Predicting the Performance of Tunnel Boring Machines using Big Operational Data

摘要

Accurate prediction of the performance of tunnel boring machines (TBMs) is important to safe and efficient tunneling. Traditional prediction method of TBM performance is restricted by the lack of sufficient rock mass data and the prediction accuracy is low. A comprehensive management and preprocessing framework of TBM big operational data is proposed in this paper, after which a new prediction method of TBM performance is proposed. The effectiveness of the proposed method is tested on 4 different tunnels. Prediction results show that the big operational data based method can predict TBM performance with high accuracy, and in most cases, the random forest algorithm generates better prediction results than the long-short term memory (LSTM) neural network. The data imbalance phenomenon leads to the emergence of big prediction errors on certain operational segments, highlighting the necessity of adopting new sampling methods to create a more balanced dataset, and incremental learning methods to update the prediction model timely.