A method for quantitatively identifying acoustic emission of structural damage and its visualization system

Abstract

The present invention discloses a method for quantitatively identifying structural damage through acoustic emission and a visualization system. The method comprises the following steps: first, acoustic emission signals emitted when the structure to be measured is damaged are collected by spatially distributed acoustic emission sensors; then, the arrival time of the acoustic emission signals is calculated, acoustic emission events are extracted, and the objective function is constructed and optimized to achieve more accurate acoustic emission source positioning; then, a hybrid hierarchical-K-means clustering analysis is performed based on the acoustic emission source positioning results to precisely identify the locations of multiple dense cracks in different damage areas; finally, a regression analysis is performed on each cluster of acoustic emission sources in the clustering results to evaluate the size of each damaged surface and visualize them in a three-dimensional model. The damage identification method of the present invention can accurately identify the damage location and quantify the damage size, and the damage identification and quantification effects are more stable in the actual operating environment of the engineering structure. The quantitative damage identification results are intuitively displayed through a three-dimensional model, providing a scientific basis for timely maintenance and repair of the structure.

Description

Technical Field

[0001] The present invention belongs to the field of civil engineering structure health monitoring and damage identification, and particularly relates to a method for quantitatively identifying structural damage using acoustic emissions and a visualization system thereof. Background Art

[0002] Steel-concrete composite beams are structures composed of a steel beam and a concrete flange plate connected by shear connectors, sharing the load. Depending on the type of steel beam, they are classified into I-shaped steel beam-concrete slab composite beams, steel box beam-concrete slab composite beams, and steel truss-concrete slab composite beams. By leveraging the tensile properties of steel beams and the compressive properties of concrete, steel-concrete composite beams leverage the strengths of different materials, significantly reducing structural volume, increasing structural rigidity, improving load-bearing capacity and ductility, and reducing costs and construction schedules.

[0003] In bridge engineering, steel-concrete composite continuous beam bridges are an important application solution for medium-span bridges. Compared with simply supported beams, composite continuous beams can achieve a higher strength-to-weight ratio and improve the continuity of the superstructure. However, a significant disadvantage is that the concrete slab in the negative bending moment zone is prone to cracking under tension, which will reduce its cross-sectional stiffness and cause corrosion of the steel bars and shear connectors over time, affecting the structural durability and ultimately leading to the destruction of the entire steel-concrete continuous composite bridge system. Although numerous methods have been studied to delay concrete cracking and control interface damage, it is difficult to truly avoid concrete cracking and composite beam interface damage in actual engineering applications. Currently, there is no effective method for quantitatively identifying and visualizing damage to concrete and its composite structures. Therefore, there is an urgent need to develop effective quantitative damage identification and visualization techniques for steel-concrete composite beams. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for quantitatively identifying structural damage acoustic emissions and a visualization system thereof, so as to solve the problems of the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for quantitatively identifying structural damage by acoustic emission, comprising the following steps:

[0006] S1. Collecting acoustic emission signals when the target structure is damaged;

[0007] S2. Calculate the arrival time of the acoustic emission signal, extract the acoustic emission event, construct and optimize the objective function of the acoustic emission source position coordinates, and obtain the accurate positioning of the acoustic emission source when the target structure is damaged. This specifically includes the following sub-steps:

[0008] S201, according to the acoustic emission signal x(t) when the target structure is damaged, N natural mode functions x i(t)(i=1,2,...,N), the Fourier spectrum of the signal is divided into N parts containing independent intrinsic mode functions, according to the empirical scaling function and empirical wavelet function The empirical wavelet is constructed as follows:

[0009]

[0010] Among them, ω n is the boundary of each Fourier spectrum, and each part is [ω n-1 ,ω n ] interval separation, where ω0=0, ω n =π, T n =2τ n =2γ×ω n is the transition phase, where γ is the coefficient to ensure that there is no overlap between transition phases, and its value range is [0, min n ((ω n+1 -ω n ) / (ω n+1 +ω n ))], the arbitrary function β(x) adopts a common form, which is expressed as: β(x) = x 4 (35-84x+70x 2 -20x 3 )x∈[0,1]; the acoustic emission signal of the target structure under test is decomposed into N amplitude-frequency modulation single-component signal modal functions x i (t), the decomposed signal is reconstructed to obtain the characteristic mode component with a preset type of energy distribution as follows:

