Real-time operational parameter recommendation system for tunnel boring machines: Application and performance analysis

The accurate selection of operational parameters is critical for ensuring the safety, efficiency, and automation of Tunnel Boring Machine (TBM) operations. This study proposes a similarity-based framework integrating model-based boring indexes (derived from rock fragmentation mechanisms) and Euclidean distance analysis to achieve real-time recommendations of TBM operational parameters. Key performance indicators—thrust (F), torque (T), and penetration (p)—were used to calculate three model-based boring indexes (a, b, k), which quantify dynamic rock fragmentation behavior. A dataset of 359 candidate samples, reflecting diverse geological conditions from the Yin-Chao water conveyance project in Inner Mongolia, China, was utilized to validate the framework. The system dynamically recommends parameters by matching real-time data with historical cases through standardized Euclidean distance, achieving high accuracy. Specifically, the mean absolute error (MAE) for rotation speed (n) was 0.10 r/min, corresponding to a mean absolute percentage error (MAPE) of 1.09%. For advance rate (v), the MAE was 3.4 mm/min, with a MAPE of 4.50%. The predicted thrust (F) and torque (T) values exhibited strong agreement with field measurements, with MAEs of 270 kN and 178 kN∙m, respectively. Field applications demonstrated a 30% reduction in parameter adjustment time compared to empirical methods. This work provides a robust solution for real-time TBM control, advancing intelligent tunneling in complex geological environments.