Sample regeneration algorithm for structural failure probability function estimation

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Xiukai Yuan
  • Shanglong Wang
  • Marcos A. Valdebenito
  • Matthias G.R. Faes
  • Michael Beer

Research Organisations

External Research Organisations

  • Xiamen University
  • TU Dortmund University
  • University of Liverpool
  • Tongji University
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Details

Original languageEnglish
Article number103387
JournalProbabilistic Engineering Mechanics
Volume71
Early online date19 Nov 2022
Publication statusPublished - Jan 2023

Abstract

An efficient strategy to approximate the failure probability function in structural reliability problems is proposed. The failure probability function (FPF) is defined as the failure probability of the structure expressed as a function of the design parameters, which in this study are considered to be distribution parameters of random variables representing uncertain model quantities. The task of determining the FPF is commonly numerically demanding since repeated reliability analyses are required. The proposed strategy is based on the concept of augmented reliability analysis, which only requires a single run of a simulation-based reliability method. This paper introduces a new sample regeneration algorithm that allows to generate the required failure samples of design parameters without any additional evaluation of the structural response. In this way, efficiency is further improved while ensuring high accuracy in the estimation of the FPF. To illustrate the efficiency and effectiveness of the method, case studies involving a turbine disk and an aircraft inner flap are included in this study.

Keywords

    Bayesian theory, Failure probability function, Maximum Entropy method, Regeneration algorithm, Reliability

ASJC Scopus subject areas

Cite this

Sample regeneration algorithm for structural failure probability function estimation. / Yuan, Xiukai; Wang, Shanglong; Valdebenito, Marcos A. et al.
In: Probabilistic Engineering Mechanics, Vol. 71, 103387, 01.2023.

Research output: Contribution to journalArticleResearchpeer review

Yuan X, Wang S, Valdebenito MA, Faes MGR, Beer M. Sample regeneration algorithm for structural failure probability function estimation. Probabilistic Engineering Mechanics. 2023 Jan;71:103387. Epub 2022 Nov 19. doi: 10.1016/j.probengmech.2022.103387
Yuan, Xiukai ; Wang, Shanglong ; Valdebenito, Marcos A. et al. / Sample regeneration algorithm for structural failure probability function estimation. In: Probabilistic Engineering Mechanics. 2023 ; Vol. 71.
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title = "Sample regeneration algorithm for structural failure probability function estimation",
abstract = "An efficient strategy to approximate the failure probability function in structural reliability problems is proposed. The failure probability function (FPF) is defined as the failure probability of the structure expressed as a function of the design parameters, which in this study are considered to be distribution parameters of random variables representing uncertain model quantities. The task of determining the FPF is commonly numerically demanding since repeated reliability analyses are required. The proposed strategy is based on the concept of augmented reliability analysis, which only requires a single run of a simulation-based reliability method. This paper introduces a new sample regeneration algorithm that allows to generate the required failure samples of design parameters without any additional evaluation of the structural response. In this way, efficiency is further improved while ensuring high accuracy in the estimation of the FPF. To illustrate the efficiency and effectiveness of the method, case studies involving a turbine disk and an aircraft inner flap are included in this study.",
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AU - Yuan, Xiukai

AU - Wang, Shanglong

AU - Valdebenito, Marcos A.

AU - Faes, Matthias G.R.

AU - Beer, Michael

N1 - Funding Information: The authors would like to acknowledge financial support from the Aeronautical Science Foundation of China (Grant No. ASFC-20170968002 ).

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KW - Bayesian theory

KW - Failure probability function

KW - Maximum Entropy method

KW - Regeneration algorithm

KW - Reliability

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