Dynamic analogue simulation test method and system for plate type structure service deformation

Abstract

The invention relates to the technical field of computer simulation testing, in particular to a dynamic analogue simulation testing method and system for plate type structure service deformation. The technical problem that the structural damage identification precision is insufficient when fixed signal decomposition parameters are used for processing all detection signals is solved. The method comprises the following steps: acquiring a mesoscopic parameter set and a bimodal ultrasonic guided wave original signal set of a to-be-detected plate type structure; based on the mesoscopic parameter set, determining optimization parameters for decomposing the bimodal ultrasonic guided wave original signal set; decomposing the bimodal ultrasonic guided wave original signal set based on the optimized parameters and a signal decomposition algorithm, and determining a target signal set related to structural damage; and performing simulation comparison on the mesoscopic parameter set and the target signal set, and determining the service damage state of the plate type structure. The method is used for plate type structure damage identification scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer simulation testing, in particular to a dynamic simulation and analog testing method and system for service deformation of a plate structure. BACKGROUND

[0002] The plate structure is widely used in the fields of aerospace, rail transportation and major infrastructure, and the deformation and damage of the plate structure during long-term service directly affect the structural safety and service life. Therefore, high-precision evaluation of the structural health state based on dynamic simulation and non-destructive testing technology has become an urgent demand in the engineering field. At present, ultrasonic guided waves are often used to excite and collect signals of the plate structure, and signal processing algorithms are used to extract damage features, and then the state is inverted combined with the simulation model. However, the inherent microscopic components such as concrete coarse aggregate and steel bars in the plate structure will produce strong scattering and reflection interference during guided wave propagation. These interference signals and real damage signals are seriously mixed in time and frequency domain. The existing method usually uses fixed signal decomposition parameters to process all detection signals, and does not fully consider the influence of the spatial and temporal differences of the internal microscopic characteristics of the specific structure on the interference characteristics, resulting in inaccurate damage feature extraction, mismatch between the simulation model and the measured signal, and insufficient structural damage identification accuracy under complex interference background. SUMMARY

[0003] In order to solve the technical problems that the existing method uses fixed signal decomposition parameters to process all detection signals, does not fully consider the influence of the spatial and temporal differences of the internal microscopic characteristics of the specific structure on the interference characteristics, and the damage feature extraction is inaccurate under complex interference background, and the structural damage identification accuracy is insufficient, the purpose of the present application is to provide a dynamic simulation and analog testing method and system for service deformation of a plate structure, and the technical scheme adopted is as follows: In the first aspect, the present application provides a dynamic simulation and analog testing method for service deformation of a plate structure, which comprises: obtaining a set of microscopic parameters of the plate structure to be tested and a set of original signals of double-mode ultrasonic guided waves; the set of microscopic parameters is used to represent the attributes and distribution of a plurality of interference sources inside the plate structure; the set of original signals of double-mode ultrasonic guided waves has a spatial correspondence with the set of microscopic parameters; based on the set of microscopic parameters, the optimal parameters for decomposing the set of original signals of double-mode ultrasonic guided waves are determined; the optimal parameters are used to match the decomposition bandwidth characteristics of the signal decomposition algorithm with the signal interference characteristics caused by the interference sources; based on the optimal parameters and the signal decomposition algorithm, the set of original signals of double-mode ultrasonic guided waves is decomposed to determine a set of target signals related to structural damage; the set of microscopic parameters and the set of target signals are simulated and compared to determine the service damage state of the plate structure.

[0004] In a possible implementation manner of the first aspect, the method specifically comprises: performing spectrum feature extraction on the set of original dual-mode ultrasonic guided wave signals to determine initial optimization parameters; determining interference influence factors corresponding to the plurality of interference sources based on the set of mesoscopic parameters and the broadening effects of the plurality of interference sources on the frequency domain features of the guided wave signals; and correcting the initial optimization parameters based on the interference influence factors to determine the optimization parameters.

[0005] In a possible implementation manner of the first aspect, the plurality of interference sources comprises: a first interference source and a second interference source; and the broadening effects comprise: a first broadening effect and a second broadening effect. The method specifically comprises: determining a first interference influence factor based on the attribute features of the first interference source in the set of mesoscopic parameters; 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 signals; and determining a second interference influence factor based on the attribute features of the second interference source in the set of mesoscopic parameters; 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 signals.

[0006] In a possible implementation manner of the first aspect, the first interference source is coarse aggregate in the plate structure concrete, and the second interference source is steel bars in the plate structure.

[0007] In a possible implementation manner of the first aspect, the method specifically comprises: determining a first contribution degree of a multi-scale scattering effect caused by uneven particle size distribution of the coarse aggregate to the signal bandwidth based on the particle size distribution features of the coarse aggregate; determining a second contribution degree of a resonance 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 degree and the second contribution degree.

[0008] In a possible implementation manner of the first aspect, the method specifically comprises: determining the density and intensity of reflection events encountered by the second-mode guided wave on the propagation path based on the diameter, number and spacing of the steel bars; determining the broadening degree of the signal bandwidth caused by the superposition of reflection signals based on the difference between the steel bar distribution features of the current region and the adjacent region; and determining the second interference influence factor based on the density and intensity of the reflection events and the broadening degree of the superposition of reflection signals.

