The usage of Ground Tire Rubber (GTR) in asphalt pavements has gained renewed interest due to its potential sustainability, economic, and performance benefits. This study focuses on asphalt mixtures designed with three different rubber modifier products including (1) a terminal-blend GTR, (2) a dry-process, chemically processed rubber product, and (3) a terminal-blend rubber-polymer hybrid product. The modifications were incorporated into Illinois Tollway’s approved Stone Matrix Asphalt (SMA) designs using (1) a base binder (PG 58-28), (2) a softer binder (PG 46-34), and (3) a softer binder with higher recycled content. Disk-shaped Compact Tension (DC(T)) test, Hamburg Wheel Tracking Test (HWTT) and Acoustic Emission (AE) tests were performed to characterize the mixtures. The fracture energy for most mixtures met the stringent criteria of 690 J/m² and the rut depths measured were less than 6 mm at 20,000 wheel passes. A Hamburg-DC(T) plot suggests that higher amounts of RAP/RAS (RAP: Reclaimed Asphalt Pavement; RAS: Reusable Asphalt Shingles) can be successfully used if a suitably soft base binder is employed.

The Hamburg wheel tracking test (HWTT) is a widely used testing procedure designed to accelerate and simulate the rutting phenomena in the laboratory. Rut depth, as one of the outputs of the HWTT, is dependent on a number of parameters related to mix design and testing conditions. This study introduces a new model for predicting the rutting depth of asphalt mixtures using a deep learning technique - the convolution neural network (CNN). A database containing 10,000 data points from a comprehensive collection of HWTT results was used to develop a CNN-based machine learning prediction model. The model has been formulated in terms of known influencing mixture variables such as asphalt binder high-temperature performance grade, mixture type, aggregate size, aggregate gradation, asphalt content, total asphalt binder recycling content, and testing parameters, including testing temperature and number of wheel passes. The model can be used as a tool to estimate the rut depth in asphalt mixtures when laboratory testing is not feasible or for cost-saving, and pre-design trials.

Rubber modification of asphalt leads to improvement in certain asphalt mixture properties in lab studies, and extension of service life has been reported in a number of field studies. However, modern laboratory cracking tests often produce conflicting results in terms of the expected cracking performance of rubber-modified asphalt mixtures. To better understand cracking/toughening mechanisms in rubber modified asphalt materials, a fracture mechanics based test was devised for various unmodified and modified binder systems (unmodified control, polymer- and rubber-modified at various treatment levels). The results of this testing showed that unmodified and polymer-modified specimens failed in a brittle fashion, while the rubber-modified specimens displayed increasing ductile-type failures as the rubber modification level increased. Analysis of the fractured faces of the specimens under a Scanning Electron Microscope also revealed the presence of characteristic tails and cavities in the rubber-modified specimens associated with a crack-pinning-type toughening mechanism.

This study employs a microstructure-based finite element modeling approach to understand the mechanical behavior of asphalt mixtures across different length scales. Specifically, this work aims to develop a multi-scale modeling approach employing representative volume elements (RVEs) of optimal size; this is a key issue in asphalt modeling for high-fidelity fracture modeling of heterogeneous asphalt mixtures. To determine the optimal RVE size, a convergence analysis of homogenized elastic properties is conducted using two types of RVEs, one made with polydisperse spherical inclusions, and another made with polydisperse truncated cylindrical inclusions, each aligned with the American Association of State Highway and Transportation Official’s maximum density gradation curve for a 12.5 mm Nominal Maximum Aggregate Size (NMAS). The minimum RVE lengths for this NMAS were found to be in the range of 32–34 mm. After the optimal RVE size for each inclusion shape is obtained, computational models of heterogeneous Indirect Tensile Asphalt Cracking Test samples are then generated. These models include the components of viscoelastic mastic, linear elastic aggregates, and cohesive zone modeling to simulate the rate-dependent failure evolution from micro- to macro-cracking. Examination of load-displacement responses at multiple loading rates shows that both heterogeneous models replicate experimentally measured data satisfactorily. Through micro- and macro-level analyses, this study enhances our understanding of the composition-performance relationships in asphalt pavement materials. The procedure proposed in this study allows us to identify the optimal RVE sizes that preserve computational efficiency without significantly compromising their ability to capture the asphalt material behavior under specific operational conditions.