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Sensitivity Analysis in Probabilistic Structural Design: A Comparison of Selected Techniques

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  • Zdeněk Kala

    (Department of Structural Mechanics, Faculty of Civil Engineering, Brno University of Technology, 602 00 Brno, Czech Republic)

Abstract

Although more and more reliability-oriented sensitivity analysis (ROSA) techniques are now available, review and comparison articles of ROSA are absent. In civil engineering, many of the latest indices have never been used to analyse structural reliability for very small failure probability. This article aims to analyse and compare different sensitivity analysis (SA) techniques and discusses their strengths and weaknesses. For this purpose, eight selected sensitivity indices are first described and then applied in two different test cases. Four ROSA type indices are directly oriented on the failure probability or reliability index beta, and four other indices (of a different type) are oriented on the output of the limit state function. The case study and results correspond to cases under common engineering assumptions, where only two independent input variables with Gaussian distribution of the load action and the resistance are applied in the ultimate limit state. The last section of the article is dedicated to the analysis of the different results. Large differences between first-order sensitivity indices and very strong interaction effects obtained from ROSA are observed for very low values of failure probability. The obtained numerical results show that ROSA methods lack a common platform that clearly interprets the relationship of indices to their information value. This paper can help orientate in the selection of which sensitivity measure to use.

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  • Zdeněk Kala, 2020. "Sensitivity Analysis in Probabilistic Structural Design: A Comparison of Selected Techniques," Sustainability, MDPI, vol. 12(11), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:11:p:4788-:d:370192
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    References listed on IDEAS

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    1. Emanuele Borgonovo & William Castaings & Stefano Tarantola, 2011. "Moment Independent Importance Measures: New Results and Analytical Test Cases," Risk Analysis, John Wiley & Sons, vol. 31(3), pages 404-428, March.
    2. Emanuele Borgonovo & Gordon B. Hazen & Elmar Plischke, 2016. "A Common Rationale for Global Sensitivity Measures and Their Estimation," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1871-1895, October.
    3. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
    4. Emanuele Borgonovo, 2006. "Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches," Risk Analysis, John Wiley & Sons, vol. 26(5), pages 1349-1361, October.
    5. Borgonovo, E., 2007. "A new uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 771-784.
    6. Xiao, Sinan & Lu, Zhenzhou & Xu, Liyang, 2016. "A new effective screening design for structural sensitivity analysis of failure probability with the epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 1-14.
    7. Plischke, Elmar & Borgonovo, Emanuele, 2019. "Copula theory and probabilistic sensitivity analysis: Is there a connection?," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1046-1059.
    8. V. Rykov, 2018. "On steady state probabilities of renewable system with Marshal–Olkin failure model," Statistical Papers, Springer, vol. 59(4), pages 1577-1588, December.
    9. Jean-Claude Fort & Thierry Klein & Nabil Rachdi, 2016. "New sensitivity analysis subordinated to a contrast," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(15), pages 4349-4364, August.
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    Cited by:

    1. Zhiwei Bai & Hongkui Wei & Yingying Xiao & Shufang Song & Sergei Kucherenko, 2021. "A Vine Copula-Based Global Sensitivity Analysis Method for Structures with Multidimensional Dependent Variables," Mathematics, MDPI, vol. 9(19), pages 1-20, October.
    2. Hector Gibson Kinmanhon Houankpo & Dmitry Kozyrev, 2021. "Mathematical and Simulation Model for Reliability Analysis of a Heterogeneous Redundant Data Transmission System," Mathematics, MDPI, vol. 9(22), pages 1-16, November.
    3. Zdeněk Kala, 2022. "Quantification of Model Uncertainty Based on Variance and Entropy of Bernoulli Distribution," Mathematics, MDPI, vol. 10(21), pages 1-19, October.
    4. Vladimir Rykov & Nika Ivanova & Dmitry Kozyrev, 2021. "Application of Decomposable Semi-Regenerative Processes to the Study of k -out-of- n Systems," Mathematics, MDPI, vol. 9(16), pages 1-23, August.
    5. Zdeněk Kala, 2021. "New Importance Measures Based on Failure Probability in Global Sensitivity Analysis of Reliability," Mathematics, MDPI, vol. 9(19), pages 1-20, September.

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