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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (4): 684-694    DOI: 10.3785/j.issn.1008-973X.2021.04.010
    
Macro and micro texture based prediction of pavement surface friction
You ZHAN1,2(),Qiang LI3,Xiao-tian MA1,Chen-ping WANG3,Yan-jun QIU1,2
1. School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
2. Highway Engineering Key Laboratory of Sichuan Province, Chengdu 610031, China
3. School of Civil and Environmental Engineering, Oklahoma State University, Stillwater 74078, USA
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Abstract  

The pavement skid resistance prediction model was analyzed based on macro and micro texture fusion using non-contact three-dimensional laser detection and machine learning. 45 pavement sites in Oklahoma were identified as the testing beds. Pavement skid resistance and surface macro-texture data were collected in parallel at highway speeds using a grip tester and three-dimensional (3D) laser dection vehicle. Four types of 3D aggregate parameters were calculated to characterize the micro-texture of aggregate surface using LS-40 3D laser imaging scanner. Relationship between pavement surface friction and texture was analyzed using machine learning model. The accuracy of the developed model was verified by model training and testing. The standard deviation of the model was 0.047, and the R squared value of the model was 0.865. 86.5% of the testing data fit the proposed friction model. Results show that the developed texture parameters and proposed friction prediction model can predict the pavement surface friction well.

Key words： road engineering      pavement friction      pavement macro-texture      aggregate surface characteristics      machine learning     
Received: 16 July 2020      Published: 07 May 2021
CLC:  U 416  
Fund:  国家自然科学基金资助项目（52008354）；中国博士后科学基金资助项目（2019M663557）；中央高校基本科研业务费专项资金资助项目（2682020CX65）
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You ZHAN
Qiang LI
Xiao-tian MA
Chen-ping WANG
Yan-jun QIU
Cite this article:

You ZHAN,Qiang LI,Xiao-tian MA,Chen-ping WANG,Yan-jun QIU. Macro and micro texture based prediction of pavement surface friction. Journal of ZheJiang University (Engineering Science), 2021, 55(4): 684-694.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.04.010     OR     http://www.zjujournals.com/eng/Y2021/V55/I4/684


基于宏微观纹理特征融合的路面摩擦性能预测

通过非接触式三维激光表面测试、机器学习，开展基于宏微观纹理特征融合的路面摩擦性能智能预测模型研究. 来自俄克拉荷马州的45个测试站点被选取作现场测试平台. 利用三维激光检测车和GripTester，分别获取行车道轮迹带路面摩擦数据、宏观纹理；利用LS-40三维激光表面分析仪获取集料表面三维微观纹理数据，测算4类微观纹理参数. 利用机器学习算法，将路面摩擦与宏微观纹理特征建立联系. 综合模型训练与测试，评价路面摩擦性能预测模型的准确率. 模型的测试标准差为0.047，测试集R2为0.865. 研究结果表明，86.5%的测试数据适用于所建立的机器学习预测模型，开发的评价指标及预测模型能够较好地预测路面摩擦性能.


