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


A.A. Chojaczyk

A.P. Teixeira

J.B. Cardosa

C. Guedes Soares


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.


Chojaczyk, A., Teixeira, A., Neves, L. C., Cardosa, J., & Soares, C. G. (2015). Review and application of Artificial Neural Networks models in reliability analysis of steel structures. Structural Safety, 52(A),

Journal Article Type Article
Acceptance Date Sep 14, 2014
Online Publication Date Oct 11, 2014
Publication Date Jan 1, 2015
Deposit Date May 22, 2017
Publicly Available Date May 22, 2017
Journal Structural Safety
Print ISSN 0167-4730
Electronic ISSN 0167-4730
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 52
Issue A
Keywords Artificial Neural Networks; Structural reliability; Monte Carlo simulation; Importance sampling; First-order reliability methods; Finite element analysis; Ultimate strength; Stiffened plates
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