[0011]

[0012] Among them, * represents the convolution operation, the detail coefficient W x (i, t) and the approximation coefficient W x (0, t) is obtained by the inner product operation of the signal with the empirical scaling function and the empirical wavelet function, as follows:

[0013]

[0014] Among them, F -1 is the inverse Fourier transform, is the Fourier transform, represents complex conjugate;

[0015] S202: For the characteristic mode component obtained in step S201, the accurate arrival time is extracted using the Akaike information criterion function, and the accurate arrival time of the acoustic emission signal is as follows:

[0016] AIC(t)=tlog10 (R(1,t))+(Tt-1)log 10 (R(t+1,T)),

[0017] Among them, R(1,t) is the variance of the time series from 1 to t. When the value of the Akaike information criterion function reaches the minimum value, it is considered to be the accurate arrival time of the corresponding acoustic emission signal;

[0018] S203, calculating the acoustic emission arrival time difference based on the time when the acoustic emission signal accurately reaches its corresponding sensor, and extracting the acoustic emission event based on the geometric relationship of the acoustic emission sensor array corresponding to the acoustic emission signal, as shown in the following formula:

[0019]

[0020] in, is an acoustic emission signal occurring at time t and the wave propagation speed v p , where (x0, y0, z0) is the position coordinate of the acoustic emission source, t i (i=1,2,…,n) is the time when the acoustic emission signal reaches the i-th sensor, d j is the distance from the acoustic emission source to the jth sensor, (x i ,y i ,z i ) The position coordinates of the i-th sensor;

[0021] S204. Based on the time difference positioning principle, an objective function of the acoustic emission source position coordinates is constructed, and a genetic algorithm with global search capability is introduced to optimize the objective function. The objective function E(x0, y0, z0) is as follows:

[0022] E(x0,y0,z0)=[(t i -t j )-(d i -d j ) / v p ] 2 ,

[0023] Among them, the value of E(x0, y0, z0) is only related to the coordinates of the acoustic emission source position. The estimated coordinates that make E(x0, y0, z0) obtain the minimum value are the coordinates of the acoustic emission source. j , is the distance from the acoustic emission source to the jth sensor, t j is the time when the acoustic emission signal reaches the jth sensor.

[0024] S3. Based on the precise location of the acoustic emission source when the target structure to be tested is damaged, a hybrid hierarchical-K-means clustering analysis is performed to obtain the dense crack locations of each damage area, which specifically includes the following sub-steps: S301. Using hierarchical clustering to accurately locate and identify the acoustic emission when the target structure to be tested is damaged, the shortest distance is obtained as the corresponding damage area, as shown in the following formula:

[0025]

[0026] Among them, u and v represent different data sets, dist(u i -v j ) represents data u i With v j the distance between them;

[0027] S302, using K-means clustering to perform fine identification of dense cracks in the damaged area, and obtain the dense crack positions of each damaged area, as shown in the following function, until the following function reaches a minimum:

[0028]

[0029] in For the data set, The Kth center, w is the iteration step.

[0030] S4. Based on the results of cluster analysis, regression analysis is performed to obtain the size of dense cracks in each damage area.

[0031] Furthermore, in the aforementioned step S1, the acoustic emission signals of the damaged area of the target structure to be measured are collected by an acoustic emission sensor array that satisfies a three-dimensional spatial arrangement.

[0032] Another aspect of the present invention provides a system for quantitatively identifying and visualizing structural damage acoustic emissions, comprising:

[0033] The signal acquisition module is configured to perform the following actions: collecting acoustic emission signals when the target structure to be tested is damaged;

[0034] The test area positioning module is configured to perform the following actions: calculate the arrival time of the acoustic emission signal, extract the acoustic emission event, construct and optimize the objective function of the acoustic emission source position coordinates, and obtain the accurate positioning of the acoustic emission source when the target test structure is damaged; specifically perform the following sub-steps: S201, according to the acoustic emission signal x(t) when the target test structure is damaged, it is N natural mode functions x i (t)(i=1,2,...,N), the Fourier spectrum of the signal is divided into N parts containing independent intrinsic mode functions, according to the empirical scaling function and empirical wavelet function The empirical wavelet is constructed as follows:

[0035]

