IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v222y2022ics0951832022000862.html
   My bibliography  Save this article

Hybrid active learning method for non-probabilistic reliability analysis with multi-super-ellipsoidal model

Author

Listed:
  • Hong, Linxiong
  • Li, Huacong
  • Fu, Jiangfeng
  • Li, Jia
  • Peng, Kai

Abstract

Non-probabilistic reliability analysis is of great importance in both reliability measure and reliability based design, the efficiency and precision of non-probabilistic reliability analysis, as well as uncertainty quantification, have attracted great attention currently. In this study, considering the coexistence of correlated and independent uncertain-but-bounded variables in engineering applications, a multi-super-ellipsoidal model is used to quantify the uncertainties. Furthermore, inspired by both “norm†based reliability index and “volume-ratio†based reliability index, a hybrid non-probabilistic reliability index is derived to measure the reliability extent more accurately and intuitively. To improve the accuracy and efficiency of solving the hybrid non-probabilistic reliability index, an effective Kriging based hybrid active learning method (HALM) is further developed. Finally, four examples are used to verify the effectiveness and robustness of the proposed HALM. The results show that the selection of multi-super-ellipsoidal model has a certain effect on the estimation of reliability index. Compared with the sampling-based analysis method, HALM presents better performance in terms of the trade-of between in computational efficiency and accuracy.

Suggested Citation

  • Hong, Linxiong & Li, Huacong & Fu, Jiangfeng & Li, Jia & Peng, Kai, 2022. "Hybrid active learning method for non-probabilistic reliability analysis with multi-super-ellipsoidal model," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
  • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000862
    DOI: 10.1016/j.ress.2022.108414
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022000862
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2022.108414?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. Chang, Qi & Zhou, Changcong & Wei, Pengfei & Zhang, Yishang & Yue, Zhufeng, 2021. "A new non-probabilistic time-dependent reliability model for mechanisms with interval uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Mara, Thierry A. & Becker, William E., 2021. "Polynomial chaos expansion for sensitivity analysis of model output with dependent inputs," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. Keshtegar, Behrooz & Kisi, Ozgur, 2018. "RM5Tree: Radial basis M5 model tree for accurate structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 49-61.
    4. Lei Wang & Xiaojun Wang & Ruixing Wang & Xiao Chen, 2015. "Time-Dependent Reliability Modeling and Analysis Method for Mechanics Based on Convex Process," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-16, June.
    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. Guo, Tiexin & Wang, Hongji & Li, Jinglai & Wang, Hongqiao, 2024. "Sampling-based adaptive design strategy for failure probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Zhang, Kun & Chen, Ning & Zeng, Peng & Liu, Jian & Beer, Michael, 2022. "An efficient reliability analysis method for structures with hybrid time-dependent uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 228(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. Wang, Zihan & Daeipour, Mohamad & Xu, Hongyi, 2023. "Quantification and propagation of Aleatoric uncertainties in topological structures," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    2. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2021. "A Kriging-assisted sampling method for reliability analysis of structures with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    3. Chunyan, Ling & Jingzhe, Lei & Way, Kuo, 2022. "Bayesian support vector machine for optimal reliability design of modular systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    4. Huang, Peng & Li, He & Gu, Yingkui & Qiu, Guangqi, 2024. "An extended moment-based trajectory accuracy reliability analysis method of robot manipulators with random and interval uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    5. Roy, Atin & Chakraborty, Subrata, 2022. "Reliability analysis of structures by a three-stage sequential sampling based adaptive support vector regression model," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    6. Shang, Xiaobing & Su, Li & Fang, Hai & Zeng, Bowen & Zhang, Zhi, 2023. "An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    7. Palar, Pramudita Satria & Zuhal, Lavi Rizki & Shimoyama, Koji, 2023. "Enhancing the explainability of regression-based polynomial chaos expansion by Shapley additive explanations," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    8. Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    9. 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).
    10. Shang, Yue & Nogal, Maria & Teixeira, Rui & Wolfert, A.R. (Rogier) M., 2024. "Extreme-oriented sensitivity analysis using sparse polynomial chaos expansion. Application to train–track–bridge systems," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    11. Kröker, Ilja & Oladyshkin, Sergey, 2022. "Arbitrary multi-resolution multi-wavelet-based polynomial chaos expansion for data-driven uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    12. Zhang, Jinhao & Xiao, Mi & Gao, Liang, 2019. "An active learning reliability method combining Kriging constructed with exploration and exploitation of failure region and subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 90-102.
    13. Xu, Hui & Grigoriu, Mircea D. & Gurley, Kurtis R., 2023. "A novel surrogate for extremes of random functions," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    14. Zhang, Ruijing & Dai, Hongzhe, 2022. "A non-Gaussian stochastic model from limited observations using polynomial chaos and fractional moments," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    15. Ballester-Ripoll, Rafael & Leonelli, Manuele, 2022. "Computing Sobol indices in probabilistic graphical models," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    16. Jiang, Chen & Qiu, Haobo & Gao, Liang & Wang, Dapeng & Yang, Zan & Chen, Liming, 2020. "EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    17. Zhang, Kun & Chen, Ning & Zeng, Peng & Liu, Jian & Beer, Michael, 2022. "An efficient reliability analysis method for structures with hybrid time-dependent uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    18. Rana Muhammad Adnan & Salim Heddam & Zaher Mundher Yaseen & Shamsuddin Shahid & Ozgur Kisi & Binquan Li, 2020. "Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches," Sustainability, MDPI, vol. 13(1), pages 1-21, December.
    19. Abdollahi, Azam & Amini, Ali & Hariri-Ardebili, Mohammad Amin, 2022. "An uncertainty-aware dynamic shape optimization framework: Gravity dam design," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    20. Jian Wang & Xiang Gao & Zhili Sun, 2021. "An Importance Sampling Framework for Time-Variant Reliability Analysis Involving Stochastic Processes," Sustainability, MDPI, vol. 13(14), pages 1-16, July.

    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:222:y:2022:i:c:s0951832022000862. 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.