Characterization method of the effect of corrosion degree on the bonding performance of steel fiber and concrete interface

Abstract

The present application relates to a method for characterizing the effect of corrosion degree on the interfacial bonding performance between steel fiber and concrete, which solves the technical problem that relying on traditional experimental test methods requires a lot of time and cost, and it is difficult to fully and accurately reflect the complex effect of corrosion on interfacial bonding performance. The method includes: taking out the steel fibers that have completed the single pull test operation and performing a preset pickling operation one by one, and calculating the mass loss rate of steel fibers with different degrees of corrosion one by one according to the mass change of the steel fibers before and after the pickling operation; using a preset statistical analysis method, fitting the corresponding interfacial bonding strength data under different degrees of corrosion, and obtaining a mathematical expression between the interfacial bonding strength and the degree of corrosion. The present application has the following effects: comprehensively evaluating the effect of corrosion on the interfacial bonding performance between steel fiber and concrete from multiple aspects, which helps to improve the safety and durability of steel fiber concrete structures in practical applications.

Description

Technical Field

[0001] The invention relates to the technical field of steel fiber concrete material performance research, and in particular to a method for characterizing the influence of corrosion degree on the bonding performance of the interface between steel fiber and concrete. Background Art

[0002] In the field of construction engineering, steel fiber concrete is widely used due to its excellent mechanical properties. It can significantly improve the tensile strength, bending strength and toughness of concrete, and enhance the durability and reliability of the structure. However, in actual use environments, steel fiber concrete structures often face various challenges, among which the corrosion of steel fibers is particularly prominent. The corrosion of steel fibers not only reduces its own mechanical properties, but also has a significant impact on the interfacial bonding performance between steel fibers and concrete, thereby endangering the safety and stability of the entire structure.

[0003] At present, the research on the effect of corrosion degree on the interfacial bonding performance between steel fiber and concrete mainly relies on traditional experimental test methods, such as pull-out test, shear test, electrochemical test and microstructure observation.

[0004] Relying on traditional experimental testing methods requires a lot of time and cost, and it is difficult to fully and accurately reflect the complex impact of corrosion on interfacial bonding performance. Summary of the invention

[0005] In order to comprehensively evaluate the impact of corrosion on the interfacial bonding performance between steel fiber and concrete, which will help improve the safety and durability of steel fiber concrete structures in practical applications, the present application provides a method and system for characterizing the impact of corrosion degree on the interfacial bonding performance between steel fiber and concrete.

[0006] In the first aspect, the present application provides a method for characterizing the effect of corrosion degree on the bonding performance of the steel fiber and concrete interface, using the following technical solution:

[0007] A method for characterizing the effect of corrosion degree on the bonding performance of the steel fiber and concrete interface, comprising:

[0008] A single steel fiber is embedded in each of the preset number of half-dog bone test blocks by pouring concrete according to the preset embedding depth, and cured according to the preset curing conditions;

[0009] The curing specimens were subjected to electrochemical corrosion test operations matched with preset different steel fiber corrosion degrees to obtain specimens with different corrosion degrees, wherein the specimens were half-dog bone specimens with a single steel fiber embedded;

[0010] Performing a preset single-pull test operation on all test pieces that have completed the electrochemical corrosion test operation to obtain the load change of the steel fiber during the pulling process, and analyzing to obtain the interface bonding strength between the steel fiber and the concrete interface with different corrosion degrees, performing a preset pickling operation on the steel fibers that have completed the single-pull test operation one by one, and calculating the mass loss rate of the steel fibers with different corrosion degrees one by one according to the mass change of the steel fibers before and after the pickling operation, and defining the mass loss rate of the steel fiber as the corrosion degree data of the steel fiber;

[0011] The preset statistical analysis method was used to fit the interface bonding strength data corresponding to different corrosion degrees, and the mathematical expression between the interface bonding strength and the corrosion degree was obtained.

[0012] By adopting the above technical solutions, the specimen preparation and curing conditions are precisely controlled to ensure the accuracy of the experiment. The electrochemical corrosion test is used to obtain specimens with different degrees of corrosion, which can truly reflect the actual situation. The single pull test obtains the load change and interface bonding strength, and quantifies the bonding performance. The mass loss rate is calculated to define the degree of corrosion, providing specific data for the study. The fitted mathematical expression clearly presents the relationship between the degree of corrosion and the interface bonding performance, providing a powerful tool for evaluating and predicting the performance of concrete structures under different corrosion conditions, and helping to take targeted protective measures to ensure the long-term stability and safety of steel fibers and concrete structures.

[0013] Optionally, the method further includes fitting the interface bonding strength data corresponding to different corrosion degrees using a preset statistical analysis method to obtain a mathematical expression between the interface bonding strength and the corrosion degree, which is specifically as follows:

[0014] The volume of rust pits was subtracted from the steel fiber through Boolean difference operation to obtain the steel fiber model of regular pits. The irregular pits on the steel fiber were defined as semi-ellipsoidal regular rust pits, and the distribution of the pits in the length direction of the steel fiber was the same.

[0015] According to the steel fiber corrosion degree data, the parametric modeling method is used to determine the number and size of corrosion pits, and multiple corrosion pits are evenly distributed along the length of the steel fiber;

[0016] According to the preset functional relationship between the corrosion degree and the mechanical property parameters of the steel fiber, the stress-strain constitutive relationship of the uncorroded steel fiber is modified;

[0017] By performing a single pull test, the relative slip between the steel fiber and the concrete under different pull-out forces is measured, and the relationship between the bonding force and the slip is established, thereby obtaining a bond-slip constitutive model. By multiplying the bond-slip constitutive model by a preset corrosion reduction coefficient, the effect of the corrosion degree on the bonding performance between the steel fiber and the concrete is quantitatively determined.

[0018] According to the measured dimensions of the specimen, a three-dimensional geometric model of the concrete specimen is created in the finite element software.

[0019] Based on the geometric shape of regular steel fibers and the assumption of semi-ellipsoidal rust pits, a steel fiber model with rust pits is created, and the concrete material properties and steel fiber material properties are set;

[0020] Define the material properties of the bonding unit and place the bonding unit at the interface between the steel fiber and concrete, ensure the correct direction and position of the bonding unit, and input the corresponding parameters into the bonding unit according to the mathematical expression between the determined interface bonding strength and the degree of corrosion to simulate the bonding failure and friction slip of the interface during the pull-out process;

[0021] According to the preset numerical simulation solution parameters, run the numerical simulation, analyze the solution results, extract the key mechanical performance indicators, and statistically analyze the results under different degrees of corrosion, and draw relevant views for visual display.

[0022] By adopting the above technical solutions, the actual corrosion situation can be simulated more accurately by creating a regular corrosion pit model and determining the number and size of rust pits. By modifying the stress-strain constitutive relationship of steel fibers and establishing a bond-slip constitutive model, the effect of corrosion on bonding performance can be quantitatively analyzed. Finite element software is used to create a model and set material properties, and mathematical expression input parameters are combined to simulate bond failure and friction slip, making the analysis more scientific. Running numerical simulations and visually displaying the results facilitates intuitive understanding of the mechanical performance indicators under different degrees of corrosion, providing a comprehensive and efficient method for evaluating the bond performance of the steel fiber and concrete interface, which helps to better predict and improve the durability of structures in actual engineering.

[0023] Optionally, the method further includes fitting the interface bonding strength data corresponding to different corrosion degrees using a preset statistical analysis method to obtain a mathematical expression between the interface bonding strength and the corrosion degree, which is specifically as follows:

[0024] Scan and obtain the surface morphology data of the corroded steel fiber, and use a preset image recognition algorithm to automatically analyze and extract features from the obtained surface morphology data of the corroded steel fiber;

[0025] The geometric morphology data of the corroded steel fiber extracted by the image recognition algorithm is imported into Matlab software for processing, and a point cloud file is created using Matlab, that is, the surface morphology of the corroded steel fiber is represented as a series of point coordinates;

[0026] The point cloud file generated by Matlab is imported into the 3D scanning data processing software for envelope processing and noise reduction, and the processed point cloud data is converted into a high-precision surface to generate the geometric data of the corroded steel fiber;

[0027] Use Hypermesh software to mesh the corroded steel fiber according to the preset meshing method, and import the mesh model divided by Hypermesh into Abaqus software to generate a numerical model of the corroded steel fiber component, and define the material properties, boundary conditions and loading conditions of the corroded steel fiber;

[0028] According to the measured size of the single steel fiber half-dog bone specimen and the precise aggregate morphology of the corroded steel fiber, a numerical model of single-fiber concrete unilateral pullout within the monitoring range of the extensometer was established, and the material properties, boundary conditions and loading conditions of the concrete were defined.

[0029] Abaqus software is used to analyze and solve the established numerical model, analyze the solution results, extract key mechanical performance indicators, statistically analyze the results under different degrees of corrosion, and draw relevant views for visual display.

[0030] By adopting the above technical solution, the surface morphology data of corroded steel fibers can be extracted through scanning and image recognition, which can accurately reflect the actual corrosion situation. By using software processing to generate high-precision geometric data and divide the grid, a numerical model can be established to accurately simulate the unilateral pulling process of single-fiber concrete. Defining material properties makes the model more realistic. Abaqus software analyzes and solves and visualizes the results, making it easy to intuitively understand the mechanical performance indicators under different degrees of corrosion. It provides an efficient and accurate method for studying the effect of corrosion on the bonding performance of the steel fiber and concrete interface, which helps to deeply understand the influence of corrosion and provide a scientific basis and technical support for improving the durability of concrete structures in engineering.

[0031] Optionally, the curing specimens are subjected to electrochemical corrosion test operations matched with preset different steel fiber corrosion degrees, and the specimens with different corrosion degrees are obtained, including:

[0032] All the specimens that have completed curing are immersed in the pre-configured salt solution, and the positive electrode of the DC power supply is connected to the steel fiber, and the negative electrode is connected to the auxiliary electrode;

[0033] According to Faraday's law of electrolysis, the required amount of electricity is calculated in combination with the surface area of ​​the steel fiber and different expected corrosion degrees, and then the combination of current intensity and power-on time is determined as the corrosion conditions for different expected corrosion degrees;

[0034] Each specimen is tested under corrosion conditions with different expected corrosion degrees, and the test is stopped until the preset conditions are reached, thereby obtaining specimens with different corrosion degrees.