[0009] In a possible implementation manner of the first aspect, the method specifically comprises: determining the energy distribution ratio of the first-mode guided wave signals and the second-mode guided wave signals in the set of original dual-mode ultrasonic guided wave signals; performing weighted fusion on the first interference influence factor and the second interference influence factor based on the energy distribution ratio to determine a 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.

[0010] In a possible implementation manner of the first aspect, the method specifically comprises: based on a signal decomposition algorithm, decomposing the set of original dual-mode ultrasonic guided wave signals into a plurality of intrinsic modal components; based on the first interference influence factor and the second interference influence factor, screening components related to structural damage from the plurality of intrinsic modal components to form the set of target signals.

[0011] In a possible implementation manner of the first aspect, the method specifically comprises: based on the set of mesoscopic parameters, constructing a library of reference finite element models containing a plurality of preset damage modes; the mesoscopic features of each model in the library of reference finite element models are defined based on the set of mesoscopic parameters; based on each model in the library of reference finite element models, simulating the excitation and reception process of the dual-mode ultrasonic guided wave to determine a set of simulation signals; constructing a mapping relationship library between the preset damage modes and the set of simulation signals; performing feature matching between the signals in the set of target signals and the simulation signals in the mapping relationship library to determine the service damage state of the plate structure.

[0012] In a possible implementation manner of the first aspect, the method specifically comprises: based on the set of mesoscopic parameters, constructing a library of reference finite element models containing a plurality of preset damage modes; the mesoscopic features of each model in the library of reference finite element models are defined based on the set of mesoscopic parameters; based on each model in the library of reference finite element models, simulating the excitation and reception process of the dual-mode ultrasonic guided wave to determine a set of simulation signals; constructing a mapping relationship library between the preset damage modes and the set of simulation signals; performing feature matching between the signals in the set of target signals and the simulation signals in the mapping relationship library to determine the service damage state of the plate structure.

[0013] The present application has the following beneficial effects: The application constructs the spatial mapping relationship between data and physical entities by acquiring and correlating the set of mesoscopic parameters inside the plate structure and the set of original signals of the double-mode ultrasonic guided waves outside, and then adaptively determines the optimization parameters of the signal decomposition algorithm using the set of mesoscopic parameters, so that the algorithm can accurately match the interference characteristics of the specific structure, and finally realizes high-confidence identification of the damage state through integrated simulation comparison. This method forms a complete closed loop from the data source to the processing logic and the verification link, significantly improving the overall accuracy and reliability of the service damage identification under the background of complex internal structures. Thus, the technical problems of the prior art that the fixed signal decomposition parameters are used to process all detection signals, the influence of the spatial and temporal differences of the internal mesoscopic characteristics of the specific structure on the interference characteristics is not fully considered, and the damage feature extraction is not accurate and the structure damage identification accuracy is insufficient under the complex interference background are solved. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, a brief introduction will be given to the drawings needed in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0015] Figure 1 A flowchart of a plate structure service deformation dynamic simulation test method provided by an embodiment of the present application; Figure 2 A schematic diagram of a plate structure service deformation dynamic simulation test system architecture provided by an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following describes the plate structure service deformation dynamic simulation test method and system according to the present application, its specific implementation, structure, features and effects in detail, combined with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0017] 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 the present application belongs.

[0018] The specific scheme of the plate structure service deformation dynamic simulation test method and system provided by the present application is described in detail below with reference to the drawings.

[0019] Referring to Figure 1 Fig. 1 shows a flow chart of a method and system for simulating and testing the dynamics of the service deformation of a plate structure according to an embodiment of the present application. The method comprises the following steps S101-S104, which are described in detail below.

[0020] S101, obtaining a set of mesoscopic parameters of the plate structure to be tested and a set of original signals of dual-mode ultrasonic guided waves.

[0021] The set of mesoscopic parameters is used to represent the properties and distribution of a plurality of interference sources inside the plate structure. The set of original signals of dual-mode ultrasonic guided waves has a spatial correspondence with the set of mesoscopic parameters.

[0022] In one possible implementation, the set of mesoscopic parameters is obtained by detecting and quantifying a preset category of physical interference sources inside the structure. The set of original signals of dual-mode ultrasonic guided waves is obtained by exciting and receiving specific modes of elastic waves on the surface of the structure. The two types of data are associated and calibrated by a unified spatial coordinate system during acquisition.

[0023] For example, for the set of mesoscopic parameters, the particle size distribution, volume fraction, and three-dimensional coordinates of the coarse aggregate in the concrete region can be obtained using industrial scanning technology, and the diameter, spacing, depth, and planar position of the steel bars can be obtained using ground penetrating radar technology, thereby forming a structured database. For the set of original signals of dual-mode ultrasonic guided waves, a piezoelectric sensor array can be arranged on the surface of the structure, a signal generator can be used to excite Lamb waves containing dual modes, and time-domain voltage signals after propagation can be recorded at multiple receiving points to form a sequence of original signals.

