50 45 50 50 26 30 30 18 15 15 6610 6021 5115 4922 5268 5058 2517 1594 1276 110 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES 118 119.5 121 122 124 126 128 Cohesionless (SM)PCM Caliche Cohesionless (SM) Caliche Cohesive Caliche Cohesionless (SM)PCM 111 4 4 4 4 4 4 4 118 140 125 140 130 140 122 40 40 42 40 40 41 30 50 20 45 1640 - APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.23: Assumed stratigraphy and material properties for data number 23 (water table depth = 14 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 2 6 10 12 14 16 18.25 20 29.5 31 36 36.5 40 45 45.5 46 48 51 51.5 59 61 62 63 71 72 75 76 90 93 95.4 100 108 Cohesionless (GP) Cohesionless (SM) Caliche Cohesionless (SM)PCM Caliche Cohesionless (SM) Caliche Cohesionless (SM) Cohesionless (SM) Cohesive Cohesionless (SM) Caliche Cohesionless (SM) Caliche Cohesionless (SM)PCM CohesivePCM Cohesionless (SM) Cohesive Caliche Cohesive Cohesionless (SM) CohesivePCM Caliche Cohesive Caliche Cohesive Caliche Cohesive Cohesionless (SM) Caliche Cohesionless (SM) Cohesive 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 98 113 140 116 140 140 140 137 117 131 140 140 127 140 140 131 138 131 140 131 100 131 140 131 140 131 140 113 119 140 118 130 39 43 40 42 40 44 40 40 44 44 40 43 40 44 44 40 43 40 40 40 41 40 41 - 10 30 35 50 20 50 35 50 35 50 50 50 50 30 50 35 30 30 26 30 30 18 5145 6173 5969 3421 3840 3170 3043 2523 1597 112 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES 109 111.75 Cohesionless (SM) Caliche 4 4 113 112 140 38 40 15 - - APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.24: Assumed stratigraphy and material properties for data number 24 (water table depth = 24 ft). Bottom Layer Depth (ft) 10 13 18 23 24 41.5 48 49 50 54.5 60 65.5 68 77 81.5 84.5 85.5 91 99.5 100.5 102 107 110 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SM) Cohesive Cohesive Cohesionless (SP) Caliche Caliche Cohesive Caliche Cohesive Caliche Cohesive Cohesive Cohesionless (SM) Caliche Cohesive Caliche Cohesive Caliche Cohesive Caliche Cohesive Caliche Cohesionless (SM) 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 138 111 130 138 140 140 113 140 131 140 132 130 129 140 122 140 131 140 130 140 126 140 128 30 44 40 40 29 40 40 37 40 40 40 40 40 37 25 22 50 50 29 20 50 30 13 12 50 50 15 16 2426 6169 3256 2182 5166 3000 1096 4442 4236 1240 - 114 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.25: Assumed stratigraphy and material properties for data number 25 (water table depth = 30 ft). Bottom Layer Depth (ft) 8 9 12.5 15 18 25 30 31.5 38 43 48.5 53 60 65 70 75 80 85 90 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SP) Caliche Cohesive Cohesive Caliche Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 117 140 125 107 140 111 123 118 128 119 125 106 131 131 131 127 131 122 129 43 40 40 - 25 27 9 50 7 5 12 6 10 1 27 50 27 14 47 10 17 3254 1235 6836 850 639 1129 710 1142 111 2896 5161 2705 1365 4464 928 1542 115 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.26: Assumed stratigraphy and material properties for data number 26 (water table depth = 28 ft). Bottom Layer Depth (ft) 5 6.5 8 10 12.5 16 17 20 25 28 30 35 40 45 55 60 65 70 75 77 85.5 90.5 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesive Cohesive Caliche Cohesionless (SM) Cohesive CohesivePCM Caliche Cohesive Cohesive Cohesive Cohesive Cohesionless (SM) Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesionless (SM) Cohesive 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 111 111 140 100 104 111 140 111 111 102 120 116 125 118 114 117 124 121 119 131 123 131 40 40 40 39 42 - 15 50 20 8 50 50 24 6 6 14 9 7 11 22 10 8 7 27 38 21 2828 8582 977 6993 6097 3319 767 741 1148 859 1286 2463 1087 845 720 2726 1995 116 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.27: Assumed stratigraphy and material properties for data number 27 (water table depth = 62 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 4.5 14 16.8 19 21.5 23.8 24.5 26 27.5 30 33 34.5 37 39 40.5 42 43.5 48 49.5 51 52.5 54 57 62 66 69 72 76 78.5 