The Mechanistic–Empirical Pavement Design Guide (MEPDG) characterizes asphalt materials through the dynamic modulus, which is a key parameter for predicting strain response under traffic loads. To extend results beyond laboratory conditions, various models have been developed to represent the dynamic modulus master curve accurately. This study evaluates the Standard Logistic Sigmoidal (SLS) and Generalized Logistic Sigmoidal (GLS) models applied to four asphalt mixtures with 50% reclaimed asphalt. To further optimize the testing process, this study focuses on developing master curves using reduced data measurements. This approach emphasizes the importance of selecting an appropriate temperature range for dynamic modulus evaluations. Such precision ensures the reliability of extrapolation techniques and supports the establishment of an efficient mechanistic–empirical pavement design system. The findings indicate that GLS model have exhibited the best fit. The results also showed that it is possible to reduce the data measurements to two temperatures (0 °C-10 °C or 10 °C-20 °C).
Belhaj et al. (Thu,) studied this question.