Study on micro-texture and skid resistance of aggregate during polishing

Abstract

The skid resistance performance of pavement is closely related to the micro-texture of pavement aggregate, while there is very few research on the relationship between micro-texture and the skid resistance. In this paper, the optical microscope is used to acquire the surface morphology of three types of aggregates including basalt, limestone and red sandstone respectively, where a total of 12 indicators are developed based on the surface texture information. The polishing effect on aggregate is simulated by Wehner/Schulze (W/S) device, during the polishing procedure, the skid resistance are measured by British Pendulum Tester (BPT). Based on the results of independent T-test and the polishing resistance analysis, it shows that the surface texture of basalt is significantly different between limestone and red sandstone. Three indicators including the average roughness (Ra), the kurtosis of the surface (Sku) and the mean summit curvature (Ssc) are selected to describe the characteristics of aggregate micro-texture based on the correlation analysis. The contribution of micro-texture to the skid resistance can be described with the secondary polynomial regression model by these indicators.

Keywords

skid resistance of pavement / micro-texture / aggregates / polishing test

Introduction

The skid resistance performance of asphalt pavement is crucial to ensure traffic safety, while it is closely related to the macro-texture and micro-texture of the pavement. The Permanent International Association of Road Congresses defined micro-texture as the amplitude of deviations from the surface plane with wavelengths less than or equal to 0.5 mm in length and depth. The flexible pavement committee of the18th World Road Congresses has pointed out that the material properties of the aggregate should be the prime consideration when improving the skid resistance performance of pavement [1]. Many researches have indicated that the higher the average height of pavement micro-texture, the lager the distribution density, which leads to a better skid resistance performance of asphalt pavement. It can reduce the hydrodynamic lubrication between tire and pavement so as to improve the skid resistance performance of pavement. Meanwhile, the skid resistance of pavements generally decreases with the polishing process of aggregates due to traffic loads. During the polishing process, the micro-texture of aggregates changes due to a gradual removal of mineral components [2,3], which will leads to a decrease of the factual contact area between tire and pavement, and further affect the durability of skid resistance performance [4–6]. Different texture of aggregates will cause different interaction with asphalt binder from both micro-scale and macro-scale [7,8], further will lead to different performance of asphalt mixture and pavement [9–12].

The measurement method of micro-texture of aggregate in most of researches are using the non-contact optical methods. Fletcher et al. using Aggregate Imaging System (AIMS) to acquire the gray image of aggregate surface, then to analyze the surface texture of aggregate based on wavelet theory, however, this method describes the aggregate micro-texture is mainly based on the gray image instead of direct measurement of texture [13]. Chen et al. using laser profiler to measure the contour of the aggregate surface with high precision, however this method is very sensitive to the color of aggregate, thus the measurement accuracy will be effected due to the aggregate sample need to vacuum coating before measurement [14].

Although there have been great progress in the pavement materials area [15–17], however, the commonly used characterize indicators cannot analyze the characteristics of micro-texture, or can only analyze part of the characteristics, while the relationship between micro-texture and skid resistance performance is still not well understood. Therefore, in this paper, the micro-texture of aggregate is studied to analyze the surface texture of different aggregates and the relationship with the skid resistance of pavement. The polishing effect of tires on aggregate is simulated by Wehner/Schulze test and skid resistance measurements using the British Pendulum Tester (BPT). The changes of the micro-texture are acquired by 3D optical microscope and characterized by a set of indicators. A function which describes the contribution of micro-texture to skid resistance is developed.

Investigation of micro texture on aggregate

Selection of aggregate and polishing test

There are three types of aggregate selected in this paper for polishing test, which are all commonly used in pavement engineering, including basalt, limestone and red sandstone, where the nominal maximum size of these aggregates are 13.2mm. The basalt samples are obtained from Zhangjiakou city, the limestone samples are obtained from Sanhe, Langfang city, and the red sandstone samples are obtained from Zhongning, Ningxia city.

Furthermore, the polishing specimen is prepared using these aggregates in accordance with EN 12697-49-2014, the aggregates are laid out manually, where the aggregates are placed in a single layer over the entire surface of the test plate and the plate was divided into three equal parts according to the type of aggregate. The diameterF of plate is about 225 mm (Fig. 1).

