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

Zia-ur-Rehman Gondal and Jongsoo Lee

Journal of Risk and Reliability, 2012, vol. 226, issue 5, 448-454

Abstract: 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.

Keywords: Limit state function; feed-forward neural network; extreme learning machine; Monte Carlo simulation; reliability index (search for similar items in EconPapers)
Date: 2012
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1748006X11433661 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:226:y:2012:i:5:p:448-454

DOI: 10.1177/1748006X11433661

Access Statistics for this article

More articles in Journal of Risk and Reliability
Bibliographic data for series maintained by SAGE Publications ().