A method for characterizing the performance of a concrete interfacial transition zone

Abstract

The present application relates to a method for characterizing the performance of the concrete interface transition zone in the complex environment of the plateau, and belongs to the method for testing and characterizing the performance of micro-nano scale materials. The measured material is subjected to nano-scratch testing according to the specified rectangular position to obtain the micro-mechanical parameters of the paste and the interface transition zone, and the region is marked. Then, the nano-scratch region is subjected to BSE testing. The method introduces a BSE image multi-phase gray identification algorithm and uses a boundary directional expansion algorithm to identify the microstructure parameters of the interface transition zone. The micro-mechanical performance parameters obtained by the nano-scratch testing at the same position are linked, complemented and compared, so as to obtain the quantitative relationship between the micro-mechanical performance and the structure parameters of the interface transition zone and the adjacent paste. The method can be used to study the inherent property parameters and damage evolution law of the concrete interface transition zone, and lays a foundation for the long-term service performance evaluation of concrete under the action of complex environmental factors in the plateau.

Description

TECHNICAL FIELD

[0001] The present application relates to a method for characterizing the performance of the interfacial transition zone of concrete in a high-altitude harsh environment, and belongs to the technical field of concrete durability evaluation. BACKGROUND

[0002] The climate conditions in the western plateau region of China are extremely harsh, such as extremely low winter temperatures, large diurnal temperature differences, and extremely dry climates. Frequent temperature and humidity changes, as well as high-frequency freeze-thaw cycles, can cause rapid deterioration and damage to structural concrete. The microstructure and physical and chemical properties of the internal interfaces of concrete, especially the interfacial transition zone between the aggregate and the cement stone, are crucial to the overall performance of the concrete. In the harsh environment of the plateau, microcracks and pores are prone to occur at the interface of the concrete, which not only reduces the mechanical strength of the concrete, but also makes it more susceptible to water and corrosive media penetration, thereby accelerating the corrosion and deterioration of the concrete. Therefore, the evaluation of the durability of concrete in the harsh environment of the plateau must consider the assessment of the performance of the interface region. However, traditional interface characterization techniques mainly evaluate the width and microtopography of the interface, and there are still limitations in the characterization of the micro-mechanical properties and structure of the interface.

[0003] The nano-scratch technique can observe the deformation and damage of the material surface by applying a small load on the surface of the concrete material and moving along the surface, thereby evaluating the mechanical properties of the material and providing detailed information about the microstructure and performance of the material. The BSE image (backscattered scanning electron microscope image) refers to an image obtained by backscattered electron microscope testing. The backscattered electron microscope generates backscattered electron signals by bombarding the material surface with an electron beam, which can provide information about the microstructure, pore structure, chemical composition, damage, and deterioration of the concrete material, thereby revealing the microfeatures of the material. The image recognition technology is based on the OpenCV computer vision library and the Shapely library in Python. OpenCV is one of the most popular and widely used open-source libraries in the field of computer vision, and is widely used in target detection and image segmentation; Shapely is used for spatial geometric object operations and analysis, supporting the creation, operation, and relationship judgment of spatial geometric objects such as points, lines, and surfaces.

[0004] Generally, the industrial CT and X-ray microscope (Chinese patent, application number: 201610224953.5; 202211057356.X) can be used to perform multi-dimensional layer-by-layer scanning on heterogeneous materials, and the collected image information can be reconstructed in three dimensions, so as to realize the characterization of the damage degree of the concrete material. The former has been widely used in the field of concrete damage characterization, but due to the limitation of sample size on its resolution, the voxel resolution of 20 microns and above cannot meet the actual needs of phase recognition and damage determination; the latter has high resolution, but it needs to accurately prepare test samples of almost "grain" size. Considering the dispersion of heterogeneous materials, the difficulty of preparing samples and the reliability of local test results, this method is not suitable for a large number of experimental studies. Another widely used test method is to perform nanoindentation and nanoscratch tests on concrete materials (Chinese patent, application number: 202010954440.6; 202311623883.7), and the results often contain the results of various phases and interfaces. However, the optical microscope function of the nanoscratch tester only has a simple indenter positioning function, and cannot accurately determine the composition of the tested phase, the interface width and the pore structure, so the accuracy of the test results needs to be improved.

