The main focus of this paper is the most recent phase of a large research project that has studied several wooden roof structures in the area of Bologna, belonging to a set of important historical buildings, all dating back to the 16th and 18th centuries. In particular, the behavior of the wooden trusses that support pitched roofs is analyzed, according to a methodological approach, based on generative algorithms that can help researchers and technicians to improve the comprehension of wooden structures’ behavior during their entire lifespan. While all the previous case studies concerned churches, this latest step extends the survey to the roofing system of the Municipal Theater of Bologna, which has a span of approximately 25 m. The core of the process concerns the automatic transformation of the point cloud into 3D models using parametric modeling tools, such as Grasshopper generative algorithms. Following this workflow, it is possible to speed up the creation of different truss models by changing only a few input parameters. This updating of the research protocol automatically creates a Building Information Modeling (BIM) model and a calculation model for the wooden trusses to perform a structural stress analysis by linking Grasshopper tools with Dynamo-Revit features. The procedure that has been developed from previous studies is still evolving and aims to speed up the modeling procedure and introduce new tools and methods for interpreting the functioning of these structural elements when surveyed through terrestrial laser scanning (TLS) devices.

Europe has numerous historic buildings that need to become more energy-efficient, which need permanent maintenance and refurbishment to fulfill sustainability and use requirements. Asset owners and asset managers need to adopt new strategies to protect listed buildings while optimizing costs and benefits during their life cycle. In this sense, the digital transition proves to be a moment to seize for opening new scenarios. The Digital Twin paradigm promises to be valuable for enabling the sustainable knowledge, conservation, restoration, and management of built assets and solving the dilemma about protecting the architectural identity of these buildings while adapting them to the functional and performance requirements dictated by the regulatory framework. This study proposes a workflow that integrates Heritage Building Information Modeling (HBIM) and Building Performance Simulation (BPS) tools for data-driving the energy improvement of Italian listed modern buildings built between the 1920s and 1960s. After acquiring information about the building, the HBIM model and the Building Energy Model (BEM) are realized based on the International Foundation Classes (IFC) standard. Energy intervention measures are defined, construction costs are computed, and benefits during the intervention life cycle are predicted in thermal demand. Finally, an expeditious multi-criteria analysis allows for comparing different intervention combinations and indicating the optimal solution for the energy improvement of the building concerning energy, economic, and financial issues. These outcomes represent the first step towards realizing a dynamic, accessible, and sharable Digital Twin.

Adapting outdated building stocks’ operations to meet current environmental and economic demands poses significant challenges that, to be faced, require a shift toward digitalization in the architecture, engineering, construction, and operation sectors. Digital tools capable of acquiring, structuring, sharing, processing, and visualizing built assets’ data in the form of knowledge need to be conceptualized and developed to inform asset managers in decision-making and strategic planning. This paper explores how building information modeling and building performance simulation technologies can be integrated into digital decision support systems (DSS) to make building data accessible and usable by non-digital expert operators through user-friendly services. The method followed to develop the digital DSS is illustrated and then demonstrated with a simulation-based application conducted on the heritage case study of the Faculty of Engineering in Bologna, Italy. The analysis allows insights into the building’s energy performance at the space and hour scale and explores its relationship with the planned occupancy through a data visualization approach. In addition, the conceptualization of the DSS within a digital twin vision lays the foundations for future extensions to other technologies and data, including, for example, live sensor measurements, occupant feedback, and forecasting algorithms.

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.

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