Research on Fracture Identification of Tunnel Face Based on the CBAM-UNet Model

The extraction of fracture parameters and the classification of surrounding strata are crucial criteria for assessing the stability of a tunnel face. To overcome the limitations of conventional manual sketching, this paper proposes a tunnel face fracture identification, extraction, and surrounding strata classification technique based on deep learning technology. Based on the collection of on-site tunnel face images, we construct a comprehensive database comprising 20,000 dataset samples. By refining the conventional UNet deep learning network model and incorporating the channel and spatial attention modules (Convolutional Block Attention Module, CBAM), we achieve automated identification of fracture traces on the tunnel face, yielding remarkable recognition outcomes. Through training and testing the CBAM-UNet network model on this extensive database, we conduct a comparative analysis with alternative deep learning approaches and conventional edge detection algorithms. The results unequivocally demonstrate the exceptional performance of the CBAM-UNet model in fracture recognition. Subsequently, we conduct statistical analysis and grouping of the identified fractures, as well as calculate the integrity indices of the surrounding rock mass. This enables the expeditious assessment of the tunnel face’s surrounding rock grade.