Prediction of tunnel convergence using Artificial Neural Networks

This research intends to develop a method based on Artificial Neural Network (ANN) for prediction of convergence in tunnels. In this respect, data sets of the convergence monitored in different section of a tunnel and geomechanical and geological parameters obtained through site investigations and laboratory tests are introduced to an ANN model. This data is used to estimate the unknown non-linear relationship between the rock parameters and convergence. Tunnel convergence model is developed, calibrated and tested using the above data from the perspective of Ghomroud water conveyance tunnel in Iran. The dominating rock masses in this case are metamorphic and sedimentary and are considered to be of weak to fair quality. In this tunnel there were some problems due to the convergence and instability of the tunnel. The tunnel boring machine has had several stoppages including a few major delays related to being trapped in squeezing ground and also delays due to face collapses. In order to predict the tunnel convergence a Multi-Layer Perceptron (MLP) analysis is used. A four-layer feed-forward back propagation neural network with topology 9-35-28-1 was found to be optimum. Simultaneously, the methods Radial Basis Function (RBF) analysis as another approach of ANN and Multi-Variable Regression (MVR) as a linear regression using statistical approach are used to analyze the problem and the results are compared. As a result, the MLP proposed model predicted values closer to the measured ones with an acceptable range of correlation. After the calibration and assessment of the ANN model, a parametric study is also carried out to estimate the intensity of the impact of the geological and rock mechanics parameters on tunnel convergence. It is observed that C, Phi, E and UCS parameters are the most effective factors and sigma(t) is the least effective one. Concluding remark is the proposed model appears to be a suitable tool for the prediction of convergence in the unexcavated zones of the tunnel as well as in new tunnels to be excavated in the similar geological environment. The results show that an appropriately trained neural network can reliably predict the convergence in tunnels. (C) 2011 Elsevier Ltd. All rights reserved.