[0024] For example, the preset category of physical interference sources includes a first type of interference source mainly caused by scattering interference of concrete coarse aggregate, and industrial CT scanning technology is used to obtain the particle size, gradation, volume fraction, and three-dimensional spatial distribution parameters of the first type of interference source. A second type of interference source mainly caused by reflection interference of steel bars, and high-frequency ground penetrating radar is used to obtain the diameter, spacing, depth, and planar position information of the second type of interference source.

[0025] S102, determining optimization parameters for decomposing the set of original signals of dual-mode ultrasonic guided waves based on the set of mesoscopic parameters.

[0026] 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.

[0027] In a possible implementation manner, a frequency domain distribution feature of the original signal is first extracted to establish an initial parameter benchmark, then a physical property of an interference source described by a set of mesoscopic parameters is deeply combined, a frequency spectrum broadening effect caused by each type of interference on a signal component is quantitatively evaluated, and a corresponding interference influence factor is generated according to the frequency spectrum broadening effect, and finally the initial parameter benchmark is corrected and optimized by using the factors.

[0028] For example, the signal decomposition algorithm can adopt a variational mode decomposition algorithm. A core idea of the algorithm is to adaptively decompose an input complex signal into a series of quasi-orthogonal intrinsic mode functions with specific center frequencies and limited bandwidths by constructing and solving a variational problem.

[0029] The working principle can be briefly described as follows: the algorithm assumes that any complex signal can be superimposed by a plurality of amplitude-frequency modulation components with different center frequencies. The algorithm determines an optimal center frequency and bandwidth for each mode component through iterative search. An optimization objective is to minimize a sum of estimated bandwidths of all mode components while ensuring that all mode components can be added to accurately reconstruct the original signal.

[0030] S103, based on the optimized parameters and the signal decomposition algorithm, decompose a set of original dual-mode ultrasonic guided wave signals to determine a set of target signals related to structural damage.

[0031] In a possible implementation manner, a signal decomposition algorithm configured with the optimized parameters is first applied to the original signal to adaptively decompose the original signal into a series of intrinsic mode components with different center frequencies and bandwidths. Then, a screening criterion is formulated according to characteristic indexes determined by the mesoscopic parameters and representing influences of each type of interference source, and a component consistent with the damage mechanism is identified and separated from all components.

[0032] S104, simulate and compare the set of mesoscopic parameters and the set of target signals to determine a service damage state of the plate structure.

[0033] In a possible implementation manner, a set of internal physical structures defined based on the set of mesoscopic parameters is first used to establish a benchmark model library covering different preset damage modes in a finite element simulation software. Then, an ultrasonic guided wave excitation and receiving process completely consistent with actual detection is reproduced on each model in the model library, a corresponding set of simulation signals is obtained, and a mapping relationship is established. Finally, a set of target signals actually measured is subjected to multi-dimensional feature matching with signals in the simulation signal library, and a most possible damage state is determined according to a best matching result.

[0034] The technical scheme provided by the above embodiment can bring at least the following beneficial effects: the embodiment acquires and associates the set of mesoscopic parameters in the plate structure and the set of original signals of the dual-mode ultrasonic guided waves, constructs the spatial mapping relationship between the data and the physical entity, and then adaptively determines the optimization parameters of the signal decomposition algorithm using the set of mesoscopic parameters, so that the algorithm can accurately match the interference characteristics of the specific structure. Finally, high-confidence recognition of the damage state is achieved through integrated simulation comparison. This method forms a complete closed loop from the data source to the processing logic and the verification link, significantly improving the overall accuracy and reliability of the in-service damage recognition under complex internal structure background. Thus, the technical problem of the prior art that fixed signal decomposition parameters are used to process all detection signals without fully considering the influence of the spatial and temporal differences of the internal mesoscopic characteristics of the specific structure on the interference characteristics, resulting in inaccurate damage feature extraction and insufficient structural damage recognition accuracy under complex interference background is solved.

[0035] In a possible implementation manner, the process of determining the optimization parameters for decomposing the set of original signals of the dual-mode ultrasonic guided waves based on the set of mesoscopic parameters can be implemented through the following S201-S203, which are described in detail below.

[0036] S201, performing frequency spectrum feature extraction on the set of original signals of the dual-mode ultrasonic guided waves to determine initial optimization parameters.

[0037] In a possible implementation manner, the power spectral density of the original signal is calculated to identify the dominant frequency peak and its bandwidth, and then the different modal components are distinguished according to the preset modal frequency reference range, and the initial decomposition layer number and the basic bandwidth constraint value of the algorithm execution are quantitatively determined based on the center frequency and bandwidth characteristics.

