Concrete tensile nonlinear creep monitoring system and method based on dic technology

Abstract

The present application relates to concrete tensile creep monitoring technical field, specifically to concrete tensive nonlinear creep monitoring system and method based on DIC technology, the method comprises the following steps: based on DIC technology, the monitoring image of concrete is acquired;Identify the speckle in the monitoring image, carry out whole pixel displacement search according to the speckle, obtain speckle whole pixel displacement point;With the speckle whole pixel displacement point as reference point, subpixel displacement is solved, and speckle subpixel coarse displacement point is obtained;The speckle subpixel coarse displacement point is carried out grey value interpolation, and speckle subpixel fine displacement point is obtained based on interpolation result, and the speckle subpixel fine displacement point is used, and concrete tensile nonlinear creep monitoring is completed.The present application uses DIC technology to acquire concrete deformation image in real time, then the displacement change result is obtained by whole pixel and subpixel analysis to image, the problems that the measuring range of traditional contact type concrete tensile creep monitoring is small and easy to slip are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of concrete tensile creep monitoring, in particular to a concrete tensile nonlinear creep monitoring system and method based on DIC technology. BACKGROUND

[0002] The tensile nonlinear creep of concrete is a plastic deformation of concrete material under long-term load due to changes in the internal microstructure, which has an important influence on the long-term stability and safety of concrete structures. The existing tensile creep test methods mostly use contact methods including strain gauge test method, optical fiber sensor method, etc., but these methods have problems such as small measurement range, easy to slip, not easy to fix, etc., especially in high temperature and complex stress environment, the measurement accuracy and reliability are greatly limited. SUMMARY

[0003] In view of the shortcomings of the existing methods and the needs of practical application, in order to improve the environmental adaptability of concrete tensile nonlinear creep monitoring, the traditional contact type concrete tensile creep monitoring has the problems of small measurement range, easy to slip and not easy to fix. On the one hand, the present application provides a concrete tensile nonlinear creep monitoring method based on DIC technology, comprising the following steps:

[0004] Based on DIC technology, the monitoring image of the concrete is obtained; the speckle in the monitoring image is identified, the whole-pixel displacement search is carried out according to the speckle, the speckle whole-pixel displacement point is obtained; the sub-pixel displacement is solved with the speckle whole-pixel displacement point as the reference point, and the speckle sub-pixel coarse displacement point is obtained; the speckle sub-pixel coarse displacement point is subjected to gray value interpolation, and the speckle sub-pixel fine displacement point is obtained based on the interpolation result, and the concrete tensile nonlinear creep monitoring is completed by using the speckle sub-pixel fine displacement point. The present application uses DIC technology to obtain the concrete deformation image in real time, then carries out whole-pixel and sub-pixel analysis on the image to obtain the displacement change result, and then completes the concrete tensile nonlinear creep monitoring according to the displacement change, which solves the problems of small measurement range, easy to slip and not easy to fix of the traditional contact type concrete tensile creep monitoring through the non-contact image data processing mode.

[0005] Optionally, the concrete tensile nonlinear creep monitoring method based on DIC technology further comprises distortion correction of the monitoring image. Distortion correction of image data can improve image quality and further improve the accuracy of the present application.

[0006] Optionally, the whole-pixel displacement search according to the speckle to obtain the speckle whole-pixel displacement point comprises the following steps:

[0007] A speckle deformation sub-region similarity model is constructed, and the similarity between the speckle deformation before and after deformation is obtained using this model. Based on the similarity, an iterative optimization method is used to search for integer pixel displacements to obtain the speckle pixel displacement points. This invention uses an iterative optimization method to quickly locate integer pixel displacement intervals, which helps improve the monitoring efficiency of this invention.

[0008] Optionally, the speckle deformation sub-region similarity model satisfies the following formula:

[0009] , in, Indicates speckle integer similarity. Represents reference speckle image grayscale value at that location Represents the target speckle image grayscale value at that location The radius of the sub-region is represented by the speckle deformation sub-region similarity model provided by this invention. This model can accurately assess the differences between the image before and after deformation, facilitating the rapid localization of integer pixel displacement intervals.

