Structural reliability analysis with extremely small failure probabilities: A quasi-Bayesian active learning method

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Chao Dang
  • Alice Cicirello
  • Marcos A. Valdebenito
  • Matthias G.R. Faes
  • Pengfei Wei
  • Michael Beer

Research Organisations

External Research Organisations

  • TU Dortmund University
  • Northwestern Polytechnical University
  • University of Liverpool
  • Tongji University
  • University of Cambridge
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Details

Original languageEnglish
Article number103613
Number of pages12
JournalProbabilistic Engineering Mechanics
Volume76
Early online date26 Mar 2024
Publication statusPublished - Apr 2024

Abstract

The concept of Bayesian active learning has recently been introduced from machine learning to structural reliability analysis. Although several specific methods have been successfully developed, significant efforts are still needed to fully exploit their potential and to address existing challenges. This work proposes a quasi-Bayesian active learning method, called ‘Quasi-Bayesian Active Learning Cubature’, for structural reliability analysis with extremely small failure probabilities. The method is established based on a cleaver use of the Bayesian failure probability inference framework. To reduce the computational burden associated with the exact posterior variance of the failure probability, we propose a quasi posterior variance instead. Then, two critical elements for Bayesian active learning, namely the stopping criterion and the learning function, are developed subsequently. The stopping criterion is defined based on the quasi posterior coefficient of variation of the failure probability, whose numerical solution scheme is also tailored. The learning function is extracted from the quasi posterior variance, with the introduction of an additional parameter that allows multi-point selection and hence parallel distributed processing. By testing on four numerical examples, it is empirically shown that the proposed method can assess extremely small failure probabilities with desired accuracy and efficiency.

Keywords

    Bayesian active learning, Learning function, Parallel computing, Small failure probability, Stopping criterion, Structural reliability analysis

ASJC Scopus subject areas

Cite this

Structural reliability analysis with extremely small failure probabilities: A quasi-Bayesian active learning method. / Dang, Chao; Cicirello, Alice; Valdebenito, Marcos A. et al.
In: Probabilistic Engineering Mechanics, Vol. 76, 103613, 04.2024.

Research output: Contribution to journalArticleResearchpeer review

Dang C, Cicirello A, Valdebenito MA, Faes MGR, Wei P, Beer M. Structural reliability analysis with extremely small failure probabilities: A quasi-Bayesian active learning method. Probabilistic Engineering Mechanics. 2024 Apr;76:103613. Epub 2024 Mar 26. doi: 10.1016/j.probengmech.2024.103613
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abstract = "The concept of Bayesian active learning has recently been introduced from machine learning to structural reliability analysis. Although several specific methods have been successfully developed, significant efforts are still needed to fully exploit their potential and to address existing challenges. This work proposes a quasi-Bayesian active learning method, called {\textquoteleft}Quasi-Bayesian Active Learning Cubature{\textquoteright}, for structural reliability analysis with extremely small failure probabilities. The method is established based on a cleaver use of the Bayesian failure probability inference framework. To reduce the computational burden associated with the exact posterior variance of the failure probability, we propose a quasi posterior variance instead. Then, two critical elements for Bayesian active learning, namely the stopping criterion and the learning function, are developed subsequently. The stopping criterion is defined based on the quasi posterior coefficient of variation of the failure probability, whose numerical solution scheme is also tailored. The learning function is extracted from the quasi posterior variance, with the introduction of an additional parameter that allows multi-point selection and hence parallel distributed processing. By testing on four numerical examples, it is empirically shown that the proposed method can assess extremely small failure probabilities with desired accuracy and efficiency.",
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note = "Funding Information: Chao Dang is mainly supported by China Scholarship Council (CSC). Alice Cicirello would like to thank the financial support provided by the Alexander von Humboldt Foundation Research Fellowship for experienced researchers. Pengfei Wei is grateful to the support from the National Natural Science Foundation of China (grant no. 51905430 and 72171194).",
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AU - Faes, Matthias G.R.

AU - Wei, Pengfei

AU - Beer, Michael

N1 - Funding Information: Chao Dang is mainly supported by China Scholarship Council (CSC). Alice Cicirello would like to thank the financial support provided by the Alexander von Humboldt Foundation Research Fellowship for experienced researchers. Pengfei Wei is grateful to the support from the National Natural Science Foundation of China (grant no. 51905430 and 72171194).

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