A dynamic simulation test method and system for service deformation of plate structures

Abstract

This invention relates to the field of computer simulation testing technology, specifically to a dynamic simulation testing method and system for the service deformation of plate structures. It solves the technical problem of insufficient accuracy in structural damage identification when using fixed signal decomposition parameters to process all detected signals. The method includes: acquiring a set of mesoscopic parameters of the plate structure under test and a set of original dual-mode ultrasonic guided wave signals; determining optimized parameters for decomposing the original dual-mode ultrasonic guided wave signals based on the mesoscopic parameter set; decomposing the original dual-mode ultrasonic guided wave signals based on the optimized parameters and a signal decomposition algorithm to determine a target signal set related to structural damage; and performing a simulation comparison between the mesoscopic parameter set and the target signal set to determine the service damage state of the plate structure. This invention is used in plate structure damage identification scenarios.

Description

Technical Field

[0001] This invention relates to the field of computer simulation testing technology, specifically to a dynamic simulation testing method and system for the service deformation of plate structures. Background Technology

[0002] Plate structures are widely used in aerospace, rail transportation, and major infrastructure fields. Deformation and damage during long-term service directly affect structural safety and service life. Therefore, high-precision assessment of structural health status based on dynamic simulation and non-destructive testing technology has become an urgent need in the engineering field. Currently, ultrasonic guided waves are commonly used to excite and acquire signals from plate structures, and signal processing algorithms are used to extract damage features, which are then combined with simulation models for state inversion. However, the inherent micro-components of plate structures, such as coarse aggregates in concrete and reinforcing steel, generate strong scattering and reflection interference during guided wave propagation. These interference signals severely overlap with the actual damage signals in the time and frequency domains. Existing methods typically use fixed signal decomposition parameters to process all detected signals, failing to fully consider the impact of the spatiotemporal differences in the micro-features within the specific structure on interference characteristics. This leads to inaccurate damage feature extraction under complex interference backgrounds, mismatch between simulation models and measured signals, and insufficient accuracy in structural damage identification. Summary of the Invention

[0003] To address the technical problems of existing methods that use fixed signal decomposition parameters to process all detection signals, failing to fully consider the impact of spatiotemporal differences in the microscopic features within specific structures on interference characteristics, resulting in inaccurate damage feature extraction and insufficient structural damage identification accuracy under complex interference backgrounds, the present invention aims to provide a dynamic simulation test method and system for service deformation of plate structures. The specific technical solution adopted is as follows:

[0004] In a first aspect, the present invention provides a dynamic simulation test method for service deformation of a plate structure. The method includes: acquiring a set of mesoscopic parameters and a set of original dual-mode ultrasonic guided wave signals for the plate structure under test; the mesoscopic parameter set is used to characterize the properties and distribution of various interference sources within the plate structure; the original dual-mode ultrasonic guided wave signal set and the mesoscopic parameter set have a spatial correspondence; based on the mesoscopic parameter set, determining optimized parameters for decomposing the original dual-mode ultrasonic guided wave signal set; the optimized parameters are used to match the decomposition bandwidth characteristics of the signal decomposition algorithm with the signal interference characteristics caused by the interference sources; decomposing the original dual-mode ultrasonic guided wave signal set based on the optimized parameters and the signal decomposition algorithm to determine a target signal set related to structural damage; and performing a simulation comparison between the mesoscopic parameter set and the target signal set to determine the service damage state of the plate structure.

[0005] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: extracting spectral features from the original signal set of the dual-mode ultrasonic guided wave to determine initial optimization parameters; determining the interference influence factors corresponding to the various interference sources based on the micro-parameter set and the broadening effect of multiple interference sources on the frequency domain features of the guided wave signal; and correcting the initial optimization parameters based on the interference influence factors to determine the optimization parameters.

[0006] In conjunction with the first aspect mentioned above, in one possible implementation, the multiple interference sources include: a first interference source and a second interference source; the broadening effect includes: a first broadening effect and a second broadening effect; the method specifically includes: determining a first interference influence factor based on the attribute characteristics of the first interference source in the mesoscopic parameter set; the first interference influence factor is used to quantify the first broadening effect of the first interference source on the bandwidth of the first mode guided wave signal; determining a second interference influence factor based on the attribute characteristics of the second interference source in the mesoscopic parameter set; the second interference influence factor is used to quantify the second broadening effect of the second interference source on the bandwidth of the second mode guided wave signal.

[0007] In conjunction with the first aspect mentioned above, in one possible implementation, the first source of interference is the coarse aggregate in the concrete of the slab structure; the second source of interference is the reinforcing steel in the slab structure.

[0008] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: determining the first contribution of the multi-scale scattering effect caused by the uneven particle size distribution to the signal bandwidth based on the particle size distribution characteristics of the coarse aggregate; determining the second contribution of the resonant scattering effect to the signal bandwidth based on the matching relationship between the average particle size of the coarse aggregate and the center wavelength of the first mode guided wave; and determining the first interference influence factor based on the first contribution and the second contribution.

[0009] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: determining the density and intensity of reflection events encountered by the second-mode guided wave on its propagation path based on the diameter, quantity, and spacing of the reinforcing bars; determining the extent to which the superposition of reflected signals broadens the signal bandwidth based on the differences in the distribution characteristics of the reinforcing bars between the current region and adjacent regions; and determining a second interference influence factor based on the density and intensity of the reflection events and the extent to which the superposition of reflected signals broadens the bandwidth.

[0010] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: determining the energy distribution ratio of the first-mode guided wave signal and the second-mode guided wave signal in the original signal set of the dual-mode ultrasonic guided wave; based on the energy distribution ratio, weightedly fusing the first interference influence factor and the second interference influence factor to determine the comprehensive correction factor; and adjusting the bandwidth constraint parameter in the initial optimization parameters based on the comprehensive correction factor to determine the optimization parameters.

[0011] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: decomposing the original signal set of the dual-mode ultrasonic guided wave into multiple intrinsic mode components based on a signal decomposition algorithm; and selecting components related to structural damage from the multiple intrinsic mode components based on a first interference influence factor and a second interference influence factor to form a target signal set.

[0012] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: constructing a reference finite element model library containing multiple preset damage modes based on a set of micro-parameters; defining the micro-features of each model in the reference finite element model library based on the set of micro-parameters; simulating the excitation and reception process of dual-mode ultrasonic guided waves based on each model in the reference finite element model library to determine the simulation signal set; constructing a mapping relationship library between preset damage modes and the simulation signal set; and performing feature matching between the signals in the target signal set and the simulation signals in the mapping relationship library to determine the service damage state of the plate structure.

