Effect of Aggregate Characteristics on Texture and Skid Resistance of Asphalt Pavement Surface

Transcription

1 Effect of Aggregate Characteristics on Texture and Skid Resistance of Asphalt Pavement Surface Chanida KANGKHAJITRE Civil Engineer Bureau of Rural Roads 12 (Songkhla) Department of Rural Roads 156, Moo 6, Toongkuanjean, Kuanlung, Hatyai Songkhla, 90110, Thailand Fax: Kunnawee KANITPONG Assistant Professor Transportation Engineering School of Engineering and Technology Asian Institute of Technology P.O. Box 4, Klong Luang, Pathumthani, 12120, Thailand Fax: Abstract: The skid resistance of asphalt concrete is highly influenced by the microtexture and macrotexture of pavement surface. It is believed that the microtexture and macrotexture are affected by the surface texture of pavement, which refers to the arrangement of aggregates and their aggregates characteristics. The objective of this study is to evaluate the effects of aggregate properties and its orientation on microtexture and macrotexture by using an image analysis technique. Test results reveal that texture values obtained through the use of an image analysis technique is highly sensitive to changes in microtexture. The aggregate types and their related properties which are polished stone value, angularity, and texture of aggregate significantly affect the microtexture whereas the macrotexture is considerably influenced by fineness modulus, angularity and texture. Key Words: skid resistance, micro- macrotexture, asphalt pavements, aggregate characteristics, image analysis 1. INTRODUCTION Skid resistance is one of the important functional properties of pavement and is related to the resistance of pavement surface to sliding or skidding of the vehicles. Inadequate skid resistance can lead to higher chances of skid related accidents. Skid resistance changes over time as it decreases over the remaining pavement life when the aggregates are polished by traffic and environment. It is well known that the skid resistance is highly influenced by the microtexture and macrotexture of pavement surface. Microtexture is the irregularities in the surface of aggregate particles and its magnitude depends on the aggregate texture characteristics, aggregate mineralogy, and the ability of the aggregate to resist the polishing effect of traffic (Noyce et al., 2005; Kandhal and Parker, 1998; Crouch et al., 1995). Macrotexture is the larger irregularities in the pavement surface involving the voids between coarse aggregate particles. The magnitude of macrotexture depends mainly on the size, shape, and gradation of coarse aggregates. While the microtexture contributes to skid resistance at all speeds and mainly affects the skid resistance at low speed, the macrotexture is more critical at higher speed levels (Galambos et al., 1977; Cenek and Jamieson, 2000; Noyce et al., 2007). The changes in pavement texture due to temperature, moisture and polishing can significantly reduce the skid resistance over time (Jayawickrama et al., 1996; Do et al., 2007; Prowell et al. 2005; Masad et al., 2007). Therefore, it is necessary to consider the texture of pavement surface and its related aggregate properties over the entire service life of the pavement. 1210

