A dynamically approach based on SVM algorithm for prediction of tunnel convergence during excavation

• Application of SVM algorithm in predicting tunnel convergence was investigated.
• Tunnel convergence was predicted using non-linear support vector regression.
• Amirkabir tunnel was selected as a case study for testing the SVM model.

The use of urban underground spaces is increasing due to the growing world population. Iran’s capital is no exception, traffic in Tehran is an annoying problem and Amirkabir tunnel is being excavated as a motor way to improve this situation. The excavation of this tunnel started in 2010 using New Austrian Tunneling Method (NATM). Since this tunnel lies in shallow depths of maximum 12 m in a residential area, a careful monitoring of the convergence mode is necessary to avoid instability, surface subsidence and unexpected incidents. This research intends to develop a dynamically model based on Support Vector Machines (SVMs) algorithm for prediction of convergence in this tunnel. In this respect, a set of data concerning geomechanical parameters and monitored displacements in different sections of the tunnel were introduced to the SVM for training the model and estimating an unknown non-linear relationship between the soil parameters and tunnel convergence. According to the obtained results, the predicted values agree well with the in situ measured ones. A high conformity (R2 = 0.941) was observed between predicted and measured convergence. Thereby the SVM provides a new approach to predict the convergence of the tunnels during excavation as well as in the unexcavated zones.