Wavelet based macrotexture analysis for pavement friction prediction

Yang, Guangwei; Li, Joshua; Zhan, You; Wang, Kelvin C. P.; Wang, Chaohui

doi:10.1007/s12205-017-1165-x

Cited by 47 publications

(22 citation statements)

References 14 publications

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“…However, the linear correlation between the wavelengths from 6.4 to 12.8mm and friction is poor. Similar results were obtained by Yang et al (2018).…”

Section: Wavelet Analysis Of Artificial Fractal Profilessupporting

confidence: 90%

“…Wavelet theory (Daubechies, 1998) with filtering function provides a method to decompose the profiles into different scales. As Daubechies (Db) are orthogonal and compactly supported, which are desirable characteristics for the iterative decomposition, they are widely used to explore the influence of the texture of each scale on pavement performance (Wei et al , 2005; Yang et al , 2018; Zelelew et al , 2014).…”

Section: Wavelet Analysis Of Artificial Fractal Profilesmentioning

confidence: 99%

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Discrete element analysis of friction performance for tire-road interaction

Pan

Che

et al. 2020

ILT

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Purpose This study aims to investigate the influence of surface texture distribution in respect to the procedure of pavement surface wear on friction performance. Design/methodology/approach The Weierstrass–Mandelbrot (W-M) equation is used to appropriate pavement surface profile. Through this approximation, artificial rough profiles by combining fractal parameters and conventional statistical parameters for different macro-texture are created to simulate the procedure of pavement surface wear. Those artificial profiles are then imported into discrete element model to calculate the interaction forces and friction coefficient between rolling tire and road. Furthermore, wavelet theory is used to decompose the profiles into different scales and explore the correlation between the profiles of each scale and pavement friction. Findings The influence of tire vertical displacement (TVD) on friction coefficient is greater than fractal dimension of road surface texture. When TVD decreases, the profiles can provide higher friction, but the rolling stability of tire is poor. The optimal fractal dimension of road surface is about 1.5 when considering friction performance. The pavement friction performance improves with wavelength from 0.4 to 6.4mm and decreases with wavelength from 12.8 to 51.2mm. Originality/value Artificial fractal curves are generated and analyzed by combining W-M function with traditional parameter, which can also be used to analyze the influence of texture distribution on other pavement performance. The preliminary research provides a potential approach for the evaluation of pavement friction performance. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0499/

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“…However, the linear correlation between the wavelengths from 6.4 to 12.8mm and friction is poor. Similar results were obtained by Yang et al (2018).…”

Section: Wavelet Analysis Of Artificial Fractal Profilessupporting

confidence: 90%

Section: Wavelet Analysis Of Artificial Fractal Profilesmentioning

confidence: 99%

Discrete element analysis of friction performance for tire-road interaction

Pan

Che

et al. 2020

ILT

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“…Another friction-texture relationship was based on a new signal processing method called Huang-Hilbert transformation [23]. The suitability of discrete wavelet transform used to decompose pavement surface macrotexture profile data into multiscale characteristics for pavement friction prediction was investigated in [24]. The fractal nature of pavement surfaces in relation to a coefficient of friction was discussed in [25][26][27].…”

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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“…Macro-texture is created by unevenness on the pavement surface of the wear layer caused by protruding grains of aggregate. Micro-texture, on the other hand, is created by small irregularities with the depth of tenths of a millimeter caused by the surface roughness of coarse grains and the occurrence of fine grains [3]. MTD research allowed characterizing the macro-texture of five different SMA samples, while microscopic analysis tested the state of the aggregate roughness.…”

Section: Discussionmentioning

confidence: 99%

“…Wet pavement skidding due to inadequate surface texture or friction contributes to 20% to 35% of all wet weather crashes. Many of the past studies of pavement surface texture and friction have deficiency in selecting the model forms or variables and interpreting the models and (or) the findings or have recommended further investigation [3]. Micro-texture depends on polishing resistance of coarse aggregate and content of fine aggregate in asphalt mixture.…”

Section: Introductionmentioning

confidence: 99%

The effective estimation of skid resistance of SMA pavements in relevant aspect of safety and durability of asphalt roads

Stankiewicz

Slabonski²,

Slabonska³

2018

MATEC Web Conf.

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The role of skid resistance of road pavements in ensuring safety of road traffic becomes particularly important when such surfaces become moist or wet. The various types of aggregates and special additives play an important role in asphalt mixtures like SMA, because they fill voids in paving mix and improve the cohesion of asphalt binder. The paper presents the evaluation of skid resistance of five kinds of SMA pavements, during exploitation, based on macro-texture and micro-texture comprehensive analysis. Tests were performed on five research pavement sections made from SMA on roads located in the southern part of Poland. SRT 3 equipment and methodology were used to all estimation of pavement roughness. The micro-texture and macro-texture of asphalt surface in pavements evolve under the influence of traffic and synergistic environmental interaction. The experimental parts of SMA pavements were observed in relevant aspect of skid resistance and durability using electronic microscopy type KEYENCE VHX-6000 Digital. The results of evaluation were compared with data from a literature review.

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Wavelet based macrotexture analysis for pavement friction prediction

Cited by 47 publications

References 14 publications

Discrete element analysis of friction performance for tire-road interaction

Discrete element analysis of friction performance for tire-road interaction

Pavement Friction Prediction Using 3D Texture Parameters

The effective estimation of skid resistance of SMA pavements in relevant aspect of safety and durability of asphalt roads

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