2 Currently, imaging analysis techniques are used to measure the characteristics of aggregate particles such as aggregate shape, angularity, and texture (Masad et al., 2007). In addition, this technique can be used to determine the air void distribution, aggregate orientation, and aggregate contact within the internal structure of the asphalt mixture. A previous study indicates that the aggregate orientation and aggregate contact are different when the asphalt mixture specimens are compacted by different methods (Masad et al., 1999). The image analysis of the internal structure of asphalt specimens compacted by the Superpave gyratory compactor shows lower level of aggregate contact when comparing to the specimens compacted by the linear kneading compactor (Masad et al., 1999). Even though the specimens are compacted by the same method, the skid resistance seems to be different when it is measured on different sides of specimens. Based on the findings from previous studies, it seems that aggregate properties and aggregate orientation are the significant factors affecting the surface texture and skid resistance of pavement. Therefore, to predict the skid resistance during the mix design process, these factors need to be considered including the compaction method used in the production of asphalt specimens. In this study, attempts were made to evaluate the effect of aggregate source and physical properties of aggregates used in the production of asphalt mixtures on the microtexture and macrotexture that influencing the skid resistance. The accelerated polishing of asphalt mixture surface was conducted to stimulate the changes in pavement texture due to traffic. 2. OBJECTIVES The objectives of this study are to: 1. Evaluate the effect of the aggregate source and aggregate physical properties on the texture and skid resistance by using the image analysis technique. 2. Evaluate the effect of mix design parameters including the aggregate orientation within the mixes on the texture and skid resistance of asphalt mixture surface. 3. Investigate the changes in microtexture and macrotexture at increasing levels of polishing over a period of time. 3. MEASUREMENT OF AGGREGATE PROPERTIES Five aggregate sources that are widely used in Thailand, including basalt, granite, and three sources of limestone, were used in this study. The abrasion test, polishing test, and the image processing analysis are selected to characterize the aggregate properties. 3.1 Abrasion and Polishing Stone Value The Los Angeles (LA) Abrasion Test was selected as a method to determine the resistance of aggregate to abrasion. The aggregate needs to be sufficiently hard to transfer load through contact points of aggregate particles. The LA abrasion test was conducted according to a standard test method ASTM C131 (Standard Test Method for Resistance to Degradation of Small-Size Coarse Aggregate by Abrasion and Impact in the Los Angeles Machine). The LA abrasion loss value is expressed as a percentage of wear (PW) or the percentage of aggregate loss due to the abrasion. Lower PW indicates an aggregate that is tougher and more resistant to abrasion. The Polished Stone Value (PSV) was also measured to evaluate the resistance of aggregate to the polishing action of vehicle tires under the similar conditions on the road surface. The testing procedure is described in the standard test method BS , Testing 1211

3 Aggregates: Method for Determination of the Polished-Stone Value. Aggregate with higher PSV is more resistant to polishing. 3.2 Image Processing Techniques The Imaging Analysis was used to analyze the characteristics of aggregate properties that are related to the skid resistance and macrotexture of mixture surface. This section describes the imaging technique used to characterize the particle shape, angularity, and texture of coarse aggregates. Several researchers have evaluated the imaging analysis methods to measure such aggregate properties (Masad et al., 2007). In this study, some of these methods were applied as explained in the NCHRP Report 555. One hundred particles of coarse aggregates from each source were randomly selected, and the images of all samples were captured by using the microscopic camera. The image analysis program called Image-Pro Plus was then used to quantify the two-dimensional shape, angularity, and texture of coarse aggregate particles Aspect Ratio Aspect Ratio (AR) is one of the aggregate particle shape measures. Rittenhouse (1943) and Krumbein (1941) suggest that the aspect ratio represents the shape, but does not correlate with angularity. Two dimensional lengths of particles were measured including the longest and shortest dimensions (Figure 1). The AR, which is the ratio of the major axis to minor axis of the ellipse equivalent to the aggregate particles, is used to measure the aggregate shape. Basically, the AR is equal to or greater than 1.0. The AR is equal to 1.0 when the particle is of cubical shape and greater than 1.0 when the particle is flat or elongated. The AR can be calculated from this equation as shown below: AR = a/b (1) where; a = length of the major axis (mm), b = length of the minor axis (mm) a b Figure 1 Calculation of Aspect Ratio (AR) of Aggregate 1212

4 3.2.2 Angularity The Fractal Behavior Technique was used to analyze the angularity of aggregate particles. The black and white 2-D images are required in the analysis. Masad et al. (2000) use the fractal behavior technique to define the irregular boundary image representing the angularity of particles. Firstly, the image of aggregate particle is captured as shown in Figure 2a and 2b. The erosion and dilation process is then operated on the boundary of the original image in the first cycle (Figure 2c-2e). In the first cycle, the boundary from the dilation and the erosion are compared by overlapping operation provided within a cycle. The pixels retained in Figure 2e are those removed during erosion and added during dilation. The boundary is formed, and the effective width that depends on the number of erosion and dilation cycles and surface angularity of the particle is determined. Figure 3 shows the graph plotted on a log scale between the effective width and the number of cycles, indicating the trend of particle angularity. The graph of aggregate particle with higher slope tends to be more angular. a) colored aggregate particle b) aggregate particle projection c) erosion - cycle 1 d) dilation - cycle 1 e) the effective width - cycle 1 f) erosion - cycle 2 g) dilation - cycle 2 h) the effective width - cycle 2 i) erosion - cycle 3 j) dilation - cycle 3 k) the effective width - cycle 3 Figure 2 Process of Erosion-Dilation Operation based on Fractal Behavior Technique 1213

