Detection methods for crack propagation and fracture toughness and related devices

Abstract

The present application relates to the technical field of image detection, and in particular provides a method and related device for detecting crack propagation and fracture toughness. An image set of the failure process is obtained, and the ratio between the pixels and the actual size of the image set of the failure process is calibrated to obtain a conversion ratio coefficient. According to the conversion ratio coefficient, reference points of the reference image and the image to be measured in the image set of the failure process are respectively determined, and crack edge features of the image set of the failure process are processed. A hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation is used for reference point matching, and the crack opening displacement of the fracture model is calculated. The load parameters corresponding to the crack opening displacement and preset geometric parameters are obtained, and the initiation toughness and instability toughness of the fracture model are calculated. The detection efficiency and effect of crack propagation and fracture toughness can be improved.

Description

Technical Field

[0001] This application belongs to the technical field of image detection, and particularly relates to a method and related device for detecting crack propagation and fracture toughness. Background Art

[0002] The double-K fracture model is used in fracture mechanics to describe the initial fracture and unstable fracture during crack propagation. Traditional crack mouth opening displacement measurements rely on mechanical, optical, or electromagnetic extensometers, which usually require direct contact with the crack surface, resulting in easy damage to the measuring equipment, complex operation, and difficulty in ensuring measurement stability, especially in harsh environments. In addition, although optical and electromagnetic extensometers avoid contact, there are still significant limitations in measurement accuracy, automation, and the processing of complex crack morphologies.

[0003] An electronic extensometer based on image recognition is a newly emerging non-contact crack measurement tool in recent years. By collecting and processing images of the crack propagation process, it can achieve high-precision and automated crack mouth opening displacement measurements. This technology has significant advantages such as reducing crack surface interference, simple operation, and full-process automation. However, existing image recognition methods still face many challenges when dealing with complex crack morphologies. Traditional image matching algorithms, such as Normalized Cross-Correlation (NCC), are less sensitive to morphological, scale changes, and rotational transformations during crack propagation, resulting in unstable crack boundary matching results. Especially in a tortuous or dynamically changing crack environment, the measurement accuracy is severely limited. Although some advanced matching algorithms, such as Scale-Invariant Feature Transform (SIFT) or Oriented FAST and Rotated BRIEF (ORB), enhance the ability to handle scale and rotation changes, due to their high computational complexity, their direct application to real-time crack monitoring is still restricted by the hardware computing power. In addition, in the face of high-resolution crack images and large amounts of image data processing, existing algorithms have significant bottlenecks in computational efficiency and are difficult to meet the requirements of real-time monitoring and precise tracking. These problems lead to the inability of image-based electronic extensometers to achieve the expected results in applications, especially in scenarios that require real-time and high-precision monitoring, and there is an urgent need to optimize the algorithms to improve adaptability and robustness.

[0004] Therefore, there are still many deficiencies in the accuracy, automation, robustness, and computational efficiency of existing technologies for crack monitoring. There is an urgent need to develop an image electronic extensometer and a double-K fracture model calculation method with high precision, automation, and the ability to adapt to complex crack propagation to overcome the defects of existing measurement methods and improve the research efficiency and effect of crack propagation behavior. Summary of the Invention

[0005] In view of this, the present application aims to provide a method and related device for detecting crack propagation and fracture toughness, which can improve the detection efficiency and effect of crack propagation and fracture toughness.

[0006] To achieve the above object, the technical solution of the present application is realized as follows:

[0007] A method for detecting crack propagation and fracture toughness, the method for detecting crack propagation and fracture toughness includes:

[0008] Obtain a set of images of the failure process, and calibrate the ratio between the pixels and the actual size of the set of images of the failure process to obtain a conversion ratio coefficient;

[0009] According to the conversion ratio coefficient, respectively determine the reference points of the reference image and the image to be measured in the set of images of the failure process, and perform crack edge feature processing on the set of images of the failure process;

[0010] Use a preset hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation to perform the reference point matching, and calculate the crack opening displacement of the fracture model;

[0011] Obtain the load parameters corresponding to the crack opening displacement and preset geometric parameters, and calculate the initiation toughness and instability toughness of the fracture model.

