A method for identifying cracks in rock mass

Abstract

The invention discloses a rock mass fracture identification method. The rock mass fracture identification method comprises the steps of carrying out edge detection on a rock mass excavating face image; thinning the image and removing a fork node; extracting a boundary line; carrying out fitting to obtain polylines; separating the polylines; removing the polylines according to a length threshold; merging similar polylines; and removing the polylines again according to the length threshold. According to a further technical scheme, the rock mass fracture identification method further comprises the steps of counting angles of all the polylines, and grouping fractures according to a distribution condition of angle values. The rock mass fracture identification method has the beneficial effects that an integral tunnel excavating face instead of part of the tunnel excavating face is subjected to fracture identification and extraction; incontinuous fractures are connected so as to be more integral; and the extracted fractures are grouped to obtain a sketch for the geology of rock mass fracture boundary line excavating faces of different groups.

Description

technical field [0001] The invention belongs to the application field of rock mass fissures, in particular to a method for identifying rock mass fissures, which can complete the extraction and grouping of rock mass fissures on a tunnel face. Background technique [0002] The tunnel face contains a huge amount of information, and the rock mass condition of the face is one of the important bases for evaluating the stability of the tunnel surrounding rock and determining the tunnel support scheme and construction technology. In actual engineering, limited by site conditions and technical conditions, in many cases, technicians still record and fill in the geological information of the face according to the established record format. If the filling personnel have no practical experience, it often leads to missing items or making mistakes, which leads to incorrect judgment or mistakes in the classification of surrounding rocks. [0003] In response to this situation, some scholar...

AI Technical Summary

Problems solved by technology

[0004] At present, the 3D laser point cloud has spatial characteristics, and the face processing based on close-range photogrammetry and 3D laser scanning can better realize the extraction of structural surface information, but its degree of automation is still not enough, and manual intervention is required to apply in reality

The face processing based on digital image processing technology is also very immature. Because the face image is very complex, most of the relevant research is on the processing of some ideal images or local area images of the face, which is difficult to apply in actual engineering.

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