[0005] So far, there have been many studies on concrete damage characterization, but there is still a lack of accurate quantitative characterization of the micro-nano performance and structure of the interface. In order to realize the characterization of the material parameters and damage evolution of the interface transition zone, the traditional method is no longer applicable, and it is necessary to accurately quantify the width, phase and mechanical properties of the interface transition zone. SUMMARY

[0006] TECHNICAL PROBLEM

[0007] At present, there have been many studies on concrete damage characterization, but there is still a lack of accurate quantitative characterization of the micro-nano performance and structure of the interface. In order to realize the characterization of the material parameters and damage evolution of the interface transition zone, the traditional method is no longer applicable, and it is necessary to accurately quantify the width, phase and mechanical properties of the interface transition zone.

[0008] TECHNICAL SCHEME

[0009] The purpose of the present application is to provide a method for characterizing the performance of the concrete interface transition zone in a harsh environment, which can effectively determine the width of the concrete transition zone, the chemical composition near the transition zone, and the microstructure, and solve the technical problem that the traditional method cannot accurately and quantitatively characterize the physical and chemical properties of the interface transition zone in the micro-nano scale.

[0010] In order to solve the above technical problems, the specific technical scheme of the present application is as follows:

[0011] A method for characterizing the performance of the concrete interface transition zone, comprising the following steps:

[0012] Step 1: The measured concrete material is subjected to nano-scratch test according to the specified scratch area containing paste, interface and aggregate, to obtain the micro-mechanical parameters of the paste and interface transition zone, calculate the fracture toughness parameters of different phases along the scratch path according to the micro-mechanical parameters, and mark different phases in the specified scratch area;

[0013] Step 2: The marked specified nano-scratch area is observed by backscattered mode scanning electron microscope to obtain a phase distribution map with obvious gray difference; the image resolution is adjusted and several BSE images are saved;

[0014] Step 3: The BSE images are read in the programming software, and the following operations are performed:

[0015] A function for converting distance to length is defined according to the image resolution and pixel size, which is used to obtain the chemical composition and microstructure information in a specified distance range later;

[0016] The BSE image is displayed to determine the aggregate area;

[0017] The creation of annular geometric area is performed on the area near the aggregate, taking the edge of the aggregate determined in step 1 as the initial boundary line, and gradually expanding the boundary area along the normal outward; the area between each two generated boundary lines is the area to be analyzed;

[0018] Starting from the annular area closest to the aggregate, the phase analysis and microstructure analysis of the annular area to be analyzed are gradually performed along the normal outward, which specifically includes: threshold segmentation according to the gray value of the image; according to the gray-recognized area, the ratio between the corresponding phase and the interval area in each statistical interval is calculated, and the phase distribution, microstructure information and interface width of the area near the aggregate are obtained by analogy along the normal outward; the statistical information is exported and the corresponding result graph is drawn;

[0019] Step 4: Based on the data obtained by nano-scratch test in step 1, the horizontal displacement-scratch depth curve is drawn, the first derivative of the curve is calculated, and the peak width length at the peak value of the first derivative curve is preliminarily determined as the width of the interface transition zone, and the different phase compositions within a certain range of the interface transition zone are obtained according to the fracture toughness calculation results;

[0020] The pore structure and interface width near the interface transition zone are obtained based on the image recognition results of step 3;

[0021] The interface width range is obtained by comparing the interface width results obtained by image recognition and nano-scratch;

[0022] The pore structure and phase composition information near the interface transition zone are obtained by combining the micro-nano phase and structure results obtained by image recognition and nano-scratch;

[0023] The relationship between the mechanical parameters of the interface within a certain range and the porosity ratio and the hydration product ratio is compared, the porosity-mechanical parameter image and the hydration product-mechanical parameter image are drawn, the images are fitted, and the correlation between the phase composition-pore structure-mechanical property is established, which is used for the performance and damage degree representation of the concrete interface.

[0024] Preferably, step 1 specifically comprises: according to the sample preparation method required by the nano-scratch test method, the measured concrete material containing aggregate, interface and paste is impregnated, inlaid, ground and polished; the optical microscope of the nano-scratch tester is positioned into a random rectangular area containing paste, interface and aggregate; the starting position of the scratch is the aggregate phase; the scratch operation is performed in parallel, equal interval and equal load, so that the indenter is scratched into the aggregate, interface and paste phases in turn; the fracture toughness parameters of different phases on the scratch path are calculated by using the fracture mechanics theory, which is used for the differentiation of different phases; and the specified scratch area is marked.

[0025] Preferably, in step 3, the aggregate area is manually determined by using polygon instruction.

