System reliability analysis of slopes using least squares support vector machines with particle swarm optimization

被引：61
|
作者：
Kang, Fei [1 ,2 ]
Li, Jing-shuang [1 ]
Li, Jun-jie [1 ]
机构：
[1] Dalian Univ Technol, Sch Hydraul Engn, Fac Infrastruct Engn, Dalian 116024, Peoples R China
[2] Dalian Univ Technol, State Key Lab Struct Anal Ind Equipment, Dalian 116024, Peoples R China
来源：
NEUROCOMPUTING | 2016年 / 209卷
基金：
中国国家自然科学基金;
关键词：
Slope stability; System probabilistic analysis; LSSVM; Particle swarm optimization; Response surface; BEE COLONY ALGORITHM; COMPUTER EXPERIMENTS; PREDICTION; REGRESSION; SURFACES;
D O I：
10.1016/j.neucom.2015.11.122
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents an intelligent response surface method for evaluating system failure probability of soil slopes based on least squares support vector machines (LSSVM) and particle swarm optimization. A novel machine learning technique LSSVM is adopted to establish the response surface to approximate the limit state function based on the samples generated by computer experiments. Subsequently, the proposed response surface is utilized in conjunction with Monte Carlo simulation to obtain the desired reliability estimation. The hyper-parameters which are crucial to the performance of LSSVM are selected by a swarm intelligence algorithm called particle swarm optimization. Experimental results on three examples show that the proposed system reliability analysis method is promising for soil slopes with obvious system effects. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码：46 / 56
页数：11
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