Slag permeability coefficient depth distribution prediction method considering particle crushing effect

Abstract

The invention discloses a slag permeability coefficient depth distribution prediction method considering a particle crushing effect, and belongs to the technical field of rock-soil body performance prediction. According to the invention, a dynamic triaxial test is carried out on a slag sample, and the dynamic evolution law of grain composition and breakage rate of slag at different depth sites after different vibration times of loading is explored. Meanwhile, a seepage test is carried out on the slag subjected to cyclic loading and crushing through a seepage erosion simulation test device, the critical hydraulic gradient of migration and structural damage of fine particles in the slag of different grades under the seepage erosion effect is explored, the degradation and damage mechanism of the slag is revealed, and a negative index continuous grade formula is used as a hub; according to the method, a prediction model of the particle crushing index and the permeability coefficient is constructed, and the internal mechanism of the particle crushing behavior and the permeability characteristic evolution of the slag roadbed under the cyclic load effect is disclosed, so that the predicted permeability coefficient is closer to the actual permeability coefficient, and the prediction precision is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of rock and soil performance prediction, and in particular relates to a method for predicting the depth distribution of slag permeability coefficient taking into account particle crushing effect. Background Art

[0002] Slag has the advantages of high strength, high compaction density, small settlement deformation, strong permeability, high anti-scouring performance, and can be obtained nearby, and is widely used as embankment filling. Under the action of vehicle cyclic load, medium-strong weathered siliceous mudstone slag is prone to particle crushing. The fine particles produced by crushing gradually fill the pores of slag particles, causing the internal particle structure (pore channels, pore structure) of the roadbed to be redistributed, which will also change the permeability characteristics of the slag roadbed. The permeability characteristics of the roadbed are closely related to the internal water migration, and the accumulation of water can lead to problems such as roadbed subsidence or collapse, which is not conducive to the long-term stability of the roadbed.

[0003] In the prior art, the research on the permeability mechanism of slag is mostly based on the test to obtain water-soil characteristic curves, settlement deformation, permeability coefficient and other parameters, that is, the permeability coefficient of slag in the vertical or depth direction is regarded as a constant, and the internal mechanism of internal structure redistribution and spatial and temporal evolution of permeability characteristics caused by particle crushing is not considered; in addition, the prior art mostly analyzes the unit body, and rarely studies the evolution of permeability characteristics at different locations of the overall structure of the roadbed. The roadbed is a structure with obvious stratification characteristics. Due to different usage requirements, the requirements for the particle size, gradation, compaction degree and moisture content of its filler are different for each layer; in addition, the confining pressure and cyclic dynamic stress at each point are different due to the change of depth, so that the particle crushing situation inside the slag embankment and its corresponding permeability characteristics are different along the depth. The above two defects make the existing technology insufficient in the prediction accuracy of the permeability coefficient of slag, and cannot truly reflect the permeability of slag. Summary of the invention

[0004] The present invention provides a method for predicting the depth distribution of slag permeability coefficient taking into account the particle crushing effect. It takes into account the influence of particle crushing effect and depth on the slag permeability coefficient, and can make the predicted permeability coefficient closer to the actual permeability coefficient, thereby improving the prediction accuracy, thereby solving at least one technical problem involved in the background technology.

[0005] In order to solve the above-mentioned technical problems, the present invention is achieved as follows:

[0006] A method for predicting the depth distribution of slag permeability coefficient considering particle crushing effect comprises the following steps:

[0007] Step S1, preparing a slag sample, performing a triaxial test on the slag sample, screening the loaded slag sample to determine the percentage of slag samples with different particle sizes in the total weight of the slag sample, and drawing a gradation curve of the slag sample;

[0008] Step S2, based on the stability theory, the gradation corresponding to the cumulative deformation convergence stage of the slag sample is defined as the final gradation, the area enclosed by the final gradation curve and the initial gradation curve is defined as the total crushing potential, the area enclosed by the final gradation curve and the initial gradation curve is defined as the potential crushing potential, the ratio of the total crushing potential to the potential crushing potential is defined as the relative crushing potential, and the relative crushing potential is used to characterize the crushing degree of the slag sample after loading;

