路面抗滑性能检测与预估方法综述

谭忆秋; 肖神清; 熊学堂

doi:10.19818/j.cnki.1671-1637.2021.04.002

路面抗滑性能检测与预估方法综述

doi: 10.19818/j.cnki.1671-1637.2021.04.002

谭忆秋^{1, 2,},
肖神清¹,
熊学堂¹

1.
哈尔滨工业大学交通科学与工程学院，黑龙江哈尔滨 150090
2.
哈尔滨工业大学城市水资源与水环境国家重点实验室，黑龙江哈尔滨 150090

基金项目:

国家自然科学基金项目 U20A20315

国家重点研发计划项目 2016YFE0202400

详细信息

作者简介:
谭忆秋(1968-)，女，吉林德惠人，哈尔滨工业大学教授，工学博士，从事道路材料功能特性与应用研究研究

中图分类号: U416.217
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- 被引次数: 0
出版历程
- 收稿日期: 2021-03-15
- 刊出日期: 2021-08-25

Review on detection and prediction methods for pavement skid resistance

1.
School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, Heilongjiang, China
2.
State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, Heilongjiang, China

Funds:

National Natural Science Foundation of China U20A20315

National Key Research and Development Program of China 2016YFE0202400

More Information

Author Bio:
TAN Yi-qiu(1968-), female, professor, PhD, yiqiutan@163.com

Article Text (Baidu Translation)

摘要

摘要: 针对道路工程中路面抗滑性能检测与预估中存在的问题，分别从力学机理、检测方法、预估模型3个方面系统梳理了路面抗滑性能相关成果及进展；基于传统的库伦摩擦定律，阐明了路面抗滑性能的摩擦力学机理，从路面、轮胎以及接触环境3个方面总结了抗滑性能的影响因素；总结了抗滑性能的直接与间接测量方法，重点分析了路表纹理检测技术的难点以及测试数据的预处理方法；对比分析了抗滑性能预估的传统经验统计模型、力学模型以及机器学习等方法的优点与不足。研究结果表明：影响路面抗滑性能的因素众多，传统的摩擦理论难以描述橡胶与粗糙表面接触界面第三体的力学行为，因此，需要进一步研究具有润滑介质的接触界面摩擦机理；抗滑性能直接检测方法功能单一，成本较高，表面纹理的高速无接触自动化检测更加符合未来智能一体化检测需求，但高精度、大量程检测以及数据清洗仍是需要突破的瓶颈；相比现行的各类预估模型，经验统计模型及机器学习弱化了胎-路接触特性，导致预估模型缺乏扩展性；推行有限元仿真力学模型方法，有望进一步揭示复杂物理场下的摩擦机理，从而开发更精准、高效的路面抗滑预估模型。
- 道路工程 /
- 抗滑性能 /
- 智能检测 /
- 预估方法 /
- 机器学习
Abstract: The relevant achievements and progress of skid resistance performance of pavements were systematically reviewed based on three aspects: mechanical mechanism, detection methods, and prediction models. The friction mechanism of pavement skid resistance was introduced based on the traditional Coulomb friction law, and the factors influencing the skid resistance were summarized based on road surface, tire, and contact environment. The direct and indirect measurement methods of skid resistance were evaluated, and the difficulties of road-surface texture detection and test data preprocessing methods were analyzed. The advantages and disadvantages of the skid resistance prediction methods, including traditional empirical statistical models, mechanical models, and machine learning, were compared and analyzed. Analysis results show that many factors influence the skid resistance of pavements, and it is difficult to describe the mechanical behavior of the third body between rubber and rough surface. Thus, further investigations are required to reveal the friction mechanism toward the contact interface with the lubrication medium.For the single function and high cost of the direct detection of skid resistance, surface texture detection using automatic high-speed noncontact measurements will be more in line with future intelligent integrated requirements. However, high-precision and large-range detection and data cleaning are still bottlenecks that need to break through. Compared to the existing prediction models, the tire-pavement contact characteristics are weakened by the empirical statistical model and machine learning, resulting in a lack of scalability in the prediction model. The implementation of the finite element simulation model method is expected to reveal the friction mechanism under complex physical fields to develop a more precise and efficient model for predicting the skid resistance of pavements. 1 tab, 13 figs, 89 refs.
- road engineering /
- skid resistance /
- intelligence detection /
- prediction method /
- machine learning

