CN115290530B - Method for determining permeability coefficient of discrete material - Google Patents
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- 239000000463 material Substances 0.000 title claims abstract description 117
- 230000035699 permeability Effects 0.000 title claims abstract description 111
- 238000000034 method Methods 0.000 title claims abstract description 20
- 239000006185 dispersion Substances 0.000 claims abstract description 90
- 239000002245 particle Substances 0.000 claims abstract description 73
- 238000012360 testing method Methods 0.000 claims abstract description 30
- 239000002910 solid waste Substances 0.000 claims description 32
- 239000013590 bulk material Substances 0.000 claims description 23
- 230000003204 osmotic effect Effects 0.000 claims description 8
- 239000010419 fine particle Substances 0.000 claims description 7
- 239000000203 mixture Substances 0.000 claims description 6
- 239000011362 coarse particle Substances 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 238000001507 sample dispersion Methods 0.000 claims description 2
- 238000007873 sieving Methods 0.000 claims 1
- 238000010276 construction Methods 0.000 description 12
- 239000004576 sand Substances 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 230000007547 defect Effects 0.000 description 4
- 238000013461 design Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 239000008188 pellet Substances 0.000 description 3
- 239000002689 soil Substances 0.000 description 3
- 238000011160 research Methods 0.000 description 2
- 239000002893 slag Substances 0.000 description 2
- 239000004575 stone Substances 0.000 description 2
- 238000010998 test method Methods 0.000 description 2
- 239000011449 brick Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000010438 granite Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/08—Investigating permeability, pore-volume, or surface area of porous materials
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Abstract
The invention discloses a method for determining the permeability coefficient of a discrete material, which comprises the following steps of firstly, a permeability coefficient model of the discrete material; step two, determining the grading and permeability coefficients of more than 6 samples of certain dispersion materials through a test; calculating to obtain the particle grading parameter of each discrete material sample; step three, obtaining a model for determining the permeability coefficient of the dispersion material according to the grading parameter by utilizing the particle grading parameter and the corresponding permeability coefficient of the permeability test; step four, obtaining porosity n of a dispersion material with a permeability coefficient to be measured and a dispersion material particle grading parameter with a permeability coefficient k to be measured; and step five, obtaining the permeability coefficient k of the dispersion material with the permeability coefficient to be measured. The method has different permeabilities for the dispersion materials with different porosities and particle grading parameters, and the permeability coefficient of the dispersion materials is accurately determined, so that the method has higher application value.
Description
Technical Field
The invention belongs to the field of municipal engineering, and particularly relates to a method for determining permeability coefficient of a solid waste dispersion material for disassembly and construction. The method can be used for determining the permeability coefficient of the construction solid waste dispersion material based on the particle size distribution data of the construction solid waste taking brick slag and concrete slag as main components.
Background
A large amount of sand materials are required for sponge city construction, but in recent years, sand resources are exhausted, and solid wastes are required to be considered as materials for sponge city facility construction. The urban construction solid waste is used for sponge urban construction in large quantity due to urgent treatment, low-cost material, innocuity and on-site utilization. However, the demolition solid waste dispersion material is obtained by crushing brick-concrete waste of each demolition site, and the produced recycled aggregate has very different grain size distribution because the raw materials are sourced from different sites.
The permeability coefficient is an important design index of various sponge facilities, and whether the discrete materials can be used for sponge facility construction is judged by judging whether the strength of the materials meets the requirement or not, and whether the permeability coefficient of the materials can meet the requirement of the sponge facilities or not is judged by judging whether the material can be used for sponge facility construction or not. The particle size distribution of the demolished and built solid waste recycled aggregate is very different, so that the permeability coefficient of the demolished and built solid waste recycled aggregate is very changed, and whether the solid waste recycled aggregate (solid waste dispersion material) produced by a certain manufacturer can be used for sponge facility construction or not needs to be solved in the permeability coefficient distribution interval.
The permeability coefficient of the dispersion material is generally determined through a permeability test, and the permeability characteristic of the natural sand material with stable grading can be mastered by only carrying out a small amount of permeability test. And (3) detaching and building the solid waste dispersion material with large grain size distribution change, a large number of tests are needed to determine the osmotic coefficient range and judge whether the solid waste dispersion material is usable.
