Artificial Bee Colony Algorithm Optimized Support Vector Regression for System Reliability Analysis of Slopes

作     者：Kang, Fei Li, Junjie 

作者机构：Dalian Univ Technol Sch Hydraul Engn Fac Infrastruct Engn 2 Linggong Rd Dalian 116024 Peoples R China 

出 版 物：《JOURNAL OF COMPUTING IN CIVIL ENGINEERING》 (土木工程计算杂志)

年 卷 期：2016年第30卷第3期

页      面：04015040-04015040页

核心收录：

学科分类：08[工学] 0813[工学-建筑学] 0814[工学-土木工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Fundamental Research Funds for the Central Universities [DUT15LK11] State Scholarship Fund of China 

主　　题：Slope stability System reliability analysis Support vector machines Artificial bee colony algorithm Particle swarm optimization Monte Carlo simulation 

摘      要：Probabilistic stability analysis is an effective way to take uncertainties into account in evaluating the stability of slopes. This paper presents an intelligent response surface method for system probabilistic stability evaluation of soil slopes. Artificial bee colony algorithm (ABC) optimized support vector regression (SVR) is used to establish the response surface to approximate the limit-state function. Then Monte Carlo simulation is performed via the ABC-SVR response surface to estimate system failure probability. The proposed methodology is verified in three case examples and is also compared with some well-known or recent algorithms for the problem. Results show that the new approach is promising in terms of accuracy and efficiency. (C) 2015 American Society of Civil Engineers.