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

Geotechnics, vol. 4, pp. 194–208

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.

Authors 3

1. Marcel Schlicke corresponding

   Technical University of Munich

   Affiliation as printed

   Chair of Construction Process Management, Technical University Munich, 80333 Munich, Germany

2. Helmut Wannenmacher Aachen Faculty of Georesources and Materials Engineering

   RWTH Aachen University

   Affiliation as printed

   Faculty of Georesources and Materials Engineering, RWTH Aachen University, 52064 Aachen, Germany

3. Konrad Nübel

   Technical University of Munich

   Affiliation as printed

   Chair of Construction Process Management, Technical University Munich, 80333 Munich, Germany