Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/54665
Type: Conference paper
Title: Future challenges for artifical neural network modelling in geotechnical engineering
Author: Jaksa, M.
Maier, H.
Shahin, M.
Citation: Proceedings of the 12th International Association of Computer Methods and Advance in Geomechanics Conference (IACMAG), 1-2 October, 2008, pp.1710-1719
Publisher: India Institute of Technology
Publisher Place: CD
Issue Date: 2008
ISBN: 9781622761760
Conference Name: International Association of Coimputer Methods and Advance in Geomechanics Conference (12th : 2008 : Goa : India)
Statement of
Responsibility: 
M. B. Jaksa, H. R. Maier and M. A. Shahin
Abstract: Artificial neural networks (ANNs) are a form of artificial intelligence and, since the mid-1990s, ANNbased models have been successfully applied to virtually every problem in geotechnical engineering. This paper briefly examines the areas of geotechnical engineering to which ANNs have been applied, provides a brief overview of the operation of ANN models, and highlights and discusses four important issues which require further attention in the future. These are model robustness, transparency and knowledge extraction, extrapolation, and uncertainty. For ANN models to be more effective and useful in the future, it is essential that further work be undertaken in these four areas, particularly in the context of geotechnical engineering.
Keywords: artificial neural networks; artificial intelligence
RMID: 0020085082
Description (link): http://www.conferencealerts.com/seeconf.mv?q=ca1300is
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

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