[0009] Step S3, considering the effect of particle crushing on the porosity of the slag sample, the porosity of the slag sample under the current gradation is calculated by converting the volume change of the slag sample after loading, and fitting the relationship between the porosity under the current gradation and the relative crushing potential as the porosity expression;

[0010] Step S4, considering the influence of depth on the porosity of the slag sample, determining the stable porosity of the slag sample at different depths, fitting the relationship between the stable porosity and depth, and then substituting it into the porosity expression to correct the porosity expression, wherein the stable porosity of the slag sample represents the porosity under the final gradation;

[0011] Step S5, substitute the porosity ratio expression corrected in step S4 into the Kozeny-Carman formula to construct a prediction model for the permeability coefficient of the slag, wherein the prediction model takes the relative crushing potential and depth of the slag as input and outputs the distribution prediction results of the permeability coefficient of the slag at different depths.

[0012] As a preferred improvement, the total crushing potential B t (N) and relative crushing potential B r *(N) are respectively represented as:

[0013]

[0014] Where P(N) is the mass percentage of slag sample smaller than a certain particle size, obtained through the grading curve; d maxN Indicates the maximum particle size of the slag sample under the current grading; d max0 The maximum particle size of the slag sample under the initial gradation; d maxM Indicates the maximum particle size of the slag sample under the final gradation; a N 、b N represents the fitting parameters of the current grading curve; a0 and b0 represent the fitting parameters of the initial grading curve; a M 、b M Represents the fitting parameters of the final grading curve.

[0015] As a preferred improvement, after the Nth loading, the porosity ratio ε of the slag sample is N It is expressed as:

[0016]

[0017] Where V0 represents the initial volume of the slag sample; ΔV represents the volume change of the slag sample after the Nth loading, which is obtained by monitoring the displacement of the pressure chamber outside the triaxial test; ε0 represents the initial porosity of the slag sample, where:

[0018]

[0019] In the formula, ρ dmax is the maximum dry density of the slag sample; G s is the relative density of the slag sample; ρ w is the water density; w opt It is the optimum moisture content of slag sample.

[0020] As a preferred improvement, the void ratio ε N With relative breaking potential B r *(N) is expressed as:

[0021]

[0022] In the formula, ε M represents the porosity ratio of the slag sample under the final gradation; β represents the material constant.

[0023] As a preferred improvement, the modified porosity ratio expression is expressed as:

[0024]

[0025] In the formula, ε M (0) represents the stable porosity of the site with a depth of 0 in the slag sample; h represents the depth of the site in the slag sample; ξ represents the fitting parameter.

[0026] As a preferred improvement, the prediction model is expressed as:

[0027]

[0028] In the formula, represents the permeability coefficient; It represents a constant related to the specific surface area of ​​slag and fluid properties; γ is the gravity of water; C is a constant; α is the dynamic viscosity of water; S0 is the specific surface area of ​​slag.

[0029] The beneficial effects of the present invention are:

[0030] (1) The present invention conducts dynamic triaxial tests on a certain slag, and explores the dynamic evolution of particle grading and crushing rate of slag at different depths of the roadbed after different vibration loadings. At the same time, a permeability test is conducted on the slag that has been crushed by cyclic loading through a seepage erosion simulation test device, and the critical hydraulic gradient of fine particle migration and structural damage inside slag with different gradations under the action of seepage erosion is explored, revealing its degradation and destruction mechanism, and using the negative exponential continuous grading equation as the hub, constructing a prediction model for particle crushing index and permeability coefficient, revealing the internal mechanism of the particle crushing behavior of the slag roadbed under cyclic loading and the evolution of its permeability characteristics, so that the predicted permeability coefficient is closer to the actual permeability coefficient, improving the prediction accuracy;

[0031] (2) The method for predicting the depth distribution of the permeability coefficient of slag proposed in the present invention has greatly improved the current empirical and extensive calculation of the permeability coefficient of slag filling materials, significantly enhanced the scientificity and reliability of the prediction of the permeability coefficient of slag filling materials under actual working conditions (vehicle cyclic load and rainwater erosion), and provided a reference for the stability assessment and long-term service performance improvement of slag roadbed / embankment in mountainous areas, which will help to further promote the resource utilization of slag and the high-quality development of roadbed construction in mountainous areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Schematic diagram showing the loading method in triaxial test;

