Deep learning, Transfer learning, Convolutional neural network (CNN), Recurrent neural network (RNN), Ground detection, Tunnel boring machine (TBM) vibration, Engineering geology. Rock mechanics. Soil mechanics. Underground construction, TA703-712

Tunnel boring machine (TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and the ground itself. In this study, deep recurrent neural networks (RNNs) and convolutional neural networks (CNNs) were used for vibration-based working face ground identification. First, field monitoring was conducted to obtain the TBM vibration data when tunneling in changing geological conditions, including mixed-face, homogeneous, and transmission ground. Next, RNNs and CNNs were utilized to develop vibration-based prediction models, which were then validated using the testing dataset. The accuracy of the long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) models was approximately 70% with raw data; however, with instantaneous frequency transmission, the accuracy increased to approximately 80%. Two types of deep CNNs, GoogLeNet and ResNet, were trained and tested with time-frequency scalar diagrams from continuous wavelet transformation. The CNN models, with an accuracy greater than 96%, performed significantly better than the RNN models. The ResNet-18, with an accuracy of 98.28%, performed the best. When the sample length was set as the cutterhead rotation period, the deep CNN and RNN models achieved the highest accuracy while the proposed deep CNN model simultaneously achieved high prediction accuracy and feedback efficiency. The proposed model could promptly identify the ground conditions at the working face without stopping the normal tunneling process, and the TBM working parameters could be adjusted and optimized in a timely manner based on the predicted results.