Assessment of liquefaction potential of the Erzincan, Eastern Turkey

Duman, E. Subası; İkizler, Sabriye Banu; Angin, Zekai; Demir, Gökhan

doi:10.12989/gae.2014.7.6.589

Cited by 2 publications

(2 citation statements)

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“…Evaluation of seismic liquefaction of soils can be performed through laboratory tests or numerical simulations (Toyota et al 2004, Lancelot et al 2004, Atigh and Byrne 2004, Lade and Yamamuro 2011, Liu et al 2014, Duman et al 2014, Chen et al 2013, 2015. As an alternative, simplified techniques based on an in situ testing measurement index are commonly used to assess seismic liquefaction potential.…”

Section: Introductionmentioning

confidence: 99%

“…Techniques using the standard penetration test (SPT) have been developed for evaluating soil liquefaction potential (Seed and Idriss 1971, Seed et al 1985, Law et al 1990, Cetin et al 2004, Duman et al 2014. Similarly, methods based on the use of the cone penetration test (CPT) have been developed (Stark and Olson 1995, Robertson and Wride 1998, Juang et al 2003, Moss et al 2006.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating seismic liquefaction potential using multivariate adaptive regression splines and logistic regression

Zhang¹,

Goh²

2016

Geomech. Eng.

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Simplified techniques based on in situ testing methods are commonly used to assess seismic liquefaction potential. Many of these simplified methods were developed by analyzing liquefaction case histories from which the liquefaction boundary (limit state) separating two categories (the occurrence or non-occurrence of liquefaction) is determined. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model using conventional modeling techniques that take into consideration all the independent variables, such as the seismic and soil properties. In this study, a modification of the Multivariate Adaptive Regression Splines (MARS) approach based on Logistic Regression (LR) LR_MARS is used to evaluate seismic liquefaction potential based on actual field records. Three different LR_MARS models were used to analyze three different field liquefaction databases and the results are compared with the neural network approaches. The developed spline functions and the limit state functions obtained reveal that the LR_MARS models can capture and describe the intrinsic, complex relationship between seismic parameters, soil parameters, and the liquefaction potential without having to make any assumptions about the underlying relationship between the various variables. Considering its computational efficiency, simplicity of interpretation, predictive accuracy, its data-driven and adaptive nature and its ability to map the interaction between variables, the use of LR_MARS model in assessing seismic liquefaction potential is promising.

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