Abstract: When full-face hard rock tunnel boring machines (TBMs) bore through complex geological conditions, they are prone to jamming. Traditional experience-based identification methods cannot provide real-time assessment and are unable to collaboratively identify multiple jamming modes. To address these challenges, a real-time intelligent early warning model for open TBM jamming risk was developed based on Transformer multi-task learning. An expert-system-based risk identification rule set is established, in which tunneling data are classified using predefined thresholds and comprehensive multi-indicator evaluation. Cutterhead and shield jamming risks are classified into three levels: 0 (low risk), 1 (medium risk), and 2 (high risk). Based on the Transformer’s multi-head attention mechanism, multi-task learning frameworks are constructed for Tunnels A and B, respectively, enabling simultaneous prediction of cutterhead and shield jamming risk levels. The results are as follows. (1) The prediction accuracy for cutterhead jamming risk reaches 97.01%, which is 2.73% higher than that of the second-best gated recurrent unit (GRU) model. The precision and recall reach 98.21% and 98.44%, respectively, and the area under curve (AUC) reaches 1. (2) For shield jamming risk prediction, the accuracy reaches 89.55%, which is 5.90% higher than that of the second-best GRU model. The precision and recall reach 90.35% and 91.70%, respectively, and the AUC reaches 0.972 6. Application of the proposed model to two open TBM tunnels shows that the jamming risk prediction accuracy reaches 90%, which agrees well with field construction conditions and enables stable and effective early warning before jamming occurs.

Key words: tunnel boring machine, jamming risk, Transformer architecture, multi-task learning, intelligent prediction