HTML全文

图 1 不同滑移率下轮胎的附着系数

Figure 1. Adhesion coefficients under different slip ratios of tire

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图 2 胎-路摩擦机理

Figure 2. Tire-pavement friction mechanism

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图 3 抗滑性能影响因素

Figure 3. Influencing factors of skid resistance

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图 4 路表状态高速自动检测系统

Figure 4. High-speed automatic detection system of road surface condition

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图 5 路表纹理激光检测

Figure 5. Road surface texture laser detection

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图 6 足尺环道横向摩擦因数与平均断面深度

Figure 6. SFC and MPD of full-scale track

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图 7 横向摩擦因数与平均断面深度的关系

Figure 7. Relationship between SFC and MPD

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图 8 基于路表纹理的抗滑性能预估方法

Figure 8. Prediction methods of skid resistance performance based on pavement surface texture

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图 9 轮胎-流体-路面有限元模型

Figure 9. Finite element model of tire-fluid-pavement

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图 10 橡胶粗糙表面多尺度接触

Figure 10. Multi-scale contact between rubber and rough surface

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图 11 人工智能方法及深度学习框架

Figure 11. Artificial intelligence methods and deep learning framework

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图 12 卷积神经网络基本框架及运算示意

Figure 12. Basic framework and operation illustration of CNN

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图 13 摩擦因数的残差网络结构

Figure 13. Structure of friction-residual networks

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表 1 路面抗滑性能检测方法

Table 1. Pavement skid resistance test methods

检测方式	检测方法	代表性装置	检测特点	文献、规范标准
直接测量	横向力检测	英国Mu-Meter、SCRIM	检测速度为64 km·h^-1; 喷水速率为1.2 L·min^-1；数据采集为25~125 mm·次^-1，以1 m间隔；适用于直线路段、曲线和陡坡路段。	ASTM E 670
	(锁轮)纵向力检测	美国Trailer	检测速度为64 km·h^-1; 水膜厚度为0.5 mm; 数据采集为完全锁定后1~3 s取均值；适用于直线路段。	ASTM E 274
	固定滑移率	英国Grip Tester、芬兰BV-11	滑移率为12%~ 20%；喷水速率为1.2 L·min^-1; 数据采集为25~125 mm·次^-1，以1 m间隔平均；适用于直线路段。	ASTM E 1844
	可变滑移率	法国IMAG、挪威RUNAR	滑移率为0~100%；水膜厚度为0.5 mm；数据采集间距小于2.5 mm；适用于直线路段、曲线和陡坡路段。	ASTM E 1859
	小型移动式摩擦测试仪	步行式摩擦测试仪(Walking Friction Tester, WFT)	检测方式为人工手推；喷水速率为45 mL·min^-1；接触压力为99.2 kPa；适用于室内或现场。	[24]
	旋转摩擦因数检测	动态摩擦因数仪(Dynamic Friction Tester, DFT)	检测速度为5~89 km·h^-1；喷水速率为3.6 L·min^-1；适用于室内及现场。	ASTM E 1911
	摆式摩擦因数检测	英式摆锤(British Pendulum Tester, BPT)	检测速度为10 km·h^-1；适用于室内及现场。	ASTM E 303
	刹车距离测量	客车或轻型卡车	检测速度为64 km·h^-1；适用于直线路段。	ASTM E 445
	减速度测量	加速度计	检测速度为32~48 km·h^-1；适用于直线路段。	ASTM E 2101
间接测量	铺沙法	铺沙仪	通过相关特征参数来表征路表的抗滑性能，如平均构造深度。	ASTM E 965、ISO 10844
	排水法	路面渗水仪		ASTM E 2380
	表面纹理	激光扫描仪		ASTM E 2157、ASTM E 1845、ISO 13473
	其他方法	力/声/温度传感器		[25]~[27]

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参考文献(89)

