Concrete pore structure parameter characterization analysis method

Abstract

The invention relates to the technical field of concrete pore structure parameter analysis, in particular to a concrete pore structure parameter characterization analysis method which comprises the following steps: (1) obtaining concrete pore size distribution data, dividing the pore size interval length L into interval sets with equal intervals epsilon according to a second-order reduced scale method, obtaining the interval number N (epsilon), and N (epsilon) = 2K, the selected value of the power value k is [0, 10]; (2) dividing each group of apertures into an interval i, and calculating and obtaining a metric pi (epsilon) through the obtained aperture distribution data vi; and (3) introducing a function Dq to define concrete local aperture distribution characteristics. D1 is a dispersion index; h is a connectivity index; d0-D10 are macropore distribution homogeneity indexes; and D-10-D0 is a pore distribution homogeneity index. According to the method, the problems of internal singularity and homogeneity characterization of concrete pore size distribution can be solved.

Description

Technical Field

[0001] The present application relates to the technical field of concrete pore structure parameter analysis, and more specifically, to a concrete pore structure parameter characterization and analysis method. Background Art

[0002] Concrete is a key basic material supporting the construction of national infrastructure, and its consumption accounts for more than 90% of the total amount of the three major basic building materials. As major national projects gradually expand to plateaus, deep earth, oceans and other regions, concrete materials and structures face huge challenges in extreme service environments such as high geothermal heat, large temperature differences, high salt, and extreme loads such as earthquakes, explosions, and typhoons. There is an urgent need for concrete materials to have significant characteristics such as high mechanical properties and high durability. However, concrete is a porous and heterogeneous material. Microscopic parameters such as pore structure directly determine the quality of macroscopic mechanical properties and durability. Obtaining a homogeneous, fine, and orderly pore structure has become an important technical approach to breakthroughs in the performance of concrete materials. The prerequisite is that key information such as the spatial distribution and morphology of the pore structure can be precisely characterized. At present, there are two types of methods for obtaining and characterizing the pore structure parameters of concrete materials. One is traditional test methods such as mercury injection and gas adsorption, and the other is image reconstruction methods such as CT and grinding. Both methods will eventually obtain the pore size distribution curve of concrete materials, and process and extract characteristic pore size parameters such as equivalent pore size, average pore size, and most probable pore size. However, these parameters cannot characterize the complexity and irregularity of the pore structure of concrete materials, especially the internal geometric characteristics such as pore size morphology and spatial distribution, which makes it difficult to support cutting-edge research on concrete materials.

[0003] Patent document "A method for calculating the porosity and characterizing pore parameters of porous concrete" with application number 201810059436.6 discloses a method for characterizing concrete pore parameters. The invention uses an optimized brittle material profile grinding chamber in combination with a grinding tool to dry-grind concrete material specimens layer by layer at fixed intervals, takes continuous profile images of the specimens, and then uses image processing software to reconstruct the concrete pore structure and calculate parameters such as porosity. However, the invention only provides a relatively simple image processing method in the process of obtaining the concrete pore structure, and does not involve the detailed characterization of the concrete pore structure after acquisition.

[0004] The patent document "Prediction model of chloride ion diffusion coefficient of recycled concrete based on random fractal characteristics of pore structure, construction method and application thereof" with application number 202310463773.2 discloses an effective chloride ion diffusion coefficient model based on the fractal characteristics of pore structure. This invention is based on the principle of Menger sponge model and uses the relative porosity of concrete to construct a three-dimensional pore volume fractal model and a two-dimensional pore tortuosity fractal model of recycled concrete. First, this method only provides a pore volume fractal model through theoretical assumptions, which makes it difficult to accurately analyze the real concrete pore structure. Secondly, this method is only based on relative porosity for analysis, and cannot analyze the complexity of the pore structure, let alone internal geometric characteristics such as pore size morphology and spatial distribution. Summary of the invention

[0005] In order to solve the problem of characterizing the internal singularity and homogeneity of the pore size distribution of concrete, the present application provides a method for characterizing and analyzing the parameters of the concrete pore structure, which can achieve a fine characterization of the internal geometric characteristics such as the pore size morphology and spatial distribution of the pore structure, meet the requirements of a profound description of the uniformity, dispersion, complexity and connectivity of the local pore size distribution of concrete, and provide an analysis method for the regulation of the microscopic pore structure of concrete, optimization of mechanical properties and improvement of durability.

