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FORM Sensitivities to Distribution Parameters with the Nataf Transformation

In: Risk and Reliability Analysis: Theory and Applications

Author

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  • Jean-Marc Bourinet

    (Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal)

Abstract

The Nataf transformation has been proven very useful in reliability assessment when marginal distributions are statistically known and linear correlation is sufficient for modeling the dependence between random inputs. Under the assumption that the use of FORM is appropriate for the problem of interest, it is often of importance to quantify how the FORM solution is sensitive to the distribution parameters of the random inputs. Such information can be exploited in different contexts including optimal design under uncertainty. This chapter describes how sensitivities to marginal distribution parameters and linear correlation can be assessed numerically in the context of FORM based on the Nataf transformation. The emphasis is on the accuracy of such sensitivities with no other approximations than the one due to numerical integration. In the presented examples, the accuracy of these sensitivities is assessed w.r.t. reference solutions. The sensitivity to correlation brings useful information which are complementary to those w.r.t. marginal distribution parameters. High sensitivities may be detected such as illustrated in the context of stochastic crack growth based on the Virkler data set.

Suggested Citation

  • Jean-Marc Bourinet, 2017. "FORM Sensitivities to Distribution Parameters with the Nataf Transformation," Springer Series in Reliability Engineering, in: Paolo Gardoni (ed.), Risk and Reliability Analysis: Theory and Applications, pages 277-302, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-319-52425-2_12
    DOI: 10.1007/978-3-319-52425-2_12
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    Cited by:

    1. Sarazin, Gabriel & Morio, Jérôme & Lagnoux, Agnès & Balesdent, Mathieu & Brevault, Loïc, 2021. "Reliability-oriented sensitivity analysis in presence of data-driven epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Chabridon, Vincent & Balesdent, Mathieu & Bourinet, Jean-Marc & Morio, Jérôme & Gayton, Nicolas, 2018. "Reliability-based sensitivity estimators of rare event probability in the presence of distribution parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 164-178.

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