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Structural reliability assessment through surrogate based importance sampling with dimension reduction

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  • Zuniga, M. Munoz
  • Murangira, A.
  • Perdrizet, T.

Abstract

We present a method for reliability assessment in extreme conditions from a numerical simulator through surrogate based importance sampling. As proposed in recent works in the literature, a Kriging surrogate is used to build an approximation of the limit state function and the optimal importance density. Our contribution is then the use of a sufficient dimension reduction method which enables the construction of the limit state function metamodel in lower dimension. The so called augmented failure probability and correction factor are recast in this dimension reduction framework. Simple strategies for metamodel refinement in the dimension reduction subspace are described and, in the case of Gaussian inputs, a computationally efficient MCMC scheme aimed at sampling the quasi-optimal importance density is presented. The case of non-Gaussian inputs is also laid out and it is argued and demonstrated through simulations that this approach can reduce the number of calls to the computer model, which is a crucial factor in reliability analysis. Advantages of this method are also supported by numerical simulations carried on an industrial case study concerned with the extreme response prediction of a wind turbine under wind loading.

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  • Zuniga, M. Munoz & Murangira, A. & Perdrizet, T., 2021. "Structural reliability assessment through surrogate based importance sampling with dimension reduction," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:reensy:v:207:y:2021:i:c:s0951832020307857
    DOI: 10.1016/j.ress.2020.107289
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    References listed on IDEAS

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    4. Kenji Fukumizu & Chenlei Leng, 2014. "Gradient-Based Kernel Dimension Reduction for Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 359-370, March.
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    Cited by:

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    2. Dang, Chao & Wei, Pengfei & Faes, Matthias G.R. & Valdebenito, Marcos A. & Beer, Michael, 2022. "Parallel adaptive Bayesian quadrature for rare event estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
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    6. Jakeman, John D. & Kouri, Drew P. & Huerta, J. Gabriel, 2022. "Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
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    8. Zhang, Xiaobo & Lu, Zhenzhou & Cheng, Kai, 2022. "Cross-entropy-based directional importance sampling with von Mises-Fisher mixture model for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    9. Ramadhani, Adhitya & Khan, Faisal & Colbourne, Bruce & Ahmed, Salim & Taleb-Berrouane, Mohammed, 2022. "Resilience assessment of offshore structures subjected to ice load considering complex dependencies," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    10. El Masri, Maxime & Morio, Jérôme & Simatos, Florian, 2021. "Improvement of the cross-entropy method in high dimension for failure probability estimation through a one-dimensional projection without gradient estimation," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    11. Perrin, G., 2021. "Point process-based approaches for the reliability analysis of systems modeled by costly simulators," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    12. Castellon, Dario Fernandez & Fenerci, Aksel & Petersen, Øyvind Wiig & Øiseth, Ole, 2023. "Full long-term buffeting analysis of suspension bridges using Gaussian process surrogate modelling and importance sampling Monte Carlo simulations," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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