A clustering-augmented transfer learning framework for tunnel boring machine cutterhead thrust prediction and risk warning

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.