[0035] By adopting the above technical solution, the actual corrosion environment is simulated by immersion in salt solution and electrode connection. The corrosion conditions are determined according to Faraday's law of electrolysis, and different expected corrosion degrees are accurately controlled. Each specimen is tested separately to obtain specimens with various corrosion degrees. It provides rich samples for studying the effect of corrosion on the bonding performance of the steel fiber and concrete interface, helps to deeply understand its changing laws, provides a basis for improving the durability of concrete structures, and provides effective methods and data support for solving corrosion problems in engineering.

[0036] Optionally, the method further includes the following steps between testing each test piece under corrosion conditions with different expected corrosion degrees and stopping the test until the preset conditions are reached:

[0037] During the corrosion process, the electrochemical impedance spectroscopy monitoring device is started to monitor and obtain electrochemical impedance spectroscopy data, and resistance and capacitance parameters are extracted from the electrochemical impedance spectroscopy data, and the electrochemical noise monitoring device is simultaneously started to obtain electrochemical noise data, and noise amplitude is extracted from the electrochemical noise data;

[0038] According to the preset comprehensive corrosion index calculation method, the comprehensive corrosion index values ​​at different time points are calculated, and a curve of the comprehensive corrosion index changing with time is drawn;

[0039] According to the curve of the comprehensive corrosion index changing over time, the comprehensive corrosion index change efficiency is analyzed and determined;

[0040] According to the correspondence between the interval range in which the comprehensive corrosion index change efficiency falls and the adjustment plan of the corrosion conditions of the expected corrosion degree, the adjustment plan of the corrosion conditions of the expected corrosion degree is analyzed and determined, and the adjustment plan of the corrosion conditions of the expected corrosion degree is executed.

[0041] By adopting the above technical solution, it is possible to monitor and extract parameters in real time during the corrosion process, calculate comprehensive corrosion indicators, draw change curves to analyze change efficiency, and then adjust corrosion conditions. It can more accurately control the specimens to achieve different expected corrosion degrees and provide accurate samples for research. It helps to gain a deeper understanding of the corrosion process, provide more reliable data support for evaluating the effect of corrosion on the bonding performance of the steel fiber and concrete interface, improve the scientificity and accuracy of the research, and provide a better method for solving corrosion problems in actual engineering.

[0042] Optionally, a preset single pull test operation is performed on all test pieces that have completed the electrochemical corrosion test operation to obtain the load change of the steel fiber during the pulling process, and the interface bonding strength between the steel fiber and the concrete interface with different corrosion degrees is analyzed to obtain:

[0043] Mount the half dog bone test block on the fixture of the electronic universal testing machine;

[0044] Start the electronic universal testing machine, perform the pulling test through the crossbeam displacement control, and collect the experimental data simultaneously. The crossbeam displacement control refers to loading the test block by controlling the crossbeam moving speed of the testing machine. The experimental data includes force, displacement and deformation data.

[0045] According to the force and displacement data contained in the experimental data, the pull-out force-displacement curve is drawn, and the peak pull-out force, that is, the maximum force that the test block bears during the pull-out process, is determined according to the pull-out force-displacement curve. At the same time, the contact area between the steel fiber and the concrete is calculated according to the geometric dimensions of the test block and the embedded depth parameters of the steel fiber;

[0046] The interfacial bond strength was calculated by dividing the peak pull-off force by the contact area.

[0047] By adopting the above technical solution, the test blocks are accurately installed, the crossbeam displacement control is used to conduct the pull-out test and collect data simultaneously. The pull-out force-displacement curve is drawn to determine the peak pull-out force, and the contact area is calculated in combination with the geometric dimensions to obtain the interface bonding strength. This method is accurate and reliable, and can effectively analyze the interface bonding performance between steel fiber and concrete under different degrees of corrosion, providing a scientific basis for evaluating the durability of the structure, and helping to take targeted measures in engineering to improve the safety and reliability of concrete structures.

[0048] Optionally, the steel fibers that have completed the single pull test operation are taken out and subjected to a preset pickling operation one by one. According to the mass change of the steel fibers before and after the pickling operation, the mass loss rate of the steel fibers with different degrees of rust is calculated one by one, including: taking out the steel fibers after the experiment, pickling them with a hydrochloric acid solution combined with a corrosion inhibitor, taking them out after the surface rust layer is dissolved, rinsing them with deionized water and anhydrous ethanol, and then drying them, and weighing the post-corrosion mass of each steel fiber with an electronic balance and comparing it with its mass before the experiment, so as to calculate the degree of rust of the steel fibers. The degree of rust is expressed by the mass loss rate, that is, the degree of rust = (mass before the experiment - mass after the experiment) / mass before the experiment × 100%.

[0049] By adopting the above technical solution, the rust layer is removed by pickling, and the mass of the steel fiber before and after the experiment is accurately measured. The mass loss rate is calculated to indicate the degree of corrosion, and the method is simple and intuitive. It can effectively quantify different degrees of corrosion and provide specific data for studying the effect of corrosion on the bonding performance of the steel fiber and concrete interface. It helps to deeply understand the impact of corrosion, provide a scientific basis for improving the durability of concrete structures, and facilitate the adoption of targeted measures in engineering to ensure the safety and reliability of the structure.

[0050] Optionally, the method further includes performing a preset pickling operation on the steel fibers taken out after the single pull test operation, and calculating the mass loss rates of the steel fibers with different degrees of corrosion one by one according to the mass changes of the steel fibers before and after the pickling operation, which is specifically as follows:

[0051] The μ-XCT device is used to scan the steel fiber at different angles to obtain scanning data at different angles, and the three-dimensional image of the steel fiber is reconstructed. The gray value distribution of the image obtained by the μ-XCT scan is analyzed to distinguish between concrete, steel fiber and corrosion products. The surface morphology characteristics of the steel fiber are extracted by morphological analysis method. The results of grayscale and morphological analysis are combined to quantify the degree of corrosion of the steel fiber.

[0052] The corrosion degree obtained by μ-XCT scanning is compared with the corrosion degree obtained by pickling. If the comparison result is within the preset difference range, the average of the corrosion degrees of the steel fiber obtained by the two methods is taken as the corrosion degree of the steel fiber confirmed this time;

[0053] If the comparison result is outside the preset difference range, the status information with large error will be sent to the terminal held by the relevant person in charge.

[0054] By adopting the above technical solution, the μ-XCT device is used to scan the steel fiber to obtain a three-dimensional image, quantify the degree of corrosion and compare it with the pickling result. If it is within the difference range, the average value is taken to improve the accuracy of the determination of the degree of corrosion; if it exceeds the range, a message is sent to facilitate timely processing.

[0055] Optionally, the method further includes comparing the corrosion degree obtained by the μ-XCT scanning result with the corrosion degree obtained by pickling. If the comparison result is within a preset difference range, the average of the corrosion degree of the steel fiber obtained by the two methods is used as the corrosion degree of the steel fiber confirmed this time, and the specific steps are as follows:

[0056] The mass loss rate after pickling, the image characteristic parameters obtained by μ-XCT scanning, and the pull-out load data of the single pull test were collected, and the degree of steel fiber corrosion was determined as the dependent variable;

[0057] A multiple linear regression model was used to establish the relationship model between the corrosion degree and the independent variables, with the corrosion degree of steel fiber as the dependent variable, the mass loss rate obtained by pickling, the characteristic parameters of μ-XCT images and the pulling load data as independent variables.

[0058] By adopting the above technical solution, by collecting a variety of data, and using a multivariate linear regression model to establish the relationship between the degree of corrosion and various variables, the degree of steel fiber corrosion can be more accurately evaluated by integrating multiple factors, providing a scientific method for studying the impact of corrosion. It helps to gain a deeper understanding of the relationship between various factors and corrosion, provide a basis for improving the durability of concrete structures, make the analysis of the bonding performance of the steel fiber and concrete interface more comprehensive and reliable, and provide a more effective way to solve corrosion problems in engineering.

[0059] In the second aspect, the present application provides a characterization system for the effect of corrosion degree on the bonding performance of the steel fiber and concrete interface, using the following technical solution:

[0060] A system for characterizing the effect of corrosion degree on the interfacial bonding performance of steel fiber and concrete, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can be loaded and executed by the processor to implement the method for characterizing the effect of corrosion degree on the interfacial bonding performance of steel fiber and concrete as described in the first aspect.

[0061] In summary, the beneficial technical effects of this application are:

[0062] 1. The corrosion process of steel fiber in the present invention goes through three stages: corrosion initiation, free expansion and cracking. The corrosion degradation process is similar to the natural corrosion of steel fiber in concrete, which can more accurately reflect the influence of corrosion degree on the bonding performance of the interface between steel fiber and concrete, and improve the accuracy of corrosion risk assessment;

[0063] 2. The invention uses impressed current to quickly achieve corrosion of steel fibers in concrete, which can save a lot of time and reduce time costs compared to natural corrosion;

[0064] 3. The solution and concentration of the sample soaked in the present invention can be determined according to the environment to which the concrete member is exposed. At the same time, the age of the concrete can be adjusted according to the needs of the project to fit the actual project conditions, so as to accurately reflect the influence of the degree of corrosion under various working conditions on the bonding performance of the steel fiber and concrete interface, evaluate the performance evolution of the structure during service, and serve as a practical basis for repair, reinforcement or demolition.

[0065] 4. The establishment of the numerical model of steel fiber pullout in the present invention can set different degrees of corrosion of steel fibers and different microstructures of the interface between steel fibers and substrates, quickly simulate and calculate the influence of different degrees of corrosion on the bonding performance of the interface between steel fibers and substrates, and timely form a repair and reinforcement plan for components in the operation stage to ensure the safe service of components. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a flow chart of a method for characterizing the effect of corrosion degree on the interfacial bonding performance between steel fiber and concrete according to an embodiment of the present application.

