Evaluation of Pavement Skid Resistance Using Surface Three-Dimensional Texture Data

Abstract

Pavement skid resistance is an important guarantee for driving safety. However, it is very difficult to determine the exact friction in a field environment. In order to overcome the limitations of traditional evaluation methods, the effect mechanism of surface 3D (three-dimensional) texture on skid resistance was firstly analyzed. Then the surface 3D texture of pavement was acquired through an improved binocular reconstruction method. Additionally, the relationship between friction coefficient and 3D texture was also analyzed. Subsequently, under the concept of IFI (international friction index) used to harmonize different detection methods of skid resistance, the evaluation model of skid resistance based 3D texture was further established. The results showed that the multiple quadratic multinomial regression model can well describe the relationship between skid resistance and texture indicators. The establishment of an improved evaluation model is simple to operate and implement. It can directly evaluate the skid resistance on pavement surface once the aggregates' type and 3D texture are known. This evaluation model not only overcomes the challenges of friction coefficient with a strong conditional restriction, but also provides a harmonious approach for different detection methods in the evaluation of pavement skid resistance.

Pavement Friction Prediction Using 3D Texture Parameters

“…The fractal nature of pavement surfaces in relation to a coefficient of friction was discussed in [25][26][27]. An evaluation model of pavement skid resistance using 3D texture data (mean texture depth, skewness, average spacing of single peaks, and root-mean-square wavelength) was established, and satisfactory test results were achieved in the study [28]. Meanwhile, [29] explored the use of spectrum analysis to investigate the texture-friction relationship; the authors found a good correlation between texture parameters and side force coefficient of friction at higher speeds, but not much significant correlation was obtained at lower speeds or by using a British pendulum tester.…”

Regeneration of pavement surface textures using M‐sigmoid‐normalized generative adversarial networks

“…(2016) adopted an effective water evacuation area to postulate tire–pavement noise pollution and friction performance. Similar applications can also be seen in pavement performance predictions using 3D statistical parameters (Cesbron et al., 2009; Q. J. Li et al., 2017; Y. Wang et al., 2020), Hilbert–Huang transform (Kane et al., 2015; Rado et al., 2014; M. Yu et al., 2021), power spectrum (D. Chen et al., 2015; Zhan et al., 2020, 2022), wavelet transform (Yang et al., 2018, 2020), and fractal analysis (Kokkalis & Olympia, 1998; Kokkalis et al., 2002; Cheng Liu et al., 2021). Among these parameters, the fractal dimension ($${D}_f$$, Miao et al., 2014; Kokkalis, 1998; Villani et al., 2014), with the corresponding root mean square roughness (RMS), seems to be the most effective.…”

Evaluate Pavement Skid Resistance Performance Based on Bayesian-LightGBM Using 3D Surface Macrotexture Data

“…To effectively describe the 3D texture information of asphalt pavements, ten macrotexture features are calculated and extracted to evaluate the skid resistance based on MATLAB [ 13 , 17 , 42 , 43 ], as shown in Table 2 .…”

“…Han et al [ 16 ] significantly improved the accuracy of the prediction model of pavement friction coefficient by analyzing the pavement skid resistance considering the micro-texture features of the pavement surface. Wang et al [ 17 ] used binocular reconstruction method to obtain 3D texture data of the pavement surface and analyzed the relationship between 3D texture features and friction coefficient. It is proved that the aggregate type and 3D texture can directly evaluate the skid resistance performance of pavement.…”