AI-Assisted Scan-to-BIM for Masonry Arch Bridges: From Point Cloud Segmentation to Parametric Heritage BIM Reconstruction

This paper shows an AI-assisted workflow supported by a Large Language Model for the geometric and informative reconstruction of masonry arch bridges from multi-source input data. The proposed methodology combines point cloud preprocessing, vegetation filtering, AI-based or semi-automatic segmentation, geometric feature extraction, 3D mesh generation, and parametric Heritage Building Information Modeling integration. The study specifically aims to examine how the quantity and type of input information influence the reconstruction process and the reliability of the resulting HBIM model. The input data considered include Point Clouds (PC), photographic images, and descriptive information related to the bridge geometry, construction features, and visible architectural components. Particular attention is given to the initial geometric characteristics of the point cloud, including its point density, spatial distribution, level of completeness, and overall number of points, to assess how point cloud numerosity affects the accuracy and level of detail of the reconstructed geometry. Starting from a dense 3D survey, the workflow identifies and reconstructs the main architectural and structural components of a masonry arch bridge, including the arch, intrados, parapets, masonry walls, roadway surface, abutments, and cutwaters. The extracted geometry is converted into a clean 3D mesh and subsequently structured into parametric HBIM objects suitable for documentation, conservation, structural assessment, and future monitoring activities. A Cloud-to-Mesh comparison is performed to evaluate the geometric accuracy of the reconstructed model with respect to the original point cloud. The results demonstrate the potential of hybrid AI and geometric approaches to improve the efficiency, repeatability, and reliability of Scan-to-BIM processes for historical masonry bridge heritage. Furthermore, they show that the geometric quality of the HBIM model depends primarily on the density, spatial distribution and completeness of the structural points, rather than on their total number. Well-distributed point clouds, in fact, allow for more reliable reconstructions than larger datasets characterised by uneven coverage.