Large steel structure damage identification method and system based on evolution mechanism analysis

Abstract

The invention belongs to the field of large-scale steel structure health state monitoring, and provides a large-scale steel structure damage identification method and system based on evolution mechanism analysis, and the technical scheme is as follows: analyzing a large-scale steel structure damage time-varying evolution law under the long-term coupling action of a load and an environment, and constructing a large-scale steel structure damage time-varying evolution model based on a constructed numerical simulation model; and the performance degradation mechanism of the steel structure in the long-term service process is disclosed. A large steel structure damage inversion identification method based on a unit sensitivity model and a sparse Bayesian theory is provided by taking a performance degradation mechanism as guidance, and effective positioning of a steel structure damage area is realized. On the basis, a large-scale steel structure damage quantitative analysis method based on a generalized genetic algorithm is researched, a prize and fine strategy is adopted in a variation mechanism, a large-scale steel structure damage accurate identification and quantitative analysis technical system is finally constructed, and a scientific basis is provided for operation risk assessment, safety control and predictive maintenance of the large-scale steel structure.

Description

Technical Field

[0001] The present invention belongs to the field of large-scale steel structure health status monitoring, and in particular relates to a large-scale steel structure damage identification method and system based on evolution mechanism analysis. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] During the erection of steel structures, steel may be impacted and deformed and bent. During the long-term service of steel structures, under the combined influence of complex natural environments, multiple load combinations, and production and construction defects, steel structures are prone to corrosion, loose screws, bending of rods, cracking of welds, internal micro-damage, reduced node stiffness and other structural damage. Structural damage will lead to a decrease in the bearing capacity and durability of steel structures, and may even cause the collapse of steel structures, resulting in serious loss of life and property. Therefore, it is crucial to solve the problem of real-time monitoring of large steel structure damage, realize health monitoring of structural acceleration, stress strain, displacement deformation, and identify and quantify steel structure damage.

[0004] Commonly used steel structure damage identification methods can be divided into non-model methods and model-based methods. Non-model methods refer to directly analyzing and processing the measured structural response to obtain parameter changes and identify structural damage. Non-model methods include identification methods based on dynamic fingerprints and identification methods based on measured time domain signals. Dynamic fingerprints mainly include: frequency type, vibration type, curvature mode type, flexibility type, energy change type, frequency response function type, etc. The method based on dynamic fingerprints has certain limitations: frequency changes contain too little structural information and are insensitive to local damage; high-order vibration modes are sensitive to local losses, but they are difficult to obtain; due to the difficulty in obtaining high-order vibration modes, curvature modes are difficult to apply in large-scale engineering facilities; flexibility matrices are sensitive to local damage, but the flexibility matrix before damage is difficult to obtain, which is not conducive to engineering applications. The identification method based on measured time domain signals contains more complete model information, but the processing process is complicated, easily affected by external environmental factors, and the influence of damping needs to be considered.

[0005] The model-based damage identification method establishes a finite element model and continuously modifies and updates the finite element model until the structural dynamic response of the finite element model is similar to the measured data. At this time, the finite element model can be regarded as the actual structure. The key to this method is to iteratively solve the inverse problem. Its limitation is that the amount of calculation is large, and the accuracy of the identification result depends on the accuracy of the established finite element model. Summary of the invention

[0006] In order to solve at least one technical problem existing in the above-mentioned background technology, the present invention provides a large-scale steel structure damage identification method and system based on evolution mechanism analysis, which is guided by the performance degradation mechanism and proposes a large-scale steel structure damage inversion identification method based on unit sensitivity model and sparse Bayesian theory to achieve effective positioning of the damaged area of ​​the steel structure.

