Reinforced concrete crack depth inversion analysis method and device and storage medium

Abstract

The invention provides a reinforced concrete crack depth inversion analysis method and device and a storage medium, and relates to the technical field of reinforced concrete crack depth inversion analysis, and the method comprises the steps: obtaining the length and position of each crack on the surface of a to-be-analyzed reinforced concrete beam based on machine vision; obtaining a surface crack form matrix C corresponding to the reinforced concrete structure to be analyzed; constructing a three-dimensional XFEM model of the reinforced concrete beam to be analyzed in preset software; prefabricating an initial crack depth D0 based on the crack surface morphology C; xFEM static analysis is carried out on the crack depth vector Dk parameter loaded in the kth iteration, and beam bottom deflection data are extracted; updating the crack depth vector Dk + 1 of the (k + 1) th iteration by adopting an improved bee colony algorithm, carrying out loop iteration until a preset condition is met, and outputting a final inversion depth; according to the invention, accurate and lossless detection of the depth of the reinforced concrete crack can be realized, and the method has high flexibility and adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of reinforced concrete crack depth inversion analysis, and in particular to a reinforced concrete crack depth inversion analysis method, equipment and storage medium. Background Art

[0002] Traditional methods for detecting defects in concrete structures, such as drilling and sampling, can provide defect information, but they can compromise structural integrity and result in low detection efficiency. To overcome these limitations, existing technologies employ a range of nondestructive testing techniques, including ultrasonic testing, infrared imaging, and ground-penetrating radar. However, in practical applications, these nondestructive testing techniques may be affected by a variety of factors, including the testing environment, test conditions, structural boundary characteristics, and economic costs, which can impact the accuracy and reliability of test results. While machine vision-based crack surface morphology recognition technology can determine crack location and apparent size, it cannot quantify internal extension depth, limiting the accuracy of structural health assessments. Accurately and nondestructively assessing the extent and location of internal structural defects remains a significant challenge. In the field of detecting internal defects in bridge structures, particularly regarding the internal crack extension state, the accurate and nondestructive determination of reinforced concrete crack depth has become a pressing technical challenge. Summary of the Invention

[0003] In view of the above technical problems, the technical solution adopted by the present invention is:

[0004] According to a first aspect of the present application, a reinforced concrete crack depth inversion analysis method is provided, the method comprising the following steps:

[0005] S100, based on machine vision, obtain the length and position of each crack on the surface of the reinforced concrete beam to be analyzed, so as to obtain the surface crack morphology matrix C = {c1, c2, ..., c i}; Among them, C i is the length and position information of the i-th crack;

[0006] S200, build a 3D XFEM model of the reinforced concrete beam to be analyzed in the preset software, use C3D8R elements to discretize concrete, T3D2 elements to simulate steel bars, and realize steel-concrete coupling through preset commands;

[0007] S300, based on C, prefabricated initial cracks: using level set function Describe the crack surface, embed the Heaviside step function H(x) and the crack tip asymptotic function Where X is the coordinate of any point in space, t is time, r(t) is the crack surface, r is the distance from point X to the crack tip, θ is the polar angle of point X relative to the crack extension direction, and x is an arbitrarily selected Gaussian integration point;

[0008] S400, load the crack depth vector D of the kth iteration k Parameters are used to perform XFEM static analysis and extract beam bottom deflection data;

[0009] S500, using the improved bee colony algorithm to update the crack depth vector D of the k+1th iteration k+1 , iterates until the preset conditions are met and outputs the final inversion depth.

[0010] According to another aspect of the present application, a non-transitory computer-readable storage medium is also provided, in which at least one instruction or at least one program is stored. The at least one instruction or at least one program is loaded and executed by a processor to implement the above-mentioned reinforced concrete crack depth inversion analysis method.

[0011] According to another aspect of the present application, an electronic device is provided, including a processor and the above-mentioned non-transitory computer-readable storage medium.

