基于智能手机图像采集方法的沥青路面表面离析评价指标优选

汪海年; 万铜铜; 刘园园; 郑文华; 高俊锋

doi:10.19818/j.cnki.1671-1637.2023.02.006

基于智能手机图像采集方法的沥青路面表面离析评价指标优选

doi: 10.19818/j.cnki.1671-1637.2023.02.006

1.
长安大学公路学院，陕西西安 710064
2.
南昌航空大学土木与建筑学院，江西南昌 330063
3.
重庆交通大学材料科学与工程学院，重庆 400074

基金项目:

国家重点研发计划 2021YFB2601000

国家自然科学基金项目 52078048

国家自然科学基金项目 51878063

详细信息

作者简介:
汪海年(1977-)，男，江苏涟水人，长安大学教授，工学博士，从事可持续路面材料开发与数值仿真、耐久性交通基础设施智慧建造及运维评估研究

中图分类号: U416.217
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出版历程
- 收稿日期: 2022-11-03
- 网络出版日期: 2023-05-09
- 刊出日期: 2023-04-25

Optimization on evaluation indicators of asphalt pavement surface segregation based on smartphone image acquisition method

1.
School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China
2.
School of Civil Engineering and Architecture, Nanchang Hangkong University, Nanchang 330063, Jiangxi, China
3.
School of Materials Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China

Funds:

National Key Research and Development Program of China 2021YFB2601000

National Natural Science Foundation of China 52078048

National Natural Science Foundation of China 51878063

More Information

Author Bio:
WANG Hai-nian(1977-), male, professor, PhD, wanghn@chd.edu.cn

Article Text (Baidu Translation)

摘要

摘要: 为优选沥青路面表面离析评价指标，室内成型了3种不同级配类型的沥青混合料车辙板试件；采用智能手机，提出了一种图像采集方法，获取了沥青混合料表面图像；基于盒子分形维数和多重分形谱算法计算试件表面二值图像的分形维数和多重分形谱指标，依托Image-Pro Plus图像处理软件提取二值图像凹形分布百分数和宏观构造宽度指标，采用室内铺砂法测试表面构造深度，计算平均构造深度来评价沥青混合料表面离析；分析了不同级配组成沥青混合料表面凹形区域分布特征，研究了沥青混合料表面离析评价指标间灰关联熵。研究结果表明：基于智能手机来获取二值图像的可靠性分析误差率不超过3%，说明基于智能手机获取试件表面图像方法具有较高可重复性；沥青混合料表面凹形构造具有显著分形特征，集料颗粒粒径越大，表面构造分形越复杂；基于图像处理方法的沥青路面表面离析评价指标与平均构造深度指标间存在线性相关性，但相关程度不一；同一级配沥青混合料表面分形特征指标计算误差范围较大，而凹形分布百分数指标误差范围最小，仅为±1.89%，且其与平均构造深度指标的熵关联度最高，为0.996 2，其次分别是多重分形谱、分型维数和宏观构造宽度指标；综合考虑推荐沥青混合料表面凹形分布百分数可作为可靠的表面离析评价指标。
- 路面工程 /
- 表面离析评价指标 /
- 灰关联熵分析 /
- 多重分形理论 /
- 铺砂法 /
- 图像采集 /
- 质量控制
Abstract: In order to optimize the evaluation indicators of asphalt pavement surface segregation, three different gradation types of asphalt mixture slab specimens were prepared in laboratory. An image acquisition method was proposed to obtain the surface images of asphalt mixture by using smartphone. The box-counting method and multifractal spectrum algorithm were used to calculate the fractal dimension and multifractal spectrum indexes of the binary image. Image-Pro Plus software was adopted to extract the concave distribution percentage and macro-structure width. The surface texture depth was tested by using the indoor sand patching method, and the mean texture depth was calculated to evaluate asphalt mixture surface segregation. The concave distribution characteristics of asphalt mixture surfaces with different gradations were analyzed. The grey relational entropy of the evaluation indicators of asphalt mixture surface segregation was studied. Research results show that the error rate of reliability analysis for obtaining binary images based on smartphones is less than 3%, indicating that the obtaining method has high repeatability. The concave asphalt mixed surface has considerable fractal characteristic, the larger the aggregate particle size, the more complex surface structure analysis is. There is a linear correlation between the evaluation indicators of asphalt pavement surface segregation based on image processing method and the mean texture depth, but the correlation varies. The surface fractal characteristic indicator of the identical asphalt mixture has wide error range, however, the concave distribution percentage index has a minimum error range, changing from -1.89% to 1.89%. The entropy correlation degree between concave distribution percentage and mean texture depth is highest, which is 0.996 2, and followed by the indexes of multifractal spectrum, fractal dimension and macro-structure width. Thus, the concave distribution percentage is recommended as a reliable indicator for evaluating asphalt mixture surface segregate.
- pavement engineering /
- evaluation indicators of surface segregation /
- grey relational entropy analysis /
- multifractal theory /
- sand patching method /
- image acquisition /
- quality control

