A method for establishing RAPC blockage model based on Image method

Abstract

The present invention provides a method for establishing a RAPC blockage model based on the Image method, first preparing RAPC, then configuring blocking materials, determining the blocking material gradation, and then configuring the blocking materials used in the test, and then measuring the porosity and water permeability coefficient of RAPC, and measuring the effective porosity. The test block is cut into three equal parts, and is respectively subjected to grinding, polishing, graying, and other treatments. The middle image area is intercepted as an analysis section and imported into Image Pro Plus software to extract pore structure parameters, and the relationship between the single pore structure parameter obtained based on the Image method and the water permeability coefficient is analyzed. Then, a blocking test is carried out, and the water permeability coefficient after each blocking is measured. Finally, a RAPC water permeability coefficient model and a blocking model that comprehensively consider different pore structure parameters are established. The present invention can determine the degree of blockage caused by the amount of blocking material on RAPC based on the changes in the water permeability coefficient and water loss rate of recycled aggregate permeable concrete in different regions, and provide technical support for further evaluation and application of RAPC.

Description

Technical Field

[0001] The invention belongs to building material technology, and in particular relates to a RAPC blockage model established based on an Image method. Background Art

[0002] Due to human or natural sedimentation and other reasons, a large amount of sediment will be generated on the road surface. As a porous and permeable road surface, RAPC pavement will be affected by these sediments entering the interior or accumulating on its surface under the influence of rainfall, human factors and other factors. This will lead to a significant reduction in permeability until it is unable to drain rainwater in time under the action of rainfall, and the infiltration function is lost. The increase in impervious areas in urban areas will cause a series of problems, the most serious of which is the increase in rainfall runoff. Urban pavements cannot infiltrate into the ground and form surface torrents, leading to urban waterlogging. Therefore, pore blockage is a potential problem that affects the functioning of RAPC. For example, it is impossible to accurately obtain the amount of blockage when RAPC is completely blocked, and it is impossible to obtain the permeability coefficient of RAPC after it is blocked by a certain amount of blockage. Therefore, the degree of blockage caused by the amount of blockage on RAPC cannot be effectively determined, which affects the evaluation and further application of RAPC. Summary of the Invention

[0003] In response to the technical problems existing in the prior art, the present invention provides a RAPC blockage model established based on the Image method. Through this model, the changes in the permeability coefficient and permeability loss rate of recycled aggregate permeable concrete in different regions can be studied, and the degree of blockage caused by the amount of blockage on RAPC can be determined, providing technical support for further evaluation and application of RAPC.

[0004] The solution adopted by the present invention to solve the technical problem is: a method for establishing a RAPC blockage model based on the Image method, comprising the following steps.

[0005] Step 1: Prepare RAPC. Concrete pavement (construction waste) is crushed and sieved to obtain two aggregate sizes: the first size is 4.75-9.5 mm, and the second size is 9.5-19 mm. The mass ratio of the first to the second size is 3:2. Ordinary Portland cement with a porosity of 20%, 25%, and 30% and a water-cement ratio of 0.3 is used to determine the RAPC test mix ratio. Based on this, RAPC test blocks are prepared using the pre-wetted aggregate mixing method and cured for 28 days.

[0006] Step 2: Prepare the plugging materials. Collect urban runoff particles and particle size distribution for different environments, determine the gradation of the plugging materials, and then prepare the plugging materials used in the test.

[0007] Step 3: Determine the porosity (porosity is the effective porosity) and water permeability of RAPC.

[0008] Step 4: Image method. Cut the test piece into three equal parts, grind and polish them, and grayscale them respectively. Finally, cut the middle image area as the analysis section and import it into Image Pro Plus software to extract the pore structure parameters. Then, analyze the relationship between the single pore structure parameter obtained based on the Image method and the water permeability coefficient. Finally, establish the RAPC water permeability coefficient model that comprehensively considers different pore structure parameters.

[0009] Step 5: Blockage test, measure the water permeability coefficient after each blockage.

[0010] Step 6: Blockage model. First, fit the blockage test results to obtain the blockage model. Then use the Pearson correlation coefficient method to analyze the factors related to the blockage coefficient. Finally, establish the RAPC blockage model.

