This paper presents a methodological framework for the design and simulation of monitoring systems for cultural heritage, integrating real-time interactive 3D environments and artificial intelligence techniques. The research focuses on preventive conservation, proposing a workflow that combines multiscalar digitization, semantic annotation, and AI-driven analysis to detect and predict the evolution of degradation phenomena.
The methodology is structured into three main phases: (i) detailed digital acquisition of the architectural artifact through integrated survey techniques (TLS, photogrammetry, and image-based methods); (ii) construction of an information-rich 3D model, semantically annotated and enriched with diagnostic data (thermal, material, and colorimetric); and (iii) simulation of monitoring systems within a real-time interactive environment using Unreal Engine 5. As illustrated in the workflow diagram (Fig. 1, p. 722), the system enables the placement of virtual sensors and the generation of synthetic datasets through simulated damage patterns. These data streams are used to train machine learning models for anomaly detection, which are integrated with natural language–based AI systems to support user interaction and decision-making. The results demonstrate the potential of combining digital twins, simulation environments, and AI to design and test monitoring infrastructures prior to on-site implementation, while highlighting the challenges related to data integration, model accuracy, and real-world validation.