A method for concrete crack detection and damage nondestructive evaluation based on image analysis

Abstract

The application relates to the technical field of nondestructive testing and structural health monitoring of concrete structures, in particular to a concrete crack detection and damage nondestructive evaluation method based on image analysis, which comprises the following steps: performing image analysis on a crack image of a surface or a near-surface region of a concrete member to obtain a crack geometric parameter field; constructing a crack multiscale fractal dimension spectrum set based on multiscale analysis of the crack structure, and extracting fractal dimension spectrum statistical characteristic parameters; constructing a crack pattern recognition index based on the crack geometric parameter field and the fractal dimension spectrum statistical characteristic parameters, classifying and recognizing a crack propagation mode, and obtaining a crack propagation mode recognition result; and constructing a damage evaluation model according to the crack geometric parameter field, the fractal dimension spectrum statistical characteristic parameters and the crack propagation mode recognition result, and evaluating a damage state of the concrete member. The method realizes nondestructive evaluation of concrete crack detection and damage based on multi-parameter coupling analysis, and improves the accuracy, stability and engineering applicability of damage identification.

Description

Technical Field

[0001] This invention belongs to the field of non-destructive testing and structural health monitoring technology for concrete structures, and particularly relates to a method for non-destructive assessment of concrete cracks and damage based on image analysis. Background Technology

[0002] As one of the most widely used engineering materials, concrete is inevitably affected by various factors during its service life, such as loads, environmental erosion, temperature and humidity changes, and material aging, resulting in various forms of cracks on its surface or near-surface area. The initiation and propagation of cracks not only weaken the overall stiffness and load-bearing capacity of concrete components but also provide pathways for the intrusion of harmful media, significantly reducing the durability and service life of concrete structures. Therefore, timely and accurate monitoring and damage assessment of concrete cracks are crucial technical means to ensure the safe operation of concrete engineering structures.

[0003] Image-based crack detection methods for concrete have become a hot research and engineering application area due to their advantages such as non-contact operation, rich information content, and suitability for long-term monitoring. However, most methods rely on single geometric parameters such as crack length, width, or area to quantitatively characterize cracks, making it difficult to reflect the complexity of crack spatial distribution and multi-scale structural characteristics. Furthermore, they lack the ability to identify early microcracks and complex crack networks. In addition, most existing methods are based on static image analysis, lacking a systematic characterization of crack propagation paths, bifurcation behavior, and directional evolution patterns, making it difficult to distinguish different crack development states and resulting in insufficient physical meaning of damage assessment results. Therefore, how to achieve multi-parameter coupled analysis for concrete crack detection and non-destructive damage assessment, improving the accuracy, stability, and engineering applicability of damage identification, has become an urgent technical problem to be solved. Summary of the Invention

[0004] In view of this, the present invention aims to provide a method for concrete crack detection and non-destructive assessment of damage based on image analysis, realizing non-destructive assessment of concrete crack detection and damage through multi-parameter coupled analysis, thereby improving the accuracy, stability and engineering applicability of damage identification.

[0005] To achieve the above objectives, the technical solution created by this invention is implemented as follows: This invention provides a method for concrete crack detection and non-destructive damage assessment based on image analysis, comprising the following steps: performing image analysis on crack images on or near the surface of a concrete component to obtain a crack geometric parameter field; constructing a multi-scale fractal dimension spectrum set of the crack based on multi-scale analysis of the crack structure, and extracting statistical feature parameters of the fractal dimension spectrum; constructing a crack pattern recognition index based on the crack geometric parameter field and the statistical feature parameters of the fractal dimension spectrum, classifying and recognizing crack propagation patterns, and obtaining crack propagation pattern recognition results; and constructing a damage assessment model based on the crack geometric parameter field, the statistical feature parameters of the fractal dimension spectrum, and the crack propagation pattern recognition results to assess the damage state of the concrete component.

[0006] Furthermore, image analysis is performed on crack images on or near the surface of concrete components to obtain crack geometric parameters. Specifically, this includes: preprocessing crack images on or near the surface of concrete components; constructing a crack segmentation energy function based on multi-scale edge enhancement response, crack orientation consistency constraints, and regional statistical features, and obtaining a binary crack image through optimization; and extracting crack geometric parameters from the binary crack image based on a crack skeletonization algorithm to construct the crack geometric parameter field.

[0007] Furthermore, a crack segmentation energy function is constructed based on multi-scale edge enhancement response, crack orientation consistency constraint, and regional statistical features. The resulting binary crack image is obtained through optimization. Specifically, this includes: constructing a crack segmentation energy function that integrates the multi-scale edge enhancement response, crack orientation consistency constraint, and regional statistical features based on the crack image; minimizing the crack segmentation energy function to obtain an optimal crack distribution function; thresholding the optimal crack distribution function to obtain an initial binary image; and constructing a crack region determination function based on the initial binary image, filtering and optimizing the initial binary image to obtain the final binary crack image.

