indian geotechnical conference th 50 igc 17 – 19 …igs/ldh/files/igc 2015 pune... ·...

50

th

IG

C

50th

INDIAN GEOTECHNICAL CONFERENCE

17th

– 19th

DECEMBER 2015, Pune, Maharashtra, India

Venue: College of Engineering (Estd. 1854), Pune, India

PROBABILISTIC ASSESSMENT OF LIQUEFACTION POTENTIAL OF GUWAHATI CITY

B, Sharma1, Assam Engineering College, [email protected],

M, Doley2, Assam Engineering College, [email protected]

ABSTRACT

This paper presents assessment of the liquefaction potential of Guwahati City, in Assam. The assessment

was done for saturated cohesionless deposits. Standard penetration test and simplified procedures has

evolved as a standard of practice for evaluating the liquefaction potential of soils through the works of

Seed and Idriss (1971, 1982) and Seed et al. ( 1983,1985), Cetin et al (2004) and the Idriss and

Boulanger (2004) method. In the context of probabilistic analysis this methods are known as deterministic

methods. Probabilistic method of liquefaction assessment is done in terms of probability of liquefaction.

In this paper an attempt has been made to predict the liquefaction susceptibility of Guwahati city based

on corrected SPT values using probabilistic performance based approach of Toprak et al. (1999) and

Zuang et al. (2002). Both the methods are based on logistic regression analysis. Selection of the two

probabilistic method is done arbitrarily and the methodology and procedures of the methods are

explained and assessment of liquefaction susceptibility done. Standard Penetration Test (SPT) N values,

engineering properties of the soils and depth of water table were taken from a data base of 200 boreholes

upto 30 meter depth covering an area of 262 km2 in Guwahati city. To determine the N value of the soil

with depth standard penetration test was done at every 1.5 m interval . Undisturbed and disturbed soil

samples were collected to determine the engineering properties of the soils in the laboratory. Guwahati

falls in zone V according to the seismic zoning map of India, so a design peak ground acceleration of 0.36

g was used . In this study the soil layers that were identified for liquefaction analysis are fine to medium

sand and silty sands that have classification of SP, SW, SC, SM, SP-SC. Inorganic silt of classification

ML, ML-CL and non plastic inorganic silts were also analysed for liquefaction susceptibility.

Liquefaction susceptibility from the methods is presented as a GIS based map showing zones of levels of

risk of liquefaction. It has been found that the two logistic regression probabilistic approach have yielded

different results. In all the bore holes it is seen that Zuang et al. (2002) is showing consistently lower

values of probability compared to Toprak et al.(1999). Results have also been presented in the form of

graphs showing probability of liquefaction with depth. Comparison of the two methods in the

probabilistic approach have shown a difference in the values of probabilities in the same depth. This is

shown in Fig.1. It is seen that Juang et al. (2002) is showing lower values of probability compared to

Toprak et al. (1999) for the bore holes 5 and 8. For an earthquake magnitude of 6 the variation is seen to

be more compared to an earthquake magnitude of 7.5 and 8.1. This observation is consistent in 30 bore

holes but only the degree of under estimation varies.However it is seen that the probabilities obtained

from logistic regression are influenced by the form of the function adopted for the regression, and by the

data set used in the regression. The percentage variation with respect to Juang et al. (2002) for an

earthquake magnitude of 7.5, being 63.9% to -13.3%.

1Sharma_Binu1, Civil Engineering, Professor, Guwahati, India, [email protected]

2Doley_Monalisha2, Civil Engineering, M.E.student, Guwahati, India, [email protected]

Binu Sharma, Monalisha Doley

14

12

10

8

6

0 50 100

Probability (%)

Depth

(m

)

Toprak et al. (1999), M = 6

Juang et al. (2002), M = 6

Toprak et al. (1999), M = 7.5

Juang et al. (2002), M = 7.5

Toprak et al. (1999), M = 8.1

Juang et al. (2002), M = 8.1

Fig.1 Comparison of probability between Toprak et al. (1999) and Juang et al. (2002) for borehole 8.

Fig.2 shows the probabilistic liquefaction potential map of Guwahati city for an earthquake

magnitude of 7.5 according to Juang et al. (2002). The map shows zones of different levels of risk of

liquefaction. It is observed from the map that the southern bank of river Brahmaputra with the areas of

Palashbari, Azara, Jhalukbari, Pandu, Bharalumukh and Uzanbazar, some areas in G.S.Road, Gorchuk

area and areas near Chandmari have probabilities of liquefaction greater than 50%. Some areas in the

northern bank of the city are also susceptible to liquefaction.

