基于支持向量机的围岩定性智能分级研究

本文将数据挖掘的新方法支持向量机应用于隧道围岩分级。支持向量机是一种基于统计学习理论的新的学习算法,比神经网络算法能更好地解决小样本问题。选用岩层厚度、岩体结构、嵌合程度、风化程度、地下水特征、节理发育程度、榔头敲击声和地应力等8个定性指标作为评判因子,用泥巴山隧道采集的实际数据作为样本对不同核函数的支持向量机进行训练,并得到评判因子与围岩级别的映射关系,从而可以对未知的围岩样本进行级别判别。判别结果表明: 采用多项式核的支持向量机对围岩级别进行判别有较高的准确率,是一种值得推广和应用的围岩智能分级方法。

SUPPORT VECTOR MACHINES BASED INTELLIGENT ROCK MASS CLASSIFICATION METHOD

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.