[0008] Furthermore, based on the crack skeletonization algorithm, crack geometric parameters are extracted from the binary crack image to construct the crack geometric parameter field. Specifically, this includes constructing crack length density parameters, crack branch complexity parameters, and crack direction dispersion parameters based on the binary crack image to form the crack geometric parameter field.

[0009] Furthermore, based on multi-scale analysis of crack structure, a set of fractal dimension spectra of crack at multiple scales is constructed to obtain statistical characteristic parameters of the fractal dimension spectra. Specifically, this includes: performing multi-scale analysis of crack structure based on the coverage statistical relationship of crack distribution at different coverage scales; constructing the set of fractal dimension spectra of crack at multiple scales; and extracting the statistical characteristic parameters of the fractal dimension spectra based on the set of fractal dimension spectra.

[0010] Furthermore, a crack pattern recognition index is constructed based on the crack geometric parameter field and the fractal dimension spectrum statistical feature parameters to classify and identify crack propagation patterns, thereby obtaining crack propagation pattern recognition results. Specifically, this includes: constructing a crack geometry-fractal coupling discrimination state vector based on the crack geometric parameter field and the fractal dimension spectrum statistical feature parameters; constructing a crack pattern recognition function using the crack geometry-fractal coupling discrimination state vector as input to obtain the crack pattern recognition index; and classifying and identifying the crack propagation patterns based on the crack pattern recognition index to obtain the crack propagation pattern recognition results.

[0011] Further, a damage assessment model is constructed based on the crack geometric parameter field, the fractal dimension spectrum statistical feature parameters, and the crack propagation pattern recognition results to assess the damage state of the concrete component. Specifically, this includes: constructing a crack damage equivalent characterization function for the damage assessment model based on the crack geometric parameter field and the fractal dimension spectrum statistical feature parameters; constructing a crack propagation driving function for the damage assessment model based on the crack pattern recognition index; constructing a final damage variable based on the crack damage equivalent characterization function and the crack propagation driving function; and classifying the damage state of the concrete component according to the final damage variable.

[0012] Further, constructing the final damage variable based on the crack damage equivalent characterization function and the crack propagation driving function specifically includes: calculating the initial damage variable according to the crack damage equivalent characterization function; calculating the evolution acceleration factor based on the crack propagation driving function; and coupling the initial damage variable and the evolution acceleration factor to obtain the final damage variable.

[0013] In addition, this application also provides a concrete crack detection and non-destructive damage assessment system based on image analysis, comprising: a crack geometric parameter field acquisition module, used to perform image analysis on crack images of the surface or near-surface region of a concrete component to obtain a crack geometric parameter field; a fractal dimension spectrum statistical feature parameter acquisition module, used to construct a multi-scale fractal dimension spectrum set of cracks based on multi-scale analysis of crack structure, and extract fractal dimension spectrum statistical feature parameters; a crack propagation pattern recognition result acquisition module, used to construct a crack pattern recognition index based on the crack geometric parameter field and the fractal dimension spectrum statistical feature parameters, classify and recognize crack propagation patterns, and obtain crack propagation pattern recognition results; and a damage state assessment module, used to construct a damage assessment model based on the crack geometric parameter field, the fractal dimension spectrum statistical feature parameters, and the crack propagation pattern recognition results, and assess the damage state of the concrete component.

[0014] Furthermore, the present invention also provides an electronic device, characterized in that it includes: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to perform the steps of concrete crack detection and non-destructive assessment of damage as described above.

[0015] In addition, the invention provides a storage medium for storing one or more programs that, when executed by an electronic device including multiple applications, cause the electronic device to perform the steps of the concrete crack detection and non-destructive assessment method described above.

[0016] Compared with existing technologies, the present invention provides an image analysis-based method for concrete crack detection and non-destructive assessment of damage, comprising the following steps: image analysis of crack images on the surface or near-surface region of a concrete component to obtain a crack geometric parameter field; constructing a multi-scale fractal dimension spectrum set of the crack based on multi-scale analysis of the crack structure, and extracting statistical feature parameters of the fractal dimension spectrum; constructing a crack pattern recognition index based on the crack geometric parameter field and the statistical feature parameters of the fractal dimension spectrum, classifying and recognizing crack propagation patterns, and obtaining crack propagation pattern recognition results; constructing a damage assessment model based on the crack geometric parameter field, the statistical feature parameters of the fractal dimension spectrum, and the crack propagation pattern recognition results, and assessing the damage state of the concrete component. This non-destructive assessment method can comprehensively characterize the geometric features and spatial complexity of cracks, depict the evolution process of cracks from initiation to development, improve the accuracy of crack recognition and the reliability of damage assessment, and is applicable to the detection and non-destructive assessment of concrete surface cracks in concrete bridges, tunnels, building structures, and hydraulic structures under service conditions, especially suitable for non-destructive testing scenarios with complex backgrounds and early micro-cracks. Attached Figure Description

