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<p>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 (<i>F</i>), torque (<i>T</i>), and penetration (<i>p</i>)—were used to calculate three model-based boring indexes (<i>a</i>, <i>b</i>, <i>k</i>), 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 (<i>n</i>) was 0.10 r/min, corresponding to a mean absolute percentage error (MAPE) of 1.09%. For advance rate (<i>v</i>), the MAE was 3.4 mm/min, with a MAPE of 4.50%. The predicted thrust (<i>F</i>) and torque (<i>T</i>) 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 realtime TBM control, advancing intelligent tunneling in complex geological environments.</p>

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

  • 期刊论文

  • 发表日期：

    2025年4月30日

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  • Shuangjing Wang,
  • Leijie Wu,
  • Xu Li

摘要

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 realtime TBM control, advancing intelligent tunneling in complex geological environments.