[0013] Secondly, this invention provides a dynamic simulation test system for the service deformation of a plate structure. The system includes: a data acquisition module for acquiring a set of mesoscopic parameters and a set of original dual-mode ultrasonic guided wave signals of the plate structure under test; the mesoscopic parameter set characterizes the properties and distribution of various interference sources within the plate structure; the original dual-mode ultrasonic guided wave signal set and the mesoscopic parameter set have a spatial correspondence; a parameter optimization module for determining optimized parameters for decomposing the original dual-mode ultrasonic guided wave signal set based on the mesoscopic parameter set; the optimized parameters are used to match the decomposition bandwidth characteristics of the signal decomposition algorithm with the signal interference characteristics caused by the interference sources; a signal processing module for decomposing the original dual-mode ultrasonic guided wave signal set based on the optimized parameters and the signal decomposition algorithm to determine a target signal set related to structural damage; and a damage state determination module for performing a simulation comparison between the mesoscopic parameter set and the target signal set to determine the service damage state of the plate structure.

[0014] The present invention has the following beneficial effects:

[0015] This invention constructs a spatial mapping relationship between data and physical entities by acquiring and associating the set of micro-parameters within a plate-like structure with the original signal set of external dual-mode ultrasonic guided waves. Then, it adaptively determines the optimized parameters of the signal decomposition algorithm using the micro-parameter set, enabling the algorithm to accurately match the interference characteristics of specific structures. Finally, high-confidence identification of damage states is achieved through integrated simulation comparison. This method forms a complete closed loop from data source to processing logic to verification, significantly improving the overall accuracy and reliability of service damage identification under complex internal structural backgrounds. This solves the technical problems of existing methods that use fixed signal decomposition parameters to process all detection signals, failing to fully consider the impact of the spatiotemporal differences in the micro-features within specific structures on interference characteristics, resulting in inaccurate damage feature extraction and insufficient structural damage identification accuracy under complex interference backgrounds. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a dynamic simulation test method for service deformation of a plate structure provided in one embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of the architecture of a dynamic simulation test system for service deformation of a plate structure, provided as an embodiment of the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a dynamic simulation test method and system for service deformation of a plate structure proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The following description, in conjunction with the accompanying drawings, details the specific scheme of the dynamic simulation test method and system for service deformation of plate structures provided by this invention.

[0022] Please see Figure 1 The present invention illustrates a dynamic simulation test method and system flowchart for service deformation of a plate structure provided by an embodiment of the present invention. The method includes the following S101-S104, which will be described in detail below.

[0023] S101. Obtain the set of mesoscopic parameters and the original signal set of dual-mode ultrasonic guided waves for the plate structure under test.

[0024] Among them, the micro-parameter set is used to characterize the properties and distribution of various interference sources inside the plate structure; the original signal set of the dual-mode ultrasonic guided wave has a spatial correspondence with the micro-parameter set.

[0025] In one possible implementation, the acquisition of the microscopic parameter set is accomplished by detecting and quantifying physical interference sources of a preset category inside the structure; the acquisition of the original signal set of the dual-mode ultrasonic guided wave is accomplished by exciting and receiving elastic waves of a specific mode on the surface of the structure. These two types of data are correlated and calibrated through a unified spatial coordinate system during acquisition.

[0026] For example, for the set of mesoscopic parameters, industrial scanning technology can be used to obtain the particle size distribution, volume fraction, and three-dimensional coordinates of coarse aggregate in the concrete area, and ground-penetrating radar technology can be used to obtain the diameter, spacing, embedment depth, and planar position of the reinforcing bars, thereby forming a structured database. For the raw signal set of dual-mode ultrasonic guided waves, a piezoelectric sensor array can be deployed on the structural surface, a signal generator can be used to excite Lamb waves containing dual modes, and the propagated time-domain voltage signals can be recorded at multiple receiving points to form the raw signal sequence.

[0027] For example, the preset categories of physical interference sources include: a first type of interference source mainly caused by scattering interference from coarse aggregate in concrete, for which industrial CT scanning technology is used to obtain its particle size, gradation, volume fraction and three-dimensional spatial distribution parameters; and a second type of interference source mainly caused by reflection interference from reinforcing steel, for which high-frequency ground penetrating radar is used to obtain its diameter, spacing, burial depth and planar location information.

[0028] S102. Based on the microscopic parameter set, determine the optimized parameters for decomposing the original signal set of the dual-mode ultrasonic guided wave.

[0029] The optimization parameters are used to match the decomposition bandwidth characteristics of the signal decomposition algorithm with the signal interference characteristics caused by the interference source.

[0030] In one possible implementation, the frequency domain distribution features of the original signal are first extracted to establish an initial parameter benchmark. Then, the physical properties of the interference source are deeply combined with the micro-parameter set to quantitatively evaluate the spectral broadening effect of various interferences on the signal components. Based on this, corresponding interference influence factors are generated. Finally, these factors are used to correct and optimize the initial parameter benchmark.

[0031] For example, the signal decomposition algorithm can adopt the variational mode decomposition algorithm. The core idea of ​​this algorithm is to construct and solve a variational problem to adaptively decompose the input complex signal into a series of quasi-orthogonal eigenmode functions with specific center frequencies and finite bandwidths.

[0032] Its working principle can be simply described as follows: The algorithm assumes that any complex signal can be composed of multiple amplitude-frequency modulated (AM-FM) components with different center frequencies. Through iterative search, it determines an optimal center frequency and bandwidth for each modal component. Its optimization objective is to minimize the sum of the estimated bandwidths of all modal components, while ensuring that the sum of all modal components can accurately reconstruct the original signal.

[0033] S103. Based on the optimization parameters and signal decomposition algorithm, the original signal set of the dual-mode ultrasonic guided wave is decomposed to determine the target signal set related to structural damage.

[0034] In one possible implementation, a signal decomposition algorithm with optimized parameters is first applied to the original signal to adaptively decompose it into a series of intrinsic mode components with different center frequencies and bandwidths. Then, based on the characteristic indicators that characterize the influence of various interference sources determined by micro-parameters, screening criteria are formulated to identify and separate the components that are consistent with the damage mechanism from all components.

[0035] S104. Perform simulation comparison between the micro-parameter set and the target signal set to determine the service damage state of the plate structure.

[0036] One possible implementation involves first establishing a series of benchmark model libraries covering different preset damage modes in finite element simulation software based on the internal physical composition defined by the mesoscopic parameter set; then, on each model in the model library, reproducing the ultrasonic guided wave excitation and reception process that is completely consistent with the actual detection, obtaining the corresponding simulation signal set and establishing a mapping relationship; finally, performing multi-dimensional feature matching between the measured target signal set and the signals in the simulation signal library, and determining the most likely damage state based on the best matching result.

[0037] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment acquires and associates the micro-parameter set inside the plate structure with the original signal set of the external dual-mode ultrasonic guided wave, constructing a spatial mapping relationship between data and physical entities. Then, it adaptively determines the optimization parameters of the signal decomposition algorithm using the micro-parameter set, enabling the algorithm to accurately match the interference features of specific structures. Finally, it achieves high-confidence identification of damage states through integrated simulation comparison. This method forms a complete closed loop from the data source to the processing logic and then to the verification stage, significantly improving the overall accuracy and reliability of service damage identification under complex internal structural backgrounds. This solves the technical problem of existing methods using fixed signal decomposition parameters to process all detection signals, failing to fully consider the impact of the spatiotemporal differences of the micro-features inside the specific structure on interference characteristics, resulting in inaccurate damage feature extraction and insufficient structural damage identification accuracy under complex interference backgrounds.

