IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v257y2025ipas0951832025000146.html
   My bibliography  Save this article

Stacking ensemble surrogate modeling method based on decomposed- coordinated strategy for structural low-cycle fatigue life reliability estimation

Author

Listed:
  • Li, Zhen-Ao
  • Li, Qing-Long
  • Liang, Jia-Hao
  • Dong, Xiao-Wei
  • Zhu, Chun-Yan
  • Wang, Ming

Abstract

Traditional reliability analysis methods for the low-cycle fatigue (LCF) of mechanical structures consistently adopt one-step modeling approach, which tends to lack satisfactory precision when dealing with highly nonlinear problems. To address above issue, the Stacking ensemble surrogate modeling method based on decomposed-coordinated strategy (DCS-SESMM) is proposed. In this study, the Manson-Coffin (M-C) formula serves as the foundation, and the LCF life estimation problem is transformed into a two-step data-driven modeling approach through the decomposed-coordinated strategy (DCS). Firstly, the sub-surrogate models are developed to accurately capture the stress-strain responses under random load variables. Then, the predictions of sub-surrogate models are combined with fatigue parameters to construct the main LCF life model based on the M-C formula. Additionally, the Stacking ensemble learning method is introduced to improve the robustness of surrogate model by integrating data features from different training rules. Finally, the general applicability of DCS-SESMM is validated through the probabilistic analysis of a nested function and the LCF life reliability estimation of turbine blisk, demonstrating its excellent modeling characteristics and simulation performance. The proposed method can be applied to the LCF life reliability estimation of various mechanical structures, providing valuable insights for the LCF life reliability assessment.

