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Adaptive learning for reliability analysis using Support Vector Machines

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  • Pepper, Nick
  • Crespo, Luis
  • Montomoli, Francesco

Abstract

Given an expensive computational model of a system subject to reliability requirements, this work shows how to approximate the failure probability by learning adaptively the high-likelihood regions of the Limit State Function using Support Vector Machines. To this end, an algorithm is proposed that selects informative parameter points to add to training data at each iteration to improve the accuracy of the approximation. Furthermore, we provide a means to quantify the uncertainty in the Limit State Function, using geometrical arguments to estimate an upper bound to the failure probability.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:226:y:2022:i:c:s0951832022002721
    DOI: 10.1016/j.ress.2022.108635
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    References listed on IDEAS

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    Cited by:

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    3. Van Huynh, Thu & Tangaramvong, Sawekchai & Do, Bach & Gao, Wei & Limkatanyu, Suchart, 2023. "Sequential most probable point update combining Gaussian process and comprehensive learning PSO for structural reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Yu, Ting & Lu, Zhenzhou & Yun, Wanying, 2023. "An efficient algorithm for analyzing multimode structure system reliability by a new learning function of most reducing average probability of misjudging system state," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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