Advancing TBM Performance: Integrating Shield Friction Analysis and Machine Learning in Geotechnical Engineering

Abstract

The Ylvie model is a novel method towards transparent Tunnel Boring Machine (TBM) data analysis for tunnel construction. The model innovatively applies machine learning to automate friction loss computation per stroke, enhancing TBM performance prediction in varying geomechanical environments. This research considers the complexities of TBM mechanics, focusing on the Thrust Penetration Gradient (TPG) and shield friction influenced by geological conditions. By integrating operational data analysis with geological exploration, the Ylvie model transcends traditional methodologies, allowing for a comprehensible and specific determination of the friction loss towards more precise penetration rate prediction. The model’s capability is validated through comparative analysis with established methods, demonstrating its effectiveness even in challenging hard rock tunneling scenarios. This study marks a significant advancement in TBM performance analysis, suggesting potential for the expanded application and future integration of additional data sources for comprehensive rock mass characterization.

Keywords

• TBM tunneling
• hard rock TBM
• shield friction
• performance prediction
• torque factor
• TBM operational data