Inverse Design of NATM Tunnels By Neural Networks

Abstract

Tunnels are often designed using uncertain geotechnical data. Insufficient boreholes, natural variation, difficulties in extracting undisturbed samples, and lack of realistic testing procedures are common impediments rendering precise prediction of the associated deformations and lining stresses practically unachievable. Faced with these uncertainties, geotechnical engineers usually opt to re-appraise the assumed parameters by inverse analysis using the monitoring measurements. The parameters obtained from such analyses can then be realistically implemented in subsequent geotechnical assessments. In this paper, artificial neural networks (ANNs) are used in an attempt to simulate the back analysis of the tunnels to obtain realistic parameters that can be used in the finite element analysis to achieve a more accurate design of tunnels. A large database of actual models for a real case study of a tunnel in Algeria is used to develop and verify the ANN model. The designed tunnel deformations determined by utilizing ANNs through the finite element analyses are compared with the measured deformations. The results indicated that ANNs are useful techniques for the inverse design of tunnels.