[0036] Among them, ω n is the boundary of each Fourier spectrum, and each part is [ω n-1 ,ω n ] interval separation, where ω0=0, ω n =π, T n =2τ n =2γ×ω n is the transition phase, where γ is the coefficient to ensure that there is no overlap between transition phases, and its value range is [0, min n ((ω n+1 -ω n ) / (ω n+1 +ω n ))], the arbitrary function β(x) adopts a common form, which is expressed as: β(x) = x 4 (35-84x+70x 2 -20x 3 )x∈[0,1]; the acoustic emission signal of the target structure under test is decomposed into N amplitude-frequency modulation single-component signal modal functions x i (t), the decomposed signal is reconstructed to obtain the characteristic mode component with a preset type of energy distribution as follows:

[0037]

[0038] Among them, * represents the convolution operation, the detail coefficient W x (i, t) and the approximation coefficient W x (0, t) is obtained by the inner product operation of the signal with the empirical scaling function and the empirical wavelet function, as follows:

[0039]

[0040] Among them, F -1 is the inverse Fourier transform, is the Fourier transform, represents complex conjugate;

[0041] S202: For the characteristic mode component obtained in step S201, the accurate arrival time is extracted using the Akaike information criterion function, and the accurate arrival time of the acoustic emission signal is as follows:

[0042] AIC(t)=tlog 10 (R(1,t))+(Tt-1)log 10 (R(t+1,T)),

[0043] Among them, R(1,t) is the variance of the time series from 1 to t. When the value of the Akaike information criterion function reaches the minimum value, it is considered to be the accurate arrival time of the corresponding acoustic emission signal;

[0044] S203, calculating the acoustic emission arrival time difference based on the time when the acoustic emission signal accurately reaches its corresponding sensor, and extracting the acoustic emission event based on the geometric relationship of the acoustic emission sensor array corresponding to the acoustic emission signal, as shown in the following formula:

[0045]

[0046] in, is an acoustic emission signal occurring at time t and the wave propagation speed v p , where (x0, y0, z0) is the position coordinate of the acoustic emission source, t i (i=1,2,…,n) is the time when the acoustic emission signal reaches the i-th sensor, d j is the distance from the acoustic emission source to the jth sensor, (x i ,y i ,z i ) The position coordinates of the i-th sensor;

[0047] S204. Based on the time difference positioning principle, an objective function of the acoustic emission source position coordinates is constructed, and a genetic algorithm with global search capability is introduced to optimize the objective function. The objective function E(x0, y0, z0) is as follows:

[0048] E(x0,y0,z0)=[(t i -t j )-(d i -d j ) / v p ] 2 ,

[0049] Among them, the value of E(x0, y0, z0) is only related to the coordinates of the acoustic emission source position. The estimated coordinates that make E(x0, y0, z0) obtain the minimum value are the coordinates of the acoustic emission source. j , is the distance from the acoustic emission source to the jth sensor, t j is the time when the acoustic emission signal reaches the jth sensor.

[0050] The precise location acquisition module is used to configure and perform the following actions: based on the precise location of the acoustic emission source when the target structure is damaged, a hybrid hierarchical-K-means clustering analysis is performed to obtain the dense crack locations in each damage area;

[0051] The analysis and display module is configured to perform the following actions: based on the results of the cluster analysis, regression analysis is performed to obtain the size of the dense cracks in each damage area; specifically, the following sub-steps are performed: S301, using hierarchical clustering to accurately locate and identify the acoustic emission when the target structure is damaged, and obtain its shortest distance as the corresponding damage area, as shown in the following formula:

[0052]

[0053] Among them, u and v represent different data sets, dist(u i -v j ) represents data u i With v j the distance between them;

[0054] S302, using K-means clustering to perform fine identification of dense cracks in the damaged area, and obtain the dense crack positions of each damaged area, as shown in the following function, until the following function reaches a minimum:

[0055]

[0056] in For the data set, The Kth center, w is the iteration step.

