- Email: [email protected]
Experimental investigation on the effect of pore characteristics on clogging risk of pervious concrete based on CT scanning
Construction and Building Materials 212 (2019) 130–139
Contents lists available at ScienceDirect
Construction and Building Materials journal homepage: www.elsevier.com/locate/conbuildmat
Experimental investigation on the effect of pore characteristics on clogging risk of pervious concrete based on CT scanning Haonan Zhou a, Hui Li a,b,⇑, Ahmed Abdelhady a, Xiao Liang a, Hanbing Wang a, Bing Yang a a b
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China University of California Pavement Research Center, University of California, Davis, USA
h i g h l i g h t s Used CT scanning and image processing technique to investigate pervious concrete. Investigated the characteristics and calculated the equivalent diameter of pores. Investigated the influence of the size of clogging particle and pores on clogging. Optimized the design of pervious concrete to avoid clogging.
a r t i c l e
i n f o
Article history: Received 6 August 2018 Received in revised form 24 March 2019 Accepted 27 March 2019
Keywords: Pervious concrete Permeability Pore characteristics Clogging CT scanning
a b s t r a c t Pervious concrete is one of the permeable pavement materials, which can improve the traffic safety and road environment. However, the clogging problem of pervious concrete is considered as a potential common deficient of the permeable pavement that causes severe loss of the ecological function. Generally, the main cause of clogging is the interlocking of the pores with clogging particles. Therefore, addressing the problem of clogging needs to explore the influence of pore characteristics and clogging particles on the pervious concrete. This study determined the equivalent diameter of pores through CT scanning and image processing using an algorithm code to calculate the geometry properties of the open and closed pores. Moreover, the variation of permeability after clogging has been analyzed. The experimental results showed that the pore size could determine whether clogging particles can block or pass through the pores or not, which is closely related to the clogging degree. Finally, the new findings can help engineers and designers to optimize the design of pervious concrete to forwardly avoid or mitigate the clogging problem, which has a promising realistic signification for permeable pavement design and maintenance. Ó 2019 Elsevier Ltd. All rights reserved.
1. Introduction 1.1. Background With the continuous development of cities, impervious pavement occupies a large number of green space, which makes the cities have to face more severe problems that caused by heavy rain and flooding. Permeable pavement is considered as one of the active techniques to solve this problem in recent years. As a kind of permeable pavement materials, pervious concrete has many ecological functions, such as water permeability, noise reduction, cooling, and driving safety [1–4]. These ecological functions are realized based on pervious performance of the pervious concrete. ⇑ Corresponding author at: 4800 Caoan Rd, Shanghai 201804, China. E-mail address: [email protected] (H. Li). https://doi.org/10.1016/j.conbuildmat.2019.03.310 0950-0618/Ó 2019 Elsevier Ltd. All rights reserved.
Generally, the permeability of pervious concrete is due to its macropore structure. However, with the continuous application of the permeable pavement, the pores of pervious concrete are easily clogged by various small particles [5–8]. Apparently, after pervious concrete is being clogged, it will gradually lose its permeability, and this will seriously reduce the service life of the permeable pavement and cause huge ecological and economic losses. At present, there are two ways to solve the problem of clogging of the permeable pavement: active and passive methods. The passive method is to rejuvenate the permeability through road maintenance. The active way is to avoid clogging of pervious concrete through optimizing the design (select a suitable aggregate size) [9–10]. It is worth noting that the cost of road maintenance is high, and the recovery effect is not satisfactory. Some studies in the past believed that the accumulation of the fine particles in the pores of pervious pavement causes the
131
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139
clogging and the mass of the cumulative sediments is the most critical factor (5). Wuguang Lin, Na Zhang and others [5,11–12] classified the types of clogging of pervious concrete according to the types of particles that cause the clogging. According to the particle size and the way to enter the pores, these particles are divided into three categories: suspension, siltation, and creep. The suspension is an unfixed particle with a tiny particle size that suspended in the air. Siltation is a particle deposited by air resistance and gravity during traffic, and creep is the particles that are attached to the tire during the running of vehicles, or the crushing aggregate of the worn road surface. Based on the clogging mechanism of pervious concrete and the classification of the particles, it is clear that clogging of pervious concrete is mainly related to the particle size of the clogging particles [11,12]. However, in the previous research that focused on the clogging of pervious concrete, the researchers usually only consider that the type of clogging and the porosity of the pervious concrete are the main factors of the clogging. Some studies have also investigated the influence of the particle size, which caused the clogging on the permeability coefficient of pervious concrete [5,8,12,13]. However, due to the pore size of pervious concrete is difficult to measure, the combined influence of particle size and pore size on clogging are usually not considered. In fact, pore size and particle size together determine whether particles can enter or pass through the pores of pervious concrete. 1.2. Objectives and scope This paper aims to optimize the design of pervious concrete through investigating the influenced factors on the clogging of the pervious concrete through the CT scanning method. Then, this study is to explore the variation of permeability coefficient before and after clogging, including the influence of pore size, particle size and porosity on the clogging. 2. Materials and methods 2.1. Materials 2.1.1. Specimen preparation In this paper, cement, water, silica fume and aggregate have been nominated to prepare the pervious concrete mixture. The cement was ordinary Portland cement whose label was P.O 42.5. Moreover, the water here was tap water. Commonly, the optimum water-cement ratio for pervious concrete is between 0.29 and 0.33 [14,15], and 0.31 was selected here for preparing samples. As a keynote, the type of aggregate was a basalt aggregate with two different sizes; fine aggregate (2.36–4.75 mm) and coarse aggregate (4.75–9.5 mm). In order to ensure the performance of the pervious concrete, the aggregate used should be clean and dry. The modifier was a kind of liquid modifier for pervious concrete. In this paper, the influence of pores size and the clogging particles on the pervious concrete clogging was studied. In addition, the size of pores was controlled by cement content or cementaggregate ratio by weight. According to the aggregate size and cement-aggregate ratio, the experimental samples were divided into 6 groups as shown in Table 2. All the pervious concrete specimens in this study were prepared with single particle size aggregate. Finally, nine same specimens were used in each group for testing, which were cylinder forms with a diameter of 100 mm and a height of 50 mm. 2.1.2. Clogging particles The clogging particle for clogging test was a limestone aggregate. And according to the calculation results of pore size, the par-
Table 1 Aggregates properties. Aggregate type
Aggregate Size (mm)
Apparent Relative Density (g*cm3)
Bulk Relative Density (g*cm3)
Raw Material of Specimens (Basalt) Clogging Particle (Limestone)
4.75–9.5 2.36–4.75
2.763 2.778
2.702 2.707
2–3 1–2 0.5–1 0–0.5
2.687 2.643 2.660 2.662
2.653 2.638 2.635 2.623
ticle size picked were 0–0.5 mm, 0.5–1 mm, 1–2 mm, and 2–3 mm. And the aggregate properties were shown in Table 1. 2.1.3. Casting procedure When preparing pervious concrete, dry materials such as aggregates, silica fume and cement should first be added to the mixer. The mixing time is determined by the amount of material, and the time should not be less than 2 min. After mixing, the water should be divided into two equal parts and added at two times. The mixing time after adding the water each time is also determined by the amount of the materials added, and the time should not be less than 2 min at a time. After mixing, the mixture should be divided into three parts and added into the mold at three times separately. Vibration is required for each addition of the mixture. The time of each vibration is 5 s. Curing is carried out under standard curing conditions. And the curing methods mainly refer to Technical Standard for Application of Pervious Concrete Pavement (DG/TJ 08-2265-2018). 2.2. Experimental methods 2.2.1. Permeability test method The test method of the permeability coefficient was a constant head test method, which has been implemented founded on the ‘‘Technical specification for permeable cement concrete pavement” (GJJ/T 132-2009). The water head difference was about 150 mm. And the height of the specimen was 50 mm, and the diameter was 100 mm. During the test, the water head difference H has been recorded every 5 s to calculate the water permeability coefficient based on the following formula.