[0007] In order to achieve the above object, the present invention adopts the following technical solution: A first aspect of the present invention provides a large steel structure damage identification method based on evolution mechanism analysis, comprising the following steps: Analyze the time-varying evolution mechanism of damage of large steel structures under the long-term coupling of load and environment, and construct a numerical simulation model of the coupling of load and environment under the damage state of large steel structures; Obtain multimodal datasets containing mode shape and frequency information for large steel structures; Determine the stiffness matrix of the damaged structure, introduce the damage coefficient of the steel structure, and calculate the vibration mode sensitivity and frequency sensitivity based on the functional relationship between the frequency, vibration mode, and the stiffness matrix of the damaged structure; The initial large-scale steel structure damage identification inversion model is constructed by combining the sensitivity matrix constructed by mode sensitivity and frequency sensitivity, the residual of the measured data and the numerical simulation model calculation data; Based on the initial large-scale steel structure damage identification inversion model, the damage coefficient of the steel structure is identified, the posterior probability density function of the damage coefficient is maximized, and the location information of the suspected damaged unit of the steel structure is determined; Based on the location information of suspected damaged units in the steel structure, the damage of the steel structure is quantitatively analyzed to determine the final damage location and damage extent of the steel structure.

[0008] Furthermore, the construction process of the numerical simulation model of the load and environment coupling includes: Based on the theory of continuum damage mechanics and considering the multiaxial stress loading, the damage evolution equation of steel material under multiaxial stress state is obtained. Conduct experiments on the degradation damage mechanism of large steel structures under complex dynamic and static loads and environmental coupling to obtain a collection of the main environmental factors related to steel structure damage; The damage parameters in the damage evolution equation of the multiaxial stress state are fitted with the set of environmental factors respectively, and the damage evolution equation of the steel material under the coupling of load and environment is established; A finite element model of steel structure buildings is established, and combined with the damage evolution equation of steel, the behavior of steel structures under complex dynamic loads and environmental factors is simulated to obtain a numerical simulation model of steel structures under the coupling of loads and environment.

[0009] Furthermore, the damage evolution equation of steel material under the coupling of load and environment is: , ， in, is the damage value caused by the combined effect of load and environment, Y It is a collection of main environmental factors related to steel structure damage. The main environmental factors that cause damage to steel structures are is the critical damage value, is the fracture strain, is the damage strain threshold, is the equivalent cumulative plastic strain, The damage index.

[0010] Furthermore, the calculation process of mode sensitivity and frequency sensitivity includes: The unit stiffness matrix and stiffness damage coefficient are used to represent the overall stiffness matrix of the damaged structure; Determine the relationship between the overall stiffness matrix and the frequency and mode shape of the steel structure; Based on the relationship between the overall stiffness matrix and body stiffness matrix of the damaged structure and the frequency and mode shape of the steel structure, the mode shape sensitivity and frequency sensitivity are obtained.

[0011] Furthermore, the functional relationship between frequency, mode shape, and damaged structure stiffness matrix is: , in, M is the mass matrix of the structure, , For the j The modal frequency, For the j The mode shape, For the The stiffness damage factor of each element is For the The stiffness matrix of each element is n is the number of units.

[0012] Furthermore, the objective function of the initial large-scale steel structure damage identification inversion model is: , =

[0013] In the formula, For the The residuals of the measured data and the simulated data during the iterations are For the The sensitivity matrix in the iteration, For the The updated value of the damage coefficient in the iteration, is the multimodal data actually measured, For the The damage coefficient in the iteration is, It is the multimodal data obtained by simulating the numerical simulation model.

[0014] Furthermore, the posterior probability density function of the damage coefficient is: , in, is the normalization constant, For the The damage coefficient in the iteration is, For the k The likelihood function of the damage coefficient in the iteration is, For the k The prior distribution function of the damage coefficient in the iteration, The residuals between the measured and simulated data during the iteration process, To measure the variance of the error in the frequency and vibration mode of steel structure buildings, Used to control the distribution density of the damage coefficient, The relationship between the stiffness damage coefficient within the control group, is the hyperparameter corresponding to each unit of the steel structure A collection of is the hyperparameter corresponding to each unit of the steel structure A collection of .

[0015] Furthermore, the variance matrix Σ of the prior distribution of the damage coefficient satisfies: , The posterior probability of the damage coefficient follows a Gaussian distribution, and its mean vector and the variance matrix satisfy: , in, is the identity matrix, is the sensitivity matrix.