[0038] In one possible implementation, the process of determining the optimized parameters for decomposing the original signal set of the dual-mode ultrasonic guided wave based on the microscopic parameter set can be specifically implemented through the following S201-S203, which will be explained in detail below.

[0039] S201. Extract spectral features from the original signal set of dual-mode ultrasonic guided waves to determine the initial optimization parameters.

[0040] In one possible implementation, the dominant frequency peak and its bandwidth are identified by calculating the power spectral density of the original signal. Then, different modal components are distinguished according to a preset modal frequency reference range. Finally, the initial decomposition layer and basic bandwidth constraint value of the algorithm are quantitatively determined by combining their center frequency and bandwidth characteristics.

[0041] For example, a Fast Fourier Transform (FFT) can be performed on the original signal to obtain its spectrum, and then the power spectral density curve can be calculated. By analyzing this curve, broadband peaks appearing in a preset low-frequency band (e.g., corresponding to the A0 mode) along with their center frequencies and bandwidths, and narrow-frequency peaks appearing in a preset high-frequency band (e.g., corresponding to the S0 mode) along with their center frequencies and bandwidths, are identified. The initial number of levels for signal decomposition is determined based on the number of identified significant peaks. Simultaneously, based on the ratio of each center frequency to its corresponding bandwidth, and in conjunction with a preset scaling factor, initial penalty components for different modal components are calculated. Finally, by integrating the energy proportions of different modal signals in the entire original signal, the aforementioned initial penalty components are weighted and fused to obtain a global initial optimization parameter, which serves as the core component of the initial optimization parameter set.

[0042] For example, initial optimization parameters Satisfy the following formula 1:

[0043]

[0044] in, The center frequency of the first mode (A0 mode) ultrasonic guided wave represents the location where the A0 mode signal energy is most concentrated in the frequency domain; The frequency width at which the power spectral density of the first mode signal drops to half of its peak value after being disturbed represents the broadening of the frequency components of the A0 mode signal. The center frequency of the second mode (S0 mode) ultrasonic guided wave represents the location where the S0 mode signal energy is most concentrated in the frequency domain; The frequency width at which the power spectral density of the second mode signal drops to half of its peak value after being disturbed represents the broadening of the frequency components of the S0 mode signal. is the proportionality coefficient, a dimensionless constant set based on historical experience (e.g., 2000), used to adjust the frequency ratio to an order of magnitude that matches the optimization parameters; The energy percentage of the first mode signal in the original dual-mode signal is a dimensionless quantity with a value range of [0, 1]. The energy percentage of the second-mode signal in the original dual-mode signal is a dimensionless quantity with a value range of [0, 1]. + =1.

[0045] and The larger the ratio, the more concentrated the signal frequency components (narrower bandwidth). According to the signal decomposition principle, larger optimization parameters need to be applied to constrain the bandwidth and prevent mode aliasing; multiplied by the scaling factor... Map the quality factor to the typical numerical range of the algorithm optimization parameters; Based on the energy proportion of each modal signal in the original signal and The initial optimization parameters are obtained by weighted averaging of the two initial penalty components. Modes with higher energy proportions have a greater contribution of their initial penalty components to the global parameters. These are the initial optimization parameters of the signal decomposition algorithm, used to control the strength of the bandwidth constraint on each intrinsic mode function when the algorithm decomposes the signal. The larger the value, the stricter the bandwidth constraint of the algorithm, and the narrower the bandwidth of the decomposed components; conversely, the looser the constraint, the wider the bandwidth of the components.

[0046] S202. Based on the microscopic parameter set and the broadening effect of multiple interference sources on the frequency domain characteristics of guided wave signals, determine the interference influence factors corresponding to each of the multiple interference sources.

[0047] One possible implementation involves establishing a mapping model between different types of interference sources and the signal bandwidth variation based on their unique physical parameters (such as size, distribution, density, etc.). A quantized value characterizing the interference intensity is then calculated using this model.

[0048] For example, for the first interference source (such as coarse aggregate in concrete), data on aggregate particle size distribution, volume fraction, and spatial location from the mesoscopic parameter set are required. By analyzing the non-uniformity of particle size distribution and the closeness of the average particle size to the guided wave wavelength, the extent to which the bandwidth of the first-mode guided wave signal is broadened due to the combined effects of multi-scale scattering and resonant scattering is evaluated; this extent is quantified as the first interference influence factor. For the second interference source (such as reinforcing steel), data on the diameter, spacing, quantity, and distribution differences of the reinforcing steel are required. By analyzing the density and intensity of the reflecting surface and the superposition effect caused by the non-uniform distribution of the reflected signal, the extent to which it broadens the bandwidth of the second-mode guided wave signal is evaluated; this extent is quantified as the second interference influence factor.

[0049] S203. Based on the interference influence factor, the initial optimization parameters are corrected to determine the optimization parameters.

[0050] One possible implementation involves first considering the energy weights of different modal guided wave signals in the actual acquired signal, using this as the basis for fusing different interference factors; then, obtaining a comprehensive correction factor through weighted calculation; finally, using this comprehensive correction factor to numerically adjust the core constraint terms in the initial optimization parameters, thus completing the directional optimization of the parameters.

[0051] For example, the power spectrum of the original dual-mode signal set can be calculated first by integration to obtain the proportions of the energy of the first and second mode signals to the total signal energy. Then, using these two energy proportions as weights, the first and second interference influence factors are weighted and summed to obtain a comprehensive correction factor reflecting the overall path interference level. The correction operation involves using this comprehensive correction factor to reduce the optimization parameters in the initial optimization parameters. The physical logic is that a larger comprehensive correction factor indicates stronger overall interference and more mixed signal components on the propagation path, thus requiring a more relaxed bandwidth constraint on the decomposition algorithm (i.e., a reduction in the optimization parameters) to prevent over-decomposition or incorrect separation of complex mixed signals. The optimization parameter values ​​obtained through this reduction calculation are the key components of the optimization parameters.

[0052] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment obtains initial parameters that match the current detection state by extracting spectral features from the original signal, and then quantitatively analyzes the broadening effect of various interference sources on the frequency domain features of the signal based on the micro-parameter set and obtains the corresponding interference influence factors. Finally, these factors are used to correct the initial parameters. This process dynamically associates the abstract algorithm parameters with the specific physical interference mechanism, so that the final generated optimized parameters can more accurately reflect the signal distortion characteristics in the actual propagation path, thereby greatly enhancing the adaptability and processing accuracy of the signal decomposition algorithm to complex interference environments.

[0053] In one possible implementation, the multiple interference sources include: a first interference source and a second interference source; the broadening effect includes: a first broadening effect and a second broadening effect; the process of determining the interference influence factors corresponding to the multiple interference sources based on the broadening effect of the multiple interference sources on the frequency domain characteristics of the guided wave signal based on the microscopic parameter set can be specifically implemented through the following S301-S302, which will be explained in detail below.

[0054] S301. Based on the attribute characteristics of the first interference source in the micro-parameter set, determine the first interference influence factor.