K¼
QL AHT
ð1Þ
where K is the water permeability; Q is the water output in T time; L is the specimen height; A is the sectional area of the specimen; H is the head difference, and T is the test time. 2.2.2. Clogging test method Wuguang Lin et al. (5) tested the amount of contaminated particles on the road, and they stated that the daily clogging particle of the unit road area was 0.33 g. If it is converted to the size of the sample that has been used in the current study, the particulate matter per year would be 1 g. To make the results more clearly, 10 g of clogging particle were selected for the suggested clogging test. The experimental process that have been suggested in this study were as following: Firstly, the permeability coefficient should be determined before doing the clogging test; Then the clogging process could be carried out through putting the specimen in a cylindrical tube. It is worth noting that the clogging particles were evenly sprinkled on the surface of the specimen, and the particles were washed into the pores of the specimen. In addition, the clogging process should not be shorter than 10 min to
132
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139
Table 2 Mixture details. Aggregate Size (mm)
2.36–4.75
4.75–9.5
Group
1–1
1–2
1–3
2–1
2–2
2–3
Aggregate (kg*m3) Cement (kg*m3) Cement-aggregate Ratio Water (kg*m3) Modifier (kg*m3) 7d Compressive Strength (MPa) 28d Compressive Strength (MPa)
1650 445.5 0.27 138.1 13.8 23.1 25.2
1650 412.5 0.25 127.9 12.8 21.7 24.7
1650 379.5 0.23 117.6 11.8 16.8 19.4
1550 356.5 0.23 110.5 11.1 25.1 27.6
1550 325.5 0.21 100.9 10.1 22.0 24.1
1550 294.5 0.19 91.3 9.1 17.9 20.3
Fig. 1. The suggested procedures of the image processing.
133
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139
Fig. 2. Different images for one sample to explain the required steps of the suggested test; a) initial image; b) image after cutting; c) pore image processing; d) calculation of the number and area of pores.
Table 3 Calculation of pore characteristics from the images processing. Group
Average Total Pores Area (mm2) Average Pore Number Equivalent Diameter Average Value (mm) Standard Deviation 15% Quantiles (mm) Median (mm) 85% Quantiles (mm) Maximum Value (mm)
2.36–4.75 mm
4.75–9.5 mm
1–1
1–2
1–3
2–1
2–2
2–3
672.78 237 1.52 1.14 0.50 1.20 2.62 10.02
741.51 222 1.64 1.25 0.53 1.28 2.84 10.90
865.24 184 1.81 1.63 0.53 1.35 3.25 18.68
755.32 87 2.22 2.33 0.53 1.62 4.60 16.01
833.33 81 2.54 2.59 0.54 1.64 4.82 20.08
1083.65 75 2.85 3.23 0.54 1.67 5.62 34.49
Note: For each group in this study, 3-specimen have been used to calculate the image data, which was 90 images in total. All results in this table refer to the mean value of each section.
make the particles entirely pass in the pores. Ultimately, determine the permeability coefficient again after the clogging is sufficient. 2.2.3. CT scanning and image processing method This study conducts 100-section scanning pictures of each specimen with the scan interval of 0.5 mm. Regarding the processes of preparing and cutting of the specimens, the two ends of a specimen will be uneven and the pores will be larger, however clogging usu-
ally occurs at small pores. Thus, other aspects should be indicated; the image results of 15 mm in the middle of the specimen were selected for the subsequent analysis in this study, and 30 pieces were selected for each specimen. In addition, the scanning precision was 1 mm per 12.5 pixels. The main procedures of the image processing have been shown in Fig. 1. In step 1, as shown in Fig. 2, due to the bondage of the mold or the spalling and lack of aggregate, the edge of the
134
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139
the image will increase the calculation results of the pores number, the cutting images need to be smoothed and de-noised (step 3) by J Image software [17]. Moreover, the gray scales of pores and mixtures are totally different especially after image processing. There are two peaks in the gray scale distribution, and the median of two peaks could be selected as the gray scale threshold. This threshold can also be automatically selected by Image J, because the gray scale difference is obvious and there will be no obvious deviation (steps 4 to 6). In the calculation process, some small pores which may cause deviation will be screened out, and the potential deviation here can be reduced at the same time. In step 7, the pores that have size are lower than the clogging particle size has little influence on the clogging test results [16,17]. As a result, the pore data with the area of pixels less than 10 were ignored in this study. In step 8, to calculate the number of the pores and its cross area with Matlab software, a related point to consider, is the equivalent diameter of pores has been calculated as a circular hole, which was shown in Eq. (2).
sffiffiffiffiffiffiffiffiffiffiffiffiffiffi P A Pi D¼2 p pi
ð2Þ
where D is the equivalent diameter or size of a single pore, Ai is the total pore area of each image, and Pi is the total number of pores in each image.
Fig. 3. The variation in pore characteristics of the examined samples; a) pore area pore number; b) equivalent diameter; c) average equivalent diameter – cementaggregate ratio.