Keywords: Liquefaction, Probability, Standard penetration test.

50

th

IG

C

50th


17th

– 19th



PROBABILISTIC ASSESSMENT OF LIQUEFACTION POTENTIAL OF

GUWAHATI CITY

Binu Sharma, Professor, Assam Engineering College, [email protected]

Monalisha Doley, Student, Assam Engineering College, [email protected]

ABSTRACT: This paper presents assessment of the liquefaction potential of Guwahati City, in Assam. The

assessment was done for saturated cohesionless deposits. Standard penetration test and simplified procedures has

evolved as a standard of practice for evaluating the liquefaction potential of soils. In the context of probabilistic

analysis this methods are known as deterministic methods. Probabilistic method of liquefaction assessment is done

in terms of probability of liquefaction. In this paper an attempt has been made to predict the liquefaction

susceptibility of Guwahati city based on corrected SPT values using probabilistic performance based approach of

Toprak et al. (1999) and Juang et al. (2002). Both the methods are based on logistic regression analysis. Corrected

SPT N values and engineering properties of the soils were taken from a data base of 200 bore holes of Guwahati

city covering an area of 262 km2. Guwahati falls in zone V according to the seismic zoning map of India, so a

design peak ground acceleration of 0.36 g was used. Liquefaction susceptibility from the methods is presented as a

GIS based map showing zones of levels of risk of liquefaction. It has been found that the two logistic regression

probabilistic approaches have yielded different results. In all the bore holes it is seen that Juang et al. (2002) is

showing consistently lower values of probability compared to Toprak et al. (1999). Results have also been

presented in the form of graphs showing probability of liquefaction with depth. Comparisons of the two methods in

the probabilistic approach have shown a difference in the values of probabilities in the same depth. However it is

seen that the probabilities obtained from logistic regression are influenced by the form of the function adopted for

the regression, and by the data set used in the regression.

Keywords: Liquefaction, Probability, Standard penetration test.

INTRODUCTION In the deterministic approach, evaluation of

liquefaction potential is done by the simplified

methods that follow Seed and Idriss (1971,1982)

and Seed et al (1983,1985).These methods were

developed from field performance case histories at

level ground sites together with in situ tests data at

these sites such as the standard penetration test (

SPT). In this approach, factor of safety (Fs),

defined as the ratio of cyclic resistance ratio (CRR)

over cyclic stress ratio (CSR) is evaluated and

liquefaction of a soil is predicted to occur if the

factor of safety (Fs) is less than or equal to 1. Due

to the uncertainties that exist in the adopted model

and the input data, the computed factor of safety

(Fs) is often required to be greater than a limiting

value. According to the Building Seismic Safety

Council [BSSC] 1997, the limiting value of factor

of safety (Fs) is required to be greater than 1.2 – 1.5

to ensure that no liquefaction will occur. A sound

engineering judgement is required to decide upon a

suitable value of factor of safety (Fs). Compared to factor of safety, the probability of

liquefaction is more suitable as an index for

assessment of liquefaction potential and for

liquefaction potential mapping. Determination of

the probability of liquefaction is important for

assessment of the performance based earthquake

engineering. Several researchers have contributed

to the work of statistical/probabilistic evaluation of

liquefaction potential. Liao et al. 1988, Youd and

Nobel 1997, Toprak et al. (1999) and Juang et al.

(2000, 2002) have done logistic regression analysis

of field records to established empirical equation to


calculate probability of liquefaction. Haldar and

Tang 1979, Yegian and Whitman, 1978 have

applied probability and statistics to deal with

uncertainties that are associated with the simplified

methods. Cetin et al. (2004) have developed

stochastic models for assessment of seismic soil

liquefaction developed within a Bayesian

framework. Juang et al. (2002) and Juang et al.

(2012) proposed a new approach to evaluate the

probability of liquefaction by using a Baysian

mapping function that depends on a particular

deterministic approach.

The logistic regression approaches are

developed based on field data and the form of

logistic function. The equation for liquefaction

probability established by the logistic regression is

independent of the deterministic methods based on

standard penetration test or the cone penetration

test.

In this paper comparison of probabilities

obtained by the logistic regression of Toprak et al.

