[1]彭毅,李强,战友,等.基于区域三维纹理特征的路面抗滑性能评估[J].东南大学学报(自然科学版),2020,50(4):667-676.[doi:10.3969/j.issn.1001-0505.2020.04.010]
 Peng Yi,Li Qiang(Joshua),Zhan You(Jason),et al.Pavement skid resistance evaluation based on 3D areal texture characterization[J].Journal of Southeast University (Natural Science Edition),2020,50(4):667-676.[doi:10.3969/j.issn.1001-0505.2020.04.010]
点击复制

基于区域三维纹理特征的路面抗滑性能评估()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
50
期数:
2020年第4期
页码:
667-676
栏目:
交通运输工程
出版日期:
2020-07-20

文章信息/Info

Title:
Pavement skid resistance evaluation based on 3D areal texture characterization
作者:
彭毅123李强3战友12杨广伟3王郴平34
1西南交通大学土木工程学院, 成都 610031; 2西南交通大学道路工程四川省重点实验室, 成都 610031; 3School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, OK 74078-5013, USA; 4广东省建筑科学研究院集团股份有限公司, 广州 510500
Author(s):
Peng Yi123 Li Qiang(Joshua)3 Zhan You(Jason)12 Yang Guangwei3 Wang Kelvin C.P.3
1School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
2Key Laboratory of Highway Engineering of Sichuan Province, Southwest Jiaotong University, Chengdu 610031, China
3School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, OK 74078-5013, USA
4Guangdong Provincial Academy of Building Research Group Co., Ltd., Guangzhou 510500, China
关键词:
路面纹理 抗滑 区域纹理参数 神经网络
Keywords:
pavement texture skid resistance areal texture parameter neural network
分类号:
U416.2
DOI:
10.3969/j.issn.1001-0505.2020.04.010
摘要:
为实现路面抗滑性能非接触式测量,提出应用新型区域三维纹理特征来表征沥青路面形貌构造并评估路面抗滑性能.使用便携式高分辨率三维激光扫描仪采集不同类型沥青路面纹理数据,同时使用动态摩擦系数测试仪采集其路面抗滑性能数据,并分别使用70和15 km/h时的动态摩擦系数值代表高速与低速状态下的路面抗滑性能.通过相关性分析和多元线性回归,发现路面抗滑性能与多个区域三维纹理特征参数的共同作用有关.建立前馈神经网络预测模型,使用多个区域三维纹理特征参数预测高速与低速状态下的路面抗滑性能.结果表明,区域三维纹理特征参数对动态摩擦系数测试仪在70 km/h时测得的路面抗滑性能预测能力为77%,对15 km/h时测得的路面抗滑性能预测能力为69%,证实了区域三维纹理特征参数与路面抗滑性能之间存在非线性联系.
Abstract:
To realize contactless skid resistance measurement, novel three-dimensional(3D)areal texture parameters are proposed to characterize the pavement surface topography and evaluate the skid resistance.A portable ultrahigh-resolution 3D laser scanner is used to collect various asphalt pavement texture data.A dynamic friction tester(DFT)is used to collect the asphalt pavement skid resistance data in parallel.The friction coefficient collected by DFT at speeds of 70 and 15 km/h represents the pavement skid resistance at the higher speed and lower speed, respectively.The joint action of the 3D areal texture parameters with each other on skid resistance is found out via the correlation analysis and multiple linear regression.The skid resistance at both higher and lower operating speeds is characterized by the 3D areal texture parameters using a multilayer feed-forward neural network model.Results indicate that the 3D areal texture parameters account for 77% contributions to higher speed skid resistance and 69% contributions to lower speed skid resistance.It proves that there is a non-linear relationship between the 3D areal texture parameters and skid resistance.

参考文献/References:

