Seismic Vulnerability Assessment of Masonry Buildings Including the Effects of Pre-existing Differential Settlements

Existing buildings are subject to various risks threatening their capacity and stability. Masonry structures, due to their construction techniques and material properties are particularly vulnerable to exceptional actions, such as seismic excitations and ground deformations. In this context, pre-existing differential settlements can induce structural damage by altering their performance towards horizontal actions, increasing the overall vulnerability of constructions. For both the considered actions, ground settlements and seismic excitations, several studies have been conducted in literature and various approaches are available to determine the expected level of damage given a certain intensity of hazard/susceptibility. Nevertheless, multi-risk approaches and combined analysis are scarce, with several open issues. In this manuscript, a methodological framework to analyze the seismic vulnerability of masonry buildings including the effects of precedent vertical settlements is proposed. The analysis is carried out at urban scale, based on semi-empirical procedures and numerical calibrations. It involves the adoption of the macroseismic approach for seismic assessment, and the limiting tensile strain method to determine the damage of buildings subjected to settlements. FE models of different building facades are realized to calibrate the equivalent E/G ratio for each construction to be used in the empirical calculation. The numerical models are later analyzed considering nonlinear constitutive behavior, applying settlement profiles to the facades; hence, the damaged models are subjected to horizontal pushover analysis determining the structural performance considering the two combined actions. This work represents a first step toward an integrated multi-risk assessment framework; further research will focus on refining the methodology calibrating empirical indicators through extended numerical analyses for large-scale assessments.