[0016] Furthermore, the quantitative analysis of the steel structure damage based on the position information of the suspected damaged unit of the steel structure to determine the final damage position and damage degree of the steel structure includes: According to the damage inversion identification results, the location, number and corresponding gene point of the suspected damaged unit are determined as parameters and input into the generalized genetic algorithm calculation model, the initial mutation probability of the gene point is assigned, and the suspected damaged unit is searched according to the initial mutation probability; During the evolution process, the initial population of the set sub-population is set using the suspected damaged units after pre-identification, and the initial population of the remaining sub-populations is distributed over the entire domain, so that the optimal solution in the solution process may jump out of the setting of the suspected damaged units in pre-identification and initialize the population distribution only within the range of the suspected damaged units; The mutation probability adjusted according to the evolutionary results of each generation guides the operator operation of the next generation, thereby realizing directional search, obtaining the global optimal solution, and obtaining the damage location and damage degree of the steel structure.

[0017] A second aspect of the present invention provides a large steel structure damage identification system based on evolution mechanism analysis, comprising: The simulation model construction module is used to analyze the time-varying evolution mechanism of damage of large steel structures under the long-term coupling of load and environment, and to construct a numerical simulation model of the coupling of load and environment under the damage state of large steel structures; The initial inversion model building module is used to determine the stiffness matrix of the damaged structure, introduce the damage coefficient of the steel structure, and calculate the vibration sensitivity and frequency sensitivity based on the functional relationship between the frequency, vibration mode, and the stiffness matrix of the damaged structure; The initial large-scale steel structure damage identification inversion model is constructed by combining the sensitivity matrix constructed by mode sensitivity and frequency sensitivity, the residual of the measured data and the numerical simulation model calculation data; A damage identification module is used to identify the damage coefficient of the steel structure based on the initial large-scale steel structure damage identification inversion model, maximize the posterior probability density function of the damage coefficient, and determine the location information of the suspected damaged unit of the steel structure; Based on the location information of suspected damaged units in the steel structure, the damage of the steel structure is quantitatively analyzed to determine the final damage location and damage extent of the steel structure.

[0018] A third aspect of the present invention provides a computer-readable storage medium.

[0019] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the large-scale steel structure damage identification method based on evolution mechanism analysis as described above.

[0020] A fourth aspect of the present invention provides a computer device.

[0021] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the large steel structure damage identification method based on evolution mechanism analysis as described above are implemented.

[0022] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention analyzes the time-varying evolution of damage of large steel structures under the long-term coupling of load and environment, and reveals the performance degradation mechanism of steel structures in the long-term service process based on the constructed numerical simulation model. Guided by the performance degradation mechanism, a large steel structure damage inversion identification method based on unit sensitivity model and sparse Bayesian theory is proposed to achieve effective positioning of the damaged area of ​​the steel structure.

[0023] 2. The present invention introduces the reward-for-excellence and punishment-for-inferiority strategy into the variation mechanism through a quantitative analysis method for damage of large steel structures based on a generalized genetic algorithm, and finally constructs a technical system for accurate identification and quantitative analysis of damage of large steel structures, providing a scientific basis for operational risk assessment, safety control and predictive maintenance of large steel structures.

[0024] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0026] Figure 1 is a flow chart of a large steel structure damage identification method based on evolution mechanism analysis provided by an embodiment of the present invention; Figure 2 is a schematic diagram of a numerical simulation model construction process provided by an embodiment of the present invention; Figure 3 It is a flow chart of a damage inversion technology based on a unit sensitivity model and sparse Bayesian theory provided by an embodiment of the present invention; Figure 4 It is a flow chart of a method for quantitatively analyzing steel structure damage using a generalized genetic algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0028] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0029] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0030] In response to the technical difficulties in steel structure damage identification mentioned in the background technology, the present invention studies the time-varying evolution of damage in large steel structures under the long-term coupling of load and environment, and relies on the constructed numerical simulation model to reveal the performance degradation mechanism of steel structures during long-term service. Guided by the performance degradation mechanism, a large-scale steel structure damage inversion identification method based on the unit sensitivity model and sparse Bayesian theory is proposed to achieve effective positioning of the damaged area of ​​the steel structure. On this basis, a large-scale steel structure damage quantitative analysis method based on a generalized genetic algorithm is studied, and a reward-for-excellence and punishment-for-inferiority strategy is introduced into the variation mechanism, and finally a large-scale steel structure damage accurate identification and quantitative analysis technology system is constructed to provide a scientific basis for the operation risk assessment, safety control and predictive maintenance of large steel structures.

