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

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Frontiers of Structural and Civil Engineering

2013, Volume 7, Issue 1, Pages 72-82

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

10.1007/s11709-013-0185-y

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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 ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), 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 ( ) 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

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Frontiers of Structural and Civil Engineering

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

Abstract

Keywords

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Outline

Figures(5)

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其他(2)

Frontiers of Structural and Civil Engineering

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

Abstract

Keywords

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