UHPC anti-bending test initial crack strength determination method and system based on image method

Abstract

The application discloses a UHPC anti-bending test initial cracking strength determination method and system based on an image method, and belongs to the technical field of mechanical property testing, and comprises the following steps: spraying random speckles on the surface of a UHPC test piece, pre-embedding an optical fiber Bragg grating sensor array in the test piece, and performing X-ray computed tomography on test pieces in the same batch to establish a fiber-matrix interface transition zone damage evolution model; when three-point anti-bending loading is performed on the test piece, surface speckle images are collected in real time, the DIC algorithm is used to calculate the full-field strain distribution, and the surface initial cracking time and the strain concentration area coordinates are determined in combination with a criterion; meanwhile, the wavelength drift of the optical fiber is monitored, the internal initial cracking time is determined by calculating the coefficient of variation; the interface debonding rate and the interface initial cracking point time are calculated according to the model and the real-time interface stress; a multi-source initial cracking characteristic data set containing the surface strain, the internal strain anomaly and the interface debonding rate is constructed, the weight coefficients are obtained by inputting the neural network model, and the initial cracking anti-bending strength is calculated by weighted summation.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of mechanical property testing, and particularly relates to a method and system for determining initial cracking strength in a UHPC (Ultra-High Performance Concrete) bending test based on an image method. BACKGROUND

[0002] In the bending test of UHPC (Ultra-High Performance Concrete), accurate determination of initial cracking strength is crucial for evaluating the performance of UHPC materials. The commonly used methods for determining initial cracking strength, such as load-displacement curve turning point method, strain gauge / extensometer method, and acoustic emission technology, all have certain limitations. The load-displacement curve turning point method is affected by the bridging effect of steel fibers, and the curve inflection point is not prominent, making it difficult to accurately identify the initial cracking time. The strain gauge / extensometer method has problems such as data distortion caused by contact slip and missing the initial point of microcracks due to a large gauge length. Although the acoustic emission technology can capture microcracks, the signal is easily disturbed by the environment, and it cannot directly reflect the crack morphology. In addition, existing methods rely on a single data indicator and lack multi-dimensional information fusion, resulting in insufficient accuracy and reliability, which cannot meet the testing requirements of the complex mechanical properties of UHPC. SUMMARY

[0003] To overcome the shortcomings of the prior art, the application provides a method and system for determining initial cracking strength in a UHPC bending test based on an image method. Random speckles are sprayed on the surface of the UHPC specimen, and an array of fiber Bragg grating sensors is pre-embedded in the specimen. X-ray computed tomography is performed on the same batch of specimens to establish a damage evolution model of the fiber-matrix interface transition zone. During the three-point bending loading of the specimen, the surface speckle images are collected in real time, the DIC algorithm is used to calculate the full-field strain distribution, and the surface initial cracking time and strain concentration zone coordinates are determined by combining the criteria. At the same time, the wavelength drift of the optical fiber is monitored, and the internal initial cracking time is calculated by calculating the coefficient of variation. The interface debonding rate and interface initial cracking point time are calculated based on the model and real-time interface stress. A multi-source initial cracking feature dataset containing surface strain, internal strain anomaly, and interface debonding rate is constructed, and the weighted coefficients are obtained by inputting the neural network model. The initial cracking bending strength is calculated by weighted summation.

[0004] To achieve the above-mentioned purposes, the application provides the following technical solutions:

[0005] The method for determining initial cracking strength in a UHPC bending test based on an image method comprises the following steps:

[0006] Spray random speckles on the surface of the UHPC specimen, pre-embed an array of fiber Bragg grating sensors in the specimen, and perform X-ray computed tomography on the same batch of UHPC specimens to establish a damage evolution model of the fiber-matrix interface transition zone;

[0007] The three-point bending loading test is performed on the test piece, the speckle image on the surface of the test piece is collected in real time, the DIC algorithm is used to calculate the full-field strain distribution, the surface initial cracking time and the local strain concentration area coordinates at the initial cracking time are determined by combining the pre-set surface initial cracking criterion, and simultaneously, the fiber Bragg grating sensor array is used to monitor the wavelength drift of the optical fiber in real time, the wavelength drift variation coefficient is calculated, and the internal initial cracking time is determined;

[0008] According to the fiber-matrix interface transition zone damage evolution model, the interface debonding rate is calculated by combining the interface stress measured in real time during the loading test, and the interface initial cracking point time is determined according to the pre-set interface initial cracking criterion;

[0009] Synchronize the surface initial cracking time, the internal initial cracking time and the interface initial cracking point time, and take the earliest time as the comprehensive initial cracking time;

[0010] Integrate the surface strain, internal strain anomaly and interface debonding rate corresponding to the comprehensive initial cracking time to form a multi-source initial cracking feature data set, and input the data set into the trained neural network model, combine the weight coefficients obtained by training, and calculate the initial cracking bending strength of the UHPC by weighted summation.

[0011] Specifically, the process of establishing the fiber-matrix interface transition zone damage evolution model by X-ray computed tomography on the same batch of UHPC test pieces includes:

[0012] Select an X-ray computed tomography device, and adjust the scanning parameters according to the size and density of the UHPC test piece; the scanning parameters include X-ray energy, scanning speed and layer thickness;

[0013] Place the same batch of UHPC test pieces on the scanning table of the scanning device for omnidirectional scanning, and the scanning device converts the collected transmission signals into three-dimensional structure images of the test piece interior through a data processing system;

[0014] Edge detection is performed on the three-dimensional structure images of the test piece interior obtained by scanning to identify the fiber-matrix interface transition zone information;

[0015] Based on the identified fiber-matrix interface transition zone information, a finite element model of the interface transition zone is established in combination with the mechanical performance parameters of the UHPC material;

[0016] In the finite element model of the interface transition zone, the material nonlinearity, geometric nonlinearity and interaction between the fiber and the matrix in the interface transition zone are considered, the boundary conditions and loading modes are set, and the stress-strain relationship of the UHPC test piece during the bending loading test is simulated by calculating the established finite element model of the interface transition zone;

[0017] According to the finite element analysis result, stress distribution, strain distribution and damage evolution of the interface transition zone under different loading stages are obtained, and a damage evolution model of the fiber-matrix interface transition zone is established; the damage evolution is described by defining a damage variable; the damage variable includes a plastic strain and an energy dissipation index.