In a standardized Wehner/Schulze (W/S) test, the specimen is polished using three rubber cones with quartz powder and water, the polishing action is carried out with a velocity of (500±5) r/min and with a load of (392±3) N. During the polishing action, the water-quartz-powder mixture is sprinkled through the center of the polishing head to the surface of the specimen which consists of a mixture of quartz powder<0,063 mm (Fig. 2).

In this paper, the polishing procedure was stopped at several passes, so that the skid resistance of specimen was measured by the British pendulum tester and the surface texture was acquired by the 3D optical microscope.

Acquisition of surface texture of coarse aggregate

The 3D optical microscope system used in this paper was a non-contact, high-resolution optical instrument produced by Bruker Inc. (Fig. 3 (a)). The principle of this microscope system is based on light interference theory, the distance between lens and sample leads the movement of the interference fringe. As a consequence, the measuring errors can be remarkable reduced due to it is not necessary to process the surface of sample such as vacuum coating, etc. The scanning resolution of the optical microscope is 640×480, and the scanning range is 4.5 mm×3.4 mm (Fig. 3 (b)), the accuracy for the measurement is 7mm, thus the micro-texture of aggregate can be captured accurately.

Taking one of the red sandstone as an example, the surface topography images is captured by the optical microscope system (Fig. 4) where the red area represents the higher texture and the blue area represents the lower texture, thus the change of micro-texture height during the course of polishing with W/S can be observed directly (Figs. 4 (a) and 4(b)). However, these changes of micro-texture needs to be described quantitatively to reveal the variation of different types of aggregates during polishing test.

Characterization indicators of micro-texture

In this paper, a total of 12 indicators are developed based on the analysis of microscope system to describe the micro-texture of aggregate from different perspectives [18–20]. The calculating procedure of each indicators is as follows.

1. The average roughness (Ra) is the arithmetic average deviation from the mean plane within the assessment area. It was a 2D profile indicator and has been extended to a 3D surface indicator as follow:

(1)Ra={\displaystyle \frac{\mathrm{1}}{N}}{\displaystyle \sum _{\mathrm{i=}\mathrm{1}}^N\left|z_i-m\right|},

where N is the number of sample points on the surface, z_i is the height of the i^th sampling point, m={\displaystyle \frac{\mathrm{1}}{N}}{\displaystyle \sum _{\mathrm{i=}\mathrm{1}}^N{z}_i} is the arithmetic mean of surface height.

2. The root mean square of roughness (Rq) is evaluated as follow:

(2)Rq=\sqrt{{\displaystyle \frac{\mathrm{1}}{N}}{\displaystyle \sum _{\mathrm{i=}\mathrm{1}}^N{(z_i-m)}^{\mathrm{2}}}}.

3. The average maximum height of the surface (Rz) is the average of the ten highest and ten lowest points in all sampling points, it can be evaluated as follows:

(3)Rz={\displaystyle \frac{\sum _{i=\mathrm{1}}^{\mathrm{10}}{z}_{pi}+\sum _{i=\mathrm{1}}^{\mathrm{10}}{z}_{vi}}{\mathrm{10}}},

where z_pi and z_vi is the height of peak and valley points.

4. The mean summit curvature (Ssc) describes the summit features of the surface, it can be evaluated as follows:

(4)Ssc={\displaystyle \frac{\mathrm{1}}{N}}{\displaystyle \sum _{\mathrm{1}}^N{\displaystyle \underset{\mathrm{Summit\_}y}{\int }{\displaystyle \underset{\mathrm{Summit\_}x}{\int }\left[\left({\displaystyle \frac{{\partial }^{\mathrm{2}}Z(x,y)}{\partial x^{\mathrm{2}}}}\right)+\left({\displaystyle \frac{{\partial }^{\mathrm{2}}Z(x,y)}{\partial y^{\mathrm{2}}}}\right)\right]}}}\mathrm{d}\mathit{x}\mathrm{d}\mathit{y},

where “summits” are derived from “peaks”, a peak is defined as any point that is above all eight nearest neighbors, x, y, z is the spatial coordinates of sampling points.