[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart illustrating a method for concrete crack detection and non-destructive damage assessment based on image analysis, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of image processing using a concrete crack detection and non-destructive assessment method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of image processing for another method of concrete crack detection and non-destructive assessment of damage provided in an embodiment of the present invention; Figure 4This is a schematic diagram of the structure of an electronic device provided for an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and do not constitute a limitation thereof. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of the invention. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, some operations related to the invention are not shown or described in the specification. This is to avoid obscuring the core parts of the invention with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined to form various implementations. Furthermore, the order of the steps or actions in the method description can be changed or adjusted in a manner readily apparent to those skilled in the art. Therefore, the various orders in the specification and drawings are merely for the clear description of a particular embodiment and do not imply a mandatory order, unless otherwise stated that a particular order must be followed.

[0020] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0021] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0022] The image analysis-based concrete crack detection and non-destructive assessment method provided in this invention can achieve multi-parameter coupled analysis for concrete crack detection and non-destructive assessment of damage, improving the accuracy, stability, and engineering applicability of damage identification. The invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] Example 1 like Figure 1 As shown, this embodiment of the invention provides a method for concrete crack detection and non-destructive damage assessment based on image analysis, including the following steps: S10: Image analysis is performed on crack images on the surface or near-surface region of a concrete component to obtain a crack geometric parameter field; S20: A multi-scale fractal dimension spectrum set of the crack is constructed based on multi-scale analysis of the crack structure, and statistical feature parameters of the fractal dimension spectrum are extracted; S30: A crack pattern recognition index is constructed based on the crack geometric parameter field and the statistical feature parameters of the fractal dimension spectrum, and crack propagation patterns are classified and identified to obtain crack propagation pattern recognition results; S40: A damage assessment model is constructed based on the crack geometric parameter field, the statistical feature parameters of the fractal dimension spectrum, and the crack propagation pattern recognition results to assess the damage state of the concrete component.

[0024] This invention provides a multi-parameter coupled analysis method for evaluating the durability and safety performance of concrete components. This method utilizes image crack geometric parameter fields, multi-scale fractal dimension spectrum statistical feature parameters, and crack propagation pattern recognition results. It addresses the shortcomings of traditional methods that typically rely solely on a single fractal dimension, failing to fully utilize the structural information of cracks at different scales and thus struggling to depict the evolution of cracks from microscopic propagation to macroscopic penetration. Furthermore, existing methods are mostly based on static image analysis, lacking a systematic characterization of crack propagation paths, bifurcation behavior, and directional evolution patterns. This makes it difficult to distinguish different crack development states, resulting in insufficient physical meaning in the damage assessment results. This invention improves the accuracy, stability, and engineering applicability of damage identification in concrete components.

[0025] Furthermore, in the concrete crack detection and non-destructive assessment method provided in this embodiment of the invention, S10: image analysis is performed on the crack image of the surface or near-surface region of the concrete component to obtain crack geometric parameters, specifically including: preprocessing the crack image of the surface or near-surface region of the concrete component; constructing a crack segmentation energy function based on multi-scale edge enhancement response, crack direction consistency constraint and regional statistical features, and obtaining a crack binary image through optimization solution; extracting crack geometric parameters from the crack binary image based on the crack skeletonization algorithm, and constructing a crack geometric parameter field.

[0026] Crack images of the surface or near-surface region of concrete components are acquired, and preprocessed images such as grayscale normalization, noise suppression, and contrast enhancement are performed to obtain preprocessed crack images. Based on multi-scale edge enhancement response, crack orientation consistency constraints, and regional statistical features, a crack segmentation energy function is constructed, and the crack distribution function and binary crack image are obtained through optimization. Based on the crack skeletonization algorithm, the binary crack image is transformed into a crack skeleton image, and a crack geometric parameter field is constructed.

[0027] Furthermore, in the concrete crack detection and non-destructive assessment method provided in this embodiment of the invention, a crack segmentation energy function is constructed based on multi-scale edge enhancement response, crack direction consistency constraint, and regional statistical features, and a binary image of the crack is obtained through optimization. Specifically, this includes: constructing a crack segmentation energy function based on the crack image that integrates multi-scale edge enhancement response, crack direction consistency constraint, and regional statistical features; minimizing the crack segmentation energy function to obtain the optimal crack distribution function; thresholding the optimal crack distribution function to obtain an initial binary image; constructing a crack region determination function based on the initial binary image; and filtering and optimizing the initial binary image to obtain a binary image of the crack.