[1]	邝宏柱, 廖志高, 柳本民. 高速公路隧道路面抗滑性能评价标准研究[J]. 公路, 2007, 4(9): 85-88. doi: 10.3969/j.issn.0451-0712.2007.09.020 KUANG Hong-zhu, LIAO Zhi-gao, LIU Ben-min. A study on evaluation standard of skid resistance performance for expressway tunnel pavement[J]. Highway, 2007, 4(9): 85-88. (in Chinese) doi: 10.3969/j.issn.0451-0712.2007.09.020
[2]	NAJAFI S, FLINTSCH G W, MEDINA A. Linking roadway crashes and tire-pavement friction: a case study[J]. International Journal of Pavement Engineering, 2017, 18(2): 119-127. doi: 10.1080/10298436.2015.1039005
[3]	KOKKALIS A G, PANAGOULI O K. Fractal evaluation of pavement skid resistance variations. I: surface wetting[J]. Chaos Solitons and Fractals, 1998, 9(11): 1875-1890. doi: 10.1016/S0960-0779(97)00138-0
[4]	AHAMMED M A, TIGHE R L. Early-life, long-term, and seasonal variations in skid resistance in flexible and rigid pavements[J]. Transportation Research Record, 2009(2094): 112-120. http://www.researchgate.net/publication/238197006_Early-Life_Long-Term_and_Seasonal_Variations_in_Skid_Resistance_in_Flexible_and_Rigid_Pavements
[5]	HALL J W, SMITH K L, TITUS-GLOVER L, et al. Guide for pavement friction[R]. Washington DC: National Cooperative Highway Research Program, 2009.
[6]	王旭东. 足尺路面试验环道路面结构与材料设计[J]. 公路交通科技, 2017, 34(6): 30-37. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK201706005.htm WANG Xu-dong. Design of pavement structure and material for full-scale test track[J]. Journal of Highway and Transportation Research and Development, 2017, 34(6): 30-37. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK201706005.htm
[7]	ZHANG Jun-ning, YANG Shao-pu, LI Shao-hua, et al. Influence of vehicle-road coupled vibration on tire adhesion based on nonlinear foundation[J]. Applied Mathematics and Mechanics (English Edition), 2021, 42: 607-624. doi: 10.1007/s10483-021-2724-6
[8]	黄晓明, 郑彬双. 沥青路面抗滑性能研究现状与展望[J]. 中国公路学报, 2019, 32(4): 32-49. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201904004.htm HUANG Xiao-ming, ZHENG Bin-shuang. Research status and progress for skid resistance performance of asphalt pavements[J]. China Journal of Highway and Transport, 2019, 32(4): 32-49. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201904004.htm
[9]	GROSCH K. Visco-elastic properties and the friction of solids: relation between the friction and visco-elastic properties of rubber[J]. Nature, 1963, 197: 858-859. doi: 10.1038/197858a0
[10]	LORENZ B, PYCKHOUT-HINTZEN W, PERSSON B N J. Master curve of viscoelastic solid: using causality to determine the optimal shifting procedure, and to test the accuracy of measured data[J]. Polymer, 2014, 55(2): 565-571. doi: 10.1016/j.polymer.2013.12.033
[11]	LORENZ B, OH Y R, NAM S K, et al. Rubber friction on road surfaces: experiment and theory for low sliding speeds[J]. Journal of Chemical Physics, 2015, 142(19): 194701. doi: 10.1063/1.4919221
[12]	SCARAGGI M, PERSSON B N J. Rolling friction: comparison of analytical theory with exact numerical results[J]. Tribology Letters, 2014, 55(1): 15-21. doi: 10.1007/s11249-014-0327-y
[13]	MATAEI B, ZAKERI H, ZAHEDI M, et al. Pavement friction and skid resistance measurement methods: a literature review[J]. Open Journal of Civil Engineering, 2016, 6(4): 537-565. doi: 10.4236/ojce.2016.64046
[14]	LEI Yong, HU Xiao-di, WANG Hai-nian, et al. Effects of vehicle speeds on the hydrodynamic pressure of pavement surface: measurement with a designed device[J]. Measurement, 2017, 98: 1-9. http://www.sciencedirect.com/science/article/pii/S026322411630673X
