Stiffness Prediction of Recycled Aged CRM Binders Using an Artificial Neural Network

This research explores the utilization of an artificial neural network (ANN) in predicting the stiffness of recycled aged binders containing crumb rubber modifier (CRM). The data were organized into six independent variables (rotational viscosity of unaged, high failure temperatures of unaged and RTFO (rolling thin film oven) residual, and large molecular sizes of unaged, RTFO residual, and RTFO+PAV (pressure aging vessel) residual) covering the binder properties and one dependent variable, the binder stiffness. The training and testing results showed that the model explains 0.943 of the variability in stiffness, indicating that the ANN techniques are effective in predicting the stiffness of recycled aged CRM binders tested in this study.


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  • Accession Number: 01518575
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Feb 27 2014 12:24PM