[0012] In some embodiments, the step of using a preset hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation to perform the reference point matching and calculate the crack opening displacement of the fracture model includes:

[0013] Downsample the reference image to form an image pyramid, wherein the images of each layer in the image pyramid are arranged according to the resolution size;

[0014] Perform a fast Fourier transform on the low-resolution layer of the image pyramid to form a normalized cross-correlation coefficient;

[0015] Use the normalized cross-correlation coefficient to perform layer-by-layer upward matching on the bottom layer of the image pyramid to locate the position of the reference image in the image to be measured; wherein, in response to the preliminary position of the reference point being matched in one layer of the image pyramid, scale the reference image back to the size of the next layer, and use the preliminary position as the starting point of the new position;

[0016] At the high-resolution layer of the image pyramid, perform key point matching on the reference image and the image to be measured to obtain a matching result;

[0017] Determine the target position of the reference point according to the matching result, calculate the pixel distance of the reference image, and the pixel distance of the image to be measured;

[0018] Calculate the crack opening displacement of the fracture model according to the pixel distance of the reference image and the pixel distance of the image to be measured.

[0019] In some embodiments, the downsampling of the reference image to form an image pyramid includes:

[0020] Perform Gaussian blur and downsampling on the reference image to obtain the image pyramid. The calculation formula for the image layer in the image pyramid is as follows:

[0021] ;

[0022] Wherein, I s represents the image pyramid; I represents the reference image; s represents the scaling factor.

[0023] In some embodiments, the normalized cross-correlation coefficient is as follows:

[0024] ;

[0025] Wherein, is a given reference image patch; is an image patch to be measured; F represents the Fourier transform; represents the inverse Fourier transform; represents the image to be measured the conjugate complex of the Fourier transform; and respectively represent the magnitudes of the Fourier transforms of the reference image and the image to be measured.

[0026] In some embodiments, the performing key point matching on the reference image and the image to be measured in the high-resolution layer of the image pyramid to obtain a matching result includes:

[0027] Use the Euclidean distance method to extract the descriptors of the key points on the reference image and the image to be measured in the high-resolution layer of the image pyramid. The calculation formula for the descriptor extraction is as follows:

[0028] ;

[0029] Wherein, d i represents thei The descriptor between the i th key point in the reference image and the x i1 th key point in the image to be measured, used to measure the similarity between two key points in the feature space; ( y i1 ) represents the coordinates of the i th key point in the reference image, x i1 is the abscissa of the i th key point in the reference image in the reference image, y i1 is the ordinate of the i th key point in the reference image in the reference image; ( x i2 ) represents the coordinates of the y i2 th key point in the image to be measured, i is the abscissa of the x i2 th key point in the image to be measured in the image to be measured, i is the ordinate of the y i2 th key point in the image to be measured in the image to be measured; i Calculate the matching degree between the key points, and use the RANSAC algorithm to refine the position and matching. The calculation formula of RANSAC is as follows:

[0030]

[0031] ;

[0032] ;

[0033] where, i represents the index of the key point pair; inliers represents the inliers, that is, the points with an error less than the preset threshold; error i represents the reprojection error of the i th key point pair; represents the function for measuring the error; x i represents the homogeneous coordinates of the i th key point in the reference image; y i represents the homogeneous coordinates of the matching key point in the image to be measured; M is the transformation matrix to be estimated.

[0034] ​In some embodiments, calculating the crack opening displacement of the fracture model based on the pixel distance of the reference image and the pixel distance of the image to be measured includes:

[0035] The calculation formula for the crack opening displacement is as follows:

[0036] ;

[0037] ;

[0038] Where, CMOD is the crack opening displacement; d0 is the pixel distance of the reference image; dn is the pixel distance of the image to be measured; k represents the conversion ratio coefficient, H is the actual distance, h is the pixel distance.

[0039] In some embodiments, the calculation expressions for the initiation toughness and the instability toughness are as follows:

[0040] ;

[0041] ;

[0042] Where, K in represents the initiation toughness; K un represents the instability toughness; P represents the concentrated load; S represents the span of the prismatic specimen; B represents the thickness of the specimen; D represents the height of the specimen; β is the ratio of the span of the prismatic specimen to the height of the specimen, ; represents the fracture height ratio at the initiation moment, , represents the length of the prefabricated notch; represents the fracture height ratio at the instability moment, , and The calculation expressions are as follows:

[0043] ;

[0044] ;

[0045] ;

[0046] ;

[0047] ;

[0048] ;

[0049] ;

[0050] Among them, is the equivalent crack length; , , , and The calculation expressions are as follows:

[0051]

[0052]

[0053] Among them, CMOD u represents the critical crack mouth opening displacement, P u represents the load applied by the press when the critical crack mouth opening displacement is reached; B represents the specimen thickness; E represents the elastic modulus, and the specific calculation expression is as follows:

[0054] ;

[0055]

[0056] Among them, S represents the span of the prismatic specimen; D represents the height of the specimen; is the prefabricated notch length; B represents the specimen thickness; C i represents the initial elastic compliance; CMOD i represents the initial linear part before the peak load in the P-CMOD curve; P i represents reaching CMOD i when the load applied by the press;

[0057] The fracture toughness at the initiation moment is calculated using the fracture height ratio ; the fracture toughness at the instability moment is calculated using the fracture height ratio .

