Reliability assessment using feed-forward neural network-based approximate meta-models

Zia Ur Rehman Gondal, Jongsoo Lee

Research output: Contribution to journalArticle

8 Citations (Scopus)


This paper deals with an adaptation of artificial neural networks in the context of the reliability analysis of non-linear limit state functions. An extreme learning machine (ELM) that is categorized as a single-hidden-layer feed-forward neural network is considered in the present study. Using a trained ELM-based approximate meta-model, the reliability analysis is conducted in conjunction with Monte Carlo simulation. The ELM is compared with both single and multiple-hidden-layer back-propagation neural networks. A number of non-linear and large-dimensionality limit state functions are explored to support the proposed method in terms of approximation accuracy and reliability index.

Original languageEnglish
Pages (from-to)448-454
Number of pages7
JournalProceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Issue number5
Publication statusPublished - 2012 Oct

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality

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