[0055] The first interference influence factor is used to quantify the first broadening effect of the first interference source on the bandwidth of the first mode guided wave signal.

[0056] One possible approach is to start from the inherent physical properties of coarse aggregate, analyze the geometric characteristics of its spatial distribution and the interaction between key dimensions and waveguide propagation parameters, evaluate the contribution of its scattering effect from different physical dimensions, and finally integrate them into a unified quantitative result.

[0057] For example, firstly, based on the particle size distribution data of coarse aggregate in the mesoscopic parameter set, statistics reflecting the dispersion of its distribution (such as the standard deviation of particle size distribution) and the difference in average particle size between adjacent analysis regions are calculated. These statistical characteristics are used to evaluate the multi-scale scattering effect caused by the uneven distribution of aggregate particle size: the greater the difference in particle size and the more uneven the distribution, the wider the scattering frequency range of the signal by scatterers of different scales, and the greater the contribution to the broadening of the signal bandwidth. This contribution is quantified as the first contribution. Secondly, based on the average particle size data of coarse aggregate provided by the mesoscopic parameter set, it is compared with the center wavelength of the first mode guided wave (which can be estimated from the center frequency obtained by spectral analysis) to evaluate the degree of matching between the two. When the average particle size is close to the center wavelength, it is easy to induce a resonant scattering effect, causing the signal energy to be dispersed in a wider frequency band, which has a significant impact on the bandwidth broadening. This impact is quantified as the second contribution. Finally, through a preset fusion rule (such as weighted average or other composite functions), the first contribution and the second contribution are combined into a single value, which is determined as the first interference influence factor.

[0058] S302. Based on the attribute characteristics of the second interference source in the micro-parameter set, determine the second interference influence factor.

[0059] The second interference influence factor is used to quantify the second broadening effect of the second interference source on the bandwidth of the second mode guided wave signal.

[0060] One possible approach is to start with the individual geometric properties and spatial arrangement characteristics of the reinforcing bars, analyze the intensity and density of discrete reflection events generated by them on specific mode guided waves, as well as the signal superposition effect caused by uneven distribution, evaluate their contribution to the broadening of signal bandwidth from different dimensions, and finally integrate them into a unified quantitative result.

[0061] For example, firstly, based on the data on the diameter, quantity, and spacing of the reinforcing bars in the mesoscopic parameter set, the total effective area of ​​the reflective surface of the reinforcing bars per unit length along the path, or a similar index, is calculated to assess the density and intensity of reflection events: the larger the diameter, the greater the quantity, and the smaller the spacing of the reinforcing bars, the more concentrated the reflected energy and the more frequent the reflection events, resulting in a more significant impact on the signal. Secondly, based on the spatial coordinates and distribution data of the reinforcing bars provided by the mesoscopic parameter set, the degree of difference in the distribution pattern (such as arrangement direction and spacing consistency) of the reinforcing bars between the current assessment area and its adjacent areas is analyzed. The greater the distribution difference, the more dispersed the arrival time distribution of the reflected signal in the time domain, and the easier it is to cause the superposition of frequency components in the frequency domain, thus leading to signal bandwidth broadening. This broadening degree is quantified separately. Finally, through a preset fusion rule, the quantification results characterizing the density and intensity of reflection events are combined with the quantification results characterizing the superposition broadening degree caused by distribution differences to generate a single value, which is determined as the second interference influence factor.

[0062] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment further distinguishes multiple interference sources into first interference sources and second interference sources, and the corresponding broadening effect is also subdivided into first broadening effect and second broadening effect. Based on this, the first interference influence factor and the second interference influence factor are determined respectively. This classification and quantification method can finely characterize the physical differences in the influence of different types of interference sources (such as scattering type and reflection type) on guided wave signals, laying a solid foundation for subsequent more targeted signal separation and parameter optimization, and effectively avoiding the analysis bias caused by conflating the characteristics of different types of interference.

[0063] In one possible implementation, the first source of interference is the coarse aggregate in the slab structure concrete; the second source of interference is the reinforcing steel in the slab structure.

[0064] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment clearly defines the first interference source as coarse aggregate in concrete and the second interference source as steel reinforcement. This directly corresponds the abstract interference source category with the specific physical entity in engineering practice, so that the analysis model, parameter calculation and subsequent simulation of the entire technical solution are all based on real engineering materials and structures, which greatly enhances the physical consistency and engineering practicality of the method and ensures a smooth transition from theoretical analysis to engineering application.

[0065] In one possible implementation, the process of determining the first interference influence factor based on the attribute characteristics of the first interference source in the microscopic parameter set can be specifically implemented through the following S401-S403, which will be explained in detail below.

[0066] S401. Based on the particle size distribution characteristics of coarse aggregate, determine the first contribution of the multi-scale scattering effect caused by uneven particle size distribution to the signal bandwidth.

[0067] In one possible implementation, based on statistical data regarding coarse aggregate particle size in the mesoscopic parameter set, the dispersion and spatial variation patterns are analyzed to quantitatively assess the specific contribution of the multi-scale scattering mechanism caused by the uneven spatial distribution of aggregate particles to the spectral broadening of the guided wave signal. This contribution is then quantified into an independent numerical index, namely the first contribution. Specifically, this process focuses on the statistical distribution characteristics of the particle size data itself and its spatial fluctuations, aiming to characterize the contribution of scatterer arrays of different sizes to broadband noise formation.

[0068] For example, the first contribution The following formula 2 is satisfied:

[0069]

[0070] Where N is a positive integer, representing the total number of spatial intervals into which the propagation path of the ultrasound is divided; For the first The average volume fraction of coarse aggregate in concrete within a spatial interval. This is a dimensionless quantity, and its physical meaning is the volume percentage of the aggregate within that interval. The larger the value, the denser the aggregate is within that interval. For the first The average volume fraction of coarse aggregate in concrete within a spatial interval; The standard deviation of the average volume fraction of aggregate across all intervals along the propagation path is represented by the physical meaning of the degree of fluctuation (dispersion) of the aggregate content in each interval relative to the average level of the path. The larger the value, the more uniform the aggregate distribution (assuming a high overall content), and the greater its potential to contribute to the overall broadening of the signal bandwidth. The smaller the value, the more sparse or uneven the aggregate distribution, and the smaller its potential to contribute to the overall broadening. This is the parameter tuning coefficient, with a value of 0.01.

[0071] molecular The larger the value, the denser the aggregate in that region, the more scatterers there are, and the greater the potential impact on signal scattering; the denominator The smaller the value, the more uniform the overall path distribution, which means that the scattering environment encountered in each interval changes less, and the accumulation and superposition of scattering effects may be more continuous and stable. The combined gain of the aggregate density and the uniformity of the overall path distribution in this interval on the scattering effect is comprehensively reflected. The larger the ratio, the more high-density scatterers this interval contributes to a uniform path, and the more significant its effect on signal broadening is expected to be. This is used to quantitatively assess the combined influence of the volume fraction and spatial uniformity of coarse aggregate in concrete along the ultrasonic guided wave propagation path on the bandwidth broadening effect of the signal (especially the A0 mode). It reflects the intensity of the scattering environment created by a dense and uniform aggregate distribution.