[0058] In some embodiments, the geometric parameters of the fracture model at least include span, cross-sectional height, cross-sectional thickness, and initial crack length, and the load parameters of the fracture model at least include initial crack opening displacement and the corresponding load, unstable crack opening displacement, and the corresponding load.

[0059] Compared with the prior art, the present invention can achieve the following beneficial effects: By improving the combination of the image electronic extensometer of normalized cross-correlation and the double-K fracture model, it effectively realizes the accurate monitoring of the crack propagation process and the effective evaluation of fracture toughness parameters. It can improve the accuracy and efficiency of crack propagation monitoring, especially under non-contact measurement conditions, and has higher practicability and reliability. It can be widely applied to the safety assessment of engineering structures and the research on the fracture properties of materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0061] Figure 1 is a schematic flowchart of the first embodiment of the present application;

[0062] Figure 2 is a schematic work flowchart of an exemplary embodiment of the present application;

[0063] Figure 3 is a schematic flowchart of the first embodiment of the present application;

[0064] Figure 4 is a schematic diagram of the setting of the image electronic extensometer in this embodiment of the present application;

[0065] Figure 5 is a schematic work flowchart of the hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation in the embodiment of the present application;

[0066] Figure 6 is a schematic structural diagram of a computer device provided in the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not constitute a limitation to the present invention. Similar elements in different embodiments are labeled with related similar element numbers. In the following embodiments, many detailed descriptions are provided to enable a better understanding of the present invention. However, those skilled in the art can easily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, or methods. In some cases, some operations related to the present invention are not shown or described in the specification, which is to avoid the core part of the present invention being overwhelmed by excessive descriptions. For those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations based on the descriptions in the specification and general technical knowledge in the art.

[0068] It should be noted that, without conflict, the embodiments and features in the embodiments of the present invention can be combined with each other to form various embodiments. At the same time, the steps or actions in the method description can also be adjusted or reordered in an obvious manner by those skilled in the art. Therefore, the various sequences in the specification and drawings are only for clearly describing a certain embodiment and do not mean that they are the necessary sequences, unless it is stated that a certain sequence must be followed.

[0069] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more.

[0070] In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0071] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. The present application aims to provide an image electronic extensometer and a double-K fracture model calculation method based on improved normalized cross-correlation to overcome the problems of insufficient accuracy, low automation, limited robustness, calculation efficiency, and ability to handle complex crack morphologies in existing technologies for crack propagation monitoring. Different from the existing normalized cross-correlation method, the present application performs image matching and crack propagation analysis through an improved hierarchical matching method based on image pyramid and frequency-domain normalized cross-correlation, which can significantly improve the accuracy of crack opening displacement measurement, realize full-process automatic analysis, and meet the monitoring requirements of complex crack propagation behaviors.

[0072] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of the first embodiment of the present application. Among them, the detection method of crack propagation and fracture toughness may include the following steps:

[0073] Step S101, obtain a set of images of the failure process, and calibrate the ratio between the pixels and the actual size of the set of images of the failure process to obtain a conversion ratio coefficient;

[0074] As an exemplary example, in this embodiment, a high-precision industrial complementary metal-oxide-semiconductor (CMOS) camera can be used to collect images of the material loading failure process. Then, select and load the images in the set of images of the failure process into MATLAB, define the ratio, and calibrate the conversion ratio relationship between the pixels and the actual size.

[0075] Among them, the set of images of the failure process may include at least one of a reference image and a to-be-tested image. The object of the image can be a material, such as ordinary concrete, and the specimen size is 40 mm × 40 mm × 160 mm. A cut with a depth of 10 mm is prefabricated in the middle of the specimen to simulate an initial crack. Images of the material during the loading failure process are collected by a high-precision industrial CMOS camera, and the resolution of the collected images is set to dpi = 500, which can ensure that the subtle changes in the crack opening can be accurately captured.

[0076] Step S102: According to the conversion ratio coefficient, determine the reference points of the reference image and the image to be measured in the set of damage process images respectively, and perform crack edge feature processing on the set of damage process images.

[0077] As an exemplary example, two reference points can be manually selected in the first frame image, located on both sides of the crack respectively, for subsequent measurement of the crack mouth opening displacement. Using these reference points, by inputting the physical actual distance of each reference point, calculate the conversion ratio coefficient between the pixel and the actual size: , H is the actual distance, h is the pixel distance.

