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Reliability analysis of structures by a three-stage sequential sampling based adaptive support vector regression model

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  • Roy, Atin
  • Chakraborty, Subrata

Abstract

A three-stage adaptive support vector regression (SVR) based metamodel is built by sampling training data sequentially close to a limit state function (LSF). The approach alleviates the difficulty of scarcity of samples in the reduced space for reliability evaluation of a structure involving implicit LSF. Specifically, importance sampling is proposed to ensure a sufficient number of simulation points near the approximated failure plane. A design of experiment is initially constructed by a space-filling design over the entire domain. The optimum choices of the hyper-parameters of the SVR model are then determined by minimizing the generalized root mean square error (GRMSE). A subset of Monte Carlo simulation samples with magnitude of approximated LSF less than the noted GRMSE values are selected. Subsequently, the data points are added sequentially from the subset, based on the maximin criterion. Finally, the SVR model is iteratively updated to improve the reliability estimation by adding more data from the latest subset until convergence. An improved stopping condition is proposed to avoid false convergence. The effectiveness of the proposed approach along with estimation of very small probability of failure is elucidated through three numerical examples.

Suggested Citation

  • Roy, Atin & Chakraborty, Subrata, 2022. "Reliability analysis of structures by a three-stage sequential sampling based adaptive support vector regression model," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:reensy:v:219:y:2022:i:c:s0951832021007365
    DOI: 10.1016/j.ress.2021.108260
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    References listed on IDEAS

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    Cited by:

    1. Dong, Manman & Cheng, Yongbo & Wan, Liangqi, 2024. "A new adaptive multi-kernel relevance vector regression for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Zhou, Jin & Li, Jie, 2022. "An enhanced method for improving the accuracy of small failure probability of structures," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    3. Zhang, Chi & Shafieezadeh, Abdollah, 2022. "Simulation-free reliability analysis with active learning and Physics-Informed Neural Network," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Pepper, Nick & Crespo, Luis & Montomoli, Francesco, 2022. "Adaptive learning for reliability analysis using Support Vector Machines," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. Roy, Atin & Chakraborty, Subrata, 2023. "Support vector machine in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    6. Zheng, Xiaohu & Yao, Wen & Zhang, Yunyang & Zhang, Xiaoya, 2022. "Consistency regularization-based deep polynomial chaos neural network method for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    7. Bo, Yimin & Bao, Minglei & Ding, Yi & Hu, Yishuang, 2024. "A DNN-based reliability evaluation method for multi-state series-parallel systems considering semi-Markov process," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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