81 91 93 Cohesive Cohesive Cohesionless (SM) Cohesionless (GP) Cohesionless (SM) Cohesionless (GP) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesive Cohesive Cohesive Cohesionless (GP) Cohesive Cohesive Cohesive Cohesionless (GP) Cohesive Cohesionless (GP) Cohesionless (GP) Cohesionless (GP) Caliche Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 111 111 109 119 140 129 111 106 111 116 111 111 111 111 104 111 111 111 102 111 99 108 109 140 131 125 131 131 130 131 131 131 43 44 44 44 42 44 42 41 39 43 43 40 - 24 30 36 50 50 49 50 32 35 50 50 38 20 45 31 50 45 35 31 44 20 45 50 35 13 50 26 19 50 50 36 4525 4601 6566 4382 6340 4704 2408 5260 5623 4973 3742 4499 3170 1159 4401 2254 1624 4230 4125 2902 117 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES 100 Cohesive 5 118 131 - 50 3965 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.28: Assumed stratigraphy and material properties for data number 28 (water table depth = 81.2 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 4.5 14 16.8 19 21.5 23.8 24.5 26 27.5 30 33 34.5 37 39 40.5 42 43.5 48 49.5 51 52.5 54 57 62 66 69 72 76 78.5 81 91 93 Cohesive Cohesive Cohesionless (SM) Cohesionless (GP) Cohesionless (SM) Cohesionless (GP) Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesive Cohesive Cohesive Cohesionless (GP) Cohesive Cohesive Cohesive Cohesionless (GP) Cohesive Cohesionless (GP) Cohesionless (GP) Cohesionless (GP) Caliche Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 111 111 109 119 140 129 111 106 111 116 111 111 111 111 104 111 111 111 102 111 99 108 109 140 131 125 131 131 130 131 131 131 43 44 44 44 42 44 42 41 39 43 43 40 - 24 30 36 50 50 49 50 32 35 50 50 38 20 45 31 50 45 35 31 44 20 45 50 35 13 50 26 19 50 50 36 4525 4601 6566 4382 6340 4704 2408 5260 5623 4973 3742 4499 3170 1159 4401 2254 1624 4230 4125 2902 119 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES 100 Cohesive 5 120 131 - 50 3965 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.29: Assumed stratigraphy and material properties for data number 29 (water table depth = 18 ft). Bottom Layer Depth (ft) 8 10 11 13 14.5 17 18 41 42 46 52 56 58 60 64.5 68.5 70 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesive Cohesionless (SM) Caliche Cohesionless (SM) Caliche Cohesionless (GP) Cohesive Cohesive Caliche Cohesionless (SM)PCM Cohesive Caliche Cohesionless (SM) Cohesive Cohesive Caliche Cohesive 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 130 130 140 105 140 109 111 131 140 128 131 140 129 131 131 140 131 39 40 42 40 43 40 43 40 43 40 - 35 35 40 35 35 40 40 45 40 40 40 5000 4167 4656 4666 4324 4232 4040 121 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.30: Assumed stratigraphy and material properties for data number 30 (water table depth = 48.1 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 15 18.5 20 22.5 25.5 28 29 32.5 33.5 36.5 37 39 40 41.5 42.5 48.1 53 59 64 64.5 68 72 74 76.5 81 82.5 86.5 92 98 100 102.5 108 Cohesionless (SP) Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (GP) Cohesionless (SM) Cohesive Caliche Cohesionless (GP) Caliche Cohesive Cohesionless (SM) CohesivePCM Caliche Cohesive Cohesionless (SM) Cohesive Cohesive Caliche Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesive Caliche Cohesionless (SM) Cohesionless (SM) Cohesive Cohesionless (GP) CohesivePCM CohesivePCM 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 133 125 111 109 130 114 102 100 140 112 140 111 114 111 140 124 114 104 131 140 130 131 123 125 131 140 129 124 121 123 131 131 44 42 36 44 41 40 42 40 44 40 38 40 43 42 40 43 42 42 - 40 30 20 11 20 46 25 27 32 50 50 50 31 14 30 20 50 20 40 26 40 50 44 32 45 50 50 2332 2638 3534 5931 5744 3373 3069 1999 1916 4234 3688 2767 4232 4170 122 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES 114 115.5 Cohesive Cohesionless (SM) 4 4 123 131 114 38 23 20 1879 - APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.31: Assumed stratigraphy and material properties for data number 31 (water table depth = 5 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 