Study on Permeability Performance and Prediction Model of Coal Gangue Pervious Concrete

Abstract

To expand the in-situ utilization of coal gangue in mining area and meet the demand of “sponge city”, the permeability coefficient, total porosity and effective porosity of coal gangue pervious concrete (CGPC) in different mixtures were deeply explored. The results show that the permeability coefficient of CGPC can reach 1.5 ~ 2 mm/s. In general, the permeability coefficient and porosity of CGPC increase as the aggregate particle size and designed porosity increase. Effective porosity is the key factor affecting the permeability coefficient of CGPC. More importantly, the prediction model of permeability coefficient of CGPC is established based on the mix proportion, providing a theoretical reference for the application of CGPC in pervious pavement of mining areas.

1 Introduction

China is rich in coal resources, a large number of solid waste-coal gangue will be produced in the process of coal mining. About 7 billion tons of coal gangue accumulated in mining areas, resulting in the land occupation, environmental pollution and even geological disasters [1,2,3]. Thus, the utilization of coal gangue in situ has become an urgent issue to be solved. Many studies indicate that the most effective way to use coal gangue is to replace ordinary gravel for the preparation of green concrete [4, 5]. On the other hand, the urbanization of concrete impervious roads leads to the un-recycling of water, causing the urban waterlogging. With the development of the “sponge city” concept, permeable concrete pavement can realize the recycling of water resources [6, 7]. Liu et al. [8] substituted 3%, 6%, 9% and 12% cement with fly ash to prepare pervious concrete, and the results showed that the addition of fly ash reduced the strength of pervious concrete at 28d in the early stage but increased the strength at 150d in the later stage. Tan et al. [9] found that iron tailing can be used as a substitute for coarse aggregate to prepare pervious concrete, and the permeability coefficient is up to 3.2 mm/s. Many scholars have carried out certain research on different types of pervious concrete, including pervious concrete modified fly ash [10], pervious recycled concrete [11], iron tailing pervious concrete [9] and so on, but there is no report on the study of coal gangue pervious concrete (CGPC).

Therefore, this study investigates the variation in the permeability coefficient and porosity of CGPC. More importantly, it delves into the correlation between the permeability coefficient and effective porosity, establishing a prediction model for the permeability coefficient in CGPC based on the mix proportion.

2 Experimental Program

2.1 Materials

The raw materials used for preparing CGPC include coal gangue coarse aggregate, cement, water, and permeable agent. The cement used in the experiment is P.O 52.5 ordinary Portland cement, meeting the Chinese standard GB175-2020. The coal gangue used is from Zhang Shuanglou Coal Mine in Xuzhou, Jiangsu. Its main chemical composition is as shown in Table 1. By crushing and sieving, four particle sizes of coal gangue coarse aggregate (4.75 ~ 9.5mm, 9.5 ~ 16mm, 16 ~ 19mm, 19 ~ 26.5mm) are obtained. The basic physical properties measured according to the Chinese standard GB/T14685-2011, as presented in Table 2. The permeable agent is from Jiangsu Guangda Ecological Engineering Technology Co., Ltd. The water used for the experiment is regular tap water.

Table 1 Chemical components of coal gangue

Table 2 Basic physical properties of coal gangue coarse aggregate

2.2 Sample Preparation

The influence of three factors aggregate particle size (A), w/c ratio (B), and designed porosity (C) on the permeability performance of CGPC is considered, as shown in Table 3. The mix design for CGPC is calculated by considering the volume of pervious concrete as a combination of raw materials and designed porosity, as shown in Formula (1). Given the designed porosity and the information about the w/c ratio and the closely packed porosity of coarse aggregates, the quantities of each component can be determined by substituting them into the formula:

$$ \frac{{{\text{M}}_{{\text{G}}} }}{{{\uprho }_{{\text{g}}} }}{ + }\frac{{{\text{M}}_{{\text{C}}} }}{{{\uprho }_{{\text{c}}} }}{ + }\frac{{{\text{M}}_{{\text{W}}} }}{{{\uprho }_{{\text{w}}} }}{\text{ + P = 1}} $$

(1)

Table 3 Test design and mix proportion

where M_G, M_C, M_W is the mass of coal gangue coarse aggregate, cement and water per unit volume (kg/m³), respectively; ρ_g, ρ_c, ρ_w is the apparent density of coarse aggregate, cement and water of coal gangue(kg/m³), respectively; P is the designed porosity (%). The mass of coarse aggregates per unit volume is determined based on Formula (2):

$$ {\text{M}}_{{\text{G}}} { }\,{ = }\,\alpha \cdot \rho_{{\text{G}}} $$

(2)

where α is the correction factor for the amount of coarse aggregates, a dimensionless parameter, taken as 0.98; ρ_G is the closely packed density of coal gangue coarse aggregates (kg/m³). The mix designs for each group of CGPC, calculated using Formulas (1) and (2), are shown in Table 3.