HTML全文

图 1 初始圆形图像获取

Figure 1. Acquisition of initial circle images

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图 2 二值图像获取方法

Figure 2. Method of obtaining binary images

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图 3 最大熵阈值分割算法实施步骤

Figure 3. Implementation steps of maximum entropy threshold segregation algorithm

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图 4 三种级配沥青混合料构造深度测试

Figure 4. Texture depth tests of three gradation types asphalt mixtures

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图 5 凹形分布宏观构造宽度

Figure 5. Macro-structure width of concave distribution

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图 6 不同级配组成类型沥青混合料表面离析程度

Figure 6. Surface segregation degrees of asphalt mixtures with different gradation types

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图 7 N_ε(P)和ε的对数关系

Figure 7. Logarithmic relation between ε and N_ε(P)

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图 8 不同级配组成类型的沥青混合料表面分形维数

Figure 8. Fractal dimensions of asphalt mixtures surfaces with different gradation types

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图 9 χ_q(ε)与ε的对数关系

Figure 9. Logarithmic relationship between χ_q(ε) and ε

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图 10 不同q取值范围下f(α)与α关系

Figure 10. Relationships between f(α) and α in different q values

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图 11 沥青混合料表面二值图像多重分形谱分布

Figure 11. Distributions of multifractal spectrum measured from surface binary images of asphalt mixtures

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图 12 沥青混合料表面凹形分布百分数和构造宽度分布

Figure 12. Distributions of concave distribution percentage and micro-structure width measured from asphalt mixture surface

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图 13 m与F、Δα、Δf(α)、e、K指标间的熵关联度

Figure 13. Entropy correlation degrees between m and F, Δα, Δf(α), e and K

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表 1 智能手机获取图像的可重复性

Table 1. Repeatability of images captured by smartphone

统计指标	不同测试次数的指标及其误差
统计指标	1	2	3
σ/mm	0.385 7	0.369 4	0.366 3
σ的相对误差	0.031 7	0.011 7	0.020 0
R/mm	0.355 5	0.335 9	0.329 5
R的相对误差	0.044 5	0.012 9	0.031 6
H/mm	1.000 0	1.000 0	1.000 0
H的相对误差	0.000 0	0.000 0	0.000 0
S	1.508 0	1.422 1	1.462 1
S的相对误差	0.030 0	0.028 7	0.001 3
F	1.837 7	1.801 2	1.792 4
F的相对误差	0.015 1	0.005 1	0.010 0
Δα	1.649 1	1.611 9	1.627 3
Δα的相对误差	0.012 1	0.010 8	0.001 3
Δf(α)/像素	1.157 8	1.144 1	1.124 0
Δf(α)的相对误差	0.013 9	0.001 9	0.015 7
e	1.904 2	1.950 0	1.967 0
e的相对误差	0.018 7	0.004 9	0.013 7
K/像素	40.96	38.97	41.35
K的相对误差	0.013 2	0.036 0	0.022 8

