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• A new index called the dynamic modulus area ratio (DMAR) is proposed. • The DMAR can be used to evaluate variations in the entire stiffness spectra of asphalt mixtures. • Tests revealed no correlations between the DMAR and the tensile strength ratio (TSR). • The 2S2P1D rheological model proved itself adequate to accurately reflected the deleterious effect of water. A methodology is proposed for evaluating the induced moisture damage in asphalt mixtures via a new index called the dynamic modulus area ratio (DMAR), which is used to calculate the difference under the areas of the dynamic modulus master curves fitted by the 2S2P1D model before and after conditioning. Were evaluated 21 mixtures applied in the field by comparing data obtained via empirical tests, e.g., the tensile strength ratio and dynamic modulus ratio, using AASHTO T283. The behavior that moisture damage analysis based on stiffness loss measurements in modulus tests can lead to different conclusions than those based on damage resistance tests like TSR. The 2S2P1D model proved to be adequate to evaluate the changes promoted by the deleterious effect of water on the stiffness of the mixtures after the induced moisture damage cycle. For the DMAR, the results clearly differed according to the type of binder, indicating that the impact of water damage on the linear viscoelastic behavior depends on the type of binder used in the mixture. The estimation of susceptibility to moisture using the DMAR, which assesses broader temperature and frequency ranges, enables a stiffness evaluation that is closely related to the field impact on this property, making it possible to evaluate variations in the entire stiffness spectra of the mixtures; thus, it can be used in methods based on the performance for the selection of asphalt mixtures. [ABSTRACT FROM AUTHOR]

• CAM, HN, Sigmoidal and 2S2P1D models were evaluated using exhaustive |E*| database. • WLF and polynomial time–temperature superposition equations were compared. • Mechanical analogs achieved better accuracy than fitting models. • A practical field situation was obtained using three climate conditions. • Sigmoidal can be a reliable stiffness prediction tool in appropriate conditions. Asphalt mixture stiffness is a key property in any pavement long-life performance analysis; it is used to predict a material's strain response for any given applied stress. To accurately describe experimental data, different mathematical models have been reported in literature, which allow for extrapolation to ranges not evaluated in laboratory tests. However, a precision analysis comparing each of them is necessary. This study evaluated 50 data points from five temperatures, ten loading frequencies, and 48 different asphalt mixtures to construct dynamic modulus master curves. Sigmoidal, Christensen-Anderson-Marasteanu (CAM), and Havriliak-Negami (HN) models were calibrated with three different target error functions, in addition to 2 Springs, 2 Parabolic creep elements, and 1 Dashpot (2S2P1D) to analyze their relative and absolute errors. In the first part, the mechanical analogs (2S2P1D and HN) achieved the best results. In the second part, the error added by the Williams-Landel-Ferry (WLF) and polynomial function, which describe the time–temperature shift factors used to construct the dynamic modulus master curves, was calculated; this suggested that the polynomial function better fits the master curves' shift factors. In the third part, three Enhanced Integrated Climate Models (EICM) with different pavement depths and temperatures throughout the year together with the influence of vehicle speed and a depth equation were used to meet a reduced frequency interval that represents practical field conditions. Therefore, by filtering the errors found in part one using the field-representative reduced frequency interval, the fitting models (based on regression adjustments) can reach lower error levels closer to those of the mechanical analog models although still greater than those of the 2S2P1D model. In summary, it is possible to obtain a reliable pavement stiffness prediction using a less robust model as long as the dynamic modulus tested database is wide enough to express the desired reduced frequency range. Nevertheless, the use of a mechanical approach model such as 2S2P1D better describes the dynamic modulus experimental results, which leads to a more reliable prediction in addition to enabling characterization of the rheological behavior of asphalt materials. [ABSTRACT FROM AUTHOR]