14.3 18.5 20 22.5 25.5 28 29 32.5 33.5 36.5 37 39 40 41.5 42.5 48.1 53 59 64 64.5 68 72 74 76.5 81 82.5 86.5 92 98 100 102.5 108 Cohesionless (SP) Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (GP) Cohesionless (SM) Cohesive Caliche Cohesionless (GP) Caliche Cohesive Cohesionless (SM) CohesivePCM Caliche Cohesive Cohesionless (SM) Cohesive Cohesive Caliche Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesive Caliche Cohesionless (SM) Cohesionless (SM) Cohesive Cohesionless (GP) Cohesive Cohesive 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 133 125 111 109 130 114 102 100 140 112 140 111 114 111 140 124 114 104 131 140 130 131 123 125 131 140 129 124 121 123 131 131 44 42 36 44 41 40 42 40 44 40 38 40 43 42 40 43 42 42 - 40 30 20 11 20 46 25 27 32 50 50 50 31 14 30 20 50 20 40 26 40 50 44 32 45 50 50 2332 2638 3534 5931 5744 3373 3069 1999 1916 4234 3688 2767 4232 4170 124 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES 114 117 123 127 Cohesive Cohesionless (SM) Cohesive Cohesive 6 6 6 6 125 131 114 131 131 38 - 23 20 50 50 1879 4712 4606 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.32: Assumed stratigraphy and material properties for data number 32 (water table depth = 19 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 3 7.5 9.67 13.5 16 17.5 19 24.5 27 28 29 31 33.5 38 42.5 50 51.5 60 63 69.5 71 74 76 76.5 85 88.5 89.5 92.5 98 105 106.5 108.5 Cohesionless (SM) Cohesive Cohesionless (SM) Cohesionless (SM) Cohesionless (SP) Cohesive Cohesionless (SP) Cohesionless (GP)PCM Caliche Cohesive Caliche Cohesive Caliche Cohesionless (SM) Cohesionless (SM) Cohesive Caliche Cohesive Cohesive Cohesive Caliche Cohesive Cohesionless (GP) Caliche Cohesive Cohesive Caliche Cohesionless (GP) Cohesive Cohesive Cohesive Caliche 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 106 111 108 108 140 111 126 140 140 131 140 131 140 123 121 131 140 131 131 131 140 131 129 140 130 131 140 126 131 131 129 140 42 43 43 44 44 44 40 40 40 42 41 40 40 43 40 40 42 40 21 34 33 33 50 45 50 50 40 29 29 27 50 50 19 14 30 50 28 20 48 46 50 18 - 6187 5752 5235 3873 6047 5605 2046 1462 3013 2687 1861 4112 4346 1536 - 126 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES 110.5 111.5 113.5 114 Cohesive Caliche Cohesive Caliche 3.75 3.75 3.75 3.75 127 131 140 131 140 40 40 50 50 - 4198 4146 - APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.33: Assumed stratigraphy and material properties for data number 33 (water table depth = 20 ft). Bottom Layer Depth (ft) 2 6 10 12 20 25 27.5 30 39 45 49.5 56 61 66 71 76.5 77 80 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SP) Cohesionless (SM) Cohesionless (SP) Cohesionless (SP) Cohesionless (SM) Cohesionless (SM) Cohesive Cohesionless (SP) Cohesionless (SM)PCM Cohesive Caliche Cohesive Cohesive Cohesive Cohesive Cohesive Cohesionless (SP) Cohesive 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 136 140 134 121 116 140 131 140 135 131 130 131 131 131 131 131 138 131 44 45 44 43 44 44 44 39 40 43 - 35 50 50 50 50 50 35 50 50 33 50 37 30 27 32 50 45 4437 3949 5515 3296 3085 2695 3102 4253 128 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.34: Assumed stratigraphy and material properties for data number 34 (water table depth = 20 ft). Bottom Layer Depth (ft) 3.5 8.5 11 13 15 19 21 24 30 35 40 41.5 48 53 60 61 66.5 74 84.5 86 90 91 97 98.5 101 103 105 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SP) Cohesionless (SM) Caliche Cohesive Cohesionless (SM) Cohesionless (SM) Caliche Cohesive Cohesive Cohesive Cohesionless (SM) Caliche Cohesive Cohesive Cohesive Cohesive Caliche Cohesive Cohesive Cohesionless (SM) Cohesive Cohesionless (SM) Caliche Cohesive Caliche Cohesive Caliche 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 152 105 140 111 137 137 140 131 120 126 117 140 123 129 130 123 140 131 131 116 131 125 140 131 140 131 140 45 42 40 43 43 40 42 40 40 38 42 40 40 40 50 25 25 41 41 30 41 33 28 49 50 43 19 50 50 16 25 40 45 45 - 2822 4009 5221 4417 5927 5794 4770 2052 5057 4808 2303 3948 