ABSTRACT

Pavement texture is an important characteristic affecting user safety and satisfaction. Despite the large number of studies on pavement texture, there is still a need to further explore the models relating texture to tyre-pavement interaction. In this study, 29 cores were extracted from 15 different pavement surfaces and tested in the laboratory by Ohio department of transportation. The data set includes high density texture scans, modified mean texture depth (MMTD) values estimated using a modified sand patch test, and friction measurements acquired using dry and wet British pendulum (BP) testes. The texture scans were characterised using the mean profile depth (MPD) and wavelets energy. The correlation between MPD values and the coefficients of friction (COF) estimated using BP tests were not as strong as the correlation between the wavelet energies and the wet and dry COFs. Statistically significant models were derived using the wavelet energies to predict the MMTD (R ² = 0.94) as well as the dry and wet COF (R ² _Dry = 0.49/R ² _Wet = 0.60). The principle component analysis was used to decorrelate the wavelet energies and overcome the multicollinearity, while the least absolute shrinkage and selection operator was used to reduce the number of variables in the model.

摘要

路面纹理是影响用户安全性和满意度的重要特征。尽管对路面纹理进行了大量研究，但仍需要进一步探索将纹理与轮胎-路面相互作用相关的模型。在这项研究中，从15个不同的路面提取了29个岩心，并在俄亥俄州交通运输部的实验室进行了测试。数据集包括高密度纹理扫描，使用改进的砂块测试估计的平均纹理深度（MMTD）值以及使用干式和湿式英国摆式（BP）睾丸获得的摩擦测量值。使用平均轮廓深度（MPD）和小波能量表征纹理扫描。MPD值与使用BP测试估算的摩擦系数（COF）之间的相关性不如小波能量与湿式和干式COF之间的相关性强。使用小波能量推导MMTD（R ²  = 0.94）以及干湿COF（R ² _干 = 0.49 / R ² _湿 = 0.60）。主成分分析被用于去相关小波能量并克服了多重共线性，而最小绝对收缩和选择算子被用于减少模型中的变量数量。