[0072] S402. Based on the matching relationship between the average particle size of coarse aggregate and the center wavelength of the first mode guided wave, determine the second contribution of the resonant scattering effect to the signal bandwidth.

[0073] In one possible implementation, the average particle size data of coarse aggregate provided by the mesoscopic parameter set and the center wavelength parameter of the first mode guided wave obtained through spectral analysis are analyzed to quantitatively assess the specific influence share of the broadening of the guided wave signal spectrum caused by the Mie scattering resonance mechanism. This share is then quantified into an independent numerical index, namely the second contribution. Specifically, this process focuses on the comparison between a single key size (average particle size) and the wave characteristic size (wavelength), aiming to characterize the broadening effect of the resonance effect on the signal bandwidth when the scatterer size is comparable to the wavelength.

[0074] For example, the second contribution The following formula 3 is satisfied:

[0075]

[0076] in, For the first The normalized value of the standard deviation of aggregate particle size distribution within a given interval has the physical meaning of the degree of dispersion of aggregate particle size within that interval. The larger the value, the more significant the differences in size of aggregate particles exist within that interval (i.e., multi-scale distribution). For the first The mean of the absolute values ​​of the differences in average aggregate size between a given interval and all its adjacent intervals, after being over-normalized, has the physical meaning of representing the degree of difference (abrupt change) in typical aggregate size between the interval and the surrounding area. The larger the value, the greater the difference between the average aggregate size of the interval and the surrounding environment. The wavelength corresponding to the center frequency of the first mode (A0 mode) ultrasonic guided wave; This is the parameter tuning coefficient, with a value of 0.01.

[0077] This factor combines the effects of particle size dispersion within a range and abrupt changes in particle size between ranges. A larger value indicates greater spatial and dimensional inhomogeneity of the aggregate within that range, making it more prone to forming multi-scale scattering arrays and potentially generating wider-bandwidth scattering noise in the signal; exponential function Used to non-linearly amplify such inhomogeneities, the potential value difference between a slightly inhomogeneous interval and a highly inhomogeneous interval becomes very significant after exponential amplification; the Softmax function normalizes the potential values ​​of each amplified interval to obtain the weight of each interval. , The larger the value, the more prominent the multi-scale scattering characteristics of that interval are throughout the entire path, and the greater the contribution it is believed to make to the final bandwidth broadening. It measures the degree of deviation between particle size and wavelength; This method is used to quantitatively assess the influence of the particle size (relative to wavelength) of concrete coarse aggregate and its multi-scale spatial distribution characteristics on the bandwidth broadening effect of the signal (A0 mode) along the ultrasonic guided wave propagation path. It simultaneously captures two physical mechanisms: resonant scattering and multi-scale scattering.

[0078] S403. Based on the first contribution and the second contribution, determine the first interference impact factor.

[0079] In one possible implementation, the first and second contributions, which have been quantified separately and reflect the influence of different physical scattering mechanisms, are first normalized to eliminate differences in their numerical magnitude and dimensions. Then, a pre-defined fusion rule is used to synthesize the normalized results into a unified numerical index characterizing the overall scattering intensity of the first type of interference source, namely, the first interference influence factor. Specifically, this process aims to fairly and evenly fuse the effects of two independent physical dimensions—multi-scale scattering caused by uneven particle size distribution and resonant scattering caused by particle size and wavelength matching—onto the same scale, so as to comprehensively evaluate the overall broadening effect of coarse aggregate on the bandwidth of guided wave signals.

[0080] For example, a preset normalization function (such as maximum-minimum normalization, Z-score normalization, etc.) can be used to process the first contribution and the second contribution separately, resulting in two dimensionless parameters with the same value range (e.g., both within the [0,1] interval). Then, these two normalized parameters are weighted and summed. The weight allocation can be determined based on prior knowledge, experimental data, or analysis of the relative importance of the two scattering mechanisms in a specific frequency band; alternatively, it can be calculated using a preset multivariate function (such as geometric mean, square root, etc.). The output value of this calculation process is the first interference influence factor. Its magnitude directly reflects the overall broadening of the signal bandwidth caused by the scattering interference from coarse aggregate: the larger the value, the stronger and more comprehensive the distortion and broadening effect of the coarse aggregate as a scatterer on the signal along the current propagation path.

[0081] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment targets coarse aggregate as the first source of interference, evaluates the multi-scale scattering effect by analyzing its particle size distribution characteristics, and evaluates the resonance scattering effect by combining the matching relationship between its average particle size and the center wavelength of the guided wave. Finally, the first interference influence factor is determined comprehensively. This method deeply characterizes the intensity and spectral characteristics of aggregate scattering interference from the two core physical dimensions of aggregate non-uniformity and particle size-wavelength resonance, making the quantitative evaluation of broadband noise caused by concrete, a complex heterogeneous material, more scientific and accurate.

[0082] In one possible implementation, the process of determining the second interference influence factor based on the attribute characteristics of the second interference source in the microscopic parameter set can be specifically implemented through the following S501-S503, which will be explained in detail below.

[0083] S501. Based on the diameter, quantity, and spacing of the reinforcing bars, determine the density and intensity of reflection events encountered by the second-mode guided wave along its propagation path.

[0084] In one possible implementation, for the geometric and arrangement parameters of the reinforcing bars in the mesoscopic parameter set, the overall situation of the reflection behavior generated by the interaction between the key attributes of the second-mode guided wave and the reinforcing mesh during propagation is quantitatively evaluated by analyzing the interrelationship between these parameters. This situation is then quantified into a comprehensive index reflecting the spatial density of reflection events and the intensity level of individual events.

[0085] For example, firstly, data on the diameter, quantity, and center-to-center distance between adjacent reinforcing bars within the area covered by the target propagation path are extracted from the mesoscopic parameter set. The intensity of reflection events is mainly related to the reflection cross-section of a single reinforcing bar, which can be quantified based on its diameter; for example, the larger the diameter, the stronger the reflection capability. The density of reflection events is closely related to the quantity and spacing of reinforcing bars on the path: the more reinforcing bars per unit length and the smaller the spacing, the denser the reflectors encountered by the wave during propagation, and the higher the frequency of reflection events. A composite quantified value is calculated using a preset data fusion rule (e.g., accumulating or averaging the diameter representation of each reinforcing bar, and then multiplying or weighting it with the spatial distribution density index derived from the quantity and spacing). This value represents the density and intensity of reflection events; the higher the value, the more concentrated and intense the reflection interference from reinforcing bars encountered by the second-mode guided wave on the current path.

[0086] S502. Based on the differences in the distribution characteristics of reinforcing bars between the current area and adjacent areas, determine the extent to which the superposition of reflected signals broadens the signal bandwidth.

[0087] In one possible implementation, based on the spatial location information of the steel reinforcement distribution data provided in the micro-parameter set, the similarity or difference in the steel reinforcement arrangement pattern between the current evaluated area and its directly adjacent areas is compared to quantitatively assess the complex superposition effect of multiple reflected signals in the time-frequency domain caused by the discontinuity of this spatial distribution. Furthermore, this superposition effect is transformed into an additional broadening of the signal spectrum width, and this broadening is quantified into an independent numerical index.

