Estimation of model parameters and ground movement in shallow NATM tunnel by means of neural network

Currently an increasing number of urban tunnels with small overburden are excavated according to the principle of the New Austrian Tunneling Method (NATM). However, it is still difficult under the present state of the art to predict deterministically the behavior of the ground and tunnel system in the planning and design stage, thereby resulting in an extensive difference between the predicted values and the actual behavior after excavation. This paper discusses back-propagation artificial neural network (ANN) for identification of unknown model parameters and predicting the ground movement using ones during tunneling. The network is trained to approximate the results of FE simulations. FE simulations used here incorporate reduction of shear stiffness, as well as strain softening effects of given material strength parameters. Based on the identified material parameter, the ground movement is predicted. (A) This paper was presented at Safety in the underground space - Proceedings of the ITA-AITES 2006 World Tunnel Congress and the 32nd ITA General Assembly, Seoul, Korea, 22-27 April 2006. For the covering abstract see ITRD E129148. “Reprinted with permission from Elsevier”.