Toward the automatic deflection analysis in historic timber slabs by combining 3D point clouds and machine learning approaches

In the field of Cultural Heritage, 3D point clouds are pivotal for representing scenes with high resolution and accuracy. Although traditionally used for planimetries and computational modelling, this study explores their potential in diagnostics. A novel methodology is proposed that leverages 3D point clouds to evaluate deflection in historic timber slabs. By combining Artificial Intelligence with advanced 3D point cloud techniques, an approach is developed for detecting beam deflections with high precision. Specifically, a multi-resolution Random Forest classifier identifies beams and their faces, while deflection is computed using connected component and minimum bounding rectangle algorithms. Applied to timber slabs in the Nuestra Señora convent in Ávila. This methodology demonstrates promising results, achieving 99% accuracy in beam detection and a high degree of automation in deflection assessment. This approach offers significant advancements in the diagnostic capabilities of 3D point clouds in heritage conservation.