Intelligent Prediction of Rock Mass Classification Using TBM Multivariate Time Series and Multichannel Deep Convolutional Neural Networks

In tunnel boring machine (TBM) operations, a large volume of feedback data related to the excavation process is collected in real time, which directly reflects the interaction between the TBM and surrounding rock. The key challenge in ensuring efficient and safe tunneling is to accurately extract sensitive features from vast, fluctuating, and complex multidimensional time series data to understand the geological conditions ahead of the cutterhead. In this study, a novel intelligent recognition method for TBM rock mass classification is proposed, based on piecewise aggregate approximation (PAA) and a multichannel one-dimensional convolutional neural network (MC-1D-CNN). The method effectively maps the interaction between the TBM and surrounding rock to the associated rock classes information ahead of the cutterhead. Five tunneling parameters are selected as input features. First, the PAA technique is applied to transform the multidimensional, raw TBM time series data, which exhibit different scales, into fixed-length one-dimensional feature vectors. This process reduces dimensionality and fuses the data along spatial channels. Then, a MC-1D-CNN model is constructed, which takes the multichannel PAA sequence matrix as input for training. Through multilayer nonlinear mappings and dynamic optimization, the model automatically learns the features of univariate time series within each channel, further reducing the dimensionality and extracting high-order features more strongly correlated with the rock classes. Additionally, to enhance the feature extraction ability and generalization capability of the model, a MC-1D dilated CNN (MC-1D-DCNN) is developed by incorporating dilated convolutions. The method is validated using TBM monitoring data from the second section of the Yinchao project, demonstrating its capability to accurately classify the rock classes. Comparative analysis with several ensemble machine learning models based on key tunneling parameter time series statistical features shows that the proposed TBM surrounding rock level intelligent method PAA-MC-1D-CNN has better feature mining ability and higher recognition accuracy and robustness.