Suggested Citation

  • Li, Zhen-Ao & Li, Qing-Long & Liang, Jia-Hao & Dong, Xiao-Wei & Zhu, Chun-Yan & Wang, Ming, 2025. "Stacking ensemble surrogate modeling method based on decomposed- coordinated strategy for structural low-cycle fatigue life reliability estimation," Reliability Engineering and System Safety, Elsevier, vol. 257(PA).
    • Handle: RePEc:eee:reensy:v:257:y:2025:i:pa:s0951832025000146
    DOI: 10.1016/j.ress.2025.110811
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832025000146
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2025.110811?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhou, Changcong & Zhang, Hanlin & Valdebenito, Marcos A. & Zhao, Haodong, 2022. "A general hierarchical ensemble-learning framework for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    3. Zhu, Chun-Yan & Li, Zhen-Ao & Dong, Xiao-Wei & Wang, Ming & Li, Qing-Da, 2024. "Collaborative modeling-based improved moving Kriging approach for low-cycle fatigue life reliability estimation of mechanical structures," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    4. Zhu, Shun-Peng & Huang, Hong-Zhong & Peng, Weiwen & Wang, Hai-Kun & Mahadevan, Sankaran, 2016. "Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 1-12.
    5. Gu, Hang-Hang & Wang, Run-Zi & Tang, Min-Jin & Zhang, Xian-Cheng & Tu, Shan-Tung, 2024. "Data-physics-model based fatigue reliability assessment methodology for high-temperature components and its application in steam turbine rotor," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. 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).
    7. Yanxu Wei & Guangchen Bai & Bowei Wang & Bin Bai, 2018. "Reliability Analysis on Structures Based on a Modified Iterative Response Surface Method," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-14, October.
    8. Lu, Cheng & Teng, Da & Chen, Jun-Yu & Fei, Cheng-Wei & Keshtegar, Behrooz, 2023. "Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    9. Teng, Da & Feng, Yun-Wen & Chen, Jun-Yu & Liu, Jia-Qi & Lu, Cheng, 2024. "Multi-polynomial chaos Kriging-based adaptive moving strategy for comprehensive reliability analyses," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    10. Bai, Song & Huang, Tudi & Li, Yan-Feng & Lu, Ning & Huang, Hong-Zhong, 2023. "A probabilistic fatigue life prediction method under random combined high and low cycle fatigue load history," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    11. Teng, Da & Feng, Yun-Wen & Lu, Cheng & Liu, Jia-Qi & Chen, Jun-Yu, 2024. "Vectorial generative adversarial surrogate modeling reliability evaluation framework for engineering structural systems," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    12. Gao, Haifeng & Wang, Anjenq & Zio, Enrico & Bai, Guangchen, 2020. "An integrated reliability approach with improved importance sampling for low-cycle fatigue damage prediction of turbine disks," Reliability Engineering and System Safety, Elsevier, vol. 199(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. Gao, Hai-Feng & Wang, Yu-Hang & Li, Yang & Zio, Enrico, 2024. "Distributed-collaborative surrogate modeling approach for creep-fatigue reliability assessment of turbine blades considering multi-source uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    2. Liu, Jiaqi & Feng, Yunwen & Lu, Cheng & Fei, Chengwei, 2025. "Operational reliability assessment of complex mechanical systems with multiple failure modes: An adaptive decomposition-synchronous-coordination approach," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    3. Jia-Qi, Liu & Yun-Wen, Feng & Da, Teng & Jun-Yu, Chen & Cheng, Lu, 2023. "Operational reliability evaluation and analysis framework of civil aircraft complex system based on intelligent extremum machine learning model," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Wang, Run-Zi & Gu, Hang-Hang & Zhu, Shun-Peng & Li, Kai-Shang & Wang, Ji & Wang, Xiao-Wei & Hideo, Miura & Zhang, Xian-Cheng & Tu, Shan-Tung, 2022. "A data-driven roadmap for creep-fatigue reliability assessment and its implementation in low-pressure turbine disk at elevated temperatures," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. Zhu, Chun-Yan & Li, Zhen-Ao & Dong, Xiao-Wei & Wang, Ming & Li, Qing-Da, 2024. "Collaborative modeling-based improved moving Kriging approach for low-cycle fatigue life reliability estimation of mechanical structures," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    6. Shen, Xingkeng & Feng, Kaixuan & Xu, Heming & Wang, Guangqiang & Zhang, Yishang & Dai, Ying & Yun, Wanying, 2023. "Reliability analysis of bending fatigue life of hydraulic pipeline," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    7. Wang, Run-Zi & Gu, Hang-Hang & Liu, Yu & Miura, Hideo & Zhang, Xian-Cheng & Tu, Shan-Tung, 2023. "Surrogate-modeling-assisted creep-fatigue reliability assessment in a low-pressure turbine disc considering multi-source uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    8. Teng, Da & Feng, Yun-Wen & Lu, Cheng & Liu, Jia-Qi & Chen, Jun-Yu, 2024. "Vectorial generative adversarial surrogate modeling reliability evaluation framework for engineering structural systems," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    9. Jia-Qi, Liu & Yun-Wen, Feng & Cheng, Lu & Wei-Huang, Pan, 2024. "Decomposed-coordinated framework with intelligent extremum network for operational reliability analysis of complex system," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    10. Gunjan, Shashi Bhushan & Srinivasu, D.S. & N, Ramesh Babu, 2025. "A new approach for product reliability prediction by considering the production factory lifecycle information," Reliability Engineering and System Safety, Elsevier, vol. 258(C).
    11. Pan, Wei-Huang & Feng, Yun-Wen & Lu, Cheng & Liu, Jia-Qi, 2023. "Analyzing the operation reliability of aeroengine using Quick Access Recorder flight data," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    12. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    13. He, Yu & Ma, Yafei & Huang, Ke & Wang, Lei & Zhang, Jianren, 2024. "Digital twin Bayesian entropy framework for corrosion fatigue life prediction and calibration of bridge suspender," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    14. Wang, Zequn & Wang, Pingfeng, 2015. "A double-loop adaptive sampling approach for sensitivity-free dynamic reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 346-356.
    15. Puppo, L. & Pedroni, N. & Maio, F. Di & Bersano, A. & Bertani, C. & Zio, E., 2021. "A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    16. Campari, Alessandro & Ustolin, Federico & Alvaro, Antonio & Paltrinieri, Nicola, 2024. "Inspection of hydrogen transport equipment: A data-driven approach to predict fatigue degradation," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    17. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    18. Chen, Edward & Bao, Han & Dinh, Nam, 2024. "Evaluating the reliability of machine-learning-based predictions used in nuclear power plant instrumentation and control systems," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    19. Wen, Zhixun & Pei, Haiqing & Liu, Hai & Yue, Zhufeng, 2016. "A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 170-179.
    20. Gaspar, B. & Teixeira, A.P. & Guedes Soares, C., 2017. "Adaptive surrogate model with active refinement combining Kriging and a trust region method," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 277-291.

    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:eee:reensy:v:257:y:2025:i:pa:s0951832025000146. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.