[0078] Then, preprocessing can be performed on the reference image and / or the image to be measured to enhance the crack edge features. For example, a method of enhancing the contrast can be adopted to improve the contrast between the crack and the concrete background, making the crack features more obvious for subsequent analysis.

[0079] Step S103: Use a preset hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation to perform the matching of the reference points, and calculate the crack mouth opening displacement of the fracture model.

[0080] As an exemplary example, a hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation can be used for reference point matching and calculating the crack mouth opening displacement, such as the method based on Fourier (FFT)-scale rotation (SIFT / ORB)-matching pyramid.

[0081] Step S104: Obtain the load parameters corresponding to the crack mouth opening displacement and the preset geometric parameters, and calculate the initiation toughness and instability toughness of the fracture model.

[0082] As an exemplary example, in this embodiment, the geometric parameters can at least include: span 100 mm, cross-section height 40 mm, cross-section thickness 40 mm, and initial crack length 10 mm. Using the input geometric parameters and load parameters, calculate the initiation toughness and instability toughness of the double-K fracture model. Among them, in this embodiment, the load parameters include: initial crack mouth opening displacement ( CMOD i ), and the corresponding load ( P i ), instability crack mouth opening displacement ( CMOD u ), and the corresponding load ( P u ).

[0083] In an exemplary embodiment, please refer to Figure 2 which is the flowchart of the present invention;

[0084] First, use a high-precision industrial CMOS camera to collect images of the material loading and failure process to form a set of failure process images; then, select and load the picture data from the set of failure process images into MATLAB to calibrate the conversion ratio between pixels and actual dimensions; then, set two reference points of the electronic extensometer and perform image preprocessing to enhance the crack edge features; then, perform hierarchical matching of the reference points based on the hierarchical matching method of normalized cross-correlation of Fourier (FFT)-scale rotation (SIFT / ORB)-matching pyramid to calculate the crack opening displacement; then, input the basic geometric parameters and load parameters of the double-K fracture model; then, based on the input geometric parameters and load parameters, calculate the initiation toughness and instability toughness of the double-K fracture model.

[0085] In this embodiment, by improving the combination of the image electronic extensometer with the normalized cross-correlation and the double-K fracture model, the accurate monitoring of the crack propagation process and the effective evaluation of the fracture toughness parameters are effectively realized. It can improve the accuracy and efficiency of crack propagation monitoring. Especially under non-contact measurement conditions, it has higher practicability and reliability and can be widely applied to the safety assessment of engineering structures and the research on the fracture properties of materials.

[0086] Please refer to Figure 3 , Figure 3 which is a schematic flow diagram of the second embodiment of this application. Among them, the detection method of crack propagation and fracture toughness may include the following steps:

[0087] Step S301, obtain a set of failure process images, and calibrate the ratio between the pixels and the actual dimensions of the set of failure process images to obtain a conversion ratio coefficient;

[0088] Among them, the implementation manner of step S301 may be as described in step S101 above and will not be elaborated here.

[0089] Step S302, according to the conversion ratio coefficient, respectively determine the reference points of the reference image and the image to be measured in the set of failure process images, and perform crack edge feature processing on the set of failure process images;

[0090] As an exemplary example, two reference points of the electronic extensometer can be set. As Figure 4 shown, manually select two reference points in the first frame of the image, which are located on both sides of the crack respectively, for subsequent measurement of the crack opening displacement. The method of enhancing the contrast is used to improve the contrast between the crack and the concrete background, making the crack features more obvious for subsequent analysis.

[0091] Step S303, downsample the reference image to form an image pyramid, where the images in each layer of the image pyramid are arranged according to the resolution size;

[0092] As an exemplary illustration, the working process of the hierarchical matching method based on image pyramid and frequency-domain normalized cross-correlation is as follows Figure 5 shown; for the reference image and the image to be tested in the image of the destruction process, 4 to 6 layers of image pyramids are respectively constructed. The resolution of each layer is 1 / 2 of the previous layer (2-fold downsampling). Gaussian blur and downsampling are used to reduce the influence of noise. The generation formula of the image layer in the image pyramid is as follows:

[0093] ;

[0094] In the expression, s is the scaling factor, which can be taken as 1 / 2 for example. Each layer in the pyramid will smooth and downsample the original image to obtain images with different resolutions.

[0095] Step S304, perform a fast Fourier transform on the low-resolution layer of the image pyramid to form a normalized cross-correlation coefficient;

[0096] As an exemplary illustration, the fast Fourier transform (FFT) can be used on the low-resolution layer to accelerate the normalized cross-correlation. Specifically, the FFT is used to accelerate the calculation of the normalized cross-correlation (NCC) at the bottom layer (the coarsest resolution) of the pyramid. First, perform FFT transforms on the image to be tested and the reference image block. Calculate its normalized cross-correlation coefficient and find the preliminary matching position. The calculation formula of the normalized cross-correlation coefficient is as follows:

[0097] ;

[0098] In the expression is the given reference image block; is the image block to be tested; F represents the Fourier transform; represents the inverse Fourier transform; represents the image to be tested the conjugate complex number of the Fourier transform; and respectively represent the amplitudes of the Fourier transforms of the reference image and the image to be tested.

