IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v167y2017icp484-493.html
   My bibliography  Save this article

Sliced inverse regression-based sparse polynomial chaos expansions for reliability analysis in high dimensions

Author

Listed:
  • Pan, Qiujing
  • Dias, Daniel

Abstract

Reliability analysis requires a large number of original model evaluations, especially for high-nonlinear and high-dimensional problems. This could be computationally expensive when a single evaluation is time-consuming, finite element models for example. Metamodeling techniques have been developed for reliability assessments in order to enhance computational efficiency. Polynomial chaos expansions are widely used for metamodel buildings, but suffering from the “curse of dimensionality†. This work proposes an efficient reliability method which combines the sliced inverse regression (SIR) with sparse polynomial chaos expansions (SPCE). The SIR technique is firstly adopted to achieve a dimension reduction by finding a new input vector which reduces the dimension of the original input vector without losing the essential information of model responses. Then a SPCE metamodel is built with respect to the reduced dimensionality by means of the stepwise regression technique. An iteration algorithm is employed to select the optimal metamodel for a given size of design of experiments. Three representative examples with random variables ranging from 6 to 300 are provided for validation, which show great effectiveness and efficiency of the proposed approach, particularly for high-dimensional problems.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:167:y:2017:i:c:p:484-493
    DOI: 10.1016/j.ress.2017.06.026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095183201630864X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2017.06.026?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. Gannoun, Ali & Girard, Stephane & Guinot, Christiane & Saracco, Jerome, 2004. "Sliced inverse regression in reference curves estimation," Computational Statistics & Data Analysis, Elsevier, vol. 46(1), pages 103-122, May.
    2. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
    3. Song, Shufang & Lu, Zhenzhou & Qiao, Hongwei, 2009. "Subset simulation for structural reliability sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 658-665.
    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. 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).
    2. 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.
    3. 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).
    4. 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).
    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. Yao, Wen & Zheng, Xiaohu & Zhang, Jun & Wang, Ning & Tang, Guijian, 2023. "Deep adaptive arbitrary polynomial chaos expansion: A mini-data-driven semi-supervised method for uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    7. Dang, Chao & Xu, Jun, 2020. "Unified reliability assessment for problems with low- to high-dimensional random inputs using the Laplace transform and a mixture distribution," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    8. Nguyen, Phong T.T. & Manuel, Lance, 2024. "Uncertainty quantification in low-probability response estimation using sliced inverse regression and polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    9. Wang, Tianzhe & Chen, Zequan & Li, Guofa & He, Jialong & Liu, Chao & Du, Xuejiao, 2024. "A novel method for high-dimensional reliability analysis based on activity score and adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    10. Ding, Jiayi & Zhou, Jianfang & Cai, Wei, 2023. "An efficient variable selection-based Kriging model method for the reliability analysis of slopes with spatially variable soils," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    11. Zhou, Yicheng & Lu, Zhenzhou & Yun, Wanying, 2020. "Active sparse polynomial chaos expansion for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    12. Song Cui & Qiang Wu & James West & Jiangping Bai, 2019. "Machine learning-based microarray analyses indicate low-expression genes might collectively influence PAH disease," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-25, August.
    13. 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).
    14. Du, Weiqi & Luo, Yuanxin & Wang, Yongqin, 2019. "Time-variant reliability analysis using the parallel subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 250-257.
    15. Cheng, Jin & Wang, Jian & Wu, Xuezhou & Wang, Shuo, 2019. "An improved polynomial-based nonlinear variable importance measure and its application to degradation assessment for high-voltage transformer under imbalance data," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 175-191.

    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. Keshtegar, Behrooz & Chakraborty, Subrata, 2018. "An efficient-robust structural reliability method by adaptive finite-step length based on Armijo line search," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 195-206.
    2. Marrel, Amandine & Chabridon, Vincent, 2021. "Statistical developments for target and conditional sensitivity analysis: Application on safety studies for nuclear reactor," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. Xu, Jun & Kong, Fan, 2018. "A new unequal-weighted sampling method for efficient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 94-102.
    4. Proppe, Carsten, 2021. "Local reliability based sensitivity analysis with the moving particles method," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    5. Dawei Zhang & Weilin Li & Xiaohua Wu & Tie Liu, 2018. "An Efficient Regional Sensitivity Analysis Method Based on Failure Probability with Hybrid Uncertainty," Energies, MDPI, vol. 11(7), pages 1-19, June.
    6. Ehre, Max & Papaioannou, Iason & Straub, Daniel, 2020. "A framework for global reliability sensitivity analysis in the presence of multi-uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    7. 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).
    8. Shang, Xiaobing & Wang, Lipeng & Fang, Hai & Lu, Lingyun & Zhang, Zhi, 2024. "Active Learning of Ensemble Polynomial Chaos Expansion Method for Global Sensitivity Analysis," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    9. Saracco, Jérôme, 2005. "Asymptotics for pooled marginal slicing estimator based on SIR[alpha] approach," Journal of Multivariate Analysis, Elsevier, vol. 96(1), pages 117-135, September.
    10. 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.
    11. Matieyendou Lamboni, 2020. "Uncertainty quantification: a minimum variance unbiased (joint) estimator of the non-normalized Sobol’ indices," Statistical Papers, Springer, vol. 61(5), pages 1939-1970, October.
    12. Daniel Harenberg & Stefano Marelli & Bruno Sudret & Viktor Winschel, 2019. "Uncertainty quantification and global sensitivity analysis for economic models," Quantitative Economics, Econometric Society, vol. 10(1), pages 1-41, January.
    13. Nguyen, Phong T.T. & Manuel, Lance, 2024. "Uncertainty quantification in low-probability response estimation using sliced inverse regression and polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    14. 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).
    15. Yu, Weichao & Huang, Weihe & Wen, Kai & Zhang, Jie & Liu, Hongfei & Wang, Kun & Gong, Jing & Qu, Chunxu, 2021. "Subset simulation-based reliability analysis of the corroding natural gas pipeline," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    16. David Breitenmoser & Francesco Cerutti & Gernot Butterweck & Malgorzata Magdalena Kasprzak & Sabine Mayer, 2023. "Emulator-based Bayesian inference on non-proportional scintillation models by compton-edge probing," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    17. 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.
    18. 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).
    19. 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).
    20. Torii, André Jacomel & Novotny, Antonio André, 2021. "A priori error estimates for local reliability-based sensitivity analysis with Monte Carlo Simulation," Reliability Engineering and System Safety, Elsevier, vol. 213(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:167:y:2017:i:c:p:484-493. 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.