Abstract: Efficient excavation and closed construction environment of double-shield TBM relies on more accurate and real-time intelligent surrounding rock classification methods. To achieve this, the strong correlation between the two indices of geology and excavation parameters is analyzed based on advanced HRNet high-resolution neural networks. Furthermore, an accurate dual-mode intelligent classification model of surrounding rock with high-resolution is built based on the fusion of these two indices. As a result, real-time surrounding rock classification can be conducted. Main findings are as follows: (1) The advance speed and cutterhead torque are strongly correlated with rock hardness. The linear characteristics of these two parameters distinguish different rock hardness. (2) The cutter rotation angle and gripper pitch angle are strongly correlated with rock integrity, and different rock integrity can be distinguished by these two parameters. (3) A dual-mode classification of surrounding rocks established by bonding and fully connecting two single-mode HRNet high-resolution neural networks fuses discrete numerical geological modal data with continuous numerical tunneling modal data, thereby more accurately reflecting the surrounding rock classification from multiple aspects such as surrounding rock stability and rock-machine interaction. The application accuracy reaches 95%. (4) The dual-mode model outperforms single-mode model on testing set, with an Macro F1 Score of 0.985.

Key words: double-shield TBM, high resolution neural network, intelligent surrounding rock classification, dual mode