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A support vector density-based importance sampling for reliability assessment

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  • Dai, Hongzhe
  • Zhang, Hao
  • Wang, Wei

Abstract

An importance sampling method based on the adaptive Markov chain simulation and support vector density estimation is developed in this paper for efficient structural reliability assessment. The methodology involves the generation of samples that can adaptively populate the important region by the adaptive Metropolis algorithm, and the construction of importance sampling density by support vector density. The use of the adaptive Metropolis algorithm may effectively improve the convergence and stability of the classical Markov chain simulation. The support vector density can approximate the sampling density with fewer samples in comparison to the conventional kernel density estimation. The proposed importance sampling method can effectively reduce the number of structural analysis required for achieving a given accuracy. Examples involving both numerical and practical structural problems are given to illustrate the application and efficiency of the proposed methodology.

Suggested Citation

  • Dai, Hongzhe & Zhang, Hao & Wang, Wei, 2012. "A support vector density-based importance sampling for reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 86-93.
    • Handle: RePEc:eee:reensy:v:106:y:2012:i:c:p:86-93
    DOI: 10.1016/j.ress.2012.04.011
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    References listed on IDEAS

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    1. Morio, Jérôme, 2011. "Non-parametric adaptive importance sampling for the probability estimation of a launcher impact position," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 178-183.
    2. Chen, Kuan-Yu, 2007. "Forecasting systems reliability based on support vector regression with genetic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 92(4), pages 423-432.
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    Citations

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    Cited by:

    1. Keshtegar, Behrooz & Kisi, Ozgur, 2018. "RM5Tree: Radial basis M5 model tree for accurate structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 49-61.
    2. Yang Liu & Naiwei Lu & Xinfeng Yin & Mohammad Noori, 2016. "An adaptive support vector regression method for structural system reliability assessment and its application to a cable-stayed bridge," Journal of Risk and Reliability, , vol. 230(2), pages 204-219, April.
    3. Roy, Atin & Chakraborty, Subrata, 2020. "Support vector regression based metamodel by sequential adaptive sampling for reliability analysis of structures," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    4. Wang, Cao & Zhang, Hao & Li, Quanwang, 2017. "Reliability assessment of aging structures subjected to gradual and shock deteriorations," Reliability Engineering and System Safety, Elsevier, vol. 161(C), pages 78-86.
    5. Keshtegar, Behrooz & Chakraborty, Subrata, 2018. "An efficient-robust structural reliability method by adaptive finite-step length based on Armijo line search," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 195-206.
    6. Dai, Hongzhe & Zhang, Boyi & Wang, Wei, 2015. "A multiwavelet support vector regression method for efficient reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 132-139.
    7. Roy, Atin & Chakraborty, Subrata, 2023. "Support vector machine in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    8. Cao, Quoc Dung & Choe, Youngjun, 2019. "Cross-entropy based importance sampling for stochastic simulation models," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    9. 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).
    10. Ling, Chunyan & Lu, Zhenzhou & Zhang, Xiaobo, 2020. "An efficient method based on AK-MCS for estimating failure probability function," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    11. Khatibinia, Mohsen & Javad Fadaee, Mohammad & Salajegheh, Javad & Salajegheh, Eysa, 2013. "Seismic reliability assessment of RC structures including soil–structure interaction using wavelet weighted least squares support vector machine," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 22-33.

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