[0033] Figure 2 It represents the gradation curve of the slag sample under working conditions A1-A4;

[0034] Figure 3 It represents the gradation curve of the slag sample under working conditions B1-B4;

[0035] Figure 4 It represents the gradation curve of the slag sample under working conditions C1-C4;

[0036] Figure 5 Schematic diagram showing total crushing potential and relative crushing potential;

[0037] Figure 6 A graph showing the variation of total crushing potential with the number of cyclic loading;

[0038] Figure 7 A graph showing the change of relative crushing potential with the number of cyclic loading;

[0039] Figure 8 A graph showing the relationship between void ratio and relative crushing potential;

[0040] Fig. 9 A graph showing the relationship between site depth and stable porosity ratio;

[0041] Fig.10 A schematic diagram showing a prediction model provided by the present application;

[0042] Fig.11 A comparison chart showing the predicted values ​​and measured values ​​of different relative crushing potential prediction models. DETAILED DESCRIPTION

[0043] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Please refer to Figure 1-Figure 11 The present invention provides a method for predicting the depth distribution of slag permeability coefficient taking into account the particle crushing effect, comprising the following steps:

[0045] Step S1, prepare slag samples, perform triaxial tests on the slag samples, screen the loaded slag samples to determine the percentage of slag samples with different particle sizes in the total weight of the slag samples, and draw a gradation curve of the slag samples.

[0046] Before the test begins, it is necessary to prepare the slag sample. The specific process is as follows: the slag is mixed at the optimal moisture content, left to stand and sealed for 24 hours, and then compacted in 6 layers to form the slag sample. The top surface of the previous layer is roughened before the next layer of slag is filled and compacted, and the last layer is leveled. The relative density of the slag sample is controlled by controlling the mass and height of each layer. The compaction degree of all slag samples is controlled at about 95%.

[0047] The test was conducted using the DYNTTS servo-controlled dynamic triaxial test system developed by the British GDS company. The selected loading frequency range was 0-5Hz, the axial load range was 0-10kN, and the rated confining pressure was 0-2000kPa. The diameter of the slag sample was 100mm and the height was 150mm. The ratio of the slag sample diameter to the maximum particle size of the soil particles was 6.67 (greater than 5), which basically eliminated the size effect.

[0048] A total of 48 slag samples were prepared for the test. The slag samples were first isotropically consolidated to a set confining pressure value σ3, and then cyclic dynamic deviator stress σ d The unsaturated drainage cyclic load test was carried out. The first cycle of all slag samples was slowly loaded at a frequency of 0.01 Hz to eliminate the irregularities of the upper and lower parts of the slag samples, thereby eliminating the initial deformation of the slag samples. The test was performed according to the sine wave loading wave, such as Figure 1 shown.

[0049] The theoretical calculation process of confining pressure σ3 is as follows:

[0050] σ3=K0(σ0+γh);

[0051] Where K0 is the lateral earth pressure coefficient, which can be calculated as K0 = μ / (1-μ), μ is Poisson's ratio, and its value range is 0.2-0.45; σ0 is the cover weight of the asphalt surface layer and the water-stabilized macadam base layer, the thickness of the asphalt surface layer is 0.12-0.32m, 23.3-24.6kN / m 3 The thickness of the water-stable crushed stone base is 0.44-0.77m, and the weight is 22-25kN / m 3 ), γ is the average bulk density of the slag; h is the depth from the top of the roadbed. The larger the confining pressure σ3 is, the more obvious the particle crushing is. In order to make the crushing obvious, the most unfavorable load value that maximizes σ3 is taken, that is, μ is taken as 0.45; the thickness of the asphalt surface layer is taken as 0.32m, and the weight is taken as 24.6kN / m 3 ; The thickness of the water-stable gravel layer is 0.77m, and the weight is 25kN / m 3 .

[0052] Combine the theoretical calculation formula of confining pressure σ3 and the empirical value to set the value of confining pressure σ3 selected for loading, and set the cyclic dynamic deviator stress σ according to the empirical value. d The value of the highway subgrade frequency is generally 0.1~10Hz, so the proposed loading frequency is 5Hz. d , confining pressure σ3 and number of cyclic loading N are divided into 12 groups of working conditions, and the loading plan is shown in Table 1.

