Prediction of maximum surface settlement caused by earth pressure balance shield tunneling using random forest

Underground tunneling for the development of underground railway lines as a rapid, clean, and efficient way to transport passengers in megacities has received a great deal of attention. Since such tunnels are generally excavated beneath important structures in urban zones, estimating the surface settlement caused by tunnel excavation is an important task. During the recent decades, many attempts have been made to investigate the influencing factors affecting the amount of surface settlement. In this study, random forest (RF) is introduced and investigated for the prediction of maximum surface settlement (MSS) caused by earth pressure balance (EPB) shield tunneling. The results obtained show that RF is a reliable technique for estimating MSS using the geometrical, geological, and shield operational parameters. The applicability and accuracy of RF, as a novel approach, is checked by comparing the results obtained with the artificial neural network (ANN), as a popular artificial intelligence algorithm. The proposed RF model shows a better performance than ANN.