[0010] Optionally, the iterative optimization method satisfies the following formula:

[0011] , in, Indicates the first The individual in the first Position at the next iteration Indicates the maximum number of iterations. Indicates the number of iterations. Indicates the first The individual in the first Position at the next iteration Indicates the first The average position of all individuals in the population at the next iteration. Indicates the first The average speckle integer pixel similarity of all individuals in the population at the next iteration. Indicates the first The speckle integer pixel similarity of the best individual in the next iteration. Indicates the first The individual in the first Speckle integer pixel similarity at the next iteration This indicates that the random number follows the flight path of Levi. Represents a random number in the range [-1, 1]. Indicates the first The optimal individual in the next iteration.

[0012] The iterative optimization method of the application can balance local optimization accuracy and global optimization speed, and is further beneficial to quickly positioning the integer-pixel displacement interval.

[0013] Optionally, the sub-pixel displacement is solved based on the integer-pixel displacement point of the speckle to obtain a speckle sub-pixel coarse displacement point, and the following formula is met:

[0014] , wherein, the first solving parameter is represented by, the gray value at the reference speckle image is represented by, the second solving parameter is represented by, the gray value at the target speckle image is represented by, the integer-pixel displacement in the direction is represented by, the integer-pixel displacement in the direction is represented by, the sub-pixel displacement in the direction is represented by, the sub-pixel displacement in the direction is represented by, the gray gradient operator in the direction is represented by, the gray gradient operator in the direction is represented by, the gray gradient operator in the direction is represented by, the gray gradient operator in the direction is represented by, the gray gradient operator in the direction is represented by, the gray gradient operator in the direction is represented by, the gray gradient operator in the direction is represented by, the gray gradient operator in the direction is represented by.

[0015] Optionally, the first solving parameter meets the following formula:

[0016] , wherein, the first solving parameter is represented by, the number of integer-pixel points in the target region is represented by, the gray value at the target region is represented by, the gray value at the reference region is represented by, the gray value at the reference region is represented by, the offset change of the gray value is represented by.

[0017] Optionally, the second solving parameter meets the following formula:

[0018] , wherein, the second solving parameter is represented by, the number of integer-pixel points in the target region is represented by, the gray value at the target region is represented by,​​​ a gray value at the reference region, a gray value at the reference region a gray value at the reference region, a scale change of the gray value.

[0019] Optionally, the gray value interpolation of the speckle sub-pixel coarse displacement point satisfies the following formula:

[0020] , wherein, a gray value at the reference region a gray value at the reference region, a to-be-determined coefficient.

[0021] Through the gray value interpolation method of the present application, the gray value of the interpolation point can be accurately reflected, which is further beneficial to accurately obtaining the final displacement change value.

[0022] In a second aspect, in order to efficiently execute the concrete tensile nonlinear creep monitoring method provided by the present application, the present application further provides a concrete tensile nonlinear creep monitoring system based on DIC technology, which comprises a processor, an input device, an output device and a memory, and the processor, the input device, the output device and the memory are connected with each other, wherein the memory is used for storing a computer program, the computer program contains program instructions, and the processor is configured to call the program instructions to execute the concrete tensile nonlinear creep monitoring method based on DIC technology as described in the first aspect of the present application. The concrete tensile nonlinear creep monitoring system based on DIC technology has a compact structure and stable performance, can stably execute the concrete tensile nonlinear creep monitoring method based on DIC technology provided by the present application, and further improves the overall applicability and practical application ability of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 A concrete tensile nonlinear creep monitoring method based on DIC technology provided by the present application is shown in the flowchart;

[0024] Figure 2 A concrete tensile nonlinear creep monitoring system based on DIC technology provided by the present application is shown in the block diagram;

[0025] Figure 3 A concrete tensile nonlinear creep monitoring device based on DIC technology provided by the present application is shown in the structural schematic diagram. DETAILED DESCRIPTION

[0026] Specific embodiments of the present application will now be described in detail with reference to the following figures. A person of ordinary skill in the art will immediately appreciate that the embodiments described herein are merely for illustration and should not be taken as limiting the application. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. It will be apparent, however, to one skilled in the art that the specific details need not be used in implementing the present application. In other instances, well-known circuits, software or processes have not been described in detail in order to avoid obscuring the present application.