[0018] Specifically, the full-field strain distribution is calculated by using the DIC algorithm, including:

[0019] The collected speckle images on the surface of the test piece are preprocessed;

[0020] An initial reference image and a deformed speckle image are selected from the preprocessed speckle images on the surface of the test piece; the initial reference image is a random speckle image sprayed on the surface of the test piece collected before the loading test; the deformed speckle image is a speckle image collected after the deformation of the surface of the test piece during the loading test according to the set time interval;

[0021] A predetermined number of sub-regions are selected on the initial reference image and the deformed speckle image, respectively;

[0022] The DIC algorithm is used to match the sub-regions on the initial reference image and the deformed speckle image, and the displacement vector of each sub-region in the deformed speckle image relative to the corresponding sub-region in the initial reference image is determined by calculating the correlation coefficient between the sub-regions;

[0023] According to the displacement vector, the least square method is used to calculate the full-field strain distribution on the surface of the test piece, and the strain information of the test piece under different loading stages is obtained.

[0024] Specifically, the process of determining the surface initial cracking time and the local strain concentration area coordinates at the initial cracking time in combination with the pre-set surface initial cracking criterion includes:

[0025] According to the characteristics of the UHPC material and the test requirements, the surface initial cracking criterion is pre-set based on the strain threshold value;

[0026] In the calculated full-field strain distribution, the strain value and the strain gradient change of each position are monitored in real time;

[0027] When the strain value of any position exceeds the pre-set surface initial cracking criterion, the time at this moment is recorded as the surface initial cracking time, and the position coordinates exceeding the pre-set surface initial cracking criterion are determined as the local strain concentration area coordinates at the initial cracking time, and are stored.

[0028] Specifically, the process of using the fiber Bragg grating sensor array to monitor the wavelength drift amount in real time, calculating the wavelength drift coefficient of variation, and determining the internal initial cracking time includes:

[0029] The fiber Bragg grating sensor array is connected to the fiber grating demodulator through an optical fiber;

[0030] Before the loading test starts, the initial wavelength of the fiber Bragg grating sensor is calibrated, and the initial wavelength value of each fiber Bragg grating sensor in the stress-free state is recorded;

[0031] During the bending resistance loading test, the fiber grating demodulator collects the wavelength data of the fiber Bragg grating sensor in real time, processes the collected wavelength data, obtains the current wavelength value, and calculates the wavelength drift of each fiber Bragg grating sensor; the wavelength drift is equal to the current wavelength value minus the initial wavelength value;

[0032] The wavelength drifts of all fiber Bragg grating sensors in the fiber Bragg grating sensor array are statistically analyzed, and the average value and the standard deviation of the wavelength drift are calculated;

[0033] According to the average value and the standard deviation of the wavelength drift, the wavelength drift coefficient of variation is calculated; the wavelength drift coefficient of variation is equal to the standard deviation of the wavelength drift divided by the average value of the wavelength drift;

[0034] During the bending resistance loading test, the change of the wavelength drift coefficient of variation is monitored in real time;

[0035] When the wavelength drift coefficient of variation exceeds the pre-set internal initial cracking judgment threshold, the time at this moment is recorded as the internal initial cracking time.

[0036] Specifically, the process of calculating the interface debonding rate according to the fiber-matrix interface transition zone damage evolution model combined with the interface stress measured in real time during the loading test includes:

[0037] During the bending resistance loading test, the stress value of the interface transition zone is measured in real time through the stress sensor arranged on the surface or inside the test piece;

[0038] The real-time measured stress value of the interface transition zone is input into the pre-established fiber-matrix interface transition zone damage evolution model;

[0039] According to the damage variable and the stress-strain relationship defined in the fiber-matrix interface transition zone damage evolution model, the damage degree of the interface transition zone at different loading times is calculated;

[0040] According to the calculated damage degree, combined with the geometric shape and size of the interface transition zone, the interface debonding rate is calculated; the interface debonding rate is the ratio of the debonded area to the total area of the interface transition zone.

[0041] Specifically, the interface initial cracking point time is determined according to the pre-set interface initial cracking judgment standard, which includes:

[0042] According to the characteristics of the UHPC material and the test requirements, an interface initial cracking judgment standard is preset; the interface initial cracking judgment standard is set based on an interface debonding rate threshold index;

[0043] Among the calculated interface debonding rate and the real-time measured interface stress data, the changes of the interface debonding rate and the interface stress are monitored in real time;

[0044] When the interface debonding rate exceeds the pre-set interface debonding rate threshold, the time at this moment is recorded as the interface initial cracking point time.

[0045] Specifically, the initial cracking flexural strength of the UHPC is calculated by weighted summation, including:

[0046] The constructed multi-source initial cracking feature data set and the obtained weight coefficient are acquired;

[0047] According to the weight coefficient obtained by the neural network model, the surface strain, the internal strain anomaly and the interface debonding rate in the multi-source initial cracking feature data set are weighted and summed to obtain the initial cracking flexural strength of the UHPC; the surface strain is obtained by the DIC algorithm; the internal strain anomaly is determined by the wavelength drift variation coefficient;

[0048] The initial cracking flexural strength of the UHPC is output to a test report or a display interface and stored in a database.

[0049] The initial cracking strength judgment system of the UHPC flexural test based on the image method includes a data acquisition module, an initial cracking feature determination module, an initial cracking time determination module and a strength calculation module.

[0050] The data acquisition module is used to acquire data of the UHPC test piece in the flexural loading test process.

[0051] The initial cracking feature determination module is used to determine the initial cracking features of the surface, the interior and the interface respectively according to the acquired data.

[0052] The initial cracking time determination module is used to synchronize the surface initial cracking time, the internal initial cracking time and the interface initial cracking point time to determine the comprehensive initial cracking time.

[0053] The strength calculation module is used to construct a multi-source initial cracking feature data set and calculate the initial cracking flexural strength of the UHPC by using a neural network model.

[0054] Specifically, the initial cracking feature determination module includes a surface initial cracking feature determination unit, an internal initial cracking feature determination unit and an interface initial cracking feature determination unit.