[0038] For example, the original signal can be subjected to fast Fourier transform to obtain its frequency spectrum, and then the power spectral density curve is calculated. By analyzing the curve, a wide frequency peak and its center frequency and bandwidth appearing in a preset low frequency band (for example, corresponding to the A0 mode) and a narrow frequency peak and its center frequency and bandwidth appearing in a preset high frequency band (for example, corresponding to the S0 mode) are identified. The initial number of layers of signal decomposition is determined according to the number of identified significant peaks. At the same time, according to the ratio relationship of each center frequency and its corresponding bandwidth, and in combination with a preset proportional coefficient, the initial penalty components for different modal components are calculated. Finally, the initial penalty components are weighted and fused by integrating the energy proportion of different modal signals in the entire original signal, so as to obtain a global initial optimization parameter as a core component of the initial optimization parameter.

[0039] For example, the initial optimization parameter satisfies the following formula 1: wherein, is the center frequency of the first modal (A0 modal) ultrasonic guided wave, representing the position where the A0 modal signal energy is most concentrated in the frequency domain; is the frequency width at which the power spectral density of the first modal signal drops to half of the peak value after being disturbed, representing the degree of broadening of the frequency components of the A0 modal signal; is the center frequency of the second modal (S0 modal) ultrasonic guided wave, representing the position where the S0 modal signal energy is most concentrated in the frequency domain; is the frequency width at which the power spectral density of the second modal signal drops to half of the peak value after being disturbed, representing the degree of broadening of the frequency components of the S0 modal signal; is a proportional coefficient, which is a dimensionless constant set based on historical experience (for example, 2000), used to adjust the frequency ratio to the order of magnitude matching the optimization parameter; is the energy proportion of the first modal signal in the bimodal original signal, which is a dimensionless quantity, with a value range of [0, 1]; is the energy proportion of the second modal signal in the bimodal original signal, which is a dimensionless quantity, with a value range of [0, 1], + =1.

[0040] The greater the ratio of , the more concentrated the signal frequency components (the narrower the bandwidth), according to the signal decomposition principle, a larger optimization parameter needs to be applied to constrain the bandwidth and prevent modal aliasing; multiplied by the proportional coefficient maps the quality factor to the typical numerical range of the algorithm optimization parameter; According to the energy proportion of each modal signal in the original signal and , the two initial penalty components are weighted and averaged to obtain the initial optimization parameter The modal with high energy proportion has greater contribution to the global parameter of the corresponding initial penalty component; is the initial optimization parameter of the signal decomposition algorithm, used to control the bandwidth constraint strength of each eigenmode function when the algorithm decomposes the signal. The greater the value, the more stringent the algorithm's bandwidth constraint, and the narrower the bandwidth of the decomposed component; otherwise, the constraint is looser, and the component bandwidth is wider.

[0041] S202, based on the mesoscopic parameter set, the broadening effect of the frequency domain characteristics of the guided wave signal by multiple interference sources, determine the interference influence factor corresponding to each of the multiple interference sources.

[0042] In one possible implementation, a mapping relationship model between the physical parameters (such as size, distribution, density, etc.) of different types of interference sources and the signal frequency band width variation is established according to the unique physical parameters of the interference sources, and a quantitative value representing the interference intensity is calculated through the model.

[0043] For example, for the first interference source (such as concrete coarse aggregate), the data about the aggregate particle size distribution, volume fraction, and spatial position in the mesoscopic parameter set are called. By analyzing the non-uniformity of the particle size distribution and the closeness of the average particle size to the guided wave wavelength, the degree of broadening of the first modal guided wave signal bandwidth caused by the combined action of multi-scale scattering and resonance scattering is evaluated, which is quantified as the first interference influence factor. For the second interference source (such as steel bars), the data about the steel bar diameter, spacing, number, and distribution difference in the mesoscopic parameter set are called. By analyzing the density and intensity of the reflection surface and the superposition effect of the reflected signal due to uneven distribution, the degree of broadening of the second modal guided wave signal bandwidth is evaluated, which is quantified as the second interference influence factor.

[0044] S203, correcting the initial optimization parameters based on the interference influence factors to determine the optimization parameters.

[0045] In one possible implementation, the energy weight of different modal guided wave signals in the actual collected signal is first considered as the basis for fusing different interference influence factors, then a comprehensive correction factor is obtained through weighted calculation, and finally the core constraint term in the initial optimization parameters is numerically adjusted by using the comprehensive correction factor to complete the directional optimization of the parameters.

[0046] For example, the power spectrum of the dual-modal original signal set can be first calculated by integral calculation to obtain the proportion of the energy of the first modal signal and the second modal signal in the total signal energy. Then, the two energy proportions are used as weights to perform weighted summation on the first interference influence factor and the second interference influence factor, thereby obtaining a comprehensive correction factor reflecting the overall path interference level. The correction operation is embodied in that the optimization parameters in the initial optimization parameters are reduced by using the comprehensive correction factor. The physical logic is that the larger the comprehensive correction factor, the stronger the overall interference on the propagation path, and the more mixed the signal components, so that the bandwidth constraint of the decomposition algorithm needs to be appropriately relaxed (i.e., the optimization parameters are reduced) to prevent over-decomposition or false separation of the complex mixed signal. The optimization parameter value obtained through the reduction calculation is used as a key component of the optimization parameters.

