A Dynamic Classification Method for Surrounding Rock Based on the Fusion of Tunnel Face Images and Ground Penetrating Radar Advanced Geological Forecast Images

Geological investigation and surrounding rock classification ahead of tunnel faces are among the most critical fundamental tasks in tunnel construction. However, current methods for obtaining rock mass parameters and conducting grade evaluation are plagued by significant subjectivity, prolonged cycles, and insufficient intelligence. To address these issues, this study takes the Shitang Tunnel, Zaoxi Tunnel, Jiangjunshan Tunnel, Kuiba Tunnel, Dapingwei Tunnel as engineering cases, integrating geological survey data from tunnel faces and GPR advance detection data. A dynamic rock mass classification method during tunnel construction is then proposed based on deep learning and digital image processing techniques. The proposed method employs MobileNetV3 to intelligently identify key features in tunnel face images, including rock lithology, rock mass structure type, fracture development degree, weathering degree, and water content, achieving an identification accuracy exceeding 95%. Additionally, MobileNet-SSD is utilized for rapid intelligent interpretation of GPR images, with an identification precision of 96.57%. By inputting the recognition results into the BQ classification formula, real-time dynamic determination of rock mass grades is achieved, yielding a prediction accuracy of 85.7%. The results demonstrate that this method significantly enhances the intelligence level and operational efficiency of tunnel rock mass classification, providing a novel technical approach for dynamic design and construction optimization in tunnel engineering.