Prediction of the tunnelling advance speed of a super-large-diameter shield machine based on a KF-CNN-BiGRU hybrid neural network

Abstract

The tunnelling advance speed (TAS) of a super-large-diameter tunnel boring machine (TBM) significantly affects project progress and safety. Effective prediction of the TAS of a TBM is crucial for early warning and evaluation of its excavation performance. This paper proposes a model based on convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) to predict TAS. This model can more effectively extract and retain many features; additionally, it considers the temporal relationships among the operating parameters of the TBM. Additionally, a Kalman filter (KF) is incorporated into the model to effectively remove input parameter noise, leading to a more accurate prediction of TBM excavation speed. A case study indicated that the pressure of the excavation chamber and the pitch angle of the shield tunnelling machine were correlated with the advance speed. The research results show that the proposed model can accurately and effectively predict TAS.