Modeling viscosity behavior of crumb rubber modified binders

Accurately predicting the viscous properties of crumb rubber modified (CRM) binders has proven difficult, especially as these properties tend to vary with changing crumb rubber concentrations and temperatures. This study explores the utilization of the statistical regression and neural network (NN) approaches in predicting the viscosity values of CRM binder at various temperatures (135 °C and greater). A total of 53 CRM binder combinations were prepared from two different rubber types (ambient and cryogenic), three different binder sources, four rubber concentrations (0%, 5%, 10%, and 15%), and five crumb rubber gradations (ADOT, SCDOT, 0.18 mm, 0.425 mm, and 0.85 mm). The results indicated that the regression model is easy to use and can be used for viscosity prediction, similarly NN-based models also provided accurate for predictions for the viscosity values of CRM binders regardless of rubber type and can easily be implemented in a spreadsheet. In addition, the developed NN model can be used to predict viscosity values of other types of CRM binders efficiently. Furthermore, the sensitivity analysis of input variables indicated that the changes of viscosity are significant as the changes of asphalt binder grade, test temperature, and rubber content. The results also show that these three independent variables are the most important factors in the developed NN models in comparison with other variables.