A Novel Hybrid Deep Neural Network Prediction Model for Shield Tunneling Machine Thrust

Abstract

Shield thrust is a critical operational parameter during shield driving, which is of vital significance for adjusting operational parameters and ensuring efficient and safe propulsion of shield tunneling machine. In this paper, a novel hybrid prediction model (CLM) combining attention mechanism, convolutional neural networks (CNN) and Bi-directional long short-term memory (BiLSTM) network is proposed for shield thrust prediction. Correlation analysis based on Maximal Information Coefficient (MIC) between the thrust and other parameters is first conducted to select optimal parameters and reduce input dimension. An attention mechanism is introduced into CNN to distinguish the importance of different features, with the convolution layer and pooling layer further extracting dimension features of the data. Then, a BiLSTM neural network integrating first attention layer is employed to extract time-varying characteristics of the data, with a second attention layer added to capture important time information. Field data during shield cutting bridge piles are investigated to support and validate the effectiveness and superiority of the proposed method. Results show that the proposed CLM model are general enough to avoid overfitting problems and have good performance at prediction. The predicted value match reasonably well the monitoring data, with coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) equaling to 0.85, root mean square error (RMSE) equaling to 0.05, mean absolute error (MAE) equaling to 0.02. The CLM model in this paper can accurately predict the thrust even under complicated construction conditions, which provides reference for similar industrial application.