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The seismic assessment of existing masonry infilled reinforced concrete (RC) building portfolios is a highly relevant issue in Italy and other Mediterranean countries, particularly in regions where a large part of the built environment was not designed according to modern seismic codes. In the past, masonry infills were generally considered as non-structural elements and were not accounted for in the design process. However, the results of experimental tests and past earthquake evidence demonstrated the influence of masonry infills on the global and local behaviour of RC buildings. Furthermore, the acknowledgment of different construction practices points out the significant variability affecting the masonry infills in terms of material properties, thickness, presence of openings and manufacturing techniques. This paper deals with the large-scale performance analysis of existing masonry infilled RC building portfolios accounting for the uncertainty related to the variability in the mechanical properties and the modelling of masonry infills. To accomplish this goal, a fully integrated building stock, representative of masonry infilled RC buildings designed according to the Italian codes in force between 1960 and 1980, was developed, using a simulated design approach. Statistical data and results of experimental tests were used to identify the main masonry infill typologies adopted in the Italian context. Detailed numerical models, whose prediction capability was appraised through the results of cyclic quasi-static tests, were developed to estimate the lateral response of the analysed RC buildings. Different response parameters, which can be easily implemented in seismic risk assessment frameworks, were considered and quantified, at the portfolio level, to understand the uncertainty propagation and impact of the masonry infills’ variability on the expected performance of existing RC buildings. The results demonstrated that the variability related to the definition and modelling of the masonry infills induces significant dispersion, with respect to the mean, on the lateral response of different classes of buildings within a given portfolio. Moreover, the provided parameters for multi-linear idealised capacity curves can be used in simplified seismic fragility and risk models for existing buildings, including a proper estimate of the associated uncertainty, leading to a more accurate quantification of economic losses.

Several research contributions stemmed from the increasing interest in progressive collapse-resistant design and assessment of structures, which was driven by extreme events all around the world. Most of studies focused on the robustness of steel and reinforced concrete (RC) structures, highlighting a knowledge gap on the progressive collapse resistance of framed buildings with partially encased (PE) continuous composite beams, which are an attractive solution compared to, for instance, their cast-in-place RC counterparts. This paper presents the main outcomes of a numerical investigation that was aimed at evaluating/exploring the vertical load-carrying capacity of framed buildings with PE beams after column loss. A numerical fibre-based model was developed and successfully validated against experimental data on two full-scale composite beam specimens, indicating a high accuracy level of the models with displacement-based fibre elements. Then, a wide set of framed building structures with PE beams was generated according to Eurocodes, evaluating their progressive collapse resistance through the alternate load path method. Pushdown analysis was carried out to investigate the gravity load capacity under increasing downward displacement at the location of the removed column. Analysis results outline a huge sensitivity of progressive collapse resistance to the beam type (i.e. PE versus RC), beam depth and span length, with minor influence of longitudinal reinforcement in the case of PE beams. The peak load capacity under column loss was characterised at several limit states corresponding to increasing levels of damage, allowing the proposal of multiple regression models for progressive collapse design and assessment.