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Derivative-Variance Hybrid Global Sensitivity Measure with Optimal Sampling Method Selection

Author

Listed:
  • Jiacheng Liu

    (School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430205, China)

  • Haiyun Liu

    (Automotive Institute, Wuhan Technical College of Communication, Wuhan 430065, China)

  • Cong Zhang

    (School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430205, China)

  • Jiyin Cao

    (School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430205, China)

  • Aibo Xu

    (Wuhan Fiberhome Technical Services Co., Ltd., Wuhan 430205, China)

  • Jiwei Hu

    (Wuhan Fiberhome Technical Services Co., Ltd., Wuhan 430205, China)

Abstract

This paper proposes a derivative-variance hybrid global sensitivity measure with optimal sampling method selection. The proposed sensitivity measure is as computationally efficient as the derivative-based global sensitivity measure, which also serves as the conservative estimation of the corresponding variance-based global sensitivity measure. Moreover, the optimal sampling method for the proposed sensitivity measure is studied. In search of the optimal sampling method, we investigated the performances of six widely used sampling methods, namely Monte Carlo sampling, Latin hypercube sampling, stratified sampling, Latinized stratified sampling, and quasi-Monte Carlo sampling using the Sobol and Halton sequences. In addition, the proposed sensitivity measure is validated through its application to a rural bridge.

Suggested Citation

  • Jiacheng Liu & Haiyun Liu & Cong Zhang & Jiyin Cao & Aibo Xu & Jiwei Hu, 2024. "Derivative-Variance Hybrid Global Sensitivity Measure with Optimal Sampling Method Selection," Mathematics, MDPI, vol. 12(3), pages 1-15, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:396-:d:1326783
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    References listed on IDEAS

    as
    1. Kucherenko, S. & Rodriguez-Fernandez, M. & Pantelides, C. & Shah, N., 2009. "Monte Carlo evaluation of derivative-based global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1135-1148.
    2. Shields, Michael D. & Zhang, Jiaxin, 2016. "The generalization of Latin hypercube sampling," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 96-108.
    3. Sobol’, I.M. & Kucherenko, S., 2009. "Derivative based global sensitivity measures and their link with global sensitivity indices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(10), pages 3009-3017.
    4. Sudret, B. & Mai, C.V., 2015. "Computing derivative-based global sensitivity measures using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 241-250.
    5. 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.
    6. Andrea Saltelli, 2002. "Sensitivity Analysis for Importance Assessment," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 579-590, June.
    Full references (including those not matched with items on IDEAS)

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