[0031] Embodiment 1 This embodiment provides a large steel structure damage identification method based on evolution mechanism analysis, such as Figure 1 As shown, it includes damage evolution mechanism research, damage area identification and location, and damage area quantitative analysis, which specifically includes the following steps: Step 1: Analyze the time-varying evolution mechanism of damage of large steel structures under the long-term coupling of load and environment, and construct a numerical simulation model of large steel structures under the coupling of load and environment in the damaged state; like Figure 2 As shown, the specific steps include: Step 101: Based on the theory of continuum damage mechanics and considering the situation of multiaxial stress loading, the damage evolution equation expression of steel material under multiaxial stress state is obtained: (1), In the formula, For cumulative damage, For the initial damage, is the critical damage value, is the equivalent cumulative plastic strain, is the damage threshold strain, is the hydrostatic pressure, is the Mises equivalent stress; and Satisfy the following formula: , is the fracture strain, is the damage strain threshold, is the fracture strain under multiaxial stress, is the injury index; Step 102: Conduct a deterioration damage mechanism experiment on large steel structures under complex dynamic and static loads and environmental coupling, explore the main factors affecting the overall bearing performance of large steel structure components, such as atmospheric corrosion, air temperature, humidity, wind and sand, etc., so as to obtain a collection of the main environmental factors related to steel structure damage Y : (2), In the formula, It is the main environmental factor causing damage to steel structures.

[0032] Step 103: Combine the experimental data and transform the damage parameters in the damage evolution equation of the multiaxial stress state into Respectively Fitting is performed to obtain the damage parameters and The functional relationship of is used to establish the damage evolution equation of steel material under the coupling of load and environment: (3), in, It is the damage value caused by the combined effect of load and environment.

[0033] Step 104: Establish a finite element model of the steel structure building, and combine it with the steel material damage evolution equation established by formula (3) to carry out computer simulation to simulate the behavior of the steel structure under the action of complex dynamic loads and environmental factors, so as to obtain a numerical simulation model of the steel structure under the coupling of load and environment, which provides the necessary theoretical basis for damage identification of large steel structures.

[0034] Studying the damage evolution mechanism of steel structures under the coupling of complex loads and environments has practical guiding significance for steel structure damage identification. Steel structure damage identification is divided into two stages: damage location and quantification. Based on measured modal data and numerical simulation models, the suspected location information of steel structure damage is determined, and the final damage location and degree are determined through further calculations. This is the basic idea for steel structure damage identification.

[0035] Step 2: Perform Gaussian mixture clustering analysis on the large steel structure monitoring data sample set collected by sensors in complex scenarios, and then perform modal analysis on the monitoring data to obtain the values ​​of each vibration mode and each frequency order, thereby obtaining a measured modal data set containing vibration mode information and frequency information; Step 3: Based on the nonlinear parameter system form and the structural sensitivity principle in the direct method of numerical analysis, the initial large steel structure damage identification inversion model is constructed by introducing the structural damage degree inversion objective function constructed based on the residual of the measured value and the simulated value of the damage degree identification index of the transient dynamic response of the large steel structure; like Figure 3 , specifically including the following steps: Step 301: Use the change of the structural unit stiffness coefficient to represent the damage of the structure and obtain the structural stiffness matrix , determine the overall stiffness matrix Relationship with structural frequency and vibration mode; Changes in stiffness and mass will cause changes in frequency and vibration mode. Since general structural damage only leads to a decrease in structural stiffness, the change in the structural unit stiffness coefficient is used to represent the damage of the structure. Therefore, the unit stiffness matrix and stiffness damage coefficient are used to represent the overall stiffness matrix of the damaged structure, as follows: (4), In the formula, is the global stiffness matrix of the damaged structure, For the The stiffness matrix of each element is For the The stiffness damage factor of each element is n is the number of units; Determine the global stiffness matrix The relationship with the frequency and vibration mode of the steel structure is expressed as: (5), in, M is the mass matrix of the structure, , For the j The modal frequency, For the j The first-order mode shape.