[0026] Preferably, in step 3, according to the actual analysis accuracy and image resolution, the range of the area to be analyzed is determined, so as to determine the number of times of expanding the aggregate edge outward and the actual distance of each expansion step.

[0027] Preferably, in step 3, zero buffer zone operation is performed to avoid the problem of intersection of multiple curves and overlapping of analysis areas.

[0028] Preferably, in the nano-scratch test of step 1, the length of the scratch along the interface is 100-120 μm.

[0029] Preferably, in the nano-scratch test of step 1, the interval between the two scratches needs to be greater than 30 μm.

[0030] Preferably, in the nano-scratch test of step 1, the starting position of the scratch is the aggregate phase, the diamond rhombus indenter is used as the probe, the cone angle of the indenter is 90°, a minimum load of 5 μm is used for pre-pressing scanning on the sample surface, and then the formal indentation test is started according to the path position recorded in the pre-scanning process; the scratch data on the scratch are recorded; a constant pressing load is maintained, and then the scratch operation is performed in parallel, equal interval and equal load, so that the indenter is scratched into the aggregate, interface and paste phases in turn.

[0031] Preferably, the programming software is python, and step 3 specifically comprises: reading the BSE image in Python, defining a function for conversion between "distance" and "micron" according to the image resolution and pixel scale; loading the Matplotlib library, displaying the BSE image, and manually determining the aggregate area by using polygon instruction; loading the Shapely library, creating a ring-shaped geometric area around the aggregate, taking the edge of the aggregate determined in step 1 as the initial boundary line, and gradually expanding the boundary area along the normal outward; according to the actual analysis accuracy and image resolution, determining the range of the area to be analyzed, thereby determining the number of times the aggregate edge is expanded outward and the actual distance of each expansion step; using the buffer(0) method for zero buffer operation to avoid the problem of intersection of multiple curves and overlapping of analysis areas; the area between each two boundary lines generated above is the area to be analyzed; loading the OpenCV library, performing phase analysis and microstructure analysis on the ring-shaped area to be analyzed; starting from the ring-shaped area closest to the aggregate, gradually progressing along the normal outward; performing threshold segmentation according to the gray value of the image; according to the gray-recognized area, calculating the ratio between the corresponding phase and the interval area in each statistical interval, and sequentially extrapolating along the normal outward, so as to obtain the phase distribution, microstructure information and interface width of the aggregate adjacent area; exporting the statistical information in CSV format, and drawing the corresponding result graph.

[0032] Preferably, in step 3, the threshold segmentation is performed according to the gray value of the image, wherein the phase with a gray value in the range of 76-125 is a pore, the aggregate phase is in the range of 125-148, and the paste phase is in the range of 148-225.

[0033] Advantages:

[0034] The nano-scratch test specific implementation system and the concrete multi-phase parameter identification and boundary directional expansion method based on Python programming language, OpenCV and Shapely library provided by the application can effectively identify the microstructure parameters of the interface transition zone and the paste adjacent area of concrete. The method involves the linkage use of nano-scratch test and BSE image recognition technology, and can be used for researching the inherent parameters and damage evolution law of the interface transition zone of concrete material, thereby laying a foundation for long-term service performance evaluation of concrete under complex environmental factors. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the implementation method and technical scheme of the application, the implementation drawings required to be used in the prior art description are simply introduced as follows.

[0036] Figure 1 The flow chart for implementation of the application.

[0037] Figure 2Indentation parameters and typical scratch data points plot for the nanoscratch of the present application.

[0038] Figure 3 Peak splitting plot of the calculated fracture toughness parameters for the nanoscratch data of Example 1.

[0039] Figure 4 Main objects and implementation effect plot for the BSE image recognition of Example 1.

[0040] Figure 5 Output result plot for the BSE image recognition of Example 1.

[0041] Figure 6 Sample image recognition objects for Example 2.

[0042] Figure 7 Sample image recognition results and nanoscratch results for Example 2. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application, and the implementation flowchart of the present application is shown in Figure 1 It should be understood that the specific embodiments described herein are only used to explain the present application and are not limited to the present application.