[0027] Reference throughout this specification to "one embodiment", "an embodiment", "one example", or "an example" means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the application. Thus, appearances of the phrases "in one embodiment" or "in an embodiment" or "one example" or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics can be combined in any suitable

[0028] Reference is made to Figure 1 In order to improve the adaptability of concrete tensile nonlinear creep monitoring to the environment, the traditional contact type concrete tensile creep monitoring has the problems of small measurement range, easy to slip and not easy to fix. The application provides a concrete tensile nonlinear creep monitoring method based on DIC technology, as shown in Figure 1 The method comprises the following steps in one embodiment:

[0029] S1, based on the DIC technology, the monitoring image of the concrete is acquired.

[0030] DIC technology, full name Digital Image Correlation, is an advanced non-contact full-field deformation measurement technology. Its basic principle is to track (or match) the position of the same pixel point in the speckle image before and after the deformation of the object surface to obtain the displacement vector of the pixel point, so as to obtain the full-field displacement of the specimen surface. In simple terms, the sample surface containing pixel feature points is photographed during the experiment, and the displacement information of the sample during the experiment can be obtained according to the mathematical algorithm after selecting the reference image.

[0031] A typical DIC measurement system is usually composed of a CCD camera, an illumination light source, an image acquisition card and a computer. During the experiment, the imaging surface of the specimen needs to have a random speckle pattern that can reflect the deformation information, then the images of the specimen surface before and after loading are collected and stored in the computer, and finally the displacement information of the specimen surface is obtained by using software program and related mathematical algorithm.

[0032] In an embodiment, based on the DIC technology, the monitoring image of the concrete is obtained, including configuring a high-resolution CCD camera for capturing the speckle image of the concrete surface; ensuring that the illumination light source is stable and uniform to reduce shadows and reflections in the image; using an image acquisition card to connect the camera with the computer to realize real-time transmission and processing of the image.

[0033] Test piece preparation: random speckle patterns are made on the surface of the concrete test piece, which can be achieved by spraying white primer and scattering black or colored speckles. The quality of the speckle pattern directly affects the measurement accuracy of the DIC technology. Ensure that the test piece surface is flat, free of oil and impurities to obtain clear images.

[0034] Image acquisition before loading: before loading the concrete test piece, use the CCD camera to capture the speckle image on its surface as the reference image, ensuring that the image is clear, free of blur and distortion.

[0035] Image acquisition during loading: gradually increase the load on the concrete test piece while capturing the speckle image on its surface in real time. According to the experimental requirements, different loading rates and load levels can be set. At each load level, multiple images should be captured to ensure data reliability and accuracy.

[0036] Camera calibration is a key step to ensure the accuracy of the Digital Image Correlation (DIC) technology. In other embodiments, after configuring a high-resolution CCD camera, the camera needs to be calibrated and the distortion of the obtained monitoring image needs to be corrected.

[0037] Camera calibration methods include traditional camera calibration, active vision camera calibration, camera self-calibration, and zero-distortion camera calibration. Traditional camera calibration requires the use of a known size calibration object, such as a flat calibration board or a three-dimensional calibration block. By establishing the correspondence between the coordinates of the points on the calibration object and their image points, the internal and external parameters of the camera model are obtained using certain algorithms. The flat calibration board is easy to make and its accuracy is easy to guarantee, but it requires the acquisition of two or more images during calibration.

[0038] Active vision camera calibration uses encoders, displacement tables and other external auxiliary items to obtain camera motion information. The algorithm is simple, but the system cost is high, the experimental equipment is expensive, and the experimental conditions are high.

[0039] Camera self-calibration uses the vanishing point formed by some parallel lines in the scene for calibration, which is flexible and can be used for online calibration of the camera.

[0040] Zero distortion camera calibration method takes LCD display screen as reference datum, takes phase shift grating as medium, establishes mapping relationship between LCD pixels and camera sensor pixels, does not need to know camera internal structure, does not need to establish geometric model, and each pixel vector is independently calibrated in space.

[0041] Further, distortion correction is performed on the obtained monitoring image, including establishing a distortion model, and establishing the distortion model according to the results of camera calibration. Common distortion models include radial distortion models, tangential distortion models and image plane distortion models. The distortion model is usually represented as a function of a series of parameters, which can be obtained through the camera calibration process.

