Sequential Prediction of the TBM Tunnelling Attitude Based on Long-Short Term Memory with Mechanical Movement Principle

Abstract

TBM tunnelling attitude controlling is a significant issue for guaranteeing the tunnel fitting the expected tunnel axis, with directly influence the tunnel quality. The key to solve the problem is to establish the relationship between the tunnelling attitude and the controlling parameters and to predict the tunnelling attitude accordingly. For this, this paper introduced a TBM tunnelling attitude predicting method. In detail, using Long-Short Term Memory (LSTM), the initial tunnelling attitude and the controlling parameters of each later ring are taken as input, while the tunnelling attitude of each later rings are regarded as the output, and the relationship between the input and output is established. Meanwhile, for avoid the over-fitting and error accumulation risk of LSTM, the theoretical relationship between the input and output is also built based on the TBM mechanical movement principle, and it is also involved into the LSTM-based relationship as constraints. The proposed method is verified by the field data collected from the 6th Section of the Qingdao Metro Project, and the results reveal that the proposed LSTM-based method is accurate and acceptable.