[0012] The present invention has at least the following beneficial effects:

[0013] The reinforced concrete crack depth inversion analysis method of the present invention confirms the shape and position of the apparent cracks in the structure through crack surface morphology recognition technology based on machine vision, and then establishes a finite element model to associate the external response of the structure with the internal defect state. By analyzing the response of the structure under different working conditions through the finite element model and comparing it with the test data, the depth of the internal cracks in the structure can be inferred. The advantage of the present invention is that it can accurately and non-destructively detect the depth of reinforced concrete cracks without the need for destructive testing of the structure, and has high flexibility and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 A flowchart of a reinforced concrete crack depth inversion analysis method provided by an embodiment of the present invention;

[0016] Figure 2 A schematic diagram of a fracture tangential level set provided by an embodiment of the present invention;

[0017] Figure 3 A schematic diagram of the software coordination process provided by an embodiment of the present invention;

[0018] Figure 4 This is a flow chart of fracture depth inversion analysis provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] It should be noted that, based on this disclosure, those skilled in the art will appreciate that an aspect described herein can be implemented independently of any other aspect, and that two or more of these aspects can be combined in various ways. For example, any number of the aspects described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement such an apparatus and / or practice such a method.

[0021] The following will refer to Figure 1 The flowchart of the reinforced concrete crack depth inversion analysis method shown in FIG1 introduces a reinforced concrete crack depth inversion analysis method.

[0022] In this embodiment, it should be noted that the bridge structure in reality is very complex. The forward analysis method that directly calculates the structural response under load based on known physical parameters is often difficult to accurately locate and describe the geometric characteristics of internal defects in the structure. Structural damage will cause variations in its internal physical properties, such as changes in key parameters such as Young's modulus and mass density, thereby affecting the dynamic and static response behavior of the structure. Therefore, a strategy for solving structural inverse problems can be adopted, using the observed structural response data to infer the state of the internal defects of the structure through optimization and inversion algorithms. The inverse analysis method provides a numerical method for quantifying defects, such as determining the size, shape, direction, etc. of the defect. In the inverse problem, the response of the defect body to external excitation is known. Therefore, it can be described as a process of finding the defect boundary required for the forward analysis problem based on known external loads, external boundaries, and structures.

[0023] Based on the above theory, the reinforced concrete crack depth inversion analysis method may include the following steps:

[0024] S100, based on machine vision, obtain the length and position of each crack on the surface of the reinforced concrete beam to be analyzed, so as to obtain the surface crack morphology matrix C = {c1, c2, ..., c i}; Among them, C i is the length and position information of the i-th crack.

[0025] In this embodiment, machine vision technology can be used to obtain the length and position of all cracks on the surface of the reinforced concrete beam to be analyzed. The position includes coordinate information. Based on this, the surface crack morphology matrix corresponding to the reinforced concrete structure to be analyzed can be obtained. It should be noted that those skilled in the art can use existing machine vision technology according to actual needs to obtain the surface crack morphology matrix corresponding to the reinforced concrete structure to be analyzed, which will not be elaborated here.

[0026] Through the above steps, damage to the structure caused by drilling sampling can be avoided; crack distribution information can be quickly obtained to provide high-precision input for subsequent inversion.

[0027] S200, build a 3D XFEM model of the reinforced concrete beam to be analyzed in the preset software, use C3D8R elements to discretize concrete, T3D2 elements to simulate steel bars, and realize steel-concrete coupling through preset commands.

[0028] In this embodiment, the preset software may be ABAQUS software, and the preset command may be the *embed command; that is, a three-dimensional extended finite element model is established in ABAQUS, using C3D8R units (concrete solid units) and T3D2 units (steel truss units), and the *embed command is used to couple the displacements of steel bars and concrete nodes.

[0029] The C3D8R unit is an eight-node hexahedral reduced integration unit suitable for complex stress analysis of concrete. The T3D2 unit is a three-dimensional truss unit that simulates the axial stress characteristics of steel bars. Through step S200, the synergistic effect of steel bars and concrete can be accurately captured. Reduced integration reduces the amount of calculation while maintaining accuracy.

[0030] S300, based on C, prefabricated initial cracks: using level set function Describe the crack surface, embed the Heaviside step function H(x) and the crack tip asymptotic function Where X is the coordinate of any point in space, t is time, r(t) is the crack surface, r is the distance from point X to the crack tip, θ is the polar angle of point X relative to the crack extension direction, and x is an arbitrarily selected Gaussian integration point.

[0031] In this embodiment, the displacement field function used by the XFEM method for overall discretization is as follows:

[0032]

[0033] Where N I (X) represents the interpolation function of the traditional finite element method associated with node I, which is used to approximate the displacement field in the continuous finite element framework; I traverses the node set of all elements in the mesh, and u I Represents the displacement vector calculated at node I in the conventional finite element solution; the function H(x) is a step function used to represent the situation where the crack penetrates the unit node; a I represents the extended degrees of freedom associated with node I; F α (x) is an asymptotic function specifically for the element nodes near the crack tip; It is an improved enrichment function related to node I, which combines the step function and the asymptotic function to enhance the ability of finite element approximation in the local area of the crack surface; where H(x) is the Heaviside function, that is, the discontinuous step function of the crack surface.

