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A jamming risk warning model for TBM tunnelling based on Bayesian statistical methods.

Shuang-Jing Wang1,2, Le-Chen Wang1, Lei-Jie Wu3

  • 1Key Laboratory of Urban Underground Engineering of Ministry of Education, Beijing Jiaotong University, Beijing, 100044, China.

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Summary
This summary is machine-generated.

This study introduces a new framework to predict Tunnel Boring Machine (TBM) jamming. It uses real-time data and a novel index to identify jamming risks with 95% accuracy, improving tunneling safety.

Keywords:
Bayesian statisticsBoring dataRisk assessmentTBM jammingTunnel boring machine

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Area of Science:

  • Civil Engineering
  • Geotechnical Engineering
  • Risk Management

Background:

  • Tunnel Boring Machine (TBM) operations face significant risks of jamming accidents.
  • Accurate prediction of TBM jamming is crucial for project efficiency and safety.
  • Existing risk assessment methods often rely on single parameters, limiting accuracy.

Purpose of the Study:

  • To develop a comprehensive jamming risk assessment framework for TBM excavation.
  • To propose a novel risk warning model using real-time boring data and Bayesian probability.
  • To enhance the safety and efficiency of tunneling projects through improved jamming prediction.

Main Methods:

  • Statistical analysis of excavation parameters to identify jamming patterns.
  • Introduction of a comprehensive jamming perception index (η) synthesizing multiple parameters.
  • Development of a quantitative model for jamming probability calculation, considering sample size differences.

Main Results:

  • A jamming perception index (η) achieved a 95% recognition rate for jamming states.
  • The quantitative model provided realistic jamming probability estimates (94% in jamming sections, 7% in normal sections).
  • Class Ⅲ surrounding rock identified as most suitable for excavation with the lowest jamming probability.

Conclusions:

  • The proposed framework offers a practical approach for predicting and managing TBM jamming accidents.
  • Integrated analysis of multiple parameters significantly improves jamming risk assessment accuracy.
  • Geological conditions are critical factors in mitigating TBM jamming risks, emphasizing the need for careful excavation planning.