[0006] This application provides a concrete pore structure parameter characterization and analysis method, which adopts the following technical solution:

[0007] A concrete pore structure parameter characterization and analysis method comprises the following steps:

[0008] (1) Obtain the pore size distribution data of concrete and divide its pore size interval length L into a set of intervals with equal spacing ε according to the second-order scaling method. The number of intervals obtained is N(ε), where N(ε)=2 K , the power value k is selected as [0, 6];

[0009] (2) For each group of pore size division intervals i, a metric value pi(ε) is calculated by using the obtained pore size distribution data vi. Specifically:

[0010]

[0011] (3) The generalized dimension Dq is introduced to define the local pore size distribution characteristics, as follows:

[0012]

[0013] D2=2H-1

[0014]

[0015] The matrix order q is a real number, [-10, 10];

[0016] D1 is the dispersion index, which characterizes the concentration and dispersion of the pore size distribution. The maximum value of D1 is 1. The closer it is to 1, the more dispersed the pore size distribution is. On the contrary, the smaller D1 is than 1, the easier it is for the pores to aggregate and distribute in certain narrow pore size intervals, that is, the more uneven the dispersion is.

[0017] H is the connectivity index, which characterizes the autocorrelation of the pore size distribution within the pore size interval length L. Its value ranges from 0.5 to 1. The closer the H value is to 1, the stronger the autocorrelation of the pore size distribution is, that is, the worse the connectivity of the pore size is.

[0018] D0-D 10 It is the macropore distribution homogeneity index, which characterizes the fluctuation trend of pore distribution with a pore size greater than or equal to 10 μm. The smaller its value, the more uniform and homogeneous the macropores are distributed within the pore size interval. On the contrary, the larger its value, the more non-uniform the macropores are distributed within the pore size interval.

[0019] D -10 -D0 is the pore distribution homogeneity index, which characterizes the fluctuation trend of pore distribution with a pore size less than 10 μm. The smaller its value, the more uniform and homogeneous the distribution of pores in the pore size division interval; on the contrary, the larger its value, the more uneven the distribution of pores in the pore size division interval.

[0020] Furthermore, in step (1), the method for obtaining concrete pore size distribution data includes an adsorption test method and an image reconstruction test method.

[0021] Furthermore, the medium involved in the adsorption test method includes one of liquid mercury, nitrogen, water vapor, carbon dioxide, argon, and krypton.

[0022] Furthermore, the image reconstruction test method includes one of a hardened concrete pore analysis test, a scanning electron microscope test, and a CT test.

[0023] In summary, this application has the following beneficial effects:

[0024] The concrete pore structure parameter characterization and analysis method described in this application is based on the in-depth analysis of the concrete pore size distribution data obtained by the traditional adsorption or image reconstruction test method. By introducing the analysis method of this application, a characteristic function D is provided. q To define the local pore size distribution characteristics of concrete, it achieves a detailed characterization of the dispersion, uniformity, connectivity and complexity of the pore size distribution inside concrete, breaking through the limitation that traditional methods are difficult to characterize the complexity and irregularity of the geometric characteristics of the internal pore structure of concrete. It can be used to guide the fine control of the concrete microstructure in order to further improve its mechanical properties and durability. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a graph of concrete pore size distribution in the embodiment;

[0026] Figure 2 is the generalized dimension D of concrete in the embodiment q Graphs;

[0027] Figure 3 is the bubble (macroporous) distribution diagram of Examples 1-3;

[0028] Figure 4 is a photo of the pore distribution of the concrete of Example 1-3;

[0029] Figure 5 It is the concrete CT scan image of Example 1-3. DETAILED DESCRIPTION

[0030] The present application is further described in detail below with reference to the accompanying drawings and embodiments.

