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Indexed by:期刊论文

Date of Publication:2017-12-25

Journal:INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS

Included Journals:SCIE、EI、Scopus

Volume:41

Issue:18

Page Number:1962-1978

ISSN No.:0363-9061

Key Words:artificial neural networks; Monte Carlo simulation; probabilistic slope stability; response surface; system reliability

Abstract:This paper presents a system reliability analysis method for soil slopes on the basis of artificial neural networks with computer experiments. Two types of artificial neural networks, multilayer perceptrop (MLP) and radial basis function networks (RBFNs), are tested on the studied problems. Computer experiments are adopted to generate samples for constructing the response surfaces. On the basis of the samples, MLP and RBFN are used for establishing the response surface to approximate the limit state function, and Monte Carlo simulation is performed via the MLP and RBFN response surfaces to estimate the system failure probability of slopes. Experimental results on 3 examples show the effectiveness of the proposed methodology.