In contrast, particle size distribution data for demolition solid wastes is more readily available because: firstly, in order to grasp the quality of materials, solid waste recycled aggregate manufacturers must periodically perform particle grading test on the products; secondly, the particle size grading test method (such as screening method) is simple to operate and high in speed. Therefore, the method for determining the permeability coefficient of the solid waste dispersion material based on the particle size distribution data has higher application value.
The prior art has not studied how to determine the permeability coefficient of urban construction solid waste dispersion materials through particle size distribution data. In analogy, in the field of civil engineering and hydraulic engineering, many such works have been made as if they were available for reference, such as: su Lijun (Su Lijun, zhang Yijian, wang Tiehang. Test study of permeability characteristics of sand of different particle size grades [ J ]. Geotechnical mechanics, 2014,35 (5): 1289-1294.):
wherein: k is the permeability coefficient of the bulk material; n is the porosity of the dispersion material; d 10、Cu、Cc is a grading parameter, and d 10 is a particle size with particle mass less than a certain particle size accounting for 10% of the total mass of the soil; or C u is a non-uniformity coefficient), cc is a curvature coefficient;
As further proposed by Yang Zhihao (Yang Zhihao, yue Zurun, feng Huaiping, et al, heavy haul railway foundation bed surface graded crushed stone permeability test study [ J ]. Geotechnical mechanics, 2021,42 (1): 1-10.):
wherein: e is the pore ratio of the dispersion material; d 50 is a grading parameter, and is the particle diameter of which the mass of particles smaller than a certain particle diameter accounts for 50% of the total mass of the soil; e is the natural logarithm.
Similar formulas are numerous, but these formulas have theoretical drawbacks and are therefore poorly applicable. The theoretical defects are as follows: the permeability coefficients of different particle size dispersions are generally different at the same porosity, but the probability of the same calculation result by using the formulas is high, because the grading parameters of the formulas are not representative enough, and the same (group) parameter value cannot uniquely correspond to one particle grading. This means that based on this type of formula, substituting a grading parameter, such as d 50 of formula (2), the calculated permeability coefficient will correspond to a plurality of particle grading curves. Therefore, to accurately calculate the permeability coefficient of the dispersion material, firstly, a scientific and reasonable particle grading model is required to be based on which the grading parameters can accurately and uniquely express the grading data.
Disclosure of Invention
In order to solve the problems, the invention provides a method for determining the permeability coefficient of a discrete material.
In order to achieve the technical effects, the technical scheme of the invention is as follows:
A method for determining permeability coefficient of a dispersion material comprises the following steps:
Step one, regarding the dispersion material as a mixture of 2 particle components with the thickness, wherein the maximum particle diameter of the coarse particle component is R T1, the mass is M T1, and the fractal dimension is D 1; the maximum particle size of the fine particle component is R T2, the mass is M T2, and the fractal dimension is D 2; if R T is the maximum particle size of the bulk material, then R T1＝RT; if M T is the total mass of the bulk material, then M T＝MT1+MT2 is present; the general model of permeability coefficient of the dispersion material is:
Wherein k is the permeability coefficient of the bulk material; n is the porosity of the bulk material; m (r < d c) is the mass of particles with a particle size smaller than d c; d 1、D2 is determined according to the second step; a 0、A1、A2、B1、B2、dc are undetermined coefficients, and are determined by fitting inversion of the actually measured permeability coefficient k;
Step two, determining M T、RT, n and R-M (R < R) relations of more than 6 samples of the dispersion material through density and screening tests, wherein M (R < R) is the mass of particles with the particle size smaller than R;
Thirdly, fitting and inverting according to the obtained R-M (R < R) relation data and the formulas (2) and R T1＝RT、MT＝MT1+MT2 to obtain the particle grading parameter of each sample dispersion material: d 1、D2、RT2、MT1,MT2;
Wherein M T1 is the mass of coarse particle components in the dispersion material; m T2 is the mass of the fine particle component in the dispersion material, and M T2＝MT-MT1;RT2 is the maximum particle size of the fine particle component in the dispersion material; d c ε R;
step four, obtaining permeability coefficients k of more than 6 samples in the step two through a permeability test;
Step five, fitting by using the particle grading parameters, n and the corresponding permeability coefficients of the permeability test by adopting a formula (1) to obtain parameters: a 0、A1、A2、B1、B2、dc; substituting the obtained A 0、A1、A2、B1、B2、dc into a formula (1) to obtain a permeability coefficient model of the dispersion material with determined parameters;
Step six, obtaining data of porosity n of the dispersion material with the osmotic coefficient to be determined, total mass M T of the dispersion material and R-M (R < R) according to the test of the step two; determining grain composition parameters according to the third step: d 1、D2、RT2、MT1,MT2;
Step seven, let r=d c, let R and the parameters of the osmotic coefficient dispersion material to be determined: d 1、D2、RT1、RT2、MT1,MT2 inputs the parameters of formula (2) to obtain M (r < D c) of the dispersion material of which the permeability coefficient is to be determined.
