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The importance measure on the non-probabilistic reliability index of uncertain structures

Author

Listed:
  • Guijie Li
  • Zhenzhou Lu
  • Longfei Tian
  • Jia Xu

Abstract

In the non-probabilistic uncertainty structural analysis, the input uncertain variables of it, such as loads and material properties, will be propagated to the output responses, which include the displacement, stress and compliance, etc. To measure the effect of these non-probabilistic input variables on the output response, two new uncertainty importance measures on the non-probabilistic reliability index are discussed. For the linear limit state function, the analytical solutions of the importance measures are derived. To reduce computational effort, the discretization method and the surrogate model method are presented to calculate the two importance measures in case of the non-linear limit state. Finally, four examples demonstrate that the proposed importance measures can effectively describe the effect of the input variables on the reliability of the structure system, and the established methods can effectively obtain the two importance measures.

Suggested Citation

  • Guijie Li & Zhenzhou Lu & Longfei Tian & Jia Xu, 2013. "The importance measure on the non-probabilistic reliability index of uncertain structures," Journal of Risk and Reliability, , vol. 227(6), pages 651-661, December.
    • Handle: RePEc:sae:risrel:v:227:y:2013:i:6:p:651-661
    DOI: 10.1177/1748006X13489069
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    References listed on IDEAS

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    1. Borgonovo, E., 2007. "A new uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 771-784.
    2. Ronald L. Iman, 1987. "A Matrix‐Based Approach to Uncertainty and Sensitivity Analysis for Fault Trees," Risk Analysis, John Wiley & Sons, vol. 7(1), pages 21-33, March.
    3. Yu, W. & Harris, T.J., 2009. "Parameter uncertainty effects on variance-based sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 596-603.
    4. Liu, Qiao & Homma, Toshimitsu, 2009. "A new computational method of a moment-independent uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1205-1211.
    5. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
    6. Emanuele Borgonovo, 2006. "Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches," Risk Analysis, John Wiley & Sons, vol. 26(5), pages 1349-1361, October.
    7. Ronald L. Iman & Stephen C. Hora, 1990. "A Robust Measure of Uncertainty Importance for Use in Fault Tree System Analysis," Risk Analysis, John Wiley & Sons, vol. 10(3), pages 401-406, September.
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    Cited by:

    1. 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.

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