This study presents a novel workflow for characterizing potentially unstable rock blocks using high-resolution 3D point clouds, addressing some of the limitations of traditional methods in rockfall risk analysis. The proposed methodology integrates block segmentation, 3D modelling using triangulation meshing algorithm, and the quantitative extraction of parameters such as volume, surface area, and shape. Moreover, a new parameter for block flattening, derived from the Oriented Minimum Bounding Box (OMBB), has been introduced and employed to conceptually update the existing shape classification system, enabling a more accurate representation of real block geometries. The workflow generates 3D Block Size Distribution and a refined Block Shape Classification, offering essential insights into fractured rock mass behavior and supporting geoengineering assessments and decision-making. Validation through synthetic datasets of primitive three-dimensional shapes demonstrates the accuracy of the proposed method in calculating volumes and surfaces, as well as for improved shape classification. Application to a real case study on Mount Gallo (Sicily, Italy) shows good agreement between directly measured and indirectly calculated block volumes and reveals the dominant morphometric classes located on the rock slope. Sensitivity analyses highlight the robustness of the volume calculation method to varying input data resolution and completeness, although large data gaps may affect accuracy. An uncertainty analysis, employing a non-parametric approach, ensures statistical reliability in defining the 95% confidence interval of the 3D Block Size Distribution. Overall, this proposed framework offers a robust and practical tool for rockfall risk management, providing detailed insights into individual potentially unstable blocks.
Mineo et al. (Fri,) studied this question.