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An active learning reliability analysis method using adaptive Bayesian compressive sensing and Monte Carlo simulation (ABCS-MCS)

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  • Li, Peiping
  • Wang, Yu

Abstract

Response surface methods (RSMs) have been developed to improve the efficiency of reliability analysis for computationally time-consuming systems. Most RSMs cannot self-evaluate the accuracy of reliability analysis results and rely on Monte Carlo simulation (MCS) for verification. Therefore, this renders a challenging question in RSM applications that how to determine whether the number of sampling points is sufficient to achieve target accuracy of reliability analysis. Adaptive Kriging MCS (AK-MCS) utilizes the advantage of self-estimated uncertainty in Kriging method and combines a learning function to sequentially select additional sampling points to improve the accuracy of reliability analysis until a target accuracy is achieved. However, extensive sampling data are required to ensure that the trend function and auto-correlation structure function of AK-MCS are reliable, and AK-MCS does not work with high-dimensional or highly non-stationary data. To address these challenges, this study develops an active learning reliability analysis method using adaptive Bayesian compressive sensing (ABCS) and MCS, denoted ABCS-MCS. ABCS-MCS can self-evaluate the uncertainty of predictions and combines a learning function to adaptively determine the minimum number of sampling points and their locations for achieving a target accuracy of reliability analysis. This approach is directly applicable to non-stationary data because BCS is non-parametric and data-driven, and thus does not incorporate a trend function or an auto-correlation function. Investigations using two highly non-stationary analytical functions and a slope reliability analysis problem reveal that ABCS-MCS outperforms AK-MCS.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:221:y:2022:i:c:s0951832022000540
    DOI: 10.1016/j.ress.2022.108377
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    References listed on IDEAS

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    Cited by:

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    8. Bao, Yuequan & Sun, Huabin & Guan, Xiaoshu & Tian, Yuxuan, 2024. "An active learning method using deep adversarial autoencoder-based sufficient dimension reduction neural network for high-dimensional reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 247(C).

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