specimen was incomplete and uneven. In addition, the background of the image was black, which was similar to the color of the pores. The two problem would affect the analysis result, therefore the image should be cut. For the convenience of the image processing computation, the image in this study was cut into a square section with a length of 60 mm by Matlab software (step 2), and the center of image after cutting was the center of the initial image. In addition, the edges of the pores was very rough to calculate the area, moreover the small fuzzy points in
2.2.4. Porosity test method In this study, two kinds of porosity are analyzed: One is the open porosity and the other is the cross-section porosity. The open pore is the part of the total pore that removes the closed pore, and the cross-section porosity is the ratio of the pore area to the total area in a cross-section image. Lu and Sun [18] indicated that the porosity test of pervious concrete usually adopts to the immersion method, which was weighed in water, and this test method was also consistent with Technical Standard for Application of Pervious Concrete Pavement (DG/TJ 08-2265-2018). Firstly, the pervious concrete specimens after 28 days should be placed at 20 in a water bath, which was different compared to the ordinary specimens. To the best of the authors’ knowledge and the mentioned reference, the porosity of pervious concrete is generally between 15% and 30% due to its multi porosity characteristics. The large porosity of pervious concrete will take a long time to soak in the water bath. Otherwise, when weighing the weight of the concrete in water, the weight will change regularly. In general, the soaking time was within 24–48 h. After soaking, measure the weight of the specimen in the water, then put the sample into the oven at 105 and removed it after 24 h. After the specimen is cooled, determine the weight of specimen in the air. Finally, estimate the height of the specimen with a caliper to calculate the volume and open porosity (Po) using the next formula:
Po ¼ 1
ma mw qw V
ð3Þ
Table 4 Porosity and permeability coefficient for the nominated samples before clogging. Group
Open Porosity
Cross-Section Porosity
Permeability Coefficient (mm*s1)
Standard Deviation
1–1 1–2 1–3 2–1 2–2 2–3
0.179 0.187 0.211 0.217 0.24 0.273
0.187 0.206 0.224 0.226 0.231 0.301
4.76 5.27 7.70 9.30 10.36 12.16
0.41 0.93 0.48 0.26 0.11 0.51
135
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139 Table 5 Permeability coefficient for the examined samples after clogging. Group
Clogging Particle Size (mm)
Permeability Coefficient (mm*s1)
Standard Deviation
Decrease in Permeability Coefficient (mm*s1)
Percentage of Decrease (%)
1–1
0.5–1 1–1.5 1.5–2 0.5–1 1–1.5 1.5–2 0.5–1 1–1.5 1.5–2 0.5–1 1–1.5 1.5–2 2–3 0.5–1 1–1.5 1.5–2 2–3 0.5–1 1–1.5 1.5–2 2–3
2.70 2.44 4.26 2.77 2.52 4.62 5.26 4.40 6.65 8.27 6.04 5.34 7.57 9.26 7.02 4.76 8.31 9.76 7.33 6.30 8.41
0.52 0.23 0.49 0.37 0.21 0.76 0.51 0.46 0.23 0.49 0.43 0.47 0.20 0.17 0.49 0.29 0.47 0.33 0.53 0.26 0.47
2.06 2.32 0.50 2.50 2.75 0.65 2.44 3.30 1.05 1.03 3.26 3.96 1.73 1.10 3.34 5.60 2.05 1.40 3.83 4.86 2.75
43 49 11 47 52 12 32 43 14 11 35 43 19 11 32 54 20 13 34 44 25
1–2
1–3
2–1
2–2
2–3
3. Results and discussion 3.1. Pore characteristics In this part, the data of the experimental tests have been recorded and the analytical study for image processing to calculate all details of the scanned images has been also conducted. Therefore, the data results will be discussed and displayed, and firstly the details of the scanned images are shown in Table 3. From Fig. 3, the number of pores were generally negatively related to the pore area. In addition, this result is not affected by the particle size of the aggregate. The main reason is that the larger selected cement-aggregate ratio, the smaller the pore area is. At the same time, the number of pores will decrease due to the increase in contact frequency between adjacent aggregate. There are many data in each group (more than 20 thousand data), therefore the standard deviation is large, and there are a lot of abnormal outlier points in Fig. 3.b (There 200–600 points in each group). In fact, from the large amount of the calculated data in this study, these statistics of equivalent diameter have a good statistical significance, which could ignore the effect of deviation that caused by some of the excluded outlier points. The average equivalent diameter decreases with the increase of cementaggregate ratio, which is consistent to the original expectation. 3.2. Variation of permeability
Fig. 4. Porosity and permeability coefficient of the examined samples before clogging; a) permeability coefficient before clogging- porosity; b) porosity.
where ma is the weight of the sample in air, mw is the weight of the sample in water, qw is the water density and V is the specimen volume. In this study, the cross-section porosity can be calculated by the following formula:
PCS ¼
Si A
ð4Þ
where Pcs is the cross section porosity, Si is the average total pore area, and A is the total area of the image.
The porosity and permeability coefficient before and after the clogging are recorded in Table 3 and Table 4, respectively. It is worth noting that only three kinds of size of clogging particle are selected for testing and this attributed to the pores in group 1, which was small. It is clear from Tables 4 and 5 that a slight difference between the two types of porosity. In addition, the variation of permeability coefficient is related to the clogging particle size. Consequently, the following figures have been drawn based on the acquired data to obtain convenient analysis. Fig. 4 displays the porosity and permeability coefficient before clogging. In the second part of this figure, the cross section porosity is usually slightly larger than open porosity, and this may be related to closing some parts in the cross section porosity. However, the method of testing open porosity could not test the closed
136
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139
Fig. 6. The influence of cement-aggregate ratio on pore characteristics; a) average pore area of cross section; b) average pore number of cross section.
Fig. 5. Variation of permeability coefficient under the influence of the ratio of clogging particle size to aggregate size; a) the relationship between the permeability coefficient and size ratio of clogging particle; b) percentage of decrease in permeability of group 1 (2.36–4.75 mm aggregate size) after clogging; c) percentage of decrease in permeability of group 2 (4.75–9.5 mm aggregate size) after clogging.
porosity. The trend linear in Fig. 1.b has been obtained from a regression analysis of the fitting data, it is apparent that there is a significant functional relationship (R2 = 0.9004) between the two porosities and the proposed model is as follows.
Po ¼ 1:1893Pcs 0:0259
ð5Þ
where Po is the open porosity and Pcs is the cross-section porosity. Although the closed pore has no effect on the permeability coefficient, it is an important consideration in the design method based on the target porosity [18], which is the main design method for pervious concrete. The reason is that the target porosity here in this design method means the total porosity of which the closed pore is a part. However, the ordinary porosity test methods are generally to test the open porosity. Therefore, investigating the proposed model (Eq. 5) can effectively estimate the open porosity, which is the main influence factor of permeability of pervious concrete in design.
Moreover, the benefit of the suggested method to determine the open porosity of pervious concrete in this study is in the first stage, which is also provide a new idea for researchers to apply it on the other different pavements such as open grade friction course of asphalt pavement. Furthermore, generally, the permeability coefficient is related to the porosity and aggregate size before clogging (14). The porosity determines the water volume flowing through the cross-section of pervious concrete. On the other side the aggregate determines the number of the pores and the complexity of concrete structure, which are the main influencing factors of permeable velocity. In order to analyze the variation of permeability coefficient, the following figures have been drawn. The abscissa here is the ratio of clogging particle size to average equivalent diameter. It is worth noting that the clogging particle size is determined by the median of its group. Some interesting results can be obtained from Fig. 5. Primarily, when the size ratio of clogging particle to pores is too small or large, the variation of permeability coefficient is not obvious, which means that the clogging is at a low level. This is due to the clogging is produced by the clogging particle being trapped in the internal pores of pervious concrete. The relationship between the size of clogging particle and pores determines this phenomenon. Firstly, when the clogging particle size is much larger than pore size, the clogging particle cannot pass in the pores in the surface of the pervious concrete. Additionally, when the clogging particle size is much smaller than pore size, the clogging particle would pass through the entire concrete structure. Therefore, these situations will not easily to cause the clogging. For such consideration, it is generally considered that the top point of the curve in the Fig. 5 is at the size ratio of 1. However, when the ratio is between 0.6 and 0.8, the clogging has reached to a serious level. The reasons can be listed as follows. Certainly, the pores on the pervious concrete surface determine whether
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139
137
Fig. 7. Flowchart of how to optimize the design of pervious concrete to prevent clogging.
