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Rutting is one of the most prevalent road distresses in asphalt pavement (AP), it is therefore critical to accurately predict the development of rutting to guide pavement design and maintenance. Based on the measured rutting data obtained from the RIOHTrack in China, the the most widely used MEPDG rutting model is evaluated for its applicability. Due to the original MEPDG rutting model being highly sensitive to a rapid increase of the number of load repetitions (NLR), a modified MEPDG rutting model is proposed by introducing a growth coefficient ($\alpha$ α) for the NLR to reduce the effect of the rapid increase of NLR on the rutting prediction. The applicability of the modified rutting model is then verified by various pavement structures tested in the RIOHTrack, and the value of coefficient ($\alpha$ α) based on the different annual average growth rates of traffic volume is provided for facilitating application. Moreover, the growth rate of the flow ruts is indicated as the critical parameter to describe the rutting development and used for rutting prediction, and the correlations between the pavement structures and the parameter are analyzed. The findings can guide for predicting the rut depth of the asphalt pavement within the whole service life. [ABSTRACT FROM AUTHOR]

Highlights: What are the main findings? Compared with the mixed-effects and the fixed-effects models, the random-effects model is the most suitable form to predict the asphalt pavement surface texture. The cumulative standard axle loads (CSAL), the monthly average humidity (MAH), the monthly rainfall (MR), the coarse aggregate ratio (CA), and the fine aggregate fine ratio (FAF) are negatively correlated with surface texture. The monthly average temperature (MAT) and the fine aggregate coarse ratio (FAC) are positively correlated with surface texture. What is the implication of the main finding? The panel data analysis model is established for predicting asphalt pavement surface texture The significance of each influencing factor on asphalt pavement surface texture is determined The surface texture of asphalt pavement is of enormous importance to skid resistance. To investigate the degradation tendency of surface texture related to the skid resistance, four years of sensor measured texture depth (SMTD) panel data measured from 19 pavement structures including 4 types of surface asphalt layer are used to develop a surface texture prediction model. The panel data-based prediction model takes into account the dependence on time scale, diversified road sections, traffic factors, environmental factors, and the Bailey method-based aggregate gradation parameters. The regression and prediction capability of the surface texture model is evaluated from both short and long-term perspective. The results indicated that the random-effects model is the most suitable form to characterize the degradation of surface texture. The cumulative standard axle loads (CSAL), monthly average humidity (MAH), and the Bailey method-based aggregate gradation parameters (the coarse aggregate ratio (CA), the fine aggregate coarse ratio (FAC), and the fine aggregate fine ratio (FAF)) have significant influences on the sensor measured texture depth. The time scale of the input sensor measured texture depth (SMTD) data has influence on the accuracy of the surface texture prediction model; therefore, long-term data series can ensure the robustness of long-term prediction. The results of this study can benefit the material design, construction, maintenance of asphalt pavement, and its evaluation pavement for longer service life. [ABSTRACT FROM AUTHOR]