Viscosity Prediction Model for Reacted and Activated Rubber Modified Binders Utilizing Artificial Neural Networks

Description

Viscosity Prediction Model for Reacted and Activated Rubber Modified Binders Utilizing Artificial Neural Networks

Mayzan M. Isied, Mena I. Souliman Ph.D.

ABSTRACT Crumb rubber surface activation and pretreatment are considered as one of the promising newly introduced methods for asphalt rubber production. Reacted and Activated Rubber (RAR) is an elastomeric asphalt extender produced by the hot blending and activation of crumb rubber with asphalt and Activated Mineral Binder Stabilizer (AMBS). Besides RAR’s ability in enhancing the performance of asphaltic mixtures, its dry granulate industrial form enabled its addition directly into the mixture utilizing pugmill or the dryer drum with very minimal to no modification required on the plant level. This study aims to develop an Artificial Neural Network (ANN) viscosity prediction model for extracting a stand-alone viscosity prediction equation. Three different Performance Graded (PG) asphalt binders modified by ten dosages of RAR were tested and evaluated under this study. Sixty-six samples that generated more than three thousand viscosity data points were utilized in ANN modeling. The developed ANN model as well as the extracted stand-alone viscosity prediction equation had a high value of the coefficient of determination and were statistically valid. Both of them can predict the RAR modified binder viscosity as a function of binder grade, temperature, testing shearing rates, and RAR content.

 KEYWORDS: Crumb rubber, surface activation, Reacted and Activated Rubber (RAR), binder viscosity, binder grade, viscosity testing, shearing rate, ANN modeling, viscosity prediction, AVTS prediction.