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表 2 凹形分布多重分形谱参数

Table 2. Multifractal spectral parameters of concave distribution

多重分形谱参数	不同q取值范围下的参数取值
多重分形谱参数	-2~2	-4~4	-6~6	-8~8
α_max	3.482 8	3.511 7	3.512 2	3.512 2
f(α_max)/像素	0.987 7	0.914 6	0.912 7	0.912 7
α_min	1.846 0	1.840 6	1.840 6	1.840 6
f(α_min)/像素	1.839 8	1.825 8	1.825 5	1.825 5
Δα	1.636 8	1.671 1	1.671 6	1.671 6
Δf(α)/像素	0.852 1	0.911 2	0.912 8	0.912 8

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表 3 m、F、Δα、Δf(α)、e、K指标计算数据

Table 3. Computational data of m, F, Δα, Δf(α), e and K

指标		指标参数
指标		m	F	Δα	Δf(α)/像素	e	K/像素
沥青混合料	O10	1.015 0	1.804 8	1.633 9	1.009 7	0.536 7	33.438 3
	O13	1.784 3	1.844 6	1.616 7	1.305 3	2.610 5	57.572 5
	O16	2.160 0	1.844 8	1.629 3	1.320 9	3.056 9	50.271 5
	S10	0.693 8	1.787 6	1.625 9	1.093 8	0.779 7	33.978 9
	S13	0.956 6	1.817 0	1.617 4	1.234 9	1.632 3	37.803 4
	S16	1.005 9	1.812 0	1.644 5	1.263 5	1.508 0	43.809 5
	A10	0.333 2	1.758 6	1.622 7	1.032 3	0.388 1	27.492 7
	A13	0.523 6	1.755 7	1.597 9	0.996 8	0.255 4	27.802 9
	A16	0.581 9	1.771 0	1.592 2	1.099 5	0.567 1	30.551 7

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表 4 数据归一化结果

Table 4. Data normalization results

指标参数	O10	O13	O16	S10	S13	S16	A10	A13	A16
m	0.93	2.34	1.98	0.64	0.88	0.92	0.31	0.48	0.53
F	1.01	1.00	1.03	1.00	1.01	1.01	0.98	0.98	0.99
e	0.46	1.57	2.60	0.66	1.39	1.28	0.33	0.22	0.48
Δα	0.94	1.51	0.94	0.94	0.93	0.95	0.94	0.92	0.92
Δf(α)/像素	0.85	1.36	1.11	0.92	1.04	1.07	0.87	0.84	0.93
K/像素	1.05	0.04	1.58	1.07	1.19	1.38	0.86	0.87	0.96

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表 5 m与F、Δα、Δf(α)、e、K的灰关联系数

Table 5. Gray relational coefficients between m and F, Δα, Δf(α), e and K

指标参数	O10	O13	O16	S10	S13	S16	A10	A13	A16
F	0.95	0.47	0.56	0.77	0.90	0.94	0.64	0.71	0.73
e	0.72	0.61	0.66	0.99	0.70	0.77	0.99	0.83	0.97
Δα	1.00	0.59	0.53	0.80	0.96	0.99	0.65	0.73	0.76
Δf(α)/像素	0.95	0.55	0.58	0.81	0.89	0.90	0.68	0.77	0.75
K/像素	0.92	0.34	0.75	0.74	0.80	0.73	0.68	0.75	0.74

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表 6 m与F、Δα、Δf(α)、e、K的灰关联熵

Table 6. Gray relational entropies between m and F, Δα, Δf(α), e and K

指标参数	F	e	Δα	Δf(α)/像素	K/像素
灰关联熵	2.173 0	2.182 0	2.174 8	2.181 8	2.172 7

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