[0099] Step S305, using the normalized cross-correlation coefficient, perform layer-by-layer upward matching on the bottom layer of the image pyramid to locate the position of the reference image in the image to be tested; wherein, in response to the preliminary position of the reference point being matched in one layer of the image pyramid, scale the reference image back to the size of the next layer, and use the preliminary position as the starting point of the new position;

[0100] As an exemplary example, the matching can start from the bottom layer of the pyramid and proceed layer by layer upwards, with each layer being refined based on the matching results of the previous layer. The normalized cross-correlation can be used to calculate the matching between the images of each layer and locate the position of the reference image in the image to be tested. If at the layer, the preliminary position of the reference point is obtained , then at the layer, the reference image is scaled back to the size of the layer, and this preliminary estimate is used as the starting point for the new position.

[0101] Step S306: At the high-resolution layer of the image pyramid, perform key-point matching on the reference image and the image to be tested to obtain the matching result;

[0102] As an exemplary example, SIFT or ORB can be used for key-point matching. At the higher-resolution layer of the pyramid (i.e., the finer image layer), the SIFT or ORB method is used to extract the key points and descriptors of the image. Key-point matching is performed on the reference image and the image to be tested. Considering scale and rotation changes, the Euclidean distance metric is used to measure the matching quality, and the calculation expression is as follows:

[0103] ;

[0104] where d i represents the descriptor between the i th key points in the reference image and the image to be tested, which can also be understood as the Euclidean distance between these two key points. This value measures the similarity between the two key points in the feature space. The smaller the distance, the more similar the two key points are; ( x i1 , y i1 ) represents the coordinates of the i th key point in the reference image, x i1 is the abscissa of this key point in the reference image, y [[ID=D9]] i1 is the ordinate of this key point in the reference image; ( x i2 , y i2 ) represents the coordinates of the i th key point in the image to be tested, x i2 is the abscissa of this key point in the image to be tested, y i2 is the ordinate of this key point in the image to be tested.

[0105] Then, the matching degree between key points can be calculated, and the RANSAC algorithm can be used to further refine the position and matching. The process of RANSAC is as follows:

[0106] ;

[0107] ;

[0108] Among them, i represents the index of the key point pair; inliers represent inlier points, that is, points with an error less than a preset threshold; error i represents the reprojection error of the i th key point pair; represents a function for measuring the error. For example, it can be a threshold function used to determine whether a point pair is an "inlier point"; x i represents the homogeneous coordinates of the i th key point in the reference image; y i the homogeneous coordinates of the matching key point in the image to be measured; M is the transformation matrix to be estimated.

[0109] Step S307, determine the target position of the reference point according to the matching result, calculate the pixel distance of the reference image, and the pixel distance of the image to be measured;

[0110] As an exemplary example, the accurate position of the reference point can be determined through the matching result extracted by SIFT or ORB. Combining with the NCC method, the matching accuracy can be further refined on the high-resolution layer. Output the matching result: output the final position of the reference point according to the optimal matching position, calculate the initial image pixel distance and the current image pixel distance to be measured; convert to the actual displacement according to the scale factor.

[0111] Step S308, calculate the crack opening displacement of the fracture model according to the pixel distance of the reference image and the pixel distance of the image to be measured.

[0112] As an exemplary example, the calculation of the crack opening displacement ( CMOD ) is as follows: the specific calculation formula is as follows:

[0113] ;

[0114] Among them, CMOD is the crack opening displacement; d0 is the pixel distance of the reference image (unit: pixels); dn is the pixel distance of the image to be measured (unit: pixels);k represents the conversion ratio coefficient, defined as the ratio of the actual distance H (in mm) to the pixel distance h (in pixels). .

[0115] Step S309: Obtain the load parameters corresponding to the crack opening displacement and the preset geometric parameters, and calculate the initiation toughness and instability toughness of the fracture model.

