Khalid Alshibli, Ayman Okeil, BasharAlramahi, and Zhongjie Zhang

• Objectives• Methodology• Data Collection and Archiving• Reliability Analysis• Results• Summary and Conclusions• Implementations• Acknowledgements

The main objective of this research is to: update the correlations that are currently

used by LADOTD to interpret CPT data for engineering design purposes, and

assess the reliability of using CPT data to predict soil shear strength in both the magnitude and spatial variations in the field with respect to the LRFD methodology.

Collect the available CPT/PCPT soundings with the correspondingboring log data from the LADOTD and other possible sources.

Create an electronic archive of the CPT sites using geographicinformation systems (GIS).

Process/ analyze the data and update the correlations of the shearstrength and soil classification, which are currently used by thedesign section of LADOTD.

Evaluate the spatial variation of soil engineering properties in thefield. Develop the Louisiana CPT database for correspondingengineering properties as a general guideline for design purposeswith reliability consideration in preparation for use in LRFD method.

Make recommendations for future field data collections withrespect to CPT and PCPT and field drilling.

CPT file header:

This information is used to locate the project documents from LADOTD archive.

05-08-20003:18pmJD STA # 10 + 4 3 ON CLRM BENT # 1JL BAYOU MACON LA 585JN 332-04-0005ID F5CKE/V539EL 3 1 . 5 GW 1 2 5 T K N

• A total of 752 CPT soundings were analyzed and archived in ArcGIS• Only 503 CPT were matched with adjacent borehole logs

Analyzed the CPT files using a specially developed excel template to predict the following soil properties:• Bulk unit weight• Undrained shear strength• Soil index properties & classification

Created a GIS database of all the CPT sites where all the CPT information can be retrieved by clicking on its location on the map.

To compare to CPT data, the logs of the boreholes adjacent to CPT sites are located and the following information is recorded: bulk density, water content, LL, PI, Su, and classification for depth intervals of 10 ft.

Archived electronic copies of the associated borehole logs in Arc GIS software.

10

Before performing any reliability calibrations, the compiled results in database were preprocessed by:• Matching boreholes and CPT locations• Averaging CPT readings over a chosen depth• Limiting undrained shear strength scope

11

Matching boreholes and CPT locations was done by setting a limit of 150 ft maximum distance to justify the hypothesis of equal soil properties.

< 25' < 50' < 75' < 100' < 125' < 150' < 200' < 300' < 400' < 500' < 800' < 5,000'

< 10,000'

No. of CPT Locations 96 140 172 205 223 251 262 275 285 300 317 319 320No. of Data Points 531 731 910 1068 1163 1263 1318 1419 1505 1636 1785 1804 1814

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0

50

100

150

200

250

300

350

No.

of D

ata

Poin

ts

No.

of C

PT L

ocat

ions

Distance Threshold = 150 ft

12

Locations where more than one borehole records were available within the distance threshold, a weighted average was used

∑∑

∑=

=

=

−

=n

in

jj

i

n

jj

i

D

DDqq

1

1

1

+

+

+

=21

12

21

21 DD

DqDD

Dqq (case of 2 boreholes)

21 2414.0 7586.0 qqq += (for D1=35 ft and D2=110 ft)

13

Raw CPT data was averaged over a depth equal to about 8 inches. This was necessary to eliminate sudden spikes without loosing the general trends.

0

25

50

75

100

0 50 100 150 200 250 300 350

Dept

h, h

(ft)

Tip Resistance, qc (tsf)

0

25

50

75

100

0 50 100 150 200 250 300 350

Dept

h, h

(ft)

Tip Resistance, qc (tsf)

spike

14

Soil properties outside of a minimum and maximum threshold were excluded, thus limiting the scope of findings from this study.

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

100 500 900 1300 1700 2100 2500 2900 3300

Freq

uenc

y (%

)

Undrained Shear Strength, Su (psf)

• Objectives• Methodology• Data Collection and Archiving•Reliability Analysis• Results• Summary and Conclusions• Implementations• Acknowledgements

16

Focused on the undrained shear strength, Su

The calibration was performed by comparing borehole data and CPT estimates

By introducing the uncertainties, the limit state function is written as the difference between both quantities

( ) CPTu

UCu SS −=Z

CPTu

Boreholeu SS ⇔

RandomVariables

(Phoon and Kulhawy 1999)

First, the three main sources of uncertainty needed to be quantified

18

soilγ

Modelγ

deviceγfrom compiled database

from repeatability tests

from literature

(Phoon and Kulhawy 1999)

19

Conducted a repeatability test to assess uncertainty in device readings

Radius = 6 ft

R = 3.