[0011] Among them, the permeability coefficient is measured using the constant head method.

[0012] The clogging test includes the following steps.

[0013] (1) Using a water permeability test device, test the initial water permeability coefficient k0 of RAPC;

[0014] (2) Turn off the water inlet valve, evenly place 10g of plugging material on the surface of the test block, and then evenly sprinkle water for 180 seconds. Then measure the water permeability coefficient of RAPC at this time;

[0015] (3) Repeat the operation in (2) until the test piece is blocked to a value below the specified minimum water permeability coefficient of 0.5 mm / s, then stop the test and record the water permeability coefficient attenuation; test three test pieces for each designed pore in each area and take the average value as the test result.

[0016] Considering the influence of different pore structure parameters on the water permeability coefficient of RAPC, the composite pore structure parameter R is defined using formula (1):

[0017] R=∑α i R i (1),

[0018] Where R i are different pore structure parameters, i=1~4, where R1 is the plane porosity, R2 is the perimeter, R3 is the specific surface area, and R4 is the shape factor; ɑ i is the influence of different pore structure parameters on the water permeability coefficient, obtained through multiple linear regression analysis.

[0019] According to formula (1), the R value under different porosities is obtained, and the relationship between the water permeability coefficient and the composite pore structure parameters is plotted. The regular relationship between the water permeability coefficient and the composite pore structure parameters is formula (2), k0 = -0.275R 2 +2.145R-1.204(2), which is used to comprehensively analyze the quantitative relationship between permeability coefficient and pore structure.

[0020] The RAPC blockage model was established as shown below:

[0021] k n =k0e -βn (3),

[0022] Where k n is the water permeability coefficient after blocking n times, mm / s; k0 is the initial water permeability coefficient of RAPC, mm / s; β is the blocking coefficient, which is generally a constant greater than 0 and can be determined by regression analysis; n is the number of blocking times, n≥0.

[0023] As can be seen from Equation (3), βn represents the decay rate. The larger its value, the faster the RAPC permeability decays, that is, the worse the anti-clogging ability. Therefore, β can be used to judge the possibility of RAPC being clogged. That is, the larger β is, the more likely it is to be clogged.

[0024] Remove irrelevant and low-correlation factors and establish a fitting model between β value and its high and medium correlation factors. The result is shown in formula (4), and the relationship between the actual β value and the fitted value is given.

[0025] β=0.7-0.529R-0.024P+0.033P0+0.058k0-0.003M3+1.146τ (4),

[0026] In the formula: β is the blockage coefficient; R is the composite pore structure parameter; P is the designed porosity, %; P0 is the effective porosity, %; k0 is the initial permeability coefficient, mm / s; M3 is the mass of sand with a particle size of 0.6-1.18 mm in the blockage, g; τ is the ratio of the median size of the blockage particle to the average equivalent diameter.

[0027] Substituting equations (2) and (4) into equation (3), we can obtain a clogging model that takes into account factors such as composite pore structure parameters, clogging amount, and clogging times. This model can be used to predict the permeability coefficient after clogging.

[0028] Beneficial effects of the present invention: Through the model method, the present invention can quickly obtain the changes in the permeability coefficient and permeability loss rate of recycled aggregate permeable concrete for different regions and environments, and then determine the degree of blockage caused by the amount of blockage on RAPC, providing technical support for further evaluation and application of RAPC. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is the distribution diagram of effective porosity and permeability;

[0030] Figure 2 This is a diagram of a water permeability test device;

[0031] Figure 3 is the relationship between the water permeability coefficient and the pore structure parameters;

[0032] Figure 4 is the fitting diagram of water permeability and composite pore structure parameters;

[0033] Figure 5 This is the permeability coefficient diagram after RAPC is blocked;

[0034] Figure 6 is the correlation coefficient matrix diagram;

[0035] Figure 7 This is the relationship between the actual value of β and the fitted value. DETAILED DESCRIPTION

[0036] The present invention will be further described below with reference to the accompanying drawings and examples.

[0037] Example 1: A method for establishing a RAPC blockage model based on the Image method is used. The method can be implemented using the following steps.

[0038] Step 1: Prepare RAPC.

