Simulation-driven Thermal Analyses for Cultural Heritage Conservation

Heritage masonry structures, with their complex geometries and variety of materials, pose significant challenges for accurate digital modelling and simulations for restoration and conservation purposes. In addition, traditional cultural heritage (CH) documentation workflows typically rely on geometric 3D acquisitions, often without supporting material diagnostics or structural insights. This work addresses this gap by introducing a data-driven workflow that leverages image-based information and 3D point clouds to extract both geometric and material-related attributes for energy analysis applications. The proposed methodology leverages Deep Learning (DL) and Finite Element Method (FEM) modelling to support energy simulation for cultural heritage assets. The proposed workflow integrates orthoimages and 3D data to segment masonry textures, estimate wall thickness, and generate a semantically enriched mesh tailored for energy analyses. A YOLO-based model identifies stone and mortar regions in high-resolution imagery, while point cloud voxelization and plane fitting are used to compute local thickness values. This information feeds into an adaptive meshing strategy, where mesh resolution is adjusted based on material texture and geometric features. A tunable parameter β enables control over mesh density, allowing for optimization of computational performance in thermal FEM simulations. This approach enables the derivation of meaningful simulation-ready 3D models from limited survey data.