[0055] The surface initial crack feature determination unit is configured to process the collected surface speckle image of the test piece by using a DIC algorithm, calculate a full-field strain distribution, determine a surface initial crack time and a local strain concentration area coordinate at the initial crack time;

[0056] The internal initial crack feature determination unit is configured to process the wavelength drift data collected by the fiber Bragg grating sensor array, calculate a wavelength drift coefficient of variation, and determine an internal initial crack time.

[0057] The interface initial crack feature determination unit is configured to calculate an interface debonding rate according to a fiber-matrix interface transition zone damage evolution model and in combination with the interface stress measured in real time during the loading test, and determine an interface initial crack point time.

[0058] Compared with the prior art, the present application has the following advantages:

[0059] 1. The present application proposes a UHPC flexural test initial crack strength determination system based on an image method, and optimizes and improves the architecture, operation steps and flow, and the system has the advantages of simple flow, low investment and operation cost, and low production cost.

[0060] 2. The present application proposes a UHPC flexural test initial crack strength determination method based on an image method, which realizes accurate acquisition of multi-dimensional damage information of the test piece surface, interior and interface by spraying speckles on the surface of the UHPC test piece, pre-embedding sensors inside and performing X-ray scanning modeling; during the three-point flexural loading test, the surface initial crack time, position and internal initial crack time can be determined in real time, and the interface initial crack point time can also be determined according to the model, so that the initial crack characteristics of the UHPC test piece during the loading test are comprehensively and meticulously captured.

[0061] 3. The present application proposes a UHPC flexural test initial crack strength determination method based on an image method, which synchronously collects multi-source initial crack times and takes the earliest one as the comprehensive initial crack time, constructs a multi-source initial crack feature data set containing surface strain, internal strain anomaly and interface debonding rate, and inputs the neural network model to calculate the initial crack flexural strength, which comprehensively considers the damage conditions of different parts of the test piece, effectively improves the accuracy and reliability of the initial crack flexural strength determination, and this comprehensive determination method can more truly reflect the initial crack characteristics of the UHPC material under actual stress conditions. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 The present application is a UHPC flexural test initial crack strength determination method based on an image method;

[0063] Figure 2 The present application is a UHPC flexural test initial crack strength determination method based on an image method;

[0064] Figure 3 FIG. 1 is a schematic diagram of a system architecture for determining the initial cracking strength of UHPC based on an image method according to an embodiment of the present application. DETAILED DESCRIPTION

[0065] Embodiment 1

[0066] Referring to FIG. 1 Figure 1 and Figure 2 An embodiment of the present application provides a method for determining the initial cracking strength of UHPC based on an image method, the method comprising steps S1-S5, including the following steps:

[0067] S1: spraying random speckles on the surface of a UHPC test piece, pre-embedding an optical fiber Bragg grating sensor array in the test piece, and at the same time, performing X-ray computed tomography on UHPC test pieces of the same batch to establish a damage evolution model of the fiber-matrix interface transition zone;

[0068] Further, the process of spraying random speckles on the surface of the UHPC test piece includes:

[0069] First, clean the surface of the UHPC test piece to remove surface dirt, dust and other impurities, ensuring that the surface is clean and smooth; then, select a spraying material and use a spray gun to evenly spray the spraying material on the surface of the test piece to form a random distribution of speckle patterns, wherein the spraying material should have good adhesion and contrast, and at the same time, control the pressure, spraying distance and spraying speed of the spray gun during the spraying process to ensure that the speckles are evenly distributed and have moderate density. In addition, the density and size of the speckles should meet the requirements of the subsequent Digital Image Correlation (DIC) algorithm processing to ensure that the deformation information of the test piece surface can be accurately captured.

[0070] Further, the process of pre-embedding an optical fiber Bragg grating sensor array in the test piece includes:

[0071] According to the size of the UHPC test piece and the expected stress distribution, design the layout of the optical fiber Bragg grating sensor array to determine the position and spacing of each sensor; before pouring the UHPC test piece, fix the optical fiber Bragg grating sensors in the pre-embedded mold according to the designed layout to ensure that the sensor position is accurate and will not be displaced during the pouring process; when pouring the UHPC material, pay attention to protect the optical fiber Bragg grating sensors to avoid damage, and at the same time, ensure that the optical fiber is in full contact with the UHPC material to ensure that the sensor can accurately sense the strain change in the test piece.

[0072] S2: Real-time speckle images of the specimen surface are collected under three-point bending loading, and the full-field strain distribution is calculated using the DIC algorithm. The surface initial cracking time and the local strain concentration area coordinates at the initial cracking time are determined by combining the pre-set surface initial cracking criterion. Meanwhile, the wavelength shift of the fiber Bragg grating sensor array is monitored in real time, and the wavelength shift coefficient of variation is calculated to determine the internal initial cracking time;

[0073] The surface initial cracking time reflects the moment when the first damage occurs on the specimen surface. The local strain concentration area coordinates indicate the specific location of the damage. The internal initial cracking time represents the moment when the first damage occurs inside the specimen, reflecting the changes in the material's internal properties.

[0074] Further, the three-point bending loading test is performed on the specimen, including:

[0075] (1) Place the UHPC specimen sprayed with random speckles and embedded with a fiber Bragg grating sensor array on the support of the three-point bending testing machine. Adjust the position of the specimen so that the center line of the specimen coincides with the loading axis.

[0076] (2) Set the loading rate according to the test requirements. The loading rate should be able to simulate the stress conditions of UHPC components in actual engineering.

[0077] (3) Start the testing machine and perform the three-point bending loading test on the specimen. Maintain a stable loading rate during the loading process, and simultaneously collect real-time speckle images of the specimen surface and monitoring data from the fiber Bragg grating sensor array.

[0078] S3: According to the fiber-matrix interface transition zone damage evolution model, calculate the interface debonding rate based on the real-time measured interface stress during the loading test. Determine the interface initial cracking point time according to the pre-set interface initial cracking criterion.

[0079] The interface initial cracking point time reflects the moment when the bonding performance between the fiber and the matrix begins to fail, which has a significant impact on the overall performance of the material.

[0080] S4: Synchronize the surface initial cracking time, internal initial cracking time, and interface initial cracking point time, and take the earliest time as the comprehensive initial cracking time.

[0081] Further, the specific steps of S4 include:

[0082] (1) Establish a unified time reference to ensure that the measurement and recording of the surface initial cracking time, internal initial cracking time, and interface initial cracking point time are based on the same time coordinate system.