[0039] Recycled coarse aggregate: Concrete pavement (construction waste) was crushed and screened to produce two aggregate fractions with particle sizes of 4.75-9.5 mm and 9.5-19 mm, respectively. The mass ratio of the two fractions was 3:2. Basic properties are shown in Table 1. Cement: Ordinary Portland Cement (PO42.5); Water: Tap Water.

[0040] Table 1 Performance indicators of recycled coarse aggregate

[0041]

[0042]

[0043] The selected design porosities were 20%, 25%, and 30%, and the water-cement ratio was 0.3. The RAPC test mix proportions were determined according to the "Technical Specification for Permeable Cement Concrete Pavement" (CJJ / T135-2009), as detailed in Table 2. Based on this, RAPC test blocks were prepared using the pre-wetted aggregate mixing method and cured for 28 days at standard conditions. The test blocks measured 150 mm × 150 mm × 150 mm. The measured average strengths of the RAPC test blocks were 18.10 MPa, 14.50 MPa, and 11.30 MPa, respectively, meeting the strength requirements for permeable concrete. Furthermore, 12 test blocks were prepared for each design porosity, for a total of 36 test blocks.

[0044] Table 2 Test mix ratio (kg / m 3 )

[0045]

[0046] Step 2: Prepare the plugging material.

[0047] Urban runoff particles and particle size distribution were collected for different environments (commercial areas, traffic areas, residential areas, and parks) to determine the blockage gradation, as shown in Table 3.

[0048] Table 3 Blockage particle size distribution (%)

[0049]

[0050] Step three: determine the porosity and water permeability of RAPC.

[0051] The pore structure of permeable concrete is a key factor influencing its strength and permeability. Pores in permeable concrete are categorized into three main types: connected pores, semi-connected pores, and closed pores. Connected and semi-connected pores are collectively referred to as effective pores, while closed pores are considered ineffective pores. Effective porosity is determined according to the "Technical Specification for the Application of Recycled Aggregate Permeable Concrete" (CJJ / T253-2016). The permeability coefficient is determined using the constant head method.

[0052] Figure 1 The relationship between RAPC effective porosity and water permeability. Overall, the water permeability increases with the increase of effective porosity. The minimum water permeability of this RAPC is 1.72 mm / s, corresponding to an effective porosity of 19.27%; the maximum is 2.2 mm / s, corresponding to an effective porosity of 30.36%. Through regression analysis, it is found that the water permeability and effective porosity meet the exponential law. The correlation coefficient R of the regression result is 2 =0.74, which is relatively small. This is because the RAPC prepared in this experiment used continuous dual-grade recycled aggregate. During the molding process, the distribution of the two aggregates was relatively random, making the pore structure more complex than that of a single-grade aggregate. In addition, the recycled aggregate used in the experiment contained a large number of needle-like particles, which also produced complex pores.

[0053] Step 4: Image method.

[0054] The specimen was cut into three equal parts and subjected to grinding, polishing, and grayscale processing. Finally, a 120 mm × 120 mm central image region was captured as an analysis section and imported into Image Pro Plus (IPP) software to extract pore structure parameters. Table 4 presents the statistical results of the planar pore structure parameters of RAPC at different designed porosities. As shown in the table, with increasing designed porosity, the total planar pore area, planar porosity, average equivalent pore diameter, and perimeter gradually increase, while the number of pores, specific surface area, and average shape factor show the opposite trend. This indicates that a greater designed porosity increases the proportion of large pores in the RAPC and reduces the proportion of small pores, resulting in a decrease in specific surface area. This suggests that a greater number of pores does not necessarily improve water permeability; the water permeability coefficient increases only when the proportion of large pores is relatively high. Furthermore, the average shape factor decreases with increasing porosity, indicating that the water permeability of RAPC is also related to pore shape. It is generally believed that a pore shape close to circularity improves water permeability, while an elliptical shape has the opposite effect.