[0047] The technical scheme provided by the above embodiment can bring at least the following beneficial effects: the embodiment extracts initial parameters matched with the current detection state from the original signal through frequency spectrum feature extraction, then quantitatively analyzes the broadening effect of various interference sources on the signal frequency domain feature based on the micro parameter set and obtains corresponding interference influence factors, finally corrects the initial parameters by using the factors, and the process dynamically associates abstract algorithm parameters with specific physical interference mechanisms, so that the finally generated optimized parameters can more accurately reflect the signal distortion characteristics in the actual propagation path, thereby greatly enhancing the adaptability and processing precision of the signal decomposition algorithm to the complex interference environment.

[0048] In a possible implementation manner, the multiple interference sources include a first interference source and a second interference source, and 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 micro parameter set and the broadening effect of the multiple interference sources on the frequency domain feature of the guided wave signal can be implemented through the following S301-S302, which are described in detail below.

[0049] S301, determining a first interference influence factor based on the attribute features of the first interference source in the micro parameter set.

[0050] The first interference influence factor is used to quantify the first broadening effect of the first interference source on the bandwidth of the first modal guided wave signal.

[0051] In a possible implementation manner, starting from the inherent physical attributes of the coarse aggregate, the interaction relationship between the geometric characteristics and key sizes of the spatial distribution and the guided wave propagation parameters is analyzed, the contribution of the scattering effect is evaluated from different physical dimensions, and finally fused into a unified quantitative result.

[0052] Exemplarily, first, based on the data about the particle size distribution of the coarse aggregate in the mesoscopic parameter set, a statistical quantity (such as the standard deviation of the particle size distribution) reflecting the dispersion degree of the distribution and the difference of the 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 the particle size of the aggregate: the greater the particle size difference and the more uneven the distribution, the wider the scattering frequency range of the scatterers of different scales on the signal, and the greater the contribution of the signal bandwidth expansion, which is quantified as the first contribution degree. Second, based on the average particle size data of the coarse aggregate provided by the mesoscopic parameter set, and compared with the center wavelength (calculated from the center frequency obtained by frequency spectrum analysis) of the first modal guided wave, the matching degree of the two is evaluated. When the average particle size is close to the center wavelength, it is easy to cause resonance scattering effect, which causes the signal energy to be dispersed in a wider frequency band, and has a significant impact on the bandwidth expansion, which is quantified as the second contribution degree. Finally, by a preset fusion rule (such as weighted average or other composite functions), the first contribution degree and the second contribution degree are integrated into a single value, which is determined as the first interference influence factor.

[0053] S302, based on the attribute characteristics of the second interference source in the mesoscopic parameter set, a second interference influence factor is determined.

[0054] Among them, the second interference influence factor is used to quantify the second bandwidth expansion effect of the second interference source on the second modal guided wave signal.

[0055] In a possible implementation manner, starting from the individual geometric attribute and spatial arrangement characteristics of the steel bars, the intensity, density and signal superposition effect caused by uneven distribution of the discrete reflection events generated by the steel bars on the specific modal guided wave are analyzed, the bandwidth expansion contribution of the steel bars to the signal is evaluated from different dimensions, and finally the unified quantization result is obtained.

[0056] Exemplarily, first, based on the data about the diameter, number and spacing of the steel bars in the mesoscopic parameter set, the total effective area of the steel bar reflection surface per unit length on the path or similar indicators are calculated, which are used to evaluate the density and intensity of the reflection events: the larger the diameter of the steel bars, the more the number of the steel bars, and the smaller the spacing, the more concentrated the reflection energy and the more frequent the reflection events, and the more significant the impact on the signal. Second, based on the spatial coordinates and distribution data of the steel bars provided by the mesoscopic parameter set, the difference degree of the distribution pattern (such as the arrangement direction and spacing consistency) between the current evaluation region and its adjacent region is analyzed. The greater the distribution difference, the more dispersed the arrival time distribution of the reflection signal in the time domain, and the more likely to cause the superposition of the frequency components in the frequency domain, thereby causing the bandwidth expansion of the signal, and the expansion degree is quantified separately. Finally, by a preset fusion rule, the quantization result representing the density and intensity of the reflection events and the quantization result representing the superposition expansion degree caused by the distribution difference are integrated to generate a single value, which is determined as the second interference influence factor.

[0057] The technical solutions provided by the above embodiments can at least bring the following beneficial effects: the embodiments further explicitly distinguish a plurality of interference sources into a first interference source and a second interference source, and correspondingly, the broadening effects are subdivided into a first broadening effect and a second broadening effect, and the first interference influence factor and the second interference influence factor are determined respectively, the classification and quantitative processing manner can finely depict the physical nature difference of the influence of different types of interference sources (such as scattering type and reflection type) on the guided wave signal, lays a solid foundation for subsequent more targeted signal separation and parameter optimization, and effectively avoids analysis deviation caused by confusing different types of interference characteristics.

[0058] In a possible implementation manner, the first interference source is coarse aggregate in the plate structure concrete, and the second interference source is the steel bar in the plate structure.