[0083] (2) In the data processing process, use a time synchronization algorithm to calibrate and synchronize the time data obtained from different monitoring methods.

[0084] (3) By comparing the numerical values at the three time points, determine the earliest time as the comprehensive initial cracking time;

[0085] (4) Store and manage the synchronized time data for subsequent analysis and verification.

[0086] S5: Integrate the surface strain, internal strain anomaly and interface debonding rate corresponding to the comprehensive initial cracking time to form a multi-source initial cracking feature dataset, and input it into the trained neural network model. Combined with the weight coefficients obtained by training, the initial cracking strength of UHPC is calculated by weighted summation.

[0087] Among them, the surface strain is obtained by DIC algorithm; the internal strain anomaly is determined by wavelength drift variation coefficient.

[0088] Further, the surface strain, internal strain anomaly and interface debonding rate corresponding to the comprehensive initial cracking time are integrated, including:

[0089] (1) From the real-time collected and calculated data, extract the surface strain distribution data, internal strain anomaly data and interface debonding rate data corresponding to the comprehensive initial cracking time, wherein the internal strain anomaly data is, for example, the strain mutation data monitored by the fiber Bragg grating sensor;

[0090] (2) Format uniform processing is performed on the extracted data to ensure that different types of data can be integrated and analyzed in the same dataset;

[0091] (3) The surface strain, internal strain anomaly and interface debonding rate data are associated and integrated to form a complete multi-source initial cracking feature dataset;

[0092] (4) Quality check is performed on the integrated multi-source initial cracking feature dataset to ensure the integrity and accuracy of the data.

[0093] Further, the process of extracting the surface strain distribution data corresponding to the comprehensive initial cracking time from the real-time collected and calculated data includes:

[0094] (1) Associate the real-time collected surface speckle image data with the time record of the loading test. Since the image acquisition device usually records the acquisition time stamp of each image, and the loading test also records the time axis of the entire loading process, by matching the time stamp, the loading time corresponding to each image can be determined;

[0095] For example, if the image acquisition frequency is 100 frames per second, and the loading test starts at 0 seconds, then the first frame of image corresponds to 0.01 seconds, the second frame corresponds to 0.02 seconds, and so on.

[0096] (2) On the basis of the determined comprehensive initial cracking time, the image frame closest in time is found. Since image acquisition is discrete, the comprehensive initial cracking time does not exactly coincide with the acquisition time of a certain frame of image, and therefore the closest frame is selected, for example, the comprehensive initial cracking time is 5.23 seconds, and the image acquisition time stamps are 5.20 seconds and 5.30 seconds, and then the image frame corresponding to 5.20 seconds is selected as the target frame image;

[0097] (3) The target frame image is preprocessed, including denoising and contrast enhancement, to improve the image quality and facilitate subsequent strain calculation;

[0098] (4) According to the set region of interest before the loading test, the region in the image that needs to be calculated for strain is determined. It should be noted that the selection of ROI should avoid the edges of the test piece, clamps and other parts that may affect the accuracy of strain calculation;

[0099] (5) In the region of interest, the surface strain distribution is calculated using the DIC algorithm. The basic principle of the DIC algorithm is to calculate the displacement field by comparing the gray scale distribution of subsets in the images before and after deformation, and then obtain the strain field. Specifically, it includes:

[0100] A series of subsets are selected in the image before deformation, each subset containing a certain number of pixel points; then the region with the most similar gray scale distribution to the subset before deformation is searched in the image after deformation to determine the displacement of the subset; the strain tensor components in the region of interest are calculated according to the displacement field by numerical differentiation method;

[0101] (6) The distribution data of each component of the strain tensor in the region of interest is extracted to form a surface strain distribution data set. The surface strain distribution data set can be stored in the form of matrix or array, and each element corresponds to the strain value at a specific position in the region of interest.

[0102] Further, the process of extracting internal strain anomaly data corresponding to the comprehensive initial cracking time from the real-time collected and calculated data includes:

[0103] (1) The wavelength shift data collected by the fiber Bragg grating sensor in real time is associated with the time record of the loading test. The data acquisition system of the fiber Bragg grating sensor usually records the time stamp of each data point. By matching with the loading test time axis, the loading time corresponding to each wavelength shift data is determined;

[0104] (2) On the basis of the comprehensive initial cracking time, the data point of the fiber Bragg grating sensor closest in time is found. Since data acquisition is also discrete, the data point closest to the comprehensive initial cracking time is also selected;

[0105] (3) According to the wavelength-strain relationship formula of the fiber Bragg grating sensor, the selected wavelength drift data is converted into a strain value, and the formula is: wherein, represents the strain value, represents the wavelength drift, represents the strain sensitivity coefficient, represents the center wavelength of the fiber Bragg grating;

[0106] (4) According to the pre-set strain anomaly judgment standard, it is judged whether the strain value is an abnormal value, wherein the judgment standard is determined based on material characteristics or engineering experience, for example, a strain threshold is set, and when the strain value exceeds the strain threshold, it is determined that the strain is abnormal;

[0107] If the strain is abnormal, the strain value and the corresponding fiber Bragg grating sensor position information are extracted as internal strain anomaly data; if it is not determined to be abnormal, the data of other fiber Bragg grating sensors near the comprehensive initial cracking time is continuously checked.

[0108] Further, input into the trained neural network model to obtain weight coefficients, including:

[0109] (1) According to the characteristics of the multi-source initial cracking feature data set and the test requirements, a convolutional neural network model is selected;

[0110] (2) The convolutional neural network model is trained using known UHPC bending resistance test data, and during the training process, a suitable optimization algorithm and loss function are used to adjust the weight and bias parameters of the model, so that the convolutional neural network model can accurately learn the mapping relationship between the multi-source data and the initial cracking bending strength;

[0111] (3) After the training is completed, the real-time multi-source initial cracking feature data set is input into the trained convolutional neural network model, and the convolutional neural network model calculates the weight coefficients corresponding to each data source according to the input multi-source data. The weight coefficients reflect the influence degree of different data sources on the initial cracking bending strength.

