Reliability-oriented sensitivity analysis in presence of data-driven epistemic uncertainty
针对小样本数据估计输入联合分布导致的认知不确定性，提出一种面向可靠性的敏感性分析方法，衡量边缘分布和连接函数建模对失效概率的影响，并通过仿真和实际案例验证。
Reliability assessment in presence of epistemic uncertainty leads to consider the failure probability as a quantity depending on the state of knowledge about uncertain input parameters. The input joint distribution is often learnt from a small-sized dataset provided by operating experience. The computed failure probability depends on the estimated marginal distributions and the estimated copula distribution. This paper develops a reliability-oriented sensitivity analysis procedure in order to measure the influence exerted by the data-driven modeling of both the margins and the copula. The proposed methodology is validated for both deterministic and stochastic reliability methods through an extensive simulation study including several analytical performance functions as well as a real-life simulation code dealing with the buckling of a laminated composite plate .