Viscosity prediction of CRM binders using artificial neural network approach

Abstract

The primary objective of this study was to develop a series of artificial neural network (ANN) models to predict the viscosity values of crumb rubber-modified (CRM) binders using four input variables: asphalt binder source, rubber size, mixing duration and rubber content. The results indicated that ANN-based models are effective in predicting the viscosity values of CRM binders regardless of rubber type and can easily be implemented in a spreadsheet. In addition, the developed ANN model can be used to predict viscosity values of other types of CRM binders. Furthermore, the results also show that asphalt binder source, rubber size and rubber content are the most important factors in the developed ANN models while the mixing duration is relatively unimportant. The sensitivity analysis of input variables indicated that the viscosity changes significantly with changes in asphalt binder source, rubber size and rubber content.

Keywords:

• artificial neural network
• viscosity
• rubber size
• rubber content
• mixing duration

Acknowledgement

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.