[0112] Further, the UHPC bending resistance test initial cracking strength determination method based on the image method, after completing the initial cracking bending strength calculation of the UHPC, further includes the process of further analyzing and applying the test results:

[0113] (1) Statistical analysis is performed on the initial cracking bending strength test results of UHPC specimens of different batches and different mix proportions, and the main factors affecting the initial cracking bending strength are found out;

[0114] (2) Based on the test results and analysis, optimize and improve the mix design and production process of UHPC to improve the initial cracking and flexural strength performance of UHPC;

[0115] (3) Provide test results and analysis reports to relevant engineering design and construction personnel to provide reference for the application of UHPC in engineering structures and ensure the safety and reliability of engineering structures.

[0116] The process of performing X-ray computed tomography on UHPC specimens of the same batch to establish a fiber-matrix interface transition zone damage evolution model includes:

[0117] S1.1: Select an X-ray computed tomography device and adjust the scanning parameters according to the size and density of the UHPC specimen; the scanning parameters include X-ray energy, scanning speed, and layer thickness;

[0118] S1.2: Place the UHPC specimen of the same batch on the scanning table of the scanning device for full-range scanning, and the scanning device converts the collected transmission signals into a three-dimensional structure image of the specimen interior through a data processing system;

[0119] S1.3: Perform edge detection on the three-dimensional structure image of the specimen interior obtained by scanning to identify the fiber-matrix interface transition zone information, wherein edge detection is a prior art in the field and is not part of the inventive concept of this application, and will not be described here;

[0120] S1.4: Based on the identified fiber-matrix interface transition zone information, establish a finite element model of the interface transition zone in combination with the mechanical performance parameters of the UHPC material;

[0121] Further, the specific steps of S1.4 include:

[0122] (1) Obtain the structural characteristics of the identified fiber-matrix interface transition zone, including the thickness and shape of the transition zone;

[0123] (2) Use CAD software to establish a geometric model of the interface transition zone based on the structural characteristic information of the identified interface transition zone;

[0124] (3) Determine the material parameters of the interface transition zone, which should be noted that the material properties of the interface transition zone are different from those of the fiber and matrix, and need to be determined through experimental testing, for example, the elastic modulus of the interface transition zone can be measured by nanoindentation experiment;

[0125] (4) Assign the experimentally measured material parameters of the interface transition zone to the geometric model to make it a finite element model with actual material properties, and also need to define the material properties of the fiber and matrix;

[0126] (5) According to the geometric shape and stress characteristics of the interface transition zone, select hexahedral element type for meshing;

[0127] (6) Discretize the geometric model into a finite number of elements;

[0128] (7) According to the actual working condition, determine the boundary conditions of the interface transition zone model, for example, if the interface transition zone is in a structure with a fixed support, set the corresponding fixed constraint in the model;

[0129] (8) According to the actual stress condition, apply load on the model, where the load can be force, pressure, temperature, for example, if the interface transition zone is subjected to external tension, apply the corresponding tension load on the model;

[0130] (9) According to the nature of the problem, select dynamic analysis;

[0131] (10) Use the solver to calculate and output the stress, strain and displacement results of the interface transition zone;

[0132] (11) Compare the calculation results with the theoretical analysis or experimental results to verify the reasonableness of the model. If the calculation results differ greatly from the theoretical analysis or experimental results, check whether there are problems in the geometric shape, material properties, boundary conditions, meshing, etc. of the model and make corresponding adjustments.

[0133] S1.5: In the finite element model of the interface transition zone, consider the material nonlinearity, geometric nonlinearity and interaction between the fiber and the matrix, set the boundary conditions and loading mode, and simulate the stress-strain relationship of the UHPC specimen in the bending test by calculating the established finite element model of the interface transition zone;

[0134] Further, the specific steps of S1.5 include:

[0135] (1) Open the finite element software and import the established finite element model of the interface transition zone. Define the material parameters of the interface transition zone, fiber and matrix in the software, including basic parameters such as elastic modulus, Poisson's ratio, density, and material nonlinearity and geometric nonlinearity related parameters. At the same time, set the contact parameters and friction coefficient between the fiber and the matrix;

[0136] (2) According to the actual test situation, set the symmetric constraint boundary condition in the model, and set the loading curve and loading parameters, for example, for a rectangular cross-section specimen, set the symmetric constraint on the symmetric surface perpendicular to the loading direction, and only calculate half of the model;

[0137] (3) Select the solver, set the convergence criteria, iteration number, sub-step number and other solving parameters;

[0138] (4) Start the solver to perform the calculation, and monitor the iteration process and real-time changes of the results during the calculation process;

[0139] (5) After the calculation is completed, extract the stress-strain data and stress distribution cloud map from the result file;

[0140] (6) Draw the stress-strain curve, analyze the nonlinear characteristics of the curve and the stress distribution, and conduct in-depth analysis combined with the model parameters and loading conditions.

[0141] S1.6: According to the results of finite element analysis, the stress distribution, strain distribution and damage evolution of the interface transition zone at different loading stages are obtained, and a damage evolution model of the fiber-matrix interface transition zone is established. The model can describe the variation law of interface damage with loading time and loading stress; the damage evolution is described by defining a damage variable; the damage variable includes plastic strain and energy dissipation index.

[0142] Further, the specific steps of S1.6 include:

[0143] (1) Run the finite element analysis program to obtain the stress, strain and damage data of the interface transition zone at different loading stages;

[0144] (2) Extract the stress, strain and damage data, draw the stress cloud map, strain cloud map and damage cloud map, and analyze the damage initiation point and propagation path to summarize the damage evolution law;

[0145] (3) Select the form of damage evolution model and determine the model parameters;

[0146] (4) Compare the model calculation results with the finite element analysis results and experimental data to verify the accuracy of the model. If the model is not accurate, adjust the model parameters or model form and re-verify;

[0147] (5) Apply the established damage evolution model to the analysis and prediction of actual engineering problems to evaluate the damage of the interface transition zone.

