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Intelligent Engineering Systems through Artificial Neural Networks

Editor
Cihan H. Dagli
Cihan H. Dagli
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K. Mark Bryden
K. Mark Bryden
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Steven M. Corns
Steven M. Corns
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Mitsuo Gen
Mitsuo Gen
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Kagan Tumer
Kagan Tumer
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Gürsel Süer
Gürsel Süer
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ISBN:
9780791802953
No. of Pages:
636
Publisher:
ASME Press
Publication date:
2009

This paper explores the feasibility of applying support vector regression (SVR) kernel-based supervised learning method to develop hot mix asphalt (HMA) dynamic modulus (|E*|) predictive models. SVR-based prediction models were developed using the latest comprehensive |E*| database that is available to the researchers. The SVR model predictions were compared with the existing regression-based prediction model which is employed in the Mechanistic-Empirical Pavement Design Guide (MEPDG). The SVR based |E*| models show better prediction accuracy compared to the existing regression models. The determination of optimal function and parameters for SVR algorithm is recommended to improve the prediction performance of SVR based |E*| models.

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