Predicting Convergence Rate of Namaklan Twin Tunnels Using Machine Learning Methods

Abstract

Convergence prediction of tunnels has always been one of the important issues of geotechnical projects. Developing prediction models is a good approach to predict convergence, when there is no knowledge of the possibility of convergence occurrence in the future. In this study, convergence rates of two tunnels from the Namaklan twin tunnel were predicted by different types of machine learning methods. Artificial neural networks (ANNs), multivariate linear regression (MLR), multivariate nonlinear regression (MNR), support vector regression (SVR), Gaussian process regression (GPR), regression trees and ensembles of trees (ET) were applied to predict the convergence rate (CR). The six parameters of cohesion (c), internal friction angle (Φ), uniaxial compressive strength of rock mass (σc), rock mass rating, overburden height (H) and the number of installed rock bolts (NB) were selected as predictor parameters. The dataset was collected via field investigations and laboratory experiments. The results showed that the MLP–ANN model can successfully predict the CR with the determination coefficient (R2) of 0.93. The RBF–ANN model is also successful in predicting the CR with R2 of 0.81. The SVR, MNR and MLR models were also constructed to obtain an empirical formula for predicting the CR. Comparing among the three models showed that the SVR model is more successful (R2 = 0.66) than the MNR and MLR models with R2 of 0.65 and 0.61, respectively. However, the SVR model is placed in the next rank of the ANN models. Among the rest models, except the ET model (R2 = 0.66), the RT and GPR models have no good capability for the prediction of the CR. In total, assessing the statistical indices indicated that the ANNs are superior to the other models in predicting the CR. However, the SVR model could be considered to be a reliable predictive model for convergence rate estimation.