[0116] As an exemplary example, the calculation expressions for the initiation toughness and the instability toughness are as follows:

[0117] ;

[0118] ;

[0119] where K in represents the initiation toughness; K un represents the instability toughness; P represents the concentrated load; S represents the span of the prismatic specimen (in mm); B represents the thickness of the specimen (in mm); D represents the height of the specimen (in mm); β is the ratio of the span of the prismatic specimen to the height of the specimen, ; represents the fracture height ratio at the initiation moment, , represents the length of the prefabricated notch; represents the fracture height ratio at the instability moment, 、 and The calculation expressions are as follows:

[0120] ;

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] ;

[0127] Among them, is the equivalent crack length; , , , and The calculation expressions are as follows:

[0128]

[0129]

[0130] Among them, CMOD u represents the critical crack mouth opening displacement (unit: mm), P u represents the load applied by the press when the critical crack mouth opening displacement is reached (unit: N); B represents the thickness of the specimen (unit: mm); E represents the elastic modulus, and the specific calculation expression is as follows:

[0131] ;

[0132]

[0133] Among them, S represents the span of the prismatic specimen (unit: mm); D represents the height of the specimen (unit: mm); is the prefabricated notch length; B represents the thickness of the specimen (unit: mm); C i represents the initial elastic compliance; CMOD i represents the initial linear part before the peak load in the P-CMOD curve; P i represents reaching CMOD i when the load applied by the press (unit: N);

[0134] The initiation toughness is calculated using the fracture height ratio at the initiation moment; the instability toughness is calculated using the fracture height ratio at the instability moment.

[0135] In an exemplary embodiment, the basic geometric parameters and load parameters of the input fracture model are provided. Among them, the geometric parameters may at least include a span of 100 mm, a cross-sectional height of 40 mm, a cross-sectional thickness of 40 mm, and an initial crack length of 10 mm; the load parameters may at least include the initial crack opening displacement ( CMOD i ) and the corresponding load (P i ), the opening displacement of the instability crack ( CMOD u ), and the corresponding load ( P u ).

[0136] Through the above operation steps, the measured results of the crack opening displacement and the double-K fracture model parameters in the embodiment are shown in Table 1 as follows:

[0137] Table 1 Results of crack opening displacement and double-K fracture model parameters in the embodiment of the present application

[0138]

[0139] The results of the crack propagation experiment show that the initiation toughness is relatively small, which indicates that the crack will start to propagate under lower load conditions, conforming to the mechanical properties of brittle materials such as concrete. The method based on the image electronic extensometer is more accurate than the traditional mechanical extensometer and can capture the initial initiation of cracks more sensitively, avoiding the interference of contact measurement. As the crack propagates, the resistance of the material to the crack increases, and the instability toughness is significantly higher than the initiation toughness. The formation of the plastic zone at the crack tip and the redistribution of the stress field are the main reasons for this enhancement.

[0140] In the experimental data, the initiation load and the instability load are 643.0 N and 1273.4 N respectively, and the latter is almost twice the former, showing a significant blunting effect after the initial crack propagation, enabling the material to withstand higher loads. The crack opening displacement ( CMOD ), is crucial for crack propagation and material properties. The small displacement change at the initial stage of the crack can significantly affect the stress state of the material, emphasizing the importance of accurately monitoring the crack opening displacement for early repair. Through the non-contact and automated measurement of the image electronic extensometer, the accuracy and efficiency of crack propagation monitoring can be improved, which has important value in engineering applications.

[0141] This embodiment uses a non-contact measurement method based on an image electronic extensometer. Through a hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation of Fourier-scale rotation-matching pyramid, using the fast Fourier transform (FFT) to accelerate the calculation, initially perform a preliminary matching on the low-resolution image, then use the method of pyramid images to refine the matching layer by layer, and finally further process the problems of rotation and scale invariance at the high-resolution level through the scale-invariant feature matching (SIFT / ORB) method. This hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation can improve the calculation efficiency and robustness of the algorithm while ensuring accuracy, overcoming the disadvantages of the traditional normalized cross-correlation (NCC) method, such as large computational amount and insensitivity to rotation and scale changes.

[0142] Meanwhile, through the image processing and numerical calculation functions of MATLAB, this application can directly extract geometric parameters from crack propagation images and automatically calculate the initial fracture toughness and unstable fracture toughness of the double-K fracture model in combination with load data. Compared with the traditional method that requires manual measurement and calculation, the automated processing flow of this application significantly reduces the operation complexity and improves the accuracy and consistency of the analysis process.

[0143] In addition, based on the combination of an image electronic extensometer and numerical calculation, it is applicable to the study of complex crack propagation behaviors and has high adaptability and practicality. By performing image preprocessing (including contrast enhancement, Gaussian filtering, and edge detection) to handle images of different qualities and features, it can effectively extract crack features and displacement parameters under various crack morphologies and background conditions, and is widely applicable to the fracture safety assessment of engineering structures and the study of the mechanical properties of materials.

