Support vector regression to predict asphalt mix performance

Maher Maalouf, Naji Khoury, Theodore B. Trafalis

    Research output: Contribution to journalArticlepeer-review

    30 Scopus citations

    Abstract

    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 (MR), 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.

    Original languageBritish English
    Pages (from-to)1989-1996
    Number of pages8
    JournalInternational Journal for Numerical and Analytical Methods in Geomechanics
    Volume32
    Issue number16
    DOIs
    StatePublished - Nov 2008

    Keywords

    • Hot mix asphalt
    • Pavement
    • Resilient modulus
    • Support vector regression

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