Xu, P.; Qian, G.; Zhang, C.; Wang, X.; Yu, H.; Zhou, H.; Zhao, C. Influence of the Surface Texture Parameters of Asphalt Pavement on Light Reflection Characteristics. Appl. Sci.2023, 13, 12824.
Xu, P.; Qian, G.; Zhang, C.; Wang, X.; Yu, H.; Zhou, H.; Zhao, C. Influence of the Surface Texture Parameters of Asphalt Pavement on Light Reflection Characteristics. Appl. Sci. 2023, 13, 12824.
Xu, P.; Qian, G.; Zhang, C.; Wang, X.; Yu, H.; Zhou, H.; Zhao, C. Influence of the Surface Texture Parameters of Asphalt Pavement on Light Reflection Characteristics. Appl. Sci.2023, 13, 12824.
Xu, P.; Qian, G.; Zhang, C.; Wang, X.; Yu, H.; Zhou, H.; Zhao, C. Influence of the Surface Texture Parameters of Asphalt Pavement on Light Reflection Characteristics. Appl. Sci. 2023, 13, 12824.
Abstract
The optical reflection characteristics of asphalt pavement are an important influencing factor in road lighting design, and the macro and micro textures of asphalt pavement significantly affect its reflection characteristics. To investigate the impact of texture parameters on the retroreflection coefficient of asphalt pavement, this study obtained the macroscopic and microscopic texture properties of rutting board specimens and on-site asphalt pavement using a pavement texture tester. The macro- and microtexture parameters, such as macroscopic texture distribution density (D1), microscopic texture distribution density (D2), profile height root-mean-square (Rq), profile slope root-mean-square (Δq), skewness Rsk and kurtosis Rku, were measured, and the corresponding retroreflection coefficient RL was measured using a retro-reflectometer. In the laboratory ex-periments, rutting specimens of AC-13, SMA-13, and OGFC-13 asphalt mixtures were formed. The changes in texture parameters and retroreflection coefficient of different rutting specimens before and after crushing were studied, and a factor influence model between macro- and microtexture parameters and RL was established. And the correlation models of texture index and RL of asphalt pavement are further established. The results showed that in the single factor model, the param-eters can be used to characterize RL with high prediction accuracy, whereas for the on-site meas-urements, the three parameters of Δq, Rsk, and Rku can characterize the RL well. The nonlinear model established on the basis of the B-P neural network algorithm improves its prediction ac-curacy. This research can provide ideas for optimizing the reflection characteristics of asphalt pavement and further provide a decision-making basis for road lighting design.
Keywords
macro-micro texture; retroreflection coefficient; rutting test; single factor model; quadratic func-tion; multifactor model
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