[0053] Table 1 Loading schedule

[0054]

[0055]

[0056] After loading, the four parallel slag samples of each working condition are fully mixed. Then the slag samples are spread into a thin layer and air-dried in the air for 1 to 2 days to evaporate the moisture in the soil. After air-drying, the lumps in the soil sample are poured into a magnetic bowl and ground with a rubber-headed pestle with appropriate force until the lumps become separate particles. Finally, all the slag samples are placed on a vibrating screening machine for screening, and the pass rate of the slag samples under each sieve hole size is calculated. The pass rate represents the percentage of slag samples of different particle sizes in the total weight of the slag samples. Finally, a coordinate system is constructed. The horizontal axis of the coordinate system represents the particle size, and the vertical axis represents the percentage of soil mass less than a certain particle size. The measured grouped data are marked in the coordinate system, and each point is connected in turn to form a gradation curve. Then, the negative exponential continuous gradation equation is used to fit the gradation curve to make the curve smooth.

[0057] Under different loading conditions, the particle gradation curve of the slag material after crushing changes as shown in the figure. Figure 2-Figure 4 As shown in the figure, based on the grading curve, the negative exponential continuous grading equation under different cycle numbers N is established, which is expressed as:

[0058]

[0059] Where P(N) is the mass percentage of slag sample with particle size smaller than a certain size; d is the particle size; d max Maximum particle size; a N 、b N are the fitting parameters that control the shape and slope of the curve respectively.

[0060] Depend on Figure 2-Figure 4 It can be seen that the particle size distribution P(N) under a certain loading number N can be obtained by a unique array (d max , a N , b N ) is determined. Under different vibration times, the fitting determination coefficient R of the particle size distribution of the slag sample under each working condition is 2 All of them are over 0.9, which meets the requirements of fitting accuracy. Therefore, within a reasonable error range, the particle gradation characteristics of the slag sample under each loading condition can be determined by fitting the negative exponential gradation equation.

[0061] Step S2, based on the stability theory, defines the gradation corresponding to the cumulative deformation convergence stage of the slag sample as the final gradation, defines the area enclosed by the final gradation curve and the initial gradation curve as the total crushing potential, defines the area enclosed by the final gradation curve and the initial grading curve as the potential crushing potential, defines the ratio of the total crushing potential to the potential crushing potential as the relative crushing potential, and uses the relative crushing potential to characterize the crushing degree of the slag sample after loading.

[0062] Total crushing potential B t (N) and relative crushing potential B r *(N) are respectively represented as:

[0063]

[0064] Where, d maxN It indicates the maximum particle size of the slag sample under the state gradation curve; d max0 The maximum particle size of the slag sample under the initial grading curve; d maxM Indicates the maximum particle size of the slag sample under the final gradation curve; a N 、b N represents the fitting parameters of the current grading curve; a0 and b0 represent the fitting parameters of the initial grading curve; a M 、b M Represents the fitting parameters of the final grading curve.

[0065] The total crushing potential and potential crushing potential are expressed as follows: Figure 5As shown. According to the total crushing potential B t (N) and relative crushing potential B t *(N) expression plot B t (N)-N and B r *(N)-N curve, such as Figure 6-Figure 7 As shown. Figure 6 and Figure 7 It can be seen that the total crushing potential B t (N), relative crushing potential B r *(N) The growth rate decreases with the number of cycles N. In this embodiment, the gradation corresponding to N=20000 is taken as the final gradation, and the total crushing potential B t (N), relative crushing potential B r *(N) did not converge when N = 20000, and continued to increase. r *(N) is slightly larger than the deep point, and increases with the depth and the number of cyclic loading. r *(N)The difference is reduced.

[0066] Step S3, considering the effect of particle crushing on the porosity of the slag sample, the porosity of the slag sample under the current grading is calculated by converting the volume change of the slag sample after loading, and the relationship between the porosity under the current grading and the relative crushing potential is fitted as the porosity expression.

[0067] During the cyclic loading process, the crushing of slag particles will lead to a decrease in the porosity ratio. This is because the crushing process produces more small particles that can fill the larger pores, and the compaction effect during the crushing process will further reduce the pore space.