[0055] Table 4 Planar pore distribution characteristics at different porosities

[0056]

[0057]

[0058] Figure 3 The relationship between single pore structure parameters and permeability coefficient is shown. The study shows that the main factor affecting the permeability of permeable concrete is the pore structure distribution, not the porosity. Figure 3 This conclusion is reflected to some extent. As can be seen from the figure, the plane porosity, perimeter, specific surface area, and shape factor have a good linear correlation with the water permeability coefficient, with the correlation coefficient ranging from 0.8 to 0.9. The greater the plane porosity and perimeter, and the smaller the specific surface area and shape factor, the higher the water permeability coefficient, and vice versa.

[0059] The above analysis shows that a single pore structure parameter can, to a certain extent, characterize the variation in RAPC permeability. However, its characterization results are often incomplete and unsystematic, and the correlation is subject to significant interference. Therefore, this paper proposes a comprehensive consideration of the effects of different pore structure parameters on the RAPC permeability coefficient. Equation (1) is used to define the composite pore structure parameter R.

[0060] R=∑α i R i (1),

[0061] Where R iare different pore structure parameters. In this paper, i is taken as 1 to 4, where R1 is the plane porosity (%), R2 is the perimeter (mm), R3 is the specific surface area (mm-1), and R4 is the shape factor; ɑ i The influence of different pore structure parameters on the water permeability coefficient can be obtained through multiple linear regression analysis. In this test, ɑ1~ɑ4 are -0.0055, 0.0766, -0.6556 and 0.3153, respectively.

[0062] According to formula (1), the R value under different porosities is obtained, and the relationship between the water permeability coefficient and the composite pore structure parameters is plotted, as shown in Figure 4 As shown in the figure, the water permeability coefficient and the composite pore structure parameters satisfy the regular relationship of quadratic polynomial, with a correlation coefficient of 0.957. This can be used to comprehensively analyze the quantitative relationship between the water permeability coefficient and the pore structure, and also provides a basis for the construction of the blockage model in this paper.

[0063] k0=-0.275R 2 +2.145R-1.204 (2),

[0064] Step 5: Blockage test.

[0065] ①Use a self-made water permeability test device, such as Figure 2 , test the initial permeability coefficient k0 of RAPC; 1 is the permeability test box, 3 is the water collection tank, 3 is the water inlet pipe, 5 is the drainage pipe,

[0066] ② Turn off the water inlet valve, evenly place 10g of plugging material on the surface of the test block, and then evenly sprinkle water for 180 seconds. Then measure the water permeability coefficient of RAPC at this time;

[0067] ③ Repeat step ② until the test piece is clogged to a minimum permeability coefficient of 0.5 mm / s or less. Stop the test and record the permeability coefficient decay. Test three test pieces per designed pore in each area and take the average value as the test result.

[0068] Step 6: Block the model.

[0069] Figure 5 The following is a curve showing the effect of the number of blockages on the RAPC water permeability coefficient in different areas. As can be seen from the figure, as the number of blockages increases, the RAPC water permeability coefficient gradually decreases, and generally speaking, the greater the porosity, the stronger the ability to resist blockage, which is manifested in the fact that more blockages are required to completely block the internal pores. When the blockage does not exceed 3 times, the RAPC water permeability coefficients of different porosities are all greater than 0.5mm / s, which means that they can still maintain their permeability requirements. When the blockage reaches 4 and 5 times, the RAPC water permeability coefficients with P = 20% and 25% are less than the specified value, and begin to lose their permeability function. When the number of blockages exceeds 6 times, the RAPC with P = 30% also begins to lose its permeability function.

[0070] For RAPCs with P = 20%, 25%, and 30%, after three clogging cycles, the RAPC permeability decreased to 61%, 48%, and 71% of the initial permeability in traffic areas, 52%, 56%, and 80% in commercial areas, 54%, 34%, and 63% in residential areas, and 40%, 51%, and 75% in parks, respectively. This indicates that, in the initial clogging phase, the greater the porosity, the less susceptible the RAPC is to clogging. For example, at P = 30% and after the third clogging cycle, the RAPC permeability in different areas still maintained approximately 70% of its initial value, while the permeability loss at the other two porosities was approximately half or even greater. This is because as the porosity of permeable concrete increases, its planar porosity and equivalent pore size increase, widening the channels and reducing resistance. This allows more obstructions to be carried out of the specimen by the water flow, reducing the permeability loss. Conversely, at lower porosity levels, more obstructions block the pore channels, significantly reducing the permeability. In addition, when P = 30%, the permeability coefficient of RAPC fluctuates greatly before and after blockage. This is because when the porosity is large, the proportion of large pores increases and the tortuosity of the pores decreases. After being blocked once, the blockage remaining inside the specimen may be flushed open by the water pressure and flushed out of the specimen through the connected pores.

