IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v239y2025i5p984-999.html

Nested surrogate model for discrete parameter optimization of structural reliability analysis

Author

Listed:
  • Hongseok Kim
  • Dooyoul Lee
  • Do-Nyun Kim

Abstract

The dynamic Bayesian network which is widely used for efficient reliability analysis, requires random variable discretization. To estimate the accuracy of the discretization results, the Kullback-Leibler (KL) divergence and reliability index errors are used as metrics. Since both errors have a trade-off relationship in a combined discretization method, determining the optimal discrete parameters is crucial to accurate and efficient reliability analysis. However, the optimization process is significantly time-consuming, so we proposed the nested surrogate model for the discrete parameter optimization in structural reliability analysis. The first-stage surrogate model based on the Gaussian process regression (GPR) method calculated the stress intensity factor (SIF). The sample points for GPR were obtained by using the finite element analysis (FEA) method. The artificial neural network for the nested surrogate model is then trained using the data generated from the first-stage surrogate model, to determine the SIF range for the crack growth behavior. The nested surrogate model was implemented to optimize the discrete parameters of the crack length distribution. The proposed method considerably reduced the computation time in obtaining the optimal discrete parameters that capture the features of regions with low probability.

Suggested Citation

  • Hongseok Kim & Dooyoul Lee & Do-Nyun Kim, 2025. "Nested surrogate model for discrete parameter optimization of structural reliability analysis," Journal of Risk and Reliability, , vol. 239(5), pages 984-999, October.
    • Handle: RePEc:sae:risrel:v:239:y:2025:i:5:p:984-999
    DOI: 10.1177/1748006X241307103
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X241307103
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X241307103?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Zwirglmaier, Kilian & Straub, Daniel, 2016. "A discretization procedure for rare events in Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 96-109.
    2. Marquez, David & Neil, Martin & Fenton, Norman, 2010. "Improved reliability modeling using Bayesian networks and dynamic discretization," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 412-425.
    3. Saraygord Afshari, Sajad & Enayatollahi, Fatemeh & Xu, Xiangyang & Liang, Xihui, 2022. "Machine learning-based methods in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lee, Dooyoul & Kwon, Kybeom, 2023. "Dynamic Bayesian network model for comprehensive risk analysis of fatigue-critical structural details," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    2. Simon, Christophe & Bicking, Frédérique, 2017. "Hybrid computation of uncertainty in reliability analysis with p-box and evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 629-638.
    3. Zhang, Haoyuan & Marsh, D. William R, 2021. "Managing infrastructure asset: Bayesian networks for inspection and maintenance decisions reasoning and planning," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    4. Wei, Pengfei & Zheng, Yu & Fu, Jiangfeng & Xu, Yuannan & Gao, Weikai, 2023. "An expected integrated error reduction function for accelerating Bayesian active learning of failure probability," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    5. Hao, Donghui & Zhang, Jian & Yue, Xinxin & Chen, Lei, 2025. "Combined dimensionality reduction based adaptive polynomial chaos expansion for high-dimensional reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 264(PA).
    6. Pan, Yongjun & Sun, Yu & Li, Zhixiong & Gardoni, Paolo, 2023. "Machine learning approaches to estimate suspension parameters for performance degradation assessment using accurate dynamic simulations," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    7. Chen, Jun-Yu & Feng, Yun-Wen & Teng, Da & Lu, Cheng & Fei, Cheng-Wei, 2022. "Support vector machine-based similarity selection method for structural transient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    8. Zhang, Tianyu & Zhang, Jize, 2025. "iCE-NGM: Improved cross-entropy importance sampling with non-parametric adaptive Gaussian mixtures and budget-informed stopping criterion," Reliability Engineering and System Safety, Elsevier, vol. 264(PA).
    9. Phan, Hieu Chi & Dhar, Ashutosh Sutra & Bui, Nang Duc, 2023. "Reliability assessment of pipelines crossing strike-slip faults considering modeling uncertainties using ANN models," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    10. Dhulipala, Somayajulu L.N. & Shields, Michael D. & Chakroborty, Promit & Jiang, Wen & Spencer, Benjamin W. & Hales, Jason D. & Labouré, Vincent M. & Prince, Zachary M. & Bolisetti, Chandrakanth & Che, 2022. "Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    11. Luo, Changqi & Zhu, Shun-Peng & Keshtegar, Behrooz & Niu, Xiaopeng & Taylan, Osman, 2023. "An enhanced uniform simulation approach coupled with SVR for efficient structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    12. Xu, Yingchun & Yao, Wen & Zheng, Xiaohu & Gong, Zhiqiang & Yan, Lei & Xu, Na, 2025. "A generic quality and accuracy driven uncertainty quantification framework for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 262(C).
    13. Roma, Giovanni & Di Maio, Francesco & Zio, Enrico, 2026. "Decision making under uncertainty by trustworthy dynamic bayesian networks for severe accident management in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 267(PA).
    14. Liu, Zengkai & Liu, Yonghong & Zhang, Dawei & Cai, Baoping & Zheng, Chao, 2015. "Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge," Energy, Elsevier, vol. 87(C), pages 41-48.
    15. Zhang, Qing & Tang, Lv & Xuan, Jianping & Shi, Tielin & Li, Rui, 2023. "An uncertainty relevance metric-based domain adaptation fault diagnosis method to overcome class relevance caused confusion," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    16. Lindhe, Andreas & Norberg, Tommy & Rosén, Lars, 2012. "Approximate dynamic fault tree calculations for modelling water supply risks," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 61-71.
    17. Jung, Yongsu & Lee, Ungki & Lee, Ikjin, 2026. "An integrated framework for reliability analysis and design optimization using input, simulation, and experimental data: Confidence-based design optimization under aleatory and epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 267(PB).
    18. 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).
    19. Ma, Yulin & Li, Lei & Yang, Jun, 2022. "Convolutional kernel aggregated domain adaptation for intelligent fault diagnosis with label noise," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    20. Qiu, Tao & Du, Xiaoqing, 2025. "Graph Neural Networks combined with PCA for predicting blast load time series on structures," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:risrel:v:239:y:2025:i:5:p:984-999. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.