[0068] The calculation process of the initial porosity ratio ε0 of the slag sample is as follows:

[0069]

[0070] In the formula, ρ dmax is the maximum dry density of the slag sample; G s is the relative density of the slag sample; ρ w is the water density; w opt It is the optimum moisture content of slag sample.

[0071] The porosity ratio ε of the slag sample loaded for the Nth time N The calculation is based on the volume change of the slag sample, which is monitored by the displacement of the triaxial pressure chamber. N The calculation process is as follows:

[0072]

[0073] Where V0 represents the initial volume of the slag sample; ΔV represents the volume change of the slag sample.

[0074] (B r *(N), ε N ) data points are marked in the coordinate system, and multiple groups of data points are fitted to obtain ε N -B r *(N) relationship curve, such as Figure 8 As shown. Figure 8 It can be seen that during the initial cyclic loading process, the void ratio ε decreases significantly, and the relative crushing potential B r * significantly increased; during the mid-term cyclic loading process, the void ratio ε continued to decrease, but the rate of decrease gradually decreased, and the relative crushing potential B r * Rapidly increase; in the later cyclic loading process, the void ratio ε tends to be stable, and the relative crushing potential B r * also tends to be stable. Based on the above law, the porosity ratio ε is obtained N The expression is:

[0075]

[0076] In the formula, ε M represents the stable porosity, i.e. the porosity under the final gradation, and β represents the material constant, with a value range of 0.0652 to 0.1283. N -B r *(N) Relationship fitting determination coefficient R 2 When it exceeds 0.9, the fitting accuracy requirement is met.

[0077] Step S4, considering the influence of depth on the porosity of the slag sample, determining the stable porosity of the slag sample at different depths, fitting the relationship between the stable porosity and depth, and then substituting it into the porosity expression to correct the porosity expression, wherein the stable porosity of the slag sample represents the porosity under the final grading.

[0078] In actual engineering applications, the stable void ratio ε of the corresponding specimens at different locations of the embankment under cyclic loading is M and external loads (confining pressure σ3, cyclic dynamic deviator stress σ d ) is closely related to the distribution of confining pressure σ3 and cyclic dynamic deviator stress σ d The attenuation is a function of depth h, and the fitting curve is as follows: Fig. 9 As shown. Then the distribution relationship of the stable porosity along the depth can be constructed, which is expressed as:

[0079] ε M (h) = ε M (0)·exp(ξh);

[0080] In the formula, εM (h) represents the stable porosity of the site at depth h in the slag sample; ε M (0) represents the stable porosity of the site with a depth of 0 in the slag sample; ξ represents the fitting parameter.

[0081] ε M Substitute (h) into the porosity expression to obtain the corrected porosity expression:

[0082]

[0083] Step S5, substitute the porosity ratio expression corrected in step S4 into the Kozeny-Carman formula to construct a prediction model for the permeability coefficient of the slag, wherein the prediction model takes the relative crushing potential and depth of the slag as input and outputs the distribution prediction results of the permeability coefficient of the slag at different depths.

[0084] The original expression of the Kozeny-Carman formula is as follows:

[0085]

[0086] Where K is the permeability coefficient of the slag; γ is the density of water; C is a constant; α is the dynamic viscosity of water; S0 is the specific surface area of ​​the slag (cm 2 / cm 3 ); n is the porosity of the slag sample;

[0087] The above formula shows that when other factors affecting the permeability coefficient remain unchanged, K and n 3 / (1-n) 2 There is a linear relationship between the porosity n and the porosity ratio ε N Satisfies the following relationship:

[0088]

[0089] The permeability coefficient K can be converted into the following formula:

[0090]

[0091] In the formula, It represents a constant related to the specific surface area of ​​slag and fluid properties, and its value ranges from 0.67412 to 0.71984.

[0092] The constructed prediction model is expressed as:

[0093]

[0094] In this embodiment, A = 0.68101, ξ = 0.41543, β = 0.10723, and the function graph of the prediction model is as follows: Fig.10As shown. The depths h are 0.0m, 0.5m, and 1.0m respectively, and the different relative crushing potentials B r *Comparison between model prediction and measured values ​​is shown in Fig.11 As shown in Figure 2, the relative error between the two is 2.29% to 33.83%. The predicted value and the measured value are in the same order of magnitude, and the relative error is less than 35%. Therefore, the prediction model K[B r *(N),h] has good applicability.