3870 - 129 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.35: Assumed stratigraphy and material properties for data number 35 (water table depth = 15.5 ft). Bottom Layer Depth (ft) 1 8 15 15.5 17 24 27 37 43 46 48.5 67 75 90 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (GP) Cohesionless (SP) Cohesionless (GP)PCM Cohesionless (GP)PCM Cohesionless (GP)PCM Caliche Cohesionless (SM) Cohesive Cohesive Caliche Cohesive Cohesionless (SM) Cohesionless (SM) Cohesionless (SM) 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 105 127 135 135 135 140 118 142 123 140 131 138 121 128 42 43 44 44 44 45 36 45 36 41 43 20 26 50 50 50 50 19 50 11 50 16 50 33 50 230000 6789 1375 230000 1880 - 130 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.36: Assumed stratigraphy and material properties for data number 36 (water table depth = 17.5 ft). Bottom Layer Depth (ft) 1 5 10 12.5 17.5 19 23 30 33.5 34.5 35.5 39 42.5 47.5 52.5 56 59 70 72.5 76 77.5 83 86 87 94 95.5 99 101 104.5 115.5 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (SM) Cohesionless (SM) Cohesive Cohesive Cohesionless (SM) Cohesionless (SM) Cohesionless (SP) Caliche Cohesionless (SM) Caliche Cohesive Caliche Cohesive Cohesionless (SM) Cohesionless (GP) Cohesionless (SM) Caliche Cohesive Cohesive Cohesionless (SM) Caliche Cohesionless (SM) Cohesionless (SM) Caliche Cohesive Caliche Cohesive Caliche Cohesive Caliche 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 120 133 129 126 135 135 140 140 148 140 131 140 131 136 118 146 140 127 122 131 140 117 123 140 114 140 131 140 131 140 40 4 43 43 44 40 44 40 40 39 41 43 40 38 40 40 42 40 40 40 40 35 15 8 13 41 41 50 50 50 40 14 24 50 13 9 50 24 40 31 40 40 - 1111 1478 6422 4894 1337 892 2813 3529 3441 - 131 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.37: Assumed stratigraphy and material properties for data number 37 (water table depth = 20 ft). Bottom Layer Depth (ft) 6 9 14.5 17 20 21.5 28 35 40 121 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (GP) Cohesionless (SM) Cohesionless (SM) Caliche Cohesive Cohesive Caliche Cohesionless (GP) Cohesive Cohesive 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 127 109 108 140 11 131 140 140 131 131 44 43 43 40 40 44 - 35 35 35 35 35 45 35 35 4306 5292 4635 3389 132 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.38: Assumed stratigraphy and material properties for data number 38 (water table depth = 15 ft). Bottom Layer Depth (ft) 1.5 3 5 6.5 12.5 15 17 26 28.5 35 36.5 40 42.5 49 52 58 116 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesionless (GP) Cohesionless (SM) Cohesive Caliche Cohesive Cohesionless (SM) Cohesionless (SM) Cohesionless (SM) Cohesionless (SP)PCM Cohesionless (SM) Cohesionless (SP) Cohesionless (SM) Cohesive Cohesionless (SM) Cohesionless (SM) Cohesionless (SM) Cohesionless (SM) 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 127 119 111 140 111 102 119 138 140 120 140 126 131 134 136 122 123 44 41 40 41 41 44 44 44 44 42 44 44 41 42 35 17 50 29 22 22 45 50 50 50 33 33 46 50 30 40 9426 3734 6079 4295 - 133 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.39: Assumed stratigraphy and material properties for data number 39 (water table depth = 80.5 ft). Bottom Layer Depth (ft) Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) 15 18.5 20 22.5 25.5 28 29 32.5 33.5 36.5 37 39 40 41.5 42.5 48.1 53 59 64 64.5 68 72 74 76.5 81 82.5 86.5 92 98 100 102.5 108 Cohesionless (SP) Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesionless (GP) Cohesionless (SM) Cohesive Caliche Cohesionless (GP) Caliche Cohesive Cohesionless (SM) CohesivePCM Caliche Cohesive Cohesionless (SM) Cohesive Cohesive Caliche Cohesionless (SM) Cohesive Cohesionless (SM) Cohesive Cohesive Caliche Cohesionless (SM) Cohesionless (SM) Cohesive Cohesionless (GP) CohesivePCM CohesivePCM 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 133 125 111 109 130 114 102 100 140 112 140 111 114 111 140 124 114 104 131 140 130 131 123 125 131 140 129 124 121 123 131 131 44 42 36 44 41 40 42 40 44 40 38 40 43 