关键词： 道路工程,  路面摩擦,  路面宏观纹理,  集料表观特性,  机器学习 
Fig.1 Key mechanisms of pavement-tire friction
Fig.2 Data collection sites map
站点ID 处治类型 集料种类 AADT A0 /a 站点ID 处治类型 集料种类 AADT A0 /a
1 碎石封层 石灰岩 30 5.7 24 Novachip 花岗岩 5185 3.8
2 碎石封层 石灰岩 550 3.2 25 Novachip 流纹岩 23439 2.3
3 碎石封层 石灰岩 1063 5.2 26 Novachip 流纹岩 2030 1.6
4 碎石封层 石灰岩 1063 5.2 27 Novachip 流纹岩 4412 7.2
5 碎石封层 石灰岩 1063 5.2 28 Novachip 石灰岩 18075 6.7
6 微表处 花岗岩 2776 2.8 29 Novachip 砂岩 9283 5.7
7 微表处 Mine Chat 2322 15.5 30 Novachip 砂岩 3200 3.5
8 微表处 花岗岩 1126 5.3 31 高抗滑 铝土矿 10672 2.1
9 微表处 花岗岩 8234 6.3 32 高抗滑 铝土矿 10672 2.1
10 薄层罩面 白云石 4930 6.9 33 高抗滑 铝土矿 25155 2.1
11 薄层罩面 白云石 2313 2.8 34 高抗滑 Mine Chat 195 2.1
12 薄层罩面 白云石 3200 3.4 35 高抗滑 Mine Chat 195 2.1
13 薄层罩面 白云石 4200 5.4 36 高抗滑 铝土矿 195 3.8
14 薄层罩面 花岗岩 470 4.7 37 高抗滑 铝土矿 195 3.8
15 薄层罩面 流纹岩 1650 4.9 38 高抗滑 铝土矿 195 3.8
16 薄层罩面 流纹岩 16006 4.2 39 高抗滑 铝土矿 195 3.8
17 薄层罩面 流纹岩 1150 3.9 40 温拌 流纹岩 2685 2.5
18 薄层罩面 花岗岩 11037 6.8 41 温拌 流纹岩 2685 2.5
19 薄层罩面 石灰岩 900 6.8 42 温拌 流纹岩 2685 2.5
20 薄层罩面 石灰岩 215 3.4 43 温拌 流纹岩 2685 2.5
21 薄层罩面 石灰岩 1128 5.8 44 温拌 流纹岩 2685 2.5
22 薄层罩面 砂岩 2912 3.8 45 温拌 流纹岩 2685 2.5
23 薄层罩面 砂岩 2800 6.5 ? ? ? ? ?
Tab.1 Summary of testing sites
Fig.3 Three-dimensional laser detection vehicle
Fig.4 Friction detection device
Fig.5 Portable three-dimensional laser surface analyzer
Fig.6 Preliminary data analysis for high friction treatment surface
Fig.7 Correlation analysis between friction and MPD
Fig.8 Three-dimensional aggregate surface characterization parameters and acquiring process
变量类型 参数类型 参数 参数解释
输出变量 摩擦性能 F 路面摩擦系数
输入变量 路表处治 Treatment 高抗滑表面处理、温拌沥青测试点、碎石封层、
微表处技术、薄层罩面、Novachip超薄磨耗层
输入变量 区域气候 T 温度
输入变量 交通特性 AADT 年平均日交通量
输入变量 交通特性 V 累计交通量（AADT × A）
输入变量 路面服役龄期 A 自路表处治实施以来使用年数
输入变量 路表特性 MPD 构造深度
输入变量 路表特性 IRI 路面平整度
输入变量 路表特性 R 车辙深度
输入变量 集料特性 Type 集料类型
输入变量 集料特性 Textural Parameters 纹理参数：Entropy（T）、Energy（E）、Homogeneity（H）、Sal、Str、Std
输入变量 集料特性 Feature Parameters 特征参数：Spd、Spc、S5p、S5v、S10z、Sda、Sha、Sdv、Shv
输入变量 集料特性 Height Parameters 高度参数：Sp、Sv、Sz、Sa、Sq、Ssk、Sku
输入变量 集料特性 Material Ratio & Volume Parameters 材料比与体积参数：Smr、Smc、Sxp、Vv、Vm、Vmp、Vmc、Vvc、Vvv
Tab.2 Input variables for machine learning model
Fig.9 Machine learning-based pavement friction prediction process
Fig.10 Diagram of random forest
Fig.11 RF pavement friction model testing
模型 R2 模型 R2
文献[23]模型 0.78 文献[42]模型 0.66
文献[24]模型 0.55 本文模型 0.86
Tab.3 Comparison of friction prediction model
参数类型 评价指标 重要性 重要性总和 重要性等级
交通特性 V 0.40 0.40 1
集料表面微观纹理特性 Str 0.17 0.32 2
集料表面微观纹理特性 H 0.15 0.32 2
路表特性 IRI 0.07 0.12 3
路表特性 MPD 0.03 0.12 3
路表特性 R 0.02 0.12 3
温度 T 0.10 0.10 4
路面服役龄期 A 0.06 0.06 5
Tab.4 RF model for friction prediction
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