[0067] Figure 2 This is a flowchart of another embodiment of the present application, which uses a preset statistical analysis method to fit the interface bonding strength data corresponding to different corrosion degrees to obtain a mathematical expression between the interface bonding strength and the corrosion degree in parallel.

[0068] Figure 3This is a flowchart of another embodiment of the present application, which uses a preset statistical analysis method to fit the interface bonding strength data corresponding to different corrosion degrees to obtain a mathematical expression between the interface bonding strength and the corrosion degree in parallel.

[0069] Figure 4 Another embodiment of the present application is to perform electrochemical corrosion test operations matching preset different steel fiber corrosion degrees on a set of specimens that have completed maintenance, to obtain a flow chart of specimens with different corrosion degrees.

[0070] Figure 5 Schematic diagram of the accelerated corrosion process of a single steel fiber half-dog bone specimen.

[0071] Figure 6 It is a schematic diagram of the tensile load curve of a single steel fiber half-dog bone specimen. DETAILED DESCRIPTION

[0072] The present application is further described in detail below in conjunction with the accompanying drawings.

[0073] Reference Figure 1 , which is a method for characterizing the effect of corrosion degree on the bonding performance of the steel fiber and concrete interface disclosed in the present application, comprising:

[0074] Step S100, a single steel fiber is embedded in each of a preset number of half-dog bone test blocks by pouring concrete according to a preset embedding depth, and cured according to preset curing conditions.

[0075] Among them, the half dog bone test block is a concrete test piece of a specific shape, similar to a part of the dog bone shape; the half dog bone test block is obtained as follows: it is cast and made through a customized mold. For example, a high-strength plastic mold is used, which is made according to the designed size and shape, and then concrete is poured into it, and a single steel fiber is embedded according to the preset embedding depth.

[0076] The preset embedding depth is a specific depth of the steel fiber embedded in the concrete test block. The appropriate embedding depth can be determined by theoretical calculation, preliminary tests or reference to relevant standards. For example, after theoretical analysis and preliminary tests, the embedding depth is determined to be one third of the test block thickness.

[0077] Preset curing conditions refer to the need to perform curing under specific environmental conditions in order for the concrete specimen to achieve a certain strength and performance. For example, in a standard curing room, the temperature is maintained at 20±2℃ and the relative humidity is above 95% for a certain period of time, such as 28 days.

[0078] More specifically, the above steps can be further divided into three steps: cleaning of steel fibers, placement of steel fibers, and casting of half dog-bone test blocks.

[0079] The specific steps for cleaning steel fibers are as follows: take out an appropriate amount of steel fibers with standard shape and no rust on the surface, put them into distilled water and anhydrous ethanol in an ultrasonic cleaning machine to clean the steel fibers for 5 minutes each to remove glue and oil on the surface of the steel fibers. After drying, the steel fibers are numbered to facilitate identification of steel fiber samples before and after the experiment, and weighed one by one (accurate to 0.0001g) to obtain the initial weight of each steel fiber, that is, the weight before corrosion pickling.

[0080] The specific steps for placing steel fibers are as follows: insert a cleaned single steel fiber into the middle hole of the PVC sheet to adjust the depth. In addition, the inclination angle of the fiber can be set according to the loading direction. After setting the embedding depth, use a foam board to fix the steel fiber to prevent the movement of the steel fiber during pouring and compaction, so as to maintain the angle and embedding depth of the steel fiber in the test block.

[0081] The specific steps for casting the half-dog bone test block are as follows: weigh the cementitious material, sand, and admixture according to a certain water-binder ratio and mortar-sand ratio, pre-mix at low speed for 20-30 seconds in a mixer, and stir at high speed for 3-5 minutes. After stirring, pour the slurry on the side where the steel fiber is embedded in the mold. After vibrating on the vibration table for 5s to 10s, scrape and smooth the slurry that is higher than the test mold along the top surface of the test mold, and then immediately cover the surface with a polyethylene film to prevent water loss. After 24 hours, remove the mold and place it in a standard curing room for curing to obtain a half-dog bone test block containing a single steel fiber.

[0082] Step S200, the curing specimens are subjected to electrochemical corrosion test operations matched with preset different steel fiber corrosion degrees to obtain specimens with different corrosion degrees, wherein the specimens are half-dog bone specimens with a single steel fiber embedded.

[0083] The electrochemical corrosion test is a test method that uses electrochemical reactions to cause different degrees of corrosion in steel fibers to simulate the corrosion conditions in the actual environment. The degree of steel fiber corrosion refers to the degree to which the steel fiber is corroded, which can usually be measured by indicators such as mass loss rate and surface morphology change.

[0084] Suppose we want to simulate three different degrees of steel fiber corrosion, namely mild corrosion, moderate corrosion and severe corrosion. For mild corrosion, you can set a smaller current density and a shorter corrosion time. For example, the current density is 0.1mA / cm² and the corrosion time is 24 hours. For moderate corrosion, you can increase the current density and corrosion time appropriately. For example, the current density is 0.5mA / cm² and the corrosion time is 48 hours. For severe corrosion, you can further increase the current density and extend the corrosion time. For example, the current density is 1mA / cm² and the corrosion time is 72 hours.

[0085] Step S300, performing a preset single pull test operation on all test pieces that have completed the electrochemical corrosion test operation to obtain the load change of the steel fiber during the pulling process, and analyzing to obtain the interface bonding strength between the steel fiber and the concrete interface with different degrees of corrosion, taking out the steel fibers that have completed the single pull test operation and performing a preset pickling operation one by one, and calculating the mass loss rate of the steel fibers with different degrees of corrosion one by one according to the mass change of the steel fibers before and after the pickling operation, and defining the mass loss rate of the steel fiber as the corrosion degree data of the steel fiber.

[0086] Among them, the single pull test is a test of unidirectional stretching of the specimen to obtain data such as load changes and interface bonding strength of the steel fiber during the pulling process; the pickling operation is an operation to remove the rust layer on the surface of the steel fiber with an acid solution in order to accurately calculate the mass loss rate of the steel fiber. The interface bonding strength refers to the bonding force between the steel fiber and the concrete, and is an important indicator for measuring the interface bonding performance between the two.

[0087] Suppose we have a set of half dog bone test pieces that have undergone electrochemical corrosion testing.

[0088] Single pull test operation: Use a universal testing machine to stretch the test block at a loading rate of 1mm / min. Record the load changes during the pulling process and find that the load gradually increases as the pulling progresses until the steel fiber is pulled out. According to the load change curve, the maximum load is calculated to be 10kN. Assuming that the cross-sectional area of ​​the steel fiber is 10mm², the interface bonding strength = 10kN / 10mm² = 1MPa.

[0089] Pickling operation: Select dilute hydrochloric acid as the acid solution and soak the steel fiber in it for 30 minutes.

[0090] After rinsing with clean water, the mass of the steel fiber before pickling was measured on a precision balance, which was 10 g, and the mass after pickling was 9.8 g.

[0091] Mass loss rate: Mass loss rate = (10g-9.8g) / 10g×100%=2%.

[0092] Step S400, using a preset statistical analysis method, fitting the interface bonding strength data corresponding to different corrosion degrees to obtain a mathematical expression between the interface bonding strength and the corrosion degree.

[0093] Statistical analysis methods can be selected according to the characteristics of the data and the purpose of the research. For example, if the data presents a linear relationship, linear regression analysis can be selected; if the data is more complex, nonlinear fitting can be tried.

[0094] Mathematical expression obtained by fitting: By fitting the data, a mathematical expression between the interface bonding strength and the degree of corrosion is obtained. Expression form: The mathematical expression can be a functional relationship, such as y=f(x), where y represents the interface bonding strength and x represents the degree of corrosion.

[0095] Assume that we obtain the interface bonding strength data of steel fiber and concrete under different degrees of corrosion through step S300, which are as follows: when the corrosion degree (mass loss rate) of the steel fiber is 5%, the interface bonding strength is 2.5 MPa; when the corrosion degree is 10%, the interface bonding strength is 2.2 MPa; when the corrosion degree is 15%, the interface bonding strength is 2.0 MPa; when the corrosion degree is 20%, the interface bonding strength is 1.8 MPa; when the corrosion degree is 25%, the interface bonding strength is 1.6 MPa.

[0096] We can use the data analysis tool in Excel to perform linear regression analysis. First, enter the data of the degree of corrosion and the interfacial bonding strength into the Excel table. Then, select "Data Analysis" in the "Data" tab, and select "Regression" in the pop-up dialog box. Set the input area and output area, and click "OK" to analyze. The fitting process is as follows: After linear regression analysis, the equation of the fitting line is y=-0.03x+2.65, where y represents the interfacial bonding strength and x represents the degree of corrosion (mass loss rate). The goodness of fit R² is 0.95, indicating that the fitting effect is good. Mathematical expression: The mathematical expression between the interfacial bonding strength and the degree of corrosion is y=-0.03x+2.65. This expression can be used to predict the interfacial bonding strength between steel fiber and concrete under different degrees of corrosion.

[0097] Reference Figure 2 The characterization method of the effect of corrosion degree on the interfacial bonding performance between steel fiber and concrete also includes the steps of fitting the interfacial bonding strength data corresponding to different corrosion degrees using a preset statistical analysis method to obtain a mathematical expression between the interfacial bonding strength and the corrosion degree, which are as follows:

[0098] Step S500, subtracting the volume of the rust pit from the steel fiber through Boolean difference operation to obtain a steel fiber model of regular pits, defining irregular pits on the steel fiber as semi-ellipsoidal regular rust pits, and the distribution pattern of the pits in the length direction of the steel fiber is the same.

