The discontinuities of natural rock mass possess specific mechanical properties that can define the vulnerable parts of the rock mass. These properties play a decisive role in the structure

strength and stability of various rock engineering projects

such as tunnel support

classification of surrounding rock mass

and slope reinforcement. Therefore

it is of crucial importance to recognize the single discontinuity and the dominant groups with relatively developed conditions. In proposed method

the automatic identification steps of dominant groups of discontinuities are divided into three parts

point clouds normal vectors calculation

single discontinuity segmentation and dominant groups clustering: 1) Calculate normal vectors based on Robust Randomized Hough Transform; 2) An improved region-growing algorithm is proposed to segment a number of discontinuities. In terms of seed points selection and region-growing conditions

curvature

planarity and roughness are considered

and dynamic outlier detection is added. In addition

the relationship between the thresholds and the number of discontinuities is used to qualitatively judge the extreme segmentation situation

and the optimal threshold range is screened out; 3) Finally

S-K-means clustering algorithm is proposed to recognize dominant groups clustering. The accuracy of the algorithm is verified by a rock slope. The results demonstrate that the inclination angle error ranges from 0.7° to 2.5°

and the average inclination angle error is 1.8° and 1.7° respectively. This method shifts from directly clustering point clouds to identify dominant groups to first segmenting several single discontinuity before clustering. This refinement enhances the robustness and accuracy of discontinuities clustering computation

increases computational speed

and maintains applicability across various discontinuities datasets. Consequently

this provides a more precise and rapid method for the intelligent identification of discontinuities.