Artificial neural network application for the prediction of ground surface movements induced by shield tunnelling

This paper presents a methodology to correlate ground surface movements and tunnel boring machine (TBM) operation parameters. Two approaches are proposed and evaluated based on a case study of a shallow tunnel in a dense urban area. The first approach is based on a least square approximation and the second one uses an artificial neural network model. Data analysed were selected from the excavation of the subway line B tunnel in Toulouse, France, which was performed mainly by a shield TBM. Ground movements measured on the 4.7 km long contract 2 are reproduced with reasonable agreement by each of the two approaches. The amount of data (in particular for TBM operation parameters), the rather small amplitude of measured movements (a few millimetres), and the accuracy of these measurements (designed for routine construction management) make it necessary to create a pre-processing technique for the data, and a step-by-step improvement of approaches used. An elimination procedure is proposed to identify the most influential operation parameters and a sensitivity analysis shows their respective effect on ground movements.