25 ft

Bore Hole

CPT

20

Tip resistance showed an average COV = 19.8%0% 10% 20% 30% 40%

5

15

25

35

45

55

65

75

COV(qc)

Dept

h, h

(ft)

0

20

40

60

80

0 50 100 150 200 250

Dept

h, h

(ft)

Tip Resistance, qc (tsf)

0

20

40

60

80

0 50 100 150 200 250

Dept

h, h

(ft)

Tip Resistance, qc (tsf)

21

Unit weight estimates showed less COV ( ~1.5%)0.0% 1.0% 2.0% 3.0%

5

15

25

35

45

55

65

75

COV(γT)

Dept

h, h

(ft)

0

20

40

60

80

75 90 105 120

Dept

h, h

(ft)

Unit Weight, γT (pcf)

0

20

40

60

80

75 90 105 120

Dept

h, h

(ft)

Unit Weight, γT (pcf)

22

Soil Classification affects COV of device readings 0

10

20

30

40

50

60

70

80

2 3 4 5 6 7

Dep

th, h

(ft)

Soil Classification

0%

5%

10%

15%

20%

25%

30%

35%

40%

2 3 4 5 6 7

COV

(qc)

Soil Classification

23

Uncertainty in transformation model is a big factor in this study Used the given Su

expression

kt

vocCPTu N

qS σ−=

0

500

1000

1500

2000

2500

0 500 1000 1500 2000 2500

Bore

Hol

e U

ndra

ined

She

ar S

tren

gth,

Su

(psf

)

CPT Undrained Shear Strength, Su (psf)

Overestimate

Underestimate

CPT overestimates Su(using Nkt=15)

24

The bias and COV were found to be

Next task was to look into effects of various factors on uncertainty

01.2Mean Bias =

= UC

u

CPTu

SSλ

%71COVScatter =

= UC

u

CPTu

SS

25

Depth No clear correlation could be established

Depth (ft) h ≤ 5 5 < h ≤ 20 20 < h ≤ 40 40 < h ≤ 60 h ≥ 60

Bias 1.473 2.203 2.191 1.826 1.940

COV(%) 85% 73% 61% 58% 73%

# 123 317 189 122 111

Nkt 22.1 33.0 32.9 27.4 29.1

26

Soil Classification (Zhang and Tumay) Clear correlation could be established

Clay % < 25 25 – 50 50 – 75 > 75

Bias 2.112 2.273 1.954 1.822

COV(%) 67% 78% 76% 87%

# 171 210 128 62

Nkt 31.7 34.1 29.3 27.3

27

Soil Classification (Robertson) Clear correlation could be established

Classification 2 3 4 5

Bias 1.358 1.899 2.067 2.643

COV(%) 71% 67% 63% 87%

# 33 478 258 87

Nkt 20.4 28.5 31.0 39.6

28

CPT Readings Clearest correlation was established

qc – σvo (tsf) < 4 4 – 8 8 – 12 12 – 16 > 16

Bias 0.849 1.751 2.215 2.339 2.491

COV(%) 72% 62% 67% 70% 69%

# 57 315 226 173 91

Nkt 12.7 26.3 33.2 35.1 37.4

29

Su Magnitude Can be correlated

using soil classification A two parameter

study (classification and another parameter) is possible, however the number of data points is not sufficient.

0

500

1000

1500

2000

2500

0 500 1000 1500 2000 2500

Bore

Hol

e Un

drai

ned

Shea

r St

reng

th,

Su

(psf

)

CPT Undrained Shear Strength, Su (psf)

Overestimate

Underestimate

30

Distribution type was then identified by performing a χ2 statistical test (Normal vs. Lognormal)

0 1 2 3 4 5 6 7 80

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Transormation Model Bias, λ

Freq

uenc

y

Normal

Lognormal

31

Based on these results, the following statistical parameters were used in the reliability study

Variable Mean COV (%) Distribution Source

Soil Uncertainty 1.0 33 LognormalPhoon and Kulhawy (1999)

Transformation Model varies Lognormal Current Study

Tip Resistance varies Normal Current Study

Overburden Pressure deterministic Current Study

32

Two approaches were followed in this study:1st Approach: A direct correlation approach

to find the CPT coefficient, Nkt, where a certain probability of exceedance, Pe, is targeted2nd Approach: A detailed reliability analysis

that accounts for all sources of uncertainty (device, expression, soil) explicitly to achieve a target reliability index, βT.

