Prediction of Resilient Modulus of Lime-Treated Subgrade Soil Using Different Kernels of Support Vector Machine
Publication: International Journal of Geomechanics
Volume 17, Issue 2
Abstract
The resilient modulus (MR) plays a crucial role in mechanistic–empirical design such that acquiring the MR of lime-treated pavement layers seems to be necessary, because the use of lime materials in road projects is generally established. However, because of the complexity of and time and equipment requirements for repeated and cyclic load testing, several methods have been proposed to apply. In this paper, the novel artificial intelligence algorithm called support vector machine regression (SVR) has been applied to evaluate accurate values of lime-treated pavement layers’ MR. Moreover, polynomial kernel, radial basis function, and linear kernel as three different kernels of SVR were used to predict the MR of lime-treated subgrade soil. To create the model and validate the algorithm’s performance, approximately 75% of the data was selected as training data sets, and the remaining ones were applied as testing data sets. For this study, the obtained results indicate that developed SVR models produce high-performance predictions, and the polynomial kernel is selected with the significant correlation coefficient (R2) value of 98% for predicting the MR of lime-treated soil.
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Acknowledgments
The resilient modulus data sets for this study were obtained from research that was carried out by Anand J. Puppala, Louay N. Mohammad, and Aaron Allen for Louisiana Transportation Research Center and Louisiana State University, the efforts of whom are gratefully acknowledged.
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Published In
International Journal of Geomechanics
Volume 17 • Issue 2 • February 2017
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Apr 28, 2015
Accepted: Apr 13, 2016
Published online: Jun 29, 2016
Discussion open until: Nov 29, 2016
Published in print: Feb 1, 2017
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