Shield machine pose prediction based on CNN-GRU-Attention

Abstract This paper presents a shield machine pose prediction method based on Convolutional Neural Network (CNN) - Gated Recurrent Unit (GRU) with Attention mechanism (Attention). Firstly, the Pearson correlation coefficient is employed to select input parameters highly related to the position and posture of the shield machine. Then, a convolutional neural network is introduced to extract the long-term short-term feature dependency features in the operation data of the shield machine, optimizing the model’s input. The attention mechanism is integrated into the gated loop unit to make the model more targeted in using key information in the input sequence and improve the accuracy of the shield machine pose prediction model. The effectiveness of this method is verified by the example of Beijing Metro Line 10. Compared with GRU-Attention and LSTM-Attention models, the mean value of determination coefficient R 2 increased from 0.872 and 0.886 to 0.959, and the mean value of root mean square error RMSE decreased from 2.78 and 2.52 to 2.14. This method can provide effective prediction for the attitude and position of shield machines in actual tunnel engineering.