42 40 43 42 42 - 40 30 20 11 20 46 25 27 32 50 50 50 31 14 30 20 50 20 40 26 40 50 44 32 45 50 50 2332 2638 3534 5931 5744 3373 3069 1999 1916 4234 3688 2767 4232 4170 134 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES 114 117 121.2 Cohesive Cohesionless (SM) Cohesive 4 4 4 135 131 114 131 38 - 23 20 50 1879 4712 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.40: Assumed stratigraphy and material properties for data number 40 (water table depth = 16 ft). Bottom Layer Depth (ft) 2 8 13 16 17 23 27 33 37 42 49 55 60 63 68 73 80 83.5 84 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive CohesivePCM Cohesionless (SM) Cohesionless (GP) Cohesive Cohesive Cohesive Cohesive Cohesionless (GP) Caliche 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 111.35 133 121 119 116 116 113 113 120 117 122 124 138 118 131 120 134 126 140 42 44 43 40 20 31 5 12 12 12 5 14 23 28 50 30 50 15 21 25 50 50 - 1250 4000 250 1250 1375 1375 250 1625 2375 3250 5500 2625 2320 1850 1650 - 136 APPENDIX C. INTERPRETED STRATIGRAPHY AND GEOMATERIAL PROPERTIES Table C.41: Assumed stratigraphy and material properties for data number 41 (water table depth = 16 ft). Bottom Layer Depth (ft) 2 8 13 16 17 23 27 33 37 42 49 55 60 63 68 73 80 82.5 83 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive CohesivePCM Cohesionless (SM) Cohesionless (GP) Cohesive Cohesive Cohesive Cohesive Cohesionless (GP) Caliche 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 111.35 133 121 119 116 116 113 113 120 117 122 124 138 118 131 120 134 126 140 42 44 43 40 20 31 5 12 12 12 5 14 23 28 50 30 50 15 21 25 50 50 - 1250 4000 250 1250 1375 1375 250 1625 2375 3250 5500 2625 2320 1850 1650 - 137 Appendix D Equivalent Top-Down Load-Settlement Curves Figure D.1: Equivalent top-down load-settlement curve for data number 1 138 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.2: Equivalent top-down load-settlement curve for data number 2 Figure D.3: Equivalent top-down load-settlement curve for data number 3 139 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.4: Equivalent top-down load-settlement curve for data number 4 Figure D.5: Equivalent top-down load-settlement curve for data number 5 140 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.6: Equivalent top-down load-settlement curve for data number 6 Figure D.7: Equivalent top-down load-settlement curve for data number 7 141 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.8: Equivalent top-down load-settlement curve for data number 8 Figure D.9: Equivalent top-down load-settlement curve for data number 9 142 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.10: Equivalent top-down load-settlement curve for data number 10 Figure D.11: Equivalent top-down load-settlement curve for data number 11 143 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.12: Equivalent top-down load-settlement curve for data number 12 Figure D.13: Equivalent top-down load-settlement curve for data number 13 144 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.14: Equivalent top-down load-settlement curve for data number 14 Figure D.15: Equivalent top-down load-settlement curve for data number 15 145 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.16: Equivalent top-down load-settlement curve for data number 16 Figure D.17: Equivalent top-down load-settlement curve for data number 17 146 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.18: Equivalent top-down load-settlement curve for data number 18 147 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.19: Equivalent top-down load-settlement curve for data number 19 Figure D.20: Equivalent top-down load-settlement curve for data number 20 148 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.21: Equivalent top-down load-settlement curve for data number 21 Figure D.22: Equivalent top-down load-settlement curve for data number 22 149 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.23: Equivalent top-down load-settlement curve for data number 23 Figure D.24: Equivalent top-down load-settlement