[1] Hall J W,Smith K L,Titus-Glover L,et al.Guide for pavement friction:Contractor’s final report for National Cooperative Highway Research Program(Nchrp)Project 01-43[R].Washington,DC,USA:Transportation Research Board of the National Academies,2009.
[2] 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.
[3] 陈嘉颖,黄晓明,郑彬双,等.基于近景摄影测量技术的沥青路面纹理实时识别系统[J].东南大学学报(自然科学版),2019,49(5):973-980.DOI:10.3969/j.issn.1001-0505.2019.05.022.
Chen J Y,Huang X M,Zheng B S,et al.Real-time identification system of asphalt pavement texture based on close-range photogrammetry[J].Journal of Southeast University(Natural Science Edition),2019,49(5):973-980.DOI:10.3969/j.issn.1001-0505.2019.05.022. (in Chinese)
[4] 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.DOI:10.19721/j.cnki.1001-7372.2019.04.003.
Huang X M,Zheng B S.Research status and progress for skid resistance performance of asphalt pavements[J].China Journal of Highway and Transport,2019,32(4):32-49.DOI:10.19721/j.cnki.1001-7372.2019.04.003. (in Chinese)
[5] Kouchaki S,Roshani H,Prozzi J A,et al.Field investigation of relationship between pavement surface texture and friction[J].Transportation Research Record:Journal of the Transportation Research Board,2018,2672(40):395-407.DOI:10.1177/0361198118777384.
[6] Garcia N Z.Predicting friction with improved texture characterization[D].Austin,TX,USA:The University of Texas at Austin.2017.
[7] Yut I,Henault J W,Mahoney J.Friction study of long-term pavement performance special pavement study 9A sections in Connecticut[J].Transportation Research Record:Journal of the Transportation Research Board,2014,2446(1):29-36.DOI:10.3141/2446-04.
[8] 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.
[9] Ueckermann A,Wang D W,Oeser M,et al.A contribution to non-contact skid resistance measurement[J].International Journal of Pavement Engineering,2015,16(7):646-659.DOI:10.1080/10298436.2014.943216.
[10] Li L,Wang K C P,Li Q.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.
[11] Kouchaki S,Roshani H,Prozzi J A,et al.Evaluation of aggregates surface micro-texture using spectral analysis[J].Construction and Building Materials,2017,156:944-955.DOI:10.1016/j.conbuildmat.2017.08.174.
[12] Doycheva K,Koch C,König M.Implementing textural features on GPUs for improved real-time pavement distress detection[J].Journal of Real-Time Image Processing,2019,16(5):1383-1394.DOI:10.1007/s11554-016-0648-1.
[13] Kane M,Rado Z,Timmons A.Exploring the texture-friction relationship:From texture empirical decomposition to pavement friction[J].International Journal of Pavement Engineering,2015,16(10):919-928.DOI:10.1080/10298436.2014.972956.
[14] Zhong K,Sun M Z,Liu Z X,et al.Research on dynamic evaluation model and early warning technology of anti-sliding risk for the airport pavement[J].Construction and Building Materials,2020,239:117820.DOI:10.1016/j.conbuildmat.2019.117820.
[15] Hartikainen L,Petry F,Westermann S.Frequency-wise correlation of the power spectral density of asphalt surface roughness and tire wet friction[J].Wear,2014,317(1/2):111-119.DOI:10.1016/j.wear.2014.05.017.
[16] 何宝凤,魏翠娥,刘柄显,等.三维表面粗糙度的表征和应用[J].光学精密工程,2018,26(8):1994-2011.DOI:10.3788/OPE.20182608.1994.
He B F,Wei C E,Liu B X,et al.Three-dimensional surface roughness characterization and application[J].Optics and Precision Engineering,2018,26(8):1994-2011.DOI:10.3788/OPE.20182608.1994. (in Chinese)
[17] Blateyron F.New 3D parameters and filtration techniques for surface metrology[C]//Proceeding of JSPE Annual Congress 2006.Tokyo,Japan,2006:1-7.
[18] Leach R.Characterisation of areal surface texture[M].Berlin:Springer,2013:20-135.
[19] ISO.ISO 25178-2 Geometrical product specifications(GPS)—Surface texture:Areal—Part 2:Terms,definitions and surface texture parameters[S].London:ISO,2012.
