Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/977
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Type: Journal article
Title: Data division for developing neural networks applied to geotechnical engineering
Author: Shahin, M.
Maier, H.
Jaksa, M.
Citation: Journal of Computing in Civil Engineering, 2004; 18(2):105-114
Publisher: ASCE-Amer Soc Civil Engineers
Issue Date: 2004
ISSN: 0887-3801
1943-5487
Statement of
Responsibility: 
Mohamed A. Shahin, Holger R. Maier, and Mark B. Jaksa
Abstract: In recent years, artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. In the majority of these applications, data division is carried out on an arbitrary basis. However, the way the data are divided can have a significant effect on model performance. In this paper, the issue of data division and its impact on ANN model performance is investigated for a case study of predicting the settlement of shallow foundations on granular soils. Four data division methods are investigated: (1) random data division; (2) data division to ensure statistical consistency of the subsets needed for ANN model development; (3) data division using self-organizing maps (SOMs); and (4) a new data division method using fuzzy clustering. The results indicate that the statistical properties of the data in the training, testing, and validation sets need to be taken into account to ensure that optimal model performance is achieved. It is also apparent from the results that the SOM and fuzzy clustering methods are suitable approaches for data division
Description: © 2004 American Society of Civil Engineers
RMID: 0020041954
DOI: 10.1061/(ASCE)0887-3801(2004)18:2(105)
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

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