[0028] Based on multi-scale edge enhancement response, crack orientation consistency constraints, and regional statistical characteristics, a crack segmentation energy function is constructed, and the crack distribution function and binary crack image are obtained through optimization. Based on preprocessed crack images, a crack segmentation energy function is constructed that integrates multi-scale edge response, crack orientation consistency constraints, and regional statistical features. E ( B ):

[0029] Where Ω is the image domain; ( , )∈{0,1} is the crack indicator function, which indicates whether a pixel belongs to a crack; ( , ) represents the crack response function; | ( , | represents the boundary regularization term, used to constrain the spatial continuity of the segmentation results; ( , ) represents the crack propagation uniformity response term; These are the boundary smoothing weight coefficients; The weighting coefficient for the consistency constraint of crack orientation.

[0030] Crack Path Consistent Response Item ( , ) is defined as:

[0031] in, λ 1, λ 2 is the eigenvalue of the structure tensor, and λ 1≥ λ 2; ( , )∈[0,1], used to characterize the consistency of local crack orientation.

[0032] Crack response function ( , ) is defined as:

[0033] in, R m ( , () represents the response value obtained based on multi-scale edge or gradient enhancement operators; ( , ) represents the local grayscale statistical response item; , Let be the weight coefficient, and satisfy... + =1.

[0034] Gray Scale Statistical Response Item ( , ) is defined as:

[0035] in, I ( x , y () represents the grayscale value of the preprocessed crack image; ( , ), ( , ( ) represents the local mean and standard deviation; It is a small positive number.

[0036] Next, the crack segmentation energy function is minimized to obtain the optimal crack distribution function:

[0037] in, ( ) represents the crack segmentation energy function constructed above; ( , )∈[0,1] is the optimal crack distribution function, used to characterize the probability or membership degree of a pixel belonging to a crack.

[0038] Then, the optimal crack distribution function is... ( , Thresholding is performed to obtain the initial binary image. ( , ):

[0039] in, ( , () represents the initial binary image; Set to a preset threshold or an adaptive threshold.

[0040] Then, the initial binary image of the crack... ( , We perform feature analysis on the connected regions in the initial binary crack image, construct a crack region determination function, and filter and optimize candidate regions in the initial binary crack image to obtain the final binary crack image. First, we define the connected regions, let the i-th region be... The connected regions are Ω. Its pixel set is Redefine the region determination function S i :

[0041] in, ω 1, ω 2, ω 3 is the weighting coefficient, and satisfies ω 1+ ω 2+ ω 3 = 1; f geo,i The geometric feature term is used to characterize the fineness of the region; the cracked region has a larger fineness value. f conn,i This is a connectivity feature term used to suppress isolated noise regions; f dir,i This is a characteristic term for the uniformity of crack direction, used to characterize the property of cracks extending in a single direction. f geo,i , f conn,i , f dir,i The definition is as follows:

[0042] in, A i For the first The area of ​​each connected region; This represents the perimeter of the corresponding region. A max The maximum area of ​​all connected regions; C ( x,y The crack orientation uniformity response is defined above. This represents the number of pixels in that region. Defined as the crack detection threshold. When the following conditions are met... If the area is found to be a cracked area, it is considered a cracked area; otherwise, it is discarded.

[0043] Furthermore, in the concrete crack detection and non-destructive assessment method provided in this embodiment of the invention, crack geometric parameters are extracted from the binary image of the crack based on the crack skeletonization algorithm to construct a crack geometric parameter field. Specifically, this includes constructing crack length density parameters, crack branch complexity parameters, and crack direction dispersion parameters based on the binary image of the crack to form a crack geometric parameter field.

[0044] Construct the crack geometric parameter field based on the crack skeleton image: Constructing crack length density parameters ρ L To avoid discontinuity errors caused by binarization, gradient integrals are used for definition, determined by the following formula:

[0045] in, A The area of ​​the image region; Define the domain for the image; Let Crack distribution function be used. The gradient magnitude is used to characterize the crack boundary strength.

[0046] Constructing the complexity parameters of crack branching C b To avoid the scale dependency problem caused by using only the "number of bifurcations" and achieve a normalized expression, the following formula is used:

[0047] in, C b For branch complexity; The number of branching nodes in the crack skeleton; The total length of the crack skeleton; To prevent tiny positive numbers with a denominator of zero.

[0048] Constructing crack direction dispersion parameters The degree of dispersion of the crack direction is quantified and expressed in the form of directional entropy, which is calculated as follows:

[0049] in, For the direction of the crack to fall into the first k The probability of each directional interval. m This represents the number of directional intervals.

