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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Hindawi Limited
2021-01-01
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Series: | Advances in Materials Science and Engineering |
Online Access: | http://dx.doi.org/10.1155/2021/6650525 |
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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 |
work_keys_str_mv |
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1714852891148156928 |