Knowledge-based data-driven prediction of shield tail clearance under karst geological condition

Abstract

Precise control of shield tail clearance is a critical factor influencing the safety and quality of shield tunneling construction. Although various methods exist for accurately measuring shield tail clearance, predictive capabilities remain insufficient. This study is based on a shield tunnel project in the karst region of Longgang, Shenzhen, China. By integrating geological parameters obtained from advanced geological prediction with shield construction monitoring data, a predictive calculation method for shield tail clearance is developed, grounded in the spatial relationship between the shield machine and the pipe segments. A knowledge-based data-driven prediction approach is proposed using a Transformer-LSTM deep learning model. Case analysis demonstrates that the proposed Transformer–LSTM model consistently outperformed baseline models such as GRU, LSTM, and pure Transformer. The predicted R² values for the four positions of the shield tail—top, bottom, left, and right—reached 0.990, 0.901, 0.976, and 0.908, respectively, while error indicators (MAE, RMSE, and MAPE) were also minimized. These results confirm that the proposed hybrid approach effectively captures both global dependencies and temporal dynamics, enabling accurate prediction of shield tail clearance and offering practical engineering significance for guiding shield tunneling construction.