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| 基于支持向量机的砂土液化预测模型 | |
| 引用本文： | 夏建中,罗战友,龚晓南,边大可.基于支持向量机的砂土液化预测模型[J].岩石力学与工程学报,2005,24(22):4139-4144. |
| 作者姓名： | 夏建中 罗战友 龚晓南 边大可 |
| 作者单位： | 1. 浙江科技学院,岩土工程研究所,浙江,杭州,310012 2. 浙江大学,岩土工程研究所,浙江,杭州,310027 3. 中国新型建筑材料工业杭州设计研究院,浙江,杭州,310003 |
| 摘 要： | 分析了砂土液化的主要影响因素,建立了砂土液化的支持向量机预测模型。该模型能通过有限经验数据的学习,建立砂土液化类型与其影响因素之间的非线性关系。运用所建立的模型对具体的砂土液化类型进行了评判,评判结果表明,基于线性核的支持向量机分类器不能有效地建立液化类型与影响因素之间的非线性映射,而基于多项式核及径向基核函数的分类器能正确判定砂土是否液化。 |
| 关 键 词： | 土力学 砂土液化 液化判别 统计学习 支持向量机 核函数 |
| 文章编号： | 1000-6915（2005）22-4139-06 |
| 收稿时间： | 2004-05-25 |
| 修稿时间： | 2004-05-252004-10-31 |
SUPPORT VECTOR MACHINE MODEL FOR PREDICTING SAND LIQUEFACTION |
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| XIA Jian-zhong,LUO Zhan-you,GONG Xiao-nan,BIAN Da-ke.SUPPORT VECTOR MACHINE MODEL FOR PREDICTING SAND LIQUEFACTION[J].Chinese Journal of Rock Mechanics and Engineering,2005,24(22):4139-4144. | |
| Authors: | XIA Jian-zhong LUO Zhan-you GONG Xiao-nan BIAN Da-ke |
| Abstract: | Based on study of main factors that have great influence on sand liquefaction,the support vector machine(SVM) model of sand liquefactions was established. The nonlinear relation between sand liquefactions and influencing factors was learned from the finite empirical data by SVM model. Then the model was applied to the practical engineering. The results show SVM model of linear kernel function is not enough effective to found the mapping between classification of sand liquefactions and influencing factors;but polynomial kernel functions and radial based function(RBF) kernel can correctly evaluate the classification of sand liquefactions. |
| Keywords: | soil mechanics sand liquefaction evaluation of liquefaction statistics learning theory support vector machine(SVM) kernel function |
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