Monitoring and managing skid resistance properties are crucial activities to reduce the number of highway accidents and fatalities. However, current methodologies to measure pavement surface friction present several disadvantages that make them impractical. Thus, it is necessary to evaluate alternative methods to estimate friction. The principal objective of this study was to develop friction models based on pavement texture. We implemented a Line Laser Scanner (LLS) to obtain an improved characterization of the pavement texture which includes macrotexture and incorporates microtexture description using eight different parameters. Field measurements of friction and texture were collected around Texas using the British Pendulum Test (BPT), the Dynamic Friction Test (DFT), the micro-GripTester, and the LLS. The experimental results showed that there is not a unique relationship between texture and friction; though strong and statistically significant, the relationship is different for each type of pavement surface. Thus, regression analysis pooling all data cannot be utilized to quantify this relationship. For this reason, we applied a panel data analysis approach that allows the incorporation of the type of surface and provides a more robust analysis. The results indicate that the prediction of friction is significantly improved when incorporating information from both macrotexture and microtexture into the prediction model. Therefore, a measure of microtexture should be included into friction models based on texture. In addition, the study of different texture parameters suggests that the mean profile depth (MPD) is the most significant parameter for macrotexture and for microtexture to explain the distinct friction measures.
High-Definition Field Texture Measurements for Predicting Pavement Friction