[0057] The visualization module is based on the size display of dense cracks in each damage area and is used to configure and perform the following actions: using 3D modeling to depict the damage source and damage surface, identifying the damage surface and evaluating its size, and displaying the structural damage.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] (1) The present invention is a method for quantitative identification and visualization of structural damage by acoustic emission, which is suitable for quantitative identification of damage in large-scale engineering structures with complex three-dimensional structures, such as steel-concrete composite beams and reinforced concrete beams. Three-dimensional acoustic emission source positioning is the basis for quantitative damage identification. By installing an acoustic emission sensor array arranged in three dimensions on the surface of the structure, the acoustic emission signal released when the structure is damaged is collected, and the time when the acoustic emission signal reaches each sensor is determined, the objective function is constructed and optimized to obtain the damage location of the structure. The method of the present invention can reduce the influence of environmental noise, dispersion phenomenon, propagation path, etc. on the accuracy of damage location during the propagation of the acoustic emission signal.

[0060] (2) The present invention is a method for quantitative identification and visualization of acoustic emission of structural damage. Based on the three-dimensional acoustic emission source positioning, a hybrid unsupervised clustering method is used to identify structural damage, evaluate the location and size of the damage surface, and visualize the damage identification and quantification results using three-dimensional modeling. Based on the visualization results, the degree of structural damage can be intuitively evaluated, providing a scientific basis for timely maintenance and repair of the structure, thereby avoiding structural damage.

[0061] (3) The present invention provides a method for quantitatively identifying and visualizing acoustic emission of structural damage. It utilizes empirical wavelet transform to adaptively decompose the original acoustic emission signal, selects the modal components where the energy is primarily concentrated, and uses the Akaike Information Criterion to determine the arrival time. It also utilizes a genetic algorithm to optimize the objective function, effectively avoiding local minima during the optimization process. Therefore, the method is less susceptible to various factors, such as material inhomogeneity, existing damage, acoustic emission signal propagation dispersion characteristics, and environmental noise, making the damage identification and quantification effects of the present invention more stable in the actual operating environment of engineering structures.

[0062] (4) The present invention provides a method for quantitatively identifying and visualizing acoustic emission signals from structural damage. Hierarchical clustering is first used to identify structural damage regions, separating acoustic emission events from areas with dense crack distribution. K-means clustering data sets are constrained, and the number of K-means clusters is determined based on the elbow method. K-means clustering is then used to precisely identify the locations of multiple cracks in different damage regions. This hybrid clustering method can effectively identify structural damage, maintaining high accuracy and robustness while maintaining low signal utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 Flowchart of the present invention.

[0064] Figure 2 This is a flow chart of the acoustic emission localization method based on empirical wavelet transform-Akaike information criterion-genetic algorithm.

[0065] Figure 3 Dimensional drawing of steel-concrete composite beam specimen.

[0066] Figure 4 This is the inverted four-point bending test diagram of the steel-concrete composite beam.

[0067] Figure 5 Damage distribution diagram of the negative bending moment zone of the steel-concrete composite beam. (a) is the damage distribution diagram of specimen 1, and (b) is the damage distribution diagram of specimen 2.

[0068] Figure 6 This is an example diagram of a typical acoustic emission signal.

[0069] Figure 7 This is the empirical wavelet transform component diagram of the acoustic emission signal example.

[0070] Figure 8 Arrival time graph determined by Akaike Information Criterion.

[0071] Figure 9 It is a hierarchical clustering tree diagram, where (a) is the clustering tree diagram of specimen 1, and (b) is the clustering tree diagram of specimen 2.

[0072] Figure 10 Figure 2 is the cluster number diagram determined by the elbow method. (a) is the cluster number diagram determined by the elbow method for specimen 1, and (b) is the cluster number diagram determined by the elbow method for specimen 2.

[0073] Figure 11 Figure 2 is a hybrid cluster analysis result diagram, in which (a) is the hybrid cluster analysis result diagram of specimen 1, and (b) is the hybrid cluster analysis result diagram of specimen 2.

[0074] Figure 12 The visualization diagram of the damage quantitative identification results, (a) is the visualization diagram of specimen 1, and (b) is the visualization diagram of specimen 2. DETAILED DESCRIPTION

[0075] In order to better understand the technical content of the present invention, specific embodiments are given and described below with reference to the accompanying drawings.

[0076] Various aspects of the present invention are described herein with reference to the accompanying drawings, which show a number of illustrative embodiments. The embodiments of the present invention are not limited to those described in the accompanying drawings. It should be understood that the present invention can be implemented by any of the various concepts and embodiments described above, as well as the concepts and implementations described in detail below, because the concepts and embodiments disclosed herein are not limited to any particular implementation. In addition, some aspects disclosed herein may be used alone or in any appropriate combination with other aspects disclosed herein.