[0050] Furthermore, in the concrete crack detection and non-destructive assessment method provided in this embodiment of the invention, a multi-scale fractal dimension spectrum set of cracks is constructed based on multi-scale analysis of the crack structure to obtain statistical characteristic parameters of the fractal dimension spectrum. Specifically, this includes: performing multi-scale analysis of the crack structure based on the coverage statistical relationship of crack distribution at different coverage scales; constructing a multi-scale fractal dimension spectrum set of cracks; and extracting statistical characteristic parameters of the fractal dimension spectrum based on the fractal dimension spectrum set.

[0051] Under different geometric scales of cracks, a coverage analysis of crack distribution is performed, the fractal dimension spectrum is calculated, and a multi-scale fractal dimension spectrum set of cracks is constructed. Statistical characteristic parameters of the fractal dimension spectrum are extracted, including: Based on the statistical relationship of crack distribution at different coverage scales, a multi-scale analysis of the crack structure is performed, defining the coverage scale as... ε ,but:

[0052] in, N ( ε ) is the scale Hourly coverage; Ω To cover sub-regions ( , () is a binary image of the crack.

[0053] Next, the fractal dimension of the crack is calculated. D Determine using the following formula:

[0054] in, ε This is the scale parameter. D The larger the value, the more complex and dense the cracks.

[0055] Construct a multi-scale fractal dimension spectrum set of cracks. , This indicates the geometrically resolvable hierarchy of cracks from the microcrack scale to the main crack scale.

[0056] Then, the fractal dimension spectrum statistical feature parameters are extracted, including:

[0057] in, Indicates the fractal spectrum mean; Represents the variance of the fractal spectrum; This represents the fractal spectrum width.

[0058] Furthermore, in the concrete crack detection and non-destructive assessment method provided in this embodiment of the invention, a crack pattern recognition index is constructed based on the crack geometric parameter field and fractal dimension spectrum statistical feature parameters to classify and identify crack propagation patterns, thereby obtaining crack propagation pattern recognition results. Specifically, this includes: constructing a crack geometry-fractal coupling discrimination state vector based on the crack geometric parameter field and fractal dimension spectrum statistical feature parameters; constructing a crack pattern recognition function using the crack geometry-fractal coupling discrimination state vector as input to obtain the crack pattern recognition index; and classifying and identifying crack propagation patterns based on the crack pattern recognition index to obtain crack propagation pattern recognition results.

[0059] Based on the crack geometric parameter field and fractal dimension spectrum statistical characteristic parameters, a crack pattern recognition index is constructed to classify and identify crack propagation patterns, yielding crack propagation pattern recognition results, including: Based on the crack geometric parameter field and fractal dimension spectrum statistical characteristic parameters obtained above, a crack geometry-fractal coupling discriminant state vector is constructed. To eliminate the influence of dimensions, the crack geometry-fractal coupling discriminant state vector is normalized to obtain... .

[0060] Then, the normalized crack geometry-fractal coupling discriminant state vector is used. As input, a crack pattern recognition function is constructed to obtain crack pattern recognition indices. :

[0061] in, These are the weighting coefficients, determined through experimental calibration.

[0062] Next, classification rules are established based on crack pattern recognition indicators. Crack propagation patterns are then classified according to these rules. The specific classification rules are as follows:

[0063] in, T 1, T 2 represents the threshold determined through material testing or historical data; Types I, II, and III extension modes represent stable, transitional, and unstable extensions, respectively.

[0064] This invention establishes a damage evaluation framework with unified dimensions by normalizing multi-source parameters and using weighted coupling modeling. At the same time, it combines multi-scale fractal analysis to improve robustness to noise and complex working conditions, thereby enhancing the stability, generalization ability and application feasibility of the method in actual engineering environments.

[0065] Furthermore, in the concrete crack detection and non-destructive damage assessment method provided in this embodiment of the invention, a damage assessment model is constructed based on the crack geometric parameter field, fractal dimension spectrum statistical characteristic parameters, and crack propagation pattern recognition results to assess the damage state of the concrete component. Specifically, this includes: constructing a crack damage equivalent characterization function for the damage assessment model based on the crack geometric parameter field and fractal dimension spectrum statistical characteristic parameters; constructing a crack propagation driving function for the damage assessment model based on the crack pattern recognition index; constructing a final damage variable based on the crack damage equivalent characterization function and the crack propagation driving function; and classifying the damage state of the concrete component according to the final damage variable.

