Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/991
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Type: Journal article
Title: Predicting settlement of shallow foundations using neural networks
Author: Shahin, M.
Maier, H.
Jaksa, M.
Citation: Journal of Geotechnical and Geoenvironmental Engineering, 2002; 128(9):785-793
Publisher: ASCE-Amer Soc Civil Engineers
Issue Date: 2002
ISSN: 1090-0241
1943-5606
Statement of
Responsibility: 
Mohamed A. Shahin; Holger R. Maier; and Mark B. Jaksa
Abstract: Over the years, many methods have been developed to predict the settlement of shallow foundations on cohesionless soils. However, methods for making such predictions with the required degree of accuracy and consistency have not yet been developed. Accurate prediction of settlement is essential since settlement, rather than bearing capacity, generally controls foundation design. In this paper, artificial neural networks ~ANNs! are used in an attempt to obtain more accurate settlement prediction. A large database of actual measured settlements is used to develop and verify the ANN model. The predicted settlements found by utilizing ANNs are compared with the values predicted by three of the most commonly used traditional methods. The results indicate that ANNs are a useful technique for predicting the settlement of shallow foundations on cohesionless soils, as they outperform the traditional methods.
Description: © 2002 American Society of Civil Engineers
RMID: 0020020721
DOI: 10.1061/(ASCE)1090-0241(2002)128:9(785)
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

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