[15]	ANUPAM K. Numerical simulation of vehicle hydroplaning and skid resistance on grooved pavement[D]. Singapore: National University of Singapore, 2012.
[16]	KOGBARA R B, MASAD E A, KASSEM E, et al. A state-of-the-art review of parameters influencing measurement and modeling of skid resistance of asphalt pavements[J]. Construction and Building Materials, 2016, 114: 602-617. doi: 10.1016/j.conbuildmat.2016.04.002
[17]	TAN Tan, FAN Ze-peng, XING Chao, et al. Evaluation of geometric characteristics of fine aggregate and its impact on viscoelastic property of asphalt mortar[J]. Applied Sciences, 2019, DOI: 10.3390/app10010130.
[18]	KANE M, EDMONDSON V. Long-term skid resistance of asphalt surfacings and aggregates' mineralogical composition: generalisation to pavements made of different aggregate types[J]. Wear, 2020, 454/455: 203339. doi: 10.1016/j.wear.2020.203339
[19]	DE LUCA M, ABBONDATI F, PIROZZI M, et al. Preliminary study on runway pavement friction decay using data mining[J]. Transportation Research Procedia, 2016, 14: 3751-3760. doi: 10.1016/j.trpro.2016.05.460
[20]	COUTERMARSH B A, SHOOP S A. Tire slip-angle force measurements on winter surfaces[J]. Journal of Terramechanics, 2009, 46(4): 157-163. doi: 10.1016/j.jterra.2008.08.002
[21]	WAMBOLDJ C, KULAKOWSKI B T. Limitations of using skid number in accident analysis and pavement management[J]. Transportation Research Record, 1991(1311): 43-50. http://onlinepubs.trb.org/Onlinepubs/trr/1991/1311/1311-007.pdf
[22]	GROSCH K A. Rubber abrasion and tire wear[J]. Rubber Chemistry and Technology, 2008, 81(3): 470-505. doi: 10.5254/1.3548216
[23]	WANG Shao-wei, VENEZIANO D, HUANG Jiang, et al. Estimating wet-pavement exposure with precipitation data: final report[R]. Sacramento: California Department of Transportation (Caltrans) Division of Research and Innovation, 2006.
[24]	AL-QADI I L, FLINTSCH G W, ROOSEVELT D S, et al. Feasibility of using friction indicators to improve winter maintenance operations and mobility[R]. Washington DC: National Cooperative Highway Research Program, 2002.
[25]	HAN Sen, LIU Meng-mei, FWA T F. Testing for low-speed skid resistance of road pavements[J]. Road Materials and Pavement Design, 2020, 21(5): 1312-1325. doi: 10.1080/14680629.2018.1552619
[26]	CHEN Bo, ZHANG Xiao-ning, YU Jiang-miao, et al. Impact of contact stress distribution on skid resistance of asphalt pavements[J]. Construction and Building Materials, 2017, 133: 330-339. doi: 10.1016/j.conbuildmat.2016.12.078
[27]	KHALEGHIAN S, EMAMI A, TAHERI S. A technical survey on tire-road friction estimation[J]. Friction, 2017, 5(2): 123-146. doi: 10.1007/s40544-017-0151-0
[28]	PERERA R W, KOHN S D. NCHRP web document 42: issues in pavement smoothness[R]. Washington DC: Transportation Research Board, 2002.
[29]	MASAD E, REZAEI A, CHOWDHURY A, et al. Predicting asphalt mixture skid resistance based on aggregate characteristics[R]. Canyon: Texas Transportation Institute, 2009.
[30]	SAYERS M W, KARAMIHAS S M. Interpretation of road roughness profile data[R]. McLean: Federal Highway Administration, 1996.
[31]	GOUBERT L, BERGIERS A. About the reproducibility of texture profiles and the problem of spikes[C]//VTTI. 7th Symposium on Pavement Surface Characteristics: SURF 2012. Norfolk: VTTI, 2012: 1-14.