[0066] By coupling the crack geometric parameter field, fractal dimension spectrum statistical characteristic parameters, and crack propagation mode recognition results, a damage assessment model is constructed to obtain damage identification parameters, and the damage state of concrete components is assessed, including: Based on the crack geometric parameters and fractal dimension spectrum statistical characteristic parameters obtained above, an equivalent characterization function for crack damage is constructed, defined as the initial damage variable:

[0067] in, P is Initial damage variable (value range 0) 1); These are weighting coefficients, reflecting the contribution of each parameter to the damage; It is a non-linear exponent used to characterize the sensitivity to different parameters.

[0068] Based on the crack pattern recognition index obtained above Construct a crack propagation driving function, defined as an evolution acceleration factor. ( ):

[0069] in, This represents the model influence coefficient. q It is a non-linear adjustment index.

[0070] The initial damage variable is coupled with the evolution acceleration factor to obtain the final damage variable. P :

[0071] Finally, based on the final damage variable P The damage state of concrete components is classified and determined:

[0072] in, , The damage grading threshold is determined through experiments or engineering experience; Grade I, II, and III damage represent the initial, developing, and critical damage stages, respectively.

[0073] The concrete crack detection and non-destructive assessment method based on image analysis provided in this invention introduces a crack propagation pattern recognition mechanism and modulates the final damage variable to realize the transformation from static geometric representation to evolution-driven representation. This enables the assessment results of the damage state to effectively distinguish different crack development stages and improve the physical interpretability and engineering discrimination ability of the assessment results.

[0074] Example 1 In this embodiment, the above-mentioned image analysis-based concrete crack detection and non-destructive damage assessment method is used to process the surface crack images of concrete components. The results are shown in Table 1 below. The image processing process is as follows: Figure 2 As shown.

[0075] Table 1 Crack identification results in this example

[0076] The fractal dimension of 1.3671 indicates that the surface cracks of the concrete component have a relatively simple geometric morphology, and the cracks have not yet formed obvious branching or network structures, exhibiting strong localization characteristics in their spatial distribution. The low crack rate (1.13%) reflects the small proportion of cracks in the overall area, while the low fractal dimension (1.3671) indicates limited complexity in the crack structure and the absence of significant multi-scale self-similarity. Furthermore, the limited number of crack endpoints and crack lines further suggests that the cracks are still in the initiation or initial propagation stage, without significant interconnection or branching evolution.

[0077] In engineering terms, this type of crack typically corresponds to localized stress concentration or early micro-damage stages within the material, before significant degradation of the overall structural performance. The non-destructive method provided in this invention enables quantitative identification of crack development stages by combining the crack geometric parameter field and fractal dimension spectrum statistical characteristic parameters, even when the crack percentage is only 1.13%, thereby accurately classifying the damage state as Type I initial damage.

[0078] Compared to traditional methods that assess cracks solely based on crack length or width, conventional methods often struggle to effectively identify cracks in their early stages and fail to characterize crack propagation trends and structural complexity. This invention, however, introduces fractal dimension spectrum statistical characteristic parameters and crack pattern recognition results to comprehensively characterize the spatial distribution complexity and evolutionary features of cracks. This allows for sensitive identification of damage states even before cracks have significantly expanded, thereby significantly improving the accuracy and reliability of early-stage damage detection in concrete components.

[0079] Therefore, this example verifies the effectiveness of the non-destructive assessment method provided by the embodiments of the present invention in low-damage and early crack identification scenarios, and can achieve refined characterization of the crack initiation stage and accurate determination of damage level.

[0080] Example 2 Based on Example 1 above, another concrete image with a more complex crack distribution was selected for further processing. The same processing procedure was followed to obtain crack feature parameters and damage assessment results, as shown in Table 2 below. The image processing procedure is as follows: Figure 3 As shown.

[0081] Table 2 Crack identification results in this example

[0082] The results show that, compared to Example 1, the cracks in this example exhibit significantly enhanced characteristics in terms of quantity, distribution range, and structural complexity. Specifically, the substantial increase in the number of crack lines indicates that the cracks have gradually evolved from a single main crack to a structure with multiple cracks coexisting, while the increase in the number of crack endpoints reflects obvious bifurcation behavior during crack propagation. Simultaneously, the crack rate increased from 1.13% to 3.05%, indicating a significant expansion of the crack coverage area, extending from a localized region to a larger area.

[0083] The fractal dimension increased from 1.3671 to 1.4252, indicating a significant increase in the complexity of the crack structure. Its spatial distribution exhibits stronger multi-scale characteristics and irregularity, reflecting that the crack has gradually moved from the initial initiation stage to the development stage. Cracks are beginning to influence each other and even partially connect, forming a preliminary crack network structure. Based on the above multi-parameter comprehensive analysis, this example classifies the crack propagation mode as Type II and assesses the structural damage state as Type II development stage.