[0088] For example, a rebar distribution parameter vector can be extracted from the mesoscopic parameter set for the current evaluation area and one or more spatially adjacent areas. This parameter vector may contain features such as the average spacing, dominant orientation, and diameter distribution of the rebars within the area. By calculating the degree of difference between the parameter vector of the current area and the parameter vectors of each adjacent area (e.g., calculating the Euclidean distance, cosine similarity, or a specially designed difference metric function between the vectors), one or more numerical values ​​characterizing the distribution differences are obtained. Subsequently, these degree of difference values ​​are combined into a single scalar value through a preset data aggregation rule (e.g., taking the maximum value, average value, or weighted sum). This scalar value is quantified as the degree to which the superposition of reflected signals broadens the signal bandwidth. The larger this value, the more significant the difference in rebar distribution patterns between the current area and the surrounding areas, the more asynchronous the reflected signals are in the time domain, and the more dispersed their components are in the frequency domain, thus resulting in a stronger broadening effect on the overall signal bandwidth.

[0089] S503. Based on the density and intensity of the reflection event, as well as the broadening of the superimposed reflection signal, determine the second interference influence factor.

[0090] In one possible implementation, the quantified density and intensity of reflection events and the intensity of reflection signal superposition broadening are integrated into a unified numerical index, namely the second interference influence factor, by using a preset comprehensive rule. This index represents the overall reflection interference intensity of the second type of interference source.

[0091] For example, the second interference influence factor Satisfy the following formula 4:

[0092]

[0093] in, For the interval of the first The normalized value of the diameter of the reinforcing bar; Within the interval The normalized value of the average spatial distance from a single rebar to all other rebars reflects the degree of isolation of that rebar. For the first The reinforcement reflection interference factor for each interval; the larger the value, the stronger the reinforcement reflection interference in that interval. For the first The cosine similarity between each interval and the reinforcement distribution parameter vector of the previous interval. The smaller the value, the greater the variation in the distribution of reinforcing bars between adjacent intervals, and the more complex the reflected wavefront, which will further aggravate the widening of the signal bandwidth; This is a normalization function that normalizes the summation result to the interval [0, 1] by normalizing it through the maximum and minimum values. The intention is to amplify the contribution of regions with strong self-reflection and significant differences from adjacent regions to the overall path bandwidth broadening. When the value is small, the ratio will increase; For parameter tuning coefficients, if When the value is 0, it is set to the minimum value other than 0. Dimensions and same; This is used to quantitatively assess the impact of the reinforcement distribution characteristics along the current ultrasonic propagation path on the bandwidth broadening of the second mode (S0) guided wave signal.

[0094] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment targets the steel bars as the second interference source, evaluates the density and intensity of reflection events by their diameter, quantity and spacing, and evaluates the superposition degree of reflected signals by combining the differences in steel bar distribution between different areas, thereby determining the second interference influence factor. This method captures the characteristics of locality and discreteness of steel bar reflection interference, and not only considers the intensity of a single reflection surface, but also the signal superposition effect caused by uneven spatial distribution, so as to more realistically quantify the impact of narrow-frequency transient interference caused by steel bars on signal integrity.

[0095] In one possible implementation, the process of correcting the initial optimization parameters based on the interference influence factor and determining the optimization parameters can be achieved through the following steps S601-S603, which will be explained in detail below.

[0096] S601. Determine the energy distribution ratio of the first mode guided wave signal and the second mode guided wave signal in the original signal set of the dual-mode ultrasonic guided wave.

[0097] In one possible implementation, for the acquired dual-mode ultrasonic guided wave raw signal set, the energy distribution of the overall energy among the different modal components distinguished by the spectral characteristics is analyzed, and the energy share of the first-mode guided wave signal and the second-mode guided wave signal is quantitatively calculated and characterized in proportion.

[0098] S602. Based on the energy distribution ratio, the first interference influence factor and the second interference influence factor are weighted and fused to determine the comprehensive correction factor.

[0099] In one possible implementation, for the already determined first interference influence factor Second interference factor and energy distribution ratio and First, the two interference factors are normalized, then their weighted sum is calculated, and this weighted sum is used to adjust the initial penalty factor. The parameters are then reduced to generate the final optimized parameters. .

[0100] For example, a preset normalization function (such as maximum / minimum value normalization) can be used first to normalize the values. and Mapping to the [0, 1] interval yields the normalized interference impact factor. and Subsequently, based on and Calculate the weighted overall interference level.

[0101] S603. Adjust the bandwidth constraint parameters in the initial optimization parameters based on the comprehensive correction factor to determine the optimization parameters.

[0102] In one possible implementation, given the determined comprehensive correction factor and initial optimization parameters, a preset mapping or operation rule is used to transform the comprehensive correction factor, which reflects the overall interference level of the path, into a specific adjustment amount for the key bandwidth constraint parameters in the signal decomposition algorithm. This adjustment amount is then applied to modify the initial values, thereby generating the final optimization parameters used to guide signal decomposition.

[0103] For example, optimize parameters The following formula 5 is satisfied:

[0104]

[0105] in, The energy percentage of the first mode guided wave signal; The energy percentage of the second-mode guided wave signal. ; The first interference factor after normalization; This is the normalized second interference factor; These are the initial optimization parameters.

[0106] The energy percentage is used as the weight, and the overall interference effect of aggregate and steel reinforcement on the signal is integrated, with the value range [0, 1]. This indicates that the stronger the interference and the more mixed the signal, the more necessary it is to relax the bandwidth constraints of the signal decomposition algorithm (i.e., smaller optimization parameters are required). The penalty factor of the signal decomposition algorithm, which is optimized by observing physical features, dynamically adjusts the constraint strength of the algorithm on the bandwidth of the decomposed signal components, so that it accurately matches the actual physical interference environment of the current detection path.

[0107] The technical solution provided by the above embodiments can bring at least the following beneficial effects: In synthesizing the final optimized parameters, this embodiment first determines the energy distribution ratio of the first mode and the second mode guided wave signals in the actual acquired signal, and then uses this ratio to weight and fuse the first and second interference influence factors to obtain a comprehensive correction factor. Finally, the bandwidth constraint of the initial parameters is adjusted using this factor. This step fully considers the difference in energy contribution of the two main mode signals, so that the adjustment direction of the optimized parameters is consistent with the dominant characteristics of the actual signal, avoiding parameter deviations that may occur due to equal weighting, thereby achieving a more balanced and effective global control of the signal decomposition bandwidth constraint.

[0108] In one possible implementation, the process of decomposing the original signal set of dual-mode ultrasonic guided waves based on optimized parameters and signal decomposition algorithms to determine the target signal set related to structural damage can be specifically implemented through the following S701-S702, which will be described in detail below.

[0109] S701. Based on the signal decomposition algorithm, the original signal set of dual-mode ultrasonic guided wave is decomposed into multiple intrinsic mode components.