[32]	KATICHA S W, MOGROVEJO D E, FLINTSCH G W, et al. Adaptive spike removal method for high-speed pavement macrotexture measurements by controlling the false discovery rate[J]. Transportation Research Record, 2015(2525): 100-101. http://www.researchgate.net/publication/290471545_Adaptive_Spike_Removal_Method_for_High-Speed_Pavement_Macrotexture_Measurements_by_Controlling_the_False_Discovery_Rate
[33]	BENJAMINI Y, HOCHBERG Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing[J]. Journal of the Royal Statistical Society, Series B: Methodological, 1995, 57(1): 289-300. doi: 10.1111/j.2517-6161.1995.tb02031.x
[34]	STOREY J D, TIBSHIRANI R. Statistical significance for genomewide studies[J]. Proceedings of the National Academy of Sciences of the United States of America, 2003, 100(16): 9440-9445. doi: 10.1073/pnas.1530509100
[35]	RICHARD C, SOHANEY, ROBERT O R. Three dimensional pavement texture evaluation at Mn/ROAD[R]. Austin: Minnesota Department of Transportation Research Services Section, 2012.
[36]	DONG N, PROZZI J A, NI F. Reconstruction of 3D pavement texture on handling dropouts and spikes using multiple data processing methods[J]. Sensors, 2019, DOI: 10.3390/s19020278.
[37]	CHU L J, FWA T F. Pavement skid resistance consideration in rain-related wet-weather speed limits determination[J]. Road Materials and Pavement Design, 2018, 19(2): 334-352. doi: 10.1080/14680629.2016.1261723
[38]	WASILEWSKA M, GARDZIEJCZYK W, GIERASIMIUK P. Comparison of measurement methods used for evaluation the skid resistance of road pavements in Poland—case study[J]. International Journal of Pavement Engineering, 2020, 21(13): 1662-1668. doi: 10.1080/10298436.2018.1562188
[39]	LEU M C, HENRY J J. Prediction of skid resistance as a function of speed from pavement texture[J]. Transportation Research Record, 1978(666): 7-13. http://www.researchgate.net/publication/282991986_PREDICTION_OF_SKID_RESISTANCE_AS_A_FUNCTION_OF_SPEED_FROM_PAVEMENT_TEXTURE_MEASUREMENTS
[40]	FUVLÖP I A, BOGÁRDI I, GULYÁS A, et al. Use of friction and texture in pavement performance modeling[J]. Journal of Transportation Engineering, 2000, 126(3): 243-248. doi: 10.1061/(ASCE)0733-947X(2000)126:3(243)
[41]	ANDRIEJAUSKASA T, VOROBJOVASA V, MIELONASB V. Evaluation of skid resistance characteristics and measurement methods[C]//VGTU. 9th International Conference on Environmental Engineering. Vilnius: VGTU, 2014: 1-8.
[42]	SENGOZ B, TOPAL A, TANYEL S. Comparison of pavement surface texture determination by sand patch test and 3D laser scanning[J]. Periodica Polytechnica Civil Engineering, 2012, 56(1): 73-78. doi: 10.3311/pp.ci.2012-1.08
[43]	UECKERMANN A, WANG D, OESER M, et al. Calculation of skid resistance from texture measurements[J]. Journal of Traffic and Transportation Engineering (English Edition), 2015, 2(1): 3-16. doi: 10.1016/j.jtte.2015.01.001
[44]	LI Lin, WANG K C P, LI Q J. Geometric texture indicators for safety on AC pavements with 1 mm 3D laser texture data[J]. International Journal of Pavement Research and Technology, 2016, 9(1): 49-62. doi: 10.1016/j.ijprt.2016.01.004
[45]	王旭东, 张蕾, 周兴业, 等. RIOHTRACK足尺路面试验环道2017年试验研究概况[J]. 公路交通科技, 2018, 35(4): 1-13. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK201804001.htm WANG Xu-dong, ZHANG Lei, ZHOU Xing-ye, et al. Review of researches of RIOHTRACK in 2017[J]. Journal of Highway and Transportation Research and Development, 2018, 35(4): 1-13. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK201804001.htm