[0148] The calculation of the full-field strain distribution using the DIC algorithm includes:

[0149] A1: Preprocess the collected speckle images on the surface of the test piece;

[0150] A2: Select an initial reference image and a deformed speckle image in the preprocessed speckle image on the surface of the test piece; the initial reference image is a random speckle image sprayed on the surface of the test piece before loading; the deformed speckle image is a speckle image collected after the deformation of the test piece surface during the loading test according to the set time interval;

[0151] A3: selecting a predetermined number of sub-regions on the initial reference image and the deformed speckle image respectively, the size and shape of the sub-regions being determined according to the speckle characteristics and deformation of the surface of the test piece;

[0152] A4: matching the sub-regions on the initial reference image and the deformed speckle image by using the DIC algorithm, determining the displacement vector of each sub-region in the deformed speckle image relative to the corresponding sub-region in the initial reference image by calculating the correlation coefficient between the sub-regions, wherein the DIC algorithm is a prior art content in the field and is not the inventive scheme of the present application, and will not be described here;

[0153] A5: calculating the full-field strain distribution of the surface of the test piece according to the displacement vector by using the least square method, obtaining the strain information of the test piece at different loading stages, wherein the least square method is a prior art content in the field and is not the inventive scheme of the present application, and will not be described here.

[0154] The process of determining the surface initial cracking time and the local strain concentration area coordinates at the initial cracking time by combining the pre-set surface initial cracking criterion comprises:

[0155] B1: pre-setting the surface initial cracking criterion based on the strain threshold according to the characteristics of the UHPC material and the test requirements;

[0156] B2: monitoring the strain value and the strain gradient change of each position in the calculated full-field strain distribution in real time;

[0157] B3: when the strain value of any position exceeds the pre-set surface initial cracking criterion, recording the time at this time as the surface initial cracking time, at the same time, determining the position coordinates exceeding the pre-set surface initial cracking criterion as the local strain concentration area coordinates at the initial cracking time, and storing them.

[0158] The process of using the fiber Bragg grating sensor array to monitor the wavelength drift amount in real time, calculating the wavelength drift coefficient of variation, and determining the internal initial cracking time comprises:

[0159] C1: connecting the fiber Bragg grating sensor array to the fiber grating demodulator through an optical fiber;

[0160] C2: calibrating the initial wavelength of the fiber Bragg grating sensor before the start of the loading test, and recording the initial wavelength value of each fiber Bragg grating sensor in the stress-free state;

[0161] C3: during the flexural loading test, the fiber grating demodulator collects the wavelength data of the fiber Bragg grating sensor in real time, processes the collected wavelength data, obtains the current wavelength value, and calculates the wavelength drift amount of each fiber Bragg grating sensor; the wavelength drift amount is equal to the current wavelength value minus the initial wavelength value;

[0162] C4: statistically analyzing the wavelength shift amount of all fiber Bragg grating sensors in the fiber Bragg grating sensor array, and calculating the average value and standard deviation of the wavelength shift amount;

[0163] C5: calculating the wavelength shift coefficient of variation according to the average value and standard deviation of the wavelength shift amount; the wavelength shift coefficient of variation is equal to the standard deviation of the wavelength shift amount divided by the average value of the wavelength shift amount;

[0164] C6: monitoring the change of the wavelength shift coefficient of variation in real time during the bending resistance loading test;

[0165] C7: when the wavelength shift coefficient of variation exceeds a pre-set internal initial cracking judgment threshold, recording the time at this moment as the internal initial cracking time; the internal initial cracking judgment threshold is determined according to test experience and the characteristics of the UHPC material.

[0166] The process of calculating the interface debonding rate according to the fiber-matrix interface transition zone damage evolution model and in combination with the interface stress measured in real time during the loading test includes:

[0167] D1: measuring the stress value of the interface transition zone in real time through the stress sensor arranged on the surface or inside of the test piece during the bending resistance loading test;

[0168] D2: inputting the stress value of the interface transition zone measured in real time into the pre-established fiber-matrix interface transition zone damage evolution model;

[0169] D3: calculating the damage degree of the interface transition zone at different loading times according to the damage variable and stress-strain relationship defined in the fiber-matrix interface transition zone damage evolution model;

[0170] D4: calculating the interface debonding rate according to the calculated damage degree, in combination with the geometric shape and size of the interface transition zone; the interface debonding rate is the ratio of the debonded area to the total area of the interface transition zone.

[0171] The process of determining the interface initial cracking point time according to the pre-set interface initial cracking judgment standard includes:

[0172] E1: pre-setting the interface initial cracking judgment standard according to the characteristics of the UHPC material and test requirements; the interface initial cracking judgment standard is set based on the interface debonding rate threshold index;

[0173] E2: monitoring the change of the interface debonding rate and the interface stress in real time among the calculated interface debonding rate and the real-time measured interface stress data;

[0174] E3: when the interface debonding rate exceeds the pre-set interface debonding rate threshold, recording the time at this moment as the interface initial cracking point time.

[0175] The initial cracking flexural strength of the UHPC is calculated by weighted summation, comprising:

[0176] S5.1: Obtain the constructed multi-source initial cracking feature dataset and the obtained weight coefficient;

[0177] S5.2: According to the weight coefficient obtained by the neural network model, the surface strain, internal strain anomaly and interface debonding rate in the multi-source initial cracking feature dataset are weighted and summed to obtain the initial cracking flexural strength of the UHPC; the surface strain is obtained by the DIC algorithm; the internal strain anomaly is determined by the wavelength drift variation coefficient; the initial cracking flexural strength of the UHPC comprehensively considers the damage information of the surface, the interior and the interface of the specimen, and can more accurately reflect the initial cracking performance of the UHPC material in the flexural loading process, wherein the neural network model is a prior art content in the art and is not the inventive scheme of the present application, and will not be described here;

[0178] S5.3: The initial cracking flexural strength of the UHPC is output to the test report or the display interface, and stored in the database.

[0179] Embodiment 2:

[0180] Please refer to Figure 3 , the present application provides another embodiment: a UHPC flexural test initial cracking strength determination system based on image method, comprising:

[0181] a data acquisition module, an initial cracking feature determination module, an initial cracking time determination module and a strength calculation module;

[0182] The data acquisition module is used to acquire various data of the UHPC specimen in the flexural loading test process.

[0183] The initial cracking feature determination module is used to determine the initial cracking features of the surface, the interior and the interface respectively according to the acquired data.

[0184] The initial cracking time determination module is used to synchronize the surface initial cracking time, the internal initial cracking time and the interface initial cracking point time, and determine the comprehensive initial cracking time.

[0185] The strength calculation module is used to construct a multi-source initial cracking feature dataset, and calculate the initial cracking flexural strength of the UHPC by using a neural network model.