[20] 全国产品几何技术规范标准化技术委员会.GB/T 33523.6—2017产品几何技术规范(GPS)表面结构 区域法 第6部分:表面结构测量方法的分类[S].北京:中国国家标准化管理委员会,2017.
[21] 杨洁,李乐.基于机器视觉的表面粗糙度测量与三维评定[J].光学技术,2016,42(6):491-495.DOI:10.13741/j.cnki.11-1879/o4.2016.06.003.
Yang J,Li L.Surface roughness measurement and three-dimensional assessment based on machine vision[J].Optical Technique,2016,42(6):491-495.DOI:10.13741/j.cnki.11-1879/o4.2016.06.003. (in Chinese)
[22] Li Q J,Yang G W,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:Journal of the Transportation Research Board,2017,2641(1):164-176.DOI:10.3141/2641-19.
[23] Priddy K L,Keller P E.Artificial neural networks:An introduction[M].Washington,DC,USA:Spie Press,2005:102-150.
[24] Graupe D.Advanced series in circuits and systems:Principles of artificial neural networks Volume 7 ‖ Statistical training[M].Singapore:World Scientific Publishing Co.Ptc.Ltd.,2013:98-115.
[25] 陈讷郁,葛耀君.基于人工神经网络的典型桥梁断面气动参数识别[J].土木工程学报,2019,52(8):91-97,128.DOI:10.15951/j.tmgcxb.2019.08.008.
Chen N Y,Ge Y J.Aerodynamic parameter identification of typical bridge sections based on artificial neural network[J].China Civil Engineering Journal,2019,52(8):91-97,128.DOI:10.15951/j.tmgcxb.2019.08.008. (in Chinese)
[26] 李伯奎.三维表面偏斜度与陡度的规律研究[J].计量技术,2008(10):3-6.
  Li B K.Research on the law of three-dimensional surface skewness and steepness[J].Measurement Technique,2008(10):3-6.(in Chinese)
[27] Leach R K.Fundamental principles of engineering nanometrology[M].Amsterdam:Elsevier,2014:34-80.
[28] 林炜轩,王江涌.高度分布函数与自相关函数对表面粗糙度参数的影响[J].表面技术,2017,46(1):241-149.DOI:10.16490/j.cnki.issn.1001-3660.2017.01.039.
Lin W X,Wang J Y.Effects of autocorrelation function and height distribution function on the 3D surface roughness parameters[J].Surface Technology,2017,46(1):241-149.DOI:10.16490/j.cnki.issn.1001-3660.2017.01.039. (in Chinese)
[29] Oufqir S,Bloom P R,Toner B M,et al.Surface characterization of natural and Ca-saturated soil humic-clay composites at the micrometer scale:Effect of calcium[J].Journal of Materials and Environmental Science,2015,6(11):3174-3183.
[30] Kampstra P.Beanplot:A boxplot alternative for visual comparison of distributions[J].Journal of Statistical Software,2007,28:1-9.DOI:10.18637/jss.v028.c01.
[31] Srirangam S K,Anupam K,Scarpas A,et al.Development of a thermomechanical tyre-pavement interaction model[J].International Journal of Pavement Engineering,2015,16(8):721-729.DOI:10.1080/10298436.2014.946927.
[32] Peng Y,Li Q J,Zhan Y J,et al.Finite element method-based skid resistance simulation using in-situ 3D pavement surface texture and friction data[J].Materials,2019,12:1-19.DOI:10.3390/ma12233821.
[33] Oden J T,Martins J A C.Models and computational methods for dynamic friction phenomena[J].Computer Methods in Applied Mechanics and Engineering,1985,52(1/2/3):527-634.DOI:10.1016/0045-7825(85)90009-x.
[34] Yang G W,Yu W Y,Li Q J,et al.Random forest-based pavement surface friction prediction using high-resolution 3D image data[J].Journal of Testing and Evaluation,2021,49(2):20180937.DOI:10.1520/jte20180937.
[35] Hornik K,Stinchcombe M,White H.Multilayer feedforward networks are universal approximators[J].Neural Networks,1989,2(5):359-366.DOI:10.1016/0893-6080(89)90020-8.
[36] Karayiannis N B,Venetsanopoulos A N.Artificial neural networks:Learning algorithms,performance evaluation,and applications[M].Norwell,MA,USA,1993:15-50.
[37] 陈明.MATLAB 神经网络原理与实例精解[M].北京:清华大学出版社,2013:1-99.

备注/Memo

备注/Memo:
收稿日期: 2020-02-16.
作者简介: 彭毅(1987—),男,博士生;战友(联系人),男,博士,助理研究员,zhanyou@swjtu.edu.cn.
基金项目: 国家自然科学基金资助项目(U1534203,51478398)、中国博士后科学基金资助项目(2019M663557)、国家留学基金委资助项目(201607000101).
引用本文: 彭毅,李强,战友,等.基于区域三维纹理特征的路面抗滑性能评估[J].东南大学学报(自然科学版),2020,50(4):667-676. DOI:10.3969/j.issn.1001-0505.2020.04.010.
更新日期/Last Update: 2020-07-20