[0034] S400, load the crack depth vector D of the kth iteration k Perform XFEM static analysis with parameters to extract beam bottom deflection data.

[0035] Furthermore, step S400 includes the following steps:

[0036] S410, calling the ABAQUS kernel through the Python script, loading the crack depth vector D of the kth iteration k parameter.

[0037] S420: Perform XFEM static analysis to extract beam bottom deflection data.

[0038] In this embodiment, the mobility of the crack interface can be mathematically expressed by the level set function, which is usually described by formula (2):

[0039]

[0040] The characterization of crack geometry usually involves two level set functions: the normal level set function Ψ[X(t), t] (used to represent the crack front or wavefront) and the tangential level set function (used to represent crack surfaces), such as Figure 2 Both functions are constructed based on the signed distance function, which assigns a sign to the distance from any point on the crack interface to the reference point to indicate the position of the point relative to the crack front. The specific mathematical expression is as shown in formula (3):

[0041]

[0042] If point X is above the crack path defined by the function r(t), the level set function takes a positive value; if point X is below the crack path defined by the function r(t), the level set function takes a negative value. In this way, the level set function can describe the distribution and opening and closing behavior of cracks in the material in a concise and continuous mathematical form, providing an effective mathematical tool for crack simulation.

[0043] S500, using the improved bee colony algorithm to update the crack depth vector D of the k+1th iteration k+1 , iterates until the preset conditions are met and outputs the final inversion depth.

[0044] Furthermore, step S500 includes the following steps:

[0045] S510, generate a sinusoidal chaotic sequence a m =sin(πa m-1 ) to obtain the initial population; where m is the element position index in the sinusoidal chaotic sequence, m = 1, 2, ..., n; n is the size of the initial population.

[0046] S520, according to the formula Map the initial population to the initial crack depth vector D = [d1, d2, d3, ..., d i ];in, is the depth of the jth crack in the i-th group of solutions; is the chaotic sequence value corresponding to the parameter on the jth dimension of the i-th nectar source; x max is the maximum depth of the crack, x min is the minimum depth of the crack.

[0047] S530, calculating the distance between the i-th group of solutions and the k-th group of solutions Eliminate similar solutions with H<ξ and retain individuals with high fitness; is the depth of the jth crack in the kth group of solutions, and ξ is the preset first threshold.

[0048] S540, iterative optimization until a preset convergence condition is met.

[0049] In this embodiment, it should be noted that the method of population initialization in the traditional ABC algorithm is to generate a set of random numbers uniformly distributed between 0 and 1, and then use formula (4) to convert the random number sequence into the initial population of the algorithm. However, this method of directly generating initial solutions based on random numbers may cause the distribution of solutions to be biased towards local areas of the search space, causing the algorithm to over-concentrate on these areas in the early stages of iteration, increasing the risk of falling into local optimal solutions.

[0050]

[0051] Therefore, a chaotic sequence is used instead of the original random sequence to obtain a better initial population. The chaotic sequence is ergodic, irregular, and random. Therefore, by avoiding local singularities, the global convergence can be improved. The chaotic sequence is a simplified version of the sine sequence, as shown in formula (5):

[0052] a m =sin(πa m-1 ) (5)

[0053] In formula (5), m = 1, 2, ..., n; n represents the total number, that is, the size of the initial population. Substituting the initial value a0 and the number n into formula (5) yields the initial population.

[0054] The specific strategies are as follows: ① The number of initial populations in the optimization problem is clearly defined, and this number is set to be larger than the value of the initial nectar source to enhance the algorithm's global search capability. ② The initial population generation method based on formula (4) is abandoned and formula (6) is adopted instead. Specifically, by defining a distance function and combining it with a preset threshold ξ, the initial population is evaluated and screened to determine the final initial nectar source set.

[0055]

[0056] In formula (6), is the chaotic sequence value corresponding to the parameter on the jth dimension of the i-th honey source (i.e., potential solution). To evaluate the similarity between two solutions, a distance function H is defined as shown in formula (7):

[0057]

[0058] When the value of the distance function H is less than a preset threshold ξ, the two solutions are considered close to each other in the solution space. In this case, a greedy strategy is used to retain the solution with higher fitness and discard the solution with lower fitness, thereby reducing redundancy in the solution space and optimizing the population structure. Finally, the final initial population is determined from the filtered solution set based on the ranking of the fitness function.

