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

  • Andrew Utomi Ebenuwa
  • , Kong Fah Tee*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

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.

Original languageEnglish
Pages (from-to)413-425
Number of pages13
JournalStructure and Infrastructure Engineering
Volume15
Issue number3
DOIs
StatePublished - 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

  • Optimisation
  • bending stress
  • buried pipes
  • fuzzy variable
  • pipe ovality
  • random variable
  • subset simulation

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Safety, Risk, Reliability and Quality
  • Geotechnical Engineering and Engineering Geology
  • Ocean Engineering
  • Mechanical Engineering

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