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Seismic fragility curves have been introduced as key components of SeismicProbabilistic Risk Assessment studies. They express the probability of failureof mechanical structures conditional to a seismic intensity measure and musttake into account the inherent uncertainties in such studies, the so-calledepistemic uncertainties (i.e. coming from the uncertainty on the mechanicalparameters of the structure) and the aleatory uncertainties (i.e. coming fromthe randomness of the seismic ground motions). For simulation-based approacheswe propose a methodology to build and calibrate a Gaussian process surrogatemodel to estimate a family of non-parametric seismic fragility curves for amechanical structure by propagating both the surrogate model uncertainty andthe epistemic ones. Gaussian processes have indeed the main advantage topropose both a predictor and an assessment of the uncertainty of itspredictions. In addition, we extend this methodology to sensitivity analysis.Global sensitivity indices such as aggregated Sobol indices and kernel-basedindices are proposed to know how the uncertainty on the seismic fragilitycurves is apportioned according to each uncertain mechanical parameter. Thiscomprehensive Uncertainty Quantification framework is finally applied to anindustrial test case consisting in a part of a piping system of a PressurizedWater Reactor.

Uncertainty quantification and global sensitivity analysis of seismic fragility curves using kriging