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Liquefaction evaluation methods

Amirtouraj Bahadori

on 27 June 2014

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Transcript of LIQUEFACTION

Table of Contents
SPT-N-based liquefaction analysis approaches
CPT-N-based liquefaction analysis approaches
Evaluation of liquefaction resistance of soils are nesseccary:
(1) the seismic demand on a soil layer, expressed in terms of
(2) the capacity of the soil to resist liquefaction, expressed in terms of

The cyclic shear resistance of soils could be evaluated :
a) in the
using cyclic triaxial or cyclic simple shear testing,
b) based upon
relationships between liquefaction case histories and on-site material parameters (e.g., SPT-N, CPT-qc, or Vs values) through various field testing programs.
Seed and Idriss (1971) formulated the following equation for calculation of the cyclic stress ratio:
To avoid the difficulties associated with sampling and laboratory testing, field tests have become the state-of-practice for routine liquefaction investigations.
s and

s are generally preferred because of the more extensive databases and past experience. Primary advantages and disadvantages of each test are listed below.
The majority of
liquefaction assessment methods
available to date are simplified-empirical; namely, the cyclic shear stress due to shaking is estimated by a simplified procedure, and the cyclic resistance of soils is based on an empirical approach.
liquefaction analysis approaches
Analysis methods considered in this presentation include:
(1) Seed’s method – modified and suggested by NCEER/NSF Workshop
(2) a new version of Japan Road Association (JRA) method
(3) Tokimatsu and Yoshimi’s (T–Y) method
(4) the Chinese Code for Seismic Design of Buildings (CSDB) method
1 - Seed's Method
This method was first proposed by Seed and Idriss in 1971. In 1996 and 1998, this method was further synthesized and updated in the workshops held by NCEER and NSF. The updated version is used in the current study.
Parameters Involved for estimating
effects of earthquake magnitude.
fines content.
effective overburden pressure.

Analysis flow chart of 2001 Seed’s method
2 - New JRA (NJRA) method
The original JRA method was promulgated in 1990. After the Hyogoken-Nambu Earthquake of Japan in 1995, this method has been considerably revised.
New screening criteria in this method:

maximum values of the CSR and CRR are computed, unlike the majority of SPT-N-based approaches, where only average values are employed.
The cyclic resistance of gravelly soils, as evidenced in the earthquake (based on limited laboratory results of frozen samples)
earthquake magnitude is not included in the CSR formulation
Analysis flow chart of 1996 JRA method
3- Tokimatsu and Yoshimi (T–Y) method
Tokimatsu and Yoshimi (1983) proposed a similar approach to Seed’s method, by estimating CSR and CRR separately prior to the computation of the factor of safety against liquefaction (FL). However, the T–Y method is different from Seed’s method in developing the CRR relationships.
Differences between this method and Seed's method:

The CRR boundary curves of soils are established based on the results of laboratory testing on high quality undisturbed (frozen) samples from Niigata, Japan, where severe liquefactions had occurred in 1964.
Cyclic strain is correlated to the level of severity in liquefaction damages observed during 70 case histories in Japan which is reflected by the coefficient, Cs.

Analysis flow chart of 1983 T–Y method
4- Chinese building code (CSDB) method
The Chinese liquefaction assessment procedure was established in the Code for Seismic Design of Buildings (CSDB) of China in 1974. After the Haichen (1975) and Tongshan (1976) earthquakes, this assessment procedure has been modified by considering the attenuation effect of ground shaking.
liquefaction analysis approaches
CPT is generally considered a more consistent and repeatable in situ test than SPT, and unlike SPT it can provide a nearly continuous soil profile.
Almost all of these methods follow the same format as in the simplified procedure originated by Seed and Idriss, in which a chart is used with a boundary curve that separates liquified and non-liquefied cases.
CPT methods also use the same procedure for determination of CSR by Seed and Idriss.
Analysis methods considered in this presentation includes:
(1) Robertson method
(2) Olsen method
(3) Juang method
1 - Robertson method
in the robertson method the
is calculated as:
clean-sand cone penetration resistance normalized to a reference stress level of 1atm.
Liquefaction Potential Index (LPI), as formulated by Iwasaki et al. (1978, 1982), is computed by integrating the “contribution” of liquefaction potential, in terms of factor of safety (FS) against the initiation of liquefaction, over the depth at a “borehole” location. Symbolically, this index is expressed as follows:
in which the depth weighting factor, w(z)=10−0.5z where z=depth (m). Thus, the weighting factor is 10 at z=0 and linearly decreases to 0 at z=20 m, which implies that the severity of surface manifestation of liquefaction (such as sand boils, lateral spreads, and settlement) is proportional to the proximity of the liquefied “layer” to the ground surface.
The variable F is defined as follows:
an example of the calculatin of LPI based on cone penetration profiles.
Iwasaki et al. (1982) concluded that severe liquefaction is very likely at sites with
and that severe liquefaction is very unlikely at sites with
In the formulation by Iwasaki et al. (1982), the factor of safety (FS) is determined using a standard penetration test (SPT)-based simplified method established by the Japan Road Association (1980).
Toprak and Holzer (2003) computed
values from cone penetration test (CPT) at sites with surface manifestations of liquefaction during the 1989 Loma Prieta, California, earthquake.
Factor of safety (FS) was calculated using the
CPT-based method by Robertson and Wride (1998)
. They reported that sand boils typically occurred at soundings where the LPI≥5, and that lateral spreads typically occurred where the LPI≥12. They suggested that LPI≥5 can be used as a threshold for the surface manifestation of liquefaction.
These studies showcase the advantages of using LPI in the regional mapping of liquefaction hazards.
Lenz and Baise (2007) computed
values for geologic units of the San Francisco Bay Area using both CPT and SPT data sets. They found that CPT-based LPI characterization results in higher hazard in the same study area than those derived from the SPT.
Another important finding of they study is that the
CPT-based LPI
values have a much higher degree of spatial correlation and a lower variance over a greater distance than those estimated from
They concluded that:

