Multi-source 3D point clouds fusion for potential rock mass hazard evaluation in high-steep rock slopes

Abstract

Accurate characterization and evaluation of hazardous rockmass sources prove essential for rockfall risk mitigation. Structural properties of rock masses play a decisive role in evaluating these risks. This study presents an integrated approach that combines Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) photogrammetry to address data limitations in complex terrain. The practical validation was carried out on the basis of a case study on a high and steep rock slope. The results demonstrate that the fusion of TLS-UAV multi-source data enhances spatial coverage and point cloud density by 19%, enabling comprehensive slope modeling and improving multi-angle structural characterization of target rock masses. An approach integrating multiple algorithms enables the automatic identification of rock joints from multi-source 3D point clouds, achieving high recognition accuracy. And the key geometric and mechanical parameters were extracted and analyzed to quantify joint properties. Furthermore, a novel rock hazard index (RHI) is proposed, which takes into account joint geometric features, joint mechanical features, and slope quality grade to assess risk levels across slope domains. The proposed framework provides an efficient solution for joint-controlled hazardous rockmass assessment, offering theoretical insights and practical applications for infrastructure-related geohazard prevention. This study contributes to enhancing risk assessment methodologies for high and steep slope environments.