Masonry buildings in historic centres are prone to seismic damage, due to either construction features or effects of changes over the time. Systematic studies on built heritage need to be updated, aimed at providing large number of data to identify recursive aspects affecting vulnerability and damage. In this study, over 2300 case studies (i.e., structural units/buildings) belonging to 20 villages struck by the 2016 Central Italy earthquake were analysed by a multi-level survey procedure recently upgraded at the University of Padova. The effect of damage was examined with respect to the local macroseismic intensity and computed by comparing the influence of features such as: settlements configuration, buildings aggregation, geometrical and architectural details, horizontal and vertical structural components, and constituent materials. This study lays the groundwork for new approaches to the analysis of construction parameters of existing masonry buildings, in view of their typological categorization and validation on a statistical base.

Residential masonry buildings represent a large stock among highly vulnerable structures in medium–high seismic hazard areas, often built without any anti-seismic provisions. Their rehabilitation and/or strengthening according to optimised intervention strategies is topical and may contribute to revaluating zones characterized by depopulation phenomena. In this paper, a terraced building struck by the 2016 Central Italy earthquake is analysed through a frame by macro element (FME) model. The building is composed of six two-storey units made of stone and clay block masonry walls and semi-rigid diaphragms. The numerical model was calibrated based on the damage pattern caused by the earthquake and then used to carry out parametric analyses on the strengthened conditions by simulating both one unit and the entire terrace. The effects of interventions applied to either vertical or horizontal components, both singularly and in combination, were analysed in terms of nonlinear static analyses, and quantified by a performance factor, according to the upgraded seismic code in Italy. Kinematic analyses also completed the assessment of the building. Results compared the capacity of interventions in attaining the targets defined for improvement at both local and overall levels.

The complexity of the historical built environment may be reduced by discretizing buildings in types. At urban scale, typological classifications feed vulnerability models. Several literature procedures define types by the evaluation of constructive, geometrical, and structural features, but none seems to consider improving or upgrading interventions. These are the expected performance levels prescribed by seismic codes for strengthening existing masonry buildings. Notwithstanding, the 2016 Central Italy earthquake revealed the potentially detrimental role of interventions on the seismic behaviour of a building. A new performance-based taxonomy of masonry buildings is here proposed starting from the analysis of 2306 inspected dwellings struck by that event. Building features, i.e., masonry and diaphragm types, were matched to strengthening techniques, thus obtaining a preliminary description of that sample. The empirical evidence showed the performance levels of buildings with interventions, which were graded in four categories: upgrading and improvement, downgrading and worsening depending on their compliance with the box-like behaviour. Therefore, an expansion of the coded possible performance levels was found. Those levels entered the final taxonomy of the sample, yielding 15 robust performance-based types, which were cross-validated through the analysis of their most common damage mechanisms.

Damage surveys following to the Central Italy 2016 seismic swarm have shown that retrofitting or strengthening interventions on masonry buildings may result in unexpected seismic behaviour, which may be defined as hybrid. In fact, the insertion within the masonry of single parts aimed at activating the box-like behaviour can worsen the overall response like a vulnerability added to a building's intrinsic ones. Existing procedures for post-earthquake visual assessment do not permit the definition of the behaviour of altered masonry buildings (substitution of horizontal structures, concrete curbs, injections, etc.). The article proposes a completion of the 'knowledge path' of an existing survey form adding a new part specifically dedicated to structural interventions. On-site data collection is performed by a digital survey form written in Google Forms. The analysis phase takes advantage of the features available in a Geographical Information System (GIS). The application of this survey form to about one hundred buildings in Castelsantangelo sul Nera (Macerata district, Central Italy) hit by the Oct. 26 and 30, 2016 quakes also helped to define the vulnerability classes for buildings with structural interventions. Empirical results show that either poor execution or conception of interventions can lead to the highest vulnerability classes.

Empirical data on the seismic behavior of masonry buildings are collected by technicians through rapid visual assessment procedures, i.e., by filling in forms that organize information in short answers or ticks. The resulting empirical database serves as a basis for prevention strategies, but the archiving and the post-processing of data are always a potential cause of losses and misinterpretations. New technologies are nowadays entering seismic risk analyses as a support to the usual visual approach. This paper proposes a new application for Android mobile devices that digitalizes an assessment form (MUSE-DV) able to collect damage and vulnerability data of masonry buildings in seismic areas, including information on strengthening interventions applied to buildings in the past. The client–server architecture of the app considers local devices communicating over the web with a central unit; data processing is split between them to reduce network and resource needs. This approach is convenient with thin devices, such as smartphones, and in post-disaster situations, when the mobile network may not be available. Data collected onsite can be stored in remote archives and therefore shared among technicians without affecting the integrity and consistency of the database. The whole dataset can be extracted and processed by a dedicated software for statistical and spatial analysis. The MUSE-DV procedure was validated in the area struck by the 2016 Central Italy earthquake and the app presented here was preliminarily tested onsite on the buildings of Castelsantangelo sul Nera (Macerata district); the results contributed to damage and vulnerability analyses by the means of thematic maps.