Nonlinear Genetic-Based Models for Prediction of Flow Number of Asphalt Mixtures
Publication: Journal of Materials in Civil Engineering
Volume 23, Issue 3
Abstract
Rutting has been considered the most serious distress in flexible pavements for many years. Flow number is an explanatory index for the evaluation of the rutting potential of asphalt mixtures. In this study, a promising variant of genetic programming, namely, gene expression programming (GEP), is utilized to predict the flow number of dense asphalt-aggregate mixtures. The proposed constitutive models relate the flow number of Marshall specimens to the coarse and fine aggregate contents, percentage of air voids, percentage of voids in mineral aggregate, Marshall stability, and Marshall flow. Different correlations were developed using different combinations of the influencing parameters. The comprehensive experimental database used for the development of the correlations was established on the basis of a series of uniaxial dynamic-creep tests conducted in this study. Relative importance values of various predictor variables were calculated to determine their contributions to the flow number prediction. A multiple-least-squares-regression (MLSR) analysis was performed to benchmark the GEP models. For more verification, a subsequent parametric study was carried out, and the trends of the results were confirmed with the results of previous studies. The results indicate that the proposed correlations are effectively capable of evaluating the flow number of asphalt mixtures. The GEP-based formulas are simple, straightforward, and particularly valuable for providing an analysis tool accessible to practicing engineers.
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Acknowledgments
The authors are thankful to Professor Mohammad Ghasem Sahab and Dr. Habib Shahnazari for their support and stimulating discussions.
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Published In
Journal of Materials in Civil Engineering
Volume 23 • Issue 3 • March 2011
Pages: 248 - 263
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Sep 17, 2009
Accepted: Jul 8, 2010
Published online: Jul 29, 2010
Published in print: Mar 1, 2011
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