A jamming risk warning model for TBM tunnelling based on Bayesian statistical methods.

This study presents a comprehensive jamming risk assessment framework for Tunnel Boring Machine (TBM) jamming accidents during excavation. Using real-time boring data and Bayesian conditional probability, a novel risk warning model is proposed to enhance safety and efficiency of tunneling projects. Through statistical analysis of excavation parameters, distinct patterns between jamming and normal excavation states are identified. A comprehensive jamming perception index (η) is introduced that synthesizes multiple parameters to accurately identify jamming states with a recognition rate of 95%. This integrated approach overcomes the limitations of single-parameter analysis and provides improved accuracy in jamming risk assessment. Additionally, a quantitative model for calculating jamming probability is developed, accounting for differences in sample size between jamming and normal excavation sections. The refined model yields realistic estimates of jamming probability, with an average of 94% in jamming sections and 7% in normal excavation sections. Furthermore, geological analysis shows that the Class Ⅲ surrounding rock is the most suitable for excavation and has the lowest jamming probability. This finding emphasizes the importance of considering geological conditions in excavation planning to effectively mitigate jamming risks. In conclusion, this research provides a practical framework for the prediction and management of TBM jamming accidents, contributing to enhanced safety and efficiency in tunneling projects.