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An integrated QMU approach to structural reliability assessment based on evidence theory and kriging model with adaptive sampling

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  • Xie, Chaoyang
  • Li, Guijie
  • Wei, Fayuan

Abstract

The objective of this paper is to propose an implementation framework of the quantification of margins and uncertainties (QMU) calculation for reliability and safety assessment of high-consequence systems in the presence of mixed (aleatory and epistemic) uncertainties. The aleatory and epistemic uncertainties are represented by a probability distribution and the Dempster–Shafer theory of evidence (DSTE), respectively. This study focuses on the need to alleviate the computational cost in terms of mixed uncertainties propagation, which is the core of the QMU process. The kriging model together with an adaptive sampling method is tailored to predict the responses of a system simulation model. The confidence factor (CF) for the QMU calculation is then evaluated by integrating the surrogate model and the DSTE analysis. The proposed approach is demonstrated by a numerical example to examine the computational efficiency. Finally, the proposed QMU framework is demonstrated via a simulation-based structural analysis of a pressure vessel with corrosion damage.

Suggested Citation

  • Xie, Chaoyang & Li, Guijie & Wei, Fayuan, 2018. "An integrated QMU approach to structural reliability assessment based on evidence theory and kriging model with adaptive sampling," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 112-122.
    • Handle: RePEc:eee:reensy:v:171:y:2018:i:c:p:112-122
    DOI: 10.1016/j.ress.2017.11.014
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    Cited by:

    1. Bansal, Parth & Zheng, Zhuoyuan & Shao, Chenhui & Li, Jingjing & Banu, Mihaela & Carlson, Blair E & Li, Yumeng, 2022. "Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    2. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Chen, Weiyi & Zhang, Limao, 2021. "Resilience assessment of regional areas against earthquakes using multi-source information fusion," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Saraygord Afshari, Sajad & Enayatollahi, Fatemeh & Xu, Xiangyang & Liang, Xihui, 2022. "Machine learning-based methods in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 219(C).

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