Spectral Techniques Applied to Evaluate Pavement Friction and Surface Texture

Yuan, Yan; Ran, Maoping; Sandberg, Ulf; Zhou, Xiao; Xiao, Shulong

doi:10.3390/coatings10040424

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Pavement Friction Prediction Using 3D Texture Parameters

2021

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This article deals with the possibility of predicting skid resistance based on non-contact scanning of the road surface. The study is based on comparing pavement texture parameters with coefficients of friction measured on a wide variety of road surfaces, while other test conditions were the same and constant. The measurements of the coefficient of friction were performed using a pendulum tester. The pavement texture was measured using a static road scanner, and 85 different 3D texture parameters were calculated. The study shows that the determination of the friction using only single texture parameters is not sufficient. Based on this statement, the next part of the research analyzed the influence of the mutual combination of surface texture parameters. A linear regression model was chosen to determine the friction coefficient prediction formula based on the combination of texture parameters. Statistically, the most significant parameters in the prediction model proved to be the valley material portion, characterizing the microtexture, and the arithmetic mean curvature, characterizing the pavement macrotexture. The obtained regression model proved to be statistically significant with R2 = 0.81 for Pendulum Test Value prediction.

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Section: Introductionmentioning

confidence: 99%

Pavement Friction Prediction Using 3D Texture Parameters

2021

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“…The R 2 of the right and left wheel tracks are 0.6585 and 0.6275, respectively. According to previous research on the correlation between road pavement texture and pavement adhesion [53], the result of this study shows a high correlation, which means that the volume parameter V mp used in this study is a reliable index to characterize the 3D aspahlt pavement texture. By observing the correlation trend in the figure, we can see that the CAT values on the left and right sides have a consistent change pattern.…”

Section: Correlation Between Cat and V Mpmentioning

confidence: 55%

Analysis of Road Surface Texture for Asphalt Pavement Adhesion Assessment Using 3D Laser Technology

Liang,

Pagano,

Oddone

et al. 2024

Remote Sensing

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Pavement adhesion plays a crucial role in driving safety, while traditional test methods exhibit some limitations. To improve the efficiency and accuracy of asphalt pavement texture characterization and adhesion assessments, this paper uses three-dimensional (3D) laser technology to detect the continuous point cloud data of road surface and reconstruct the 3D topography of pavement texture. On this basis, a volume parameter Volume of peak materials (Vmp) is innovatively proposed to comprehensively characterize the 3D spatial characteristics of road surface texture. The correlation analysis between the proposed Vmp and the traditional adhesion evaluation index Transversal Adhesion Coefficient (CAT) is conducted, and then refined graded adhesion prediction models based on the proposed Vmp are proposed. Results show that the proposed volume parameter Vmp can reliably and accurately characterize the asphalt pavement texture by considering more structural properties of the road surface texture. According to the research findings of this paper, it is feasible to achieve rapid and correct assessment of asphalt pavement adhesion using 3D laser detection technology by comprehensively considering the 3D characteristics of the road surface texture.

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“…Given various advantages of non-contact measurement methods, many studies have demonstrated the link between pavement texture and pavement skid resistance (Dong et al, 2022;Han et al, 2019;Kogbara et al, 2016). Yan et al (2020) adopted a laser scanner to obtain point clouds data. They characterize pavement texture features with texture spectra using selected in-service pavements.…”

Section: Introductionmentioning

confidence: 99%

“…Although such methods (ASTM E303‐93, 2018, JTG 3450‐2019, 2019) and equipment (Han et al., 2018; Wasilewska et al., 2020) are continually being improved, the conventional direct testing method will always be time‐consuming and inefficient concerning the traffic opening. The non‐contact testing and pavement texture analysis techniques based on digital images are becoming increasingly indispensable tools for skid resistance studies due to the portability of the sampling equipment (Puzzo et al., 2017; Vilaca et al., 2010) and the efficacy of the analysis (Wesolowski et al., 2018; Yan et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effective contact texture region aware pavement skid resistance prediction via convolutional neural network

Shi

Niu

et al. 2023

Computer aided Civil Eng

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The surface texture of asphalt pavement has a significant effect on skid resistance performance. However, its contribution to the performance of skid resistance is non‐homogeneous and subjects to local validity. There are also a few deep learning models that take into account the effective contact texture region. This paper proposes a convolutional neural network model based on the effective contact texture region, containing macro‐ and micro‐scale awareness sub‐modules. In this study, the asphalt mixture with varying gradations was designed to accurately obtain the effective contact texture region. Then, the textures were disentangled into macro‐ and micro‐texture scales by applying the fast Fourier transform and fed into the model for training. Finally, the area of effective contact texture region was calculated, and the effective contact ratio parameter was then proposed using the triangulation algorithm. The results showed that the effective contact texture area of pavement varies by the asphalt mixture type. The effective contact ratio parameter exhibited a significant positive correlation (Pearson correlation coefficient is 0.901, R2= 0.8129) with skid resistance performance and was also influenced by key sieve aggregate content from 2.36 to 4.75 mm. The data of effective contact texture region following disentanglement significantly released the model performance (the relative error dropped to 1.81%). The model exhibited improved precision and performance, which can be utilized as an efficient, non‐contact alternative method for skid resistance analysis.

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Spectral Techniques Applied to Evaluate Pavement Friction and Surface Texture

Cited by 11 publications

References 12 publications

Pavement Friction Prediction Using 3D Texture Parameters

Pavement Friction Prediction Using 3D Texture Parameters

Analysis of Road Surface Texture for Asphalt Pavement Adhesion Assessment Using 3D Laser Technology

Effective contact texture region aware pavement skid resistance prediction via convolutional neural network

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