[46]	廖亦源. 基于足尺环道的沥青路面抗滑性能衰变规律的研究[D]. 重庆: 重庆交通大学, 2019. LIAO Yi-yuan. Research on regularity of skid resistance regradation of asphalt pavement based on full-scale pavement loop[D]. Chongqing: Chongqing Jiaotong University, 2019. (in Chinese)
[47]	LI Q, YANG G, WANG K C P, et al. Novel macro- and microtexture indicators for pavement friction by using high-resolution three-dimensional surface data[J]. Transportation Research Record, 2017(2641): 164-176. http://www.researchgate.net/publication/319605459_Novel_Macro-_and_Microtexture_Indicators_for_Pavement_Friction_by_Using_High-Resolution_Three-Dimensional_Surface_Data
[48]	陈德. 沥青混合料表面构造图像评价方法及抗滑降噪性能预测研究[D]. 西安: 长安大学, 2015. CHEN De. Study on image-based texture analysis method and prediction of skid-resistance and tire/pavement noise reduction of HMA[D]. Xi'an: Chang'an University, 2015. (in Chinese)
[49]	RADO Z, KANE M. An initial attempt to develop an empirical relation between texture and pavement friction using the HHT approach[J]. Wear, 2014, 309(1/2): 233-246. doi: 10.1080/10298436.2014.972956
[50]	ZELELEW H, KHASAWNEH M, ABBAS A. Wavelet-based characterisation of asphalt pavement surface macro-texture[J]. Road Materials and Pavement Design, 2014, 15(3): 622-641. doi: 10.1080/14680629.2014.908137
[51]	周兴林, 肖神清, 刘万康, 等. 沥青路面表面纹理的多重分形特征及其磨光行为[J]. 东南大学学报(自然科学版), 2018, 48(1): 175-180. https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX201801027.htm ZHOU Xing-lin, XIAO Shen-qing, LIU Wan-kang, et al. Multifractal characteristics and polishing behaviors of surface texture on asphalt pavement[J]. Journal of Southeast University (Natural Science Edition), 2018, 48(1): 175-180. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX201801027.htm
[52]	周兴林, 肖神清, 肖旺新, 等. 粗集料表面纹理粗糙度的多重分形评价[J]. 华中科技大学学报(自然科学版), 2017, 45(2): 29-33. https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG201702006.htm ZHOU Xing-lin, XIAO Shen-qing, XIAO Wang-xin, et al. Multi-fractal evaluation on roughness of coarse aggregate surface texture[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2017, 45(2): 29-33. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG201702006.htm
[53]	XIAO Shen-qing, TAN Yi-qiu, XING Chao, et al. Scale demarcation of self-affine surface of coarse aggregate and its relationship with rubber friction[J]. Road Materials and Pavement Design, 2020, DOI: 10.1080/14680629.2020.1728365.
[54]	YU Miao, YOU Zhan-ping, WU Guo-xiong, et al. Measurement and modeling of skid resistance of asphalt pavement: a review[J]. Construction and Building Materials, 2020, 260: 119878. doi: 10.1016/j.conbuildmat.2020.119878
[55]	BROWNE A, CHENG H, KISTLER A. Dynamic hydroplaning of pneumatic tires[J]. Wear, 1972, 20(1): 1-28. doi: 10.1016/0043-1648(72)90284-0
[56]	GROGGER H, WEISS M. Calculation of the three-dimensional free surface flow around an automobile tire[J]. Tire Science and Technology, 1996, 24(1): 39-49. doi: 10.2346/1.2137511
[57]	MARTIN C. Hydroplaning of tire hydroplaning: final report[R]. Atlanta: Georgia Institute of Technology, 1966.
[58]	STOCKER A J, DOTSON J T, IVEY D L. Automobile tire hydroplaning: a study of wheel spin-down and other variables[R]. Canyon: Texas Transportation Institute, 1974.
[59]	DINESCU C, HIRSCH C, LEONARD B, et al. Fluid-structure interaction model for hydroplaning simulations[J]. SAE International, 2006, DOI: 10.4271/2006-01-1190.
[60]	CHO J, LEE H, SOHN J, et al. Numerical investigation of hydroplaning characteristics of three-dimensional patterned tire[J]. European Journal of Mechanics A: Solids, 2006, 25(6): 914-926. doi: 10.1016/j.euromechsol.2006.02.007
