Abstract:Structural planes play a crucial role in evaluating the mechanical properties of rock masses and slope stability. Compared to traditional measurement methods, an accurate and efficient method for recognizing structural plane parameters is particularly important. This paper proposes a new meth od for extracting structural planes of rock masses from 3D point clouds based on Support Vector Ma chines (SVM). First, point cloud coordinates, RGB values, normal vectors, curvature, and density were used as feature vectors for the machine learning model. By combining manually and automatically selected learning samples, the samples were divided into training and testing sets to train and test the SVM model. The accepted model was then used for point cloud prediction and classification, en abling the identification of structural planes and information extraction. This method was applied to a publicly available slope dataset and a structural plane survey at a mining area in Fa'er Town. The re sults showed that: the combination of LOF and PCA methods effectively improved the accuracy of normal vector estimation, while the DetRD-PCA method provided more accurate results for estimat ing normal vectors and calculating orientation for individual planes. When applied to the publicly avail able point cloud dataset, the SVM identified 881552 points in just 9 seconds, successfully extracting four sets of structural planes. Compared with previous results, the maximum average deviation in the dip direction was 3.12°, and the maximum average deviation in the dip angle was 1.54°. When applied to the structural plane survey in the Fa'er Town mining area slope, the SVM identified 1450148 points in 18 seconds, successfully extracting two sets of structural planes. Compared with the classic three-point method, the deviations in dip direction and dip angle ranged from 0.7° to 3.3° and 0.1° to 3.3°, respectively. The method demonstrated high accuracy even with small training sample sizes.
