This paper proposes an image-based method for automated rock fracture segmentation and fracture trace quantification. It is integrated using a CNN-based model named FraSegNet, a skeleton extraction algorithm, and a chain code-based polyline approximation algorithm. A rock tunnel fracture database with a total of 3,000 images of rock tunnel faces is established and selected to train and test the FraSegNet model. A comparison study is further conducted and shows that the FraSegNet model shows advanced performance in pixel-level fracture trace map extraction and noise reduction compared to other deep learning approaches and traditional image edge detection algorithms. Next, the skeletons of the predicted fracture trace maps are extracted and the corre- sponding polyline for each fracture skeleton is thus obtained and output as a text file composed of key nodes coordinates. The fracture trace characteristics (trace length, dip angle, density, and intensity) are acquired using node-based files. The quantitative evaluation of the proposed method illustrates that it can extract trace oc- currences effectively and accurately. A case study of three full scale tunnel sections demonstrates the proposed method to be an efficient approach for acquiring and evaluating 2D fracture occurrences of under-construction rock tunnel faces.

deep learning;rock mass;rock fracture