5 [µm] Figure 3 Log-log graph Plotted between Effective Width and Number of Erosion-Dilation Cycles Texture Masad et al. (2001) suggest the use of the intensity histogram method to determine the texture. According to the image analysis program, a histogram is created by using the histogram command on the measure menu. The black and white images need to be converted to an 8-bit gray scale image. Figure 4 shows the X-axis in a histogram represents the intensity scale (0 to 255) and the Y-axis measures the number of pixels. The output of the image processing technique is also evaluated as shown in the intensity histogram. It represents the mean and the standard deviation of the entire image. Given that the standard deviation of gray intensity is correlated to the surface texture, the standard deviation can represent the surface texture of the particle. The lower the standard deviation is, the smoother the particles will be. Figure 4 shows the standard deviation calculated from the intensity histogram of the aggregate particle. Color Values [-] Number of Pixels Color Values [-] Figure 4 Intensity Histogram to Represent Texture of Aggregate Particle 1214

6 4. MEASUREMENT OF MICROTEXTURE AND MACROTEXTURE OF ASPHALT MIXTURES In this study, the aggregates from five sources were mixed with the asphalt binder AC 60/70 to produce the asphalt mixture specimens. The 19-mm nominal maximum aggregate size was selected in the mix design. The gradation used in this study is illustrated in Figure 5. The asphalt mixture specimens were prepared according to the Marshall mix design method (ASTM D3515) which is a commonly used design method in Thailand. Other aggregate properties and volumetric properties are shown in Table 1. Percent Passing Sieve Size Raised to Power 0.45 Lower Upper Basalt Granite Limestone1 Limestone2 Limestone3 Max Size Figure 5 Gradation of the Aggregate Used in this Study Table 1 Aggregate Properties and Volumetric Properties Aggregate Type Criteria Basalt Granite Limestone1 Limestone2 Limestone3 LA Abrasion,% PSV Sieve Size Percent Passing (mm.) Binder Content, % Voids, % VMA, % 14 min

7 The specimens were compacted by both the Marshall Impact Hammer and the Gyratory Compactor for the comparison of the microtexture and macrotexture of asphalt mixtures. Von Quintus et al. (1991) stated that while the gyratory compactor compacts the specimens by the kneading action, the Marshall hammer compacts by impact action. The difference in two compaction methods could affect the aggregate orientation and texture of asphalt mixture surface. Goodman et al. (2006) also indicated that the arrangement of aggregate particles in mixture specimens compacted by the gyratory compactor is different from those compacted by the Marshall hammer. The top and bottom surfaces of the lab compacted specimen could have an effect on the microtexture and macrotexture. The 4 inch-diameter gyratory and Marshall compacted specimens were cut in two halves (top and bottom). The top and bottom surfaces were then tested for macrotexture and skid resistance at microtexture level. The specimens were polished by the Wet Track Abrasion Tester to determine the resistance of mixture surface to the wearing under wet conditions. The skid and texture were measured before the polishing and after 5, 10, 15, 30, 45, 60, 90, 120, and 180 minutes of polishing. Figure 6 shows the polishing conducted in this study. Figure 6 Polishing by Wet Track Abrasion The skid resistance of the mixture specimen was measured by using the British Pendulum Tester with the length of slider contact path between 75 and 78 mm according to the ASTM E303 (the Standard Test Method for Measuring Surface Frictional Properties Using the British Pendulum Tester). The British Pendulum Number (BPN) is determined to represent the microtexture of mixture surface. The macrotexture was measured by the Sand Patch Test in accordance with the ASTM E965 (the Standard Test Method for Measuring Pavement Macrotexture Depth Using a Volumetric Technique). The test was conducted by placing a known volume of sand on the mixture surface and spreading it using a circular motion until the sand is dispersed around the voids in mixture surface. The mean texture depth (MTD) of specimen macrotexture is then determined from the diameter of area covered with sand. 1216