(1999) and Juang et al. (2000, 2002) is presented of

Guwahati city. A soil database from 200 boreholes

of Guwahati city was used for the purpose. The soil

database was from a project funded by the

Directorate of Science and Technology, India for

microzonation of Guwahati City.

According to Toprak et al. (1999) SPT-based

probabilistic liquefaction boundary curves were

developed using logistic regression analyses. The

logistic regression equation, obtained from the

world wide liquefaction database (total number of

data points = 440) is given by the following

equation

(1)

Where CSR = cyclic stress ratio and MSF =

magnitude scaling factor.

In Eq. (1) CSR were calculated according to Youd

and Idriss (1997). All CSR were adjusted to Mw =

7.5 using the Idriss (1999) magnitude scaling factor

(MSF). (N1)60cs is determined according to Youd

and Idriss (1997). In logistic regression, the

classification or prediction is generally considered

a success for a liquefied case if PL > 50%, whereas

the prediction is considered a success for a

nonliquefied case if PL < 50%.

Probability of liquefaction, according to Juang et

al. (2002), was calculated using two different

approaches. One was by the logistic regression and

the other was by the Bayesian mapping. Logistic

regression is a well-established statistical

procedure, whereas Bayesian mapping is a

relatively new application of the Bayes’ theorem to

evaluate probability of soil liquefaction. Logistic

regression was performed on a database of 243

cases with SPT measurements that were taken from

a database of field performance cases compiled by

Fear and McRoberts (1995). The logistic

regression analysis of these data gave the following

probability equation:

(2)

Where PL = probability of liquefaction.

The cyclic stress ratio (CSR) in Eq. (2) is

calculated according to Seed and Idriss (1985). In

the formulation of CSR in Eq. (2), the term rd,

which provides an approximate correction for

flexibility of the soil profile, are calculated using

the Liao et al. (1988) equation:

for z < 9.15m (3a)

for 9.15m < z < 23m (3b)

(4)

where Mw is the moment magnitude.

The normalized SPT N-values in the relationships

(1) and (2) were corrected for overburden pressure

CN, for the energy ratio of the hammer CE, for bore

hole diameter CB, for rod length CR and for

correction for samplers with or without liners CS as

shown below

(5)

Where N = measured standard penetration

resistance; CN = factor to normalize N to a

50

th

IG

C

50th


17th

– 19th



common reference effective over burden stress; CE

= correction for hammer energy ratio(ER); CR =

correction factor for rod length; CS = correction for

samplers with or without liners; CB = correction for

bore hole diameter. The equivalent clean sand

corrected N values i.e. the (N1)60cs values are

obtained according to Youd and Idriss (1997).

For evaluation of (N1)60cs, the following

equation was developed by I. M. Idriss with the

assistance of R. B. Seed (1985).

(6)

Where α and β = coefficients were determined

from the following relationships:

for FC (7a)

for (7b)

for FC (7c)

for FC (8a)

for (8b)

for FC (8c)

Equations (5) and (6) and (7a) to (8c) are

incorporated in Youd and Idriss (1997).

Soil study of Guwahati city

Guwahati city lies between latitude 26.1833o N and

longitude 91.733o E. The mighty river

Brahmaputra flows to its north, the south and the

eastern sides are surrounded by two rows of semi-

circular hillocks. A soil database from 200

boreholes was used to determine probability of

liquefaction for areas in Guwahati city. The soil

database was from a project funded by the


Microzonation of Guwahati City. The project was

to study the soil properties of Guwahati City. For

this, bore holes of 30 m depth were made in 200

locations covering an area of 262 km2. The bore

hole location map along with the river

Brahmaputra in Guwahati city is shown in Fig. --.

Standard penetration test was done at every 1.5 m

interval to determine the N value of the soil with

depth. SPT-N value of the soils varied from 4 to >

50 (refusal). Undisturbed and disturbed soil

samples were collected to determine the

engineering properties of the soils in the

laboratory.

Guwahati soil mostly consists of alluvial deposits

with alternating layers of both fined grained and

coarse grained soils. There is a great deal of

variation in the thickness of these layers. The fine

grained fraction mostly consists of red, brown and

gray colored silty clay and clay of classification

CL, CI and CH. In some locations inorganic silt of

classification ML and MI and CL–ML and non

plastic inorganic silts were also encountered. The

coarse grained fraction is mostly of classification

SP, SW, SC, SM, SP-SC. Gravel deposits were

also encountered in certain bore holes. Some bore

holes were found to consist of fine grained deposits

of cohesive soils up to the full depth of 30 m. The

200 boring logs showed the water table to be

within 0–6 m meter of the ground surface. The

depth to ground water table in Guwahati city is

given in Sharma and Hazarika (2013).