[0110] In one possible implementation, a signal decomposition algorithm with optimized parameters is used to perform the core decomposition operation, which adaptively decomposes the original mixed time-domain signal into a set of sub-signal components with a certain number and different frequency characteristics, namely multiple intrinsic mode components.

[0111] S702. Based on the first interference influence factor and the second interference influence factor, components related to structural damage are selected from multiple intrinsic mode components to form a target signal set.

[0112] In one possible implementation, by calling the physical interference intensity information characterized by the quantified first interference influence factor and the second interference influence factor, an objective screening criterion is formulated and applied to identify and separate those components whose characteristics are significantly different from known strong interference modes. These components are identified as responses related to structural damage and set as a target signal set.

[0113] For example, a mapping relationship is established between the first interference influence factor 1 and the typical aggregate scattering interference bandwidth, and a linear mapping relationship is established between the second interference influence factor and the typical steel reinforcement reflection interference bandwidth. Based on the linear mapping relationship, the bandwidth reference values ​​of the first and second interference influence factors are determined. Then, for each intrinsic mode component, its power spectral density is calculated, and its actual bandwidth value is determined using the half-power bandwidth method. A preset difference threshold (e.g., 20%) is set. When the relative difference percentage between the actual bandwidth value and the bandwidth reference value of the first interference influence factor is greater than 20%, and the relative difference percentage between the actual bandwidth value and the bandwidth reference value of the second interference influence factor is greater than 20%, it is determined that the characteristics of this component do not conform to the known strong physical interference mode and are more likely to originate from other scattering mechanisms such as structural damage, and therefore it is screened out. All components that pass this screening constitute the target signal set.

[0114] The technical solution provided by the above embodiments can bring at least the following beneficial effects: In the signal decomposition and target signal extraction stages, this embodiment directly uses the optimized signal decomposition algorithm to process the original signal set, and based on the interference intensity information characterized by the first and second interference influence factors, it selects the damage-related components from the multiple intrinsic mode components obtained by decomposition to form the target signal set. This process makes the signal decomposition more targeted, effectively removes weak damage signals that are submerged by strong background interference, significantly improves the extraction purity and signal-to-noise ratio of damage feature signals, and provides a clean and reliable data foundation for subsequent high-precision damage inversion.

[0115] In one possible implementation, the process of comparing the microscopic parameter set with the target signal set to determine the service damage state of the plate structure can be specifically implemented through the following S801-S804, which will be explained in detail below.

[0116] S801. Based on the microscopic parameter set, construct a benchmark finite element model library containing multiple preset damage modes.

[0117] In one possible implementation, for the acquired set of mesoscopic parameters, numerical simulation technology is used to construct digital structural models that are consistent with the entity under test in terms of mesoscopic physical composition and have different types and sizes of damage. These models are then systematically organized to form a benchmark model library for subsequent simulation comparison.

[0118] For example, a baseline geometric model of the plate structure is first constructed based on the geometric dimensions and boundary conditions provided by the mesoscopic parameter set. For the concrete portion, non-uniform mechanical properties are assigned to the material using a random aggregate placement algorithm or an equivalent homogenization method, based on the aggregate size, volume fraction, and spatial distribution data in the parameter set. For the reinforcing steel portion, rod element or solid element models are precisely established based on the diameter, spacing, and location data in the parameter set. On this basis, a series of model variants containing single or combined damage are systematically generated by modifying the geometry of local areas (e.g., introducing slits to simulate cracks) or material properties (e.g., reducing the elastic modulus to simulate voids). These damage modes can cover common cracking, peeling, voids, etc., and each mode can be set with different damage degrees (e.g., crack length, void area). All these models are centrally managed, forming a structured baseline finite element model library, where each model has a unique identifier associated with its corresponding mesoscopic parameter subset and preset damage description.

[0119] S802. Based on each model in the benchmark finite element model library, simulate the excitation and reception process of dual-mode ultrasonic guided waves to determine the simulation signal set.

[0120] In one possible implementation, for each digital model containing preset damage in the benchmark finite element model library, the entire process of ultrasonic guided wave excitation, propagation and signal acquisition corresponding to the actual physical detection is fully reproduced in the numerical simulation environment. The dynamic response time history data of the structure under specific excitation at the preset receiving point is calculated and exported, thereby generating a set of simulation signals that correspond one-to-one with the model.

[0121] S803. Construct a mapping library between preset damage modes and simulation signal sets.

[0122] In one possible implementation, for each model in the benchmark finite element model library and its corresponding simulation signal, a systematic data organization and management method is used to establish and solidify the precise and searchable correspondence between the complete description of each preset damage mode and the simulated wave signal it excites. This forms a standardized knowledge base for damage inversion comparison, thereby integrating the discrete model and signal data generated in the previous stage into a structured database with clear logical connections, so that any preset damage state can be quickly located to its corresponding complete simulation signal data through query.

[0123] S804. Perform feature matching between the signals in the target signal set and the simulation signals in the mapping relationship library to determine the service damage status of the plate structure.

[0124] In one possible implementation, for each target signal in the extracted target signal set, a subset of simulated signals with similar microscopic backgrounds is retrieved from the mapping relation library, and the feature similarity between the target signal and each simulated signal in the subset is calculated in multiple preset dimensions. Based on the damage mode information associated with the simulated signal corresponding to the highest similarity, the actual damage type, location, and extent of the plate structure at the current measurement position are finally determined and output.

[0125] For example, feature matching includes, but is not limited to, matching of peak amplitude, signal flight time, center frequency offset, and energy attenuation coefficient.

[0126] The technical solution provided by the above embodiments can bring at least the following beneficial effects: In the simulation comparison stage, this embodiment emphasizes the construction of a benchmark finite element model library that matches the microscopic features based on the microscopic parameter set, and the simulation signal set is obtained on this model library to establish a mapping relationship. Finally, the target signal is matched with the simulation signal to determine the damage state. This method ensures the physical consistency between the simulation model and the entity under test in terms of microscopic composition, making the damage inversion results based on simulation more convincing and accurate, and significantly improving the reliability and practicality of the entire testing method.

[0127] Please see Figure 2 This illustration shows a schematic diagram of the system architecture of a dynamic simulation test system 200 for service deformation of a plate structure according to an embodiment of the present invention. The system includes: a data acquisition module 201, used to acquire the micro-parameter set and the original signal set of dual-mode ultrasonic guided waves of the plate structure under test; the micro-parameter set is used to characterize the properties and distribution of various interference sources inside the plate structure; the original signal set of dual-mode ultrasonic guided waves and the micro-parameter set have a spatial correspondence; a parameter optimization module 202, used to determine the optimization parameters for decomposing the original signal set of dual-mode ultrasonic guided waves based on the micro-parameter set; the optimization parameters are used to match the decomposition bandwidth characteristics of the signal decomposition algorithm with the signal interference characteristics caused by the interference sources; a signal processing module 203, used to decompose the original signal set of dual-mode ultrasonic guided waves based on the optimization parameters and the signal decomposition algorithm to determine the target signal set related to structural damage; and a damage state determination module 204, used to perform simulation comparison between the micro-parameter set and the target signal set to determine the service damage state of the plate structure.