8 5. RESULTS AND ANALYSIS [mm/mm] [µm] (a) Aspect Ratio (b) Angularity [-] (c) Texture Figure 7 Physical Properties of Aggregate (a) Aspect Ratio, (b) Angularity, and (c) Texture Specifically, the aggregate properties were measured by image processing technique as measured in section 3.2. Figure 7(a) shows the results of the measurement of particle shape of coarse aggregate from five different sources as evaluated by the Aspect Ratio (AR) analysis. It was observed that the overall particle shapes of coarse aggregate from the source of Limestone 1 are more flat and elongated, while Limestone 2 and Granite have aggregates with more cubical shape. Limestone 3 contains the coarse aggregate with the highest angularity as shown in Figure 7(b), while Basalt aggregates show the least angularity among five aggregate sources. Figure 7(c) shows the standard deviation of the intensity of the image analyzed from the coarse aggregate particles which representing the surface texture of aggregate particles. Granite appears to have the largest surface texture, while Limestone 2 has the smallest one. 5.1 Comparison of BPN and MTD between Gyratory and Marshall Compacted Specimens In this study, the gyratory and Marshall compacted specimens were prepared for the BPN and MTD measurements. Two types of compaction method were compared to determine the difference in the BPN and MTD. Figure 8 shows the relationship between the BPNs of the 1217

9 gyratory compacted specimens and the Marshall compacted specimens. It can be seen that the BPNs of the specimens compacted by both methods are fairly correlated with R 2 of 0.53, even though the gyratory compacted specimens have lower BPN than the Marshall specimens. The opposite trend was observed in the MTD values as shown in Figure 9. The MTDs of specimens compacted by both methods are highly correlated with R 2 of 0.67 and the gyratory compacted specimens have higher macrotexture than the marshall specimens. The results indicate that gyratory compacted specimens have microtexture with less exposed area of aggregate surface contacting the tire, thus the BPN becomes lower. However, the arrangement of coarse aggregate particles in the gyratory compacted specimens creates a larger macrotexture. BPN Figure 8 Comparison of the BPN between Gyratory and Marshall Compacted Specimens MTD [mm] Figure 9 Comparison of the MTD between Gyratory and Marshall Compacted Specimens 5.2 Comparison of BPN and MTD between Top and Bottom Surfaces of Compacted Specimens The BPN and MTD of top and bottom surfaces of gyratory compacted specimens were compared as shown in Figure 10 and 11. It was found that the BPN and MTD of top and bottom surfaces are somewhat correlated with R 2 of 0.56 and 0.74, respectively. The BPN and MTD of the bottom surface of specimens appear to be higher than the top surface of specimens as can be seen from the coefficient values of the regression equation. The results suggest that the gyratory compactor differently orients aggregate particles at the top and bottom surfaces of specimens. 1218

10 BPN Figure 10 Comparison of the BPN between Top and Bottom Surfaces of Gyratory Compacted Specimens MTD [mm] Figure 11 Comparison of the MTD between Top and Bottom Surfaces of Gyratory Compacted Specimens 5.3 Relationship between Initial BPN and MTD and Time Duration of Loading Figure 12 shows the MTD values of different mixes before and after polishing at different time duration of loading. The overall trends show that as the time duration of polishing increases, the MTD values decrease for all mix types. The test results indicate that the MTD values for Granite mixes were greater than other mixes, while the MTD values for Limestone 2 mixes were the smallest comparing to other mixes. According to the surface texture of aggregate measured by the standard deviation of the intensity from the image analysis shown in Figure 7(c), it appears that when a granite aggregate with the largest surface texture was used in the asphalt mixtures, the greater MTD values were observed. The similar finding was observed with the limestone 2 aggregate which has the smallest surface texture, and resulting in lower MTD values of asphalt mixtures. Therefore, the use of aggregate with larger surface texture tends to result in asphalt mixtures with larger macrotexture. Figure 13 shows the BPN 1219