[0036] Step 302: Substitute equation (4) into equation (5) to obtain the following expression: (6), Equation (6) establishes the modal frequency , vibration shape , the element stiffness matrix and damage factor The functional relationship between Based on this, the vibration sensitivity can be calculated and frequency sensitivity , based on vibration mode sensitivity and frequency sensitivity Construct the sensitivity matrix .

[0037] Step 303: Based on the principle of structural sensitivity, the multimodal data actually measured is Expand into coefficients to be identified The first-order Taylor expansion of , the relationship between the structural modal parameters and the structural physical parameters is obtained as follows: (7), In the formula, is the multimodal data actually measured, is the multimodal data obtained by numerical simulation model. is the change in the element stiffness damage coefficient, is the sensitivity matrix, which includes vibration shape sensitivity and frequency sensitivity.

[0038] Step 304: According to the relationship between the structural modal parameters and the structural physical parameters, an initial inversion objective function is constructed as follows: (8), In the formula, = For the The residuals of the measured data and the simulated data during the iterations are For the The sensitivity matrix in the iteration, For the The damage coefficient in the iteration, For the Updated value of the damage coefficient in iterations.

[0039] Step 4: Based on the initial large-scale steel structure damage identification inversion model, the damage coefficient is identified, the posterior probability density function of the damage coefficient is maximized, and the location information of the suspected damaged unit of the steel structure is obtained; In order to make the response of the finite element model closest to the actual measured data, an iterative optimization method is required to continuously modify the parameters in the mechanical response system to minimize the objective function.

[0040] For large steel structures, the use of health monitoring methods can obtain dynamic response data before and after structural damage more accurately and timely, and establish a damage inversion problem containing structural damage information.

[0041] The specific steps include: Considering that the dimension of the element stiffness damage coefficient vector is much larger than the number of measurement points, equation (8) is a set of ill-conditioned linear equations. Since structural damage has sparse characteristics, this embodiment adopts the sparse Bayesian method to identify the loss coefficient of the steel structure. The specific steps are as follows: Step 401: Take the multimodal data in each monitoring period and the model parameter change information before and after the structural damage as the target data, and , , , Initialize, where To measure the variance of the error in the frequency and vibration mode of steel structure buildings, Used to control the distribution density of the damage coefficient, The relationship between the stiffness damage coefficient within the control group, is the element stiffness damage coefficient, is the hyperparameter corresponding to each unit of the steel structure A collection of is the hyperparameter corresponding to each unit of the steel structure A collection of .

[0042] Step 402: Solve the frequency and mode shape in the current iteration process with respect to the stiffness damage coefficient vector The sensitivity matrix : (9), in, For the The order vibration mode i The sensitivity of the damage factor is For the The frequency eigenvalues ​​are i The sensitivity of the damage factor.

[0043] Step 403: In the k During the iteration process, the EM algorithm is used to continuously correct the hyperparameters and the unit stiffness damage coefficient of the steel structure building. , as follows: Step 4031: According to Bayesian theory, the posterior probability density function of the damage coefficient is: (10), In the formula, is the normalization constant, For the k The likelihood function of the damage coefficient in the iteration is, No. k Prior distribution function of the damage coefficient in iterations; The residuals between measured and calculated data during the iteration process; The likelihood function and prior distribution of the damage coefficient both obey Gaussian distribution, where the variance matrix Σ of the prior distribution of the damage coefficient satisfies: (11), Therefore, the posterior probability of the damage coefficient also follows a Gaussian distribution, and the mean vector and the variance matrix satisfy: , in, is the identity matrix.

