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Experimental estimation of time variant system reliability of vibrating structures based on subset simulation with Markov chain splitting

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  • Sonal, S.D.
  • Ammanagi, S
  • Kanjilal, O
  • Manohar, C.S.

Abstract

The study investigates the application of ideas from variance reduction schemes, developed in the area of computational structural reliability modelling, to the problems of experimental estimation of time variant reliability of randomly excited vibrating structures. The study considers series/parallel system reliability of vibrating systems under multi-component random excitations. An experimental protocol, based on subset simulation with Markov chain splitting, is proposed to estimate probabilities of failure as low as 10−5 to 10−4 with a relatively smaller number of samples and hence with reduced test times. Illustrative examples consist of earthquake shake table studies on a three-storied bending-torsion coupled building frame under bi-axial nonstationary, random earthquake support motions.

Suggested Citation

  • Sonal, S.D. & Ammanagi, S & Kanjilal, O & Manohar, C.S., 2018. "Experimental estimation of time variant system reliability of vibrating structures based on subset simulation with Markov chain splitting," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 55-68.
    • Handle: RePEc:eee:reensy:v:178:y:2018:i:c:p:55-68
    DOI: 10.1016/j.ress.2018.05.007
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    References listed on IDEAS

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    1. Li, Yuyin & Zhang, Yahui & Kennedy, David, 2018. "Reliability analysis of subsea pipelines under spatially varying ground motions by using subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 74-83.
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    4. Lambros Katafygiotis & Sai Hung Cheung & Ka-Veng Yuen, 2010. "Spherical subset simulation (S³) for solving non-linear dynamical reliability problems," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 4(2/3), pages 122-138.
    5. Ahmed, Hussam & Chateauneuf, Alaa, 2014. "Optimal number of tests to achieve and validate product reliability," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 242-250.
    6. Zdravko I. Botev & Dirk P. Kroese, 2008. "An Efficient Algorithm for Rare-event Probability Estimation, Combinatorial Optimization, and Counting," Methodology and Computing in Applied Probability, Springer, vol. 10(4), pages 471-505, December.
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    Cited by:

    1. Jian Wang & Xiang Gao & Zhili Sun, 2021. "A Multilevel Simulation Method for Time-Variant Reliability Analysis," Sustainability, MDPI, vol. 13(7), pages 1-16, March.
    2. Zhang, Yu & Dong, You & Frangopol, Dan M., 2024. "An error-based stopping criterion for spherical decomposition-based adaptive Kriging model and rare event estimation," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Jian Wang & Xiang Gao & Zhili Sun, 2021. "An Importance Sampling Framework for Time-Variant Reliability Analysis Involving Stochastic Processes," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    4. Qian, Hua-Ming & Li, Yan-Feng & Huang, Hong-Zhong, 2021. "Time-variant system reliability analysis method for a small failure probability problem," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    5. Jiang, Chen & Qiu, Haobo & Yang, Zan & Chen, Liming & Gao, Liang & Li, Peigen, 2019. "A general failure-pursuing sampling framework for surrogate-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 47-59.
    6. Wang, Zhonglai & Liu, Jing & Yu, Shui, 2020. "Time-variant reliability prediction for dynamic systems using partial information," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    7. Du, Weiqi & Luo, Yuanxin & Wang, Yongqin, 2019. "Time-variant reliability analysis using the parallel subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 250-257.

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