the clogging particles can pass in the pores. However, the smallest pore size of concrete is most likely to make it clogged. Moreover, the pores are generally depressed by the mixtures, and this can also be confirmed in the scanning image of Fig. 1. In this paper, the pores are regarded as circular for convenience of calculation. Therefore, the equivalent diameter is larger than the actual size of the pores. Comparing the Fig. 5b and c, it is clear that though the pore sizes and the porosity of the two groups are totally different, the
trend of these curves are similar.. This indicates that the clogging level is mainly influenced by the size ratio of clogging particle and pore size, but not only one of them or the porosity. 3.3. The proposed design of the pervious concrete Apparently, if the sizes of the available aggregates, which have been used to construct a road pavement, is known, it is entirely possible to optimize the design of permeable concrete by selecting
138
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139
the suitable aggregate sizes and the amount of cement and other materials to avoid being clogged of the permeable pavement. Therefore, the following section will provide the procedures that will help the designers to design the pervious concrete mixture in safely way. To optimize the design, initially, the curves of Fig. 6 should be drawn to determine the influence of cement-aggregate ratio on pore characteristics. It is obvious that there is a linear relationship between pore characteristics and cement-aggregate ratio. The specific formulas are shown below:
AP1 ¼ 4811:5R þ 1962:7
ð6:1Þ
AP2 ¼ 8208:3R þ 2614:5
ð6:2Þ
N1 ¼ 1325R 116:92
ð7:1Þ
N2 ¼ 300R þ 18
ð7:2Þ
where R is the cement-aggregate ratio, Ap1 is the average pore area of 2.36–4.75 mm aggregate size, Ap2 is the average pore area of 4.75–9.5 mm aggregate size, N1 is the pore number of 2.36– 4.75 mm aggregate size, and the N2 is the pore number of 4.75– 9.5 mm aggregate size. Fig. 7 explains the suggested procedures for optimization of the pervious concrete design to prevent clogging or decrease the clogged pores. In this figure, firstly, the materials should be selected and the mix proportion should be designed, especially the size data of contaminants (d1) should be collected before design (in step 2). Secondly (in step 3), due to the target open porosity is determined in step 2, the cross-section porosity could be calculated by Eq. 5, and the total area of pores that equal the cross-section porosity could also be acquired (in step 4). Get the cement-aggregate ratio by Eq.6 and then get the number of pores by Eq. 7 through the cement-aggregate ratio (in step 5) to calculate (in step 6) the pore size or the equivalent diameter of pore (d2). If the ratio of d1 and d2 is between 0.6 and 0.8, the cement-aggregate ratio should be redesigned properly (back to step 5), or the convenient materials and optimum ratios can be obtained. 4. Conclusions and recommendations In this paper, the pores characteristics of pervious concrete were explored using CT scanning, and the analysis of the image processing has been conducted by Matlab and Image J software. In addition, the relationship between clogging and the combined effects of clogging particle size and size of pores were also investigated. Moreover, a flowchart to optimize the design of pervious concrete has been displayed. Through the above discussion, the following conclusions and recommendations can be drawn from the test results and data analysis: The pervious concrete mixture influences the pore characteristics, including the number of pores and its size. Whereas, at specific aggregate gradation and the porosity before clogging will be affected by the pore size of pervious concrete. In addition, the number of pores is related to the pore size. Both the aggregate size and cement-aggregate ratio exhibited an evident effect on the pore size. At the same time, aggregate size is the primary influenced factor while the cementaggregate ratio is the secondary factor. The clogging level is mainly influenced by the size ratio of clogging particle to the pores of pervious concrete. In addition, when the size ratio is between 0.6 and 0.8, it is easy to cause the clogging.
The proposed model between open porosity and cross-section porosity exhibited a significant result to predict the expected open porosity, which can be considered the same value of the cross-section porosity or (Po = 1.05 Pcs). The proposed design method in this study to prevent the occurring of clogging in the pervious concrete will help the designers and engineers to provide a safe design of the new porous concrete pavement or predict the porosity of the current pervious concrete pavement. Certainly, there are still some shortcomings in this study. More influenced factors should be taken into consideration in a further study especially when the size of the clogging particle is uneven. Conflict of interest There is no conflict of interest. Acknowledgement The research is supported by grants from Ministry of Science and Technology of the People’s Republic of China (Grant No. 2016YFE0108200), National Natural Science Foundation of China (Grant No. 5150080567), the Fundamental Research Funds for the Central Universities (Grant No. 22120180093), and Science and Technology Commssion Shanghai Municipality (Grant No. 17230711300, 16DZ1202004), Fund of Shanghai Peak Discipline (Grant No. 2016J012309), and Department of Transportation of Hebei Province (QG2018-5). The sponsorships are gratefully acknowledged. The contents of this paper reflect the views of the authors and do not necessarily reflect the official views or policies of the sponsors. This paper does not represent any standard or specification. References [1] Ivanka Netinger Grubeša, Ivana Barišic´, Vilma Ducman, Lidija Korat, Draining capability of single-sized pervious concrete, Construct. Build. Mater. 169 (2018) 252–260. [2] Putra Jaya Ramadhansyah, Mohd Yusak Mohd Ibrahim, Hainin Mohd Rosli, Wan Ibrahim Mohd Haziman, A review of porous concrete pavement: applications and engineering properties, Appl. Mech. Mater. 554 (2014) 37–41. [3] Sonia Rahman, Andrew B. Northmore, Vimy Henderson, Susan L. Tighe, Developing a framework for low-volume road implementation of pervious concrete pavements, Int. J. Transp. Sci. Technol. 4 (1) (2015) 77–91. [4] Hanbin Wang, Hui Li, Xiao Liang, Haonan Zhou, Ning Xie, Zhen Dai, Investigation on the mechanical properties and environmental impacts of pervious concrete containing fly ash based on the cement-aggregate ratio, Constr. Build. Master. 202 (2019) 387–395. [5] Wuguang Lin, Dae-Geun Park, Sung Woo Ryu, Byeong-Tae Lee, Yoon-Ho Cho, Development of permeability test method for porous concrete block pavement materials considering clogging, Construct. Build. Mater. 118 (2016) 20–26. [6] Na Zhang, Experimental Investigation on Clogging Mechanism of Pervious Concrete, Shandong University, 2014. [7] Liv M. Haselbach, Potential for clay clogging of pervious concrete under extreme conditions, J. Hydrol. Eng. 15 (1) (2010). [8] Jiayi Yuan, Xudong Chen, Saisai Liu, Shengtao Li, Nan Shen, Effect of water head, gradation of clogging agent, and horizontal flow velocity on the clogging characteristics of pervious concrete, J. Mater. Civil Eng. 30 (9) (2018). [9] Michael F. Hein, Mark Dougherty, Turner Hobbs, Cleaning methods for pervious concrete pavements, Int. J. Construct. Educ. Res. 9 (2) (2013). [10] Liv M. Haselbach, Srinivas Valavala, Felipe Montes. Permeability predictions for sand-clogged Portland cement pervious concrete pavement systems, J. Environ. Manage. 81 (1) (2005). [11] Alalea Kia, Hong S. Wong, Christopher R. Cheeseman, Clogging in permeable concrete: a review, J. Environ. Manage. 193 (2017). [12] Sean Patrick Walsh, Amy Rowe, Qizhong Guo, Laboratory scale study to quantify the effect of sediment accumulation on the hydraulic conductivity of pervious concrete, J. Irrig. Drain. Eng. (2014). [13] Alalea Kia, Hong S. Wong, Christopher R. Cheeseman, Defining clogging potential for permeable concrete, J. Environ. Manage. 220 (2018).
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139 [14] Baoshan Huang, Wu. Hao, Xiang Shu, Edwin G. Burdette, Laboratory evaluation of permeability and strength of polymer-modified pervious concrete, Construct. Build. Mater. 24 (5) (2009). [15] Xuzhao Qin, Study on Properties of Porous Cement Concrete Material and Design of Pavement Structure, Changan University, 2011. [16] Masoud Kayhanian, Dane Anderson, John T. Harvey, David Jones, Balasingam Muhunthan, Permeability measurement and scan imaging to assess clogging
139
of pervious concrete pavements in parking lots, J. Environ. Manage. 95 (1) (2011). [17] Xu. Zongheng, Study on Macroporous Soil in Unsaturated Zone of Vegetation Development Slope, Kunming University of Science and Technology, 2014. [18] Lu. Hewei, Lijun Sun, Mixture design of porous cement concrete based on target porosity, Shanghai Highway 4 (2013) 63–65.
