Seismic fragility curves have been introduced as key components of seismic probabilistic risk assessment studies. They express the probability of failure of mechanical structures conditional to a seismic intensity measure and must take into account various sources of uncertainties, the so-called epistemic uncertainties (i.e., coming from the uncertainty on the mechanical parameters of the structure) and the aleatory uncertainties (i.e., coming from the randomness of the seismic ground motions). For simulation-based approaches we propose a methodology to build and calibrate a Gaussian process surrogate model to estimate a family of nonparametric seismic fragility curves for a mechanical structure by propagating both the surrogate model uncertainty and the epistemic ones. Gaussian processes have indeed the main advantage to propose both a predictor and an assessment of the uncertainty of its predictions. In addition, we extend this methodology to sensitivity analysis. Global sensitivity indices such as aggregated Sobol' indices and kernel-based indices are proposed to know how the uncertainty on the seismic fragility curves is apportioned according to each uncertain mechanical parameter. This comprehensive uncertainty quantification framework is finally applied to an industrial test case consisting of a part of a piping system of a pressurized water reactor.

中文翻译：

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使用克里格法进行地震易损性曲线的不确定性量化和全局敏感性分析

地震易损性曲线已被引入作为地震概率风险评估研究的关键组成部分。它们表达了以地震烈度测量为条件的机械结构失效的概率，并且必须考虑各种不确定性来源，即所谓的认知不确定性（即来自结构机械参数的不确定性）和偶然不确定性（即来自地震地面运动的随机性）。对于基于仿真的方法，我们提出了一种建立和校准高斯过程代理模型的方法，通过传播代理模型不确定性和认知模型来估计机械结构的一系列非参数地震易损性曲线。高斯过程确实具有提出预测器及其预测不确定性评估的主要优​​势。此外，我们将此方法扩展到敏感性分析。提出了全局敏感性指数，例如聚合 Sobol 指数和基于核的指数，以了解如何根据每个不确定的力学参数分配地震易损性曲线上的不确定性。该综合不确定性量化框架最终应用于由压水堆管道系统的一部分组成的工业测试案例。