[0031] Unless otherwise specified, the raw materials involved in the examples of this application can be obtained from commercial sources, as follows:

[0032] 1. Test raw materials:

[0033] The "cement" in the examples is P·II52.5 silicate cement.

[0034] The “silicon ash” in the embodiment is silicon ash with a silicon content of 92% and a surface area of ​​16.19 m 2 / g.

[0035] The "fine aggregate" in the embodiment is river sand, a continuously graded sand with a particle size of 0.075 to 4.75 mm.

[0036] The "superplasticizer" in the embodiment is a polycarboxylic acid additive with selective adsorption, and the model is

[0037] The "nano material" in the embodiment is nano silicon oxide. The surface area is 100-150m 2 / g.

[0038] 2. Example mix ratio

[0039] Table 1 Content of each component of concrete in each embodiment

[0040] Components cement Silica Fume fine aggregate Nanomaterials Superplasticizer water Example 1 100 40 140 0 3 20 Example 2 100 40 140 5 3 20 Example 3 100 40 140 10 3 20

[0041] When preparing the materials in the above Examples 1-3, cement, silica fume, fine aggregate and nanomaterials were first mixed in a vertical planetary forced mixer for 1 minute, and then water and superplasticizer were added to the resulting mixture and stirred for 2 minutes to obtain concrete, which was then placed in a standard curing environment for 28 days.

[0042] Example

[0043] A concrete pore structure parameter characterization and analysis method comprises the following steps:

[0044] (1) Obtain the pore size distribution data of concrete and divide its pore size interval length L into a set of intervals with equal spacing ε according to the second-order scaling method. The number of intervals obtained is N(ε), where N(ε)=2 K , the power value k is selected as [0, 10];

[0045] (2) For each group of pore size division intervals i, a metric value pi(ε) is calculated by using the obtained pore size distribution data vi. Specifically:

[0046]

[0047] (3) The generalized dimension Dq is introduced to define the local pore size distribution characteristics, as follows:

[0048]

[0049] D2=2H-1

[0050]

[0051] The matrix order q is a real number, [-10, 10];

[0052] D1 is the dispersion index, which characterizes the concentration and dispersion of the pore size distribution. The maximum value of D1 is 1. The closer it is to 1, the more dispersed the pore size distribution is. On the contrary, the smaller D1 is than 1, the easier it is for the pores to aggregate and distribute in certain narrow pore size intervals, that is, the more uneven the dispersion is.

[0053] H is the connectivity index, which characterizes the autocorrelation of the pore size distribution within the pore size interval length L. Its value ranges from 0.5 to 1. The closer the H value is to 1, the stronger the autocorrelation of the pore size distribution is, that is, the worse the connectivity of the pore size is.

[0054] D0-D 10 It is the macropore distribution homogeneity index, which characterizes the fluctuation trend of pore distribution with pore size greater than or equal to 10nm. The smaller its value is, the more uniform and homogeneous the macropores are distributed in the pore size interval. On the contrary, the larger its value is, the more non-uniform the macropores are distributed in the pore size interval.

[0055] D -10 -D0 is the pore distribution homogeneity index, which characterizes the fluctuation trend of pore distribution with a pore size less than 10nm. The smaller its value, the more uniform and homogeneous the distribution of pores in the pore size division interval; on the contrary, the larger its value, the more uneven the distribution of pores in the pore size division interval.

[0056] The software used may be Matlab, Python, etc., and in this embodiment, Python is used.

[0057] Furthermore, in step (1), a conventional test method is used to obtain the pore size distribution data of concrete, and the method for obtaining the pore size distribution data of concrete includes an adsorption test method and an image reconstruction test method. The medium involved in the adsorption test method includes one of liquid mercury, nitrogen, water vapor, carbon dioxide, argon, and krypton. The image reconstruction test method includes one of hardened concrete pore analysis test, scanning electron microscope test, and CT test.

