边坡系统可靠度分析智能响应面法框架  

Framework of intelligent response surface methods for system reliability analysis of slopes

作　　者：康飞[1] 李俊杰[1] 李守巨[2] 李红军[3] 

机构地区：[1]大连理工大学水利工程学院,辽宁大连116024 [2]大连理工大学工业装备结构分析国家重点实验室,辽宁大连116024 [3]中国水利水电科学研究院,北京100048

出　　处：《武汉大学学报（工学版）》2016年第5期654-660,共7页Engineering Journal of Wuhan University

基　　金：国家重点研发计划资助项目(编号:2016YFC0401600);国家自然科学基金项目(编号:51109028);工业装备结构分析国家重点实验室开放基金(编号:GZ15207);中央高校基本科研业务费专项资金资助项目(编号:DUT15LK11);中国水科院科研专项青年专项(编号:GE0145B112016)

摘　　要：多失效模式情况下,边坡体系可靠性分析较为复杂.提出了基于计算机试验结合机器学习理论建立边坡系统(体系)可靠度分析智能响应面法的一般框架.边坡体系可靠性分析智能响应面法步骤包括:样本生成、模型参数优化、智能响应面建立、蒙特卡罗模拟计算失效概率.对支持向量机、高斯过程等多种智能机器学习理论用于建立智能响应面的性能进行了对比分析.通过典型边坡的体系可靠度分析,验证了小样本情况下智能响应面法在边坡体系可靠性分析中的可行性和优越性.System reliability analysis of soil slopes is a quite complex task under the multiple failure modes situation. This study proposes a general framework of intelligent response surface methods, which com- bine computer experimental methods with machine learning theories, for slope system reliability analysis. The main steps of the proposed method include, samples generation, model parameters optimization, intel- ligent response surface establishment and Monte Carlo simulation for calculating the failure probability. Performance of several intelligent response surfaces established by support vector machines（SVM） and Gaussian process regression are compared. The proposed methods are tested on three typical slope exam- ples with obvious system effect. The results show that the intelligent response surface methods with small samples are effective for slope system reliability analysis.

关 键 词：边坡稳定 系统可靠性 响应面法 人工蜂群算法 高斯过程 支持向量机 最小二乘支持向量机 

分 类 号：TV31[水利工程—水工结构工程]