Abstract. Fragility curves (FCs) are key tools for seismic probabilistic safety assessments that are performed at the level of the nuclear power plant (NPP). These statistical methods relate the probabilistic seismic hazard loading at the given site to the required performance of the NPP safety functions. In the present study, we investigate how the tools of non-stationary extreme value analysis can be used to model in a flexible manner the tail behaviour of the engineering demand parameter as a function of the considered intensity measure. We focus the analysis on the dynamic response of an anchored steam line and of a supporting structure under seismic solicitations. The failure criterion is linked to the exceedance of the maximum equivalent stress at a given location of the steam line. A series of three-component ground-motion records (∼300) were applied at the base of the model to perform non-linear time history analyses. The set of numerical results was then used to derive a FC, which relates the failure probability to the variation in peak ground acceleration (PGA). The probabilistic model of the FC is selected via information criteria completed by diagnostics on the residuals, which support the choice of the generalised extreme value (GEV) distribution (instead of the widely used log-normal model). The GEV distribution is here non-stationary, and the relationships of the GEV parameters (location, scale and shape) are established with respect to PGA using smooth non-linear models. The procedure is data-driven, which avoids the introduction of any a priori assumption on the shape or form of these relationships. To account for the uncertainties in the mechanical and geometrical parameters of the structures (elastic stiffness, damping, pipeline thicknesses, etc.), the FC is further constructed by integrating these uncertain parameters. A penalisation procedure is proposed to set to zero the variables of little influence in the smooth non-linear models. This enables us to outline which of these parametric uncertainties have negligible influence on the failure probability as well as the nature of the influence (linear, non-linear, decreasing, increasing, etc.) with respect to each of the GEV parameters.

Historical seismicity is mainly defined from historical sources which are not always available. Yet historical buildings are an unique opportunity to record and study effects of past earthquakes at a given place. An innovative methodology is defined to improve knowledge of local historical seismicity. Such a methodology is based on an interdisciplinary approach combining : analysis of historical sources, stratigraphic analysis and structural analysis of an historical building. The church of Sant'Agata del Mugello (Italy, Tuscany) is considered as a case of study. The stratigraphic analysis is performed by identifying the repairs using the RECAP methodology. 80 repairs units using 13 building techniques are identified in the church. The identified repairs are associated with unknown events, earthquakes or routine reconstructions. When post-earthquake reconstructions are found, damage mechanisms are associated with them. 13 constructive phases of the church have been traced combining stratigraphic analysis and historical sources. A proto-church was built before 948 A.D. and is nowadays below the current one. The first phase of the current church appears between the 9 th and the 12 th century. A significant event of unknown origin occurred during the 12 th century which probably led to an important collapse and then a significant reconstruction of the church. The church is then deeply affected by the 1542 seismic event (epicentral macroseismic intensity 9, deduced magnitude 6.02) which resulted in the collapse of the upper part of the bell tower and the two lateral chapels as well as the overturning of the front wall and of the two lateral walls of the nave. The 1611 seismic event (epicentral macroseismic intensity 7, deduced magnitude 5.1) damaged the upper part of the bell tower as described in historical records. In spite of the confirmed occurrence of seismic events in the area from the middle of the 17 th century and the beginning of the 20 th century, no information relating seismic damage of the church has been found in historical records nor in the startigraphic analysis. The most important earthquake which struck the area on June 19th, 1919, produced only some small cracks in the church (magnitude 6.38).

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In nuclear applications, fragility curves are an essential element of the seismic probabilistic safety assessment that is performed at the level of the power plant. They are required to account for the aleatory randomness and the epistemic uncertainty generated by various sources of variability, such as the representation of the seismic input by intensity measures, the assumptions in the structural model (e.g., mechanical or geometrical parameters) and the confidence in the statistical estimation of the fragility parameters (i.e., related to number of data points used). Therefore, this study investigates the relative contributions of such variables to the dispersion of the resulting fragility functions, while ensuring the separation between aleatory and epistemic uncertainty sources, as advocated by the standards in effect in the nuclear industry. To this end, vector-valued fragility functions, based on two intensity measures, are also investigated: it appears that they allow for a partial transfer from the record-to-record variability to an epistemic uncertainty component that is related to the description of the seismic loading given the hazard at the studied site. The proposed uncertainty decomposition is applied to the fragility assessment of the main steam line of a nuclear reactor: the total dispersion of the resulting fragility models is then decomposed into different aleatory and epistemic components. Although it is found that vector-valued intensity measures contribute to a significant part of the total dispersion, the uncertainty due to the variability of mechanical and geometrical parameters appears to be even larger.

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