is a more reliable and consistent measure of liquefaction potential.
Iwasaki criterion is not "universally" applicable
Lee et al. (2004) reported that when the
CPT-based method by Robertson and Wride (1998)
was used to determine the factor of safety (FS), the calculated LPI values did not match either the Iwasaki criterion or the results by Toprak and Holzer (2003).
The results by Lee et al. (2004) showed that 50% of liquefied cases had an LPI>15 with only 10% of liquefied cases having an LPI<5. To the contrary, 85% of non-liquefied cases had an LPI>5, and 30% of non-liquefied cases even had an LPI>15.
Li et al. (2006) conducted a fundamental study by using 155 CPT soundings with field observations of liquefaction/no liquefaction in various seismic events in the United States, Taiwan and Turkey. LPI values were computed using a
CPT-based method by Juang et al. (2006)
four models of liquefaction resistance (and thus FS) were examined. These models were all variants of the CPT-based method by Juang et al. (2006) and each with a different degree of conservatism.
The results of LPI calibration analyses showed the obvious effect of adopting different models for FS.
As another point of interest the Li et al. investigated the possible use of
probability of liquefaction (PL)
at a given depth, in lieu of factor of safety (FS), in the LPI framework.
Li et al. retained the Iwasaki equation but variable F was defined based on the
probability of liquefaction
at a given depth as follows:
This cut-off value (.35) was chosen for two reasons:
According to a classification criterion by Chen and Juang (2000), the liquefaction potential of a soil is rated as “low” if
A sensitivity analysis involving five different cut off probabilities ranging from 0.15 to 0.50, showed that use of the cut-off probability of
yielded the best results.
Chen and Juang suggested that any deterministic method must be calibrated so that the meaning of the calculated
is well understood in terms of the likelihood or
probability of liquefaction
this mapping function should be used only for robertson method.
2 - Olsen method
in the simplified Olsen method the
is calculated as:
friction ratio in percent
The calibrated mapping function of Olsen method is the same from Robertson, with A=1.0 and B=2.87
3 - Juang method
in the Juang method the
is calculated as:
The calibrated mapping function of Juang method is the same from Robertson, with A=0.96 and B=4.5
CPT data at eight sites are used as example to compare the three CPT-based methods.
The basic information about these sites is shown in table2.
It is noted that at deeper strata the Juang method yielded lower probabilities, whereas the Olsen and Robertson methods remained quite high.
obtained from
Juang method
is seen sensitive to the change in the effective stress
Based on the results presented in table 4,
Juang method
appears to be more accurate than the other two, although all three are quite comparable in accuracy.
Next figures show the CPT profile along with profiles of probability of liquifaction (PL) obtained from the three CPT-based method described. in driving these PL profiles, the factor of safety was first calculated and the corresponding mapping function was then used to determine PL.
For conducting a parametric study comparison, the Iwasaki’s depth weighted average technique is uilitzed, and the associated liquefaction potential index (PL) is computed for the sensitivity study among various factors.
Table 1 shows a set of common parameters adopted in SPT-based assessment methods.
For comparison, Three types of SPT-N profile of soil deposit are postulated, namely: constant distribution, linearly-increasing distribution, and linearly-decreasing distributions with depth, all with an average blow count of 10.
Results of the
sensitivity study
are summarized in Table 2. For all the parameters examined, SPT blow count (N) and peak ground acceleration (a max) appear to be most sensitive in the computed liquefaction potential.
With exception to the CSDB method,
Seed’s method
appears most sensitive in the computed liquefaction potential due to SPT blow count (N), hammer energy ratio (ER), and earthquake magnitude (M); while NJRA method is least sensitive.
Comparison of accuracy in predicting liquefaction and non-liquefaction
the use of a single depth to compute the safety factor would normally be very difficult to relate to surface observations. An improved method would be to use a section or an entire depth interval to compute a depth-weighted average safety factor, or liquefaction potential index (PL), as suggested by Iwasaki et al.
To form a basis of comparison, this study adopts the analysis framework by Seed and converts the
estimations of the other two methods into the same platform;
Under the same basis of comparison, results of the CRR predictions by the three methods for the soils in the study area during the 1999 earthquake (M = 7.6) are shown in Fig. 12.
By comparing predictions and observations during the earthquake, locations of liquefaction boreholes appear better matched with the computed CRR curves by
Seed’s method
at a depth interval of
6.75 9.75 m
. Similarly, better matched cases for the
T–Y method
are at depth intervals of
3.75–6.75 m and 6.75–9.75 m
, and for the
NJRA method
a depth interval of
6.75–9.75 m
Based on computations of liquefaction and non-liquefaction cases for the entire depth range of a borehole, the OPER (overall weighted average prediction error ratio) ranking shows that the
T–Y method
would yield the
most accurate
(i.e., the smallest OPER) prediction and
Seed’s method
second most accurate
prediction, while the
NJRA method
yields the
least accurate
prediction (i.e., the greatest OPER).
By: Amirtouraj Bahadori
Professor: Dr. Ahmadreza Mazaheri
Full transcript