[0128] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment integrates four major functional modules: data acquisition, parameter optimization, signal processing, and damage state determination. It solidifies and encapsulates the parameter adaptive optimization, signal processing, and simulation comparison process based on microscopic physical characteristics, and provides a standardized operation and calculation framework. This not only reduces the complexity of method implementation and dependence on operator experience, but also ensures the consistency of the processing process and the repeatability of the results, providing powerful tool support for the efficient, accurate, and automated evaluation of the service status of plate structures.

[0129] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0130] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A dynamic simulation test method for service deformation of plate structures, characterized in that, The method includes: A set of mesoscopic parameters and a set of raw dual-mode ultrasonic guided wave signals are obtained for the plate structure under test. The set of mesoscopic parameters is used to characterize the properties and distribution of various interference sources inside the plate structure. The set of raw dual-mode ultrasonic guided wave signals and the set of mesoscopic parameters have a spatial correspondence. Based on the micro-parameter set, optimized parameters are determined for decomposing the original signal set of the dual-mode ultrasonic guided wave; the optimized parameters are used to match the decomposition bandwidth characteristics of the signal decomposition algorithm with the signal interference characteristics caused by the interference source. Based on the optimized parameters and signal decomposition algorithm, the original signal set of the dual-mode ultrasonic guided wave is decomposed to determine the target signal set related to structural damage. The service damage state of the plate structure is determined by comparing the microscopic parameter set with the target signal set through simulation.

2. The dynamic simulation test method for service deformation of a plate structure according to claim 1, characterized in that, The step of determining optimized parameters for decomposing the original signal set of the dual-modal ultrasonic guided wave based on the mesoscopic parameter set includes: Spectral features are extracted from the original signal set of the dual-mode ultrasonic guided wave to determine the initial optimization parameters; Based on the microscopic parameter set and the broadening effect of the various interference sources on the frequency domain characteristics of the guided wave signal, the interference influence factors corresponding to the various interference sources are determined respectively. Based on the interference influence factor, the initial optimization parameters are corrected to determine the optimization parameters.

3. The dynamic simulation test method for service deformation of a plate structure according to claim 2, characterized in that, The multiple interference sources include: a first interference source and a second interference source; the broadening effect includes: a first broadening effect and a second broadening effect; determining the interference influence factors corresponding to each of the multiple interference sources based on the microscopic parameter set and the broadening effect of the multiple interference sources on the frequency domain characteristics of the guided wave signal includes: Based on the attribute characteristics of the first interference source in the micro-parameter set, a first interference influence factor is determined; the first interference influence factor is used to quantify the first broadening effect of the first interference source on the bandwidth of the first mode guided wave signal. Based on the attribute characteristics of the second interference source in the micro-parameter set, a second interference influence factor is determined; the second interference influence factor is used to quantify the second broadening effect of the second interference source on the bandwidth of the second mode guided wave signal.

4. The dynamic simulation test method for service deformation of a plate structure according to claim 3, characterized in that... The first source of interference is the coarse aggregate in the slab structure concrete; the second source of interference is the reinforcing steel in the slab structure.

5. The dynamic simulation test method for service deformation of a plate structure according to claim 4, characterized in that, The determination of the first interference influence factor based on the attribute characteristics of the first interference source in the mesoscopic parameter set includes: Based on the particle size distribution characteristics of the coarse aggregate, the first contribution of the multi-scale scattering effect caused by the uneven particle size distribution to the signal bandwidth is determined. Based on the matching relationship between the average particle size of the coarse aggregate and the center wavelength of the first mode guided wave, the second contribution of the resonant scattering effect to the signal bandwidth is determined. The first interference influence factor is determined based on the first contribution and the second contribution.

6. The dynamic simulation test method for service deformation of a plate structure according to claim 5, characterized in that, The determination of the second interference influence factor based on the attribute characteristics of the second interference source in the mesoscopic parameter set includes: Based on the diameter, quantity, and spacing of the reinforcing bars, the density and intensity of reflection events encountered by the second mode guided wave on the propagation path are determined; Based on the differences in the distribution characteristics of reinforcing bars between the current area and adjacent areas, the extent to which the superposition of reflected signals broadens the signal bandwidth is determined. The second interference influence factor is determined based on the density and intensity of the reflection event and the broadening of the superimposed reflection signal.

7. The dynamic simulation test method for service deformation of a plate structure according to claim 6, characterized in that, The step of correcting the initial optimization parameters based on the interference influence factor to determine the optimization parameters includes: Determine the energy distribution ratio between the first-mode guided wave signal and the second-mode guided wave signal in the original signal set of the dual-mode ultrasonic guided wave; Based on the energy distribution ratio, the first interference influence factor and the second interference influence factor are weighted and fused to determine the comprehensive correction factor; The bandwidth constraint parameter in the initial optimization parameters is adjusted based on the comprehensive correction factor to determine the optimization parameters.

8. The dynamic simulation test method for service deformation of a plate structure according to claim 7, characterized in that, The process of decomposing the original signal set of the dual-mode ultrasonic guided wave based on the optimized parameters and signal decomposition algorithm to determine the target signal set related to structural damage includes: Based on the signal decomposition algorithm, the original signal set of the dual-mode ultrasonic guided wave is decomposed into multiple intrinsic mode components; Based on the first interference influence factor and the second interference influence factor, components related to structural damage are selected from the plurality of intrinsic mode components to form the target signal set.

9. The dynamic simulation test method for service deformation of a plate structure according to claim 1, characterized in that, The step of performing simulation comparison between the microscopic parameter set and the target signal set to determine the service damage state of the plate structure includes: Based on the micro-parameter set, a benchmark finite element model library containing multiple preset damage modes is constructed; the micro-features of each model in the benchmark finite element model library are defined based on the micro-parameter set. Based on each model in the aforementioned benchmark finite element model library, the excitation and reception process of the dual-mode ultrasonic guided wave is simulated to determine the simulation signal set; Construct a mapping library between the preset damage modes and the simulation signal set; The service damage status of the plate structure is determined by feature matching between the signals in the target signal set and the simulation signals in the mapping relationship library.

10. A dynamic simulation and testing system for service deformation of a plate structure, characterized in that, The system includes: The data acquisition module is used to acquire the micro-parameter set and the original signal set of dual-mode ultrasonic guided waves of the plate structure under test; the micro-parameter set is used to characterize the properties and distribution of various interference sources inside the plate structure; the original signal set of dual-mode ultrasonic guided waves and the micro-parameter set have a spatial correspondence. The parameter optimization module is used to determine optimized parameters for decomposing the original signal set of the dual-mode ultrasonic guided wave based on the micro-parameter set; the optimized parameters are used to match the decomposition bandwidth characteristics of the signal decomposition algorithm with the signal interference characteristics caused by the interference source; The signal processing module is used to decompose the original signal set of the dual-mode ultrasonic guided wave based on the optimized parameters and the signal decomposition algorithm to determine the target signal set related to structural damage. The damage state determination module is used to perform simulation comparison between the microscopic parameter set and the target signal set to determine the service damage state of the plate structure.

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