Research on TBM parameter optimization based on failure probability

Abstract

This study addresses the over-reliance on operator experience in Tunnel Boring Machine (TBM) operations by proposing a failure probability-based decision-making method to enhance the scientific accuracy and reliability of construction decisions. In large deformation soft rock zones, TBMs are significantly affected by surrounding rock stress and deformation, which can lead to reduced tunneling speed or even shield jamming, severely impacting construction progress and safety. To reduce subjective biases in operator decision-making, this study combines the stress release coefficient with a failure probability model to establish a limit equilibrium equation. Using Monte Carlo simulations, failure probabilities under varying geological conditions are evaluated, and the optimal tunneling parameters are selected by analyzing the cumulative effect over time. Additionally, the study incorporates a Bayesian updating method, dynamically adjusting model parameters based on periodic monitoring data, further reducing uncertainty and improving the accuracy of the decision support system. The results show that while higher tunneling speeds increase the instantaneous failure probability, considering the cumulative effect over time, the overall failure index decreases with increased speed. Conversely, lower speeds result in a lower instantaneous failure probability but prolonged exposure to high-risk conditions increases the overall failure index. With this decision-making method, operators can quantitatively and in real-time adjust tunneling parameters under complex geological conditions, minimizing failure risks and improving construction efficiency.