基于非线性支持向量机的隧道煤与瓦斯突出危险性预测

为了有效地预测桃子垭隧道揭煤段是否存在煤与瓦斯突出危险性,文章根据煤与瓦斯突出综合假说及《防治煤与瓦斯突出规定》,确定了影响煤与瓦斯突出的9个关键因素。由于评价因子与突出危险程度之间存在着复杂的非线性映射关系,因此选择了非线性支持向量机(SVM)方法对隧道煤与瓦斯突出危险性进行预测研究。结合项目实际情况确定了各训练样本的具体参数,采用单项指标法、最优分类决策函数及MATLAB SVM Toolbox软件对选定的训练样本进行了煤与瓦斯突出危险性预测对比。通过N7和N8两个测点的预测结果表明,桃子垭隧道揭煤段存在煤与瓦斯突出危险性,必须做好相应的揭煤防突工作。

Prediction of Tunnel Coal and Gas Burst Hazard Based on Nonlinear Support Vector Machine

In order to effectively predict the risk of coal and gas outburst in a coal seam disclosed section of Taozi ya tunnel,nine key factors affecting coal and gas burst are determined in light of the comprehensive hypothesis about coal and gas burst and the Regulation of Preventing Coal and Gas Burst.Because of the complex nonlinear mapping relationship between evaluation factors and risk degree of burst,a nonlinear support vector machine(SVM)method is adopted to predict the risk of tunnel coal and gas burst.The specific parameters of all training samples are determined,a comparison of risk prediction of coal and gas burst is conducted to the selected training samples by single index method,optimal classification decision function and the MATLAB SVM Toolbox software based on the actual situation.The prediction results of the two measuring points of N7 and N8 show that there are risks of coal and gas outburst in the disclosed coal section of Taoziya tunnel,so it is necessary to take relevant measures to prevent coal outburst.