curve for data number 24 150 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.25: Equivalent top-down load-settlement curve for data number 25 Figure D.26: Equivalent top-down load-settlement curve for data number 26 151 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.27: Equivalent top-down load-settlement curve for data number 27 Figure D.28: Equivalent top-down load-settlement curve for data number 28 152 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.29: Equivalent top-down load-settlement curve for data number 29 Figure D.30: Equivalent top-down load-settlement curve for data number 30 153 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.31: Equivalent top-down load-settlement curve for data number 31 Figure D.32: Equivalent top-down load-settlement curve for data number 32 154 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.33: Equivalent top-down load-settlement curve for data number 33 Figure D.34: Equivalent top-down load-settlement curve for data number 34 155 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.35: Equivalent top-down load-settlement curve for data number 35 Figure D.36: Equivalent top-down load-settlement curve for data number 36 156 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.37: Equivalent top-down load-settlement curve for data number 37 Figure D.38: Equivalent top-down load-settlement curve for data number 38 157 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.39: Equivalent top-down load-settlement curve for data number 39 Figure D.40: Equivalent top-down load-settlement curve for data number 40 158 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.41: Measured load-settlement curve for data number 41 Figure D.42: Equivalent top-down load-settlement curve for data number 42 159 APPENDIX D. EQUIVALENT TOP-DOWN LOAD-SETTLEMENT CURVES Figure D.43: Equivalent top-down load-settlement curve for data number 43 Figure D.44: Equivalent top-down load-settlement curve for data number 44 160 Appendix E Recommended Procedure for Analysis of Axially Loaded Drilled Shafts in Las Vegas 1. Carry out a detailed site investigation. - Only fully-cemented material (i.e. determined from core samples) which reaches refusal during SPT may be classified as caliche. - Unconfined compression tests must be performed on samples collected from all caliche layers to determine the unconfined compressive strengths, qu . - Any material which is not strongly cemented is to be treated the same as the parent material in design. 2. Determine the nominal resistances of all non-cemented material according to AASHTO (2014) design guidelines. 3. Side resistance in caliche is determined using Equation E.1. 4. Base resistance in caliche is computed according to AASHTO (2014) design guidelines for rock. 5. The factored resistance is evaluated using a total resistance factor of 0.66. This is applied to the sum of all individual nominal side resistances and nominal base resistance. 161 APPENDIX E. RECOMMENDED PROCEDURE FOR ANALYSIS OF AXIALLY LOADED DRILLED SHAFTS IN LAS VEGAS fSN = 0.85 pa r qu ≤ 15.8 pa (E.1) where fSN = Unit side resistance in caliche pa = Atmosphereic pressure qu = Unconfined compressive strength of caliche Notes: • If the qu of caliche cannot be determined for a given layer, a value no greater than qu = 100 ksf (or fSN = 12.4 ksf) may be assumed. This is to account for the exceptionally high variation in caliche strength. • The upper limit for caliche qu determined from lab testing is 729 ksf. • The site investigation should ensure that any caliche layers into which a shaft is tipped are at least as thick as 2 shaft diameters. • It is highly recommended that measures be taken to verify the lateral extent of cemented layers before relying on their strength in design. 