[61]	FWA T F. Skid resistance determination for pavement management and wet-weather road safety[J]. International Journal of Transportation Science and Technology, 2017, 6(3): 217-227. doi: 10.1016/j.ijtst.2017.08.001
[62]	CHU L, FWA T F. Incorporating pavement skid resistance and hydroplaning risk considerations in asphalt mix design[J]. Journal of Transportation Engineering, 2016, 142(10): 0401603. http://www.researchgate.net/publication/303358028_Incorporating_Pavement_Skid_Resistance_and_Hydroplaning_Risk_Considerations_in_Asphalt_Mix_Design
[63]	FWAT F, PASINDU H R, ONG G P. Critical rut depth for pavement maintenance based on vehicle skidding and hydroplaning consideration[J]. Journal of Transportation Engineering, 2012, 138(4): 423-429. doi: 10.1061/(ASCE)TE.1943-5436.0000336
[64]	ANUPAM K, SRIRANGAM S K, SCARPAS A, et al. Influence of temperature on tire-pavement friction: analyses[J]. Transportation Research Record, 2013(2369): 114-124. http://www.researchgate.net/publication/262419305_Influence_of_Temperature_on_Tire-Pavement_Friction-I_Laboratory_Tests_and_Finite_Element_Modeling
[65]	SRIRANGAM S K, ANUPAM K, KASBERGEN C, et al. Analysis of asphalt mix surface-tread rubber interaction by using finite element method[J]. Journal of Traffic and Transportation Engineering (English Edition), 2017, 4(4): 395-402. doi: 10.1016/j.jtte.2017.07.004
[66]	SRIRANGAM S K, ANUPAM K, SCARPAS A, et al. Development of a thermomechanical tyre-pavement interaction model[J]. International Journal of Pavement Engineering, 2014, 16(8): 721-729. http://www.researchgate.net/profile/Santosh_Srirangam/publication/262418801_Development_of_a_Thermomechanical_Tire-Pavement_Interaction_Model/links/55db4cb508aec156b9afe73b.pdf
[67]	SRIRANGAM S K, ANUPAM K, SCARPAS A, et al. Safety aspects of wet asphalt pavement surfaces through field and numerical modeling investigations[J]. Transportation Research Record, 2014(2446): 37-51. http://www.researchgate.net/publication/262419448_Safety_Aspects_of_Wet_Asphalt_Pavement_Surfaces_Through_Field_and_Numerical_Modeling_Investigations
[68]	TANG T, ANUPAM K, KASBERGEN C, et al. A finite element study of rain intensity on skid resistance for permeable asphalt concrete mixes[J]. Construction and Building Materials, 2019, 220: 464-475. doi: 10.1016/j.conbuildmat.2019.05.185
[69]	PERSSON B N J. Theory of rubber friction and contact mechanics[J]. The Journal of Chemical Physics, 2001, 115(8): 3840-3861. doi: 10.1063/1.1388626
[70]	PERSSON B N J. Rubber friction: role of the flash temperature[J]. Journal of Physics: Condensed Matter, 2006, 18(32): 1-22.
[71]	KLVPPEL M, HEINRICH G. Rubber friction on self-affine road tracks[J]. Rubber Chemistry and Technology, 2000, 73(4): 578-606. doi: 10.5254/1.3547607
[72]	LEGAL A, KLVPPEL M. Investigation and modelling of rubber stationary friction on rough surfaces[J]. Journal of Physics: Condensed Matter, 2007, 20(1): 015007. http://adsabs.harvard.edu/abs/2008JPCM...20a5007L
[73]	LORENZ B, CARBONE G, SCHULZE C. Average separation between a rough surface and a rubber block: comparison between theories and experiments[J]. Wear, 2010, 268(7/8): 984-990. http://www.sciencedirect.com/science/article/pii/S0043164809006516
[74]	MOTAMEDI M. Road surface measurement and multi-scale modeling of rubber road contact and adhesion[D]. Blacksburg: Virginia Polytechnic Institute and State University, 2015.
