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Accurately predicting tunnel boring machine (TBM) performance is beneficial for excavation efficiency enhancement and risk mitigation of TBM tunneling. In this paper, we develop a long short-term memory (LSTM) based hybrid intelligent model to predict two key TBM performance parameters (advance rate and cutterhead torque). The model combines the LSTM, BN, Dropout and Dense layers to process the raw data and improve the fitting quality. The features, including the ground formation properties, tunnel route curvature, tunnel location and TBM operational parameters, are divided into historical/real-time time-varying parameters, time-invariant parameters and historical/real-time output prediction data. The effectiveness of the proposed model is verified based on a large monitoring database of the Baimang River Tunnel Project in Shenzhen, south China. We then discuss the influence of the prediction mode, neural network structure and time division interval length of historical data on the prediction accuracy. The significance evaluation of input features shows that the historical output prediction has the largest influence on the prediction accuracy, and the influence of ground properties is secondary. It is also found that the correlations between input features and the output prediction are coincident with their interrelationships with the ground properties and ease of TBM excavation. Finally, it is found that the prediction results are most affected by the total propulsion force followed by the rotation speed of the cutterhead. The established model can provide useful guidance for construction personnel to roughly grasp the possible TBM status from the prediction results when adjusting the operational parameters.
Xu et al. (Tue,) studied this question.