Forecasting maximum surface settlement caused by urban tunneling

Abstract In this article, maximum surface settlement (MSS) of urban tunnels was investigated on the basis of three operational parameters of tunnel width, tunnel depth, excavation method, as well as three soil parameters of cohesion, friction angle and elasticity modulus. Seven intelligent methods of long short-term memory (LSTM), deep neural networks (DNNs), K-nearest neighbor (KNN), Gaussian process regression (GPR), support vector regression (SVR), decision tree (DT), and linear regression (LR) were used to perform investigation. The intelligent methods were studied on the basis of 300 datasets accessed from 8 urban tunnels in Iran. Two cross-validation methods of hold-out and 5-fold were utilized for analyzing the prediction results. Finally, the DNNs method with R2 = 0.9939 and RMSE = 3.396301689 mm in the hold-out cross-validation mode and R2 = 0.9937 and RMSE = 2.199337605 mm in the 5-fold cross-validation mode, was recommended and suggested as the best prediction method for MSS.