Step eight, the porosity n and the particle size distribution parameters of the dispersion material with the osmotic coefficient to be determined are as follows: d 1、D2 And inputting the permeability coefficient model of the dispersion material with the determined parameters to obtain the permeability coefficient k of the dispersion material with the determined permeability coefficient.
Further improved, the particle grading parameter of the dispersion material is obtained through a screening test, and the permeability coefficient is obtained through a constant head permeability test.
Further improvements are made in which the dispersion material is a solid waste dispersion material or a natural dispersion material.
Further improvement, when the dispersion material is solid waste dispersion material, the permeability coefficient model of the dispersion material determined by parameters is as follows:
further improvements, when the dispersion material is a natural dispersion material, the permeability coefficient model of the dispersion material determined by parameters is as follows:
The invention has the following advantages:
the method has different permeabilities for the dispersion materials with different porosities and particle grading parameters, and the permeability coefficient of the dispersion materials is accurately determined, so that the method has higher application value.
Drawings
FIG. 1 is a graph of a sand coarse grain test grading curve and a double fractal model fitting curve.
Detailed Description
The technical scheme of the invention is specifically described below through the specific embodiments and with reference to the accompanying drawings.
Based on a fractal theory, the dispersion material is regarded as a mixture of 2 particle components with the thickness, the maximum particle diameter of the coarse particle component is R T1, the mass is M T1, and the fractal dimension is D 1; the maximum particle size of the fine particle component is R T2, the mass is M T2, the fractal dimension is D 2, a 'dispersion material double fractal grading model' is provided, and the model is as follows:
Wherein R is any known particle size, R is any particle size smaller than R, M (R < R) is the mass of the particle smaller than R, and if: when R is more than or equal to R T2, taking
The model is derived based on fractal theory, is not an empirical formula, and can uniquely determine the particle size distribution curve of the discrete material, and the publication of results is published in journal Journal ofmaterials IN CIVIL ENGINEERING. If the parameters of the model are used as grading parameters to establish a permeability coefficient calculation formula of certain type of dispersion materials, the permeability coefficient calculation formula of dispersion materials with no theoretical defects and higher universality can be obtained.
Through research, based on the grading parameters of a 'dispersion material double fractal grading model', the inventor obtains the following formula for determining the permeability coefficient of the dispersion material:
Wherein k is a permeability coefficient calculation value, and n is porosity; m (r < d c) is the mass of the particles with a particle size smaller than d c. A 0、A1、A2、B1、B2、dc was determined by fitting to the measured permeability coefficient.
The formula has the advantages that:
(1) The formula adopts model parameter modeling of a dispersion material double-fractal grading model, model parameters correspond to grading curves one by one, and the problem that one (a group of) model parameters correspond to a plurality of grading curves is solved. Therefore, the result calculated according to one grading curve parameter only corresponds to the permeability coefficient of the material, and the intrinsic defect (theoretical defect) of the existing formula is avoided.