[0058] The following is an explanation through specific embodiments.

[0059] Examples 1-3

[0060] (1) The pore size distribution of concrete is determined by mercury intrusion testing. The pressure range of the mercury intrusion instrument is 0.10psia-61000psia, and the measurable pore size range is 5nm-1mm. The measurement resolution of mercury intrusion and decompression volume can be accurate to 0.1μL. The pore size distribution curve obtained is as follows: Figure 1 shown.

[0061] (2) According to the aperture interval length L (35000 nm) in the embodiment, the power value k is selected as [0, 10], the aperture interval is divided, and the measurement value pi (ε) of each group of aperture division intervals is obtained.

[0062] (3) Introducing characteristic function D q To define the local pore size distribution characteristics, the characteristic function D in Examples 1-3 q Curves and their associated eigenvalues, such as Figure 2 And as shown in Table 2.

[0063] Table 2 Pore structure analysis index of concrete in practice

[0064] Example 1 <![CDATA[D1]]> H <![CDATA[D0-D 10 ]]> <![CDATA[D -10 -D0]]> Example 1 1 0.893 0.841 0.105 0.551 Example 2 1 0.936 0.906 0.127 0.423 Example 3 1 0.927 0.931 0.064 0.335

[0065] From the results in Table 2, it can be found that the use of nanomaterials can clearly distinguish D1, H, D0-D in the embodiment. 10 , D -10 -The change of D0 characteristic value is described in detail below:

[0066] D1 is the dispersion index. It can be found that with the addition of nanomaterials, the dispersion index of the pore size in Examples 2 and 3 is significantly improved, indicating that the addition of nanomaterials can improve the dispersion of the pore size inside the concrete, but the continuous increase in the amount of nanomaterials will not continue to significantly improve the dispersion of the pore size of the concrete. This may be because the nanomaterials are prone to local aggregation after being added, which limits their improvement effect on the pore size.

[0067] H is the connectivity index. The order of H values ​​in the embodiments from large to small is embodiment 3, embodiment 2, and embodiment 1. Therefore, the connectivity of the pore size distribution is the largest in embodiment 1, followed by embodiment 2, and finally embodiment 3. Therefore, the addition of nanomaterials can significantly reduce the connectivity of the pore size distribution of concrete, and it increases with the increase of the amount of nanomaterials added.

[0068] D0-D 10 is the macropore distribution homogeneity index. 10 The order of the values ​​from large to small is Example 2, Example 1, and Example 3. Therefore, Example 3 has the greatest uniformity of macropore distribution, followed by Example 1, and finally Example 2. 10 Therefore, it can be found that the addition of nanomaterials can improve the uniformity of macropore distribution in concrete, but the dosage requirement is high.

[0069] D -10 -D0 is the pore distribution homogeneity index. -10 The order of -D0 values ​​from large to small is Example 1, Example 2, and Example 3, so the uniformity of small pore distribution is the greatest in Example 3, followed by Example 2, and finally Example 1. Therefore, it can be found that the addition of nanomaterials can improve the uniformity of small pore distribution in concrete, achieve the effect of refining pore diameter, and improve with the increase of nanomaterial dosage.

[0070] Proof of Accuracy

[0071] In order to verify the accuracy of the characterization method of this application, this application conducted the following tests.

[0072] (1) Concrete air permeability test

[0073] The connectivity index H is indirectly characterized by the air permeability test of concrete. The larger the H index is, the worse the connectivity of the concrete pore size is, that is, the stronger the concrete's ability to resist air permeability is, that is, the lower the air permeability coefficient of the concrete is.

