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Soft ground micro TBM jack speed and torque prediction using machine learning models through operator data and micro

Kursat Kilic1, Owada Narihiro2, Hajime Ikeda3

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Summary

This study uses machine learning and Optuna to predict Tunnel Boring Machine (TBM) jack speed and torque, improving excavation efficiency. The AI model assists operators by learning from their actions and TBM data.

Keywords:
Machine learningMicro slurry TBMOperational parametersOptunaSoft ground tunnellingTBM jack speed controlTBM torque control

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Area of Science:

  • Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Tunnel Boring Machines (TBMs) are essential for underground infrastructure development.
  • Optimizing TBM excavation efficiency relies on precise monitoring and control of jack speed and torque.
  • Current manual parameter adjustments by operators are being enhanced by machine learning (ML) approaches.

Purpose of the Study:

  • To develop an innovative ML-driven framework for enhanced operator monitoring and TBM data comprehension.
  • To establish a robust correlation between TBM operator behavior and logged TBM data.
  • To autonomously optimize TBM excavation parameters like jack speed and torque.

Main Methods:

  • Leveraging an Optuna-assisted ML methodology for hyperparameter optimization.
  • Collecting operational data from micro slurry tunnel boring machine (MSTBM) excavations.
  • Comparing and tuning various ML models including Random Forest (RF), kNN, DT, XGBoost, SVM, and ANN.

Main Results:

  • The Random Forest (RF) model demonstrated high performance with R² of 96% for jack speed and 83% for torque prediction.
  • Achieved low error rates: MSE of 119.7 and MAE of 4.42 for jack speed; MSE of 0.62 and MAE of 0.42 for torque.
  • Successfully established a strong correlation between operator actions and TBM logged data.

Conclusions:

  • The developed AI model effectively assists TBM operators in making informed control decisions.
  • This approach enhances TBM data comprehension and optimizes excavation parameters.
  • The Optuna-assisted ML framework offers a significant advancement in TBM operation and efficiency.