[0059] Furthermore, step S540 includes the following steps:

[0060] S541, hired bee press Update the solution and follow the bee probability Select nectar source; among them, is the updated lth parameter value of the i-th nectar source; is the lth parameter value of the current i-th nectar source; The chaotic sequence value corresponding to the parameter on the lth dimension of the i-th nectar source; is the lth parameter value of the kth nectar source selected randomly; p i is the probability of the i-th nectar source being selected; fit i is the fitness value of the i-th nectar source, N food is the total number of nectar sources.

[0061] S542, dynamically adjust the search dimension L: in the initial iteration, L=1 to perform a single-dimensional search, and in the later iteration, L>1 to expand to a multi-dimensional space.

[0062] S543, calculate the cost function Deflection measured value Compared with XFEM calculation value The error is the convergence criterion; among them, J(C) min is the cost function value; NS is the total number of sensors in the model; is the true value of the characteristic point response at the sensor arrangement point, that is, the deflection value; ||·|| represents the norm; f[·] is an iterative gradient direction vector function defined based on the selected optimization algorithm, and its form depends on the specific optimization strategy adopted; It is based on the disease parameter value C in the k-1th iteration step k-1 The function calculated to guide the increment of the disease parameter value in the kth step; k and K represent the current number of iterations and the total number of iterations respectively.

[0063] S544, if |J(C k ) min -J(C k-1 ) min |<J', the iteration is terminated and the depth of each fracture obtained by inversion is output; where J' is the preset second threshold.

[0064] Furthermore, the value of J' ranges from 0.02 to 0.04.

[0065] In this embodiment, the value of J' may be 0.03.

[0066] The introduction of chaotic sequences brings randomness and dynamic traversal to the initial population generation of the artificial bee colony algorithm. Even so, the standard ABC algorithm still faces the risk of being trapped in a local optimum during the optimization process. This phenomenon is partly due to the algorithm's persistence within a fixed neighborhood search range, which leads to the search being trapped in a local extreme point due to the persistence of the local fitness function value.

[0067] In this embodiment, to overcome the above limitations, a strategy of adaptively adjusting the search dimension is adopted. Specifically, the search dimension in the standard ABC algorithm is dynamically expanded through formula (8), gradually transitioning from a single-dimensional search to a multi-dimensional search space, thereby enhancing the algorithm's global exploration capability.

[0068]

[0069] In formula (8), L represents the current search dimension; W represents the total parameter dimension of the problem; the variable iteration is used to track the current number of iterations; and MEN represents the total number of iterations set by the algorithm. In the early stages of the iterative process, the search dimension L is set to 1. This means that each neighborhood search is single-dimensional and is performed only on one variable in the problem parameter space. As the number of iterations increases, the search dimension L is adjusted according to predefined rules to gradually expand the search range to multiple dimensions, thereby enhancing the global search capability of the algorithm and achieving more detailed local exploration in later iterations. The purpose of this method of adaptively adjusting the search dimension is to balance the efficiency of the algorithm between global search and local refinement search to increase the probability of finding the global optimal solution. By dynamically adjusting the search dimension, the algorithm can gradually narrow the search range while maintaining sufficient randomness, focusing on those potential areas, thereby improving the overall performance of the optimization process.

[0070] At the same time, the corresponding search function is adjusted and the employed bees are updated according to formula (9) to adapt to the multi-dimensional search requirements.

[0071]

[0072] In this example, the ABAQUS script performs parametric modeling of the diseased structure:

[0073] Python is a high-level, general-purpose, interpreted programming language. ABAQUS provides a Python scripting interface that allows users to skip the GUI interface and interact directly with the ABAQUS kernel through Python scripts to build, set up, run, and post-process simulations. Through Python scripts, users can automate the entire simulation process, including batch processing of different models and parameter combinations. Therefore, in this embodiment, Python scripts are used to create models, define materials, boundary conditions, and loading, and then the ABAQUS kernel is called to perform numerical simulations and obtain result files. The result files are fed into the improved artificial bee colony algorithm implemented in Python to optimize new crack parameters. On this basis, continuous iterations are performed to perform inverse analysis of reinforced concrete structure defects. The software coordination process is shown in Figure 3.