The HJW-60 concrete mixer is used to prepare CGPC in the experiment. The sequence is as shown in Fig. 1. Finally, the mixture is poured into the 100 mm × 100 mm × 100 mm mold by three times, inserted into shape, and the top surface is smoothed with a scraper. After 24 h, the specimens were demolded and placed in a standard curing room (at 20 ± 2℃ and 95 ± 5% relative humidity) for 28 d. After curing, the basic properties of the specimens were tested.

Fig. 1

Preparation process of CGPC

2.3 Test Methods

Permeability coefficient. In accordance with the Chinese standard CJJ/T135-2009, this paper measures the permeability coefficient of CGPC with the fixed water level, as shown in Fig. 2.

Fig. 2

Porosity test

First, use Vaseline to smear the side of the test block and wrap it with PVC film to prevent side seepage; Then put the test block into the steel mold, and fill the edge gap with cement; Next, assemble the upper and lower parts of the device into a whole, set up the inlet pipe and outlet, and begin testing the permeability coefficient. During the test, the water head in the container maintains a height of 150 mm. Once the water level stabilizes, measure the water level difference (H) using a steel ruler. Measure the amount of water outflowed in t seconds, and after repeating the measurement three times, take the average value as Q. Then the permeability coefficient of CGPC is calculated as follows:

$$ {\text{K}}_{{\text{T}}} { = }\frac{{{\text{QL}}}}{{{\text{AHt}}}} \cdot \frac{{{\upeta }_{{\text{T}}} }}{{{\upeta }_{{{15}}} }} $$

(3)

where KT is the permeability coefficient of pervious concrete specimen at a water temperature of T ℃ (mm/s); Q is the volume of water seeping out in t seconds (m³); L is the thickness of the specimen (mm); A is the surface area of the specimen's top surface (mm²); H is the water level difference (mm); t is the during time (s); ηT and η15 is the temperature T and 15 ℃ relative viscosity of water, dimensionless parameters. The results are the average value of three identical test blocks.

Porosity. Porosity test refers to the measurement of the total porosity and effective porosity. Referring to the ASTM C1754/C1754M-2012 standard, the volume method was used to determine the total porosity. The testing setup is as shown in Fig. 3.

Fig. 3

Porosity test

The testing procedure for total porosity is as follows: (1) Place the test specimen in an oven at 110 ± 5 ℃ for continuous drying for 24 h. After drying, remove the specimen and weigh its mass, denoted as A. (2) Measure and calculate the volume with a steel ruler, denoted as V. (3) Using a suspension basket, immerse the test specimen in water, allowing it to float up and down 10 times. Once no more air bubbles emerge, weigh the specimen's mass in water, denoted as B. (4) The total porosity ρ can be calculated using the following formula:

$$ {\rho }\,{ = }\,{ 1 } - { }\frac{{{\text{A}} - {\text{B}}}}{{{\uprho }_{{\text{w }}} \cdot {\text{ V}}}} $$

(4)

The testing procedure for effective porosity is similar to that of the total porosity testing in the initial steps (1) and (2). After drying and measuring the mass and volume, the test specimen is soaked for 48 h to saturate it with water. Then, the specimen is removed and allowed to drain for approximately 2 min. After draining, surface moisture is wiped off, and the mass is measured again, denoted as C.

To calculate the effective porosity ρ_e, you can substitute these values into the appropriate formula.

$$ \rho_{{\text{e}}} { }\,{ = }\,\rho - \frac{{{\text{C }} - {\text{ A}}}}{{\rho_{{\text{w }}} \cdot {\text{V}}}} $$

(5)

3 Results and Discussion

3.1 Permeability Coefficient

The permeability coefficient represents the amount of water that passes through within a unit of time. Calculated through permeability tests, the permeability coefficients for various groups are shown in Fig. 4. It can be observed that with an increase in aggregate particle size, the overall trend of CGPC changes from decreasing to increasing. For group A1, even though the 4.75 ~ 9.5 mm aggregate has a higher bulk density, its surface mortar thickness is smaller, and it has more internal porosity, resulting in a permeability coefficient of 1.67 mm/s, slightly higher than the 1.58 mm/s for group A2. In Fig. 4b, within the range of w/c ratios from 0.25 to 0.29, there is not a significant change in the permeability coefficient of CGPC. However, when the w/c ratio increases from 0.29 to 0.31, the permeability coefficient drops rapidly from 1.51 mm/s to 0.45 mm/s. The primary reason is that some mortar flows to the bottom, preventing the formation of connected pores and resulting in a decrease in the permeability coefficient. From Fig. 4b, it can be observed that as the designed porosity increases, the permeability coefficient gradually increases. The permeability coefficients of groups C2, C3, C4, and C5 increase by 25.64%, 61.47%, 143.12%, and 172.48% compared to group C1. A higher designed porosity leads to larger internal pores, resulting in a higher permeability coefficient.

