Prediction of Fatigue Life of Rubberized Asphalt Concrete Mixtures Containing Reclaimed Asphalt Pavement Using Artificial Neural Networks
Publication: Journal of Materials in Civil Engineering
Volume 21, Issue 6
Abstract
Accurate prediction of the fatigue life of asphalt mixtures is a difficult task due to the complex nature of materials behavior under various loading and environmental conditions. This study explores the utilization of an artificial neural network (ANN) in predicting the fatigue life of rubberized asphalt concrete mixtures containing reclaimed asphalt pavement (RAP). Over 190 fatigue beams were made with two different rubber types (ambient and cryogenic), two different RAP sources, four rubber contents (0, 5, 10, and 15%), and tested at two different testing temperatures of 5 and . The data were organized into nine or 10 independent variables covering the material engineering properties of the fatigue beams and one dependent variable, the ultimate fatigue life of the modified mixtures. The traditional statistical method was also used to predict the fatigue life of these mixtures. The results of this study showed that the ANN techniques are more effective in predicting the fatigue life of the modified mixtures tested in this study than the traditional statistical-based prediction models.
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Acknowledgments
The financial support of the South Carolina Department of Health and Environmental Control (SC DHEC) is greatly appreciated. However, the results and opinions presented in this paper do not necessarily reflect the view and policy of the SC DHEC.
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Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 21 • Issue 6 • June 2009
Pages: 253 - 261
Copyright
© 2009 ASCE.
History
Received: May 8, 2007
Accepted: Dec 9, 2008
Published online: May 15, 2009
Published in print: Jun 2009
Notes
Note. Associate Editor: Eyad Masad
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