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Treatment of dependency in sensitivity analysis for industrial reliability

Abstract : Structural reliability studies use probabilistic approaches to quantify the risk of an accidental event occurring. The dependence between the random input variables of a model can have a significant impact on the results of the reliability study. This thesis contributes to the treatment of dependency in structural reliability studies. The two main topics covered in this document are the sensitivity analysis for dependent variables when the dependence is known and, as well as the assessment of a reliability risk when the dependence is unknown. First, we propose an extension of the permutation-based importance measures of the random forest algorithm towards the case of dependent data. We also adapt the Shapley index estimation algorithm, used in game theory, to take into account the index estimation error. Secondly, in the case of dependence structure being unknown, we propose a conservative estimate of the reliability risk based on dependency modelling to determine the most penalizing dependence structure. The proposed methodology is applied to an example of structural reliability to obtain a conservative estimate of the risk.
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Submitted on : Friday, September 11, 2020 - 11:55:14 AM
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  • HAL Id : tel-02936431, version 1


Nazih Benoumechiara. Treatment of dependency in sensitivity analysis for industrial reliability. Statistics [math.ST]. Sorbonne Université, 2019. English. ⟨NNT : 2019SORUS047⟩. ⟨tel-02936431⟩



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