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

Zia Ur Rehman Gondal; Jongsoo Lee

doi:10.1177/1748006X11433661

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

Zia Ur Rehman Gondal, Jongsoo Lee

Department of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

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.

Original language	English
Pages (from-to)	448-454
Number of pages	7
Journal	Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Volume	226
Issue number	5
DOIs	https://doi.org/10.1177/1748006X11433661
Publication status	Published - 2012 Oct

Bibliographical note

Funding Information:
This research constitutes part of the Basic Science Research Program of the National Research Foundation of Korea (NRF) which is funded by the Ministry of Education, Science and Technology under grant 2011-0024829.

All Science Journal Classification (ASJC) codes

Safety, Risk, Reliability and Quality

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10.1177/1748006X11433661

Cite this

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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.",

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AB - 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.

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

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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