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Reliability analysis of structures using neural network method

Lookup NU author(s): Ahmed El-Hewy, Professor Ehsan Mesbahi, Dr Yongchang Pu

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Abstract

In order to predict the failure probability of a complicated structure, the structural responses usually need to be estimated by a numerical procedure, such as finite element method. To reduce the computational effort required for reliability analysis, response surface method could be used. However the conventional response surface method is still time consuming especially when the number of random variables is large. In this paper, an artificial neural network (ANN)–based response surface method is proposed. In this method, the relation between the random variables (input) and structural responses is established using ANN models. ANN model is then connected to a reliability method, such as first order and second moment (FORM), or Monte Carlo simulation method (MCS), to predict the failure probability. The proposed method is applied to four examples to validate its accuracy and efficiency. The obtained results show that the ANN-based response surface method is more efficient and accurate than the conventional response surface method.

Publication metadata

Author(s): Hosni Elhewy A, Mesbahi E, Pu Y

Publication type: Article

Publication status: Published

Journal: Probabilistic Engineering Mechanics

Year: 2006

Volume: 21

Issue: 1

Pages: 44-53

Print publication date: 22/09/2005

Date deposited: 18/04/2008

ISSN (print): 0266-8920

ISSN (electronic): 1878-4275

Publisher: Elsevier Ltd.

URL: http://dx.doi.org/10.1016/j.probengmech.2005.07.002

DOI: 10.1016/j.probengmech.2005.07.002

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