5. The mean roughness (Sa) is evaluated over the complete 3D surface which is extended based on Ra, it can be evaluated as follows:

(5)Sa={\displaystyle \frac{\mathrm{1}}{A}}{\displaystyle \underset{A}{\iint }\left|Z(x, y)\right|}\mathrm{d}\mathit{x}\mathrm{d}\mathit{y},

where A is the sampling area.

6. The root mean square roughness (Sq) is evaluated as follow:

(6)Sq=\sqrt{{\displaystyle \frac{\mathrm{1}}{A}}{\displaystyle \underset{A}{\iint }{Z}^{\mathrm{2}}(x, y)}\mathrm{d}\mathit{x}\mathrm{d}\mathit{y}}.

7. The skewness of the surface (Ssk) represents the degree of symmetry of the surface heights about the mean plane, it can be evaluated as follows:

(7)Ssk={\displaystyle \frac{\mathrm{1}}{A\cdot S{q}^{\mathrm{3}}}}{\displaystyle \underset{A}{\iint }{Z}^{\mathrm{3}}(x, y)}\mathrm{d}\mathit{x}\mathrm{d}\mathit{y}.

The Ssk indicates the preponderance of peaks (that is, Ssk>0) or valley structures (Ssk<0) comprising the surface.

8. The kurtosis of the surface (Sku) describes the kurtosis and steepness of texture, it indicates the nature of the height distribution and can be evaluated as follows:

(8)Sku={\displaystyle \frac{\mathrm{1}}{A\cdot S{q}^{\mathrm{4}}}}{\displaystyle \underset{A}{\iint }{Z}^{\mathrm{4}}(x, y)}\mathrm{d}\mathit{x}\mathrm{d}\mathit{y}.

9. The ten point height over the complete 3D surface (Sz). It represents the average difference between the five highest peaks and five lowest valleys. The formula is similar toRz, while the difference with Rz is that a peak is defined as any point above all eight nearest neighbors, while a valley is any point that is below all eight nearest neighbors.

10. The 3D fractal dimension of surface (D), many studies shows that it can effectively describe the surface roughness [21,22], this paper using box-counting method to describe the 3D surface morphology of aggregate. The 3D fractal dimension is calculated by MATLAB based on the 3D surface which is acquired by optical microscope.

11. The linearized power spectral density (LPSD) has been proved that there is a correlation between the LPSD_q=1 values and the skid resistance performance [23], which the LPSD_q=1 is the logarithm of the power spectral density (PSD) value at a spatial angular frequency of q=1 (1/m), while the power spectral density can be calculated as follows:

(9)C(q)={\displaystyle \frac{\mathrm{1}}{{(\mathrm{2}\mathrm{\pi })}^{\mathrm{2}}}}{\displaystyle \int {\mathrm{d}}^{\mathrm{2}}x}h(\mathit{x})h(\mathrm{0}){\mathrm{e}}^{-iqx},

where h(x) is the surface height from the average plane with x=(x,y) and 〈h〉=0, the statistical properties of the texture are assumed to be isotropic so thatC(q) only depends on the magnitude of q=|q| is the norm of the wave vector q.

12. The waviness w of micro-texture is derived from the linearized power spectral density to describe the roughness, which is the slope of thePSD line in double-logarithmic scale.

Result

Comparison of initial surface morphology of aggregates

In this paper, five samples are selected from each types of aggregates, thus the number of sample is 15. The 3D optical microscope is used to acquire the surface texture of aggregate samples before polishing, and the characterization indicators of aggregate texture are calculated according to the presented equations.

In order to analyze the differences of surface textures of different aggregates in the initial state, the independent T-test is used in the statistical analysis software SPSS. Table 1 shows the T-test results with equality of variances, where B represents basalt, L represents limestone and R represents red sandstone. Due to the sample size of the T-test is 15, according to the statistical experience, thus the confidence level of the T-test is determined to be 90%. As can be seen the bold value of significant level in Table 1, most of the characterization indicators includingRa, Rq, Sa, Sq, Sz, and D shows that the surface texture of basalt is significantly different from limestone and red sandstone. In other words, it can be inferred based on the results that pavement with basalt aggregates will contains more abundant texture. However, the sample size of aggregates analyzed in this paper was not large enough, more samples are expected to be tested in further studies.