[0074] The specific operation is to use Torrent permeability tester to test the permeability of each embodiment. The instrument consists of a suction cup, a vacuum pump, a regulator and a display. Based on Darcy's permeability law, the instrument creates a vacuum on the concrete surface, and under negative pressure conditions, the air escapes into the vacuum chamber through the tiny cracks in the concrete protective layer. The instrument automatically calculates its air permeability coefficient by measuring the maximum pressure difference before and after the test. The larger the air permeability coefficient, the weaker the concrete's ability to resist air permeation. The results are shown in Table 3.

[0075] Table 3, gas permeability test results of the embodiment

[0076] Example <![CDATA[Air permeability coefficient (10 -17 / m 2 )]]> Example 1 15 Example 2 2.1 Example 3 0.15

[0077] It can be found that the addition of nanomaterials significantly reduces the air permeability coefficient of concrete, indicating that the interconnected pores inside it are significantly reduced, which is consistent with the law described by the H index in this application.

[0078] (2) Analysis of pore structure of hardened concrete

[0079] The porosity inside the concrete was tested by the hardened concrete pore structure analyzer to analyze and compare D0-D 10 (macropore distribution homogeneity index) accuracy.

[0080] The specific operation is as follows: Cut the 100mm*100mm concrete specimen (thickness is 20mm), and carefully grind it with 400 and 800 emery papers respectively. After grinding, use a wide-headed black oil-based marker to blacken the observation surface of the specimen until the color of the specimen itself cannot be seen. Then fill the bubbles inside the concrete with white calcium carbonate powder with a particle size less than 10um. Finally, use the high-definition camera in the hardened concrete pore structure analysis tester to automatically scan, and use image software analysis and calculation to measure the concrete air content, bubble spacing coefficient, pore size range and corresponding bubble frequency and other bubble energy parameters. This paper measures the actual distribution of bubbles in each embodiment, see Figure 3 , and the bubble spacing coefficient was calculated. The results are shown in Table 4.

[0081] Table 4 Bubble spacing coefficient results of the embodiment

[0082]

[0083]

[0084] Depend on Figure 3It can be found that after adding a small amount of nanomaterials, the macropore distribution of concrete is not greatly affected (refer to the bubble distribution diagrams of Example 1 and Example 2), and the bubble spacing coefficient is not much different. This is because the nanomaterial has a small particle size. In theory, it can only improve the small pore size distribution of concrete when the nanomaterial is added in a small amount. However, as the amount of nanomaterials added increases, Figure 3 It can be found that the number of bubbles in Example 3 is significantly reduced, and its bubble spacing coefficient is significantly increased. This is because the nanomaterial effectively closes the internal pore size of the concrete, resulting in an increase in the dispersion of the pore size distribution in the concrete, that is, effectively improving the homogeneity of the macropore distribution in the concrete, which is consistent with the D0-D 10 (macropore distribution homogeneity index) is consistent with the law described.

[0085] (3) Concrete scanning electron microscope test

[0086] The D -10 -D0 (pore distribution homogeneity index) for intuitive comparison. Backscattering test is a more sophisticated test than hardened concrete pore structure analyzer. Its resolution can reach micron or even submicron level, which is an effective tool for comparing small pore characteristics inside concrete.

[0087] The specific operation is as follows: first, the concrete sample is cut, and then the sample is finely prepared, and the sample preparation process includes epoxy resin curing, sandpaper grinding and fine polishing of the sample, and finally the backscattering mode of the scanning electron microscope is used to take the backscattering photo. In the backscattering photo, since the epoxy resin fills the pores inside the concrete specimen, it appears black during the scanning electron microscope shooting process. At this time, as long as the backscattering photo is binarized, the actual photo of the small hole distribution of each embodiment can be obtained, that is, Figure 4 .

[0088] Depend on Figure 4 It can be found that compared with the pores that are aggregated in large numbers in Example 1 (by Figure 4 It can be found that the pores have been clearly connected. After the addition of nanomaterials, the pore distribution of Examples 2 and 3 has become significantly less and more uniform. As the amount of nanomaterials added increases, the degree of improvement in the pore distribution gradually increases, which is consistent with the D -10 -D0 (hole distribution homogeneity index) describes the same law.

