Recurrent neural networks for real-time prediction of TBM operating parameters

With tunnel boring machines (TBMs) widely used in tunnel construction, the adaptable adjustment of TBM operating status has become a research focus. Since the prediction of tunnel geological conditions is still challenging before excavation, the prediction of important TBM operating parameters plays an important role in the research on TBM adaptable adjustment. In this paper, we use three kinds of recurrent neural networks (RNNs), including traditional RNNs, long-short term memory (LSTM) networks and gated recurrent unit (GRU) networks, to deal with the real-time prediction of TBM operating parameters based on TBM in-situ operating data. The experimental results show that the proposed three kinds of RNN-based predictors can provide accurate prediction values of some important TBM operating parameters during next period including the torque, the velocity, the thrust and the chamber pressure. We also make a comparison with several classical regression models (e.g., support vector regression (SVR), random forest (RF) and Lasso) which actually cannot act as real-time predictors in a real sense, and the comparative experiments show that the proposed RNN-based predictors outperform the regression models in most cases. The feasibility of RNNs for the real-time prediction of TBM operating parameters indicates that RNNs can afford the analysis and the forecasting of the time-continuous in-situ data collected from various construction equipments.