[75]	ALHASAN A, SMADI O, BOU-SAAB G, et al. Pavement friction modeling using texture measurements and pendulum skid tester[J]. Transportation Research Record, 2018(2672): 440-451. http://www.researchgate.net/publication/325462461_Pavement_Friction_Modeling_using_Texture_Measurements_and_Pendulum_Skid_Tester
[76]	KANE M, CEREZO V. A contribution to tire/road friction modeling: from a simplified dynamic frictional contact model to a "dynamic friction tester" model[J]. Wear, 2015, 342/343: 163-171. doi: 10.1016/j.wear.2015.08.007
[77]	TAN Tan, XING Chao, TAN Yi-qiu, et al. Safety aspects on icy asphalt pavement in cold region through field investigations[J]. Cold Regions Science and Technology, 2019, 161: 21-31. doi: 10.1016/j.coldregions.2019.02.010
[78]	TAN Tan, XING Chao, TAN Yi-qiu, et al. Rubber friction on icy pavement: experiments and modeling[J]. Cold Regions Science and Technology, 2020, 174: 103022. doi: 10.1016/j.coldregions.2020.103022
[79]	沙爱民, 童峥, 高杰. 基于卷积神经网络的路表病害识别与测量[J]. 中国公路学报, 2018, 31(1): 1-10. doi: 10.3969/j.issn.1001-7372.2018.01.001 SHA Ai-min, TONG Zheng, GAO Jie. Recognition and measurement of pavement disasters based on convolutional neural networks[J]. China Journal of Highway and Transport, 2018, 31(1): 1-10. (in Chinese) doi: 10.3969/j.issn.1001-7372.2018.01.001
[80]	CHEN Wei-wei, WANG Wei-xing, WANG Kevin, et al. Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: a review[J]. Journal of Traffic and Transportation Engineering (English Edition), 2020, 7(6): 748-774. doi: 10.1016/j.jtte.2020.10.002
[81]	MAEDA H, SEKIMOTO Y, SETO T, et al. Road damage detection and classification using deep neural networks with smartphone images[J]. Computer-Aided Civil and Infrastructure Engineering, 2018, 33(12): 1127-1141. doi: 10.1111/mice.12387
[82]	NAJAFI S, FLINTSCH G W, KHALEGHIAN S. Pavement friction management-artificial neural network approach[J]. International Journal of Pavement Engineering, 2016, 20(2): 125-135.
[83]	SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958. http://dl.acm.org/citation.cfm?id=2670313
[84]	MARCELINO P, LURDES A M, FORTUNATO E, et al. Machine learning for pavement friction prediction using scikit-learn[C]//Springer. 18th EPIA Conference on Artificial Intelligence. Berlin: Springer, 2017: 331-342.
[85]	TONG Zheng, GAO Jie, SHA Ai-min, et al. Convolutional neural network for asphalt pavement surface texture analysis[J]. Computer-Aided Civil and Infrastructure Engineering, 2018, 33(12): 1056-1072. doi: 10.1111/mice.12406
[86]	ZHAN Y, LI J Q, YANG G W, et al. Friction-ResNets: deep residual network architecture for pavement skid resistance evaluation[J]. Journal of Transportation Engineering, Part B: Pavements, 2020, 146(3): 04020027. doi: 10.1061/JPEODX.0000187
[87]	KANAFI M M, TUONONEN A J. Top topography surface roughness power spectrum for pavement friction evaluation[J]. Tribology International, 2017, 107: 240-249. doi: 10.1016/j.triboint.2016.11.038
[88]	KOGBARA R B, MASAD E A, WOODWARD D, et al. Relating surface texture parameters from close range photogrammetry to grip-tester pavement friction measurements[J]. Construction and Building Materials, 2018, 166: 227-240. doi: 10.1016/j.conbuildmat.2018.01.102
[89]	DING S, WANG K, YANG E, et al. Influence of effective texture depth on pavement friction based on 3D texture area[J]. Construction and Building Materials, 2021, 287(5/6): 123002. http://www.sciencedirect.com/science/article/pii/S0950061821007625