[0084] It should be noted that cracks at this stage no longer exhibit simple geometric propagation but rather show clear structural evolution characteristics, and their impact on the structural mechanical properties begins to increase. If left uncontrolled, they may further develop into through cracks or unstable failure. Therefore, accurate identification of cracks at this stage is of great significance for engineering safety assessment.

[0085] Traditional assessment methods typically rely solely on crack length, width, or area, failing to reflect the spatial relationships between cracks and their overall complexity changes. Consequently, they struggle to distinguish the evolution of cracks from simple morphologies to complex network structures. In contrast, the non-destructive assessment method provided in this invention, by incorporating a comprehensive analysis of crack geometric parameter fields, fractal dimension spectrum statistical characteristic parameters, and crack pattern recognition results, not only quantitatively characterizes the number and scale of cracks but also delineates the evolutionary trend of crack structural complexity, thereby achieving accurate determination of crack development stages.

[0086] Example 2 Furthermore, this invention also provides an image analysis-based concrete crack detection and non-destructive testing system, comprising: a crack geometric parameter field acquisition module, used to perform image analysis on crack images of the surface or near-surface region of a concrete component to obtain a crack geometric parameter field; a fractal dimension spectrum statistical feature parameter acquisition module, used to construct a multi-scale fractal dimension spectrum set of cracks based on multi-scale analysis of the crack structure, and extract fractal dimension spectrum statistical feature parameters; a crack propagation pattern recognition result acquisition module, used to construct a crack pattern recognition index based on the crack geometric parameter field and fractal dimension spectrum statistical feature parameters, classify and identify crack propagation patterns, and obtain crack propagation pattern recognition results; and a damage state assessment module, used to construct a damage assessment model based on the crack geometric parameter field, fractal dimension spectrum statistical feature parameters, and crack propagation pattern recognition results, and assess the damage state of the concrete component. This non-destructive testing system can comprehensively characterize the geometric features and spatial complexity of cracks, depict the evolution process of cracks from initiation to development, improve crack recognition accuracy and damage assessment reliability, and is particularly suitable for non-destructive testing scenarios with complex backgrounds and early micro-cracks.

[0087] Furthermore, embodiments of the present invention also provide an electronic device, a readable storage medium, and a computer program product. These include a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate via the bus. The machine-readable instructions are executed by the processor to perform the steps of the image analysis-based concrete crack detection and non-destructive damage assessment method described above. Figure 4 This invention provides a computer device, a readable storage medium, and a computer program product in its embodiments.

[0088] Figure 4 This is a schematic diagram of the structure of a computer device 12 provided in an embodiment of the present invention. Figure 4 A block diagram of an exemplary computer device 12 suitable for implementing embodiments of the present invention is shown. Figure 4 The computer device 12 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0089] like Figure 4As shown, computer device 12 is represented in the form of a general-purpose computing device. 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. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0090] The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0091] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0092] 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 volatile and non-volatile media, removable and non-removable media.

[0093] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 3 Not shown; usually referred to as a "hard drive"). Although Figure 3 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0094] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0095] Computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with computer device 12, and / or with any device that enables computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, computer device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with 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.

[0096] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the image analysis-based concrete crack detection and non-destructive assessment method provided in the embodiments of the present invention.

[0097] This invention also provides a non-transitory computer-readable storage medium storing computer instructions, on which a computer program is stored, wherein the program, when executed by a processor, is the image analysis-based concrete crack detection and non-destructive assessment method provided in all embodiments of this application.

[0098] The computer storage medium of this invention can be 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 (a non-exhaustive list) of computer-readable storage media include: electrical connections having 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 fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0099] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0100] The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. The computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as 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, 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 cases involving a remote computer, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0101] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described image analysis-based method for concrete crack detection and non-destructive damage assessment.

[0102] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0103] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. 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 this invention should be included within the scope of protection of this invention.

Claims

1. A method for concrete crack detection and non-destructive assessment of damage based on image analysis, characterized in that: Includes the following steps: Image analysis is performed on crack images on or near the surface of concrete components to obtain the crack geometric parameter field. Based on multi-scale analysis of crack structure, a set of fractal dimension spectra of crack at multiple scales is constructed, and statistical characteristic parameters of fractal dimension spectra are extracted; Based on the crack geometric parameter field and the fractal dimension spectrum statistical feature parameters, a crack pattern recognition index is constructed to classify and identify crack propagation patterns, and the crack propagation pattern recognition result is obtained. A damage assessment model is constructed based on the crack geometric parameter field, the fractal dimension spectrum statistical characteristic parameters, and the crack propagation pattern recognition results to assess the damage state of the concrete component.

