The digitalisation of architectural heritage represents a valuable opportunity to improve the management and transfer of technical knowledge related to refurbishment and conservation practices in historic districts. Within this context, the CityGML standard has emerged as a robust framework for organising and sharing structured, multiscale, and multidisciplinary information. However, to fully meet the requirements of heritage rehabilitation planning, CityGML-based models must evolve from static data containers into dynamic decision-support tools.
This paper presents a methodology for developing a Technical Digital Model enhanced with a Decision Support System (T-DM DSS), designed to support the refurbishment and conservation process of historic buildings through the integration of technical, regulatory, and semantic knowledge. The method converts traditional refurbishment practices (such as manuals, codes of practice, and administrative constraints) into a coherent CityGML-based structure, enriched with logical rules that guide intervention choices and ensure regulatory compliance. The approach is supported by a modular workflow composed of a structured technical database, a CityGML-based model, and a web-based sharing platform with differentiated user access.
The application to the historic district of Corleto Perticara (PZ) demonstrates the system’s ability to unify fragmented data sources and provide a scalable tool for both practitioners and policymakers. Through the use of the FME visual programming environment, conditional rules were embedded to automate the classification of intervention strategies based on physical and regulatory parameters. Moreover, the use of a platform to share database contents not only ensures consistent data interpretation but also enhances transparency, interoperability, and accessibility of recovery-related information.
