Inferred Geological Data and Stability Analysis of Tunnel Surrounding Rock by Drilling and Blasting Method Based on Digital Twin

Digital twin technology is commonly applied in tunnel engineering to manage and design mechanical and electrical equipment. However, the geological data of the tunnel surrounding rock during construction can also be inferred using digital twin models, providing a reference for tunnel construction and stability analysis. First, the structural information of the tunnel face is obtained through 3D laser scanning and point cloud analysis, establishing a digital twin model of the tunnel face. Then, an intelligent classification model of the rock mass is established by combining the collected prior information of the rock mass with Bayesian networks and junction tree algorithms. Formulas are developed to correlate the rock mass deformation modulus with the classification standard GSI, RMR, and BQ. The deformation modulus of the rock mass is inferred based on the measured field information and empirical data using Bayesian inference combined with Markov Chain Monte Carlo simulation, achieving a posterior probability distribution. Finally, this method is applied to the Dongpo Tunnel of the Taihang Expressway, with an accuracy rate of over 85% for rock mass classification. The inferred parameters of the rock mass deformation modulus are obtained using the prior information provided by the rock mass classification. Finite element modelling is conducted based on the inferred geological information, preliminarily establishing that the stability of the surrounding rock mass in the Dongpo Tunnel is relatively good.