Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network

Shield tunneling machine is an important large-scale engineering machine used for tunnel excavation. During the tunneling process, precise cutterhead torque prediction is of vital significance for adjusting operational parameters and avoiding cutterhead jamming, which seriously affects the efficiency, cost and safety of tunneling process. In this paper, a novel hybrid deep neural network (HDNN) is presented for accurately predicting the cutterhead torque for shield tunneling machines based on the equipment operational and status parameters. To begin with, correlation analysis based on cosine similarity between the parameters and the cutterhead torque are conducted for parameter selection and input dimension reduction. Then, selected parameters are fed into the proposed hybrid deep neural network, combing convolutional neural network (CNN) and long short-term memory (LSTM) to extract implicit features and sequential features. On this basis, useful deep information can be fully exploited and utilized for cutterhead torque prediction. Moreover, to further improve the prediction performance and alleviate the gradient disappearing during deep-layer network training, we integrate the residual network module into the proposed neural network. Finally, 15 different datasets constructed from the actual project data are utilized to validate the effectiveness and superiority of the proposed method. The results show that the coincidence degree between the predicted curves and the actual curves of the proposed HDNN is much higher than that of the existing machine learning-based and deep learning-based models. Therefore, the prediction accuracy and the generalization ability of the proposed method outperforms the other data-driven methods. Moreover, on the 15 different datasets with different geological conditions, the highest prediction accuracy of the proposed HDNN is up to 97.4%, and the average prediction accuracy is approximately 96.2%. It can be concluded that the proposed HDNN is capable of accurately predicting the cutterhead torque even under complicated geological conditions, which is provided with high industrial application value. (c) 2020 Elsevier Ltd. All rights reserved.