Research Article Transportation Research Record 1–14 Ó National Academy of Sciences: Transportation Research Board 2020 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/0361198120914292 journals.sagepub.com/home/trr Effect of Asphalt Mixture Components on the Uncertainty in Dynamic Modulus Mastercurves Hussein Kassem 1 , Ghassan Chehab 2 , and Shadi Najjar 2 Abstract Practitioners and researchers in the paving industry have highlighted the importance of the adoption of reliability-based pave- ment design. The goal of developing reliable pavements with optimum performance over their design life has become a key factor to be considered during both pavement design and construction processes. This requires the adoption of statistical and probabilistic-based analyses for the formulation of the properties and behavior of pavement materials. Thus, many researchers worked on the quantification and modeling of the uncertainty caused by the inherent variability in pavement materials in general and that of asphalt concrete (AC) in particular. The dynamic modulus (|E*|), a fundamental property for mechanistic-empirical and purely mechanistic pavement designs, has been proven to have a significant level of uncertainty that is dependent on climatic and traffic loading conditions. The main objective of this study is to investigate the effect of the AC mixture properties and components on the uncertainty in the |E*| mastercurve. This objective is achieved by conducting an experimental program incorporating four different mixtures having the same material sources but different binder types and gradations. Monte Carlo simulations are used to model the uncertainty of |E*| for each of these mixtures. The paper shows that the uncertainty is dependent on mixture type, as the presence of larger nominal maximum aggregate size, modified bin- der, or additive can increase the uncertainty in the |E*| mastercurve, especially at high temperatures or slow loading rates. The uncertainty is proven to be material related and not imposed by the testing instrumentation. The field of uncertainty quantification has recently gained significant attention in the pavement engineering community. Uncertainty quantification is essential to render predictions of complex systems credible and use- ful for decision making. Such systems include random heterogeneous media and composite materials such as asphalt concrete (AC). Ignoring statistical analysis in for- mulating the properties and behavior of such materials might lead to false predictions jeopardizing the function- ality and economy of built engineering systems (1). AC is a unique material, the composition and mechan- ical behavior of which differ from those of other com- monly used materials. This uniqueness is imposed by different factors related to its chemical composition, pro- duction process, environmental conditions, and in-service performance, which is dictated by different modes of fail- ure. AC is of a heterogeneous nature induced by its microstructure, the interaction of its components, and their spatial distribution. Even though the asphalt binder constitutes only about 3–7% by weight of the AC mix- ture, its sensitivity to temperature and loading rate ren- ders the analytical and numerical characterization of AC complex and necessitates experimental testing be done at a wide range of loading rate–temperature combinations. Such conditions can be considered as factors that might affect the inherent uncertainty in various AC properties (2). In addition, its testing is carried out within protocols that exhibit high variability in instrumentation and equipment from one lab to another (3). Experimentally, sources for uncertainty range from control of loading rate and temperature conditions, testing equipment and instrumentation, mixture and specimen fabrication, test- ing methods, lab-to-lab and operator-to-operator vari- abilities, data processing and analysis, among other factors (4–6), in addition to inherent variability in the material because of its heterogeneous nature. As any other engineering application, the uncertainty in AC 1 Department of Civil and Environmental Engineering, Beirut Arab University, Beirut, Lebanon 2 Department of Civil and Environmental Engineering, American University of Beirut, Beirut, Lebanon Corresponding Author: Hussein Kassem, h.kassem@bau.edu.lb