[0044] The present application provides a method for characterizing the performance of a concrete interfacial transition zone, comprising the following steps:

[0045] Step 1, according to the sample preparation method required by the nanoindentation / nanoscratch test method, immerse, grind and polish the measured concrete material containing aggregate, interface and paste. Fix the sample on the test table of the nanoscratch tester, and position it in a random rectangular area containing paste, interface and aggregate through the optical microscope of the instrument. The starting position of the scratch is the aggregate phase, the diamond rhombus indenter is used as the probe, the indenter cone angle is 90°, the minimum load of 5 μm is used to pre-scan the sample surface, and then the formal indentation test is started according to the path position recorded in the pre-scan process. The load of the formal indentation test is 1000 μN, the indentation speed is 0.4 μm / s, the scratch speed is constant 5 μm / s, and 2000 scratch data on the scratch length of 100-120 μm are recorded. Pre- indentation is performed before the starting point. The test parameters are determined, including the vertical loading rate, the scratch length, the vertical force and the lateral force. Keep the constant indentation load, and then perform the scratch operation according to the parallel, equal interval and equal load, so that the indenter is scratched into the aggregate, interface and paste phases in turn. The interval between the two scratches needs to be greater than 30 μm to avoid the interference of residual stress.

[0046] The fracture mechanics theory is used to calculate the fracture toughness parameters of different phases along the scratch path, which can be used to distinguish different phases.

[0047] The scratch area is marked for subsequent backscattered electron microscopy observation. The scratch length-depth rectangular coordinate system is established with the center of the indentation depth jump as the zero point, and the scratch data and calculated fracture toughness data are imported into Origin. Origin is used to plot the indentation depth-displacement data graph of nano-scratch, the fracture toughness distribution graph near the interface, and the porosity distribution graph near the interface obtained by image recognition.

[0048] Step 2: Since the concrete sample does not have electrical conductivity, it cannot be observed by scanning electron microscopy. To obtain high-quality backscattered images, the sample is gold-coated in a vacuum for 180 seconds. Then, the gold-coated sample is placed in the vacuum sample stage. In backscattered mode, the observation position is the designated nano-scratch area marked. In the observation area, aggregate, interface, paste phase, and scratch can be observed. Adjust the electron microscope test parameters and image contrast parameters to ensure that the phase distribution map has obvious gray difference. Adjust the image resolution according to the needs of local observation, and the magnification can be 200 to 1000 times. Save several BSE images.

[0049] Step 3: Identify and analyze the interface transition zone BSE images. Identify 10-15 BSE images from different positions for each sample to avoid randomness. Specifically:

[0050] 1) Read the BSE images obtained by testing in Python one by one. Define the function for converting between "distance" and "micron (μm)" according to the image resolution and pixel size, which is used to obtain the chemical composition and microstructure information within the specified distance range later.

[0051] 2) Load matplotlib library, display BSE image, due to the difference in uneven gray value of aggregate phase, threshold segmentation method cannot be used to accurately determine the boundary and area of aggregate, polygon instruction is used to manually determine the aggregate area. Load Shapely library, create annular geometric area for the adjacent area of aggregate, the edge of the aggregate determined in the above step is used as the initial boundary line, and the boundary area is gradually expanded outward along the normal direction. According to the actual analysis accuracy and image resolution, the range of the area to be analyzed is determined, so as to determine the number of outward expansion of the aggregate edge and the actual distance of each expansion step. Since the aggregate may have local sharp points or areas, multiple curves may intersect and the analysis area may overlap when expanding the boundary of the analysis area outward along the normal direction each time. Buffer (0) method is used for a zero buffer operation, which will reduce the analysis boundary inward by a small distance, thereby eliminating the self-intersection problem. The area between each two boundary lines generated above is the area to be analyzed.

[0052] 3) Load OpenCV library, perform phase analysis and microstructure analysis on the annular area to be analyzed. Starting from the annular area closest to the aggregate, gradually proceed outward along the normal direction. According to the gray value of the image, the phase in the gray value range of 76-125 is the pore phase, the phase in the gray value range of 125-148 is the aggregate phase, and the phase in the gray value range of 148-225 is the paste phase. According to the gray value recognized area, the ratio between the corresponding phase and the interval area in each statistical interval is calculated, and the phase distribution, microstructure information and interface width of the adjacent area of the aggregate are obtained by analogy outward along the normal direction. The statistical information is exported in CSV format, and the corresponding result graph is drawn.

