## 3 Example Analyses  
The data used in this paper was obtained from [PERSON]'s personal page on Research Gate, available at: [[https://www.researchgate.net/publication/289523409_raw_point_cloud_data_ascii_x_y_z_intensity-_metadata](https://www.researchgate.net/publication/289523409_raw_point_cloud_data_ascii_x_y_z_intensity-_metadata)]([https://www.researchgate.net/publication/289523409_raw_point_cloud_data_ascii_x_y_z_intensity-_metadata](https://www.researchgate.net/publication/289523409_raw_point_cloud_data_ascii_x_y_z_intensity-_metadata)), accessed on 27 May 2024 [32]. The data was collected along the slopes of highway TP 7403 km 06, near the village of Torroja, Spain. The study area consists entirely of Carboniferous rocks, primarily composed of shales and sandstones, which are commonly interbedded and resemble flysch deposits. These sediments have undergone low-grade metamorphism, resulting in the formation of slates,  
Figure 4: Cluster Analysis Graph of the Regular Hexagonal Pyramid Structural Planes. (**a**) Clustering distance calculation result graph; (**b**) Polar density map; (**c**) Decision graph; (**d**) Clustering result graph. metasandstones, and greywackes, reflecting their complex geological history. The thickness of the Carboniferous sediments can reach up to 2000 m and experienced folding during the Hercynian orogen, leading to the development of distinct layering and structural surfaces. The well-developed structural surfaces of the Carboniferous rocks make the exposures within these rocks highly suitable for demonstrating the methods proposed in this thesis. The research data were obtained using the ILRIS_3D three-dimensional laser scanner from Optech, with an average point spacing of 6 mm. The data is stored in PCD format and includes three-dimensional coordinates (X, Y, Z) and RGB attribute information. Figure 5a shows a field image of the scanned area. This study only selected a portion of the point cloud data from the slope, indicated by the black rectangular box in Figure 5a, which is enlarged in Figure 5b. The overall dimensions of the selected research area are 3.8 m \(\times\) 3 m. The point cloud data for the selected area underwent filtering to remove vegetation points or isolated points. The final number of points in the selected research area is 30,633, as illustrated in Figure 5.  
Figure 6b shows the identification results of rock mass structural planes after applying the Hough space transformation and normal vector difference region growing method. Different colored areas represent different rock mass structural planes, with a total of 281 planes identified. For the regular planar rock mass structural planes, the region growing identification performs well, with only a few points at the edges of the planes not successfully segmented. The black rectangular box in Figure 6 highlights the locations of non-planar rock mass structural planes or complex areas of rock surfaces, where the segmentation of rock mass structural planes is fragmented. This fragmentation is due to the inherent randomness when using only the normal vector as the criterion for region growing. After successfully identifying the rock mass structural e planes, the clustering distances (sine squared values), local density, and control distances between any two the rock mass structural planes are calculated, followed by clustering analysis using the CFSFDP clustering algorithm. As shown in Figure 7a,b, the number of clustered structural planes in the study area is two, indicating the presence of two dominant structural plane groups. Additionally, the 281 identified structural planes are imported into RocScience Dips 7.0 software to generate poles and equal density plots. The number of dominant structural plane groups and their orientations are obtained, and a comparative analysis of the orientations derived from the two methods is conducted. Figure 5: Field images of the study area. (**a**) Schematic diagram of the study area location; (**b**) Local magnification of the study area.