Multiple Geological Information Recognition Techniques for Tunnel Face Information Recognition and Engineering Use Using Convolutional Neural Networks

The BQ method, the most widely used technique for classifying tunnel surrounding rocks, requires correction factors including groundwater conditions, structural surface characteristics, and initial ground stress. However, existing deep learning approaches address only a single parameter and rely heavily on empirical judgment, lacking the capability for comprehensive multi-parameter intelligent assessment. To address these limitations, a large-scale database of 21,800 tunnel face images was constructed through on-site data collection, data aggregation, and image augmentation. Five convolutional neural network (CNN) models were trained and evaluated using a proposed multi-indicator scoring method comprising seven performance metrics: loss value, accuracy, precision, recall, confusion matrix, frames per second (FPS), and model size. EfficientNet-B2, ResNet101, and DenseNet121 achieved the highest scores of 100, 84, and 87 for groundwater classification, rock structure type classification, and weathering degree classification, respectively. These three optimized models were integrated into a unified software platform that recognizes multiple geological attributes from a single tunnel face image. Field validation across multiple sections of the Jiaozhou Bay Second Undersea Tunnel shows that the platform achieves a recognition accuracy of over 90%. The results demonstrate that the proposed multi-indicator evaluation method yields more-comprehensive model selection, and the integrated platform can directly support BQ value correction, contributing to intelligent surrounding rock classification in complex tunnel construction environments.