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分离宏观纹理和微观纹理以表征沥青路面的抗滑性能。

Separation of Macro- and Micro-Texture to Characterize Skid Resistance of Asphalt Pavement.

作者信息

Xie Tao, Yang Enhui, Chen Qiang, Rao Junying, Zhang Haopeng, Qiu Yanjun

机构信息

College of Civil Engineering, Guizhou University, Guiyang 550025, China.

Guizhou University Survey and Design Institute Co., Ltd., Guizhou University, Guiyang 550025, China.

出版信息

Materials (Basel). 2024 Oct 11;17(20):4961. doi: 10.3390/ma17204961.

Abstract

The skid resistance of asphalt pavement is an important factor affecting road safety. However, few studies have characterized the contribution of the macro- and micro-texture to the skid resistance of asphalt pavement. In this paper, the generalized extreme studentized deviate (GESD) and neighboring-region interpolation algorithm (NRIA) were used to identify and replace outliers, and median filters were used to suppress noise in texture data to reconstruct textures. On this basis, the separation of the macro- and micro-texture and the Monte Carlo algorithm were used to characterize the skid resistance of asphalt pavement. The results show that the GESD method can accurately identify outliers in the texture, and the median filtering can eliminate burrs in texture data while retaining more original detail information. The contribution of the macro-texture on the skid resistance is mainly attributed to the frictional resistance caused by the adhesion and elastic hysteresis, and the main contribution of the micro-texture is a micro-bulge cutting part in the friction mechanism. This investigation can provide inspiration for the interior mechanism and the specific relationship between the pavement textures and the skid resistance of asphalt pavement.

摘要

沥青路面的抗滑性能是影响道路安全的重要因素。然而,很少有研究对宏观纹理和微观纹理对沥青路面抗滑性能的贡献进行表征。本文采用广义极端学生化偏差(GESD)和邻域插值算法(NRIA)来识别和替换异常值,并使用中值滤波器抑制纹理数据中的噪声以重建纹理。在此基础上,利用宏观纹理和微观纹理的分离以及蒙特卡罗算法来表征沥青路面的抗滑性能。结果表明,GESD方法能够准确识别纹理中的异常值,中值滤波可以消除纹理数据中的毛刺,同时保留更多的原始细节信息。宏观纹理对抗滑性能的贡献主要归因于粘附和弹性滞后引起的摩擦阻力,微观纹理的主要贡献是摩擦机制中的微凸起切割部分。本研究可为沥青路面纹理与抗滑性能之间的内在机理及具体关系提供启示。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02e6/11509104/a504633dfa1b/materials-17-04961-g001.jpg

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