Impact of ground conditions on slurry TBM learning phase performance in soft ground tunneling
摘要
Accurate prediction of the learning phase period (LPP) for slurry tunnel boring machines (STBMs) is essential for effective scheduling, cost control, and risk management in soft ground and mixed-face tunneling. Traditional models often oversimplify TBM performance and do not fully account for geological variability at the cutterhead depth. This study presents a predictive model that integrates ground conditions, represented by the average standard penetration test N-value (N(av)), with operational learning dynamics. The model incorporates ground factor (Gf ) and a learning parameter (c) to describe advance rate (AR) growth during the LPP as an exponential function. The model was calibrated using field data from multiple tunnel phases, with c value of 0.25 accurately representing observed AR progression. LPP was defined as the time required for AR to reach 95% of its steady-state value. Validation shows strong agreement with measured data, yielding a mean absolute error (MAE) of 0.61 m/day, root mean squared error (RMSE) of 0.673 m/day, mean squared error (MSE) of 0.453 (m/day)2, and mean absolute percentage error (MAPE) of 8.54%. Sensitivity analyses indicate that steady-state AR, c, and Gf strongly influence LPP duration and performance stabilization. By quantitatively linking geotechnical indicators with TBM operational behavior, the model provides a practical decision-support tool for forecasting productivity, optimizing tunneling strategies, and enabling real-time performance adjustments. This framework enhances mechanized tunneling efficiency, especially during the critical early phase of operations.