[0099] Among them, the irregular pits on the steel fiber are defined as semi-ellipsoidal regular rust pits. The reason why the pits have the same distribution pattern in the length direction of the steel fiber is as follows: considering that pit-shaped rust has a more significant effect on the degradation of the mechanical properties of the steel fiber than uniform rust, the influence of the pits can be considered in the rough simulation stage. In order to improve the calculation efficiency, the following assumptions are made here: a) In view of the large discreteness of the pit shape, the irregular pits of the steel fiber are simplified to semi-ellipsoidal regular rust pits, and b) In view of the random distribution of steel fiber rust, it is assumed that the pits have the same distribution pattern in the length direction of the steel fiber during the rough simulation.

[0100] The Boolean difference operation is a commonly used operation in 3D modeling, which obtains a new shape by subtracting one entity from another.

[0101] The steel fiber model of regular rust pits is a model based on regular rust pits defined as semi-ellipsoidal on the steel fiber after Boolean difference operation.

[0102] Step S600, according to the steel fiber corrosion degree data, a parameterized modeling method is used to determine the number and size of the corrosion pits, and a plurality of rust pits are evenly distributed along the length direction of the steel fiber.

[0103] The "parametric modeling method" is a modeling technique that controls the shape and size of the model by defining parameters. In this step, it is used to determine the number and size of corrosion pits based on the steel fiber corrosion degree data.

[0104] The number and size of pits refer to the number of rust pits on the steel fiber and the size parameters of each rust pit, such as the major semi-axis, minor semi-axis, depth, etc. These parameters will change with the degree of corrosion of the steel fiber.

[0105] “Multiple rust pits are evenly distributed along the length direction of the steel fiber”: means that rust pits of a determined number and size are evenly arranged along the length direction of the steel fiber to simulate actual corrosion conditions.

[0106] Step S700, correcting the stress-strain constitutive relationship of the uncorroded steel fiber according to the preset functional relationship between the corrosion degree and the mechanical property parameters of the steel fiber.

[0107] The functional relationship between the degree of corrosion and the mechanical properties of steel fibers is a mathematical relationship that describes the degree of corrosion of steel fibers and their mechanical properties (such as elastic modulus, yield strength, etc.). Through this functional relationship, the mechanical properties of steel fibers can be corrected according to the degree of corrosion. This functional relationship can be used to obtain data on the change of the mechanical properties of steel fibers with the degree of corrosion by conducting mechanical property tests on steel fibers with different degrees of corrosion, such as tensile tests and bending tests. Then, the functional relationship between the degree of corrosion and the mechanical properties is established by using the data fitting method.

[0108] The specific method of correcting the stress-strain constitutive relationship of uncorroded steel fibers is as follows: 1. Determine the correction method: Select an appropriate correction method based on the functional relationship between the degree of corrosion and the mechanical properties of steel fibers. Common correction methods include direct correction method, equivalent damage method, etc. 2. Perform correction calculation: Correct the mechanical properties parameters in the stress-strain constitutive relationship of uncorroded steel fibers according to the selected correction method and the degree of corrosion. Obtain the corrected stress-strain constitutive relationship.

[0109] Assume that the functional relationship between the corrosion degree of steel fiber and the elastic modulus is obtained through experimental research: E = E 0 ×(1-k×corrosion degree), where E represents the elastic modulus of the corroded steel fiber, E 0 It represents the elastic modulus of uncorroded steel fiber, k is a constant, and the degree of corrosion is the mass loss rate.

[0110] For uncorroded steel fibers, the stress-strain constitutive relation is: σ=E 0 ×ε, where σ represents stress and ε represents strain.

[0111] Now we need to modify the constitutive relationship of the uncorroded steel fiber to take into account the effect of corrosion. Assuming that the corrosion degree of the steel fiber is 20%, according to the above functional relationship, the elastic modulus of the corroded steel fiber is: E = E 0 ×(1-k×0.2).

[0112] The corrected stress-strain constitutive relation is: σ=E×ε=E 0 ×(1-k×0.2)×ε.

[0113] Step S800, according to the single pull test operation, the relative slip between the steel fiber and the concrete under different pull-out forces is measured, and the relationship between the bonding force and the slip is established, so as to obtain a bonding-slip constitutive model. By multiplying the bonding-slip constitutive model by a preset corrosion degree reduction coefficient, the influence of the corrosion degree on the bonding performance between the steel fiber and the concrete is quantitatively determined.

[0114] Relative slip refers to the relative displacement between steel fiber and concrete under force in a single pull test. The relationship between bond strength and slip is used to describe the law of change of bond strength between steel fiber and concrete with relative slip. The bond-slip constitutive model is a mathematical model used to characterize the bond-slip behavior between steel fiber and concrete. The corrosion reduction factor is a coefficient determined according to the corrosion degree of steel fiber, which is used to adjust the bond-slip constitutive model to reflect the influence of corrosion on the bond performance between steel fiber and concrete.

[0115] Assume that the relative slip data between steel fiber and concrete under different pull-out forces are obtained through a single pull test. For example, when the pull-out force is 10kN, the relative slip is 0.1mm; when the pull-out force is 20kN, the relative slip is 0.2mm; when the pull-out force is 30kN, the relative slip is 0.3mm; when the pull-out force is 40kN, the relative slip is 0.4mm; when the pull-out force is 50kN, the relative slip is 0.5mm.

[0116] By fitting the curve of these data, the relationship between adhesion and slip can be obtained as follows: F=100s, where F represents adhesion (kN) and s represents relative slip (mm).

[0117] Based on this relationship, a linear bond-slip constitutive model can be established. Assume that the bond-slip constitutive model when not corroded is F=k o s, where k o is the bond stiffness coefficient when not corroded.

[0118] Now consider the effect of corrosion. Assume that the corrosion reduction factor is 0.8 when the corrosion degree is 20% as determined by experimental research. Then the bond-slip constitutive model after corrosion is F=0.8k o s.

[0119] Step S900, based on the measured size of the specimen, a three-dimensional geometric model of the concrete specimen is created in the finite element software, based on the geometric shape of the regular steel fiber and the assumption of the semi-ellipsoidal rust pit, a steel fiber model with corrosion pits is created, and the concrete material properties and the steel fiber material properties are set.

[0120] The measured size of the specimen refers to the size of the concrete specimen and steel fiber actually made. Concrete material properties are parameters that describe the mechanical properties of concrete, such as elastic modulus, Poisson's ratio, compressive strength, etc.

[0121] Steel fiber material properties are parameters that describe the mechanical properties of steel fibers, such as elastic modulus, yield strength, etc.

[0122] Step SA00, define the material properties of the bonding unit and place the bonding unit at the interface between the steel fiber and the concrete, ensure that the direction and position of the bonding unit are correct, and input the corresponding parameters into the bonding unit according to the mathematical expression between the determined interface bonding strength and the degree of corrosion to simulate the bonding failure and friction slip of the interface during the pull-out process.

[0123] The bonding unit is a specific unit type used to simulate the bonding performance of the steel fiber and concrete interface in the finite element simulation. It can reflect the bonding force, friction slip and other characteristics at the interface. Material properties refer to the mechanical performance parameters of the bonding unit, such as bonding strength, stiffness, etc. Bond failure and friction slip refer to the two main phenomena that may occur at the interface during the steel fiber pull-out process. Bond failure indicates the loss of interfacial bonding, and friction slip indicates the relative sliding between the steel fiber and the concrete.

[0124] Suppose we use a finite element software for simulation, which has a bonding unit type specifically used to simulate interface bonding.

[0125] Select bond element: Select a bond element suitable for simulating the interface between steel fiber and concrete in the software, such as an element with specific bond strength and stiffness parameters.

[0126] Determine material properties: Through experimental determination, the bond strength between the steel fiber and concrete interface in the uncorroded state is 5MPa. As the degree of corrosion increases, the bond strength gradually decreases. Assuming that the mathematical expression between the interface bond strength and the degree of corrosion (expressed as mass loss rate) obtained through statistical analysis is: bond strength corrosion degree. Based on this expression and the known degree of corrosion, determine the bond strength parameters of the bond unit under different degrees of corrosion. At the same time, based on experience or theoretical analysis, determine other material properties of the bond unit, such as stiffness.

[0127] Place the bonding element: In the finite element model, find the exact interface between the steel fiber and the concrete. Place the bonding element at this location and ensure that its direction is consistent with the axial direction of the steel fiber to correctly simulate the interface behavior during the pull-out process.

[0128] Input parameters: According to the mathematical expression between the determined interface bonding strength and the degree of corrosion, the corresponding parameters are input into the bonding unit. For example, for a 20% degree of corrosion, the calculated bonding strength is 5×(1-0.2×20%)=4MPa, and this value is input into the bonding strength parameter of the bonding unit.

[0129] Simulate bond failure and friction slip: Set parameters such as the friction coefficient of the bond unit to simulate friction slip behavior. During the simulation, observe the stress changes and displacements at the interface. If the stress exceeds the bond strength, bond failure may occur; at the same time, as the steel fiber is pulled out, relative sliding, i.e. friction slip, will occur at the interface.

[0130] Step SB00, according to the preset numerical simulation solution parameters, run the numerical simulation, analyze the solution results, extract the key mechanical performance indicators, and statistically analyze the results under different corrosion degrees, and draw relevant views for visual display.

[0131] The solution parameters of numerical simulation refer to a series of parameters that need to be set when performing finite element numerical simulation, including solution algorithm, time step, convergence criterion, etc. These parameters determine the accuracy and efficiency of the simulation. Mechanical performance indicators refer to various parameters obtained through numerical simulation that reflect the bonding performance of the steel fiber and concrete interface, such as bond strength, slip, stress distribution, etc. Drawing relevant views for visual display refers to using the visualization function of drawing software or finite element software to display the numerical simulation results in the form of graphics, such as stress cloud diagrams, displacement vector diagrams, etc., so as to understand and analyze the results more intuitively.