[0044] Substitute the hyperparameters obtained in the previous iteration into the maximum a posteriori estimate during the iteration. , the frequency value matrix and mode value matrix Calculated by the numerical simulation model of the steel structure building, the Newton method is used to minimize , get the damage coefficient value; Step 4032: Based on the damage coefficient value and mean vector obtained in step 4031 and the variance matrix Calculate the first The revised value of each hyperparameter in the iteration; Step 4033: Determine the maximum a posteriori estimate of the damage coefficient Whether the convergence condition is met, if the condition is met, the calculation is stopped, if not, jump to step 402 to perform the Iterations.

[0045] Step 404: Set a pre-identification damage threshold W, substitute the monitored modal data into steps 401-403 to obtain the damage coefficient of each unit, and regard the unit whose damage coefficient value exceeds the threshold W as a suspected damaged unit, otherwise, it is regarded as a lossless unit, thereby achieving the purpose of damage identification and positioning.

[0046] Step 5: Based on the determined damage location information of the steel structure, quantitatively analyze the damage of the steel structure to obtain the final damage location and damage degree of the steel structure.

[0047] Damage inversion positioning can determine whether there is damage in the steel structure and the initial location and nature of the damage, providing important reference information for subsequent quantitative analysis. On the basis of achieving effective damage positioning of large steel structures, this embodiment applies a generalized genetic algorithm to the numerical optimization of the damage quantitative analysis model, such as Figure 4 The specific steps include: Step 501: First, according to the damage pre-identification result, the position, number and corresponding gene point of the suspected damaged unit are determined as damage information and input into the generalized genetic algorithm calculation model, and then the damage information is encoded using Gray code to ensure global search capability and local stability.

[0048] Step 502: The above-mentioned generalized genetic algorithm is used to optimize and solve the stiffness change of the suspected damaged unit, and the final damage position and damage degree are solved.

[0049] Specifically include: Step 5021: Determine the probability of gene mutation at different locations , sub-population size , number of sub-populations , maximum number of iterations The initial parameters are used to assign a larger initial mutation probability to the gene sites corresponding to the suspected damaged units, so that each sub-population has a greater probability to search near these units first. At the same time, for these specific gene sites, the mutation probability does not change with the reward-for-excellence and punishment-for-inferiority strategy, and always remains at a high value. .

[0050] Step 5022: For the set N sub-communities, Initialize the population within the range of the suspected damaged unit, and then The sub-populations are initialized in the global scope, which enables the population to evolve better in a given direction. At the same time, the optimal solution in the solution process may jump out of the setting of the suspected damaged unit in the pre-identification.

[0051] Step 5023: Each sub-population performs an evolution operation and selects a parent from the population with probability. and ,right and Single point hybridization forms two intermediate individuals and ,right , , , Perform selection operations to obtain surviving individuals, perform mutation operations on surviving individuals to obtain new individuals and , entering the next generation population. It is worth noting that if the mutation of a certain point causes the offspring to perform better than the parent, the mutation probability of the point will be increased, otherwise it will be reduced. That is, the mutation probability of other points except specific gene points will be rewarded and punished to achieve directional search.

[0052] Step 5024: Check whether the current number of iterations is greater than the number of iterations gen, or the optimal individual remains unchanged for multiple consecutive generations. If so, the algorithm converges and outputs the number of iterations and the optimal individual. Otherwise, the number of iterations gen = gen + 1, and jump to step 5023 to continue execution.

[0053] The above-mentioned generalized genetic algorithm is used to optimize the stiffness change of the suspected damaged unit, obtain the accurate solution of the damage location and damage degree, improve the calculation efficiency and accuracy, and finally complete the damage location and quantitative analysis of the large steel structure.

