关键词: 支持向量机, 砂土液化, 聚类, 二叉树, 统计学习, 预测模型

Abstract: Support vector machine (SVM) is a novel and powerful learning method which is derived based on statistical learning theory (SLT) and the structural risk minimization principle. Traditional SVM only deals with the binary classification problems, however, there are large numbers of multi-classification problems in practical engineering. With a view to the main factors with important influence on sand liquefaction, multi-class support vector machines model based on clustering-binary tree is established. The nonlinear relationship between sand liquefaction and influencing factors is obtained from the finite empirical data by SVM. The results indicate that the structure of model base on SVM is reasonable; this algorithm is feasible; and it can predict the sand liquefaction more accurately．

Key words: support vector machine(AVM), sand liquefaction, clustering, binary tree, statistical learning theory, prediction model