Rapid Recognition of Rock Mass Fractures in Tunnel Faces

Tunnel faces contain much geological information, which, if fully extracted and analyzed, will help to evaluate the geological state of tunnel engineering and guide tunnel design and construction. In this paper, rock mass crack detection, extraction and grouping algorithms of the tunnel face are studied on the basis of the tunnel face image. First, the image segmentation algorithm for face rock mass cracks is analyzed using digital image processing technology. According to the segmentation results, the discontinuous boundaries are connected and the short boundaries are filtered through image thinning and boundary fitting, separation, merging and filtering to form a relatively complete recognition result of rock mass boundary lines. Then, the apparent dip angle of rock mass boundary line is calculated, and the boundary lines with similar apparent dips are merged into a group to achieve automatic grouping. This method is applied to real tunnel face images to test its effectiveness. Results show that the proposed method can basically realize automatic extraction and grouping of the rock mass cracks in the funnel face. For rock masses with obvious cracks, the algorithm can extract the cracks with an error rate of more than 10% and implement automatic grouping with an error rate of no and 5%. This method improves automation degree of rock mass analysis of the tunnel face, and can be used for geological sketching map, providing references for the surrounding rock classification of tunnel faces.