Machine learning approach for prediction of TBM performance and risk of jamming in Himalayan geology using a cross-project tunnelling database

Tunnel Boring Machine (TBM) excavation in the Himalayan region presents significant challenges due to complex geological conditions. The identification of tunnelling risk under such conditions is crucial for optimizing TBM performance. This study proposes a machine learning (ML)-based framework to predict TBM performance and assess associated jamming risk using a cross-project TBM database from the Himalayan region. The study employs ML approaches, including random forest, bagging, XGBoost, stacking ensemble, and artificial neural network. The combined stratified cross-project database results in improved model performance, with R² values ranging from 0.960 to 0.965. Shapley Additive exPlanations (SHAP)…

• NT
  Norges Teknisk-Naturvitenskapelige Universitet
• TU
  Tribhuvan University
• DF
  Direktoratet for Utviklingssamarbeid