利用 0.01 毫米 3D 图像实地研究沥青路面纹理和交通抛光下的抗滑性

Guangwei Yang, Kuan-Ting Chen, Kelvin C. P. Wang, J. Li, Yiwen Zou
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摘要

路面纹理和防滑性是影响交通安全的关键路面特征,尤其是在潮湿天气下。当路面纹理和抗滑性在交通抛光作用下随着时间的推移而退化时,应定期进行工程干预,以恢复这些特征。虽然许多研究通过实验室实验调查了交通抛光对路面纹理和防滑性的影响,但由于这些研究没有考虑真实世界的交通和环境因素,可能会限制研究结果的推广。本研究针对这一研究空白,对现实世界中交通抛光条件下的路面纹理和抗滑性进行了全面的实地研究。在美国俄克拉荷马州的一处沥青路面上,我们系统地收集了三个不同地点的路面纹理和摩擦力数据(中间、右轮径和边缘)。数据采集利用激光成像设备重建 0.01 毫米的三维图像来描述路面纹理,并利用动态摩擦力测试仪评估不同速度下的路面摩擦力。在整体图像、宏观纹理图像和微观纹理图像上计算了 20 个三维面积参数,从不同角度研究了交通抛光对路面纹理的影响。然后,结合纹理参数和测试速度,通过线性和非线性方法建立摩擦力预测模型。结果表明,采用识别输入的随机森林模型在非接触摩擦力评估中表现出色。最后,还讨论了摩擦力下降率,以估算未来维护的时机,从而恢复防滑性能。这项研究为工程师提供了更多洞察力,帮助他们在考虑到实际交通抛光的情况下,制定恢复路面纹理和摩擦力的维护计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Field Study of Asphalt Pavement Texture and Skid Resistance under Traffic Polishing Using 0.01 mm 3D Images
Pavement texture and skid resistance are pivotal surface features of roadway to traffic safety, especially under wet weather. Engineering interventions should be scheduled periodically to restore these features as they deteriorate over time under traffic polishing. While many studies have investigated the effects of traffic polishing on pavement texture and skid resistance through laboratory experiments, the absence of real-world traffic and environmental factors in these studies may limit the generalization of their findings. This study addresses this research gap by conducting a comprehensive field study of pavement texture and skid resistance under traffic polishing in the real world. A total of thirty pairs of pavement texture and friction data were systematically collected from three distinct locations with different levels of traffic polishing (middle, right wheel path, and edge) along an asphalt pavement in Oklahoma, USA. Data acquisition utilized a laser imaging device to reconstruct 0.01 mm 3D images to characterize pavement texture and a Dynamic Friction Tester to evaluate pavement friction at different speeds. Twenty 3D areal parameters were calculated on whole images, macrotexture images, and microtexture images to investigate the effects of traffic polishing on pavement texture from different perspectives. Then, texture parameters and testing speeds were combined to develop friction prediction models via linear and nonlinear methodologies. The results indicate that Random Forest models with identified inputs achieved excellent performance for non-contact friction evaluation. Last, the friction decrease rate was discussed to estimate the timing of future maintenance to restore skid resistance. This study provides more insights into how engineers should plan maintenance to restore pavement texture and friction considering real-world traffic polishing.
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