[0132] Suppose we numerically simulate the interface between steel fiber and concrete with different degrees of corrosion. For example, when the degree of corrosion is 5%, the bond strength extracted from the output results of the finite element software is 2.8MPa and the slip is 0.15mm; when the degree of corrosion is 10%, the bond strength is 2.5MPa and the slip is 0.2mm; when the degree of corrosion is 15%, the bond strength is 2.2MPa and the slip is 0.25mm; when the degree of corrosion is 20%, the bond strength is 2.0MPa and the slip is 0.3mm.

[0133] Using the regression analysis method, it was found that the bond strength decreased linearly with the increase of corrosion degree, while the slip amount gradually increased with the increase of corrosion degree.

[0134] The drawing software was used to draw a line graph of the bond strength and slip amount as the degree of corrosion changes, which intuitively showed the change law of the two. At the same time, the stress cloud map under different degrees of corrosion was generated in the finite element software to observe the stress distribution at the interface between steel fiber and concrete.

[0135] Reference Figure 3 The characterization method of the effect of corrosion degree on the interfacial bonding performance between steel fiber and concrete also includes the steps of fitting the interfacial bonding strength data corresponding to different corrosion degrees using a preset statistical analysis method to obtain a mathematical expression between the interfacial bonding strength and the corrosion degree, which are as follows:

[0136] Step Sa00, scanning to obtain the surface morphology data of the corroded steel fiber, and using a preset image recognition algorithm to automatically analyze and extract features from the obtained surface morphology data of the corroded steel fiber.

[0137] “Corroded steel fiber surface morphology data” refers to the shape, texture, roughness and other characteristic information of the surface of the corroded steel fiber, which can be obtained through scanning equipment.

[0138] Image recognition algorithm is an algorithm that can automatically identify and analyze targets in an image. Here, it is used to process the surface morphology data of corroded steel fibers and extract feature information. Common image recognition algorithms include convolutional neural networks (CNN), support vector machines (SVM), Hough transform, etc.

[0139] Suppose we want to perform surface topography analysis on a set of corroded steel fibers.

[0140] Obtaining the surface morphology data of the corroded steel fiber: Use a 3D scanner to scan the steel fiber, set the scanning resolution to 0.1 mm, and scan the entire steel fiber surface. Preprocess the scanned data to remove noise and correct the color to obtain a clear steel fiber surface morphology image.

[0141] Select image recognition algorithm: Considering the complexity of the steel fiber surface morphology, convolutional neural network (CNN) was selected as the image recognition algorithm. Training data was prepared and the corrosion features of a part of the steel fiber surface, such as corrosion area, depth and shape, were manually annotated.

[0142] Automatic analysis and feature extraction: Define the feature parameters to be extracted as corrosion area, depth and shape. Use the trained CNN algorithm to analyze the steel fiber surface morphology image and extract feature parameters such as corrosion area, depth and shape.

[0143] For example, the analysis revealed that the corrosion area of ​​a certain steel fiber was 10 mm², the depth was 0.5 mm, and the shape was an irregular oval.

[0144] Step Sb00, importing the geometric morphology data of the corroded steel fiber extracted by the image recognition algorithm into Matlab software for processing, and using Matlab to create a point cloud file, that is, representing the surface morphology of the corroded steel fiber as a series of point coordinates.

[0145] Matlab software is a powerful mathematical computing and data analysis software that can be used for data processing, modeling, visualization and other tasks. Point cloud files are composed of a series of data sets representing the coordinates of points on the surface of an object, which are used to describe the shape of the object.

[0146] Step Sc00, import the point cloud file generated by Matlab into the three-dimensional scanning data processing software for envelope processing and noise reduction, and convert the processed point cloud data into a high-precision surface to generate the geometric data of the corroded steel fiber.

[0147] Envelope processing is to process the point cloud data, determine the outer contour of the object, remove redundant points, and make the point cloud data more concise and accurately represent the shape of the object. Noise reduction processing is to remove noise points in the point cloud data to improve the quality and accuracy of the data. Noise may come from errors in the scanning process, environmental interference and other factors. High-precision surface is a smooth surface obtained by fitting and reconstructing the processed point cloud data, which can more accurately represent the geometric shape of the corroded steel fiber.

[0148] 3D scanning data processing software You can choose a powerful 3D scanning data processing software, such as GeomagicWrap, PolyWorks, etc. These software usually provide rich point cloud processing tools and functions.

[0149] Step Sd00, use Hypermesh software to mesh the corroded steel fiber according to a preset meshing method, and import the mesh model divided by Hypermesh into Abaqus software to generate a numerical model of the corroded steel fiber component, and define the material properties, boundary conditions and loading conditions of the corroded steel fiber.

[0150] Hypermesh software is a professional finite element pre-processing software used to mesh and optimize geometric models and provide high-quality mesh models for subsequent numerical simulations. The preset meshing method is a meshing strategy that is pre-set according to the characteristics of the research object and the requirements of numerical simulation, including the selection of parameters such as mesh type, size, and density. Meshing is to discretize the geometric model into several small units for calculation and analysis in numerical simulation. Abaqus software is a widely used finite element analysis software used for numerical simulation and analysis of various engineering problems. Material properties are parameters that describe the mechanical properties of materials, such as elastic modulus, Poisson's ratio, yield strength, etc. Boundary conditions are physical conditions on the model boundary in numerical simulation, such as displacement constraints, force loading, etc. Loading conditions are external forces or loads applied to the model in numerical simulation, such as pull-out force, pressure, etc.

[0151] Step Se00, based on the measured size of a single steel fiber semi-dog bone specimen and the precise aggregate morphology of the corroded steel fibers, a numerical model of single-fiber concrete unilateral pullout within the monitoring range of the extensometer is established, and the material properties, boundary conditions and loading conditions of the concrete are defined.

[0152] "Within the extensometer monitoring range": An extensometer is an instrument used to measure the deformation of an object. Here it refers to the area within which the extensometer can effectively measure, as determined in the numerical model.

[0153] The “single-fiber concrete unilateral pull-out numerical model” refers to a model established using numerical simulation methods to simulate the process of a single steel fiber being pulled out from concrete on one side.

[0154] Assume that we have obtained the dimensions of a single steel fiber half-dog bone specimen through actual measurement: the concrete specimen is 100 mm long, 50 mm wide, and 50 mm high, the steel fiber is 30 mm long, and the length buried in the concrete is 20 mm. After the previous steps, the precise collective morphology of the corroded steel fiber is obtained.

[0155] Establish a numerical model: Use Abaqus software to construct the geometric shapes of concrete specimens and steel fibers according to the measured dimensions. The concrete specimen can be represented by a cuboid, and the steel fiber can be represented by a cylinder. Mesh the model and select the appropriate unit type and mesh size.

[0156] Define concrete material properties: Through experimental testing, the elastic modulus of concrete is 30GPa, Poisson's ratio is 0.2, and the compressive strength is 30MPa. In Abaqus software, these parameters are input into the definition of concrete material.

[0157] Set boundary conditions and loading conditions: Set fixed constraints at the bottom of the concrete specimen to simulate the fixed state of the specimen during the test. Apply a gradually increasing pull-out force at one end of the steel fiber to simulate the loading process of a unilateral pull-out test. Assume that the pull-out force starts from 0 and increases at a rate of 1 kN / s.

[0158] Through the above steps, we established a numerical model of unilateral pull-out of single-fiber concrete within the monitoring range of the extensometer, and defined the material properties, boundary conditions and loading conditions of the concrete, preparing for the subsequent numerical simulation analysis.

[0159] Step Sf00, using Abaqus software to analyze and solve the established numerical model, analyze the solution results, extract key mechanical performance indicators, statistically analyze the results under different corrosion degrees, and draw relevant views for visual display.

[0160] Assume that we have established a numerical model of single-sided pullout of single-fiber concrete with different degrees of corrosion, and analyzed and solved it in Abaqus software. The following is the specific operation process of step Sf00:

[0161] Use Abaqus software to analyze and solve: set the solution parameters to static analysis, the time step to 0.1s, and the convergence criterion to residual less than 1e-5. Submit the solution task and let Abaqus software calculate the numerical model.

[0162] Extract solution results: After the numerical model is solved, open the post-processing module of Abaqus software to view the stress, strain and displacement data in the result file. Extract the stress and displacement data at the interface between steel fiber and concrete from the result file.

[0163] Calculate key mechanical performance indicators: define bond strength as the maximum shear stress at the interface, and slip as the displacement of the steel fiber in the pulling direction. Based on the extracted stress and displacement data, calculate the bond strength and slip at different corrosion levels.

[0164] Based on the extracted stress and displacement data, the bonding strength and slippage under different degrees of corrosion are calculated. For example, when the degree of corrosion is 5%, the bonding strength is calculated to be 2.8MPa and the slippage is 0.15mm; when the degree of corrosion is 10%, the bonding strength is 2.5MPa and the slippage is 0.2mm; when the degree of corrosion is 15%, the bonding strength is 2.2MPa and the slippage is 0.25mm; when the degree of corrosion is 20%, the bonding strength is 2.0MPa and the slippage is 0.3mm.

[0165] Statistical results under different degrees of corrosion: Using regression analysis method, it was found that the bonding strength showed a linear downward trend with the increase of the degree of corrosion, while the slip amount gradually increased with the increase of the degree of corrosion.

[0166] Draw relevant views for visual display: Use Matlab software to draw a line graph of the bond strength and slip amount as the degree of corrosion changes, and intuitively show the change law of the two. Generate stress cloud maps at different degrees of corrosion in Abaqus software to observe the stress distribution at the interface between steel fiber and concrete.

[0167] Reference Figure 4 The curing specimens were subjected to electrochemical corrosion test operations matching the preset different steel fiber corrosion degrees, and the specimens with different corrosion degrees were obtained, including:

[0168] Step S210, all the test pieces that have completed curing are immersed in a pre-configured salt solution, and the positive electrode of the DC power supply is connected to the steel fiber, and the negative electrode is connected to the auxiliary electrode.

[0169] Step S210 can refer to Figure 5 shown.