11 values of different mixes before and after polishing at different time duration of loading. Similar results were obtained as shown that when the time duration of polishing increases, the BPN values also decrease. However, the differences in BPN values of all mix types are not clearly observed in the graph. Figure 12 MTD of Different Mixes Before and After Polishing at Different Time Duration Figure 13 BPN of Different Mixes Before and After Polishing at Different Time Duration 1220

12 5.4 Factors Affecting Microtexture and Macrotexture In this study, the linear regression technique was applied to evaluate the factor affecting the microtexture and macrotexture of asphalt mixtures. The dependent variables used in this analysis are the BPN and MTD values. The independent variables, as summarized in Table 2, include the aggregate type and aggregate properties that could affect the BPN and the MTD of the mixture surface, such as PSV, fineness modulus, aspect ratio, angularity, and texture of aggregate. As some aggregate properties represent the properties of each specific aggregate type; therefore, the aggregate type and its properties were separately analyzed in the regression model. However, among the aggregate properties variables, the test of multicollinearity indicates the existence of strong correlation among four variables: LA, PSV, FM, and ASP (i.e. all pair-wise correlation coefficients are higher than 0.6). The LA and ASP variables are therefore excluded in the preferred model specification. Table 3 presents estimation results from the regression models. In Model 1, the coefficients of all independent variables are statistically significant at the 99 % level except for Limestone 1. It means that the mixtures produced with Basalt aggregate have the highest BPN values followed by Limestone 2 and Granite. The BPN values of the mixtures produced with Limestone 1 and Limestone 3 are not significantly different. The coefficients shown in Model 2 indicate that the aggregate type significantly affects the MTD values at the level of %. The mixtures produced with Granite aggregate show the highest MTD values followed by Basalt, Limestone 1, Limestone 3, and Limestone 2. The results show that aggregate type is a significant factor influencing the skid resistance and macrotexture of the asphalt mixture surface. The analysis of Model 3 reveals that PSV, angularity, and texture of aggregate significantly affect the BPN values. It could be explained by the fact that the BPN value represents the microtexture which is affected by aggregate particle mineralogy and depends on the initial texture of aggregate surface and the ability of the aggregate to resist the polishing action due to traffic and environment. The PSV which is a physical property of aggregate, therefore significantly affects the BPN. The negative coefficients of angularity and surface texture indicate that the more angular and the larger surface texture of aggregate is, the lower BPN values are measured. The result shown is unexpected as the angularity and texture of aggregate have a potential of improving the skid resistance of mixture. However, it could be explained that as the BPN values represent the skid resistance at microtexture level of pavement surfaces, the angularity particles can generate a microtexture with less area of aggregate surface in contact with the tire and pavement surface, and thus result in lower BPN values. In Model 4, PSV does not significantly affect the MTD values, but the fineness modulus is found to be a significant factor affecting MTD. As the macrotexture depends on the size, shape, and gradation of the mixes, it is expected that the fineness modulus is found to significantly affect the MTD. The negative coefficient of angularity shows the similar effect on the MTD values in Model 4. On the other hand, the MTD value increases as the surface texture become larger. 1221

13 Table 2 Definitions of the Variables Used in the Linear Regression Analysis Variables Definition Basalt 1 if the mix is produced with Basalt aggregate, 0 otherwise Granite 1 if the mix is produced with Granite aggregate, 0 otherwise Lime1 1 if the mix is produced with Limestone1 aggregate, 0 otherwise Lime2 1 if the mix is produced with Limestone2 aggregate, 0 otherwise LA LA Abrasion Value (continuous variable) PSV Polishing Stone Value (continuous variable) FM Fineness Modulus (continuous variable) ASP Aspect Ratio (continuous variable) ANG Angularity (continuous variable) TEXT Surface texture of aggregate (continuous variable) Table 3 Results from Linear Regression Analysis BPN and MTD Models Variables Model 1: BPN Model 2: MTD Coeff t-stat Coeff t-stat Basalt *** *** Granite *** *** Lime ** Lime *** * Const *** *** R-squared No.of observation Model 3: BPN Model 4: MTD Coeff t-stat Coeff t-stat PSV ** 4.75x FM ** ANG -5.01x *** -4.96x * TEXT *** *** Const *** *** R-squared No.of observation Note: *** indicates significance at the 99 % confidence level. ** indicates significance at the 95 % confidence level. * indicates significance at the 90 % confidence level. 6. SUMMARY OF FINDINGS The following is a summary of findings according to the results of the study. Based on the aspect ratio analysis, Limestone 1 appears to be more flat and elongated whereas aggregates of Limestone 2 and granite tend to be cubical. Limestone 3 was found to have the highest angularity, with the least angularity being Basalt aggregates. Given the standard deviation of the intensity from coarse aggregate particles, Limestone 2 appears to have the smallest surface texture whilst Granite has the largest one. The Gyratory and Marshall compacted specimens were utilized for the BPN and MTD measurements. Marshall compacted specimens were found to have higher BPN than 1222

