A new active learning surrogate model for time- and space-dependent system reliability analysis

Hongyou Zhan and Ning-Cong Xiao

Reliability Engineering and System Safety, 2025, vol. 253, issue C

Abstract: This study introduces a novel method for time- and space-dependent system reliability analysis, integrating an active learning surrogate model with an innovative parallel updating strategy. A global Kriging model is developed to represent the signs of random samples using efficient global optimization. From a Bayesian perspective, the prediction probabilities of random sample signs within the time-space domain are calculated, and the sample with the lowest prediction probability is chosen to update the global Kriging model. The system extremum for each sample in the time-space domain is determined, and the corresponding random variables, time-space coordinates, and failure modes are selected. To further decrease iteration times, a parallel updating strategy that considers both the predicted probability and the correlation among candidate samples is proposed. Additionally, a new stopping criterion is introduced to balance accuracy and efficiency, terminating the updating process appropriately. The method's accuracy and efficiency are validated through three examples.

Keywords: Structural system; Time- and space-dependent reliability; Active learning Kriging model; Parallel learning; Multiple failure modes (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:253:y:2025:i:c:s0951832024006082

DOI: 10.1016/j.ress.2024.110536

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