[0053] Step 4, based on the data obtained from step 1 nanoscratch test, draw the horizontal displacement-scratch depth curve, take the first derivative of the curve, and preliminarily determine the width of the interface transition zone according to the peak width length where the peak value of the first derivative curve is located (consistent with the steep drop area of the horizontal displacement-scratch depth curve). According to the fracture toughness calculation results, the different phase composition within a certain range of the interface transition zone is obtained. Based on the image recognition results, the pore structure near the interface transition zone and the interface width can be obtained. By comparing the interface width results obtained by image recognition and nanoscratch, the approximate width range of the interface can be obtained (the union or average of the two test values can be taken). Combined with the results of image recognition and nanoscratch on micro-nano phase and structure, the pore structure, phase composition information near the interface transition zone can be obtained. By comparing the relationship between the mechanical parameters and the pore ratio, the hydration product ratio within a certain range of the interface, draw the pore-mechanical parameter image, hydration product-mechanical parameter image, and fit the image to obtain the phase composition-pore structure-mechanical property correlation relationship, and establish the phase composition-pore structure-mechanical property correlation relationship for concrete interface performance and damage degree characterization.

[0054] Example 1:

[0055] Prepare ordinary Portland cement concrete specimens with a water-cement ratio of 0.45, and the specimen size is 100mm*100mm*100mm. The concrete mix proportion per square is as follows: cement 400kg, water 180kg, sand 546kg, stone 1274kg. The water reducing agent dosage is 0.6% of the mass of cement. The concrete specimens are cured in a standard curing room (temperature 20℃, humidity 95%) for 28d, and after curing, the microstructure-mechanical property near the interface, phase composition and their mutual relationship are characterized.

[0056] First, use a precision cutting machine to cut the ordinary Portland cement concrete structure member to be studied into a sample block of about 20mm*20mm*5mm, which contains aggregate, interface and paste. Then immerse the sample in low heat epoxy resin, then polish the sample surface using 800 mesh sandpaper, and finally polish the sample surface with 9μm, 3μm, 1μm polishing cloth and polishing paste in turn to make the sample meet the surface flatness of nanoscratch test. Position the rectangular area 500μm*500μm containing different composition phases through the optical microscope of the nanoscratch tester. As shown in FIG. 1, the interface transition zone is located in the middle of the sample, and the interface transition zone is about 20μm wide. The interface transition zone is the area where the interface between the aggregate and the paste is located, and the interface transition zone is the area where the interface between the aggregate and the paste is located. Figure 1, the scratch test starts from the aggregate end, the indenter cone angle is set to 90°, the pre-pressing load is 5 μm, the normal indentation speed is 0.4 μm / s, and the horizontal scratch speed is constant 5 μm / s. The number of scratches is set to 10, which are parallel to each other, and the distance between every two scratches is greater than or equal to 30 μm. Record 2000 scratch data on the scratch length of about 120 μm. Based on the obtained scratch point data, the following fracture mechanics formula is applied to analyze the phase composition and its micro fracture performance on the scratch path.

[0057]

[0058] where p(d) and A(d) are functions of scratch depth d and indenter inherent parameters, which can be calculated by the following formula:

[0059]

[0060] A(d)=r 2 ·(θ-sinθ·cosθ)

[0061] where r is the radius of the indenter, F L is the horizontal force, and θ is the half-angle value of the indenter center and the indentation plane.

[0062] Based on the above calculation method, the phase fracture toughness in the concrete scratch range is calculated, and the possible phase composition of the data points in the scratch process is judged, such as Figure 2 , where the fracture toughness of the transition zone is about 0-0.3 MPa·m 0.5 (the peak value is about 0.18); the fracture toughness value of the aggregate is about 0.4-0.6 MPa·m 0.5 (the peak value is about 0.51); the fracture toughness value of low-density C-S-H is about 0.7-0.8 MPa·m 0.5 (the peak value is about 0.75); and the fracture toughness value of high-density C-S-H is about 0.8-1.1 MPa·m 0.5 (the peak value is about 0.95). After the phase composition analysis is completed, the results are recorded in Origin. The specified scratch area is marked for subsequent backscattered electron microscope observation. The center of the indentation depth mutation segment is set as the zero point to establish a scratch length-scratch depth rectangular coordinate system, and the scratch data and the calculated fracture toughness data are imported into Origin.

[0063] The test sample was sputtered with gold under vacuum conditions for 180 s. Then, the sputtered concrete sample was placed in the vacuum sample stage. The instrument voltage was set to 10 KV, the current was set to 0.2 nA, the T1 mode was set, the observation position was the designated nanoscratch area marked, and the aggregate, interface, paste phase, and scratch in the observation area could be observed. The electron microscope test parameters and image contrast parameters were adjusted to ensure that the phase distribution map with obvious gray difference was obtained. The image resolution was adjusted, and the magnification could be 200 to 1000 times according to the needs of local observation. 10-15 images were saved.