[0144] Correspondingly, according to the embodiments of this application, this application also provides a computer device, a readable storage medium, and a computer program product.

[0145] Figure 6 It is a schematic structural diagram of a computer device 12 provided in the embodiments of this application. Figure 6 It shows a block diagram of an exemplary computer device 12 suitable for implementing the embodiments of this application. Figure 6 The shown computer device 12 is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of this application.

[0146] As Figure 6 shown, the computer device 12 is presented in the form of a general-purpose computing device. The computer device 12 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of this application described and / or claimed herein.

[0147] The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0148] Bus 18 represents one or more of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, an Accelerated Graphics Port, a processor bus, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, these architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0149] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including both volatile and nonvolatile media, removable and non-removable media.

[0150] System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 can further include other removable / non-removable, volatile / nonvolatile computer system storage media. By way of example only, storage system 34 can be used for reading from and writing to non-removable, nonvolatile magnetic media ( Figure 6 not shown and typically called a "hard disk drive"). Although Figure 6 not shown in the figures, a disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from and writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) can be provided. In these instances, each drive can be connected to bus 18 by one or more data media interfaces. Memory 28 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of the embodiments of the present application.

[0151] A program / utility 40 having a set (at least one) of program modules 42 can be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may include an implementation of a networking environment. The program modules 42 typically carry out the functions and / or methods of the embodiments described in the present application.

[0152] The computer device 12 can also communicate with one or more external devices 14 (such as keyboards, pointing devices, displays 24, etc.), and can also communicate with one or more devices that enable users to interact with the computer device 12, and / or communicate with any device that enables the computer device 12 to communicate with one or more other computing devices (such as network cards, modems, etc.). Such communication can be carried out through the input / output (I / O) interface 22. Moreover, the computer device 12 can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through the network adapter 20. As shown in the figure, the network adapter 20 communicates with other modules of the computer device 12 through the bus 18. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0153] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing the crack propagation and fracture toughness detection methods provided by the embodiments of the present application.

[0154] Embodiments of the present application also provide a non-transitory computer-readable storage medium storing computer instructions, on which a computer program is stored. When the program is executed by a processor, the crack propagation and fracture toughness detection methods provided by all inventive embodiments of the present application are implemented.

[0155] The computer storage medium of the embodiments of the present application can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. More specific examples (non-exhaustive list) of the computer-readable storage medium include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, the computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in combination with an instruction execution system, apparatus, or device.

[0156] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0157] The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination of the foregoing. The computer program code for performing the operations of the present application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0158] The embodiments of the present application also provide a computer program product, including a computer program, which when executed by a processor implements the detection method for crack propagation and fracture toughness according to the foregoing.

[0159] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the disclosure of the present application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution disclosed in the present application can be achieved, and no limitation is imposed herein.

[0160] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present application shall be included within the protection scope of the present application.

Claims

1. A method for detecting crack propagation and fracture toughness, characterized in that, The detection method for crack propagation and fracture toughness includes: Obtaining a set of images of the failure process, and calibrating the ratio between the pixels and the actual size of the set of images of the failure process to obtain a conversion ratio coefficient; According to the conversion ratio coefficient, respectively determining reference points of a reference image and a to-be-tested image in the set of images of the failure process, and performing crack edge feature processing on the set of images of the failure process; Using a preset hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation to perform the reference point matching, and calculating the crack opening displacement of the fracture model; Obtaining the load parameters corresponding to the crack opening displacement and preset geometric parameters, and calculating the initiation toughness and instability toughness of the fracture model; The step of using a preset hierarchical matching method based on image pyramid and frequency domain normalized cross-correlation to perform the reference point matching and calculating the crack opening displacement of the fracture model includes: Performing downsampling on the reference image to form an image pyramid, wherein each layer of images in the image pyramid is arranged according to the resolution size; Performing a fast Fourier transform on the low-resolution layer of the image pyramid to form a normalized cross-correlation coefficient; Using the normalized cross-correlation coefficient to perform layer-by-layer upward matching on the bottom layer of the image pyramid to locate the position of the reference image in the to-be-tested image; wherein, in response to the preliminary position of the reference point being matched in one layer of the image pyramid, scaling the reference image back to the size of the next layer, and using the preliminary position as the starting point of the new position; Using a key point matching algorithm to extract key points and descriptors of the images on the high-resolution layer of the image pyramid, and performing key point matching on the reference image and the to-be-tested image to obtain a matching result; Determining the target position of the reference point according to the matching result, calculating the pixel distance of the reference image, and the pixel distance of the to-be-tested image; Calculating the crack opening displacement of the fracture model according to the pixel distance of the reference image and the pixel distance of the to-be-tested image.