[0170] Salt solution refers to a solution containing specific salts used for electrochemical corrosion tests, usually a corrosive medium that simulates the actual environment. A DC power supply is a device that provides direct current and is used to form an electric field in an electrochemical corrosion test. The positive electrode and steel fiber connection is to electrically connect the positive electrode of the DC power supply to the steel fiber in the test piece.

[0171] Step S220, according to Faraday's law of electrolysis, combined with the surface area of ​​the steel fiber and different expected corrosion degrees, the required amount of electricity is calculated, and then the combination of current intensity and power-on time is determined as the corrosion conditions of different expected corrosion degrees.

[0172] Faraday's law of electrolysis is a law that describes the relationship between the amount of substance precipitated and the amount of electricity passed during electrolysis. Different expected corrosion degrees refer to the pre-set corrosion degrees that the steel fiber needs to achieve.

[0173] The combination of current intensity and power-on time is confirmed as follows: the current intensity and power-on time are combined to determine the corrosion conditions of different desired corrosion degrees. For example, if light corrosion is required, a smaller current intensity and a shorter power-on time can be selected; if severe corrosion is required, a larger current intensity and a longer power-on time can be selected. By conducting multiple tests, the corrosion conditions are continuously optimized to improve the accuracy and reliability of the test. The combination of current intensity and power-on time can be adjusted according to the test results.

[0174] Step S230, each test piece is tested under corrosion conditions with different expected corrosion degrees, and the test is stopped until a preset condition is reached, thereby obtaining test pieces with different corrosion degrees.

[0175] Among them, the preset conditions can be corrosion degree indicators or time limits. Corrosion degree indicators: determine the corrosion degree indicators used to judge whether the test has reached the preset conditions, such as mass loss rate, surface morphology changes, etc. The relationship between different corrosion degree indicators and test piece performance can be determined through experiments or theoretical analysis to reasonably set the preset conditions. Time limit: In addition to the corrosion degree indicator, the time limit of the test can also be set. If the preset corrosion degree is not reached within the specified time, the corrosion conditions can be adjusted or the test time can be extended according to the actual situation.

[0176] After obtaining the specimens with different degrees of corrosion, they need to be classified and marked according to their degrees of corrosion for subsequent analysis and research. Specimens with different degrees of corrosion can be distinguished by using different colors, numbers or labels.

[0177] For example, in actual operation, after the sample has been cured for 14 days, the specimen is taken out of the curing environment and placed in a 3.5% by mass NaCl solution for immersion. After the immersion, the positive electrode of the DC power supply is connected to the steel fiber, and the negative electrode is connected to the copper rod, and the current is set to 5mA. According to Faraday's law of electrolysis, the degree of corrosion of the steel fiber is controlled by adjusting the power-on time. A total of 30 test blocks were prepared and divided into 5 groups, namely a control group without power-on treatment and four experimental groups with power-on times of 80min, 160min, 240min, and 320min, each containing 6 samples.

[0178] The method for characterizing the effect of corrosion degree on the interfacial bonding performance between steel fiber and concrete also includes steps between testing each specimen under corrosion conditions with different expected corrosion degrees and stopping the test until the preset conditions are reached, as follows:

[0179] Step S2a0, during the corrosion process, start the electrochemical impedance spectroscopy monitoring device to monitor and obtain electrochemical impedance spectroscopy data, and extract resistance and capacitance parameters from the electrochemical impedance spectroscopy data, and simultaneously start the electrochemical noise monitoring device to obtain electrochemical noise data, and extract the noise amplitude from the electrochemical noise data.

[0180] Among them, the electrochemical impedance spectroscopy monitoring equipment is an instrument used to measure the impedance characteristics of the electrochemical system. By applying a small-amplitude AC signal and measuring the response of the system, the electrochemical impedance spectroscopy data is obtained. The electrochemical noise monitoring equipment is an instrument used to measure the tiny current and voltage fluctuations naturally generated in the electrochemical system, that is, the electrochemical noise data.

[0181] The resistance and capacitance parameters can be analyzed by extracting the resistance and capacitance parameters using a suitable method based on the characteristics of the electrochemical impedance spectrum. For example, the values ​​of the resistance and capacitance can be determined by fitting an equivalent circuit model.

[0182] The noise amplitude can be analyzed by using data analysis software to analyze the electrochemical noise data and extract the noise amplitude parameters. The noise amplitude can be determined by calculating the standard deviation, peak value, etc. of the noise.

[0183] Step S2b0, calculating the comprehensive corrosion index values ​​at different time points according to a preset comprehensive corrosion index calculation method, and drawing a curve of the change of the comprehensive corrosion index over time.

[0184] Among them, the comprehensive corrosion index calculation method is a calculation method used to comprehensively evaluate the degree of steel fiber corrosion, which usually combines multiple electrochemical parameters.

[0185] Assuming that we have acquired the parameters such as resistance, capacitance and noise amplitude in step S2a0, we now proceed to step S2b0.

[0186] Determine the calculation method of comprehensive corrosion index: select resistance, capacitance and noise amplitude as parameters for calculating comprehensive corrosion index. According to experience and theoretical analysis, the weights of resistance, capacitance and noise amplitude are determined to be 0.4, 0.3 and 0.3 respectively. The calculation formula of comprehensive corrosion index is established as follows: comprehensive corrosion index value = 0.4 × resistance value + 0.3 × capacitance value + 0.3 × noise amplitude value.

[0187] The different time points are determined as follows: the time interval for monitoring the comprehensive corrosion index is set to once every hour. During the corrosion test, the time point of each hour is recorded.

[0188] Plot a curve of the change of the comprehensive corrosion index over time: with time as the horizontal axis and the comprehensive corrosion index value as the vertical axis, the plotted curve is used to intuitively show the change trend of the corrosion degree over time.

[0189] Step S2c0, analyzing and determining the comprehensive corrosion index change efficiency based on the drawn curve of the comprehensive corrosion index change over time.

[0190] The comprehensive corrosion index change efficiency is used to measure the rate at which the degree of steel fiber corrosion changes over time, and is usually determined by calculating the ratio of the change in the comprehensive corrosion index within a certain time range to the time span.

[0191] Assume that the curve of the change of the comprehensive corrosion index over time is obtained through step S2b0. For example, at the beginning, the comprehensive corrosion index value is low, and as time goes by, the index value gradually increases. After a period of monitoring, it is found that in the first few hours, the comprehensive corrosion index value increases slowly, but in the subsequent time period, the growth rate accelerates.

[0192] We select the starting time point and the ending time point to calculate the comprehensive corrosion index change efficiency. Assuming that the comprehensive corrosion index value at the starting time point is 50, after a certain period of time, the comprehensive corrosion index value at the ending time point is 80, and the time span between these two time points is 6 hours.

[0193] Then the change of comprehensive corrosion index is 80-50=30. The change efficiency of comprehensive corrosion index is 30÷6=5.

[0194] Step S2d0, analyzing and determining the adjustment scheme of the corrosion conditions for the expected corrosion degree according to the correspondence between the interval range in which the comprehensive corrosion index change efficiency falls and the adjustment scheme of the corrosion conditions for the expected corrosion degree, and executing the adjustment scheme of the corrosion conditions for the expected corrosion degree.

[0195] The adjustment plan refers to a specific method for adjusting the corrosion conditions of the desired corrosion degree according to the interval range of the comprehensive corrosion index change efficiency.

[0196] Plan formulation: formulate corresponding corrosion condition adjustment plans for different ranges. For example, if the comprehensive corrosion index change efficiency is in the low efficiency range, you can consider increasing the current intensity or extending the power-on time; if it is in the high efficiency range, you can appropriately reduce the current intensity or shorten the power-on time to control the development of the corrosion degree.

[0197] According to the formulation of the interval range and adjustment plan, the corresponding relationship between the interval range of the comprehensive corrosion index change efficiency and the corrosion condition adjustment plan of the expected corrosion degree is established. This corresponding relationship can be expressed in the form of a table, a chart or an algorithm to quickly determine the adjustment plan in the actual test.

[0198] Assume that the comprehensive corrosion index change efficiency is determined to be 5% per hour through step S2c0. Based on experience and theoretical analysis, the comprehensive corrosion index change efficiency is divided into the following intervals: low efficiency interval: less than or equal to 3% per hour; medium efficiency interval: greater than 3% and less than or equal to 7% per hour;

[0199] High efficiency range: greater than 7% per hour.

[0200] For the test with the expected corrosion degree as moderate corrosion, the initial corrosion conditions are set as current intensity of 0.5A and power-on time of 10 hours. If the comprehensive corrosion index change efficiency is in the low efficiency range, the current intensity can be increased to 0.7A while keeping the power-on time unchanged; if it is in the high efficiency range, the current intensity can be reduced to 0.3A, or the power-on time can be shortened to 8 hours.

[0201] In actual tests, when the comprehensive corrosion index change efficiency is determined to be 5% per hour, it is in the medium efficiency range. At this time, the current corrosion conditions can be maintained unchanged and the test observation can be continued. If in subsequent monitoring, the comprehensive corrosion index change efficiency exceeds the medium efficiency range, then the corresponding adjustment plan is determined and implemented according to the corresponding relationship.

[0202] The preset single pull test operation was performed on all the specimens that completed the electrochemical corrosion test operation to obtain the load change of the steel fiber during the pulling process, and the interfacial bonding strength between the steel fiber and concrete interface with different corrosion degrees was analyzed, including:

[0203] Step S310, installing the half dog bone test block on the fixture of the electronic universal testing machine.

[0204] The fixture is installed on the electronic universal testing machine and is used to fix the test piece to ensure that the test piece will not loosen or fall off during the test.

[0205] Step S320, start the electronic universal testing machine, perform a pulling test through beam displacement control, and synchronously collect experimental data, wherein beam displacement control refers to loading the test block by controlling the beam movement speed of the testing machine, and the experimental data includes force, displacement and deformation data.

[0206] The pull-out test is an experimental process of pulling steel fibers out of concrete, which is used to study the bonding properties of the interface between steel fibers and concrete.

