Quantitative analysis of structural surface roughness based on UAV point cloud and improved RS characterization method

In order to study the impact of sampling size and spacing on the roughness evaluation results of rock mass structural planes on high and steep slopes, a high and steep rock slope on the left bank of a certain railway station in southeastern Tibet was selected as the research area, and multi-angle approach photogrammetry technology of UAV was proposed to acquire millimeter-level point cloud data to establish a high-precision three-dimensional model of rock mass on slopes in the study area. 27 planar structural planes with point cloud information were clipped from the typical region, and the Delaunay triangulation principle was used to reconstruct the structural plane meshing. Based on this, a new method was proposed to replace the vertical projection plane in the RS characterization method with point cloud fitting plane, and the variation law of structural surface roughness under different sampling sizes and sampling spacing was studied. The results showed that: different triangulation methods have little impact on RS characterization values. The structural surface roughness has the size effect and the spacing effect, and the characteristic value of the roughness tends to be stable with the increase of the structural surface size, and decreases with the increase of the sampling spacing of the structural surface. Some structural planes with size effect have “false effective sampling size” and “true effective sampling size”. Therefore, the effective sampling size obtained should be “true effective sampling size” when conducting roughness evaluation. © 2023 Harbin Institute of Technology. All rights reserved.