[0074] The overall inversion process is as follows:

[0075] like Figure 4 As shown in Figure 1, first, the cracks on the component surface are recorded and grouped. In this process, a crack information matrix C is established to store the morphology and location information of the surface cracks, where c iThe vector is responsible for recording the key geometric properties of the length and position of the crack. When performing the inversion analysis of the structural crack depth, a D vector array is constructed, d i This function is used to store and manage the depth information of each surface crack. A non-zero initial crack depth vector, D0, is randomly initialized and used as the starting value for the inversion process. It is noteworthy that the inversion process for crack propagation depth does not consider the dynamic mechanism of crack propagation, but instead focuses on analyzing the static mechanical response of the structure after crack propagation. By comparing measured data with numerical simulation results based on the structural state after crack propagation, the internal crack depth of the structure is inferred.

[0076] A Python script (XFEM.py) calls the ABAQUS static general solver kernel (StaticGeneral) to perform static response analysis of reinforced concrete components with initial cracks. Specifically, the program extracts deflection data at a specified location on the beam bottom from the generated result file and compares it with the measured deflection to calculate the cost function. Subsequently, an improved artificial bee colony algorithm is used for iterative optimization to obtain the next optimized crack depth vector and update the depth vector (D). Importing D into the ABAQUS parametric modeling process for analysis via the Python script is a key step in structural damage simulation. This process drives the ABAQUS kernel to repeatedly solve, generating new deflection result files for further optimization analysis. When the optimization algorithm meets the preset convergence criteria, indicating that the model iteration process has stabilized, the final crack depth vector (DP) obtained by inversion is compared with the crack depth vector (DT) obtained through experimental testing. This comparison process is crucial for verifying the accuracy and rationality of the inversion analysis method and ensuring the reliability of structural damage assessment.

[0077] In this embodiment, the shape and location of the apparent cracks in the structure are confirmed through machine vision-based crack surface morphology recognition technology. A finite element model is then established to correlate the external response of the structure with the internal defect state. By analyzing the response of the structure under different working conditions through the finite element model and comparing it with the test data, the depth of the internal cracks in the structure can be inferred. The advantage of this invention is that it can accurately and non-destructively detect the depth of reinforced concrete cracks without the need for destructive testing of the structure, and it has high flexibility and adaptability.

[0078] Specifically:

[0079] (1) The artificial bee colony algorithm is improved by introducing a chaotic sequence instead of the original random sequence to obtain a better initial population, avoid local singularities, and improve global convergence. In addition, the method of adaptively adjusting the search dimension is adopted to improve the algorithm's ability to escape the local optimal trap.

[0080] (2) Three-point and four-point bending reloading tests were conducted on cracked concrete beams, an XFEM finite element model was constructed, and a reinforced concrete crack depth inversion method was proposed. The test results show that this method can effectively determine the extension depth of single or multiple cracks inside the structure, and the errors of all inversion analyses are controlled within 5%, which is acceptable in engineering applications. This method supplements the limitations of traditional machine vision technology in surface crack identification to a certain extent, expands its application scope from surface cracks to internal crack identification, and provides a new technical means for internal disease detection of infrastructure such as bridges.

[0081] (3) The crack depth inversion analysis process found that it is necessary to select an appropriate number of measured deflections. Too few constraints will result in a variety of crack distributions in the theoretical model, all of which make the deflection of a specified point outside the beam close to the measured value. In order to obtain a unique and realistic structural crack depth distribution, it is necessary to increase the deflection value. The relationship between the specific number of required deflection values and the number of cracks is still an issue worth discussing.

[0082] (4) Experiments have shown that the method in this embodiment is feasible.

[0083] Furthermore, although the steps of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0084] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one program related to implementing a method in a method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0085] The program product may be implemented in any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0086] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0087] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0088] The program code for performing the operations of the present application can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and conventional procedural programming languages such as "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0089] An embodiment of the present invention further provides an electronic device including a processor and the aforementioned non-transitory computer-readable storage medium.

[0090] The electronic device is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0091] The electronic device is implemented as a general-purpose computing device. Components of the electronic device may include, but are not limited to, the aforementioned at least one processor, the aforementioned at least one memory, and a bus connecting different system components (including the memory and the processor).

[0092] The memory stores program codes, which can be executed by the processor, so that the processor performs the steps of various embodiments described in this specification.