33

Su are determined using the given expression and correlated with corresponding borehole results

A CPT coefficient, Nkt, is determined for each datapoint and the results are statistically studied

34

0102030405060708090

100

< 3 6 - 9 12 - 15 18 - 21 24 - 27 30 - 33 36 - 39 42 - 45 48 - 51 54 - 57 60 - 63 > 66

Fre

qu

en

cyCPT Coefficient, Nkt

Overestimated if Nkt = 15 is usedUnderestimated

80.7%

0

0.25

0.5

0.75

1

0 25 50 75 100

Prob

abili

ty

Correlated Nkt

15 21

40%

19%

PDF and CDF plots are generated and used to determine Nkt for any desired Pe value

35

The CPT Coefficient, Nkt, is calibrated for different target reliability levels, βT, using a Limit State Function (LSF)

CPTu

UCu SS −=Z

P D

F

S , S

f (s )S

f (s )S

µS µSuCPT

uUC

u

uu

uCPT

UC

uUC

uCPT µZ

P D

F

Z

f (z)Z Probability of Exceedance

(Z>0)

βσZ

−−=

kt

vocUCu N

qS σ ηξγ

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0 10 20 30 40 50 60

(β-β

T )2

CPT Coefficient, Nkt

Optimum Nkt

36

First, reliability is assessed for different assumed Nktvalues Then, an optimum Nkt is identified through a

minimization process

Repeat for: Soil classification qc /σvo ratio Target reliability, βT

• Objectives• Research Tasks• Data Collection and Archiving• Reliability Analysis•Results• Summary and Conclusions• Implementations• Acknowledgements

38

The following Nkt values were determined

0

5

10

15

20

0 500 1000 1500 2000 2500

Tip

Res

ista

nce

-Ove

rbur

den

Pres

sure

, qc-σ

vo(ts

f)

Borehole Undrained Shear Strength, Su (psf)

Pe Nkt

50.0% 24.9

55.0% 26.9

66.7% 32.3

39

The following Nkt values were determined

PeNkt

ALL >75% 50-75% 25-50% <25%50% 27.5 26.9 25.3 28.8 31.555% 31.1 30.0 29.3 32.5 35.3

66.7% 42.0 38.4 39.3 45.2 50.1

PeNkt

ALL 2 3 450% 27.5 18.6 26.2 30.955% 31.1 21.0 28.6 34.3

66.7% 42.0 28.5 35.7 45.0

Zhang and Tumay

Robertson

40

The study also showed that the CPT is capable of assessing the soil unit weight, γT, using

0

25

50

75

100

125

0 25 50 75 100 125

Bore

Hol

e U

nit W

eigh

t, γ

T(p

cf)

CPT Soil Unit Weight, γu (pcf)

Overestimate

Underestimate

98.0Mean Bias =

= Borehole

u

CPTu

γγλ

%4.12COVScatter =

= Borehole

u

CPTu

γγ

)log(61.1)log(32.8)/( 3 zVmkN sT −=γ

[ ]3.0

67.1 100*4.11)log(1.10)/(

−⋅=

t

scs q

fqsmV

41

Modern design codes specify design coefficients that vary based on site variabilityA procedure is established for assisting

engineers in determining site variability based on CPT undrained shear strength estimatesThe procedure relies on comparing the COV

of SuCPT and expected COVs from repeatability

and soil (low, medium, high) variations combined

• Objectives• Research Tasks• Data Collection and Archiving• Reliability Analysis• Results•Summary and Conclusions• Implementations• Acknowledgements

The findings of this study can be summarized in the following : The database was integrated in a GIS system

for ease of access and retrieval Sources of uncertainty were identified and

quantified Reliability-based calibration methods were used

to estimate CPT coefficient, Nkt A framework for site variability assessment

based on CPT results was proposed

• Objectives• Research Tasks• Data Collection and Archiving• Reliability Analysis• Results• Summary and Conclusions• Implementations• Acknowledgements

The study demonstrated the importance of performing in depth statistical analyses of soil property estimation methods. It is therefore recommended that the LADOTD : Maintain the compiled database of CPT soundings

and matching boreholes Perform periodic updates of the coefficients

proposed in this study as more data become available

Use recommended Nkt values in conjunction with borehole results for a pilot testing period

Document inconsistencies to assist in future calibrations

CPTuS

The authors gratefully acknowledged the financial support provided by the Louisiana Transportation Research Center (LTRC Project No. 06-6GT) and Louisiana Department of Transportation and Development (State Project No. 736-99-1406).

We thank Benjamin Fernandez from LADOTD materials laboratory and Jesse Rauser from Ardaman & Associates, Inc. for providing the CPT and borehole data.

The authors acknowledge the assistance of the graduate students Chaytanya Mamidala and Ashwin Bommathanahalliwho helped in data archiving and analysis.

The constructive criticism and suggestions of Zhongjie "Doc" Zhang, Pavement Geotechnical Research Administrator, PRC, Report reviewers, and Mark Morvant, Associate Director of Research at LTRC are highly valued and appreciated.