[0095] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for predicting the depth distribution of slag permeability coefficient considering particle crushing effect, characterized in that: The steps include: Step S1, preparing a slag sample, performing a triaxial test on the slag sample, screening the loaded slag sample to determine the percentage of slag samples with different particle sizes in the total weight of the slag sample, and drawing a gradation curve of the slag sample; Step S2, based on the stability theory, the gradation corresponding to the cumulative deformation convergence stage of the slag sample is defined as the final gradation, the area enclosed by the final gradation curve and the initial gradation curve is defined as the total crushing potential, the area enclosed by the final gradation curve and the initial gradation curve is defined as the potential crushing potential, the ratio of the total crushing potential to the potential crushing potential is defined as the relative crushing potential, and the relative crushing potential is used to characterize the crushing degree of the slag sample after loading; Step S3, considering the effect of particle crushing on the porosity of the slag sample, the porosity of the slag sample under the current gradation is calculated by converting the volume change of the slag sample after loading, and fitting the relationship between the porosity under the current gradation and the relative crushing potential as the porosity expression; Step S4, considering the influence of depth on the porosity of the slag sample, determining the stable porosity of the slag sample at different depths, fitting the relationship between the stable porosity and depth, and then substituting it into the porosity expression to correct the porosity expression, wherein the stable porosity of the slag sample represents the porosity under the final gradation; Step S5, substitute the porosity ratio expression corrected in step S4 into the Kozeny-Carman formula to construct a prediction model for the permeability coefficient of the slag, wherein the prediction model takes the relative crushing potential and depth of the slag as input and outputs the distribution prediction results of the permeability coefficient of the slag at different depths.

2. The method for predicting the depth distribution of slag permeability coefficient considering particle crushing effect according to claim 1 is characterized in that: Total crushing potential B t (N) and relative crushing potential B r *(N) are respectively represented as: Where P(N) is the mass percentage of slag sample smaller than a certain particle size, obtained through the grading curve; d maxN Indicates the maximum particle size of the slag sample under the current grading; d max0 The maximum particle size of the slag sample under the initial gradation; d maxM Indicates the maximum particle size of the slag sample under the final gradation; a N 、b N represents the fitting parameters of the current grading curve; a0 and b0 represent the fitting parameters of the initial grading curve; a M 、b M Represents the fitting parameters of the final grading curve.

3. The method for predicting the depth distribution of slag permeability coefficient considering particle crushing effect according to claim 2 is characterized in that: After the Nth loading, the porosity ratio of the slag sample is ε N It is expressed as: Where V0 represents the initial volume of the slag sample; ΔV represents the volume change of the slag sample after the Nth loading, which is obtained by monitoring the displacement of the pressure chamber outside the triaxial test; ε0 represents the initial porosity of the slag sample, where: In the formula, ρ dmax is the maximum dry density of the slag sample; G s is the relative density of the slag sample; ρ w is the water density; w opt It is the optimum moisture content of slag sample.

4. The method for predicting the depth distribution of slag permeability coefficient considering particle crushing effect according to claim 3 is characterized in that: Porosity ratio ε N With relative breaking potential B r *(N) is expressed as: In the formula, ε M represents the porosity ratio of the slag sample under the final gradation; β represents the material constant.

5. The method for predicting the depth distribution of slag permeability coefficient considering particle crushing effect according to claim 4 is characterized in that: The corrected porosity ratio expression is expressed as: In the formula, ε M (0) represents the stable porosity of the site with a depth of 0 in the slag sample; h represents the depth of the site in the slag sample; ξ represents the fitting parameter.

6. The method for predicting the depth distribution of slag permeability coefficient considering particle crushing effect according to claim 5 is characterized in that: The prediction model is expressed as: In the formula, represents the permeability coefficient; It represents a constant related to the specific surface area of ​​slag and fluid properties; γ is the gravity of water; C is a constant; α is the dynamic viscosity of water; S0 is the specific surface area of ​​slag.

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