Abstract: Accurately forecasting the operational performance of a tunnel boring machine (TBM) in advance is useful for making timely adjustments to boring parameters, thereby enhancing overall boring efficiency. In this study, we used the Informer model to predict a critical performance parameter of the TBM, namely thrust. Leveraging data from the Guangzhou Metro Line 22 project on the big data platform in China, the model’s performance was validated, while data from Line 18 were used to assess its generalization capability. Results revealed that the Informer model surpasses random forest (RF), extreme gradient boosting (XGB), support vector regression (SVR), k-nearest neighbors (KNN), back propagation (BP), and long short-term memory (LSTM) models in both prediction accuracy and generalization performance. In addition, the optimal input lengths for maximizing accuracy in the single-time-step output model are within the range of 8–24, while for the multiple-time-step output model, the optimal input length is 8. Furthermore, the last predicted value in the case of multiple-time-step outputs showed the highest accuracy. It was also found that relaxation of the Pearson analysis method metrics to 0.95 improved the performance of the model. Finally, the prediction results were most affected by earth pressure, rotation speed, torque, boring speed, and the surrounding rock grade. The model can provide useful guidance for constructors when adjusting TBM operation parameters.

Key words: Boring machine performance; Informer model; Deep learning; Thrust force

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