Accurate prediction of TBM (Tunnel Boring Machine) operational parameters is critical to ensure safe and efficient tunnel construction. However, traditional high-accuracy models often face scalability issues due to excessive parameter counts which limit their applicability for real-time updates with on-site data streams. To address this, we propose a novel cutterhead thrust prediction framework integrating clustering-based transfer learning and optimized data augmentation strategy. The three-tiered approach includes: (1) clustering raw thrust data into sub-databases by analyzing excavation cycles' variation forms; (2) a transfer learning classifier is trained to recognize the variation form of incoming data and route samples to specialized predictors, within which multiple candidate models are trained and the best-performing expert is selected for deployment; (3) employing LAFPI (Leading Averaged Force-Penetration Index) for risk warning of abrupt surrounding rock type changes during TBM excavation. Experimental results indicate that the proposed cutterhead thrust prediction framework attains high accuracy (R² > 0.85) over prediction horizons exceeding 1,000 time steps, while deploying only a 0.015B-parameter expert model at inference and requiring about 2 minutes of training. Compared with the smallest conventional baseline models that can stably fit the dataset (LSTM/GRU/Transformer), the deployed expert model achieves an approximately 95% reduction in static parameters and an approximately 85% reduction in per-epoch training time. This establishes its superiority in predictive accuracy, computational efficiency, and adaptability for real-time TBM monitoring systems, enabling fine-tuning the model using on-site data streams.
Cheng et al. (Sun,) studied this question.