A Comparative Study of Ensemble Methods for Prediction of Surface Settlement Induced by TBM Tunneling

The purpose of this study is to apply ensemble methods to predict surface settlement induced by earth pressure balance tunnel boring machine. Random forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms are applied on 1,101 settlement measurements collected from the Grand Paris Express project. The results are compared with the performance of the back-propagation artificial neural networks (BPNN). Finally, the results show that both ensemble methods XGBoost and RF are better than BPNN based on R² and RMSE indicators.