Establishment and Analysis of the Relationship Model between Macro-Texture and Skid Resistance Performance of Asphalt Pavement

Abstract

Pavement surface texture evaluation is mainly analyzed based on elevation data in previous research, and attention also need to be paid to wavelength information. Furthermore, a well-established relationship model between surface texture and skid resistance for real road sections still needs further investigation to help provide useful information on appropriate maintenance time considering skid resistance attenuation. In this research, the macro-texture of asphalt pavement was evaluated from different aspects, including elevation, wavelength information, and geometry, and the relationship models between the macro-texture and skid resistance (at both low and high speeds) were established and compared using the multiple linear regression (MLR) and back propagation (BP) neural network to recommend a suitable one. In order to achieve this, this study monitored anti-skidding performance and the macro-texture of six road sections for 18 months. Firstly, the Dynamic Friction Coefficient (DFC) test and core drilling were conducted on site at three different service times. Additionally, a laboratory accelerated loading test was carried out on specimens prepared by similar material composition to one of the road sections, and the British Pendulum Number (BPN) was tested after different passes of loading. Secondly, 3D laser scanning was carried out on core samples from road sections and laboratory specimens after different passes of loading. The correlation degree between macro-texture indexes and anti-skidding performance was analyzed with the grey correlation entropy analysis method. Finally, the relationship models between the anti-skidding performance at high and low speeds and macro-texture were established based on the MLR and BP neural network. The results indicate that the macro-texture indexes calculated based on elevation data to characterize vertical irregularities have a good correlation with the skid resistance despite the different service times and pavement types. Compared with the BP neural network model, the MLR model has low correlation and noticeable error. The relationship model between F60 (DFC at the speed of 60 km/h) and macro-texture could be well established by the BP neural network. In addition, the relationship between F20, BPN, and pavement surface macro-texture is poor, making it impossible to establish a model with good correlation. Generally, it is recommended to use the BP neural network to establish the relationship model between macro-texture and skid resistance.