Liquefaction prediction using support vector machine model based on cone penetration data

doi:10.1007/s11709-013-0185-y

Front Struc Civil Eng

0, Vol.

Issue () : 72-82 https://doi.org/10.1007/s11709-013-0185-y

RESEARCH ARTICLE

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI(

)

Centre for Disaster Mitigation and Management, VIT University, Vellore-632014, India

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Abstract

A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibility as a classification problem, which is an imperative task in earthquake engineering. This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) data from the 1999 Chi-Chi, Taiwan earthquake. The SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM) induction principle to minimize the error. Using cone resistance (q_c) and cyclic stress ratio (CSR), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters (q_c and maximum horizontal acceleration a_max), for prediction of liquefaction. Further, developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model. For Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVM model predicts with accuracy of 89%. The effect of capacity factor (C) on number of support vector and model accuracy has also been investigated. The study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data.

Keywords earthquake cone penetration test liquefaction support vector machine (SVM) prediction

Corresponding Author(s): SAMUI Pijush,Email:pijush.phd@gmail.com

Issue Date: 05 March 2013

Cite this article:

Pijush SAMUI. Liquefaction prediction using support vector machine model based on cone penetration data[J]. Front Struc Civil Eng, 0, (): 72-82.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-013-0185-y
https://academic.hep.com.cn/fsce/EN/Y0/V/I/72

Tab.1 Data from Chi-Chi earthquake

Tab.2 Data from different case histories presented by Goh (1996)

Fig.1 Support vectors with maximum margin

Tab.3 General performance of SVM for different kernels for MODEL-I

Fig.2 Variation of testing accuracy and number of support vectors with values for MODEL-I. (a) Linear; (b) polynomial; (c) radial basis function; (d) bspline kernel

Fig.3 Variation of testing accuracy and number of support vectors with values for MODEL-I. (a) Fourier; (b) simple dot product; (c) spline; (d) sigmoid kernel

Tab.4 General performance of SVM for different kernels for MODEL-II

Fig.4 Variation of testing accuracy and number of support vectors with values for MODEL-II. (a) Linear; (b) polynomial; (c) radial basis function; (d) bspline kernel

Fig.5 Variation of testing accuracy and number of support vectors with values for MODEL-II. (a) Fourier; (b) simple dot product; (c) spline; (d) sigmoid kernel

Fig.6 Number of support vectors for different kernels using design value for MODEL-I

Fig.7 SVM prediction for MODEL-I

Fig.8 Number of support vectors for different kernels using design value for MODEL-II

Fig.9 SVM prediction for MODEL-II

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