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Computational Intelligence and Neuroscience
Volume 2016, Article ID 6708183, 16 pages
http://dx.doi.org/10.1155/2016/6708183
Review Article

Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks

1Shaanxi Provincial Major Laboratory for Highway Bridge & Tunnel, Chang’an University, Xi’an 710064, China
2School of Highway, Chang’an University, Xi’an 710064, China

Received 5 August 2015; Accepted 2 November 2015

Academic Editor: Saeid Sanei

Copyright © 2016 Jinxing Lai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability.