Frequency-wise correlation of the power spectral density of asphalt surface roughness and tire wet friction

Abstract

Modern rubber friction theories predict the friction coefficient of rubber sliding against a rough surface. The key inputs of these theories are the roughness of the surface, the viscoelastic properties of the rubber, and the operating parameters like contact pressure, sliding speed and temperature. With regard to the surface roughness, it is important to know how road surface roughness should be analyzed. To increase the understanding on this topic, the surface topographies of different asphalt surfaces were measured and the roughness results at different spatial frequencies were correlated with wet tire friction results. The surface topographies were analyzed using a top-cutting technique and calculating the power spectral densities, or C(q) functions of the resulting data. The value of the C(q) function at each evaluated spatial frequency was then correlated individually to the friction results using linear regression. The results showed that the highest correlation was found at the highest evaluated frequencies, as limited by the spatial resolution of the measurement. When building a linear least-square fit model with the surface data and road surface temperature information, a good fit between the model parameters and the friction results could be achieved.