Efficient automated method for characterizing discontinuities in tunnel face rock mass point clouds

Current methods for identifying discontinuities in rock mass point clouds do not fully consider the unique characteristics of tunnel face rock masses. Excavation profiles reduce the accuracy of discontinuity characterization, and the short exposure time of tunnel face rock masses necessitates more efficient identification methods to guide excavation and support strategies. To address these issues, this paper proposes a new method for quickly characterizing discontinuities in tunnel face rock mass point clouds. This method automatically calculates the optimal tunnel face plane and uses distance thresholds to segment the tunnel face rock mass area from the excavation profile area, eliminating the influence of excavation profiles. Additionally, an optimized fuzzy C-means (OFCM) algorithm is designed to improve the accuracy and efficiency of discontinuity identification. The superiority of this method is demonstrated through three examples: polyhedral point clouds, a slope rock mass, and a tunnel face rock mass. In the slope point cloud test, the proposed method resulted in a dip difference of 2° and a dip direction difference of 0.6° compared with the DSE method, with an identification time of 52 s, compared with 7 min and 15 s for the DSE method. In a real tunnel face application in northwestern China, the proposed method showed an average difference from manual field measurements of 4.8° in the dip direction and 5° in the dip direction, with an identification time of 19 s, compared with 2 min and 52 s for the DSE method. Finally, this paper discusses the impact of distance threshold selection on the segmentation results and further verifies the method’s generality through applications on four other tunnel faces. These results indicate that the proposed method is highly accurate and efficient in identifying discontinuities in tunnel face rock masses and can be effectively applied in practical engineering.