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An active learning method using deep adversarial autoencoder-based sufficient dimension reduction neural network for high-dimensional reliability analysis

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  • Bao, Yuequan
  • Sun, Huabin
  • Guan, Xiaoshu
  • Tian, Yuxuan

Abstract

Reliability analysis often requires time-consuming evaluations, especially when dealing with high-dimensional and nonlinear problems. To address this challenge, surrogate model methods are frequently employed. One way to improve the efficiency of surrogate model methods involves selecting informative samples that significantly enhance the accuracy of the surrogate model. This paper introduces a novel approach to facilitate the construction of surrogate models and selection of informative samples in high-dimensional reliability analysis, through an active learning method based on a deep adversarial autoencoder-based sufficient dimension reduction (AAE-SDR) neural network. The AAE-SDR neural network serves as a surrogate model, transforming complex high-dimensional variables into tractable, low-dimensional embeddings relevant to the target. These embeddings are Gaussian-distributed with a distinct latent limit state boundary. A new sampling strategy is proposed to select informative misclassified samples by iteratively identifying candidate samples near the latent limit state boundary and uniformly sampling from the candidate sample dataset based on the latent Gaussian distribution. The effectiveness of the proposed approach is demonstrated through two high-dimensional numerical examples and a cable-stayed bridge case study. Results show that the proposed method simplifies complex high-dimensional reliability problems and provides a relatively accurate estimated failure probability with a limited number of samples.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:reensy:v:247:y:2024:i:c:s095183202400214x
    DOI: 10.1016/j.ress.2024.110140
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    References listed on IDEAS

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    1. Bourinet, J.-M., 2016. "Rare-event probability estimation with adaptive support vector regression surrogates," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 210-221.
    2. Cadini, F. & Santos, F. & Zio, E., 2014. "An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 109-117.
    3. Chen, Jiahui & Chen, Zhicheng & Xu, Yang & Li, Hui, 2021. "Efficient reliability analysis combining kriging and subset simulation with two-stage convergence criterion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    4. Zhou, Jin & Li, Jie, 2023. "IE-AK: A novel adaptive sampling strategy based on information entropy for Kriging in metamodel-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    5. Xu, Jun & Wang, Ding, 2019. "Structural reliability analysis based on polynomial chaos, Voronoi cells and dimension reduction technique," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 329-340.
    6. Pan, Qiujing & Dias, Daniel, 2017. "Sliced inverse regression-based sparse polynomial chaos expansions for reliability analysis in high dimensions," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 484-493.
    7. Shi, Yan & Lu, Zhenzhou & He, Ruyang & Zhou, Yicheng & Chen, Siyu, 2020. "A novel learning function based on Kriging for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    8. Jiang, Zhong-ming & Feng, De-Cheng & Zhou, Hao & Tao, Wei-Feng, 2021. "A recursive dimension-reduction method for high-dimensional reliability analysis with rare failure event," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    9. 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).
    10. Xiang, Zhengliang & Bao, Yuequan & Tang, Zhiyi & Li, Hui, 2020. "Deep reinforcement learning-based sampling method for structural reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    11. Bao, Yuequan & Xiang, Zhengliang & Li, Hui, 2021. "Adaptive subset searching-based deep neural network method for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    12. Xiao, Ning-Cong & Zuo, Ming J. & Zhou, Chengning, 2018. "A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 330-338.
    13. Zhao, Wei & Fan, Feng & Wang, Wei, 2017. "Non-linear partial least squares response surface method for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 161(C), pages 69-77.
    14. Ameryan, Ala & Ghalehnovi, Mansour & Rashki, Mohsen, 2022. "AK-SESC: a novel reliability procedure based on the integration of active learning kriging and sequential space conversion method," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    Full references (including those not matched with items on IDEAS)

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