Research on the Prediction Model of the Friction Coefficient of Asphalt Pavement Based on Tire-Pavement Coupling

The correlations between pavement texture and tire pressure with the actual tire-road contact area were first investigated according to the tire-road static contact characteristics; on this basis, the influence mechanisms of speed and pavement texture on the pavement friction coefficient were system...

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Main Authors: Miao Yu, Xinquan Xu, Chuanhai Wu, Shanqiang Li, Mingxia Li, Haifeng Chen
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Advances in Materials Science and Engineering
Online Access:http://dx.doi.org/10.1155/2021/6650525
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spelling doaj-18362d45403848088e46c562e8d8c02c2021-02-22T00:01:54ZengHindawi LimitedAdvances in Materials Science and Engineering1687-84422021-01-01202110.1155/2021/6650525Research on the Prediction Model of the Friction Coefficient of Asphalt Pavement Based on Tire-Pavement CouplingMiao Yu0Xinquan Xu1Chuanhai Wu2Shanqiang Li3Mingxia Li4Haifeng Chen5National and Regional Engineering Lab for Transportation Construction MaterialsHualu Transport Technology Co.,Ltd.Hualu Transport Technology Co.,Ltd.Hualu Transport Technology Co.,Ltd.National and Regional Engineering Lab for Transportation Construction MaterialsHighway and Transportation Management Center of Shaoxing CityThe correlations between pavement texture and tire pressure with the actual tire-road contact area were first investigated according to the tire-road static contact characteristics; on this basis, the influence mechanisms of speed and pavement texture on the pavement friction coefficient were systematically explored from the angle of tire-road coupling system dynamics via the self-developed dynamic testing system of tire-pavement friction. By integrating the above influence factors, the BP neural network method was applied to the regression of the prediction model for the asphalt pavement friction coefficient. Through the comparison between the model measured value and estimated value, their correlation coefficient R2 reached 0.73, indicating that this model is of satisfactory prediction accuracy and applicable to the antiskid design of asphalt pavement.http://dx.doi.org/10.1155/2021/6650525
collection DOAJ
language English
format Article
sources DOAJ
author Miao Yu
Xinquan Xu
Chuanhai Wu
Shanqiang Li
Mingxia Li
Haifeng Chen
spellingShingle Miao Yu
Xinquan Xu
Chuanhai Wu
Shanqiang Li
Mingxia Li
Haifeng Chen
Research on the Prediction Model of the Friction Coefficient of Asphalt Pavement Based on Tire-Pavement Coupling
Advances in Materials Science and Engineering
author_facet Miao Yu
Xinquan Xu
Chuanhai Wu
Shanqiang Li
Mingxia Li
Haifeng Chen
author_sort Miao Yu
title Research on the Prediction Model of the Friction Coefficient of Asphalt Pavement Based on Tire-Pavement Coupling
title_short Research on the Prediction Model of the Friction Coefficient of Asphalt Pavement Based on Tire-Pavement Coupling
title_full Research on the Prediction Model of the Friction Coefficient of Asphalt Pavement Based on Tire-Pavement Coupling
title_fullStr Research on the Prediction Model of the Friction Coefficient of Asphalt Pavement Based on Tire-Pavement Coupling
title_full_unstemmed Research on the Prediction Model of the Friction Coefficient of Asphalt Pavement Based on Tire-Pavement Coupling
title_sort research on the prediction model of the friction coefficient of asphalt pavement based on tire-pavement coupling
publisher Hindawi Limited
series Advances in Materials Science and Engineering
issn 1687-8442
publishDate 2021-01-01
description The correlations between pavement texture and tire pressure with the actual tire-road contact area were first investigated according to the tire-road static contact characteristics; on this basis, the influence mechanisms of speed and pavement texture on the pavement friction coefficient were systematically explored from the angle of tire-road coupling system dynamics via the self-developed dynamic testing system of tire-pavement friction. By integrating the above influence factors, the BP neural network method was applied to the regression of the prediction model for the asphalt pavement friction coefficient. Through the comparison between the model measured value and estimated value, their correlation coefficient R2 reached 0.73, indicating that this model is of satisfactory prediction accuracy and applicable to the antiskid design of asphalt pavement.
url http://dx.doi.org/10.1155/2021/6650525
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