Hu Yuanjiao, Sun Zhaoyun, Han Yuxi, Li Wei, Pei Lili
School of Information Engineering, Chang'an University, Xi'an 710064, China.
Materials (Basel). 2022 Jul 30;15(15):5275. doi: 10.3390/ma15155275.
The lack of skid resistance performance is a crucial factor leading to road traffic accidents. The pavement surface friction is an essential indicator for measuring the skid resistance. The surface texture structure significantly affects the friction between the tire and the pavement, determining the pavement skid resistance. To deeply study the relationship between surface texture structure and pavement skid resistance performance, two types of asphalt mixture specimens, asphalt concrete (AC) and open-graded friction course (OGFC), are prepared for the skid resistance performance test. Firstly, a high-precision 3D smart sensor Gocator 3110 is used to collect the 3D point cloud data of the asphalt mixture surface texture. The British pendulum tester is used to measure the friction. Secondly, ten feature parameters are extracted to describe the 3D macrotexture characteristics. A data set containing 10 features and 200 groups of texture and friction data was also constructed. Meanwhile, the influence of macro-texture features on the skid resistance performance is discussed. Finally, an optimized Bayesian-LightGBM model is trained based on the constructed dataset. Compared with LightGBM, XGBoost, RF, and SVR algorithms, the Bayesian-LightGBM model can evaluate skid resistance more accurately. The R value of the proposed model is 92.83%. The research results prove that ten macrotexture features contribute to the evaluation of skid resistance to varying degrees. Furthermore, compared with AC mixture specimen, the texture surface of OGFC mixture specimen has more obvious height characteristics and higher roughness. The skid resistance of OGFC mixture specimens is better than that of AC.
抗滑性能不足是导致道路交通事故的关键因素。路面表面摩擦力是衡量抗滑性能的重要指标。表面纹理结构显著影响轮胎与路面之间的摩擦力，决定着路面的抗滑性能。为深入研究表面纹理结构与路面抗滑性能之间的关系，制备了两种类型的沥青混合料试件，即沥青混凝土（AC）和开级配磨耗层（OGFC），用于抗滑性能试验。首先，使用高精度3D智能传感器Gocator 3110采集沥青混合料表面纹理的3D点云数据。使用英国摆式摩擦系数测定仪测量摩擦力。其次，提取十个特征参数来描述3D宏观纹理特征。还构建了一个包含10个特征以及200组纹理和摩擦数据的数据集。同时，讨论了宏观纹理特征对抗滑性能的影响。最后，基于构建的数据集训练了优化的贝叶斯-轻梯度提升机（Bayesian-LightGBM）模型。与轻梯度提升机（LightGBM）、极端梯度提升（XGBoost）、随机森林（RF）和支持向量回归（SVR）算法相比，贝叶斯-轻梯度提升机模型能够更准确地评估抗滑性能。所提模型的R值为92.83%。研究结果证明，十个宏观纹理特征对不同程度的抗滑性能评估有贡献。此外，与AC混合料试件相比，OGFC混合料试件的纹理表面具有更明显的高度特征和更高的粗糙度。OGFC混合料试件的抗滑性能优于AC。
