Peak Friction Prediction Model Based on Surface Texture Characteristics

The paper proposes a new model for predicting the speed gradient of peak friction values on asphalt pavements based on surface characteristics. The innovative feature of the proposed model is the reliable estimation of peak friction values experienced by vehicles equipped with anti-locking brake system (ABS) at a certain vehicle speed. In order to define the experimental model, several types of Dense Asphalt Concrete (DAC) surface layers, with different surface characteristics, are analyzed by in situ tests. Friction is measured by the Skiddometer BV11 and the British Pendulum Tester whilst texture properties are measured by a laser profilometer. The Rado model is used to predict peak friction values at 3 different vehicle speeds and, by using these data, the gradient of peak friction values is determined for each pavement section. The spectral analysis of pavement profile data allows to define a texture parameter that is negatively correlated with peak friction values; this parameter is introduced in a new formulation of the speed number SP* that is a measure of the pavement macrotexture influencing peak friction values. The speed number SP* is used in the new exponential model proposed to defining the gradient of peak friction values. The obtained results show the model is highly reliable and, as it enables to identify texture characteristics to be modified in order to optimize peak friction values, it is particularly useful for the optimization of the mix design and maintenance of pavement surfaces.