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System reliability analysis of slopes using multilayer perceptron and radial basis function networks
Author(s) -
Kang Fei,
Li Junjie,
Xu Qing
Publication year - 2017
Publication title -
international journal for numerical and analytical methods in geomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.419
H-Index - 91
eISSN - 1096-9853
pISSN - 0363-9061
DOI - 10.1002/nag.2709
Subject(s) - radial basis function , artificial neural network , basis (linear algebra) , reliability (semiconductor) , multilayer perceptron , monte carlo method , perceptron , computer science , radial basis function network , function (biology) , artificial intelligence , algorithm , mathematics , statistics , power (physics) , physics , geometry , quantum mechanics , evolutionary biology , biology
Summary 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.