GNGTS 2023 - Atti del 41° Convegno Nazionale

Session 2.2 GNGTS 2023 Fragility curves for Out-Of-Plane mechanisms of UnReinforced Masonry buildings in aggregate: the case of an Italian historical centre V. Cima 1 , V. Tomei 1 , E. Grande 2 , M. Imbimbo 1 1 Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino, Italy 2 Department of Engineering Science, University G. Marconi, Rome, Italy Keywords: fragility curves, unreinforced masonry building, out-of-plane mechanisms, aggregate configuration, historical centre. Introduction The buildings composing most Italian historical centres consist often of aggregates where the adjacent Structural Units (SUs) interact with each other when subjected to seismic actions. The perimeter façades of the buildings generally results particularly prone to Out-Of-Plane (OOP) mechanisms, as underlined by recent and past earthquakes (Sorrentino et al., 2019). The protection of these buildings necessarily requires a seismic vulnerability assessment that, at the level of territorial scale, can be carried out by using fragility curves. The present study proposes an approach for the derivation of fragility curves for the most probable OOP mechanisms of perimeter façades of unreinforced masonry buildings in aggregate configuration. In particular, the effect of the mutual interaction among adjacent SUs is introduced in the approach by considering the effect of frictional forces, which act at the connections between the façade wall subject to the mechanism and the adjacent SUs (Cima et al., 2021). The approach has been applied to building typologies characterizing the historical centre of Sora (Lazio region). Fragility curves for URM buildings in aggregate The proposed approach is divided into 6 steps and its application requires, as first step, the identification of the main building typologies of the area of study. Once the building typologies have been defined, for each of them the first step of the approach foresees a qualitative analysis finalized to identify the most probable OOP mechanism occurring in each building (Saccucci et al., 2021). Subsequently, based on the results of the qualitative analysis, the buildings of each typology are divided into categories having the same number of floors and the same type of most probable OOP mechanism (Cima et al., 2021). The approach moves to (step 2) with the generation of virtual populations of 3000 buildings for each building category. The