Digital Twin for Cultural Heritage

A Computational Approach to Predictive Conservation

Bibliographic Data

ID7472572
AuthorsFrancesco Colace (0000-0003-2798-5834, University of Salerno, corresponding author), Marco Limongiello (0000-0001-6755-7928, University of Salerno, corresponding author), Angelo Lorusso (0000-0002-0831-5694, University of Salerno, corresponding author), Michele Pellegrino (0000-0002-4046-4243, University of Salerno, corresponding author), Domenicoo Santaniello (Department of Cultural Heritage, corresponding author), Alfonso Santoriello (0000-0001-5908-530X, Department of Cultural Heritage, corresponding author)
Year2026
Volume40
Pagese00519
Publication date2026-02-01
Open AccessYes
TypeARTICLE
VenueDigital Applications in Archaeology and Cultural Heritage (JOURNAL)
Journal identifiersISSN: 2212-0548
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.daach.2026.e00519
OpenAlexW7128296174
LanguageEN
References cited42

The shift towards predictive and intelligent management models in cultural asset conservation has gained significance due to the intricacies of degradation processes and the necessity for sustainable preservation solutions. This project aims to develop a methodological framework for creating a Digital Twin (DT) for architectural heritage, which can integrate geometric, historical, environmental, and predictive data into a cohesive, dynamic system. The suggested method integrates high-accuracy surveying techniques with semantic modeling using Heritage Building Information Modelling (HBIM), augmented by real-time data collection using Internet of Things (IoT) devices. Environmental and structural parameters are perpetually monitored and integrated with the digital model using visual programming procedures, facilitating real-time changes and interactions. Machine Learning (ML) techniques are employed to analyze time-series data for the identification of deterioration trends and the simulation of predictive maintenance scenarios. The technique was validated by its application to the Ponte Leproso, a Roman bridge in Benevento, Italy, noted for its intricate stratifications and susceptibility to environmental stresses. The development of a DT of the structure facilitated the dynamic integration of sensor data with historical and architectural knowledge, hence enabling the formulation of data-driven conservation plans. This integrated workflow illustrates how the collaboration of HBIM, IoT, and AI technologies may facilitate the transition of cultural heritage management from reactive intervention to proactive, intelligent, and sustainable preservation methods

Asset (computer security · Bridge (graph theory · Building Information Modeling · Cultural heritage · Field (mathematics · Identification (biology · Photogrammetry · Sustainable development · Workflow · 3D Surveying and Cultural Heritage · BIM and Construction Integration · Infrastructure Maintenance and Monitoring

Tools

Open DOIOpen Access