Correlation analysis between characterization indicators and skid resistance value

In order to investigate the correlation between micro-texture of aggregate and the friction coefficient, the Pearson correlation coefficient matrix is used to analyze the correlation between characterization indicators of micro-texture and the skid resistance performance which is represented by British Pendulum Number (BPN) and measured by BPT. Since the polishing procedure has been stopped 6 times, the BPN and characterization indicators of aggregate has been totally measured 7 times, while every 5 samples has been selected from 3 types of aggregates, therefore, the total number of samples is 105.

As shown in Table 2, all the characterization indicators had a poor correlation with skid resistance BPN, the maximum correlation coefficient is between Sku and BPN which is ‒0.200. The results shows that using only single indicator is difficult to describe the correlation between micro-texture and skid resistance of pavement. It requires several indicators together which characterize various characteristics of micro-texture to describe the correlation with skid resistance. Besides, there are some large correlation coefficients between many indicators, includingRa and Sa, Ra and Rq, Rz, and Sz and so on, in which their correlation coefficients are all above 0.9 (Table 2). Result shows that these indicators all describes the similar characteristics of micro-texture. While the poor correlation coefficients between indicators indicates that these indicators are all describes the different characteristics of the micro-texture. Only the indicators which has a high correlation coefficient withBPN and has no significant correlation with other indicators, can be selected to describe the correlation with skid resistance.

Deriving the link between micro-texture and skid resistance

A multiple linear regression model is employed first to describe the correlation between characterization indicators and BPN. The multiple linear stepwise regression has been analyzed by SPSS, in which the characterization indicators value of every 5 samples has been averaged, however, the modified coefficient of determination R2=0.454. It shows that multiple linear regression model is not suitable for this problem.

Therefore, the secondary polynomial regression model is tested in this paper, the selection of independent variables is based on the correlation coefficient withBPN (Table 2), while the regression model is fitted by eliminating the independent variables individually which is not sensitive to the dependent variable.

The independent variables of the first regression model is Sku², Sku, Ra, LPSD_q=1, Ra·Ssc and Ssc·Sku, the modified coefficient of determination R²=0.911, however, the significant level of the independent variable LPSD_q=1 is 0.174, which means that the independent variable LPSD_q=1 is not sensitive enough to the regression model, therefore, it was eliminated from the regression model, which is presented as the following equation:

(10)BPN=-\mathrm{847.994}-\mathrm{108.291}Sk{u}^{\mathrm{2}}+\mathrm{0.005}Ra+\mathrm{557.034}Sku-\mathrm{0.001}Ra\cdot Ssc+\mathrm{17.669}Ssc\cdot Sku,

The modified coefficient of determination R²=0.898, which is acceptable, the significant level of each independent variable and constant are all less than 0.001 (Table 3), which means that the coefficients of each independent variable in the model are also significant. The ANOVA analysis also shows that the significance level of this model is 0.000, which means the model is significant and sensitive enough to the dependent variable.

Summary

• The initial characterization indicators of texture of the three types of aggregates are compared, where the results of independent T-test shows that the surface texture of basalt is significantly different from limestone and red sandstone, which can also be observed from the boxplot of indicator.

• The characterization indicators Ra, Rq, and Rz are extended from 2D profile indicators, and the correlation coefficient between these indicators and the surface indicatorsSa, Sq, and Sz are all greater than 0.9. It shows that both these two set of indicators can be used to characterize the surface roughness of the aggregate.

• The single indicator is difficult to describe the correlation between micro-texture and skid resistance of pavement. It should be described by several indicators. According to the Pearson correlation coefficient matrix, these four indicators includingRa, Sku, Ssc, and LPSD_q=1 can be used to describe characteristics of aggregate micro-texture from different features. The relationship between aggregate texture and skid resistance is complicated, it is hardly to described with a linear model, while the secondary polynomial regression model can be used to describe this relationship accurately.

• If sufficient studies on different aggregates are available, a better understanding of the polishing process and micro-texture changes can be derived. It would help to determine the aggregate type of asphalt pavement, based on the actual engineering requirements.

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