Contents lists available at ScienceDirect
Construction and Building Materials journal homepage: www.elsevier.com/locate/conbuildmat
Experimental investigation on the effect of pore characteristics on clogging risk of pervious concrete based on CT scanning Haonan Zhou a, Hui Li a,b,⇑, Ahmed Abdelhady a, Xiao Liang a, Hanbing Wang a, Bing Yang a a b
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China University of California Pavement Research Center, University of California, Davis, USA
h i g h l i g h t s Used CT scanning and image processing technique to investigate pervious concrete. Investigated the characteristics and calculated the equivalent diameter of pores. Investigated the influence of the size of clogging particle and pores on clogging. Optimized the design of pervious concrete to avoid clogging.
a r t i c l e
i n f o
Article history: Received 6 August 2018 Received in revised form 24 March 2019 Accepted 27 March 2019
Keywords: Pervious concrete Permeability Pore characteristics Clogging CT scanning
a b s t r a c t Pervious concrete is one of the permeable pavement materials, which can improve the traffic safety and road environment. However, the clogging problem of pervious concrete is considered as a potential common deficient of the permeable pavement that causes severe loss of the ecological function. Generally, the main cause of clogging is the interlocking of the pores with clogging particles. Therefore, addressing the problem of clogging needs to explore the influence of pore characteristics and clogging particles on the pervious concrete. This study determined the equivalent diameter of pores through CT scanning and image processing using an algorithm code to calculate the geometry properties of the open and closed pores. Moreover, the variation of permeability after clogging has been analyzed. The experimental results showed that the pore size could determine whether clogging particles can block or pass through the pores or not, which is closely related to the clogging degree. Finally, the new findings can help engineers and designers to optimize the design of pervious concrete to forwardly avoid or mitigate the clogging problem, which has a promising realistic signification for permeable pavement design and maintenance. Ó 2019 Elsevier Ltd. All rights reserved.
1. Introduction 1.1. Background With the continuous development of cities, impervious pavement occupies a large number of green space, which makes the cities have to face more severe problems that caused by heavy rain and flooding. Permeable pavement is considered as one of the active techniques to solve this problem in recent years. As a kind of permeable pavement materials, pervious concrete has many ecological functions, such as water permeability, noise reduction, cooling, and driving safety [1–4]. These ecological functions are realized based on pervious performance of the pervious concrete. ⇑ Corresponding author at: 4800 Caoan Rd, Shanghai 201804, China. E-mail address: [email protected] (H. Li). https://doi.org/10.1016/j.conbuildmat.2019.03.310 0950-0618/Ó 2019 Elsevier Ltd. All rights reserved.
Generally, the permeability of pervious concrete is due to its macropore structure. However, with the continuous application of the permeable pavement, the pores of pervious concrete are easily clogged by various small particles [5–8]. Apparently, after pervious concrete is being clogged, it will gradually lose its permeability, and this will seriously reduce the service life of the permeable pavement and cause huge ecological and economic losses. At present, there are two ways to solve the problem of clogging of the permeable pavement: active and passive methods. The passive method is to rejuvenate the permeability through road maintenance. The active way is to avoid clogging of pervious concrete through optimizing the design (select a suitable aggregate size) [9–10]. It is worth noting that the cost of road maintenance is high, and the recovery effect is not satisfactory. Some studies in the past believed that the accumulation of the fine particles in the pores of pervious pavement causes the
131
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139
clogging and the mass of the cumulative sediments is the most critical factor (5). Wuguang Lin, Na Zhang and others [5,11–12] classified the types of clogging of pervious concrete according to the types of particles that cause the clogging. According to the particle size and the way to enter the pores, these particles are divided into three categories: suspension, siltation, and creep. The suspension is an unfixed particle with a tiny particle size that suspended in the air. Siltation is a particle deposited by air resistance and gravity during traffic, and creep is the particles that are attached to the tire during the running of vehicles, or the crushing aggregate of the worn road surface. Based on the clogging mechanism of pervious concrete and the classification of the particles, it is clear that clogging of pervious concrete is mainly related to the particle size of the clogging particles [11,12]. However, in the previous research that focused on the clogging of pervious concrete, the researchers usually only consider that the type of clogging and the porosity of the pervious concrete are the main factors of the clogging. Some studies have also investigated the influence of the particle size, which caused the clogging on the permeability coefficient of pervious concrete [5,8,12,13]. However, due to the pore size of pervious concrete is difficult to measure, the combined influence of particle size and pore size on clogging are usually not considered. In fact, pore size and particle size together determine whether particles can enter or pass through the pores of pervious concrete. 1.2. Objectives and scope This paper aims to optimize the design of pervious concrete through investigating the influenced factors on the clogging of the pervious concrete through the CT scanning method. Then, this study is to explore the variation of permeability coefficient before and after clogging, including the influence of pore size, particle size and porosity on the clogging. 2. Materials and methods 2.1. Materials 2.1.1. Specimen preparation In this paper, cement, water, silica fume and aggregate have been nominated to prepare the pervious concrete mixture. The cement was ordinary Portland cement whose label was P.O 42.5. Moreover, the water here was tap water. Commonly, the optimum water-cement ratio for pervious concrete is between 0.29 and 0.33 [14,15], and 0.31 was selected here for preparing samples. As a keynote, the type of aggregate was a basalt aggregate with two different sizes; fine aggregate (2.36–4.75 mm) and coarse aggregate (4.75–9.5 mm). In order to ensure the performance of the pervious concrete, the aggregate used should be clean and dry. The modifier was a kind of liquid modifier for pervious concrete. In this paper, the influence of pores size and the clogging particles on the pervious concrete clogging was studied. In addition, the size of pores was controlled by cement content or cementaggregate ratio by weight. According to the aggregate size and cement-aggregate ratio, the experimental samples were divided into 6 groups as shown in Table 2. All the pervious concrete specimens in this study were prepared with single particle size aggregate. Finally, nine same specimens were used in each group for testing, which were cylinder forms with a diameter of 100 mm and a height of 50 mm. 2.1.2. Clogging particles The clogging particle for clogging test was a limestone aggregate. And according to the calculation results of pore size, the par-
Table 1 Aggregates properties. Aggregate type
Aggregate Size (mm)
Apparent Relative Density (g*cm3)
Bulk Relative Density (g*cm3)
Raw Material of Specimens (Basalt) Clogging Particle (Limestone)
4.75–9.5 2.36–4.75
2.763 2.778
2.702 2.707
2–3 1–2 0.5–1 0–0.5
2.687 2.643 2.660 2.662
2.653 2.638 2.635 2.623
ticle size picked were 0–0.5 mm, 0.5–1 mm, 1–2 mm, and 2–3 mm. And the aggregate properties were shown in Table 1. 2.1.3. Casting procedure When preparing pervious concrete, dry materials such as aggregates, silica fume and cement should first be added to the mixer. The mixing time is determined by the amount of material, and the time should not be less than 2 min. After mixing, the water should be divided into two equal parts and added at two times. The mixing time after adding the water each time is also determined by the amount of the materials added, and the time should not be less than 2 min at a time. After mixing, the mixture should be divided into three parts and added into the mold at three times separately. Vibration is required for each addition of the mixture. The time of each vibration is 5 s. Curing is carried out under standard curing conditions. And the curing methods mainly refer to Technical Standard for Application of Pervious Concrete Pavement (DG/TJ 08-2265-2018). 2.2. Experimental methods 2.2.1. Permeability test method The test method of the permeability coefficient was a constant head test method, which has been implemented founded on the ‘‘Technical specification for permeable cement concrete pavement” (GJJ/T 132-2009). The water head difference was about 150 mm. And the height of the specimen was 50 mm, and the diameter was 100 mm. During the test, the water head difference H has been recorded every 5 s to calculate the water permeability coefficient based on the following formula.
