A fast weighted optimal spectral clustering method for rock discontinuity orientation grouping based on 3D point clouds.

Discontinuity properties largely influence the mechanical behavior of rock mass. Orientation grouping is an important part of rock discontinuity characterization. This paper presents a fast weighted optimal spectral clustering method for orientation grouping of rock discontinuity based on 3D point clouds. The key steps include: (1) point cloud preprocessing with normal vector calculation and sharp point filtering, (2) hemispherical raster point generation based on Fibonacci sequence (3) fast spectral clustering algorithm based on raster point weighting (4) optimal group number selection using CHI validity index. Two benchmark rock slope models are adopted for analysis. The results show that the proposed method can effectively improve the computational efficiency and accuracy of orientation grouping. [ABSTRACT FROM AUTHOR]