[0207] Step S330, drawing a pull-out force-displacement curve according to the force and displacement data contained in the experimental data, and determining the peak pull-out force, that is, the maximum force that the test block bears during the pulling process, according to the pull-out force-displacement curve, and calculating the contact area between the steel fiber and the concrete according to the geometric dimensions of the test block and the embedding depth parameters of the steel fiber.

[0208] Step S340: Divide the peak pull-out force by the contact area to calculate the interface bonding strength.

[0209] For example, an electronic universal testing machine, an extensometer, and a dynamic acquisition box are used with a corresponding data acquisition system. Through beam displacement control, the loading rate is 1.5 mm / min, and a single steel fiber half-dog bone specimen is subjected to a pulling test. Figure 6 As shown. Each group of tests takes an average value of 6 samples. The peak load error of each test should be within 10% of the average peak load of each group of tests. The pull-out performance of the steel fiber used in this test in concrete is represented by the pull-out load and displacement curve (Ps curve), where P represents the pull-out force and s represents the displacement. The bond strength calculation formula is as follows:

[0210] ;

[0211] —bond strength, in megapascals (MPa);

[0212] —Maximum pull-out load, in Newton (N);

[0213] —Steel fiber diameter, in millimeters (mm);

[0214] —Indicates the embedded length of steel fiber in millimeters (mm).

[0215] The experimental results are as follows Figure 6 As shown in the figure, at the beginning of the stretching, the pull-out load increases rapidly with the increase of the slip amount. The load at this stage mainly comes from the static friction and adhesion between the straight steel fiber and the concrete. As the load continues to increase, the matrix on the surface of the steel fiber is damaged until debonding occurs. When the tensile load reaches the peak value, it is mainly the dynamic friction between the steel fiber and the matrix channel that plays a role and maintains a certain load. Finally, as the slip amount increases, the steel fiber is gradually pulled out, and the load decreases rapidly until it disappears.

[0216] The steel fibers that have completed the single pull test are taken out and subjected to the preset pickling operation one by one. According to the mass change of the steel fibers before and after the pickling operation, the mass loss rate of the steel fibers with different degrees of rust is calculated one by one, including: the steel fibers after the experiment are taken out, pickled with a hydrochloric acid solution and a corrosion inhibitor, taken out after the surface rust layer is dissolved, rinsed with deionized water and anhydrous ethanol, and then dried. The post-corrosion mass of each steel fiber is weighed with an electronic balance and compared with its mass before the experiment, and the degree of rust of the steel fiber is calculated. The degree of rust is expressed by the mass loss rate, that is, the degree of rust = (mass before the experiment - mass after the experiment) / mass before the experiment × 100%.

[0217] The characterization method of the influence of the degree of corrosion on the bonding performance of the interface between steel fiber and concrete includes the steps of performing a preset pickling operation on the steel fibers taken out after the single pull test operation, and calculating the mass loss rate of steel fibers with different degrees of corrosion one by one according to the mass change of the steel fibers before and after the pickling operation, as follows:

[0218] Step SC00, synchronously use μ-XCT equipment to scan the steel fiber at different angles, obtain scanning data at different angles, and reconstruct a three-dimensional image of the steel fiber, analyze the gray value distribution of the image obtained by the μ-XCT scan, distinguish between concrete, steel fiber and corrosion products, and use morphological analysis methods to extract the surface morphology characteristics of the steel fiber, and comprehensively combine the results of grayscale and morphological analysis to quantify the degree of corrosion of the steel fiber.

[0219] The μ-XCT device is a micro-focus X-ray computed tomography device that can perform high-resolution three-dimensional imaging of objects.

[0220] Gray value distribution: In the image obtained by μ-XCT scanning, the gray values ​​of different areas reflect the attenuation degree of X-rays, so that different materials can be distinguished.

[0221] The morphological analysis method is an analysis method based on image shape and structure, which is used to extract the surface morphology characteristics of steel fibers.

[0222] The result of the comprehensive grayscale and morphological analysis is to integrate the information obtained from the grayscale value analysis and the morphological analysis to more accurately quantify the degree of corrosion of the steel fiber. The process of the results of the comprehensive grayscale and morphological analysis is as follows: Result integration: The results obtained from the grayscale value analysis and the morphological analysis are integrated to comprehensively consider the information of the two analysis methods. The two analysis results can be fused by weighted average, logical judgment, etc. Quantification of the degree of corrosion: According to the integrated results, the degree of corrosion of the steel fiber is quantified. The numerical representation of the degree of corrosion of the steel fiber can be determined by empirical formulas, mathematical models or statistical analysis. Taking the empirical formula as an example, based on previous research and experimental experience, an empirical formula is established to quantify the degree of corrosion of the steel fiber. This formula can be a linear combination of multiple parameters or a nonlinear function. For example, it can be assumed that the relationship between the degree of corrosion (R) and the surface roughness (S), grayscale value change (G) and the area proportion of corrosion products (C) is: R=aS+bG+cC+d, where a, b, c and d are coefficients obtained by fitting the experimental data.

[0223] Step SD00, compare the corrosion degree obtained by μ-XCT scanning results with the corrosion degree obtained by pickling. If the comparison result is within the preset difference range, the average of the corrosion degree of the steel fiber obtained by the two methods is used as the corrosion degree of the steel fiber confirmed this time.

[0224] The preset difference range is a difference within a pre-set allowable range, which is used to determine whether the rust degrees obtained by the two methods are relatively consistent.

[0225] Comparative calculation: Compare the degree of rust obtained by μ-XCT scanning with the degree of rust obtained by pickling. The difference between the two, relative error and other indicators can be calculated to evaluate the comparison results. For example, if the degree of rust obtained by μ-XCT scanning is R1 and the degree of rust obtained by pickling is R2, the difference is |R1-R2| and the relative error is (|R1-R2| / R1)×100%.

[0226] Step SE00: If the comparison result is outside the preset difference range, the status information of the large error is sent to the terminal held by the relevant person in charge.

[0227] The terminal held by the relevant person in charge can be a mobile phone, computer or other terminal device.

[0228] The method for characterizing the effect of corrosion degree on the bonding performance of the steel fiber and concrete interface also includes comparing the corrosion degree obtained by μ-XCT scanning results with the corrosion degree obtained by pickling. If the comparison result is within the preset difference range, the average of the corrosion degree of the steel fiber obtained by the two methods is used as the corrosion degree of the steel fiber confirmed this time. The specific steps are as follows:

[0229] The mass loss rate after pickling, the image characteristic parameters obtained by μ-XCT scanning, and the pull-out load data of the single pull test were collected, and the degree of steel fiber corrosion was determined as the dependent variable.

[0230] A multiple linear regression model was used to establish the relationship model between the corrosion degree and the independent variables, with the corrosion degree of steel fiber as the dependent variable, the mass loss rate obtained by pickling, the characteristic parameters of μ-XCT images and the pulling load data as independent variables.

[0231] The dependent variable is the variable that is predicted or explained in the regression analysis, which is the degree of corrosion of the steel fiber. The independent variable is the variable used to predict or explain the dependent variable in the regression analysis, which includes the mass loss rate obtained by pickling, the characteristic parameters of the μ-XCT image, and the pulling load data.

[0232] The multivariate linear regression model was formed as follows: Data preparation: A sufficient number of steel fiber sample data were collected, including mass loss rate, μ-XCT image characteristic parameters and pull-out load data as well as the corresponding steel fiber corrosion degree.

[0233] Model establishment: Use the regression analysis tool in statistical software or programming language, take the corrosion degree of steel fiber as the dependent variable, and the mass loss rate, μ-XCT image characteristic parameters and pulling load data as independent variables to establish a multivariate linear regression model. Model evaluation: Judge the reliability and effectiveness of the model by evaluating the goodness of fit, significance test and other indicators of the model.

[0234] The model is generally established by using the regression analysis function in the selected tool, inputting the independent variables and dependent variables into the function, and establishing a multiple linear regression model. The general form of the model is y=β0+β1x1+β2x2+β3x3+…+ε, where β0 is the intercept, β1, β2, β3, etc. are regression coefficients, and ε is the error term.

[0235] Model evaluation can be done by goodness of fit evaluation, as follows: The coefficient of determination (R²) is often used to evaluate the goodness of fit of the model. R² values ​​range from 0 to 1, and the closer it is to 1, the better the model fits the data. The calculation formula is R²=1-(residual sum of squares / total sum of squares). For example, the R² of the model is calculated to be 0.85, which means that the model can explain 85% of the variation of the dependent variable.

[0236] Based on the same inventive concept, an embodiment of the present invention provides a characterization system for the effect of corrosion degree on the bonding performance of the steel fiber and concrete interface, including a memory and a processor, wherein the memory stores a data stream that can be executed on the processor to implement the following Figures 1 to 4 Procedure for either method.

