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 . . . . .
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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 . . . . . . . . . . . . . . . . . .
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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
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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
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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
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top-down
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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
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138
139
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151
152
152
153
153
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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
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157
157
158
158
159
159
160
160
List of Tables
1.1
1.2
1.3
. . . .
. . . .
in the
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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)
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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)
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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
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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.
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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.
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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
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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
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CHAPTER 2. MEASURED DATA
Figure 2.4: Main search window in the NDFLTD
Figure 2.5: Example GI report in the NDFLTD
39
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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
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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
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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
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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
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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
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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.
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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
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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
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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.
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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.
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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.
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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.
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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)
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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
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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
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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
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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.
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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
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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
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Kulhawy, F. H., Prakoso, W. A., & Akbas, S. O. (2005). Evaluation of capacity of rock
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74
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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