Fig. 4

Permeability coefficient of CGPC in different groups

3.2 Porosity

The porosity is divided into total and effective porosity, to evaluate the permeability performance of CGPC. The porosity for different aggregate particle sizes, w/c ratios, and designed porosity groups are shown in Fig. 5. The figure illustrates that there is a good correlation between the total porosity and effective porosity. The effective proportion for each group is shown in Table 4. From Fig. 5a, it can be observed that as the aggregate particle size increases, both total porosity and effective porosity decrease at first and then increase. Table 4 shows that the effective proportion increases gradually with the increase in aggregate particle size, from 47.40% for A1 to 79.80% for A4. Additionally, from Fig. 5b and Table 4, it can be seen that as the w/c ratio increases, both total porosity and effective porosity, along with the effective proportion, generally decrease. When the w/c ratio increases from 0.29 to 0.31, the total porosity decreases by 30.82%, the effective porosity decreases by 82.42%, and the effective proportion rapidly drops from 59.20 to 15.20%. In Fig. 5c, with the designed porosity increases, both the total and effective porosity of CGPC increase, along with the effective proportion.

Fig. 5

Porosity of CGPC in different groups

Table 4 Effective pore/total pore ratio of CGPC in different groups

3.3 Relationship Between Permeability and Porosity

It is evident that side pores are sealed. Effective pores are the connected pores that enable permeability. Therefore, there is a close relationship between the permeability coefficient and the effective porosity of CGPC. The correlation coefficients between permeability coefficient and porosity under different groups are calculated, as shown in Fig. 6. The figure demonstrates that the change in permeability coefficient and effective porosity for CGPC follows a similar curve, with correlation coefficients R > 0.99.

Fig. 6

Correlation between permeability coefficient and effective porosity

Additionally, it's observed that there is a clear functional relationship between the permeability coefficient and effective porosity, as shown in Fig. 7. A regression fit is used to establish the relationship between the permeability coefficient of CGPC and the effective porosity:

$$ {\text{K = 0}}{\text{.3977e}}^{{{9}{\text{.6789}}\rho_{{\text{e}}} }} $$

(6)

Fig. 7

Regression curve of effective porosity and permeability coefficient

where K is the permeability coefficient of CGPC (mm/s); ρ_e is the effective porosity (%). As shown in Fig. 7, the test data points are distributed on both sides near the fitting curve, and the correlation coefficient R² is 0.9844, indicating a good fitting effect.

4 Prediction Model of CGPC

From the previous analysis, it is clear that the permeability coefficient of coal gangue concrete is closely related to aggregate particle size, w/c ratio, and designed porosity. Therefore, to better guide real-world projects, this study takes 9.5-16mm coal gangue coarse aggregate as a reference and uses the w/c ratio, and designed porosity as independent variables to establish a model for the permeability coefficient. The experimental data is subjected to binary surface fitting. The formula takes the form:

$$ {\text{K = ax}}_{{1 }} {\text{ + bx}}_{{2}} {\text{ + cx}}_{{1}}^{{2}} {\text{ + dx}}_{{2}}^{{2}} {\text{ + ex}}_{{1}} {\text{x}}_{{2}} {\text{ + f}} $$

(7)

where K is the permeability coefficient; x₁, x₂ is the w/c ratio and designed porosity, respectively; a, b, c, d, e, f is the coefficients to be determined. The values for these coefficients, obtained through data fitting, are shown in Table 5, resulting in the formula:

$$ \begin{aligned} {\text{K }}\,{ = }\,{ } & {144}{\text{.90538x}}_{{1 }} - { 221}{\text{.60461x}}_{{2}} - {435}{\text{.22126x}}_{{1}}^{{2}} { } \\ & { + 315}{\text{.45521x}}_{{2}}^{{2}} { + 445}{\text{.62063x}}_{{1}} {\text{x}}_{{2}} { + 1}{\text{.90161}} \\ \end{aligned} $$

(8)

Table 5 Binary nonlinear fitting coefficient of permeability coefficient

The correlation of each parameter is close to 1, indicating a good correlation. As shown in Fig. 8, the predictive model aligns well with the experimental results. The permeability coefficient prediction model for CGPC holds significant importance for its application.

Fig. 8

Regression curve of effective porosity and permeability coefficient

5 Conclusion

• (1) The permeability coefficient of CGPC can meet the requirements of 1.5 ~ 2 mm/s. With the increase of aggregate particle size and designed porosity, the permeability coefficient generally shows an upward trend, improving the permeability performance.

• (2) There is a positive correlation between the total porosity and the effective porosity of CGPC. In general, the larger the aggregate particle size and designed porosity, the larger the total porosity and effective porosity, and the larger the effective proportion of CGPC.

• (3) Effective porosity is the key factor affecting the permeability coefficient of CGPC. The relationship between the permeability coefficient and effective porosity of CGPC is obtained.

• (4) The prediction model of permeability coefficient of CGPC is established, in good agreement with the experimental results.