IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v225y2022ics0951832022002769.html
   My bibliography  Save this article

Adaptive structural reliability analysis method based on confidence interval squeezing

Author

Listed:
  • Chen, Zequan
  • Li, Guofa
  • He, Jialong
  • Yang, Zhaojun
  • Wang, Jili

Abstract

Kriging-based adaptive structural reliability analysis methods can replace the structural performance function and perform reliability analysis accurately and efficiently by using appropriate learning functions. In this context, a novel adaptive structural reliability analysis strategy, namely, confidence interval squeezing (CIS) method, is presented in this paper. After obtaining the confidence interval of the failure probability on the basis of the given significance level α, CIS considers squeezing the confidence interval at the highest speed possible to improve the accuracy of estimating the failure probability. CIS strives to improve the estimation accuracy of failure probability rapidly rather than paying too much attention to the state of a single candidate sample, which is the primary difference between the CIS method and other learning functions. In addition, a new global convergence condition is proposed based on the confidence interval of failure probability. Considering the difficulty of direct application of CIS, three variants of the CIS method, namely, sCIS method for sequence additions, pCIS method for parallel additions based on a clustering algorithm, and improved pCIS (ipCIS) method, are proposed. Several examples are used to demonstrate that each CIS method can handle the complex limit state function and the engineering problem of implicit functions efficiently and accurately.

Suggested Citation

  • Chen, Zequan & Li, Guofa & He, Jialong & Yang, Zhaojun & Wang, Jili, 2022. "Adaptive structural reliability analysis method based on confidence interval squeezing," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
  • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002769
    DOI: 10.1016/j.ress.2022.108639
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022002769
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2022.108639?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. Sun, Zhili & Wang, Jian & Li, Rui & Tong, Cao, 2017. "LIF: A new Kriging based learning function and its application to structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 152-165.
    2. Chen, Junhua & Chen, Longmiao & Qian, Linfang & Chen, Guangsong & Zhou, Shijie, 2022. "Time-dependent kinematic reliability analysis of gear mechanism based on sequential decoupling strategy and saddle-point approximation," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    3. Zhang, Xufang & Wang, Lei & Sørensen, John Dalsgaard, 2019. "REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 440-454.
    4. Li, Min & Wang, Ruo-Qian & Jia, Gaofeng, 2020. "Efficient dimension reduction and surrogate-based sensitivity analysis for expensive models with high-dimensional outputs," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    5. Wang, Zeyu & Shafieezadeh, Abdollah, 2019. "REAK: Reliability analysis through Error rate-based Adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 33-45.
    6. Teixeira, Rui & Nogal, Maria & O’Connor, Alan & Martinez-Pastor, Beatriz, 2020. "Reliability assessment with density scanned adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    7. Echard, B. & Gayton, N. & Lemaire, M. & Relun, N., 2013. "A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 232-240.
    8. 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).
    9. 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.
    10. Chen, Zequan & Li, Guofa & He, Jialong & Yang, Zhaojun & Wang, Jili, 2022. "A new parallel adaptive structural reliability analysis method based on importance sampling and K-medoids clustering," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    11. Pedroni, N. & Zio, E. & Apostolakis, G.E., 2010. "Comparison of bootstrapped artificial neural networks and quadratic response surfaces for the estimation of the functional failure probability of a thermal–hydraulic passive system," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 386-395.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Yang, Bin & Yang, Wenyu, 2023. "Modular approach to kinematic reliability analysis of industrial robots," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Yang, Chen & Lu, Wanze & Xia, Yuanqing, 2023. "Reliability-constrained optimal attitude-vibration control for rigid-flexible coupling satellite using interval dimension-wise analysis," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. Zeng, Chen-dong & Qiu, Zhi-cheng & Zhang, Fen-hua & Zhang, Xian-min, 2023. "Error modelling and motion reliability analysis of a multi-DOF redundant parallel mechanism with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. Li, Guofa & Wang, Tianzhe & Chen, Zequan & He, Jialong & Wang, Xiaoye & Du, Xuejiao, 2023. "RBIK-SS: A parallel adaptive structural reliability analysis method for rare failure events," Reliability Engineering and System Safety, Elsevier, vol. 239(C).

    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. Chen, Zequan & Li, Guofa & He, Jialong & Yang, Zhaojun & Wang, Jili, 2022. "A new parallel adaptive structural reliability analysis method based on importance sampling and K-medoids clustering," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Dang, Chao & Wei, Pengfei & Faes, Matthias G.R. & Valdebenito, Marcos A. & Beer, Michael, 2022. "Parallel adaptive Bayesian quadrature for rare event estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    3. Wang, Jinsheng & Xu, Guoji & Li, Yongle & Kareem, Ahsan, 2022. "AKSE: A novel adaptive Kriging method combining sampling region scheme and error-based stopping criterion for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. Teixeira, Rui & Martinez-Pastor, Beatriz & Nogal, Maria & O’Connor, Alan, 2021. "Reliability analysis using a multi-metamodel complement-basis approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    5. Zhang, Jinhao & Gao, Liang & Xiao, Mi, 2020. "A composite-projection-outline-based approximation method for system reliability analysis with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    6. Teixeira, Rui & Nogal, Maria & O’Connor, Alan & Martinez-Pastor, Beatriz, 2020. "Reliability assessment with density scanned adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    7. Yi, Jiaxiang & Cheng, Yuansheng & Liu, Jun, 2022. "A novel fidelity selection strategy-guided multifidelity kriging algorithm for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    8. Wang, Jian & Sun, Zhili & Cao, Runan, 2021. "An efficient and robust Kriging-based method for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    9. Chen, Weidong & Xu, Chunlong & Shi, Yaqin & Ma, Jingxin & Lu, Shengzhuo, 2019. "A hybrid Kriging-based reliability method for small failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 31-41.
    10. 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).
    11. Song, Kunling & Zhang, Yugang & Shen, Linjie & Zhao, Qingyan & Song, Bifeng, 2021. "A failure boundary exploration and exploitation framework combining adaptive Kriging model and sample space partitioning strategy for efficient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2020. "A system active learning Kriging method for system reliability-based design optimization with a multiple response model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    13. Wang, Jinsheng & Xu, Guoji & Yuan, Peng & Li, Yongle & Kareem, Ahsan, 2024. "An efficient and versatile Kriging-based active learning method for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    14. Xiong, Yifang & Sampath, Suresh, 2021. "A fast-convergence algorithm for reliability analysis based on the AK-MCS," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    15. 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).
    16. Cui, Da & Wang, Guoqiang & Lu, Yanpeng & Sun, Kangkang, 2020. "Reliability design and optimization of the planetary gear by a GA based on the DEM and Kriging model," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    17. Bolin Liu & Liyang Xie, 2020. "An Improved Structural Reliability Analysis Method Based on Local Approximation and Parallelization," Mathematics, MDPI, vol. 8(2), pages 1-13, February.
    18. Li, Junxiang & Chen, Jianqiao, 2019. "Solving time-variant reliability-based design optimization by PSO-t-IRS: A methodology incorporating a particle swarm optimization algorithm and an enhanced instantaneous response surface," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    19. Jiang, Chen & Qiu, Haobo & Yang, Zan & Chen, Liming & Gao, Liang & Li, Peigen, 2019. "A general failure-pursuing sampling framework for surrogate-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 47-59.
    20. 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).

    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:225:y:2022:i:c:s0951832022002769. 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.