Data-driven multi-output prediction for TBM performance during tunnel excavation: An attention-based graph convolutional network approach

A deep learning-based multi-output prediction model is developed to better understand and more accurately estimate tunnel boring machine (TBM) performance in each segment ring during the deep excavation under complex underground environments. The novelty lies in the development of a new deep learning approach named att-GCN, which feasibly integrates the graph convolutional networks (GCN) and scaled dot-product attention mechanism to improve model performance and interpretability. It is proved that our proposed att-GCN model is outstanding in significantly enhancing the prediction performance and effectively capturing the influence between monitoring points. As a case study, the proposed method is validated in a Singapore Mass Rail Transit (MRT) construction project, where seven features associated with the TBM machine are input for att-GCN training and testing. Experimental results reveal that the att-GCN model can exhibit a powerful capability in simultaneously predicting two targets named penetration rate (y1) and energy consumption (y2), reaching the mean absolute percentage error (MAPE) value at 15.475% and 15.173%, respectively. In terms of prediction accuracy, att-GCN is superior to some state-of-the-art algorithms, including deep neural network (DNN), random forest (RF), and support vector regression (SVR). Moreover, an online-learning version of att-GCN is designed. When the objective value is gradually known and fed into att-GCN during the tunneling procedure, the model can yield more impressive performance under the MAPE of 8.504% (y1) and 7.934% (y2). Accordingly, the real-time estimation of TBM performance based on the time-varying monitoring data provides valuable evidence to realize the intelligent control of TBM tunneling, which can ultimately improve construction efficiency and reliability. • A deep learning-based multi-output prediction model is developed to estimate TBM performance. • The novelty lies in the integration between graph convolutional networks and attention mechanism. • The proposed method is validated in a Singapore Mass Rail Transit (MRT) construction project. • Mean absolute percentage error reaches up around 15% in simultaneously predicting two targets. • The proposed method is superior to the state-of-the-art algorithms, including DNN, RF, and SVR.