Real-Time Rock Mass Class Identification of the Tunnel Face Based on the Spatial-Temporal Characteristics of TBM Tunneling Parameters

The tunneling parameters of tunnel boring machines (TBMs) exhibit distinct spatiotemporal characteristics under different rock mass conditions, particularly their non-Euclidean spatial properties. This paper developed a real-time rock mass identification model based on the spatial-temporal graph convolutional network (STGCN) to simultaneously capture the spatiotemporal characteristics of tunneling parameters. TBM tunneling data and geological data were collected from an 8.41-km-long tunneling section of the YinChaoJiLiao (YCJL) project in China. By matching the data based on tunnel mileage, 2,367 valid samples were obtained. A downsampling strategy was applied to reduce data redundancy and computational cost while maintaining model performance. The optimized model achieved an accuracy of 93.04% and an F 1 -score of 89.75%, and comparative experiments verified its superior performance compared with the mainstream model in this task. A statistical analysis of the adjacency matrix derived from the graph convolutional network (GCN) module was conducted and the influence of input time length on model performance was analyzed to interpret the spatial and temporal characteristics of the tunneling parameters, respectively. Furthermore, the model was validated on a extension section of the same tunnel, confirming its accuracy and efficiency for practical application. The results indicate that the STGCN model can simultaneously capture the complex spatiotemporal characteristics of TBM data and provide accurate real-time rock mass identification results, showing great potential for engineering application in intelligent tunneling.