[0042] Distortion correction implementation: distortion correction is performed on the monitoring image using the distortion model and the results of camera calibration, usually including an iterative optimization process to minimize the difference between the actual image points and the theoretical image points. The corrected image should be as close to ideal imaging as possible, i.e. the object is completely similar and there is no local deformation.

[0043] Verification of correction effect: quality evaluation is performed on the corrected image, including clarity, contrast, distortion degree and other indicators. Known geometric structures can be used for verification to compare the shape and size of the object in the image before and after correction.

[0044] It should also be noted that the accuracy of camera calibration directly affects the effect of distortion correction, so high-quality calibration boards should be used in the calibration process and the calibration environment should be stable; different distortion models are suitable for different cameras and lenses, so the distortion model should be selected according to the characteristics of the camera and lens; optimization of the distortion correction algorithm can improve the correction effect and calculation efficiency, so the robustness and calculation efficiency of the algorithm should be fully considered when implementing the correction algorithm; the method of verifying the correction effect should be scientific and accurate, and known geometric structures can be used for verification, or image processing software can be used for quality evaluation.

[0045] S2, identifying the speckle in the monitoring image, performing integer pixel displacement search according to the speckle, and obtaining a speckle integer pixel displacement point.

[0046] Specifically, identifying the speckle in the monitoring image first requires extracting speckle features, such as speckle contrast, which is an important indicator of the degree of light and dark change in the speckle pattern. By calculating the difference in gray value between adjacent pixels in the speckle pattern, the speckle contrast can be obtained. The extracted speckle features are matched with the known speckle feature library to identify the speckle in the monitoring image.

[0047] Further, the integer pixel displacement search according to the speckle to obtain a speckle integer pixel displacement point includes the following steps:

[0048] S21. Construct a speckle deformation sub-region similarity model, and use the speckle deformation sub-region similarity model to obtain the similarity of the speckle before and after deformation.

[0049] Specifically, the speckle deformation sub-region similarity model satisfies the following formula:

[0050] , in, Indicates speckle integer similarity. Represents reference speckle image grayscale value at that location Represents the target speckle image grayscale value at that location This represents the sub-region radius. In the embodiments, the sub-region radius is typically 3 or 4.

[0051] S22. Based on the similarity, perform integer pixel displacement search using an iterative optimization method to obtain speckle integer pixel displacement points.

[0052] It should be understood that images are stored in computers as binary data. The color of all image pixels can be represented by numbers, hence the name digital image. In an 8-bit system, if the possible brightness values ​​of pixels are arranged horizontally from left to right in order from 0 to 255, with 0 corresponding to pure black and 255 corresponding to pure white, a continuous scale is formed. This scale is called the grayscale scale, and the brightness corresponding to each scale is called a grayscale value.

[0053] An integer pixel, also known as a coded valid pixel, is the basic pixel unit in an image. In image processing, an integer pixel represents the smallest resolvable unit of an image. When an image is captured or displayed, integer pixels are the basic elements that make up the image. For example, in a physical scanning process, a line of scanning may contain 300 pixels, and these pixels are integer pixels.

[0054] Subpixels are even smaller units between two whole pixels. During camera imaging, due to the limitations of the image sensor, each pixel on the imaging surface only represents the nearby color. However, at the microscopic level, there are countless tiny things between two actual physical pixels; these pixels existing between two physical pixels are called subpixels. Due to the deformation of the measured surface, points in the deformed image will inevitably fall on subpixel positions that have no grayscale value.

[0055] Specifically, based on a heuristic swarm intelligence optimization search algorithm, a population is formed with reference points and target points as individuals, and the optimal individual is searched using an iterative optimization method to obtain speckle integer pixel displacement points.

[0056] Furthermore, the similarity is used as the evaluation function for an individual. The closer the similarity is to 1, the more similar the two are, and the better the solution represented by the individual.

[0057] Furthermore, the iterative optimization method satisfies the following formula:

[0058] , in, Indicates the first The individual in the first Position at the next iteration Indicates the maximum number of iterations. Indicates the number of iterations. Indicates the first The individual in the first Position at the next iteration Indicates the first The average position of all individuals in the population at the next iteration. Indicates the first The average speckle integer pixel similarity of all individuals in the population at the next iteration. Indicates the first The speckle integer pixel similarity of the best individual in the next iteration. Indicates the first The individual in the first Speckle integer pixel similarity at the next iteration This indicates that the random number follows the flight path of Levi. Represents a random number in the range [-1, 1]. Indicates the first The optimal individual in the next iteration. The target point represented by the optimal individual is the speckle integer pixel displacement point.