2. The detection method of crack propagation and fracture toughness according to claim 1, characterized in that, The step of performing downsampling on the reference image to form an image pyramid includes: Performing Gaussian blur and downsampling on the reference image to obtain the image pyramid, and the calculation formula for the image layer in the image pyramid is as follows: ； Among them, I s represents the image pyramid; I represents the reference image; s represents the scaling factor.

3. The detection method for crack propagation and fracture toughness according to claim 1, characterized in that, The normalized cross-correlation coefficient is as follows: ； Among them, is a given reference image block; is an image block to be measured; F represents the Fourier transform; represents the inverse Fourier transform; represents the image to be measured the conjugate complex number of the Fourier transform; and respectively represent the amplitudes of the Fourier transforms of the reference image and the image to be measured.

4. The method for detecting crack propagation and fracture toughness according to claim 1, wherein The step of performing key point matching on the reference image and the to-be-tested image on the high-resolution layer of the image pyramid to obtain a matching result includes: Using the Euclidean distance method to extract descriptors of the key points on the high-resolution layer of the image pyramid for the reference image and the to-be-tested image, and the calculation formula for the descriptor extraction is as follows: ； Among them, d i represents the descriptor between the i -th key point in the reference image and the i -th key point in the image to be measured, and is used to measure the similarity degree of the two key points in the feature space; ( x i1 , y i1 ) represents the coordinates of the i -th key point in the reference image, x i1 is the abscissa of the i -th key point in the reference image in the reference image, y i1 is the ordinate of the i -th key point in the reference image in the reference image; ( x i2 , y i2 ) represents the coordinates of the i -th key point in the image to be measured, x i2 is the abscissa of the i -th key point in the image to be measured in the image to be measured, y i2 the ordinate of the i -th key point in the image to be measured in the image to be measured; Calculating the matching degree between the key points, and using the RANSAC algorithm to refine the position and matching, and the calculation formula of RANSAC is as follows: ； ； Among them, i represents the index of the key point pair; inliers represent inlier points, that is, points with an error less than a preset threshold; error i represents the reprojection error of the i th key point pair; represents a function for measuring the error; x i represents the homogeneous coordinates of the i th key point in the reference image; y i represents the homogeneous coordinates of the matching key point in the image to be measured; M is the transformation matrix to be estimated.

5. The detection method of crack propagation and fracture toughness according to claim 1, characterized in that, The step of calculating the crack opening displacement of the fracture model according to the pixel distance of the reference image and the pixel distance of the to-be-tested image includes: The calculation formula for the crack opening displacement is as follows: ； ； Wherein, CMOD is the crack opening displacement; d0 is the pixel distance of the reference image; dn is the pixel distance of the image to be measured; k represents the conversion ratio coefficient, H is the actual distance, h is the pixel distance.

6. The detection method for crack propagation and fracture toughness according to claim 1, wherein The calculation expressions of the initiation toughness and the instability toughness are as follows: ； ； Among them, K in represents the crack initiation toughness; K un represents the instability toughness; P represents the concentrated load; S represents the span of the prismatic specimen; B represents the thickness of the specimen; D represents the height of the specimen; β is the ratio of the span of the prismatic specimen to the height of the specimen, ; represents the fracture height ratio at the crack initiation moment, , represents the prefabricated notch length; represents the fracture height ratio at the instability moment, 、 and The calculation expressions are as follows: ； ； ； ； ； ； ； Among them, is the equivalent crack length; , , , and The calculation expressions are as follows: Among them, CMOD u represents the critical crack mouth opening displacement, P u represents the load applied by the press when the critical crack mouth opening displacement is reached; B represents the thickness of the specimen; E represents the elastic modulus, and the specific calculation expression is as follows: ； Among them, S represents the span of the prismatic specimen; D represents the height of the specimen; is the precast notch length; B represents the thickness of the specimen; C i represents the initial elastic flexibility; CMOD i represents the initial linear part before the peak load in the P-CMOD curve; P i represents reaching CMOD i when the press applies the load; Using the fracture height ratio at the crack initiation moment calculate the crack initiation toughness; using the fracture height ratio at the instability moment calculate the instability toughness.

7. The detection method for crack propagation and fracture toughness according to claim 1, characterized in that The geometric parameters of the fracture model at least include the span, the cross-sectional height, the cross-sectional thickness, and the initial crack length. The load parameters of the fracture model at least include the initial crack opening displacement and the corresponding load, the instability crack opening displacement, and the corresponding load.

8. A computer device, characterized in that, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for detecting crack propagation and fracture toughness according to any one of claims 1 to 7.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the method for detecting crack propagation and fracture toughness according to any one of claims 1 to 7.

Images