K¼
QL AHT
ð1Þ
where K is the water permeability; Q is the water output in T time; L is the specimen height; A is the sectional area of the specimen; H is the head difference, and T is the test time. 2.2.2. Clogging test method Wuguang Lin et al. (5) tested the amount of contaminated particles on the road, and they stated that the daily clogging particle of the unit road area was 0.33 g. If it is converted to the size of the sample that has been used in the current study, the particulate matter per year would be 1 g. To make the results more clearly, 10 g of clogging particle were selected for the suggested clogging test. The experimental process that have been suggested in this study were as following: Firstly, the permeability coefficient should be determined before doing the clogging test; Then the clogging process could be carried out through putting the specimen in a cylindrical tube. It is worth noting that the clogging particles were evenly sprinkled on the surface of the specimen, and the particles were washed into the pores of the specimen. In addition, the clogging process should not be shorter than 10 min to
132
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139
Table 2 Mixture details. Aggregate Size (mm)
2.36–4.75
4.75–9.5
Group
1–1
1–2
1–3
2–1
2–2
2–3
Aggregate (kg*m3) Cement (kg*m3) Cement-aggregate Ratio Water (kg*m3) Modifier (kg*m3) 7d Compressive Strength (MPa) 28d Compressive Strength (MPa)
1650 445.5 0.27 138.1 13.8 23.1 25.2
1650 412.5 0.25 127.9 12.8 21.7 24.7
1650 379.5 0.23 117.6 11.8 16.8 19.4
1550 356.5 0.23 110.5 11.1 25.1 27.6
1550 325.5 0.21 100.9 10.1 22.0 24.1
1550 294.5 0.19 91.3 9.1 17.9 20.3
Fig. 1. The suggested procedures of the image processing.
133
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139
Fig. 2. Different images for one sample to explain the required steps of the suggested test; a) initial image; b) image after cutting; c) pore image processing; d) calculation of the number and area of pores.
Table 3 Calculation of pore characteristics from the images processing. Group
Average Total Pores Area (mm2) Average Pore Number Equivalent Diameter Average Value (mm) Standard Deviation 15% Quantiles (mm) Median (mm) 85% Quantiles (mm) Maximum Value (mm)
2.36–4.75 mm
4.75–9.5 mm
1–1
1–2
1–3
2–1
2–2
2–3
672.78 237 1.52 1.14 0.50 1.20 2.62 10.02
741.51 222 1.64 1.25 0.53 1.28 2.84 10.90
865.24 184 1.81 1.63 0.53 1.35 3.25 18.68
755.32 87 2.22 2.33 0.53 1.62 4.60 16.01
833.33 81 2.54 2.59 0.54 1.64 4.82 20.08
1083.65 75 2.85 3.23 0.54 1.67 5.62 34.49
Note: For each group in this study, 3-specimen have been used to calculate the image data, which was 90 images in total. All results in this table refer to the mean value of each section.
make the particles entirely pass in the pores. Ultimately, determine the permeability coefficient again after the clogging is sufficient. 2.2.3. CT scanning and image processing method This study conducts 100-section scanning pictures of each specimen with the scan interval of 0.5 mm. Regarding the processes of preparing and cutting of the specimens, the two ends of a specimen will be uneven and the pores will be larger, however clogging usu-
ally occurs at small pores. Thus, other aspects should be indicated; the image results of 15 mm in the middle of the specimen were selected for the subsequent analysis in this study, and 30 pieces were selected for each specimen. In addition, the scanning precision was 1 mm per 12.5 pixels. The main procedures of the image processing have been shown in Fig. 1. In step 1, as shown in Fig. 2, due to the bondage of the mold or the spalling and lack of aggregate, the edge of the
134
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139
the image will increase the calculation results of the pores number, the cutting images need to be smoothed and de-noised (step 3) by J Image software [17]. Moreover, the gray scales of pores and mixtures are totally different especially after image processing. There are two peaks in the gray scale distribution, and the median of two peaks could be selected as the gray scale threshold. This threshold can also be automatically selected by Image J, because the gray scale difference is obvious and there will be no obvious deviation (steps 4 to 6). In the calculation process, some small pores which may cause deviation will be screened out, and the potential deviation here can be reduced at the same time. In step 7, the pores that have size are lower than the clogging particle size has little influence on the clogging test results [16,17]. As a result, the pore data with the area of pixels less than 10 were ignored in this study. In step 8, to calculate the number of the pores and its cross area with Matlab software, a related point to consider, is the equivalent diameter of pores has been calculated as a circular hole, which was shown in Eq. (2).
sffiffiffiffiffiffiffiffiffiffiffiffiffiffi P A Pi D¼2 p pi
ð2Þ
where D is the equivalent diameter or size of a single pore, Ai is the total pore area of each image, and Pi is the total number of pores in each image.
Fig. 3. The variation in pore characteristics of the examined samples; a) pore area pore number; b) equivalent diameter; c) average equivalent diameter – cementaggregate ratio.