[0054] Embodiment 2 This embodiment provides a large steel structure damage identification system based on evolution mechanism analysis, including: The simulation model construction module is used to analyze the time-varying evolution mechanism of damage of large steel structures under the long-term coupling of load and environment, and to construct a numerical simulation model of the coupling of load and environment under the damage state of large steel structures; The initial inversion model building module is used to determine the stiffness matrix of the damaged structure, introduce the damage coefficient of the steel structure, and calculate the vibration sensitivity and frequency sensitivity based on the functional relationship between the frequency, vibration mode, and the stiffness matrix of the damaged structure; The initial large-scale steel structure damage identification inversion model is constructed by combining the sensitivity matrix constructed by mode sensitivity and frequency sensitivity, the residual of the measured data and the numerical simulation model calculation data; A damage identification module is used to identify the damage coefficient of the steel structure based on the initial large-scale steel structure damage identification inversion model, maximize the posterior probability density function of the damage coefficient, and determine the location information of the suspected damaged unit of the steel structure; Based on the location information of suspected damaged units in the steel structure, the damage of the steel structure is quantitatively analyzed to determine the final damage location and damage extent of the steel structure.

[0055] Embodiment 3 This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the large steel structure damage identification method based on evolution mechanism analysis as described above are implemented.

[0056] Embodiment 4 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the large steel structure damage identification method based on evolution mechanism analysis as described above are implemented.

[0057] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A damage identification method for large steel structures based on evolution mechanism analysis, characterized in that: The steps include: Analyze the time-varying evolution mechanism of damage of large steel structures under the long-term coupling of load and environment, and construct a numerical simulation model of the coupling of load and environment under the damage state of large steel structures; Obtain multimodal datasets containing mode shape and frequency information for large steel structures; Determine the stiffness matrix of the damaged structure, introduce the damage coefficient of the steel structure, and calculate the vibration mode sensitivity and frequency sensitivity based on the functional relationship between the frequency, vibration mode, and the stiffness matrix of the damaged structure; The initial large-scale steel structure damage identification inversion model is constructed by combining the sensitivity matrix constructed by mode sensitivity and frequency sensitivity, the residual of the measured data and the numerical simulation model calculation data; Based on the initial large-scale steel structure damage identification inversion model, the damage coefficient of the steel structure is identified, the posterior probability density function of the damage coefficient is maximized, and the location information of the suspected damaged unit of the steel structure is determined; Based on the location information of suspected damaged units in the steel structure, the damage of the steel structure is quantitatively analyzed to determine the final damage location and damage extent of the steel structure.

2. The large-scale steel structure damage identification method based on evolution mechanism analysis according to claim 1 is characterized in that: The construction process of the numerical simulation model of the load and environment coupling includes: Based on the theory of continuum damage mechanics and considering the multiaxial stress loading, the damage evolution equation of steel material under multiaxial stress state is obtained. Conduct experiments on the degradation damage mechanism of large steel structures under dynamic and static loads and environmental coupling to obtain a collection of the main environmental factors related to steel structure damage; The damage parameters in the damage evolution equation of the multiaxial stress state are fitted with the set of environmental factors to establish the damage evolution equation of steel materials under the coupling of load and environment. A finite element model of steel structure buildings is established, and combined with the damage evolution equation of steel, the behavior of steel structures under complex dynamic loads and environmental factors is simulated to obtain a numerical simulation model of steel structures under the coupling of loads and environment.

3. The large steel structure damage identification method based on evolution mechanism analysis according to claim 1 is characterized in that: The damage evolution equation of steel material under the coupling of load and environment is: ， ， in, is the damage value caused by the coupling of load and environment, Y It is a collection of main environmental factors related to steel structure damage. The main environmental factors that cause damage to steel structures are is the critical damage value, is the fracture strain, is the damage strain threshold, is the equivalent cumulative plastic strain, The damage index.

4. The large-scale steel structure damage identification method based on evolution mechanism analysis according to claim 1 is characterized in that: The calculation process of vibration mode sensitivity and frequency sensitivity includes: The unit stiffness matrix and stiffness damage coefficient are used to represent the overall stiffness matrix of the damaged structure; Determine the relationship between the overall stiffness matrix and the frequency and mode shape of the steel structure; Based on the relationship between the overall stiffness matrix and body stiffness matrix of the damaged structure and the frequency and mode shape of the steel structure, the mode shape sensitivity and frequency sensitivity are obtained.

