Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/57498
Type: | Journal article |
Title: | State of the Art of Artificial Neural Networks in Geotechnical Engineering |
Author: | Shahin, M. Jaksa, M. Maier, H. |
Citation: | Electronic Journal of Geotechnical Engineering, 2008; online:www1-www26 |
Publisher: | Electronic Journal of Geotechnical Engineering |
Issue Date: | 2008 |
ISSN: | 1089-3032 |
Statement of Responsibility: | Mohamed A. Shahin, Mark B. Jaksa, Holger R. Maier |
Abstract: | Over the last few years, artificial neural networks (ANNs) have been used successfully for modeling almost all aspects of geotechnical engineering problems. Whilst ANNs provide a great deal of promise, they suffer from a number of shortcomings such as knowledge extraction, extrapolation and uncertainty. This paper presents a state-of-the-art examination of ANNs in geotechnical engineering and provides insights into the modeling issues of ANNs. The paper also discusses current research directions of ANNs that need further attention in the future. |
Keywords: | artificial neural networks artificial intelligence geotechnical engineering. |
Description: | © 2008 ejge |
Appears in Collections: | Aurora harvest Civil and Environmental Engineering publications Environment Institute publications |
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