This research discusses the effect of Sasobit on asphalt binder rheology and how different percentages of Sasobit affect its performance. Evaluation is performed according to the Superpave test specifications. Tests include the rotational viscosity (RV), the dynamic shear rheometer (DSR), and the bending beam rheometer (BBR). Several rheological parameters are investigated: the dynamic viscosity ($ή$ ), the complex modulus ($G^{*}$ ), the phase angle ($\delta$ ), the creep stiffness ($S$ ), and the m-value. The behavior of modified asphalt binder is investigated at a wide range of temperatures ($-6$ –160°C). Results show an improvement of the asphalt binder performance at high temperatures, but adverse effects on asphalt binder performance at the low temperatures are observed. The optimum percentage of Sasobit is found to be 2% by mass of the asphalt binder; higher percentages are not recommended.

Tire-pavement friction is a key component in road safety. Adhesion and hysteresis are the two main mechanisms that affect the friction between rubber tires and pavements. This study experimentally examined the relationship between rubber–pavement adhesion and friction. The adhesive bond energy between rubber and pavement surfaces was calculated by measuring the surface energy components of rubber and aggregates. The friction was measured in the laboratory using a dynamic friction tester. The results revealed that there is a fair correlation between the adhesive bond energy and measured coefficient of friction. A rubber–pavement system with higher adhesion provided higher friction at low speed. In addition, the results demonstrated that there is a strong correlation between rubber–pavement friction and rubber properties. Softer rubber provided higher friction and vice versa. The results of this study provide an experimental verification of the relationship between adhesion and pavement surface friction. The adhesive bond energy and rubber rheological properties could be incorporated in computational models to study tire-pavement friction in different conditions (e.g., speed and temperature).

Skid resistance is a key factor in road safety. Surface friction characteristics of roads are dependent on the microtexture and macrotexture of the surface. The decay of skid resistance with time is a function of traffic level and aggregate characteristics. This study developed predictive models for skid resistance of asphalt pavements and seal coat surfaces. The researchers examined the surface friction characteristics of 35 asphalt pavement test sections and 35 seal coat test sections. The skid number was measured using a skid trailer, while the microtexture and macrotexture of the test sections were measured using a dynamic friction tester and a circular texture meter, respectively. The Aggregate Image Measurement System (AIMS) and Micro-Deval test were also used to evaluate the aggregate shape properties and its resistance to polishing and abrasion. The developed skid prediction models express the skid number over time as a function of aggregate gradation, aggregate resistance to abrasion and polishing, and traffic level. The models showed good correlations with skid numbers measured in the field. These models can be used to optimise the mix design to provide adequate level of friction and estimate the skid number of asphalt pavements and seal coat surfaces over time.

Knowledge of the predicted strains and deformations of the individual pavement layers under loading is required for the purpose of accurate pavement design and evaluation. In the current study, a sensitivity analysis of predicted flexible pavement responses is performed through Finite Element Method (FEM) in order to quantify potential variations in the mechanical responses. Variations in responses are investigated depending on different underlying assumptions including the unbound materials variability (nonlinear anisotropic behavior with initial stress-state), variable asphalt concrete layer thickness and modulus of elasticity. The importance of the input assumptions on the derived responses estimated in the nonlinear analysis up to 3.5 times larger than the responses from a corresponding linear analysis is highlighted. This study provides evidence that performance modeling should potentially act as a risk assessment tool for pavement scientists and engineers engaged in both pavement design and QA/QC practices in order to assist reliable performance prediction in a way that bridges the knowledge gap between modeling and pavement construction processes.