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论文类型：期刊论文

发表时间：2016-06-01

发表刊物：APPLIED MATHEMATICAL MODELLING

收录刊物：SCIE、EI

卷号：40

期号：11-12

页面范围：6105-6120

ISSN号：0307-904X

关键字：Slope reliability; Support vector regression; Spencer's method; Probabilistic analysis; Artificial bee colony algorithm

摘要：Surrogate model methods are attractive ways to improve the efficiency of Monte Carlo simulation (MCS) for structural reliability analysis. An intelligent surrogate model based method for slope system reliability analysis is presented in this study. The novel machine learning technique nu-support vector machine (nu-SVM) is adopted to establish the surrogate model to predict the factor of safety via the samples generated by Latin hypercube sampling. Global optimization algorithms particle swarm optimization and artificial bee colony algorithm are adopted to select the hyper-parameters of nu-SVM model. The applicability of the nu-SVM based surrogate model for slope system reliability analysis is tested on four examples with obvious system effects. It is found that the proposed surrogate model combined with MCS can achieve accurate system failure probability evaluation using fewer deterministic slope stability analyzes than other approaches. (C) 2016 Elsevier Inc. All rights reserved.