[0186] The data acquisition module comprises an image acquisition unit and a data acquisition unit.

[0187] The image acquisition unit is used to spray random speckles on the surface of the UHPC specimen, and then in the three-point flexural loading test process, real-time acquisition of speckle images on the specimen surface is performed at a set time interval, which is used for subsequent calculation of full-field strain distribution by the DIC algorithm.

[0188] A data acquisition unit is configured to monitor the wavelength drift of the fiber Bragg grating sensor array embedded in the test piece in real time, and provide data support for determining the internal initial cracking time.

[0189] The initial cracking feature determination module comprises a surface initial cracking feature determination unit, an internal initial cracking feature determination unit, and an interface initial cracking feature determination unit.

[0190] The surface initial cracking feature determination unit is configured to process the collected surface speckle image of the test piece by using the DIC algorithm, calculate the full-field strain distribution, determine the surface initial cracking time and the local strain concentration area coordinates at the initial cracking time.

[0191] The internal initial cracking feature determination unit is configured to process the wavelength drift data collected by the fiber Bragg grating sensor array, calculate the wavelength drift variation coefficient, and determine the internal initial cracking time.

[0192] The interface initial cracking feature determination unit is configured to calculate the interface debonding rate according to the fiber-matrix interface transition zone damage evolution model and in combination with the interface stress measured in real time during the loading test, and determine the interface initial cracking point time.

[0193] The initial cracking time determination module comprises a time synchronization unit and a time determination unit.

[0194] The time synchronization unit is configured to establish a time synchronization mechanism to ensure that the records of the surface initial cracking time, the internal initial cracking time and the interface initial cracking point time have a unified time reference.

[0195] The time determination unit is configured to compare the recorded surface initial cracking time, internal initial cracking time and interface initial cracking point time, and select the earliest time as the comprehensive initial cracking time.

[0196] The strength calculation module comprises a data set construction unit and a model calculation unit.

[0197] The data set construction unit is configured to construct a multi-source initial cracking feature data set containing multi-source information.

[0198] The model calculation unit is configured to input the constructed multi-source initial cracking feature data set into the trained neural network model to obtain the weight coefficient, and then calculate the initial cracking bending strength of the UHPC by using the weighted summation method.

[0199] The embodiments of the present application are described above with reference to the accompanying drawings, but the present application is not limited to the above-described specific embodiments, and the above-described specific embodiments are merely illustrative, but not restrictive, and a person of ordinary skill in the art can make changes, modifications, replacements and variations to the above-described embodiments without departing from the purpose of the present application and the protected scope, and these are all within the protection of the present application.

Claims

1. A method for determining the initial cracking strength of UHPC based on the image method, characterized in that, The application relates to a method for predicting the initial flexural strength of ultra-high performance concrete (UHPC) and a device thereof. The method comprises the following steps: Spraying random speckles on the surface of the UHPC specimen, embedding an array of fiber Bragg grating sensors in the interior of the specimen, and performing X-ray computed tomography on the same batch of UHPC specimens to establish a fiber-matrix interface transition zone damage evolution model; The process of establishing the fiber-matrix interface transition zone damage evolution model comprises the following steps: Selecting an X-ray computed tomography device, adjusting scanning parameters according to the size and density of the UHPC specimen, wherein the scanning parameters include X-ray energy, scanning speed and layer thickness; Placing the same batch of UHPC specimens on the scanning table of the scanning device for omnidirectional scanning, and converting the collected transmission signals into three-dimensional structure images of the interior of the specimen through a data processing system of the scanning device; Performing edge detection on the three-dimensional structure images of the interior of the specimen obtained through scanning to identify the fiber-matrix interface transition zone information; Based on the identified fiber-matrix interface transition zone information and the mechanical performance parameters of the UHPC material, an interface transition zone finite element model is established; In the interface transition zone finite element model, the material nonlinearity, geometric nonlinearity and interaction between the fiber and the matrix of the interface transition zone are considered, the boundary conditions and the loading mode are set, and the stress-strain relationship of the UHPC specimen in the process of the flexural loading test is simulated by calculating the established interface transition zone finite element model; According to the results of the finite element analysis, the stress distribution, strain distribution and damage evolution of the interface transition zone at different loading stages are obtained, and a fiber-matrix interface transition zone damage evolution model is established; the damage evolution is described by defining a damage variable; the damage variable includes a plastic strain-based and energy dissipation index-based damage variable; Performing a three-point flexural loading test on the specimen, collecting real-time surface speckle images of the specimen, calculating the full-field strain distribution by using a DIC algorithm, determining the surface initial cracking time and the local strain concentration area coordinates at the initial cracking time by combining a pre-set surface initial cracking criterion, simultaneously, monitoring the wavelength drift of the fiber Bragg grating sensor array in real time, calculating the wavelength drift variation coefficient, and determining the internal initial cracking time; According to the fiber-matrix interface transition zone damage evolution model and the real-time measured interface stress in the loading test process, the interface debonding rate is calculated, and the interface initial cracking point time is determined according to a pre-set interface initial cracking criterion; Synchronizing the surface initial cracking time, the internal initial cracking time and the interface initial cracking point time, and taking the earliest time as the comprehensive initial cracking time; 2. The image method-based initial cracking strength determination method for the UHPC flexural test according to claim 1, characterized in that, Integrating the surface strain, internal strain anomaly and interface debonding rate corresponding to the comprehensive initial cracking time to form a multi-source initial cracking feature data set, and inputting the multi-source initial cracking feature data set into a neural network model which has been trained, combining the weight coefficients obtained through training, and calculating the initial flexural strength of the UHPC by using a weighted summation method. The method for calculating the full-field strain distribution by using the DIC algorithm comprises the following steps: Preprocessing the collected surface speckle images of the specimen; An initial reference image and a deformed speckle image are selected from the pre-processed speckle images on the surface of the test piece; the initial reference image is a random speckle image sprayed on the surface of the test piece collected before loading; the deformed speckle image is a speckle image collected after deformation of the surface of the test piece at a set time interval during the loading test; A predetermined number of sub-regions are selected on the initial reference image and the deformed speckle image, respectively; The DIC algorithm is used to match the sub-regions on the initial reference image and the deformed speckle image, and the displacement vector of each sub-region in the deformed speckle image relative to the corresponding sub-region in the initial reference image is determined by calculating the correlation coefficient between the sub-regions; According to the displacement vector, the least square method is used to calculate the full-field strain distribution on the surface of the test piece, and the strain information of the test piece at different loading stages is obtained.

