The advancement of digital technologies is radically transforming the ways in which historical and architectural heritage is studied, preserved and passed on to future generations. This work proposes an integrated methodology for the digital reconstruction and management of historical monuments, based on the integrated use of Heritage Building Information Modeling (HBIM), Digital Twin (DT), IoT sensors and Machine Learning (ML) algorithms. Through the creation of high-fidelity digital models, updated in real time through environmental monitoring systems, it is possible to obtain a dynamic and predictive vision of the state of health of historical structures. The case study of the Leproso Bridge in Benevento, constitutes a concrete example of the application of this methodology: starting from surveys performed with terrestrial laser scanners and drone photogrammetry, an HBIM model was generated and subsequently integrated with data from environmental sensors, to develop a functional DT for the simulation of evolutionary scenarios, both historical and future. This digital platform makes it possible to visualise the transformations undergone by the artefact over time, monitor its current state, and predict structural degradation phenomena through automated analyses. In addition to its contribution in terms of conservation, the proposed approach represents an innovative educational resource. Students and teachers are involved in immersive, interdisciplinary learning experiences that combine art history, structural engineering, digital modelling and materials science. The interactive and data-driven environment fosters the development of transversal skills such as critical thinking, collaboration between disciplines and the conscious use of technology in heritage conservation. In conclusion, the combination of HBIM, DT, IoT and AI emerges as a powerful tool for both research and conservation design, as well as for historical memory education in a sustainable and participatory perspective
Reconstructing Historical Monuments and Environments: A Digital Approach to Education
Casillo M.;Cecere L.;Colace F.;Lorusso A.;Pellegrino M.
2025
Abstract
The advancement of digital technologies is radically transforming the ways in which historical and architectural heritage is studied, preserved and passed on to future generations. This work proposes an integrated methodology for the digital reconstruction and management of historical monuments, based on the integrated use of Heritage Building Information Modeling (HBIM), Digital Twin (DT), IoT sensors and Machine Learning (ML) algorithms. Through the creation of high-fidelity digital models, updated in real time through environmental monitoring systems, it is possible to obtain a dynamic and predictive vision of the state of health of historical structures. The case study of the Leproso Bridge in Benevento, constitutes a concrete example of the application of this methodology: starting from surveys performed with terrestrial laser scanners and drone photogrammetry, an HBIM model was generated and subsequently integrated with data from environmental sensors, to develop a functional DT for the simulation of evolutionary scenarios, both historical and future. This digital platform makes it possible to visualise the transformations undergone by the artefact over time, monitor its current state, and predict structural degradation phenomena through automated analyses. In addition to its contribution in terms of conservation, the proposed approach represents an innovative educational resource. Students and teachers are involved in immersive, interdisciplinary learning experiences that combine art history, structural engineering, digital modelling and materials science. The interactive and data-driven environment fosters the development of transversal skills such as critical thinking, collaboration between disciplines and the conscious use of technology in heritage conservation. In conclusion, the combination of HBIM, DT, IoT and AI emerges as a powerful tool for both research and conservation design, as well as for historical memory education in a sustainable and participatory perspectiveI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


