Prediction of ground subsidence by shield tunneling using ensemble learning

•1800 sets of data to study shield tunnelling–ground subsidence relationship.•XGBoost model established for ground subsidence prediction, better than DT and RF.•Nonlinear complex relationships between parameters and ground subsidence analysed.•Key parameters for controlling ground subsidence were identified.•MAE and RMSE used to analyse performance of intelligent prediction model.