[0064] As Figure 3 , the BSE image obtained by the test was read using Python, and a function for converting between “pixels” and “microns (pm)” was defined according to the image resolution and pixel size. The pixel of this embodiment is 1536*1026, and the corresponding observation area is 1200 pm*800 pm. The matplotlib library was loaded, the concrete BSE image was displayed, and the reference aggregate area was manually determined using the polygon instruction. The Shapely library was loaded, the annular geometric area of the adjacent area of the aggregate was created, and the boundary area was gradually expanded outward along the normal. The range of the area to be analyzed was determined, and the microstructure, mechanical properties, and chemical composition information within 600 pm of the normal of the aggregate were studied in this embodiment. The edge of the aggregate was expanded by 120 times, and each expansion step was 5 pm. Due to the existence of local sharp points on the upper part of the aggregate boundary, the buffer(0) method was used for zero buffer operation to avoid the problem of multiple curves intersecting and overlapping analysis areas. The OpenCV library was loaded, and the phase analysis and microstructure analysis of the annular area enclosed between each edge line were performed. Starting from the annular area closest to the aggregate, it gradually progressed outward along the normal. According to the gray value of the image, the threshold segmentation was performed, the phase with a gray value in the range of 76-125 was a pore, the aggregate phase was in the range of 125-148, and the paste phase was in the range of 148-225. According to the gray recognition area, the ratio between the corresponding phase in each statistical interval and the interval area was calculated, and the phase distribution, microstructure information, and interface width of the aggregate adjacent area were obtained by sequentially extrapolating outward along the normal. The statistical information was exported in CSV format, and the corresponding result graph was drawn, as Figure 4 .

[0065] Based on the atlas obtained by nanoscratch, the approximate width of the interface transition zone can be preliminarily determined according to the image slope drop area, as Figure 1 . According to the fracture toughness calculation result, the different phase composition within a certain range of the interface transition zone can be obtained, as Figure 2 . Based on the image recognition result, the pore structure and interface width near the interface transition zone can be obtained, as Figure 4The interface width range can be obtained by comparing the results of image recognition and nano-scratch. The information of pore structure and phase composition near the interface transition zone can be obtained by combining the results of micro-nano phase and structure of image recognition and nano-scratch. Further, the correlation between phase composition-pore structure-mechanical properties is established to characterize the interface performance and damage degree of concrete.

[0066] Example 2

[0067] Ordinary Portland cement concrete specimens with a water-cement ratio of 0.55 are prepared, and the size of the specimens is 100mm*100mm*100mm. The concrete mix proportion per square is as follows: cement 327kg, water 180kg, sand 568kg, stone 1325kg. The dosage of water reducing agent is 0.6% of the mass of cement. After the concrete specimens are cured in the standard curing room (temperature 20℃, humidity 95%) for 28d, temperature variation degradation test is carried out in the rapid temperature variation test box: the temperature variation range is -10-10℃, and the temperature rise and fall rate is 1.5℃ / min. The microstructure-mechanical properties near the interface, phase composition and their mutual relationship are characterized before temperature variation, and after 50 times and 500 times of temperature variation cycles.

[0068] The sampling method, sample preparation method, nano-scratch test system, fracture mechanics parameter calculation principle, backscattered scanning electron microscope test parameters and BSE image recognition parameters of example 2 are the same as those of example 1. Here, they will not be repeated.

[0069] The samples after freeze-thaw damage for different times are sprayed with gold for 180s under vacuum conditions. Then, the concrete samples after spraying are placed in the vacuum sample table. The instrument voltage is set to 15KV, the current is set to 0.4nA, the T1 mode is set, and the observation position is the designated nano-scratch area marked. In the observation area, aggregate, interface, paste phase and scratch can be observed. The electron microscope test parameters and image contrast parameters are adjusted to ensure that the phase distribution map with obvious gray difference is obtained. The image resolution is adjusted according to the needs of local observation, and the magnification can be 200 times. 10-15 images are saved.