(2) The formula logic is tight, scientific and reasonable, the porosity of the dispersion material (item 1 of the formula) is considered, the integral grading characteristic of the dispersion material (item 2) and the characteristic particle size of the dispersion material (item 3) are considered.
Based on the two formulas, the method for determining the permeability coefficient of the dispersion material comprises the following steps:
the first step: the method establishes a formula for calculating the permeability coefficient of a certain dispersion material, and comprises the following steps:
(1) According to the related test method specified by the current national and industry standards, the grain composition and permeability coefficient of 10 dispersion materials are determined. The particle analysis method is generally obtained through a screening test, and the permeability coefficient is generally obtained through a constant head permeability test.
(2) Calculating corresponding M (R < R j) of R j with different particle diameters by using grading particle grading data of the dispersion material, and inverting the undetermined coefficient by adopting the following formula to obtain parameters: d 1、D2、RT2、MT1、MT2.
(3) And carrying out parameter inversion by using the particle grading parameters obtained by inversion fitting and the corresponding permeability coefficients of the permeability test, and obtaining parameters by adopting the following formula: a 0、A1、A2、B1、B2、dc.
And a second step of: determining the permeability coefficient of the dispersion material in a certain grading state by using the established permeability coefficient calculation formula:
(1) The porosity n and particle size parameters of the discrete materials are obtained by testing or using size data provided by the discrete material manufacturer and using the formulas (3) and R T1＝RT、MT＝MT1+MT2: d 1、D2、RT1、RT2、MT1、MT2.
(2) Let r=d c, let R and the parameters of the osmotic coefficient dispersion material to be determined: d 1、D2、RT1、RT2、MT1,MT2 is input into equation (3) to obtain M (r < D c) of the dispersion material for which the permeability coefficient is to be determined.
(3) The porosity n and inversion of the bulk material according to design requirementsD 1、D2 is substituted into the formula (4), and the permeability coefficient of the dispersion material is calculated.
Example 1:
The inventor applies the demolition solid waste dispersion material to illustrate the implementation steps of the invention.
The first step: establishing a permeability coefficient calculation formula of the demolition and construction solid waste
(1) And (3) taking samples of the demolished and built solid wastes from the long sand to obtain 6 recycled aggregates with different grading, wherein the maximum particle size of the recycled aggregates is 20mm. The saturation permeability coefficient of the sample was measured by the constant head permeation test. The actual measured values of dry density and permeability coefficient of the solid waste dispersion material are shown in table 2 (for simplicity, M T =100).
Table 16 sets of solid waste sample gradations (i.e., relation of R to M (R < R))
TABLE 2 actual measurement values of the dry Density and permeability coefficients of solid waste Dispersion Material samples
Calculating corresponding M (R < R j) of R j with different particle diameters by using grading particle grading data of the dispersion material, and inverting the undetermined coefficient by adopting the following formula (1) to obtain parameters: r T2、MT1、MT2、D1、D2.
Wherein R is any known particle size, R is any particle size smaller than R, M (R < R) is the mass of the particle smaller than R, and if: when R is more than or equal to R T2, taking
TABLE 3 gradation model parameter inversion values for solid waste bulk materials
(3) And carrying out parameter inversion by using the inversion obtained particle grading parameters and the corresponding permeability coefficients of the permeability tests by using a formula (4) to obtain a permeability coefficient calculation formula of the solid waste material, wherein R 2 = 0.9992:
TABLE 4 measured and calculated values of permeability coefficients of solid waste bulk materials
And a second step of: determining the permeability coefficient of the dispersion material under a certain grading state by using the established permeability coefficient calculation formula
(1) The gradation data provided by the test or by the discrete material manufacturer are shown in Table (5):
TABLE 5 grading of samples to be tested of solid waste Dispersion materials
Inversion of the undetermined coefficient is carried out by adopting the formula (1) to obtain parameters
TABLE 6 grading parameters of samples to be tested of solid waste Dispersion materials
(2) Let R＝dc＝5.614,RT1＝20,RT2＝4.08,MT＝100,MT1＝44.45,MT2＝55.55,D1＝1.80,D2＝2.40 denote equation (1), resulting in M (r < 5.614) =65.23;
The resulting parameters were calculated based on the design requirements of porosity n=0.32 and the permeability coefficient of the bulk material (i.e.:
D1＝1.80,D2＝2.40， ) Substituting formula (5), and calculating to obtain the permeability coefficient k=0.592×10 -2cm·s-1 of the dispersion material.