Results and Discussion

The hazard associated with soil liquefaction during

earthquakes has been known to be encountered in

deposits consisting of fine to medium sands and

silty sand and sands containing low plasticity. Seed

et al. (1983) stated that based on both laboratories

testing and field performance, the great majority of

cohesive soils will not liquefy during earthquakes.

The soil layers that were identified for liquefaction

analysis are fine to medium sand and silty sands

that have classification of SP, SW, SC, SM, SP-

SC. Inorganic silt of classification ML, ML-CL


and nonplastic inorganic silts were also analyses

for liquefaction susceptibility.

Guwahati city is situated along the river

Brahmaputra in the North Eastern region of India.

The Indian standard code of practice (IS 1893)

identified North East India including Assam as a

highly seismic zone by placing it in the highest

seismic zonal level i.e. zone V. The peak ground

acceleration specified for Guwahati city is 0.36 g.

This is for an 8.1 magnitude earthquake occurring

on a fault at an epicentral distance of 50 km from

Guwahati city. The probability of liquefaction is

determined for the bore holes susceptible to

liquefaction using the logistic regression equation

of Toprak et al. (1999) and the logistic regression

equation of Juang (2000, 2002). For both the

methods, (N1)60 is calculated using the corrected N-

value for overburden pressure using Eq. (3). The

equivalent clean sand corrected N values i.e. the

(N1)60csvalues were determined according to Eq.4.

The Cyclic stress ratio produced by an earthquake

is calculated according to Youd and Idriss (1997)

and Youd et al (2001).

For the analysis according to Toprak et

al.(1999), first the magnitude scaling factors were

calculated according to Idriss(1999). The cyclic

stress ratios were calculated according to Youd and

Idriss (1997). For the analysis of probability

according to Juang et al. (2002), the cyclic stress

ratios were calculated according to Seed and Idriss

(1985). Using Eq. (1) and (2) the probability of

liquefaction were finally calculated. The values

were then adjusted for earthquake magnitudes 6

and 8.1. Of the 200 sites, 49 sites in Guwahati have

been found to be susceptible to liquefaction

according to the Toprak et al. (1999) method of

probability analysis and 50 according to Juang et

al. (2002) method of analysis. The rest of the sites

where the bore holes are located are not susceptible

to liquefaction. Although the probabilities are

calculated for all the 200 bore holes, Figs. 1, 2 and

3 show the probability of liquefaction with depth of

only three bore holes for earthquake magnitude 6,

7.5 and 8.1 for the Toprak et al. method.

14

12

10

80 50 100

Probability (%)

Depth

(m

)


Toprak et al. (1999), M = 7.5

Toprak et al. (1999), M = 8.1

Fig.1 Probability with depth for borehole 5

(Toprak et al. (1999))

20

18

16

14

12

10

8

60 50 100

Probability (%)

Depth

(m

)


Toprak et al. (1999), M = 7.5

Toprak et al. (1999), M = 8.1

Fig.2 Probability with depth for borehole16


14

12

10

8

6

0 50 100

Dep

th (m

)

Probability (%)

Toprak et al. (1999), M=6

Toprak et al. (1999), M=7.5

Toprak et al. (1999), M=8.1

Fig.3 Probability with depth for bore hole8


Similarly Figs. 4, 5 and 6 show the same according

to the Juang et al. (2002) method.

50

th

IG

C

50th


17th

– 19th



14

12

10

0 50 100

Probability (%)D

epth

(m

)

Juang et al. (2002), LR, M = 6

Juang et al. (2002), LR, M = 7.5

Juang et al. (2002), LR, M = 8.1


(Juang et al. (2002))

14

12

10

8

6

0 50 100

Probability (%)

Depth

(m

)

Juang et al. (2002), LR, M=6

Juang et al. (2002), LR, M=7.5

Juang et al. (2002), LR, M=8.1



18

16

14

12

10

8

60 50 100

Probability (%)

Depth

(m

)