[0059] The technical solutions provided by the above embodiments can at least bring the following beneficial effects: the embodiments explicitly limit the first interference source to be coarse aggregate in the concrete and the second interference source to be the steel bar, which directly corresponds the abstract interference source category to the specific physical entity in the engineering practice, so that the analysis model, parameter calculation and subsequent simulation of the entire technical solution are established on the basis of real engineering materials and structures, greatly enhances the physical consistency and engineering practicability of the method, and ensures smooth transition from theoretical analysis to engineering application.

[0060] In a possible implementation manner, the process of determining the first interference influence factor based on the attribute characteristics of the first interference source in the mesoscopic parameter set can be implemented through the following S401-S403, which will be described in detail below.

[0061] S401, based on the particle size distribution characteristics of the coarse aggregate, determine the first contribution degree of the multi-scale scattering effect caused by the uneven particle size distribution to the signal bandwidth.

[0062] In a possible implementation manner, for the statistical data of the coarse aggregate particle size in the mesoscopic parameter set, the specific influence share of the multi-scale scattering mechanism caused by the different sizes and uneven spatial distribution of the aggregate particles on the spectral broadening of the guided wave signal is quantitatively evaluated by analyzing the dispersion degree and spatial variation law, and the share is quantified as an independent numerical index, i.e. the first contribution degree. Specifically, this process focuses on the statistical distribution characteristics of the particle size data itself and its fluctuation in space, aiming to depict the contribution of different size scattering body arrays to the formation of broadband noise.

[0063] For example, the first contribution degree satisfies the following formula 2: wherein N is a positive integer, representing the total number of spatial intervals into which the propagation path of the ultrasonic wave 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] For example, the second contribution The following formula 3 is satisfied: 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.

[0068] 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.

[0069] S403. Based on the first contribution and the second contribution, determine the first interference impact factor.

[0070] In a possible implementation manner, for the first contribution degree and the second contribution degree respectively quantified and reflecting influences of different physical scattering mechanisms, first, differences in numerical magnitude and dimension of the first contribution degree and the second contribution degree are eliminated through normalization processing, and then the normalized results are integrated into a unified numerical index representing overall scattering intensity of the first type of interference source, that is, the first interference influence factor, through a preset integration rule. Specifically, the process aims to place influences of two independent physical dimensions, that is, multi-scale scattering caused by uneven particle size distribution and resonance scattering caused by matching of particle size and wavelength, on the same scale for fair and balanced integration, so as to comprehensively evaluate the overall broadening effect of coarse aggregate on the bandwidth of guided wave signals.

[0071] Exemplarily, the first contribution degree and the second contribution degree are first processed through a preset normalization function, such as maximum-minimum normalization, Z-score standardization and the like, to obtain two dimensionless parameters with consistent value ranges, for example, in the interval [0, 1]. Then, the two normalized parameters are weighted and summed, and distribution of the weights can be determined according to prior knowledge, experimental data or analysis of relative importance of the two scattering mechanisms in a specific frequency band; or the two normalized parameters are calculated through a preset multivariate function, such as geometric mean, square root of sum and the like. The output value of the calculation process is the first interference influence factor. The numerical value directly reflects the overall broadening degree of scattering interference caused by coarse aggregate on the signal bandwidth: the larger the numerical value, the stronger and more comprehensive the distortion and broadening effect of coarse aggregate as a scatterer on the signal in the current propagation path.

[0072] The technical scheme provided by the above embodiment can at least bring the following beneficial effects: the embodiment analyzes particle size distribution characteristics of coarse aggregate as the first interference source to evaluate multi-scale scattering effect, and combines the matching relationship between the average particle size and the center wavelength of the guided wave to evaluate resonance scattering effect, and finally comprehensively determines the first interference influence factor. The method deeply characterizes strength and spectral characteristics of aggregate scattering interference from two core physical dimensions of unevenness of aggregate distribution and particle size-wavelength resonance, so that quantitative evaluation of wideband noise caused by concrete, a complex and heterogeneous material, is more scientific and accurate.

[0073] In a possible implementation manner, the process of determining the second interference influence factor based on the attribute characteristics of the second interference source in the mesoscopic parameter set can be implemented through the following S501-S503, which are described in detail as follows.

[0074] S501, based on the diameter, number and spacing of the steel bars, the density and intensity of reflection events encountered by the second modal guided wave in the propagation path are determined.

[0075] In a possible implementation, for the geometric and arrangement parameters of the steel bars in the mesoscopic parameter set, the overall trend of the reflection behavior generated by the interaction between the second modal guided wave and the steel bar mesh during propagation is quantitatively evaluated by analyzing the correlation between key attributes, and the trend is quantified as a comprehensive index reflecting the spatial density of reflection events and the intensity level of a single event.

[0076] For example, first, the diameter, number, and center distance between adjacent steel bars in the area covered by the target propagation path are extracted from the mesoscopic parameter set. The intensity of the reflection event is mainly related to the reflection cross section of a single steel bar, which can be quantified according to the diameter, for example, the larger the diameter, the stronger the reflection ability. The density of the reflection event is closely related to the number and spacing of the steel bars on the path: the more the number of steel bars per unit length and the smaller the spacing, the more concentrated the reflection bodies encountered by the wave during propagation, and the higher the frequency of reflection events. Through a preset data fusion rule (for example, the diameter representation of each steel bar is added or averaged, and then multiplied or weighted combined with the spatial distribution density index derived from the number and spacing), a composite quantitative value is calculated. The value represents the density and intensity of the reflection event, and the higher the value, the more concentrated and intense the reflection interference from the steel bars on the current path.

