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论文类型：期刊论文

发表时间：2017-11-02

发表刊物：EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING

收录刊物：Scopus、SCIE、EI

卷号：21

期号：11

页面范围：1341-1362

ISSN号：1964-8189

关键字：slope reliability analysis; extreme learning machine; artificial bee colony algorithm; surrogate model

摘要：Geotechnical engineering problems are characterised by many sources of uncertainty, and reliability analysis is needed to take the uncertainties into account. An intelligent surrogate model based on extreme learning machine is proposed for slope system reliability analysis. The weights and bias which play an important role in the performance of ELM are optimised by a nature inspired artificial bee colony algorithm. The system failure probability of soil slopes is estimated by Monte Carlo simulation via the proposed surrogate model. Experimental results show that the proposed method is feasible, effective and simple to implement system reliability analysis of soil slopes.