Juang et al. (2002), LR, M = 6

Juang et al. (2002), LR, M = 7.5

Juang et al. (2002), LR, M = 8.1



A comparison of probability between the two

methods are shown in Figs 7and 8. It is seen that

Juang et al. (2002) is showing lower values of

probability compared to Toprak et al. (1999) for

the bore holes 5 and 8. For an earthquake

magnitude of 6 the variation is seen to be more

compared to an earthquake magnitude of 7.5 and

8.1. This observation is consistent in 30 bore holes

but only the degree of under estimation varies. In

the rest of the bore holes, Juang et al. (2002) is

showing higher values of probability compared to

Toprak et al.(1999). The percentage variation with

respect to Juang et al. (2002) for an earthquake

magnitude of 7.5, being 63.9% to -13.3%.

14

13

12

11

10

9

80 50 100

Dep

th (m

)

Probability (%)


Juang et al. (2002), M = 6

Toprak et al. (1999), M = 7.5

Juang et al. (2002), M = 7.5

Toprak et al. (1999), M = 8.1

Juang et al. (2002), M = 8.1

Fig.7 Comparison of probability between Toprak

et al. (1999) and Juang et al. (2002) for borehole 5.

14

12

10

8

6

0 50 100

Probability (%)

Depth

(m

)


Juang et al. (2002), M = 6

Toprak et al. (1999), M = 7.5

Juang et al. (2002), M = 7.5

Toprak et al. (1999), M = 8.1

Juang et al. (2002), M = 8.1

Fig.8 Comparison of probability between Toprak

et al. (1999) and Juang et al. (2002) for borehole 8.


The SPT- based liquefaction evaluation

probabilistic procedures have been found to yield

significantly different predictions. Comparisons of

the two logistic methods in the probabilistic

approach have shown a difference in the values of

probabilities in the same depth. However it is seen

that the probabilities obtained from logistic

regression are influenced by the form of the

function adopted for the regression, and by the data

used in the regression. The comparison of the

probabilities of liquefaction obtained from

regression equations which are developed using

different data sets and/or different forms of logistic

function is not perfect.

Fig. 9 shows the probabilistic liquefaction

potential map of Guwahati city for an earthquake

magnitude of 7.5 according to Juang et al. (2002).

Fig.9 Probabilistic liquefaction potential map of

Guwahati city

The map shows zones of different levels of risk of

liquefaction. It is observed from the map that the

southern bank of river Brahmaputra with the areas

of Palashbari, Azara, Jhalukbari, Pandu,

Bharalumukh and Uzanbazar, some areas in

G.S.Road, Gorchuk area and areas near Chandmari

have probabilities of liquefaction greater than 50%.

Some areas in the northern bank of the city are also

susceptible to liquefaction.

CONCLUSIONS

Liquefaction potential of soil sites susceptible to

liquefaction have been determined using two SPT

based probabilistic approach. One is according to

the logistic regression approach of Toprak et al.

(1999) and the other is also a logistic regression

approach by Juang et a. (2002). Comparisons of the

two logistic methods in the probabilistic approach

have shown a difference in the values of

probabilities in the same depth. GIS based

probabilistic liquefaction map of Guwahati city

shows that the southern bank of river Brahmaputra

with the areas of Palashbari, Azara, Jhalukbari,

Pandu, Bharalumukh and Uzanbazar, some areas in

G.S.Road, Gorchuk area and areas near Chandmari

have probabilities of liquefaction greater than 50%.

Some areas in the northern bank of the city is also

susceptible to liquefaction.

ACKNOWLEDGMENTS

The Geotechnical data of the 200 boreholes were

taken from a project work given to Assam

Engineering College, titled ‘‘Liquefaction potential

determination of Guwahati city’’ funded by The


Microzonation of Guwahati city. We acknowledge

the help and assistance given by DST, India for the

study.

REFERENCES

1. Cetin, K.O., Seed, R.B., Der Kiureghian, A.,

Tokimatsu, K., Harder Jr., L.F., Kayen, R.E.,

Moss, R.E.S., (2004). Standard penetration test-

based probabilistic and deterministic

assessment of seismic soil liquefaction

potential. Journal of Geotechnical and

Geoenvironmental Engineering 130 (12), 1314–

1340.

2. Fear, C. E., and McRoberts, E. C. (1995).

Report on liquefaction potential and catalogue

of case records. Internal Res. Rep., Dept. of

Civil

Engineering, Univ. of Alberta, Edmonton,

Alberta, Canada.