14 Gyratory specimens while the opposite trend is shown in the MTD values. The arrangement of coarse aggregate within the gyratory compacted specimens gives a lower microtexture, but higher macrotexture. The coefficient values of the regression equation used in this study illustrates that the BPN and MTD of the bottom surface of specimens are higher than the top surface of specimens implicitly reflecting that the gyratory compactor differently orients aggregate particles at the top and bottom surfaces of specimens. Relationships between BPN and MTD values, and time duration of loading found in this research highlights that as the time duration of polishing mixtures increases, BPN and MTD values decrease for all asphalt mix types. The MTD values for Limestone 2 mixes are the smallest amongst other mixes whereas the values for Granite mixes are higher than the others. Nonetheless, the disparity in BPN values of all mix types is not evident. Aggregate type is found to be a significant factor influencing the skid resistance and macrotexture of asphalt mixture surface. Out of six properties of aggregate types investigated, only PSV, angularity, and texture of aggregate significantly affect the BPN values whereas the MTD values is considerably influenced by three aggregate properties: fineness modulus, angularity and texture. REFERENCES Cenek, P.D., and Jamieson, N.J. (2000) Correlation of Skid Resistance Measuring Devices under Normal State Highway Survey Conditions. Lower Hutt, New Zealand: Opus International Consultants Central Laboratories. Crouch, L., J. Gothard, G. Head, and W. Goodwin (1995) Evaluation of Textural Retention of Pavement Surface Aggregates. In Transportation Research Record: Journal of the Transportation Research Board, No. 1486, Do M.-T., Z. Tang, M. Kane, and F.D. Larrard (2007) Pavement Polishing Development of a Dedicated Laboratory Test and Its Correlation with Road Result. WEAR, No. 263, Galambos, Winer, Hegmon, Balmer, Rice, Kopac, and Brinkman (1997) Pavement Texture and Available Skid Resistance. Federal Highway Administration, U.S. Department of Transportation, Washington, D.C. Goodman, S.N., Hassan, Y., and Abd El Halim, A.O.A. (2006) Preliminary Estimation of Asphalt Pavement Frictional Properties from Superpave Gyratory specimens and Mix Parameters. In Transportation Research Record: Journal of the Transportation Research Board, No. 1949, Jayawickrama, P.W., R. Prasanna, and S.P. Sebadheera (1996) Survey of State Practices to Control Skid Resistance on Hot-Mix Asphalt Concrete Pavement. In Transportation Research Record: Journal of the Transportation Research Board, No. 1536, Kandhal, P., and F. Parker, Jr. (1998) Aggregate Tests Related to Asphalt Concrete Performance in Pavements. NCHRP Report 405. Krumbein, W.C. (1941) Measurement and Geological Significance of Shape and roundness of Sedimentary Particles, Journal of Sedimentary Petrology, No.11, Masad E., B.Muhunthan, Naga Shashidhar, and Thomas Harman (1999) Quantifying Laboratory Compaction Effects on the Internal Structure of Asphalt Concrete. In Transportation Research Record: Journal of the Transportation Research Board, No. 1681, Masad E, Button J.W. and Papagiannakis T. (2000) Fine Aggregate Angularity: Automated Image Analysis Approach. In Transportation Research Record: Journal of the 1223

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