[0089] (4) Concrete CT scanning test

[0090] D1 (dispersion index) describes the overall pore size dispersion in concrete, where the pores include large and small pores. The concrete specimens in each example were cut into 2 cm cube specimens and then CT scanned to visually compare their pores. Figure 5 .

[0091] By observation Figure 5 It can be found that after adding the nanomaterial, the porosity in Examples 2 and 3 is significantly reduced compared to Example 1, and the pore size distribution is more dispersed. Comparing Example 2 with Example 3, it can be found that the overall pore dispersion of the two is not very different. This may be because the nanomaterial is easy to agglomerate when the dosage is large, so that the continuous increase in its dosage cannot continue to improve the overall pore structure characteristics of the concrete, which is consistent with the law described by D1 (dispersion index) in this application.

[0092] By referring to the proof means in each embodiment, the superiority of the present application can be clearly compared, that is, the concrete pore structure parameter characterization and analysis method discussed in the present application can replace the traditional pore structure testing methods, such as hardened concrete pore analyzer, backscatter scanning electron microscope and CT, so as to examine the internal pore structure characteristics of concrete from a comprehensive new perspective.

[0093] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make modifications to the present embodiment without any creative contribution as needed. However, as long as it is within the scope of the claims of the present application, it shall be protected by the patent law.

Claims

1. A method for characterizing and analyzing concrete pore structure parameters, characterized in that: The following steps are involved: (1) Obtain the pore size distribution data of concrete, and divide its pore size interval length L into a set of intervals with equal spacing ε according to the second-order scaling method. The number of intervals obtained is N(ε), N(ε) = 2 K , the power value k is selected as [0, 10]; (2) Divide each group of pore sizes into intervals i, and obtain the pore size distribution data v i Calculate and obtain a metric value p i (ε), specifically: (3) The function Dq is introduced to define the local pore size distribution characteristics of concrete, as follows: D22H1 The matrix order q is a real number, [-10, 10]; D1 is the dispersion index, which characterizes the concentration and dispersion of the pore size distribution. The maximum value of D1 is 1. The closer it is to 1, the more dispersed the pore size distribution is. On the contrary, the smaller D1 is than 1, the easier it is for the pores to aggregate and distribute in certain narrow pore size intervals, that is, the more uneven the dispersion is. H is the connectivity index, which characterizes the autocorrelation of the pore size distribution within the pore size interval length L. Its value ranges from 0.5 to 1. The closer the H value is to 1, the stronger the autocorrelation of the pore size distribution is, that is, the worse the connectivity of the pore size is. D0-D 10 It is the macropore distribution homogeneity index, which characterizes the fluctuation trend of pore distribution with a pore size greater than or equal to 10 μm. The smaller its value, the more uniform and homogeneous the macropores are distributed within the pore size interval. On the contrary, the larger its value, the more non-uniform the macropores are distributed within the pore size interval. D -10 -D0 is the pore distribution homogeneity index, which characterizes the fluctuation trend of pore distribution with a pore size less than 10 μm. The smaller its value, the more uniform and homogeneous the distribution of pores in the pore size division interval; on the contrary, the larger its value, the more uneven the distribution of pores in the pore size division interval.

2. A concrete pore structure parameter characterization and analysis method according to claim 1, characterized in that: In the step (1), the method for obtaining the concrete pore size distribution data includes an adsorption test method and an image reconstruction test method.

3. A concrete pore structure parameter characterization and analysis method according to claim 2, characterized in that: The medium involved in the adsorption test method includes one of liquid mercury, nitrogen, water vapor, carbon dioxide, argon, and krypton.

4. A concrete pore structure parameter characterization and analysis method according to claim 2, characterized in that: The image reconstruction test method includes one of a hardened concrete pore analysis test, a scanning electron microscope test, and a CT test.

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