50

th

IG

C

50th


17th

– 19th



3. Idriss, I. M. (1999). An update of the Seed–

Idriss simplified procedure for evaluating

liquefaction potential. Proc., TRB Workshop on

New Approaches to Liquefaction Analysis,

FHWA-RD-99-165, Federal Highway

Administration, Washington, D.C.

4. Juang, C. H., and Jiang, T. (2000). Assessing

probabilistic methods for liquefaction potential

evaluation. Soil dynamics and liquefaction

2000, R. Y. S. Pak and J. Yamamura, eds.,

Geotechnical Special Publication No. 107,

ASCE, New York, 148–162.

5. Juang, C.H., Jiang, T., Andrus, R.D., 2002.

Assessing probability-based methods for

liquefaction evaluation. Journal of

Geotechnical and Geoenvironmental

Engineering 128 (7), 580–589.

6. Haldar, A., and Tang, W. H. (1979).

Probabilistic evaluation of liquefaction

potential. J. Geotech. Eng. Div., Am. Soc. Civ.

Eng., 104(2), 145–162.

7. Liao, S. S. C., Veneziano, D., and Whitman,

R. V. (1988). Regression models for

evaluating liquefaction probability. J. Geotech.

Eng. Div.,

Am. Soc. Civ. Eng., 114(4), 389–411.

8. Seed, H. B., and Idriss, I. M. (1971). Simplified

procedure for evaluating soil liquefaction

potential. J. Soil Mech. Found. Div., 97(SM9),

1249–1273.

9. Seed, H. B., and Idriss, I. M. (1982). Ground

motion and soil liquefaction during

earthquakes. Earthquake Engineering Res.

Institute Mono-graph, Oakland, CA.

10. Seed HB, Idriss IM, Arango I (1983)

Evaluation of liquefaction potential using

field performances data. J Geotech Eng

ASCE 109(3):458–483.

11. Seed, H. B., Tokimatsu, K., Harder, L. F., and

Chung, R. M. (1985). The influence of SPT

procedures in soil liquefaction resistance

evaluations. J. Geotech. Engrg., ASCE,

111(12), 1425–1445.

12. Sharma, B and Hazarika, P (2013) Assessment

of Liquefaction Potential of Guwahati city. A

case study. International Journal of

Geotechnical and Geological Eng, Springer,

Vol.31, issue5, pp1437-1452.

13. Toprak, S., Holzer, T. L., Bennett, M. J., and

Tinsley, J. C. (1999). CPT and SPT-based

probabilistic assessment of liquefaction

potential. Proc., 7th U.S.–Japan Workshop on

Earthquake Resistant Design of Lifeline

Facilities and Countermeasures Against

Liquefaction, Seattle.

14. Yegian, M. K., and Whitman, R. V. (1978).

Risk analysis for ground failure by

liquefaction. J. Geotech. Eng. Div., Am. Soc.

Civ. Eng.,

104(7), 921–938.

15.Youd, T. L., and Noble, S. K. (1997). Liquefaction criteria based on statistical and

probabilistic analyses. Proc., NCEER

Workshop on Evaluation of Liquefaction

Resistance of Soils, NCEER Technical Rep.

No: NCEER-97-0022, 201–205.

16. Youd, T. L., and Idriss, I. M. (1997). Proc.,

NCEER Workshop on Evaluation of

Liquefaction Resistance of Soils, Tech. Rep.

No. NCEER-97-

0022, T. L. Youd and I. M. Idriss, eds., State

Univ. of New York at Buffalo, Buffalo, N.Y.

17.Youd, T. L., Idriss, I. M., Andrus, R. D.,

Arango, I., Castro, G., Christian, J. T., Dobry,

R., Liam Finn, W. D., Harder, L. F., Jr.,

Hynes, M. E.,

Ishihara, K., Koester, J. P., Laio, S. S. C.,

Marcuson, III,W. F., Martin, G. R., Mitchell, J.

K., Moriwaki, Y., Power, M. S., Robertson, P.

K.,Seed, R. B., Stokoe, II, K. H. (2001).

Liquefaction resistance of soils: Summary

report from the 1996 NCEER and 1998

NCEER/NSF workshops on evaluation of

liquefaction resistance of soils. J.

Geotech.Geoenviron. Eng., 127(10), 817–833.

indian geotechnical conference th 50 igc 17 – 19 …igs/ldh/files/igc 2015 pune... ·...

Documents