[0077] S502, based on the difference in steel bar distribution characteristics between the current area and the adjacent area, determine the expansion degree of the signal bandwidth caused by the superposition of the reflection signal.

[0078] In a possible implementation, for the steel bar distribution data provided by the mesoscopic parameter set and having spatial position information, by comparing the similarity or difference in steel bar arrangement mode between the current area to be evaluated and its directly adjacent area, the complex superposition effect of multiple reflection signals in the time-frequency domain caused by the discontinuity of the spatial distribution is quantitatively evaluated, and the superposition effect is further converted into an additional expansion amount of the signal spectrum width, and the expansion amount is quantified as an independent numerical index.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] For example, the second interference influence factor Satisfy the following formula 4: 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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. .

[0089] 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.

[0090] S603. Adjust the bandwidth constraint parameters in the initial optimization parameters based on the comprehensive correction factor to determine the optimization parameters.

[0091] 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.

[0092] For example, optimize parameters The following formula 5 is satisfied: 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.

[0093] 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.

[0094] The technical solutions provided by the above embodiments can bring at least the following beneficial effects: in the embodiment, when the final optimization parameter is synthesized, the energy distribution ratio of the first modal and the second modal guided wave signals in the actual collected signals is first determined, then the first and second interference influence factors are weighted and fused according to the ratio to obtain a comprehensive correction factor, and finally the factor is used to adjust the bandwidth constraint of the initial parameter. This step fully considers the difference in energy contribution of the two main modal signals, so that the adjustment direction of the optimization parameter is consistent with the dominant feature of the actual signal, avoids the parameter deviation that may be caused by equal weight processing, and thus realizes more balanced and effective global regulation and control of the signal decomposition bandwidth constraint.

[0095] In a possible implementation manner, the process of decomposing the dual-modal ultrasonic guided wave original signal set based on the optimization parameter and the signal decomposition algorithm to determine the target signal set related to the structural damage can be implemented through the following S701-S702, which are described in detail as follows.

[0096] S701, decomposing the dual-modal ultrasonic guided wave original signal set into a plurality of eigenmodal components based on the signal decomposition algorithm.

[0097] In a possible implementation manner, the signal decomposition algorithm configured with the optimization parameter is used to perform core decomposition operation to adaptively decompose the original mixed time domain signal into a group of sub-signal components with a determined number and different frequency characteristics, i.e., a plurality of eigenmodal components.

[0098] S702, screening the components related to the structural damage from the plurality of eigenmodal components based on the first interference influence factor and the second interference influence factor to constitute the target signal set.

[0099] In a possible implementation manner, by calling the physical interference intensity information represented by the quantized first interference influence factor and the second interference influence factor, an objective screening criterion is formulated and applied to identify and separate the components that are significantly different from the known strong interference mode, identify these components as the response related to the structural damage, and collect them into the target signal set.

[0100] Exemplarily, a mapping relationship between the first interference influence factor 1 and a typical aggregate scattering interference bandwidth is established, and a linear mapping relationship between the second interference influence factor and a typical steel bar reflection interference bandwidth is established; a bandwidth reference value of the first interference influence factor and the second interference influence factor is determined based on the linear mapping relationship; then, for each intrinsic modal component, a power spectral density thereof is calculated, and a half-power bandwidth method is used to determine an actual bandwidth value thereof; a preset difference threshold (for example, 20%) is set; when a relative difference percentage of the actual bandwidth value and the bandwidth reference value of the first interference influence factor is greater than 20%, and a relative difference percentage of the actual bandwidth value and the bandwidth reference value of the second interference influence factor is greater than 20%, it is determined that the feature of the component does not conform to the known strong physical interference mode, and the component is more likely to be derived from other scattering mechanisms such as structural damage, and therefore the component is screened out. All the components screened out through the screening form the target signal set.

[0101] The technical solutions provided by the above embodiments can at least bring the following beneficial effects: in the signal decomposition and target signal extraction stage, the original signal set is directly processed by using the optimized signal decomposition algorithm, and based on the interference intensity information represented by the first and second interference influence factors, a component related to damage is screened out from the plurality of intrinsic modal components obtained through decomposition to form the target signal set, and this process makes the signal decomposition more targeted, can effectively strip the weak damage signal submerged by strong background interference, significantly improves the extraction purity and signal-to-noise ratio of the damage feature signal, and provides a clean and reliable data basis for subsequent high-precision damage inversion.

[0102] In a possible implementation manner, the process of simulating and comparing the mesoscopic parameter set and the target signal set to determine the service damage state of the plate structure can be implemented through the following S801-S804, which are described in detail as follows.

[0103] S801, based on the mesoscopic parameter set, a reference finite element model library containing a plurality of preset damage modes is constructed.

