基于灰色关联支持向量机的地表沉降预测

Predicting surface settlement of tunnel using grey relational-support vector machine

基于隧道地表沉降是一个非常复杂的系统工程,受到多种高维非线性、随机性和有限样本等因素的影响,提出隧道地表沉降预测的灰色关联支持向量机分析方法。该方法基于灰色关联分析确定影响沉降量的主要因子,利用学习样本构建数值模型,并通过支持向量机学习建立沉降与随机变量之间的非线性映射关系。为避免人为选择参数的盲目性,采用模拟退火算法搜索支持向量机核函数和参数,进而对未来的变形进行预测。通过对工程实例样本进行学习和预测,并将误差结果与单一的支持向量机模型进行对比。研究结果表明:该方法科学可靠;可用于含有大量随机变量的隧道沉降分析。

Based on the fact that surface settlement was a very complex system engineering,the geo-material was complex,fuzzy and nonlinear in geotechnical engineering,a new model based on grey system was presented,grey relational was used to find the key components.The kernel function and penalty factor was determined by simulated annealing.The nonlinear relation between surface settlement and influencing factors was obtained from the key components by SVM model and the model was applied to the practical engineering.GR-SVM enhances the efficiency and the capability of forecasting.The results of experiment show that the model is valid and can be used to analysis tunnel settlement.