3. The initial cracking strength determination method of the UHPC flexural test based on the image method according to claim 2, characterized in that, The process of determining the surface initial cracking time and the local strain concentration area coordinates at the initial cracking time by combining the pre-set surface initial cracking criterion includes: According to the characteristics of the UHPC material and the test requirements, the surface initial cracking criterion is pre-set based on the strain threshold value; The strain value and the strain gradient change of each position are monitored in real time in the calculated full-field strain distribution; When the strain value of any position exceeds the pre-set surface initial cracking criterion, the time at this moment is recorded as the surface initial cracking time, and the position coordinates exceeding the pre-set surface initial cracking criterion are determined as the local strain concentration area coordinates at the initial cracking time, and are stored.

4. The initial cracking strength determination method of the UHPC flexural test based on the image method according to claim 3, characterized in that, The process of using the fiber Bragg grating sensor array to monitor the wavelength drift amount in real time, calculating the wavelength drift coefficient of variation, and determining the internal initial cracking time includes: The fiber Bragg grating sensor array is connected to the fiber grating demodulator through an optical fiber; Before the start of the loading test, the initial wavelength of the fiber Bragg grating sensor is calibrated, and the initial wavelength value of each fiber Bragg grating sensor under the stress-free state is recorded; During the bending resistance loading test, the fiber grating demodulator collects the wavelength data of the fiber Bragg grating sensor in real time, processes the collected wavelength data, obtains the current wavelength value, and calculates the wavelength drift amount of each fiber Bragg grating sensor; the wavelength drift amount is equal to the current wavelength value minus the initial wavelength value; The wavelength drift amounts of all fiber Bragg grating sensors in the fiber Bragg grating sensor array are statistically analyzed, and the average value and the standard deviation of the wavelength drift amount are calculated; According to the average value and the standard deviation of the wavelength drift amount, the wavelength drift coefficient of variation is calculated; the wavelength drift coefficient of variation is equal to the standard deviation of the wavelength drift amount divided by the average value of the wavelength drift amount; During the bending resistance loading test, the change of the wavelength drift coefficient of variation is monitored in real time; When the wavelength drift coefficient of variation exceeds the pre-set internal initial cracking threshold value, the time at this moment is recorded as the internal initial cracking time.

5. The image method-based initial cracking strength determination method for the UHPC flexural test according to claim 4, characterized in that, The process of calculating the interface debonding rate according to the fiber-matrix interface transition zone damage evolution model and combining the interface stress measured in real time during the loading test includes: During the bending resistance loading test, the stress value of the interface transition zone is measured in real time by the stress sensor arranged on the surface or inside the test piece; inputting the stress value of the interface transition zone measured in real time into a pre-established damage evolution model of the fiber-matrix interface transition zone; calculating the damage degree of the interface transition zone at different loading times according to the damage variable and the stress-strain relationship defined in the damage evolution model of the fiber-matrix interface transition zone; calculating the interface debonding rate according to the calculated damage degree, in combination with the geometric shape and size of the interface transition zone; the interface debonding rate is the ratio of the debonded area to the total area of the interface transition zone.

6. The image method-based initial cracking strength determination method for the UHPC flexural test according to claim 5, characterized in that, The interface initial cracking point time is determined according to a preset interface initial cracking determination standard, which comprises: presetting the interface initial cracking determination standard according to the characteristics of the UHPC material and the test requirements; the interface initial cracking determination standard is set based on the interface debonding rate threshold index; real-time monitoring of the changes of the interface debonding rate and the interface stress in the calculated interface debonding rate and the real-time measured interface stress data; when the interface debonding rate exceeds the pre-set interface debonding rate threshold, the time at this moment is recorded as the interface initial cracking point time.

7. The image method-based initial cracking strength determination method for the UHPC flexural test according to claim 6, characterized in that, The initial flexural strength of the UHPC is calculated by weighted summation, which comprises: obtaining the constructed multi-source initial cracking feature data set and the obtained weight coefficient; performing weighted summation on the surface strain, internal strain anomaly and interface debonding rate in the multi-source initial cracking feature data set according to the weight coefficient obtained by the neural network model to obtain the initial flexural strength of the UHPC; the surface strain is obtained by the DIC algorithm; the internal strain anomaly is determined by the wavelength drift variation coefficient; outputting the initial flexural strength of the UHPC to a test report or a display interface and storing it in a database.

8. System for determining the initial cracking strength in a UHPC flexural test based on the image method, for implementing the method for determining the initial cracking strength in a UHPC flexural test based on the image method according to any one of claims 1 to 7, characterized in that, It comprises: a data acquisition module, an initial cracking feature determination module, an initial cracking time determination module and a strength calculation module; The data acquisition module is used to acquire data of the UHPC specimen during the flexural loading test; The initial cracking feature determination module is used to determine the initial cracking features of the surface, the interior and the interface respectively according to the acquired data; The initial cracking time determination module is used to synchronize the surface initial cracking time, the internal initial cracking time and the interface initial cracking point time to determine the comprehensive initial cracking time; The strength calculation module is used to construct a multi-source initial cracking feature data set and calculate the initial flexural strength of the UHPC by using a neural network model.

9. The image method-based UHPC flexural test initial cracking strength determination system of claim 8, wherein, The initial cracking feature determination module comprises a surface initial cracking feature determination unit, an internal initial cracking feature determination unit and an interface initial cracking feature determination unit. The surface initial cracking feature determination unit is used to process the acquired specimen surface speckle image by using the DIC algorithm, calculate the full-field strain distribution, determine the surface initial cracking time and the local strain concentration area coordinates at the initial cracking time; The internal initial cracking feature determination unit is used to process the wavelength drift data acquired by the fiber Bragg grating sensor array, calculate the wavelength drift variation coefficient and determine the internal initial cracking time; The interface initial cracking feature determination unit is used to calculate the interface debonding rate according to the damage evolution model of the fiber-matrix interface transition zone in combination with the real-time measured interface stress during the loading test to determine the interface initial cracking point time.

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