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Support vector regression to predict the performance of stabilized aggregate bases subject to wet–dry cycles
Author(s) -
Maalouf Maher,
Khoury Naji,
Laguros Joakim G.,
Kumin Hillel
Publication year - 2011
Publication title -
international journal for numerical and analytical methods in geomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.419
H-Index - 91
eISSN - 1096-9853
pISSN - 0363-9061
DOI - 10.1002/nag.1023
Subject(s) - support vector machine , aggregate (composite) , durability , regression , regression analysis , mean squared error , subject (documents) , statistics , mathematics , machine learning , computer science , engineering , geotechnical engineering , materials science , database , library science , composite material
SUMMARY Durability is a notion that is integrated with the performance of stabilized pavement materials. Also, because it can be quantified and measured, it carries significant influence on the design of pavements. This study focuses on using support vector machine, a machine learning algorithm, in assessing the performance of stabilized aggregate bases subject to wet–dry cycles. Support Vector Regression (SVR) is a statistical learning algorithm that is applied to regression problems and is gaining popularity in pavement and geotechnical engineering. In our study, SVR was shown to be superior to the least‐squares (LS) method. Results of this study show that SVR significantly reduces the mean‐squared error (MSE) and improves the coefficient of determination ( R 2 ) compared to the widely used LS method. Copyright © 2011 John Wiley & Sons, Ltd.