Review and application of Artificial Neural Networks models in reliability analysis of steel structures

Chojaczyk, A.A. and Teixeira, A.P. and Neves, Luís C. and Cardosa, J.B. and Soares, C. Guedes (2015) Review and application of Artificial Neural Networks models in reliability analysis of steel structures. Structural Safety, 52 (A). pp. 78-89. ISSN 0167-4730

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Abstract

This paper presents a survey on the development and use of Artificial Neural Network (ANN) models in structural reliability analysis. The survey identifies the different types of ANNs, the methods of structural reliability assessment that are typically used, the techniques proposed for ANN training set improvement and also some applications of ANN approximations to structural design and optimization problems. ANN models are then used in the reliability analysis of a ship stiffened panel subjected to uniaxial compression loads induced by hull girder vertical bending moment, for which the collapse strength is obtained by means of nonlinear finite element analysis (FEA). The approaches adopted combine the use of adaptive ANN models to approximate directly the limit state function with Monte Carlo simulation (MCS), first order reliability methods (FORM) and MCS with importance sampling (IS), for reliability assessment. A comprehensive comparison of the predictions of the different reliability methods with ANN based LSFs and classical LSF evaluation linked to the FEA is provided.

Item Type: Article
Keywords: Artificial Neural Networks; Structural reliability; Monte Carlo simulation; Importance sampling; First-order reliability methods; Finite element analysis; Ultimate strength; Stiffened plates
Schools/Departments: University of Nottingham, UK > Faculty of Engineering > Department of Civil Engineering
Identification Number: 10.1016/j.strusafe.2014.09.002
Depositing User: Eprints, Support
Date Deposited: 22 May 2017 09:52
Last Modified: 13 Oct 2017 00:34
URI: http://eprints.nottingham.ac.uk/id/eprint/42998

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