1374316 - Exploring the effectiveness of Statistical and Mechanical surface texture parameters to predict skid resistance performance of Asphalt Pavements

Abstract

The skid resistance of asphalt pavements is an important factor to assure the safe operation of aircrafts. However, Aviation agencies and contractors have difficulty controlling or predicting the skid resistance of freshly paved pavements due to the sensitivity of skid resistance to several uncontrollable factors. Surface texture is one of the most important factors to affect the pavement’s skid resistance. Moreover, it is one of the few factors that can be controlled by engineers and contractors through the mix design. Traditionally, the sand patch method (SPM) is used to quantify the macrotexture component of the surface with an ordinary correlation to the pavement skid resistance. The SPM uses a volumetric parameter known as Mean Texture Depth (MTD) which cannot fully characterize the skid resistance–surface texture relationship. Recently, 3D laser technique has gained attention to collect surface texture information. This study aimed to evaluate the effectiveness of two approaches to analyze 3D surface texture data and their correlation to skid resistance. One based on the theoretical tire-pavement friction mechanical model which can incorporate the rolling speed and tire characteristics. The other method employed the statistical method to analyze surface topography to yield statistical parameters based on height and volume features. In this study, the British Pendulum Tester (BPT) was used to characterize the low-speed skid resistance along with the SPM and 3D scanner to evaluate the surface texture. A correlation was done to evaluate the effectiveness of both approaches in predicting skid resistance. The results revealed that both approaches showed better predictability to skid resistance than the traditional sand patch method, the statistical approach showed up to 28% improvement over the MTD (Mean Texture Depth) while the mechanical approach showed up to 42% improvement.