162 Appendix F Example: Recommended LRFD Procedure for a Drilled Shaft in Las Vegas The following example covers the recommended implementation of LRFD with M4 for a drilled shaft in Las Vegas with relatively high data quality scores (GI score = 4, load test score = 4) and a measured resistance of 3682 kip. The test shaft characteristics are taken from data number 26 (see Table 2.2) which is associated with the Trendwest Resorts (owned by Cendent). The shaft diameter (B) is 4.0 ft and its embedded length (L) extends from the ground surface to a depth of 90.5 ft. Table F.1 describes the stratigraphy along the embedded length of the shaft. For the sake of this example, it will be assumed that laboratory testing has validated a caliche qu = 625 ksf. In reality, however, no such information was available for this data point. 163 APPENDIX F. EXAMPLE: RECOMMENDED LRFD PROCEDURE FOR A DRILLED SHAFT IN LAS VEGAS Table F.1: Assumed stratigraphy and material properties for data number 26 (water table depth = 28 ft). Bottom Layer Depth (ft) 5 6.5 8 10 12.5 16 17 20 25 28 30 35 40 45 55 60 65 70 75 77 85.5 90.5 Soil Type B (ft) γ (pcf) φ◦ NSP T su (psf) Cohesive Cohesive Caliche Cohesionless (SM) Cohesive CohesivePCM Caliche Cohesive Cohesive Cohesive Cohesive Cohesionless (SM) Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesive Cohesionless (SM) Cohesive 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 111 111 140 100 104 111 140 111 111 102 120 116 125 118 114 117 124 121 119 131 123 131 40 40 40 39 42 - 15 50 20 8 50 50 24 6 6 14 9 7 11 22 10 8 7 27 38 21 2828 8582 977 6993 6097 3319 767 741 1148 859 1286 2463 1087 845 720 2726 1995 Note: PCM = moderately cemented material The total nominal resistance, RN , is calculated according to Equations F.1 and F.2. RSN i = fSN i πB∆zi 164 (F.1) APPENDIX F. EXAMPLE: RECOMMENDED LRFD PROCEDURE FOR A DRILLED SHAFT IN LAS VEGAS RN = RBN + X RSN i (F.2) where fSN i = nominal unit side resistance of an indidual soil layer ∆zi = individual layer thickness RN = total nominal axial resistance RBN = nominal base resistance RSN i = nominal side resistance of an individual soil layer Thus, the nominal side resistances are as follows: • Layers 1 and 2 - Cohesionless (SM) → β Method (AASHTO, 2014, Section 10.8.3.5.2b) Depth < 5 ft → neglect RSN 1 ; RSN 2 = 72.8 kip • Layer 3 - Caliche → proposed approach (Equation 3.1) qu = 625 ksf → RSN 3 = 575.5 kip • Layer 4 - Cohesionless (SM) → β Method (AASHTO, 2014, Section 10.8.3.5.2b) RSN 4 = 33.7 kip • Layer 5 - Cohesive → α Method (AASHTO, 2014, Section 10.8.3.5.1b) RSN 5 = 16.9 kip • Layer 6 - Partially cemented clay kip Treat as parent (cohesive) (AASHTO, 2014, Section 10.8.3.5.1b) → RSN 6 = 263.89 • Layer 7 - Caliche → proposed approach (Equation 3.1) qu = 625 ksf → RSN 7 = 383.7 kip • Layers 8 through 11 - Cohesive → α Method (AASHTO, 2014, Section 10.8.3.5.1b) RSN 8 = 103.4 kip, RSN 9 = 113.3 kip, RSN 10 = 15.9 kip, RSN 11 = 10.2 kip 165 APPENDIX F. EXAMPLE: RECOMMENDED LRFD PROCEDURE FOR A DRILLED SHAFT IN LAS VEGAS • Layer 12 - Cohesionless (SM) → β Method (AASHTO, 2014, Section 10.8.3.5.2b) RSN 12 = 140.2 kip • Layers 13 through 20 - Cohesive → α Method (AASHTO, 2014, Section 10.8.3.5.1b) RSN 13 = 39.7 kip, RSN 14 = 29.7 kip, RSN 15 = 88.9 kip, RSN 16 = 85.1 kip, RSN 17 = 37.6 kip, RSN 18 = 29.2 kip, RSN 19 = 24.9 kip, RSN 20 = 37.7 kip • Layer 21 - Cohesionless (SM) → β Method (AASHTO, 2014, Section 10.8.3.5.2b) RSN 21 = 518.5 kip • Layer 22 - Cohesive → α Method (AASHTO, 2014, Section 10.8.3.5.1b) RSN 22 = 68.9 kip Following the methodology presented in Chapter 3, the nominal base resistance, RBN , is computed according to AASHTO (2014) for the cohesive soil. In this case, the mean undrained strength over a depth 2B below the base of the shaft is 1330 psf which leads to an estimated RBN = 139.2 kip. Next, summing RSN i for layers 1 through 22 and RBN yields a total nominal resistance RN = 2828.6 kip. The factored axial resistance, RR is then calculated using a total resistance factor of 0.66 according to Equation F.3 as follows: RR = φRT RN = 0.66(2828.6) = 1866.9 kip 166 (F.3) Nevada Department of Transportation Rudy Malfabon, P.E. Director Ken Chambers, Research Division Chief (775) 888-7220 kchambers@dot.state.nv.us 1263 South Stewart Street Carson City, Nevada 89712