  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 the inherent uncertainties in such studies, 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 non-parametric 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 in a part of a piping system of a Pressurized
Water Reactor.
