• Home
  • Journals
  • Focus
  • Conferences
  • Researchers
  • Sign in

Outline

  • Abstract
  • Keywords

Figures(5)

标签(1)

Table 1

其他(2)

PDF
Document

Frontiers of Structural and Civil Engineering

2013, Volume 7,  Issue 1, Pages 72-82
    • PDF
    • collect

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

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

    Available online:2013-03-05
    Show More
    10.1007/s11709-013-0185-y
    Cite this article
    Pijush SAMUI.Liquefaction prediction using support vector machine model based on cone penetration data[J].Frontiers of Structural and Civil Engineering,2013,7(1):72-82.

    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
    Previous article in issue
    article in issue Next
    登录后,您可以进行评论。请先登录

    评论

    评论

    • 所有评论
     咋就跳到顶部了
    2019-04-23 11:24:14
    回复 (0)
    inspur  手机账号
    2019-05-10 11:30:17
    回复 (0)

    Read

    142

    Download

    7

    Related Research

    Current Issue
      Current Issue
        Follow us
        Copyright © 2015 China Engineering Science Press.
        京ICP备11030251号
        Follow us
        Copyright © 2015 China Engineering Science Press.
        京ICP备11030251号