Support Vector Machines Approach to HMA Stiffness Prediction
Publication: Journal of Engineering Mechanics
Volume 137, Issue 2
Abstract
The application of artificial intelligence (AI) techniques to engineering has increased tremendously over the last decade. Support vector machine (SVM) is one efficient AI technique based on statistical learning theory. This paper explores the SVM approach to model the mechanical behavior of hot-mix asphalt (HMA) owing to high degree of complexity and uncertainty inherent in HMA modeling. The dynamic modulus , among HMA mechanical property parameters, not only is important for HMA pavement design but also in determining HMA pavement performance associated with pavement response. Previously employed approaches for development of the predictive models concentrated on multivariate regression analysis of database. In this paper, SVM-based prediction models were developed using the latest comprehensive database containing 7,400 data points from 346 HMA mixtures. The developed SVM models were compared with the existing multivariate regression-based model as well as the artificial neural networks (ANN) based models developed recently by the writers. The prediction performance of SVM model is better than multivariate regression-based model and comparable to the ANN. Fewer constraints in SVM compared to ANN can make it a promising alternative considering the availability of limited and nonrepresentative data frequently encountered in construction materials characterization.
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Acknowledgments
The HMA data referenced in this paper was obtained from the NCHRP 9-19 project final report DVD. The writers are grateful to Prof. Charles W. Schwartz from the University of Maryland and Prof. Halil Ceylan from Iowa State University for their thoughtful discussions and inputs. The contents of this paper reflect the views of the writers who are responsible for the facts and accuracy of the data presented within. This paper does not constitute a standard, specification, or regulation.
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Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 137 • Issue 2 • February 2011
Pages: 138 - 146
Copyright
© 2011 ASCE.
History
Received: Nov 19, 2009
Accepted: Jul 29, 2010
Published online: Aug 23, 2010
Published in print: Feb 2011
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