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Automated truck platooning may cause up to 85% reduction in truck following distance compared to conventional vehicles and therefore reduces rest periods between loading cycles on pavement materials. Fatigue cracking is one of the major distresses in asphalt pavement structures, and pavement fatigue life is highly dependent on rest periods between loading cycles. In this paper, Artificial Neural Network (ANN) modeling was applied on the existing NCHRP 09-44A project's database of extensive laboratory beam fatigue testing results on asphalt mixtures with various rest periods. The developed ANN model was used to predict number of cycles to failure as a function of rest periods and to estimate the impact of truck platooning on pavement fatigue life. In addition, a stand-alone linear multivariate regression equation of fatigue life was developed independently from the ANN model. Based on the results, an 85% reduction in the following distance of platooned trucks may lead to between 7% and 25% reduction in pavement fatigue life. The Platooning Fatigue Life Ratio (PFLR) was found to be dependent on temperature, applied strain level, and mixture parameters. Finally, the applied strain level was the most significant testing factor and binder grade was the most significant mixture parameter on PFLR. [ABSTRACT FROM AUTHOR]

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]