Modeling Pile Capacity Using Support Vector Machines and Generalized Regression Neural Network
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 134, Issue 7
Abstract
This note investigates the potential of support vector machines based regression approach to model the static pile capacity from dynamic stress-wave data. A data set of 105 prestressed precast high strength concrete spun pipe piles is used. Radial basis function and polynomial kernel based support vector machines were used to model the total pile capacity and results were compared with a generalized regression neural network approach. A total of 81 data set were used to train, whereas the remaining 24 data sets were used to test the created model. A correlation coefficient value of 0.977 was achieved by generalized regression neural network in comparison to values of 0.967 and 0.964 achieved by radial basis function and polynomial kernel based support vector machines, respectively. Results suggest an improved performance by generalized regression neural network based approach in comparison to support vector machines but polynomial kernel based support vector machines provide a linear relationship to predict total pile capacity using stress-wave data.
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Acknowledgments
The stress-wave data set used in this study was provided by Dr. T. M. H. Lok, Faculty of Science and Technology, University of Macau, Av. Padre Tomás Pereira S.J., Taipa, Macao, China. The writers would like to thank three anonymous reviewers for their constructive comments that led to the improvement of this manuscript.
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Information & Authors
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Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 134 • Issue 7 • July 2008
Pages: 1021 - 1024
Copyright
© 2008 ASCE.
History
Received: May 24, 2007
Accepted: Oct 8, 2007
Published online: Jul 1, 2008
Published in print: Jul 2008
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