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Error Quantification and Control for Adaptive Kriging-Based Reliability Updating with Equality Information

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  • Zhang, Chi
  • Wang, Zeyu
  • Shafieezadeh, Abdollah

Abstract

New information obtained through measurements provide an opportunity to update estimates of a system's reliability. For equality type information, reliability updating is a daunting task. The current state-of-the-art method, reliability updating with equality information using adaptive Kriging (RUAK), integrates an adaptive Kriging process with a transformation of equality information into inequality information. The stopping criterion for training the Kriging model relies on the estimated error for prior failure probability, thus leaving the potential for the true error in posterior failure probability to exceed acceptable thresholds. This study presents an approach to estimate the maximum error in posterior failure probability for a given confidence level . Moreover, a two-phase approach is proposed for active learning and adaptive training of Kriging models in reliability updating problems. The new stopping criterion based on the maximum error of posterior failure probability ensures the accuracy of Kriging and thus the reliability estimates, while the two-phase scheme avoids unnecessary training hence improving the efficiency of reliability updating. Four numerical examples are considered to investigate the performance of the proposed approach. It is demonstrated that this method offers the ability to set and meet target accuracies for reliability updating, which is critical for applications where the consequences of decisions are significant.

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  • Zhang, Chi & Wang, Zeyu & Shafieezadeh, Abdollah, 2021. "Error Quantification and Control for Adaptive Kriging-Based Reliability Updating with Equality Information," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:reensy:v:207:y:2021:i:c:s0951832020308164
    DOI: 10.1016/j.ress.2020.107323
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    References listed on IDEAS

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    6. Wang, Zeyu & Shafieezadeh, Abdollah, 2020. "Real-time high-fidelity reliability updating with equality information using adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
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    Citations

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    Cited by:

    1. Oluwatuyi, Opeyemi E. & Ng, Kam & Wulff, Shaun S., 2023. "Improved resistance prediction and reliability for bridge pile foundation in shales through optimal site investigation plans," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
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    3. Zhang, Yu & Dong, You & Xu, Jun, 2023. "An accelerated active learning Kriging model with the distance-based subdomain and a new stopping criterion for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. 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).
    5. 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).
    6. Li, Peiping & Wang, Yu, 2022. "An active learning reliability analysis method using adaptive Bayesian compressive sensing and Monte Carlo simulation (ABCS-MCS)," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    7. Wang, Zeyu & Shafieezadeh, Abdollah, 2023. "Bayesian updating with adaptive, uncertainty-informed subset simulations: High-fidelity updating with multiple observations," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    8. Wang, Zeyu & Shafieezadeh, Abdollah & Xiao, Xiong & Wang, Xiaowei & Li, Quanwang, 2022. "Optimal monitoring location for tracking evolving risks to infrastructure systems: Theory and application to tunneling excavation risk," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    9. Li, Pei-Pei & Zhang, Yi & Zhao, Yan-Gang & Zhao, Zhao & Cai, Enjian, 2023. "An information reuse-based method for reliability updating," Reliability Engineering and System Safety, Elsevier, vol. 239(C).

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