[0059] S3. Using the speckle pixel displacement point as the reference point, solve for the sub-pixel displacement to obtain the speckle sub-pixel coarse displacement point.

[0060] In this embodiment, the sub-pixel displacement is calculated using the speckle integer pixel displacement point as the reference point to obtain the speckle sub-pixel coarse displacement point, which satisfies the following formula:

[0061] , in, Indicates the first solution parameter. Represents reference speckle image grayscale value at that location Indicates the second solution parameter. Represents the target speckle image grayscale value at that location express Integer pixel displacement in direction express Integer pixel displacement in direction express Subpixel displacement in direction, express Subpixel displacement in direction, express Gray-scale gradient operator in direction, express The gray-level gradient operator can be any of the Horn operator, natural spline operator, or Barron operator.

[0062] Furthermore, the first solution parameter satisfies the following formula:

[0063] , in, Indicates the first solution parameter. This indicates the number of integer pixels in the target area. Indicates the target area grayscale value at that location Indicates reference area grayscale value at that location This indicates the shift in grayscale.

[0064] The second solution parameter satisfies the following formula:

[0065] , in, Indicates the second solution parameter. This indicates the number of integer pixels in the target area. Indicates the target area grayscale value at that location Indicates reference area grayscale value at that location This indicates the scale variation of grayscale.

[0066] S4. Perform grayscale interpolation on the speckle subpixel coarse displacement points, obtain speckle subpixel fine displacement points based on the interpolation results, and use the speckle subpixel fine displacement points to complete the monitoring of concrete tensile nonlinear creep.

[0067] In this embodiment, step S4 involves interpolating the grayscale values ​​of the speckle sub-pixel coarse displacement points, satisfying the following formula:

[0068] , in, express Gray value interpolation at the location, This represents an undetermined coefficient. Undetermined coefficients can be obtained from the grayscale values ​​of integer pixels.

[0069] Further, based on the interpolation result, a speckle sub-pixel fine displacement point is obtained, and a correlation function is used to fit the sub-pixel around the center point of the target sub-region by a fitting rule, and an extreme point of the fitting result is found, that is, the speckle sub-pixel fine displacement point.

[0070] In the embodiment, the correlation function includes a cross-correlation function, a least square distance correlation function and a parameter least square distance correlation function, the cross-correlation function includes a direct cross-correlation function, a normalized cross-correlation function, a zero-mean normalized cross-correlation function and a direct least square distance correlation function, the least square distance correlation function includes a direct least square distance correlation function, a normalized least square distance correlation function and a zero-mean normalized least square distance correlation function, and the parameter least square distance correlation function includes a least square distance correlation function with an unknown parameter a, a least square distance correlation function with an unknown parameter b and a least square distance correlation function with two unknown parameters a and b.

[0071] The fitting method mainly includes a surface fitting method and a moving least square method, the surface fitting method is mainly used for general speckle image analysis, but if the data is a sequence image, the moving least square method can be used.

[0072] Further, the actual displacement of the speckle image is solved through the coordinates of the speckle sub-pixel fine displacement point and the coordinates of the reference point, and then the displacement value is used to analyze the concrete tensile nonlinear creep monitoring result.

[0073] The present application adopts non-contact measurement to avoid interference, does not exert additional load or influence on the measured object, thereby obtaining more real deformation data; can simultaneously obtain full-field deformation and displacement data of the material or structure surface, avoiding the problem that the traditional point measurement method can only obtain local information; adopts DIC to detect tiny deformation, has high measurement precision and sensitivity, is suitable for high-precision material performance testing, can also perform two-dimensional or three-dimensional deformation analysis according to application requirements, adapts to different experimental conditions and requirements, can also monitor deformation in real time during the experiment, is suitable for dynamic load, fatigue testing and the like scenes; is suitable for various types of materials (such as metals, polymers, composite materials and the like), and can be used under different environmental conditions (such as high temperature, low temperature, humidity change and the like), even to utilize the natural texture or pattern on the object surface to perform measurement, without exerting special markers on the sample surface, reducing experimental preparation time, meanwhile, the generated data can be analyzed through various software, such as stress distribution, strain field, fatigue life and the like, which is helpful for in-depth understanding of the behavior of materials and structures.

