Table of Contents
Advances in Artificial Neural Systems
Volume 2009, Article ID 308239, 9 pages
http://dx.doi.org/10.1155/2009/308239
Review Article

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

1Department of Civil Engineering, Curtin University of Technology, Perth, WA 6845, Australia
2School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA 5005, Australia

Received 28 April 2009; Accepted 1 September 2009

Academic Editor: Frederic Maire

Copyright © 2009 Mohamed A. Shahin 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

Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.