[0104] In a possible implementation manner, for the obtained mesoscopic parameter set, a numerical simulation technology is used to construct a digital structure model which is consistent with the entity to be measured in terms of mesoscopic physical composition, and different types and sizes of damages are preset, and these models are systematically organized to form a reference model library for subsequent simulation comparison.

[0105] Exemplarily, firstly, a reference geometric model of the plate structure is constructed according to geometric sizes and boundary conditions provided by the mesoscopic parameter set. For the concrete part, the random aggregate placement algorithm or the equivalent homogenization method can be used to give the material non-uniform mechanical properties according to the particle size, volume fraction and spatial distribution data of the aggregate in the parameter set; for the steel bar part, the rod element or solid element model is accurately established according to the diameter, spacing and position 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 (such as introducing a narrow gap to simulate a crack) or material properties (such as reducing the elastic modulus to simulate a void area) of the local area. These damage modes can cover common cracking, peeling, cavitation, etc., and each mode can be set to different damage degrees (such as crack length, void area). All these models are centrally managed to form a structured reference finite element model library, in which each model has a unique identifier associated with its corresponding mesoscopic parameter subset and preset damage description.

[0106] S802, based on each model in the reference finite element model library, simulating the excitation and receiving process of the dual-mode ultrasonic guided wave, determining a simulation signal set.

[0107] In a possible implementation, for each digital model containing a preset damage in the reference finite element model library, the whole process of ultrasonic guided wave excitation, propagation and signal acquisition corresponding to the actual physical detection is completely reproduced in the numerical simulation environment, the dynamic response time history data of the structure under a specific excitation at a preset receiving point is calculated and exported, thereby generating a simulation signal set corresponding to the model.

[0108] S803, constructing a mapping relationship library between the preset damage mode and the simulation signal set.

[0109] In a possible implementation, for each model in the reference finite element model library and its corresponding simulation signal, a systematic data organization and management method is used to establish and solidify the accurate and retrievable correspondence between the complete description of each preset damage mode and the simulation wave signal excited thereby, thereby forming 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 association, so that any preset damage state can be quickly located to its corresponding all simulation signal data through query.

[0110] S804, performing feature matching on the signals in the target signal set and the simulation signals in the mapping relationship library, and determining the service damage state of the plate structure.

[0111] In a possible implementation, for each target signal in the extracted target signal set, the feature similarity of the target signal and each simulation signal in a simulation signal subset with similar micro background is calculated by retrieving and calling the simulation signal subset from the mapping relationship library, and the type, location and degree of the actual damage of the plate structure at the current measurement position are finally determined and output according to the damage mode information associated with the simulation signal corresponding to the highest similarity.

[0112] Exemplarily, the feature matching includes but is not limited to the matching of the peak amplitude, the signal flight time, the center frequency offset and the energy attenuation coefficient.

[0113] The technical scheme provided by the above embodiment can bring at least the following beneficial effects: in the simulation comparison stage, the embodiment emphasizes the construction of a reference finite element model library based on the micro parameters and the matching of the micro features, and simulates the simulation signal set on the model library to establish the mapping relationship, and finally matches the target signal with the simulation signal to determine the damage state, which ensures the physical consistency of the simulation model and the entity to be measured in the micro structure, makes the damage inversion result based on the simulation more persuasive and accurate, and significantly improves the reliability and practicality of the whole test method.

[0114] Please refer to Figure 2 which shows a system architecture schematic diagram of a dynamic simulation and test system 200 for the service deformation of a plate structure provided by an embodiment of the present application, and the system comprises: a data acquisition module 201 configured to acquire a micro parameter set of a plate structure to be measured and a dual-mode ultrasonic guided wave original signal set; the micro parameter set is used to represent the attributes and distribution of a plurality of interference sources inside the plate structure; the dual-mode ultrasonic guided wave original signal set has a spatial corresponding relationship with the micro parameter set; a parameter optimization module 202 configured to determine an optimization parameter for decomposing the dual-mode ultrasonic guided wave original signal set based on the micro parameter set; the optimization parameter is used to match the decomposition bandwidth characteristics of a signal decomposition algorithm with the signal interference characteristics caused by the interference sources; a signal processing module 203 configured to decompose the dual-mode ultrasonic guided wave original signal set based on the optimization parameter and the signal decomposition algorithm, and determine a target signal set related to the structural damage; and a damage state determination module 204 configured to simulate and compare the micro parameter set and the target signal set, and determine the service damage state of the plate structure.

[0115] The technical scheme provided by the above embodiment can bring at least the following beneficial effects: the embodiment integrates four function modules of data acquisition, parameter optimization, signal processing and damage state determination, solidifies and encapsulates the parameter self-adaptive optimization, signal processing and simulation comparison process based on the mesoscopic physical characteristics, and provides a standardized operation and calculation framework, which not only reduces the complexity of method implementation and the dependence on the experience of operators, but also guarantees the consistency of the processing process and the repeatability of the results, and provides strong tool support for efficient, accurate and automatic evaluation of the service state of the plate structure.

[0116] It should be noted that the above-mentioned sequence of the embodiments of the application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0117] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the difference 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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