Semi-automatic Identification of Tunnel Discontinuity Based on 3D Laser Scanning

Obtaining accurate discontinuity information on a tunnel is essential for tunnel stability assessment, and usually requires geological surveys on the tunnel surface. However, traditional manual measurement methods are time-consuming, labor-intensive, and provide limited data, particularly when dealing with complex tunnel rock masses. To address this problem, this paper proposes a method to quickly obtain the point cloud model of the tunnel surface and semi-automatically identify discontinuity using 3D laser scanner. The method is centered on an improved Regional Growth (RG) algorithm, with key principles and processing flow encompassing: (1) Voxel filtering; (2) Normal calculation for point clouds; (3) Improved RG algorithm; (4) Calculation of discontinuity orientation. An analysis of parametric sensitivity which proved its good robustness was carried out to assess the performance of the method. To ascertain the effectiveness of the method in semi-automatically identifying tunnel discontinuities, three sets of test data (standard cube, rock slope in Colorado, and Xulong hydroelectric station tunnel) were chosen. By comparing the analysis results of the proposed method with those of alternative methods (DSE and CloudCompare), the validation of its efficacy in tunnel discontinuity detection was achieved.