2. The method for concrete crack detection and non-destructive assessment of damage according to claim 1, characterized in that: Image analysis of crack images on or near the surface of concrete members yields crack geometric parameters, specifically including: Preprocess the crack images on the surface or near-surface area of ​​the concrete component; A crack segmentation energy function is constructed based on multi-scale edge enhancement response, crack orientation consistency constraint and regional statistical features, and a binary image of the crack is obtained by optimization solution. Crack geometric parameters are extracted from the binary image of the crack based on the crack skeletonization algorithm, and the crack geometric parameter field is constructed.

3. The method for concrete crack detection and non-destructive assessment of damage according to claim 2, characterized in that: A crack segmentation energy function is constructed based on multi-scale edge enhancement response, crack orientation consistency constraints, and regional statistical features. The binary image of the crack is obtained through optimization. Specifically, this includes: Based on the crack image, a crack segmentation energy function is constructed that integrates the multi-scale edge enhancement response, the crack orientation consistency constraint, and the region statistical features. The optimal crack distribution function is obtained by minimizing the crack segmentation energy function. The optimal crack distribution function is thresholded to obtain an initial binary image; A crack region determination function is constructed based on the initial binary image, and the initial binary image is filtered and optimized to obtain the crack binary image.

4. The method for concrete crack detection and non-destructive assessment of damage according to claim 2 or 3, characterized in that: Based on the crack skeletonization algorithm, crack geometric parameters are extracted from the binary crack image, and the crack geometric parameter field is constructed, specifically including: Based on the binary image of the crack, crack length density parameters, crack branch complexity parameters, and crack direction dispersion parameters are constructed to form the crack geometric parameter field.

5. The method for concrete crack detection and non-destructive assessment of damage according to claim 4, characterized in that: Based on multi-scale analysis of crack structures, a set of fractal dimension spectra at multiple scales of cracks is constructed to obtain statistical characteristic parameters of the fractal dimension spectra, specifically including: Based on the coverage statistics of crack distribution at different coverage scales, a multi-scale analysis of crack structure is performed. Construct the set of fractal dimension spectra for multi-scale cracks; The fractal dimension spectrum statistical feature parameters are extracted based on the fractal dimension spectrum set.

6. The method for concrete crack detection and non-destructive assessment of damage according to claim 5, characterized in that: Crack pattern recognition indices are constructed based on the crack geometric parameter field and the fractal dimension spectrum statistical feature parameters to classify and identify crack propagation patterns, thereby obtaining crack propagation pattern recognition results, specifically including: A crack geometry-fractal coupling discrimination state vector is constructed based on the crack geometric parameter field and the fractal dimension spectrum statistical feature parameters. A crack pattern recognition function is constructed using the crack geometry-fractal coupling discriminant state vector as input, and the crack pattern recognition index is obtained. The crack propagation patterns are classified and identified based on the crack pattern recognition index to obtain the crack propagation pattern recognition result.

7. The method for concrete crack detection and non-destructive assessment of damage according to claim 6, characterized in that: A damage assessment model is constructed based on the crack geometric parameter field, the fractal dimension spectrum statistical characteristic parameters, and the crack propagation pattern recognition results to assess the damage state of the concrete component, specifically including: The crack damage equivalent characterization function of the damage assessment model is constructed based on the crack geometric parameter field and the fractal dimension spectrum statistical characteristic parameters. The crack propagation driving function of the damage assessment model is constructed based on the crack pattern recognition index. The final damage variables are constructed based on the crack damage equivalent characterization function and the crack propagation driving function. The damage state of the concrete member is classified and determined based on the final damage variable.

8. A concrete crack detection and non-destructive assessment system based on image analysis, characterized in that: include: The crack geometry parameter field acquisition module is used to perform image analysis on crack images on or near the surface of concrete components to obtain the crack geometry parameter field. The fractal dimension spectrum statistical feature parameter acquisition module is used to construct a set of fractal dimension spectra of cracks at multiple scales based on multi-scale analysis of crack structures and extract the fractal dimension spectrum statistical feature parameters. The crack propagation pattern recognition result acquisition module is used to construct crack pattern recognition index based on the crack geometric parameter field and the fractal dimension spectrum statistical feature parameters, classify and recognize crack propagation patterns, and obtain crack propagation pattern recognition results. The damage state assessment module is used to construct a damage assessment model based on the crack geometric parameter field, the fractal dimension spectrum statistical characteristic parameters, and the crack propagation pattern recognition results, and to assess the damage state of the concrete component.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. The machine-readable instructions are executed by the processor to perform the steps of the concrete crack detection and non-destructive assessment method as described in any one of claims 1 to 7.

10. A storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the steps of the concrete crack detection and nondestructive assessment method as claimed in any one of claims 1 to 7.

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