[0070] As Figure 6, the function of conversion between "pixel" and "micron (pm)" is defined according to the image resolution and pixel scale. The pixel of this embodiment is 1536*1094, which corresponds to an observation area of about 600pm*400pm. The Matplotlib library is loaded, the concrete BSE image is displayed, and the reference aggregate area is manually determined using polygon instructions. The Shapely library is loaded, and the creation of the annular geometric area of the adjacent area of the aggregate is performed, and the boundary area is gradually expanded outward along the normal. The range of the area to be analyzed is determined, and the microstructure, mechanical properties and chemical composition information within 240pm of the normal of the aggregate are studied in Embodiment 2, and the edge of the aggregate is expanded by 120 times, with a step of 2pm each time. Due to the existence of local sharp points on the upper part of the aggregate boundary, the zero buffer operation is performed using the buffer(0) method to avoid the problem of multiple curves intersecting and overlapping the analysis area. The OpenCV library is loaded, and the phase analysis and microstructure analysis of the annular area surrounded by each edge line are performed. Starting from the annular area closest to the aggregate, it gradually progresses outward along the normal phase. According to the gray value of the image, threshold segmentation is performed, the phase with a gray value in the range of 76-125 is a pore, the aggregate phase in the range of 125-148, and the paste phase in the range of 148-225. According to the gray recognition area, the ratio between the corresponding phase and the interval area in each statistical interval is calculated, and the phase distribution, microstructure information and interface width of the aggregate adjacent area are obtained by sequentially extrapolating outward along the normal. The statistical information is exported in CSV format, and the corresponding result graph is drawn, such as Figure 7 .

[0071] Based on the results obtained by nano-scratching, the approximate width of the interface transition zone can be preliminarily determined according to the image slope drop region, such as Figure 7 . According to the fracture toughness calculation results, the different phase compositions within a certain range of the interface transition zone can be obtained. Based on the image recognition results, the pore structure and interface width near the interface transition zone can be obtained, such as Figure 7 . By comparing the interface width results obtained by image recognition and nano-scratching, the approximate width range of the interface can be obtained. Combined with the results of image recognition and nano-scratching on micro-nano phase and structure, the pore structure and phase composition information near the interface transition zone can be obtained. Considering the damage of the interface after different freeze-thaw times, the interface width increase value, the interface pore coarsening degree, and the interface phase transition can be used to comprehensively characterize the multi-dimensional parameters of the interface transition zone, and the macroscopic performance and damage development of the concrete can be calculated and predicted by combining with the damage degree theory.

Claims

1. A method of characterizing the performance of a concrete interfacial transition zone, comprising: The method comprises the following steps: Step 1: The concrete material to be tested is subjected to nano-scratch testing according to a specified scratch area comprising a paste, an interface and aggregates, so as to obtain micro-mechanical parameters of the paste and the interface transition zone, calculate fracture toughness parameters of different phases along the scratch path according to the micro-mechanical parameters, and mark different phases in the specified scratch area; Step 2: The marked specified nano-scratch area is observed by using a backscattered mode scanning electron microscope to obtain a phase distribution image with obvious gray difference; the image resolution is adjusted, and a plurality of BSE images are saved; Step 3: The BSE images are read in a programming software, and the following operations are performed: A function for converting distance and length is defined according to the image resolution and pixel size, which is used to obtain chemical composition and microstructure information in a specified distance range in the future; The BSE image is displayed to determine the aggregate area; A ring-shaped geometric area is created in the vicinity of the aggregate, taking the edge of the aggregate determined in step 1 as the initial boundary line, and the boundary area is gradually expanded outward along the normal direction; the area between each two generated boundary lines is the area to be analyzed; Starting from the ring-shaped area closest to the aggregate, the ring-shaped area to be analyzed is gradually progressed outward along the normal direction, and phase analysis and microstructure analysis are performed on the ring-shaped area to be analyzed, specifically including: threshold segmentation is performed according to the gray value of the image; the ratio between the corresponding phase and the interval area in each statistical interval is calculated according to the gray-recognized area, and the phase distribution, microstructure information and interface width of the vicinity of the aggregate are obtained by sequentially extrapolating outward along the normal direction; the statistical information is exported, and the corresponding result image is drawn; Step 4: Based on the data obtained by the nano-scratch testing in step 1, a horizontal displacement-scratch depth curve is drawn, a first derivative of the curve is calculated, the width of the interface transition zone is preliminarily determined according to the peak width length at which the peak value of the first derivative curve is located, and the composition of different phases within a certain range of the interface transition zone is obtained according to the fracture toughness calculation result; The pore structure and the interface width in the vicinity of the interface transition zone are obtained based on the image recognition result of step 3; The width range of the interface is obtained by comparing the interface width results obtained by image recognition and nano-scratch; The pore structure and phase composition information in the vicinity of the interface transition zone are obtained by combining the micro / nano-phase and structure results obtained by image recognition and nano-scratch; The relationship between the mechanical parameters and the pore ratio and the hydration product ratio within a certain range of the interface is compared, pore-mechanical parameter images and hydration product-mechanical parameter images are drawn, the images are fitted, and the phase composition-pore structure-mechanical property correlation is established, which is used for concrete interface performance and damage degree representation.