Example 2:
The invention can be used for determining the permeability coefficient of solid waste dispersion materials, and can also be used for the permeability calculation of general natural dispersion materials, and the data of the literature (Liu Li. Coarse grain permeability characteristic and permeability rule test research [ D ]. Chengdu: university of Sichuan, 2006.) are used for explaining the superiority of the formula of the invention.
This document carried out grading and permeability coefficient tests on coarse sandstone pellets. The maximum grain diameter of the sandstone coarse grain sample is 60mm, and the sandstone coarse grain sample is taken from an extension engineering stock ground of a Sichuan Shujiang oil power plant. The particle size distribution and permeability coefficient data for a total of 6 samples were as follows (let M T =100):
Table 76 sandstone coarse grain sample gradation information (i.e., relationship of R-M (R < R))
Table 8 6 Dry Density and permeability coefficient measured values for coarse sandstone pellet samples
The grading curve obtained by fitting the double fractal model through inversion analysis is shown in figure 1, and the corresponding double fractal grading model parameters and the fitting goodness analysis result are shown in table 9.
Table 9 6 double fractal grading parameters for coarse sandstone sample
Based on the grading double fractal model parameters (table 9) and the porosities of each sample in the document, fitting the measured permeability coefficients of each sample can obtain a calculation formula of the granite grading crushed stone permeability coefficient, wherein R 2 = 0.9995:
in order to compare the analysis effect of the formula of the invention, the penetration coefficient of the coarse-grained soil sample is calculated by adopting a Terzaghi calculation formula, a Carrier formula and a Yang Zhihao calculation formula respectively to obtain corresponding calculated values, wherein the corresponding calculated values are expressed as: k T、kC、k Water department 、k Yang Zhihao A , as follows:
Wherein f i is the mass fraction of particles between two screen sizes, and d li and d li are the adjacent large screen size and small screen size, respectively; c u is the non-uniformity coefficient of the grading, and e is the porosity of the dispersion material.
The calculation results are shown in table 10. As can be seen from the various calculated values and calculation errors presented in table 10, the prediction effect of the present invention is best.
Table 10 measured and calculated values of permeability coefficients of sandstone coarse pellets
The foregoing is merely a specific guiding embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modification of the present invention by using the concept should be construed as infringement of the protection scope of the present invention.
Claims (5)
1. The method for determining the permeability coefficient of the discrete material is characterized by comprising the following steps of:
Step one, regarding the dispersion material as a mixture of 2 particle components with the thickness, wherein the maximum particle diameter of the coarse particle component is R T1, the mass is M T1, and the fractal dimension is D 1; the maximum particle size of the fine particle component is R T2, the mass is M T2, and the fractal dimension is D 2; if R T is the maximum particle size of the bulk material, then R T1＝RT; if M T is the total mass of the bulk material, then M T＝MT1+MT2 is present; the general model of permeability coefficient of the dispersion material is:
Wherein k is the permeability coefficient of the bulk material; n is the porosity of the bulk material; m (r < d c) is the mass of particles with a particle size smaller than d c; d 1、D2 is determined according to the second step; a 0、A1、A2、B1、B2、dc are undetermined coefficients, and are determined by fitting inversion of the actually measured permeability coefficient k;
Step two, determining M T、RT, n and R-M (R < R) relations of more than 6 samples of the dispersion material through density and screening tests, wherein M (R < R) is the mass of particles with the particle size smaller than R;
Thirdly, fitting and inverting according to the obtained R-M (R < R) relation data and the formulas (2) and R T1＝RT、MT＝MT1+MT2 to obtain the particle grading parameter of each sample dispersion material: d 1、D2、RT2、MT1,MT2;
