Water and Energy Abstracts
  • Year: 2004
  • Volume: 14
  • Issue: 4

99. Modelling TBM Performance with Artificial Neural Networks

  • Author:
  • A.G. Benardos, D.C. Kaliampakos
  • Total Page Count: 1
  • Page Number: 39 to 39

(Tunnelling and Underground Space Technology, Vol. 19, 2004, pp. 597–605).

Abstract

Assessing TBM performance is an important parameter for the successful accomplishment of a tunnelling project. This paper presents an attempt to model the advance rate of tunnelling with respect to the geological and geotechnical site conditions. The model developed for this particular task is implemented through the use of an artificial neural network (ANN) that allows the identification and understanding of both the way and the extent that the involved parameters affect the tunnelling process. The model described in the paper is customised for the construction of an interstation section of the Athens metro tunnels, where the ANN generalisations provided precise estimations regarding the anticipated advance rate.

Keywords

TBM tunnelling, Artificial neural networks, Advance rate modelling