[0071] The RAPC blockage model was established as shown in the following formula.

[0072] k n =k0e -βn (3),

[0073] Where k n is the water permeability coefficient after blocking n times, mm / s; k0 is the initial water permeability coefficient of RAPC, mm / s; β is the blocking coefficient, which is generally a constant greater than 0 and can be determined by regression analysis; n is the number of blocking times, n≥0.

[0074] As can be seen from formula (3), βn represents the decay rate. The larger its value, the faster the RAPC permeability coefficient decays, that is, the worse the anti-clogging ability. Therefore, β can be used to judge the possibility of RAPC being blocked, that is, the larger β is, the easier it is to be blocked. Table 5 shows the permeability decay model of each RAPC specimen in the blockage test and its correlation obtained by fitting using formula (3). As can be seen from the table, the blockage model can fit the RAPC blockage results well, and the correlation coefficient is above 0.80; at the same time, it is found that, in general, the larger the design porosity, the smaller the blockage coefficient β. In addition, the β value in the residential area is larger than that in other areas, indicating that the RAPC in this area is more likely to be blocked.

[0075] Table 5 Summary of blocking models

[0076]

[0077] It is generally believed that β is related to the inherent properties of permeable concrete and plugging materials. Based on this, 10 parameters were selected as influencing factors of β: composite pore structure parameter R, design porosity P, effective porosity P0, initial permeability coefficient k0, fineness modulus M of plugging material (sand) x , the amount of sand M1, M2, M3, M4 (calculated from the number of blockages and the gradation distribution of the blockage material) of different particle sizes (<0.15mm, 0.15-0.6mm, 0.6-1.18mm, 1.18-2.36mm) and the ratio τ of the median size of the blockage particle to the average equivalent diameter. In order to understand the correlation between each factor and the blockage coefficient β, as well as the correlation between each factor, the Pearson correlation coefficient method was used to obtain the correlation coefficient matrix. The results are shown in the figure. Figure 6 shown.

[0078] Depend on Figure 6 It can be seen that except for τ which is positively correlated with β, other indicators such as R, P, P0, k0, etc. are negatively correlated with β, that is, the larger the value, the smaller β, and the less likely RAPC is to be blocked. According to the size of the correlation coefficient, the degree of correlation between each factor and β can be determined, specifically: P, R, P0 are highly correlated with β, k0, M3, τ are moderately correlated with β, M2, M4 are weakly correlated with β, and M x , M1 has no correlation with β.

[0079] Remove irrelevant and low-correlation factors and establish a fitting model between β value and its high and medium correlation factors. The result is shown in formula (4), and the relationship between the actual value of β and the fitted value is given. The results are as follows: Figure 7 As shown in the figure, the average absolute error of the fitting results is 0.016, and the average relative error is 7.64%, indicating that the fitting accuracy is relatively high.

[0080] β=0.7-0.529R-0.024P+0.033P0+0.058k0-0.003M3+1.146τ (4),

[0081] In the formula: β is the blockage coefficient; R is the composite pore structure parameter; P is the designed porosity, %; P0 is the effective porosity, %; k0 is the initial permeability coefficient, mm / s; M3 is the mass of sand with a particle size of 0.6-1.18 mm in the blockage, g; τ is the ratio of the median size of the blockage particle to the average equivalent diameter.

[0082] Substituting equations (2) and (4) into equation (3), we can obtain a clogging model that takes into account factors such as composite pore structure parameters, clogging amount, and clogging times. This model can be used to predict the permeability coefficient after clogging.

