工程地质学报2011，Vol.19Issue(1)：88-92,5.
基于支持向量机的围岩定性智能分级研究
SUPPORT VECTOR MACHINES BASED INTELLIGENT ROCK MASS CLASSIFICATION METHOD
摘要
Abstract
A new data mining method of Support Vector Machines (SVM)is applied on the classification of rock mass in tunnels. SVM is a novel powerful leaning method that based on Statistical Learning Theory. SVM can solve small-sample learning problems better than neural network. Parameters including rock layer thickness, rock mass structure, inlay condition, weathering condition, groundwater characteristic, joint condition, hammer knocking sound and ground stress, are chose as the judge factors. Data samples from Niba Mountain tunnel are used to train the SVM with different kernels. The mapping relationship between judge factors and rock mass classes is used. The SVM can discriminate and provide class-unknown data samples of rock mass. Result of the classification shows that SVM with polynomial kernel has a high accuracy when it is used to classify the rock mass. So this is an intelligent classification of rock mass method that can be applied to classify rock mass in tunnels.关键词
围岩分级/支持向量机/隧道Key words
Rock mass classification/ Support vector machines /Tunnel分类
建筑与水利引用本文复制引用
牛文林,李天斌,熊国斌,张广洋..基于支持向量机的围岩定性智能分级研究[J].工程地质学报,2011,19(1):88-92,5.基金项目
国家自然科学基金项目(40772176)和四川省青年科技基金项目(09ZQ026-083). （40772176）
