ABSTRACT

The pavement friction has been recognised as a critical contributor to traffic safety and an important pavement functional characteristic. The sideway force coefficient routine investigation machine (SCRIM) has been used in many studies for pavement friction evaluation. However, this device is costly to purchase and run which limits its accessibility to many transportation agencies. This study explores an artificial neural network model to predict the sideway force coefficient (SFC) from SCRIM using pavement micro- and macro-texture information. On the selected field site, pavement texture was evaluated by a digital sand patch tester and a portable high-resolution 3D laser scanner, while pavement friction was measured using a SCRIM. The obtained high-resolution 3D texture data was decomposed into micro- and macro-textures via discrete Fourier transform and Butterworth filters. Then, height, feature, and hybrid texture parameters were calculated to characterise pavement 3D texture at the micro- and macro-levels. Next, the obtained pavement texture parameters were used to predict SFC through linear and neural network models. The neural network model, including pavement micro- and macro-texture parameters, shows better performance than other models and is adequate to predict SFC. Besides, pavement micro-texture shows more contribution to SFC than macro-texture.

摘要

路面摩擦力已被认为是交通安全的关键因素和重要的路面功能特性。侧向力系数常规调查机 (SCRIM) 已在许多研究中用于路面摩擦评估。然而，这种设备的购买和运行成本很高，这限制了它对许多运输机构的可及性。本研究探索了一种人工神经网络模型，利用路面微观和宏观纹理信息从 SCRIM 预测侧向力系数 (SFC)。在选定的现场，路面纹理通过数字砂斑测试仪和便携式高分辨率 3D 激光扫描仪进行评估，同时使用 SCRIM 测量路面摩擦力。获得的高分辨率 3D 纹理数据通过离散傅里叶变换和巴特沃斯滤波器分解为微观和宏观纹理。然后，计算高度、特征和混合纹理参数，以在微观和宏观层面表征路面 3D 纹理。接下来，获得的路面纹理参数通过线性和神经网络模型用于预测 SFC。包括路面微观和宏观纹理参数的神经网络模型显示出比其他模型更好的性能，并且足以预测 SFC。此外，路面微观纹理比宏观纹理对 SFC 的贡献更大。获得的路面纹理参数通过线性和神经网络模型用于预测 SFC。包括路面微观和宏观纹理参数的神经网络模型显示出比其他模型更好的性能，并且足以预测 SFC。此外，路面微观纹理比宏观纹理对 SFC 的贡献更大。获得的路面纹理参数通过线性和神经网络模型用于预测 SFC。包括路面微观和宏观纹理参数的神经网络模型显示出比其他模型更好的性能，并且足以预测 SFC。此外，路面微观纹理比宏观纹理对 SFC 的贡献更大。