[0237] The embodiments of this specific implementation method are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Therefore, all equivalent changes made based on the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for characterizing the effect of corrosion degree on the bonding performance of the steel fiber and concrete interface, characterized in that: include: A single steel fiber is embedded in each of the preset number of half-dog bone test blocks by concrete pouring according to the preset embedding depth, and cured according to the preset curing conditions; The curing specimens were subjected to electrochemical corrosion test operations matched with preset different steel fiber corrosion degrees to obtain specimens with different corrosion degrees, wherein the specimens were half-dog bone specimens with a single steel fiber embedded; Performing a preset single-pull test operation on all test pieces that have completed the electrochemical corrosion test operation to obtain the load change of the steel fiber during the pulling process, and analyzing to obtain the interface bonding strength between the steel fiber and the concrete interface with different corrosion degrees, performing a preset pickling operation on the steel fibers that have completed the single-pull test operation one by one, and calculating the mass loss rate of the steel fibers with different corrosion degrees one by one according to the mass change of the steel fibers before and after the pickling operation, and defining the mass loss rate of the steel fiber as the corrosion degree data of the steel fiber; The preset statistical analysis method is used to fit the interface bonding strength data corresponding to different corrosion degrees, and the mathematical expression between the interface bonding strength and the corrosion degree is obtained; The method also includes the following steps: fitting the interface bonding strength data corresponding to different corrosion degrees by using a preset statistical analysis method to obtain a mathematical expression between the interface bonding strength and the corrosion degree. The volume of rust pits was subtracted from the steel fiber through Boolean difference operation to obtain the steel fiber model of regular pits. The irregular pits on the steel fiber were defined as semi-ellipsoidal regular rust pits, and the distribution of the pits in the length direction of the steel fiber was the same. According to the steel fiber corrosion degree data, the number and size of corrosion pits are determined by parametric modeling method, and multiple corrosion pits are evenly distributed along the length direction of the steel fiber; According to the preset functional relationship between the corrosion degree and the mechanical property parameters of the steel fiber, the stress-strain constitutive relationship of the uncorroded steel fiber is modified; By performing a single pull test, the relative slip between the steel fiber and the concrete under different pull-out forces is measured, and the relationship between the bonding force and the slip is established, thereby obtaining a bond-slip constitutive model. By multiplying the bond-slip constitutive model by a preset corrosion reduction coefficient, the effect of the corrosion degree on the bonding performance between the steel fiber and the concrete is quantitatively determined. According to the measured dimensions of the specimen, a three-dimensional geometric model of the concrete specimen is created in the finite element software; Based on the geometric shape of regular steel fibers and the assumption of semi-ellipsoidal rust pits, a steel fiber model with rust pits is created, and the concrete material properties and steel fiber material properties are set; Define the material properties of the bonding unit and place the bonding unit at the interface between the steel fiber and concrete, ensure the correct direction and position of the bonding unit, and input the corresponding parameters into the bonding unit according to the mathematical expression between the determined interface bonding strength and the degree of corrosion to simulate the bonding failure and friction slip of the interface during the pull-out process; According to the preset numerical simulation solution parameters, run the numerical simulation, analyze the solution results, extract the key mechanical performance indicators, and statistically analyze the results under different corrosion degrees, and draw relevant views for visual display; The curing specimens were subjected to electrochemical corrosion test operations matching the preset different steel fiber corrosion degrees, and the specimens with different corrosion degrees were obtained, including: All the specimens that have completed curing are immersed in the pre-configured salt solution, and the positive electrode of the DC power supply is connected to the steel fiber, and the negative electrode is connected to the auxiliary electrode; According to Faraday's law of electrolysis, the required amount of electricity is calculated in combination with the surface area of ​​the steel fiber and different expected corrosion degrees, and then the combination of current intensity and power-on time is determined as the corrosion conditions for different expected corrosion degrees; Each specimen is tested under different corrosion conditions with different expected corrosion degrees, and the test is stopped until the preset conditions are reached, so as to obtain specimens with different corrosion degrees; The steps include testing each specimen under different corrosion conditions with different expected corrosion degrees and stopping the test until the preset conditions are reached, which are as follows: During the corrosion process, the electrochemical impedance spectroscopy monitoring device is started to monitor and obtain electrochemical impedance spectroscopy data, and resistance and capacitance parameters are extracted from the electrochemical impedance spectroscopy data, and the electrochemical noise monitoring device is simultaneously started to obtain electrochemical noise data, and noise amplitude is extracted from the electrochemical noise data; According to the preset comprehensive corrosion index calculation method, the comprehensive corrosion index values ​​at different time points are calculated, and the curve of the comprehensive corrosion index changing with time is drawn. The calculation formula of the comprehensive corrosion index is established as follows: comprehensive corrosion index value = 0.4 × resistance value + 0.3 × capacitance value + 0.3 × noise amplitude value; According to the curve of the comprehensive corrosion index changing over time, the comprehensive corrosion index change efficiency is analyzed and determined; According to the correspondence between the interval range in which the comprehensive corrosion index change efficiency falls and the adjustment plan of the corrosion conditions of the expected corrosion degree, the adjustment plan of the corrosion conditions of the expected corrosion degree is analyzed and determined, and the adjustment plan of the corrosion conditions of the expected corrosion degree is executed.

2. The method for characterizing the effect of corrosion degree on the interfacial bonding performance between steel fiber and concrete according to claim 1, characterized in that: The method also includes the following steps: fitting the interface bonding strength data corresponding to different corrosion degrees by using a preset statistical analysis method to obtain a mathematical expression between the interface bonding strength and the corrosion degree. Scan and obtain the surface morphology data of the corroded steel fiber, and use a preset image recognition algorithm to automatically analyze and extract features from the obtained surface morphology data of the corroded steel fiber; The geometric morphology data of the corroded steel fiber extracted by the image recognition algorithm is imported into Matlab software for processing, and a point cloud file is created using Matlab, that is, the surface morphology of the corroded steel fiber is represented as a series of point coordinates; The point cloud file generated by Matlab is imported into the 3D scanning data processing software for envelope processing and noise reduction, and the processed point cloud data is converted into a high-precision surface to generate the geometric data of the corroded steel fiber; Use Hypermesh software to mesh the corroded steel fiber according to the preset meshing method, and import the mesh model divided by Hypermesh into Abaqus software to generate a numerical model of the corroded steel fiber component, and define the material properties, boundary conditions and loading conditions of the corroded steel fiber; According to the measured size of the single steel fiber half-dog bone specimen and the precise aggregate morphology of the corroded steel fiber, a numerical model of single-fiber concrete unilateral pullout within the monitoring range of the extensometer was established, and the material properties, boundary conditions and loading conditions of the concrete were defined. Abaqus software is used to analyze and solve the established numerical model, analyze the solution results, extract key mechanical performance indicators, statistically analyze the results under different degrees of corrosion, and draw relevant views for visual display.

3. The method for characterizing the effect of corrosion degree on the interfacial bonding performance between steel fiber and concrete according to any one of claims 1 to 2, characterized in that: The preset single pull test operation was performed on all the specimens that completed the electrochemical corrosion test operation to obtain the load change of the steel fiber during the pulling process, and the interfacial bonding strength between the steel fiber and concrete interface with different corrosion degrees was analyzed, including: Mount the half dog bone test block on the fixture of the electronic universal testing machine; Start the electronic universal testing machine, perform the pulling test through the crossbeam displacement control, and collect the experimental data simultaneously. The crossbeam displacement control refers to loading the test block by controlling the crossbeam moving speed of the testing machine. The experimental data includes force, displacement and deformation data. According to the force and displacement data contained in the experimental data, the pull-out force-displacement curve is drawn, and the peak pull-out force, that is, the maximum force that the test block bears during the pull-out process, is determined according to the pull-out force-displacement curve. At the same time, the contact area between the steel fiber and the concrete is calculated according to the geometric dimensions of the test block and the embedded depth parameters of the steel fiber; The interfacial bond strength was calculated by dividing the peak pull-off force by the contact area.

4. The method for characterizing the effect of corrosion degree on the interfacial bonding performance between steel fiber and concrete according to claim 1, characterized in that: The steel fibers that have completed the single pull test are taken out and subjected to the preset pickling operation one by one. According to the mass change of the steel fibers before and after the pickling operation, the mass loss rate of the steel fibers with different degrees of rust is calculated one by one, including: the steel fibers after the experiment are taken out, pickled with a hydrochloric acid solution and a corrosion inhibitor, taken out after the surface rust layer is dissolved, rinsed with deionized water and anhydrous ethanol, and then dried. The post-corrosion mass of each steel fiber is weighed with an electronic balance and compared with its mass before the experiment, and the degree of rust of the steel fiber is calculated. The degree of rust is expressed by the mass loss rate, that is, the degree of rust = (mass before the experiment - mass after the experiment) / mass before the experiment × 100%.

5. The method for characterizing the effect of corrosion degree on the interfacial bonding performance between steel fiber and concrete according to claim 4, characterized in that: The method further includes performing a preset pickling operation on the steel fibers taken out after the single pull test operation, and calculating the mass loss rates of the steel fibers with different degrees of corrosion one by one according to the mass changes of the steel fibers before and after the pickling operation, which are specifically as follows: The μ-XCT device is used to scan the steel fiber at different angles to obtain scanning data at different angles, and the three-dimensional image of the steel fiber is reconstructed. The gray value distribution of the image obtained by the μ-XCT scan is analyzed to distinguish between concrete, steel fiber and corrosion products. The surface morphology characteristics of the steel fiber are extracted by morphological analysis method. The results of grayscale and morphological analysis are combined to quantify the degree of corrosion of the steel fiber. The corrosion degree obtained by μ-XCT scanning is compared with the corrosion degree obtained by pickling. If the comparison result is within the preset difference range, the average of the corrosion degrees of the steel fiber obtained by the two methods is taken as the corrosion degree of the steel fiber confirmed this time; If the comparison result is outside the preset difference range, the status information with large error will be sent to the terminal held by the relevant person in charge.

6. The method for characterizing the effect of corrosion degree on the interfacial bonding performance between steel fiber and concrete according to claim 5, characterized in that: The method also includes comparing the corrosion degree obtained by μ-XCT scanning with the corrosion degree obtained by pickling. If the comparison result is within the preset difference range, the average of the corrosion degree of the steel fiber obtained by the two methods is used as the corrosion degree of the steel fiber confirmed this time, and the specific steps are as follows: The mass loss rate after pickling, the image characteristic parameters obtained by μ-XCT scanning, and the pull-out load data of the single pull test were collected, and the degree of steel fiber corrosion was determined as the dependent variable; A multiple linear regression model was used to establish the relationship model between the corrosion degree and the independent variables, with the corrosion degree of steel fiber as the dependent variable, the mass loss rate obtained by pickling, the characteristic parameters of μ-XCT images and the pulling load data as independent variables.

7. A characterization system for the effect of corrosion degree on the bonding performance of the steel fiber and concrete interface, characterized in that: The invention comprises a memory, a processor and a program stored in the memory and executable on the processor, wherein the program can be loaded and executed by the processor to implement a method for characterizing the influence of the degree of corrosion on the interfacial bonding performance of steel fiber and concrete as described in any one of claims 1 to 6.

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