Frequency- and temperature-dependent dynamic shear modulus and phase angle prediction models based on existing asphalt binder viscosity data using Artificial Neural Network (ANN).

Viscoelastic characterization of asphalt binders is critical for selecting suitable binders for a given climatic region and for improving long-term performance prediction using mechanistic-empirical pavement analysis and design methods, such as the Mechanistic-Empirical-Pavement-Design-Guide (MEPDG). A-VTS information (viscosity data) was used in the past and existing databases have this type of information. Nowadays, Dynamic Shear Modulus (|G*|) and Phase Angle (δ) determination using the Dynamic Shear Rheometer (DSR) is common practice. This resulted in the existence of old and new databases in highway agencies. The goal is to find a way to combine these separate data sets and merge them into a unified national database based on |G*| and δ. The work discussed in this paper is critical to make this merger possible and to use this in future binder to mixture predictive model developments and in MEPDG. In this study, |G*| and δ prediction models were developed. A machine learning technique named Artificial Neural Network (ANN) was employed in this process. 8940 data points from 41 binders were utilized in the model development process. After robust training, validation, and testing process, a |G*| prediction model with seven neurons and a δ prediction model with three neurons were reported. The models were compared with previous Bari-Witczak and Onifade-Birgisson models. ANN-based models performed better in every statistical parameter. Two equations were also extracted from the ANN models, which can be used reliably to predict |G*| and δ from A-VTS data. These two models with closed-form equations can be incorporated into the MEPDG for |G*| and δ predictions. • New models for Binder dynamic shear modulus and phase angle using binder viscosity data with temperature and frequency. • Artificial Neural Network (ANN) is utilized in the model development process. • An equation is extracted from each ANN-based model. • The prediction performance of new models is better than existing models in every statistical parameter. [ABSTRACT FROM AUTHOR]