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Support vector regression to predict asphalt mix performance
Author(s) -
Maalouf Maher,
Khoury Naji,
Trafalis Theodore B.
Publication year - 2008
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.718
Subject(s) - support vector machine , asphalt pavement , asphalt , regression analysis , regression , correlation coefficient , mean squared error , engineering , linear regression , mathematics , statistics , computer science , machine learning , materials science , composite material
Material properties are essential in the design and evaluation of pavements. In this paper, the potential of support vector regression (SVR) algorithm is explored to predict the resilient modulus ( M R ), which is an essential property in designing and evaluating pavement materials, particularly hot mix asphalt typically used in Oklahoma. SVR is a statistical learning algorithm that is applied to regression problems; in our study, SVR was shown to be superior to the least squares (LS). Compared with the widely used LS method, the results of this study show that SVR significantly reduces the mean‐squared error and improves the correlation coefficient. Copyright © 2008 John Wiley & Sons, Ltd.