[0093] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0094] The memory may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0095] The bus may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0096] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figures, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0097] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0098] An embodiment of the present invention further provides a computer program product comprising program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0099] Although some specific embodiments of the present invention have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A reinforced concrete crack depth inversion analysis method, characterized in that: The method comprises the following steps: S100, obtaining the length and position of each crack on the surface of the reinforced concrete beam to be analyzed based on machine vision, so as to obtain the surface crack morphology matrix C = {c1, c2, ..., ci} corresponding to the reinforced concrete structure to be analyzed; wherein C i is the length and position information of the i-th crack; S200, build a 3D XFEM model of the reinforced concrete beam to be analyzed in the preset software, use C3D8R elements to discretize concrete, T3D2 elements to simulate steel bars, and realize steel-concrete coupling through preset commands; S300, based on C, prefabricated initial cracks: using level set function Describe the crack surface, embed the Heaviside step function H(x) and the crack tip asymptotic function Where X is the coordinate of any point in space, t is time, r(t) is the crack surface, r is the distance from point X to the crack tip, θ is the polar angle of point X relative to the crack extension direction, and x is an arbitrarily selected Gaussian integration point; S400, load the crack depth vector D of the kth iteration k Parameters are used to perform XFEM static analysis and extract beam bottom deflection data; S500, using the improved bee colony algorithm to update the crack depth vector D of the k+1th iteration k+1 , iterates until the preset conditions are met and outputs the final inversion depth.

2. The reinforced concrete crack depth inversion analysis method according to claim 1, characterized in that: Step S500 includes the following steps: S510, generate a sinusoidal chaotic sequence a m =sin(πa m-1 ) to obtain the initial population; where m is the element position index in the sinusoidal chaotic sequence, m = 1, 2, ..., n; n is the size of the initial population; S520, according to the formula Map the initial population to the initial crack depth vector D = [d1, d2, d3, ..., d i ];in, is the depth of the jth crack in the i-th group of solutions; is the chaotic sequence value corresponding to the parameter on the jth dimension of the i-th nectar source; x max is the maximum depth of the crack, x min is the minimum depth of the crack; S530, calculating the distance between the i-th group of solutions and the k-th group of solutions Eliminate similar solutions with H<ξ and retain individuals with high fitness; is the depth of the jth crack in the kth group of solutions, ξ is the preset first threshold; S540, iterative optimization until a preset convergence condition is met.

3. The reinforced concrete crack depth inversion analysis method according to claim 2, characterized in that: Step S540 includes the following steps: S541, hired bee press Update the solution and follow the bee probability Select nectar source; among them, is the updated lth parameter value of the i-th nectar source; is the lth parameter value of the current i-th nectar source; The chaotic sequence value corresponding to the parameter on the lth dimension of the i-th nectar source; is the lth parameter value of the kth nectar source selected randomly; p i is the probability of the i-th nectar source being selected; fit i is the fitness value of the i-th nectar source, N food is the total number of nectar sources; S542, dynamically adjust the search dimension L: in the initial iteration, L=1, perform a single-dimensional search; in the later iteration, L>1, expand to a multi-dimensional space; S543, calculate the cost function Deflection measured value Compared with XFEM calculation value The error of is the convergence criterion; among them, J(C) min is the cost function value; NS is the total number of sensors in the model; is the true value of the characteristic point response at the sensor arrangement point, that is, the deflection value; ||·|| represents the norm; f[·] is an iterative gradient direction vector function defined based on the selected optimization algorithm, and its form depends on the specific optimization strategy adopted; It is based on the disease parameter value C in the k-1th iteration step k-1 The function calculated to guide the increment of the disease parameter value in the kth step; k and K represent the current number of iterations and the total number of iterations respectively. S544, if |J(C k ) min -J(C k-1 ) min |<J', the iteration is terminated and the depth of each fracture obtained by inversion is output; where J' is the preset second threshold; J(C k ) min The disease parameter value is C k The cost function value at that time.

4. The reinforced concrete crack depth inversion analysis method according to claim 3 is characterized in that: The value of J' ranges from 0.02 to 0.

04.

5. The reinforced concrete crack depth inversion analysis method according to claim 1, characterized in that: The preset software includes: ABAQUS software.

6. The reinforced concrete crack depth inversion analysis method according to claim 1, characterized in that: The preset commands include: *embed command.

7. The reinforced concrete crack depth inversion analysis method according to claim 1, characterized in that: Step S400 includes the following steps: S410, calling the ABAQUS kernel through the Python script, loading the crack depth vector D of the kth iteration k parameter; S420: Perform XFEM static analysis to extract beam bottom deflection data.

8. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, characterized in that: The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the reinforced concrete crack depth inversion analysis method according to any one of claims 1 to 7.

9. An electronic device, characterized in that: The device comprises a processor and the non-transitory computer-readable storage medium of claim 8.

Images