Data-driven real-time advanced geological prediction in tunnel construction using a hybrid deep learning approach

This paper investigates the prediction of geological conditions ahead of tunnel boring machines (TBM) using a hybrid deep learning approach. By integrating graph convolutional network (GCN) and long short-term memory (LSTM) networks, the spatial and temporal features from TBM parameters and geological information are extracted for accurate prediction. The results from the case study indicate that (1) The proposed approach provides estimation with a high accuracy of 0.9986; (2) The past geological information has a significant contribution to the model; (3) The proposed approach outperforms several state-of-the-art methods including support vector machine (SVM), extreme gradient boosting (XGBoost) and LSTM method. The proposed hybrid deep learning approach can be a useful tool that provides reliable estimation of the advanced geological conditions in real-time.

Automated summary

This paper investigates a hybrid deep learning approach, combining graph convolutional network (GCN) and long short-term memory (LSTM) networks, for accurate prediction of geological conditions ahead of tunnel boring machines (TBM). The results from the case study demonstrate that the proposed approach provides estimation with high accuracy, outperforming state-of-the-art methods.