Wherein M T1 is the mass of coarse particle components in the dispersion material; m T2 is the mass of the fine particle component in the dispersion material, and M T2＝MT-MT1;RT2 is the maximum particle size of the fine particle component in the dispersion material; d c ε R;
step four, obtaining permeability coefficients k of more than 6 samples in the step two through a permeability test;
Step five, fitting by utilizing the particle grading parameters, n and the corresponding permeability coefficients of the permeability test by adopting a formula (1) to obtain parameters: a 0、A1、A2、B1、B2、dc; substituting the obtained A 0、A1、A2、B1、B2、dc into a formula (1) to obtain a permeability coefficient model of the dispersion material with determined parameters;
Step six, obtaining data of porosity n of the dispersion material with the osmotic coefficient to be determined, total mass M T of the dispersion material and R-M (R < R) according to the test of the step two; determining grain composition parameters according to the third step: d 1、D2、RT2、MT1,MT2;
Step seven, let r=d c, let R and the parameters of the osmotic coefficient dispersion material to be determined: d 1、D2、RT1、RT2、MT1,MT2 inputting parameters of a formula (2) to obtain M (r < D c) of a dispersion material with a permeability coefficient to be determined;
step eight, the porosity n and the particle size distribution parameters of the dispersion material with the osmotic coefficient to be determined are as follows: d 1、D2 And inputting the permeability coefficient model of the dispersion material with the determined parameters to obtain the permeability coefficient k of the dispersion material with the determined permeability coefficient.
2. The method for determining the permeability coefficient of a discrete material according to claim 1, wherein the particle size distribution parameter of the discrete material is obtained by a sieving test, and the permeability coefficient is obtained by a constant head permeation test.
3. The method for determining the permeability coefficient of a bulk material according to claim 1, wherein the bulk material is a solid waste bulk material or a natural bulk material.
4. The method for determining the permeability coefficient of a bulk material according to claim 3, wherein when the bulk material is a solid waste bulk material, the permeability coefficient model of the parametrically determined bulk material is:
5. The method for determining the permeability coefficient of a bulk material according to claim 3, wherein when the bulk material is a natural bulk material, the model of the permeability coefficient of the parametrically determined bulk material is:
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SU744286A1 (en) * | 1977-06-24 | 1980-06-30 | За витель- | Method of determining penetration coefficient of porous materials with known mean porosity |
| JP2014032125A (en) * | 2012-08-03 | 2014-02-20 | Central Research Institute Of Electric Power Industry | Device for testing air permeability of concrete, and method, device and program for estimating air permeability coefficient distribution of concrete |
| CN104931401A (en) * | 2015-06-02 | 2015-09-23 | 中国科学院力学研究所 | Dynamic changing model for permeability coefficient in sandy gravel soil piping erosion process |
| CN107543775A (en) * | 2017-05-12 | 2018-01-05 | 河海大学 | The method that stockpile fills standard and live filled soils detect is determined based on fractal theory |
-
2022
- 2022-08-02 CN CN202210920504.XA patent/CN115290530B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SU744286A1 (en) * | 1977-06-24 | 1980-06-30 | За витель- | Method of determining penetration coefficient of porous materials with known mean porosity |
| JP2014032125A (en) * | 2012-08-03 | 2014-02-20 | Central Research Institute Of Electric Power Industry | Device for testing air permeability of concrete, and method, device and program for estimating air permeability coefficient distribution of concrete |
| CN104931401A (en) * | 2015-06-02 | 2015-09-23 | 中国科学院力学研究所 | Dynamic changing model for permeability coefficient in sandy gravel soil piping erosion process |
| CN107543775A (en) * | 2017-05-12 | 2018-01-05 | 河海大学 | The method that stockpile fills standard and live filled soils detect is determined based on fractal theory |
Non-Patent Citations (2)
| Title |
|---|
| 基于级配方程的粗粒土渗透系数经验公式及其验证;包孟碟;朱俊高;吴二鲁;王龙;陈鸽;;岩土工程学报;20200815(08);全文 * |
| 颗粒级配与孔隙比对粗粒土渗透系数的影响;丁瑜;饶云康;倪强;许文年;刘大翔;张恒;;水文地质工程地质;20190515(03);全文 * |
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