A Surface Texture Prediction Model Based on RIOHTrack Asphalt Pavement Testing Data

Abstract

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.