[0077] like Figure 1 As shown, a method for quantitatively identifying structural damage by acoustic emission includes the following steps: S1, collecting acoustic emission signals when the target structure to be tested is damaged, specifically as follows:

[0078] During implementation, an array of four or more acoustic emission sensors arranged in a three-dimensional space is used to collect acoustic emission signals from the damaged area of the target structure. The number and array of acoustic emission sensors are determined based on the characteristics of the structure to be tested, and the sensor installation quality is verified through lead breaking tests. A Cartesian coordinate system is established by selecting an appropriate location within the structure to be tested, such as the center of the area as the origin. The sensors are numbered and their coordinates are clearly defined to ensure that no four sensors are arranged on the same plane.

[0079] S2. Calculate the arrival time of the acoustic emission signal, extract the acoustic emission event, construct and optimize the objective function of the acoustic emission source position coordinates, and obtain the accurate positioning of the acoustic emission source when the target structure is damaged; the acoustic emission positioning process is as follows: Figure 2 As shown, specifically steps S201 to S204:

[0080] S201. Considering the influence of various factors such as environmental noise, dispersion caused by material inhomogeneity, and propagation path during the propagation of acoustic emission signals, an empirical wavelet transform method is used to adaptively extract characteristic modal components.

[0081] The acoustic emission signal x(t) is composed of N natural mode functions x i (t)(i=1,2,...,N), the Fourier spectrum of the signal is divided into N parts containing independent intrinsic mode functions. According to the empirical scaling function and empirical wavelet function The empirical wavelet is constructed as follows:

[0082]

[0083]

[0084] Among them, ω n is the boundary of each Fourier spectrum, and each part is [ω n-1 ,ω n ] interval separation, where ω0=0, ω n =π, T n =2τ n =2γ×ω n is the transition phase, where γ is the coefficient to ensure that there is no overlap between transition phases, and its value range is [0, min n ((ω n+1 -ω n ) / (ω n+1 +ω n ))], the arbitrary function β(x) adopts a common form, which is expressed as: β(x) = x 4 (35-84x+70x 2 -20x 3 )x∈[0,1];

[0085] After constructing a suitable wavelet filter bank, the structural damage acoustic emission signal is decomposed into N amplitude-frequency modulation single component signal modal functions x i (t), the decomposed signal is reconstructed to obtain the characteristic mode component with a preset type of energy distribution as follows:

[0086]

[0087] Where * represents the convolution operation, the detail coefficient W x (i, t) and the approximation coefficient W x (0, t) is obtained by the inner product operation of the signal with the empirical scaling function and the empirical wavelet function, as follows:

[0088]

[0089] Where, F -1 is the inverse Fourier transform, is the Fourier transform, represents the complex conjugate.

[0090] S202. For the characteristic mode component obtained in step S201, use the Akaike information criterion to extract its exact arrival time as the accurate arrival time of the acoustic emission signal. The acoustic emission signal is a time series signal. Therefore, the arrival time of the characteristic mode component can be determined by using the Akaike information criterion as the accurate time when the acoustic emission signal emitted when the structure is damaged reaches the acoustic emission sensor. The formula for calculating the function value of the Akaike information criterion is as follows:

[0091] AIC(t)=tlog 10 (R(1,t))+(Tt-1)log 10 (R(t+1,T)),

[0092] Where R(1,t) is the variance of the time series from 1 to t. When the Akaike Information Criterion function value reaches the minimum value, it is considered to correspond to the accurate arrival time of the acoustic emission signal.

[0093] S203, according to an acoustic emission signal occurring at time t and the wave propagation speed v p , the relationship: and the accurate arrival time of the acoustic emission signal calculates the acoustic emission arrival time difference, and extracts the acoustic emission event according to the geometric relationship of the acoustic emission sensor array corresponding to the acoustic emission signal. Specifically: for its coordinates (x0, y0, z0), the time when the signal arrives at the i-th sensor is t i (i=1,2,…,n), and the coordinates of the i-th sensor are known, and the wave propagation speed is v p , (x0, y0, z0) is the position coordinate of the acoustic emission source, d j is the distance from the acoustic emission source to the jth sensor, (x i ,y i ,z i ) The position coordinates of the i-th sensor;

