HEIstorical project addresses critical gaps in seismic risk mitigation, environmental sustainability in historic building preservation and integration of digital tools and AI in higher education institutions (HEIs). The project proposes the implementation of a model for assessing damages in historic buildings by applying machine learning methods and data analysis using artificial intelligence for predicting damages to buildings of historical value. The data analysis using predictive methods will lead to recommendations that integrate energy, seismic, and microclimatic aspects into the restoration of buildings to predict damages for structural reinforcement, preventing potential harm and mitigating risks to buildings with historical significance.

As part of the project, 20 historic buildings in 5 transnational regions will be monitored to measure energy efficiency, microclimate, and record dynamic structural parameters of the buildings, collecting over 50,000 data measurements from installed sensors. For the Transylvania region, a database will be established with over 3,000 characteristics for 100 historic buildings, which, together with data collected from sensors installed in 4 buildings, will form the necessary database for applying machine learning algorithms. The assessment models will be integrated into several existing educational programs to reflect the latest advancements in the field, providing students with updated practical knowledge in the rehabilitation of historic buildings.

All collected data will be made available in an “open access” format, ensuring future availability for subsequent scientific research, application, and broader operational implementation of the collected data.

Project will contribute to reducing costs through optimized interventions, offering a methodological framework for renovation strategies that align with EU sustainability goals and national funding programs.