2. The method for characterizing the performance of a concrete interface transition zone according to claim 1, wherein: The step 1 specifically comprises: according to the sample preparation method required by the nano-scratch test method, impregnating, grinding and polishing the measured concrete material containing aggregate, interface and paste; positioning into a random rectangular area containing paste, interface and aggregate through the optical microscope of the nano-scratch tester; the starting position of the scratch is the aggregate phase; performing scratch operation in parallel, equal interval and equal load, so that the indenter is scratched into the aggregate, interface and paste phases in turn; using fracture mechanics theory to calculate the fracture toughness parameters of different phases on the scratch path for distinguishing different phases; marking the specified scratch area.

3. The method of concrete interface transition zone performance characterization according to claim 1, wherein: In step 3, the aggregate area is manually determined by using polygon instruction.

4. The method of concrete interface transition zone performance characterization according to claim 1, wherein: In step 3, according to the actual analysis accuracy and image resolution, the range of the area to be analyzed is determined, so as to determine the number of times of outward expansion of the aggregate edge and the actual distance of each expansion step.

5. The method for characterizing the performance of a concrete interface transition zone according to claim 1, wherein: In step 3, zero buffer zone operation is performed to avoid the problem of intersection of multiple curves and overlapping of analysis areas.

6. The method for characterizing the performance of a concrete interface transition zone according to claim 1, wherein: In step 1, the length of the scratch along the interface is 100-120 μm.

7. The method for characterizing the performance of a concrete interface transition zone according to claim 1, wherein: In step 1, the interval between the two scratches needs to be greater than 30 μm.

8. The method for characterizing the performance of a concrete interface transition zone according to claim 1, wherein: In step 1, the starting position of the scratch is the aggregate phase, the diamond rhombus indenter is used as the probe, the indenter cone angle is 90°, a small load of 5 μm is used for pre-pressing scanning on the sample surface, then the formal indentation test is started according to the path position recorded in the pre-scanning process; the scratch data on the scratch are recorded; keeping constant indentation load, then performing scratch operation in parallel, equal interval and equal load, so that the indenter is scratched into the aggregate, interface and paste phases in turn.

9. The method for characterizing the performance of a concrete interface transition zone according to claim 1, wherein: The programming software is python, and step 3 specifically comprises: reading the BSE image in Python, defining the function for converting between "distance" and "micron" according to the image resolution and pixel scale; loading the Matplotlib library, displaying the BSE image, and manually determining the aggregate area by using polygon instruction; loading the Shapely library, creating a ring-shaped geometric area in the vicinity of the aggregate, using the edge of the aggregate determined in step 1 as the initial boundary line, and gradually expanding the boundary area outward along the normal direction; according to the actual analysis accuracy and image resolution, the range of the area to be analyzed is determined, so as to determine the number of times of outward expansion of the aggregate edge and the actual distance of each expansion step; using the buffer(0) method to perform zero buffer zone operation to avoid the problem of intersection of multiple curves and overlapping of analysis areas; the area between each two boundary lines generated above is the area to be analyzed; loading the OpenCV library, performing phase analysis and microstructure analysis on the ring-shaped area to be analyzed; starting from the ring-shaped area closest to the aggregate, gradually progressing outward along the normal direction; performing threshold segmentation according to the gray value of the image; according to the gray-recognized area, calculating the ratio between the corresponding phase and the interval area in each statistical interval, and sequentially extrapolating outward along the normal direction, so as to obtain the phase distribution, microstructure information and interface width of the aggregate adjacent area; exporting the statistical information in CSV format, and drawing the corresponding result graph.

10. The method for characterizing the performance of a concrete interface transition zone according to claim 1, wherein: In step 3, threshold segmentation is performed according to the gray value of the image, wherein the phase in the range of 76-125 of the gray value is pore phase, the aggregate phase in the range of 125-148, and the paste phase in the range of 148-225.

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