[0074] Please refer to Figure 2In the embodiment, in order to efficiently execute the concrete tensile nonlinear creep monitoring method based on the DIC technology provided by the present application, the present application further provides a concrete tensile nonlinear creep monitoring system based on the DIC technology, which comprises an input device, an output device, a processor, and a memory, wherein the input device, the output device, the processor, and the memory are connected to each other, the memory contains program instructions for the steps of the concrete tensile nonlinear creep monitoring method based on the DIC technology. The concrete tensile nonlinear creep monitoring system based on the DIC technology has a compact structure and stable performance, can stably execute the concrete tensile nonlinear creep monitoring method based on the DIC technology, and further improves the overall applicability and practical application ability of the present application.

[0075] In the embodiment, the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The input device can be used to obtain data information. The output device can be used to output the results obtained by the program instructions contained in the computer program stored in the memory provided by the present application. The memory can include read-only memory and random access memory, and provide instructions and data for the processor. Part of the memory can also include non-volatile random access memory.

[0076] In another optional embodiment, please refer to Figure 3 In order to efficiently execute the concrete tensile nonlinear creep monitoring method based on the DIC technology provided by the present application, the present embodiment further provides a concrete tensile nonlinear creep monitoring device based on the DIC technology, as shown in Figure 3 The concrete tensile nonlinear creep monitoring device based on the DIC technology comprises:

[0077] The memory 10 is used to store a computer program, and the processor 20 is used to execute the computer program to realize the concrete tensile nonlinear creep monitoring method based on the DIC technology. The memory 10, the processor 20, the communication interface 31, and the communication bus 32. The memory 10, the processor 20, the communication interface 31 all complete the communication with each other through the communication bus 32.

[0078] In an embodiment, the memory 10 is configured to store one or more program instructions. The memory 10 can store program instructions for implementing the following functions:

[0079] Based on the DIC technology, a monitoring image of the concrete is acquired; speckles in the monitoring image are identified, integer-pixel displacement searching is performed according to the speckles, integer-pixel displacement points of the speckles are obtained; sub-pixel displacement is solved with the integer-pixel displacement points of the speckles as reference points, sub-pixel coarse displacement points of the speckles are obtained; the sub-pixel coarse displacement points of the speckles are subjected to gray value interpolation, sub-pixel fine displacement points of the speckles are obtained based on the interpolation results, and the sub-pixel fine displacement points of the speckles are used to complete the monitoring of the tensile nonlinear creep of the concrete.

[0080] In a possible implementation, the memory 10 can include a program storage area and a data storage area. The program storage area can store an operating system and application programs required by at least one function, etc. The data storage area can store data created during use. In addition, the memory 10 can include a read-only memory and a random access memory, and provide instructions and data for the processor. A part of the memory can also include an NVRAM. The memory stores an operating system and operation instructions, executable modules or data structures, or subsets thereof, or an extended set thereof, wherein the operation instructions can include various operation instructions for implementing various operations. The operating system can include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0081] The processor 20 can be a central processing unit (CPU), an application-specific integrated circuit, a digital signal processor, a field programmable gate array, or other programmable logic devices. The processor 20 can be a microprocessor or any conventional processor, etc. The processor 20 can invoke programs stored in the memory 10. The communication interface 31 can be an interface of a communication module, used for connecting with other devices or systems.

[0082] Of course, it should be noted that, Figure 3 The structure shown does not constitute a limitation on the concrete tensile nonlinear creep monitoring device based on the DIC technology in the embodiment. In actual applications, the concrete tensile nonlinear creep monitoring device based on the DIC technology can include more or fewer components than Figure 3 those shown, or combine certain components.

[0083] In the embodiment, a storage medium is also provided, and the storage medium stores a computer program. When the computer program is executed by a processor, the steps of the concrete tensile nonlinear creep monitoring method based on the DIC technology described above are implemented.

[0084] The storage medium can include a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media capable of storing program codes.

[0085] In summary, the present application uses DIC technology to obtain concrete deformation images in real time, then performs whole-pixel and sub-pixel analysis on the images to obtain displacement change results, and further completes concrete tensile nonlinear creep monitoring according to the displacement change, thereby solving the problems of small measurement range, easy slipping and difficult fixation of the traditional contact type concrete tensile creep monitoring through a non-contact image data processing mode.

