The paper describes a novel Adriseismic method for expeditious assessment of seismic risk associated with unreinforced masonry buildings. The methodology was developed for the Adriseismic project of the Interreg ADRION programme, with the aim to develop and share tools for increasing cooperation and reducing seismic risk for six participating countries within the region surrounding the Adriatic and the Ionian Seas. The method is applicable to unreinforced masonry buildings characterised by three main seismic failure mechanisms, namely masonry disintegration, out-of-plane failure, and in-plane damage/failure. Depending on the input parameters for a specific structure, the assessment yields a qualitative output that consists of the masonry quality index, the index of structural response, the level of seismic risk, and the most probable collapse mechanism. Both input and output of the method are applied in the spreadsheet form. The method has so far been applied in urban areas of participating countries in the project, including Mirandola, Italy; Kaštela, Croatia; Belgrade, Serbia. In parallel, the methodology has been validated by performing a detailed seismic assessment of more than 25 buildings, and the results have been compared with the results of the proposed expeditious method. The results show a good correlation between the two methods, for example, the structural response index obtained from the expeditious method and the capacity/demand ratio obtained from the conventional assessment method.

Most of Italy’s residential building stock predates contemporary structural safety and energy efficiency regulatory frameworks. Today, policymakers face the challenge of choosing whether to prioritise renovation or opt for demolition and reconstruction; both options carry significant socio-economic and environmental consequences and require extensive knowledge of the built heritage. However, detailed architecture-specific data remain scarce, as existing databases lack granular information. Moreover, traditional urban-level knowledge mapping approaches may be resource-intensive. To address this data gap, this study proposes a semi-automated methodology for generating graph-based digital models representing residential building floor plans. Using graph theory, floor spatial layouts are mapped into connectivity graphs and transformed into topological models. These models are enriched with functional data about spaces by assigning conditional topological rules based on node centrality metrics. The method was tested on 98 buildings in Bologna, Italy, yielding an 89.8% success rate and demonstrating its effectiveness in data-limited contexts. The resulting dataset facilitates the analysis of floor spatial configurations and the extraction of geometric attributes, laying the foundation for future analyses that will integrate machine learning techniques for functional detection and typological clustering.

Preliminary investigations and analyses for building assets’ renovation require considerable time and resources. Being able to assess, in advance and on a large scale, how to address these assets would allow reducing issues to existing buildings as a priority. This research project aims to demonstrate how a data-driven approach can help make informed decisions and better allocate available resources. Starting with a sample of 468 buildings constructed in the city of Bologna between 1945 and 1965, the study led to the creation of a comprehensive database consisting of 468 records and 211 fields, grouped into 16 macro-categories. These fields represent the catalogued characteristics for each building; 95 of them were directly surveyed, and 116 were derived from the processing performed on the first 95 attributes. Each building is associated with various types of information to provide an overall framework (e.g., technical, typological, morphological, etc.). At the end of the database development, queries were performed to assess both the construction characteristics of the building sample and the qualitative performance in terms of structural, energy, and planimetric distribution. The results provide an indication of the building and show how in-depth analyses can serve as the foundation for a decision support system (DSS).