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Fuzzy-based optimised subset simulation for reliability analysis of engineering structures

Fuzzy-based optimised subset simulation for reliability analysis of engineering structures

Ebenuwa, Andrew Utomi and Tee, Kong Fah ORCID: 0000-0003-3202-873X (2019) Fuzzy-based optimised subset simulation for reliability analysis of engineering structures. Structure and Infrastructure Engineering: Maintenance, Management, Life-Cycle Design and Performance, 15 (3). pp. 413-425. ISSN 1573-2479 (Print), 1744-8980 (Online) (doi:https://doi.org/10.1080/15732479.2018.1552977)

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Abstract

This paper presents a numerical strategy for the efficient reliability assessment of engineering structures with random variables and fuzzy variables using fuzzy based optimised subset simulation (SS) approach. The proposed method relies on the performance function of the structure, which involves probability distribution functions and fuzzy variables for the modelling of the structural system. The values of the fuzzy variables for every alpha level are first obtained using the membership function. Therefore, the set values of the fuzzy variable bound the reliability of the structure, and this is evaluated using optimisation and efficient SS approach. The rationale behind the proposed strategy is to locate a failure domain or region where the objective function is minimised or maximised and compute the reliability using SS. The proposed algorithm in this study inherits the benefits of direct Monte Carlo approach in propagating the uncertainties associated with structural parameters but also demonstrate more robustness against the latter. The methodology can be applied to any engineering structures, and the applicability is demonstrated here, using a buried pipeline.

Item Type: Article
Uncontrolled Keywords: optimisation; random variable; fuzzy variable; subset simulation; pipe ovality; bending stress of buried pipe
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Last Modified: 24 Mar 2019 00:41
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/23186

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