Porous asphalt mixtures are increasingly used in pavement infrastructure because of their environmental benefits and enhanced surface drainage. However, their unique open-graded structure and reduced stiffness present challenges for rutting prediction using traditional dense-graded asphalt models. This study developed and calibrated a rutting prediction model specifically for porous asphalt pavements using data from full-scale accelerated pavement testing with a Heavy Vehicle Simulator. The first section, consisting of a 2-in. modified open-graded friction course (MOGFC) over a 10-in. asphalt-stabilized drainage course (ASDC), was subjected to 1 million equivalent single axle loads (ESALs) under controlled conditions at 85°F. The second test section consisted of a 6-in. MOGFC layer over 13-in. ASDC, and was designed for 13 million ESALs. Laser profilometer measurements were used to track surface deformation. A power law model was fitted between the measured rut depth and ESALs, yielding a strong correlation (coefficient of determination = 0.97). Vertical compressive strains within each asphalt layer were computed using a structural response model that incorporated modulus values from laboratory testing. These strain values were input into the Pavement mechanistic–empirical viscoplastic rutting model, which revealed that over 64% of the rutting occurred in the MOGFC layer because of its high strain and shallow depth. The default global Pavement mechanistic–empirical coefficients significantly underpredicted the measured rutting, prompting calibration. Optimized model parameters aligned the predictions with observed performance. This calibrated model enhances the rutting prediction for porous asphalt systems and supports performance-based design under high-traffic loading.
Eleyedath et al. (Tue,) studied this question.