specimen was incomplete and uneven. In addition, the background of the image was black, which was similar to the color of the pores. The two problem would affect the analysis result, therefore the image should be cut. For the convenience of the image processing computation, the image in this study was cut into a square section with a length of 60 mm by Matlab software (step 2), and the center of image after cutting was the center of the initial image. In addition, the edges of the pores was very rough to calculate the area, moreover the small fuzzy points in
2.2.4. Porosity test method In this study, two kinds of porosity are analyzed: One is the open porosity and the other is the cross-section porosity. The open pore is the part of the total pore that removes the closed pore, and the cross-section porosity is the ratio of the pore area to the total area in a cross-section image. Lu and Sun [18] indicated that the porosity test of pervious concrete usually adopts to the immersion method, which was weighed in water, and this test method was also consistent with Technical Standard for Application of Pervious Concrete Pavement (DG/TJ 08-2265-2018). Firstly, the pervious concrete specimens after 28 days should be placed at 20 in a water bath, which was different compared to the ordinary specimens. To the best of the authors’ knowledge and the mentioned reference, the porosity of pervious concrete is generally between 15% and 30% due to its multi porosity characteristics. The large porosity of pervious concrete will take a long time to soak in the water bath. Otherwise, when weighing the weight of the concrete in water, the weight will change regularly. In general, the soaking time was within 24–48 h. After soaking, measure the weight of the specimen in the water, then put the sample into the oven at 105 and removed it after 24 h. After the specimen is cooled, determine the weight of specimen in the air. Finally, estimate the height of the specimen with a caliper to calculate the volume and open porosity (Po) using the next formula:
Po ¼ 1
ma mw qw V
ð3Þ
Table 4 Porosity and permeability coefficient for the nominated samples before clogging. Group
Open Porosity
Cross-Section Porosity
Permeability Coefficient (mm*s1)
Standard Deviation
1–1 1–2 1–3 2–1 2–2 2–3
0.179 0.187 0.211 0.217 0.24 0.273
0.187 0.206 0.224 0.226 0.231 0.301
4.76 5.27 7.70 9.30 10.36 12.16
0.41 0.93 0.48 0.26 0.11 0.51
135
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139 Table 5 Permeability coefficient for the examined samples after clogging. Group
Clogging Particle Size (mm)
Permeability Coefficient (mm*s1)
Standard Deviation
Decrease in Permeability Coefficient (mm*s1)
Percentage of Decrease (%)
1–1
0.5–1 1–1.5 1.5–2 0.5–1 1–1.5 1.5–2 0.5–1 1–1.5 1.5–2 0.5–1 1–1.5 1.5–2 2–3 0.5–1 1–1.5 1.5–2 2–3 0.5–1 1–1.5 1.5–2 2–3
2.70 2.44 4.26 2.77 2.52 4.62 5.26 4.40 6.65 8.27 6.04 5.34 7.57 9.26 7.02 4.76 8.31 9.76 7.33 6.30 8.41
0.52 0.23 0.49 0.37 0.21 0.76 0.51 0.46 0.23 0.49 0.43 0.47 0.20 0.17 0.49 0.29 0.47 0.33 0.53 0.26 0.47
2.06 2.32 0.50 2.50 2.75 0.65 2.44 3.30 1.05 1.03 3.26 3.96 1.73 1.10 3.34 5.60 2.05 1.40 3.83 4.86 2.75
43 49 11 47 52 12 32 43 14 11 35 43 19 11 32 54 20 13 34 44 25
1–2
1–3
2–1
2–2
2–3
3. Results and discussion 3.1. Pore characteristics In this part, the data of the experimental tests have been recorded and the analytical study for image processing to calculate all details of the scanned images has been also conducted. Therefore, the data results will be discussed and displayed, and firstly the details of the scanned images are shown in Table 3. From Fig. 3, the number of pores were generally negatively related to the pore area. In addition, this result is not affected by the particle size of the aggregate. The main reason is that the larger selected cement-aggregate ratio, the smaller the pore area is. At the same time, the number of pores will decrease due to the increase in contact frequency between adjacent aggregate. There are many data in each group (more than 20 thousand data), therefore the standard deviation is large, and there are a lot of abnormal outlier points in Fig. 3.b (There 200–600 points in each group). In fact, from the large amount of the calculated data in this study, these statistics of equivalent diameter have a good statistical significance, which could ignore the effect of deviation that caused by some of the excluded outlier points. The average equivalent diameter decreases with the increase of cementaggregate ratio, which is consistent to the original expectation. 3.2. Variation of permeability
Fig. 4. Porosity and permeability coefficient of the examined samples before clogging; a) permeability coefficient before clogging- porosity; b) porosity.
where ma is the weight of the sample in air, mw is the weight of the sample in water, qw is the water density and V is the specimen volume. In this study, the cross-section porosity can be calculated by the following formula:
PCS ¼
Si A
ð4Þ
where Pcs is the cross section porosity, Si is the average total pore area, and A is the total area of the image.
The porosity and permeability coefficient before and after the clogging are recorded in Table 3 and Table 4, respectively. It is worth noting that only three kinds of size of clogging particle are selected for testing and this attributed to the pores in group 1, which was small. It is clear from Tables 4 and 5 that a slight difference between the two types of porosity. In addition, the variation of permeability coefficient is related to the clogging particle size. Consequently, the following figures have been drawn based on the acquired data to obtain convenient analysis. Fig. 4 displays the porosity and permeability coefficient before clogging. In the second part of this figure, the cross section porosity is usually slightly larger than open porosity, and this may be related to closing some parts in the cross section porosity. However, the method of testing open porosity could not test the closed
136
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139
Fig. 6. The influence of cement-aggregate ratio on pore characteristics; a) average pore area of cross section; b) average pore number of cross section.
Fig. 5. Variation of permeability coefficient under the influence of the ratio of clogging particle size to aggregate size; a) the relationship between the permeability coefficient and size ratio of clogging particle; b) percentage of decrease in permeability of group 1 (2.36–4.75 mm aggregate size) after clogging; c) percentage of decrease in permeability of group 2 (4.75–9.5 mm aggregate size) after clogging.
porosity. The trend linear in Fig. 1.b has been obtained from a regression analysis of the fitting data, it is apparent that there is a significant functional relationship (R2 = 0.9004) between the two porosities and the proposed model is as follows.
Po ¼ 1:1893Pcs 0:0259
ð5Þ
where Po is the open porosity and Pcs is the cross-section porosity. Although the closed pore has no effect on the permeability coefficient, it is an important consideration in the design method based on the target porosity [18], which is the main design method for pervious concrete. The reason is that the target porosity here in this design method means the total porosity of which the closed pore is a part. However, the ordinary porosity test methods are generally to test the open porosity. Therefore, investigating the proposed model (Eq. 5) can effectively estimate the open porosity, which is the main influence factor of permeability of pervious concrete in design.
Moreover, the benefit of the suggested method to determine the open porosity of pervious concrete in this study is in the first stage, which is also provide a new idea for researchers to apply it on the other different pavements such as open grade friction course of asphalt pavement. Furthermore, generally, the permeability coefficient is related to the porosity and aggregate size before clogging (14). The porosity determines the water volume flowing through the cross-section of pervious concrete. On the other side the aggregate determines the number of the pores and the complexity of concrete structure, which are the main influencing factors of permeable velocity. In order to analyze the variation of permeability coefficient, the following figures have been drawn. The abscissa here is the ratio of clogging particle size to average equivalent diameter. It is worth noting that the clogging particle size is determined by the median of its group. Some interesting results can be obtained from Fig. 5. Primarily, when the size ratio of clogging particle to pores is too small or large, the variation of permeability coefficient is not obvious, which means that the clogging is at a low level. This is due to the clogging is produced by the clogging particle being trapped in the internal pores of pervious concrete. The relationship between the size of clogging particle and pores determines this phenomenon. Firstly, when the clogging particle size is much larger than pore size, the clogging particle cannot pass in the pores in the surface of the pervious concrete. Additionally, when the clogging particle size is much smaller than pore size, the clogging particle would pass through the entire concrete structure. Therefore, these situations will not easily to cause the clogging. For such consideration, it is generally considered that the top point of the curve in the Fig. 5 is at the size ratio of 1. However, when the ratio is between 0.6 and 0.8, the clogging has reached to a serious level. The reasons can be listed as follows. Certainly, the pores on the pervious concrete surface determine whether
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139
137
Fig. 7. Flowchart of how to optimize the design of pervious concrete to prevent clogging.