[0086] Therefore, the present application effectively overcomes various shortcomings in the prior art and has high industrial utilization value.

[0087] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope recorded in the present application.

Claims

1. A method for monitoring tensile nonlinear creep of concrete based on DIC technology, characterized in that, The concrete tensile nonlinear creep monitoring method based on DIC technology includes the following steps: Based on DIC technology, monitoring images of concrete are acquired; Identify speckle in the monitored image, and perform integer pixel displacement search based on the speckle to obtain speckle integer pixel displacement points; Using the speckle integer pixel displacement point as the reference point, the sub-pixel displacement is solved to obtain the speckle sub-pixel coarse displacement point; Grayscale values ​​are interpolated for the speckle subpixel coarse displacement points, and speckle subpixel fine displacement points are obtained based on the interpolation results. The speckle subpixel fine displacement points are then used to complete the monitoring of concrete tensile nonlinear creep. The step of performing integer pixel displacement search based on the speckle to obtain speckle integer pixel displacement points includes the following steps: A speckle deformation sub-region similarity model is constructed, and the similarity of the speckle deformation sub-region before and after deformation is obtained using the speckle deformation sub-region similarity model; Based on the similarity, an iterative optimization method is used to search for integer pixel displacements to obtain speckle integer pixel displacement points. The speckle deformation sub-region similarity model satisfies the following formula: in, Indicates speckle integer similarity. Represents reference speckle image grayscale value at that location Represents the target speckle image grayscale value at that location Indicates the radius of the sub-region; The iterative optimization method satisfies the following formula: in, Indicates the first The individual in the first Position at the next iteration Indicates the maximum number of iterations. Indicates the number of iterations. Indicates the first The individual in the first Position at the next iteration Indicates the first The average position of all individuals in the population at the next iteration. Indicates the first The average speckle integer pixel similarity of all individuals in the population at the next iteration. Indicates the first The speckle integer pixel similarity of the best individual in the next iteration. Indicates the first The individual in the first Speckle integer pixel similarity at the next iteration This indicates that the random number follows the flight path of Levi. Represents a random number in the range [-1, 1]. Indicates the first The optimal individual in the next iteration; The sub-pixel displacement is calculated using the speckle integer pixel displacement point as the reference point to obtain the speckle sub-pixel coarse displacement point, which satisfies the following formula: in, Indicates the first solution parameter. Represents reference speckle image grayscale value at that location Indicates the second solution parameter. Represents the target speckle image grayscale value at that location express Integer pixel displacement in direction express Integer pixel displacement in direction express Subpixel displacement in direction, express Subpixel displacement in direction, express Gray-scale gradient operator in direction, express Gray-scale gradient operator in direction.

2. The method for monitoring concrete tensile nonlinear creep based on DIC technology according to claim 1, characterized in that, It also includes distortion correction of the monitored images.

3. The method for monitoring concrete tensile nonlinear creep based on DIC technology according to claim 1, characterized in that, The first solution parameter satisfies the following formula: in, This indicates the number of integer pixels in the target area. Indicates the target area grayscale value at that location Indicates reference area grayscale value at that location This indicates the shift in grayscale.

4. The method for monitoring concrete tensile nonlinear creep based on DIC technology according to claim 1, characterized in that, The second solution parameter satisfies the following formula: in, This indicates the number of integer pixels in the target area. Indicates the target area grayscale value at that location Indicates reference area grayscale value at that location This indicates the scale variation of grayscale.

5. The method for monitoring concrete tensile nonlinear creep based on DIC technology according to claim 1, characterized in that, The grayscale interpolation of the speckle sub-pixel coarse displacement points satisfies the following formula: in, express Gray value interpolation at the location, This represents the coefficients to be determined.

6. A concrete tensile nonlinear creep monitoring system based on DIC technology, characterized in that, The concrete tensile nonlinear creep monitoring system based on DIC technology includes: an input device, an output device, a processor, and a memory. The input device, output device, processor, and memory are interconnected. The memory includes program instructions, which are used to execute the concrete tensile nonlinear creep monitoring method based on DIC technology according to any one of claims 1-5.

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