5. The large-scale steel structure damage identification method based on evolution mechanism analysis according to claim 4 is characterized in that: The functional relationship between frequency, mode shape, and damaged structure stiffness matrix is: ， in, M is the mass matrix of the structure, , For the j The modal frequency, For the j The mode shape, For the The stiffness damage factor of each element is For the The stiffness matrix of each element is n is the number of units.

6. The large steel structure damage identification method based on evolution mechanism analysis according to claim 1 is characterized in that: The objective function of the initial large-scale steel structure damage identification inversion model is: ， = In the formula, For the The residuals of the measured data and the simulated data during the iterations are For the The sensitivity matrix in the iteration, For the The updated value of the damage coefficient in the iteration, is the multimodal data actually measured, For the The damage coefficient in the iteration is, It is the multimodal data obtained by simulating the numerical simulation model.

7. The large steel structure damage identification method based on evolution mechanism analysis according to claim 1 is characterized in that: The posterior probability density function of the damage coefficient is: ， in, is the normalization constant, For the The damage coefficient in the iteration is, For the k The likelihood function of the damage coefficient in the iteration is, For the k The prior distribution function of the damage coefficient in the iteration, The residuals between the measured and simulated data during the iteration process, To measure the variance of the error in the frequency and vibration mode of steel structure buildings, Used to control the distribution density of the damage coefficient, The relationship between the stiffness damage coefficient within the control group, is the hyperparameter corresponding to each unit of the steel structure A collection of is the hyperparameter corresponding to each unit of the steel structure A collection of .

8. The large steel structure damage identification method based on evolution mechanism analysis according to claim 7 is characterized in that: The variance matrix Σ of the prior distribution of the damage coefficient satisfies: ， The posterior probability of the damage coefficient follows a Gaussian distribution, and its mean vector and the variance matrix satisfy: ， in, is the identity matrix, is the sensitivity matrix.

9. The large steel structure damage identification method based on evolution mechanism analysis according to claim 1, characterized in that: The method of quantitatively analyzing the damage of the steel structure based on the position information of the suspected damaged unit of the steel structure to determine the final damage position and damage degree of the steel structure includes: According to the damage inversion identification results, the location, number and corresponding gene point of the suspected damaged unit are determined as parameters and input into the generalized genetic algorithm calculation model, the initial mutation probability of the gene point is assigned, and the suspected damaged unit is searched according to the initial mutation probability; During the evolution process, the initial population of the set sub-population is set using the suspected damaged units after pre-identification, and the initial population of the remaining sub-populations is distributed over the entire domain, so that the optimal solution in the solution process may jump out of the setting of the suspected damaged units in pre-identification and initialize the population distribution only within the range of the suspected damaged units; The mutation probability adjusted according to the evolutionary results of each generation guides the operator operation of the next generation, thereby realizing directional search, obtaining the global optimal solution, and obtaining the damage location and damage degree of the steel structure.

10. A large steel structure damage identification system based on evolution mechanism analysis, characterized in that: include: The simulation model construction module is used to analyze the time-varying evolution mechanism of damage of large steel structures under the long-term coupling of load and environment, and to construct a numerical simulation model of the coupling of load and environment under the damage state of large steel structures; The initial inversion model building module is used to determine the stiffness matrix of the damaged structure, introduce the damage coefficient of the steel structure, and calculate the vibration sensitivity and frequency sensitivity based on the functional relationship between the frequency, vibration mode, and the stiffness matrix of the damaged structure; The initial large-scale steel structure damage identification inversion model is constructed by combining the sensitivity matrix constructed by mode sensitivity and frequency sensitivity, the residual of the measured data and the numerical simulation model calculation data; A damage identification module is used to identify the damage coefficient of the steel structure based on the initial large-scale steel structure damage identification inversion model, maximize the posterior probability density function of the damage coefficient, and determine the location information of the suspected damaged unit of the steel structure; Based on the location information of suspected damaged units in the steel structure, the damage of the steel structure is quantitatively analyzed to determine the final damage location and damage extent of the steel structure.

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