the clogging particles can pass in the pores. However, the smallest pore size of concrete is most likely to make it clogged. Moreover, the pores are generally depressed by the mixtures, and this can also be confirmed in the scanning image of Fig. 1. In this paper, the pores are regarded as circular for convenience of calculation. Therefore, the equivalent diameter is larger than the actual size of the pores. Comparing the Fig. 5b and c, it is clear that though the pore sizes and the porosity of the two groups are totally different, the
trend of these curves are similar.. This indicates that the clogging level is mainly influenced by the size ratio of clogging particle and pore size, but not only one of them or the porosity. 3.3. The proposed design of the pervious concrete Apparently, if the sizes of the available aggregates, which have been used to construct a road pavement, is known, it is entirely possible to optimize the design of permeable concrete by selecting
138
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139
the suitable aggregate sizes and the amount of cement and other materials to avoid being clogged of the permeable pavement. Therefore, the following section will provide the procedures that will help the designers to design the pervious concrete mixture in safely way. To optimize the design, initially, the curves of Fig. 6 should be drawn to determine the influence of cement-aggregate ratio on pore characteristics. It is obvious that there is a linear relationship between pore characteristics and cement-aggregate ratio. The specific formulas are shown below:
AP1 ¼ 4811:5R þ 1962:7
ð6:1Þ
AP2 ¼ 8208:3R þ 2614:5
ð6:2Þ
N1 ¼ 1325R 116:92
ð7:1Þ
N2 ¼ 300R þ 18
ð7:2Þ
where R is the cement-aggregate ratio, Ap1 is the average pore area of 2.36–4.75 mm aggregate size, Ap2 is the average pore area of 4.75–9.5 mm aggregate size, N1 is the pore number of 2.36– 4.75 mm aggregate size, and the N2 is the pore number of 4.75– 9.5 mm aggregate size. Fig. 7 explains the suggested procedures for optimization of the pervious concrete design to prevent clogging or decrease the clogged pores. In this figure, firstly, the materials should be selected and the mix proportion should be designed, especially the size data of contaminants (d1) should be collected before design (in step 2). Secondly (in step 3), due to the target open porosity is determined in step 2, the cross-section porosity could be calculated by Eq. 5, and the total area of pores that equal the cross-section porosity could also be acquired (in step 4). Get the cement-aggregate ratio by Eq.6 and then get the number of pores by Eq. 7 through the cement-aggregate ratio (in step 5) to calculate (in step 6) the pore size or the equivalent diameter of pore (d2). If the ratio of d1 and d2 is between 0.6 and 0.8, the cement-aggregate ratio should be redesigned properly (back to step 5), or the convenient materials and optimum ratios can be obtained. 4. Conclusions and recommendations In this paper, the pores characteristics of pervious concrete were explored using CT scanning, and the analysis of the image processing has been conducted by Matlab and Image J software. In addition, the relationship between clogging and the combined effects of clogging particle size and size of pores were also investigated. Moreover, a flowchart to optimize the design of pervious concrete has been displayed. Through the above discussion, the following conclusions and recommendations can be drawn from the test results and data analysis: The pervious concrete mixture influences the pore characteristics, including the number of pores and its size. Whereas, at specific aggregate gradation and the porosity before clogging will be affected by the pore size of pervious concrete. In addition, the number of pores is related to the pore size. Both the aggregate size and cement-aggregate ratio exhibited an evident effect on the pore size. At the same time, aggregate size is the primary influenced factor while the cementaggregate ratio is the secondary factor. The clogging level is mainly influenced by the size ratio of clogging particle to the pores of pervious concrete. In addition, when the size ratio is between 0.6 and 0.8, it is easy to cause the clogging.
The proposed model between open porosity and cross-section porosity exhibited a significant result to predict the expected open porosity, which can be considered the same value of the cross-section porosity or (Po = 1.05 Pcs). The proposed design method in this study to prevent the occurring of clogging in the pervious concrete will help the designers and engineers to provide a safe design of the new porous concrete pavement or predict the porosity of the current pervious concrete pavement. Certainly, there are still some shortcomings in this study. More influenced factors should be taken into consideration in a further study especially when the size of the clogging particle is uneven. Conflict of interest There is no conflict of interest. Acknowledgement The research is supported by grants from Ministry of Science and Technology of the People’s Republic of China (Grant No. 2016YFE0108200), National Natural Science Foundation of China (Grant No. 5150080567), the Fundamental Research Funds for the Central Universities (Grant No. 22120180093), and Science and Technology Commssion Shanghai Municipality (Grant No. 17230711300, 16DZ1202004), Fund of Shanghai Peak Discipline (Grant No. 2016J012309), and Department of Transportation of Hebei Province (QG2018-5). The sponsorships are gratefully acknowledged. The contents of this paper reflect the views of the authors and do not necessarily reflect the official views or policies of the sponsors. This paper does not represent any standard or specification. References [1] Ivanka Netinger Grubeša, Ivana Barišic´, Vilma Ducman, Lidija Korat, Draining capability of single-sized pervious concrete, Construct. Build. Mater. 169 (2018) 252–260. [2] Putra Jaya Ramadhansyah, Mohd Yusak Mohd Ibrahim, Hainin Mohd Rosli, Wan Ibrahim Mohd Haziman, A review of porous concrete pavement: applications and engineering properties, Appl. Mech. Mater. 554 (2014) 37–41. [3] Sonia Rahman, Andrew B. Northmore, Vimy Henderson, Susan L. Tighe, Developing a framework for low-volume road implementation of pervious concrete pavements, Int. J. Transp. Sci. Technol. 4 (1) (2015) 77–91. [4] Hanbin Wang, Hui Li, Xiao Liang, Haonan Zhou, Ning Xie, Zhen Dai, Investigation on the mechanical properties and environmental impacts of pervious concrete containing fly ash based on the cement-aggregate ratio, Constr. Build. Master. 202 (2019) 387–395. [5] Wuguang Lin, Dae-Geun Park, Sung Woo Ryu, Byeong-Tae Lee, Yoon-Ho Cho, Development of permeability test method for porous concrete block pavement materials considering clogging, Construct. Build. Mater. 118 (2016) 20–26. [6] Na Zhang, Experimental Investigation on Clogging Mechanism of Pervious Concrete, Shandong University, 2014. [7] Liv M. Haselbach, Potential for clay clogging of pervious concrete under extreme conditions, J. Hydrol. Eng. 15 (1) (2010). [8] Jiayi Yuan, Xudong Chen, Saisai Liu, Shengtao Li, Nan Shen, Effect of water head, gradation of clogging agent, and horizontal flow velocity on the clogging characteristics of pervious concrete, J. Mater. Civil Eng. 30 (9) (2018). [9] Michael F. Hein, Mark Dougherty, Turner Hobbs, Cleaning methods for pervious concrete pavements, Int. J. Construct. Educ. Res. 9 (2) (2013). [10] Liv M. Haselbach, Srinivas Valavala, Felipe Montes. Permeability predictions for sand-clogged Portland cement pervious concrete pavement systems, J. Environ. Manage. 81 (1) (2005). [11] Alalea Kia, Hong S. Wong, Christopher R. Cheeseman, Clogging in permeable concrete: a review, J. Environ. Manage. 193 (2017). [12] Sean Patrick Walsh, Amy Rowe, Qizhong Guo, Laboratory scale study to quantify the effect of sediment accumulation on the hydraulic conductivity of pervious concrete, J. Irrig. Drain. Eng. (2014). [13] Alalea Kia, Hong S. Wong, Christopher R. Cheeseman, Defining clogging potential for permeable concrete, J. Environ. Manage. 220 (2018).
H. Zhou et al. / Construction and Building Materials 212 (2019) 130–139 [14] Baoshan Huang, Wu. Hao, Xiang Shu, Edwin G. Burdette, Laboratory evaluation of permeability and strength of polymer-modified pervious concrete, Construct. Build. Mater. 24 (5) (2009). [15] Xuzhao Qin, Study on Properties of Porous Cement Concrete Material and Design of Pavement Structure, Changan University, 2011. [16] Masoud Kayhanian, Dane Anderson, John T. Harvey, David Jones, Balasingam Muhunthan, Permeability measurement and scan imaging to assess clogging
139
of pervious concrete pavements in parking lots, J. Environ. Manage. 95 (1) (2011). [17] Xu. Zongheng, Study on Macroporous Soil in Unsaturated Zone of Vegetation Development Slope, Kunming University of Science and Technology, 2014. [18] Lu. Hewei, Lijun Sun, Mixture design of porous cement concrete based on target porosity, Shanghai Highway 4 (2013) 63–65.