[0083] It should be understood that the above-described specific embodiments of the present invention are merely illustrative of or explanation of the principles of the present invention and are not intended to limit the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for establishing a RAPC blockage model based on the Image method, characterized in that: The following steps are involved: Step 1: Prepare RAPC by crushing and screening concrete pavement or construction waste to obtain two aggregate sizes: the first size has a particle size of 4.75-9.5 mm, and the second size has a particle size of 9.5-19 mm. The mass ratio of the first to the second size is 3:

2. Ordinary Portland cement with a porosity of 20%, 25%, and 30% and a water-cement ratio of 0.3 is used to determine the RAPC test mix ratio. Based on this, RAPC test blocks are prepared using the pre-wetted aggregate mixing method and cured for 28 days. Step 2: Prepare the plugging materials. Collect urban runoff particles and their particle size distribution according to different environments, determine the gradation of the plugging materials, and prepare the plugging materials used in the test. Step 3, determining the porosity and water permeability coefficient of RAPC; Step 4: Image method. Cut the test piece into three equal parts, grind and polish them, and grayscale them respectively. Cut the middle image area as the analysis section and import it into Image Pro Plus software to extract the pore structure parameters. Then analyze the relationship between the single pore structure parameter obtained based on the Image method and the water permeability coefficient. Finally, establish the RAPC water permeability coefficient model that comprehensively considers different pore structure parameters. Considering the influence of different pore structure parameters on the water permeability coefficient of RAPC, the composite pore structure parameter R is defined using formula (1): (1)， Where R i are different pore structure parameters, i=1~4, where R1 is the plane porosity, R2 is the perimeter, R3 is the specific surface area, and R4 is the shape factor; ɑ i The influence of different pore structure parameters on the water permeability coefficient is obtained through multivariate linear regression analysis; the R value under different porosities is obtained according to formula (1), and the relationship between the water permeability coefficient and the composite pore structure parameters is plotted, and the regular relationship between the water permeability coefficient and the composite pore structure parameters is obtained as a quadratic polynomial. k0=-0.275R 2 +2.145R-1.204 (2) It is used to comprehensively analyze the quantitative relationship between water permeability and pore structure. Where, k0 is the initial water permeability of RAPC, mm / s; Step 5: Blockage test, measuring the water permeability coefficient after each blockage; Step 6: Blockage model. First, fit the blockage test results to obtain the blockage model. Then use the Pearson correlation coefficient method to analyze the factors related to the blockage coefficient. Finally, establish the RAPC blockage model, as shown in the following formula: (3) Where k n is the water permeability coefficient after blocking n times, mm / s; β is the blocking coefficient, which is generally a constant greater than 0, n is the number of blocking times, n≥0; Using the Pearson correlation coefficient method, we removed irrelevant and low-correlation factors and established a fitting model of β value and its high and medium correlation factors. β=0.7-0.529R-0.024P+0.033P0+0.058k0-0.003M3+1.146τ (4) In the formula: β is the blockage coefficient; R is the composite pore structure parameter; P is the designed porosity, %; P0 is the effective porosity, %; k0 is the initial permeability coefficient, mm / s; M3 is the mass of sand with a particle size of 0.6-1.18 mm in the blockage, g; τ is the ratio of the median size of the blockage particle to the average equivalent diameter.

2. The method for establishing a RAPC blockage model based on the Image method according to claim 1, characterized in that: The water permeability coefficient is determined by the constant head method.

3. The method for establishing a RAPC blockage model based on the Image method according to claim 1, characterized in that: The clogging test includes the following steps: S1 uses a water permeability test device to test the initial water permeability coefficient k0 of RAPC; S2 turns off the water inlet valve, evenly put 10g of plugging material on the surface of the test block, and then evenly sprinkle water for 180s. Then measure the water permeability coefficient of RAPC at this time; S3 repeats the operation of S2 until the specimen is blocked to below the specified minimum permeability coefficient of 0.5 mm / s, then stops the test and records the permeability coefficient attenuation; 3 specimens are tested for each designed pore in each area, and the average value is taken as the test result.

4. The method for establishing a RAPC blockage model based on the Image method according to any one of claim 1, characterized in that: Substituting equations (2) and (4) into equation (3), we obtain a clogging model that takes into account the composite pore structure parameters, the amount of clogging material, and the number of clogging times. This model is used to predict the permeability coefficient after clogging.

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