Abstract
Rapid identification of the rock mass condition at the tunnel face is a key problem for TBM operating parameters optimization and subsequent tunnel support measures selection. The vibration induced by the rock breaking contains essential information for evaluating the tunnel face condition. However, conventional vibration-based methods face difficulties in continuously obtaining vibration records for long tunnel sections. Additionally, there's a lack of TBM cutterhead vibration monitoring, and they heavily depend on expertise and prior knowledge. In this study, an end-to-end deep learning (DL) method was developed for rock mass class identification of TBM tunnel working faces based on the measurement of TBM cutterhead vibration signals, including cutterhead vibration signal measurement, signal preprocessing, model training and optimization, and application verification. The model combines the advantages of 1DCNN, BiLSTM, and self-attention mechanisms, where the structural innovation of 1DCNN inspired by Inception v2 for multi-scale feature extraction. Which can automatically extract the spatial and temporal domain features in the signals to promptly identify the rock mass class at the working face without stopping the normal tunneling process. The accuracy on the test set is 95.89% compared to 85.34% for a traditional ML model, and it has better performance than other DL model architectures. The model underwent validation during subsequent TBM tunneling within the same project, successfully proving its practical reliability.