A Gene Expression Programming Model for Predicting Tunnel Convergence

Underground spaces have become increasingly important in recent decades in metropolises. In this regard, the demand for the use of underground spaces and, consequently, the excavation of these spaces has increased significantly. Excavation of an underground space is accompanied by risks and many uncertainties. Tunnel convergence, as the tendency for reduction of the excavated area due to change in the initial stresses, is frequently observed, in order to monitor the safety of construction and to evaluate the design and performance of the tunnel. This paper presents a model/equation obtained by a gene expression programming (GEP) algorithm, aiming to predict convergence of tunnels excavated in accordance to the New Austrian Tunneling Method (NATM). To obtain this goal, a database was prepared based on experimental datasets, consisting of six input and one output parameter. Namely, tunnel depth, cohesion, frictional angle, unit weight, Poisson’s ratio, and elasticity modulus were considered as model inputs, while the cumulative convergence was utilized as the model’s output. Configurations of the GEP model were determined through the trial-error technique and finally an optimum model is developed and presented. In addition, an equation has been extracted from the proposed GEP model. The comparison of the GEP-derived results with the experimental findings, which are in very good agreement, demonstrates the ability of GEP modeling to estimate the tunnel convergence in a reliable, robust, and practical manner.