Establishment of Relationship between Pavement Surface Friction and Mixture Design Properties

Abstract

In recent years, pavement designers have been increasingly challenged to achieve ideal surface friction properties to enhance driver safety through mixture design manipulation and material selection. A clear understanding of the relationship between pavement mixture design properties and surface friction is needed. In this study, the surface laser profilometer device was used to measure surface texture by scanning the surface of laboratory-compacted and field core samples, from which a surface texture profile was derived. Surface texture data were analyzed to calculate the mean profile depth. Statistical analysis and neural networks modeling were used to find the relationship between the mean profile depth results estimated from laser profilometer measurements and mixture design properties (i.e., aggregate size, gradation curve shape, binder content, air voids, and volumetric properties). Promising trends were observed in the data that could be used as guidance by mixture designers to optimize mixture design properties, such as to achieve better pavement surface friction and hence enhanced driver safety. Relationships between field-measured friction (mainly friction number) and laboratory-measured texture parameters were developed. Friction number values measured with the locked-wheel tire test in the field were taken from the MnROAD test track database and used for analysis. The study shows a correlation between friction number and mean profile depth that is of potential applicability to pavement designers for achieving target field friction with the aid of laboratory measurement and analysis. Such relationships between laboratory measurements and field friction could lead to better control of pavement surface friction.