[0094] Then we can get the following relationship:

[0095]

[0096] The distance difference between each pair of sensor groups and the acoustic emission source is as follows:

[0097]

[0098] S204. Construct an objective function for the position coordinates of the acoustic emission source based on the time difference positioning method, and introduce a genetic algorithm with global search capability to optimize the objective function, as follows:

[0099] Construct the error objective function E(x0,y0,z0) based on time difference as follows:

[0100] E(x0,y0,z0)=[(t i -t j )-(d i -d j ) / v p ] 2 ,

[0101] The value of E(x0, y0, z0) is only related to the coordinates of the acoustic emission source. Under ideal conditions, the coordinates that make the error function E(x0, y0, z0) equal to 0 are the coordinates of the acoustic emission source. In practical applications, the estimated coordinates that make E(x0, y0, z0) take the minimum value are the reasonable coordinates of the acoustic emission source. The acoustic emission source positioning problem is transformed into an error function optimization problem, and the genetic algorithm is used to optimize the objective function. j , is the distance from the acoustic emission source to the jth sensor, t j is the time when the acoustic emission signal reaches the jth sensor.

[0102] S3. Based on the precise location of the acoustic emission source when the target structure is damaged, a hybrid hierarchical-K-means cluster analysis is performed to obtain the dense crack locations in each damage area;

[0103] S301. Use hierarchical clustering to accurately locate and identify the acoustic emission of the target structure when it is damaged, and obtain its corresponding loss area; hierarchical clustering calculates a hierarchical tree based on the correlation coefficient between the data, and divides the data at different levels, without having to determine the number of clusters in advance. In the present invention, the data set is divided through a bottom-up aggregation strategy. The key to hierarchical clustering is to calculate the distance between clusters, such as the shortest distance, the longest distance, and the average distance. For different data sets u and v, the shortest distance used in the present invention is expressed as follows,

[0104]

[0105] Among them, dist(u i -v j ) represents data u i With v jThe distance between them.

[0106] S302. Use K-means clustering to finely identify dense cracks in the damaged area and obtain the dense crack locations of each damaged area. K-means clustering is more suitable for large amounts of dense data. The number of clusters is determined by the elbow method. After initializing K centers, the data is divided into each cluster based on the minimum distance between each sample and the center of the class to which it belongs, until the following function reaches a minimum:

[0107]

[0108] in For the data set, The Kth center, w is the iteration step.

[0109] S4. Based on the results of cluster analysis, regression analysis is performed to obtain the size of dense cracks in each damage area.

[0110] The specific implementation of the present invention is further illustrated below with reference to a specific engineering example.

[0111] (1) The main content of this embodiment is the identification and quantification of acoustic emission damage in the negative bending moment zone of steel-concrete, which is used to verify the effectiveness and practicality of the quantitative identification method of structural damage proposed in this patent. The experimental equipment used is a multi-channel acoustic emission data acquisition system. The acoustic emission sensor is an R15 model sensor with a resonant frequency of 150kHz. Eight acoustic emission sensors are installed on the surface of the composite beam concrete slab using a magnetic clamp and a coupling agent. Then, they are connected to the data acquisition board through a preamplifier for acoustic emission data acquisition. The amplification factor is set to 40dB, the acquisition threshold is set to 35dB, and the sampling rate is set to 2MS / s.

[0112] (2) This embodiment is an inverted four-point bending test of a steel-concrete composite beam under laboratory conditions. The dimensions of the steel-concrete composite beam four-point bending test specimen are as follows: Figure 3As shown, the H-shaped steel beam is welded from three steel plates. The overall length of the H-shaped steel beam is 2500 mm, the height is 240 mm, and the upper and lower flanges and web are 8 mm thick. Two rows of large-head studs are welded to the flanges of the steel plates, with a longitudinal spacing of 100 mm and a transverse spacing of 120 mm. The difference between the two specimens is that the shear studs have diameters of 10 mm and 13 mm, respectively, and a height of 80 mm. The reinforced concrete slab is 110 mm thick and 500 mm wide, the same length as the steel beam. The longitudinal and transverse steel bars are both 8 mm in diameter and installed at the top and bottom layers of the concrete slab, respectively. The longitudinal and transverse spacing of the steel bars is 100 mm. Given the high attenuation of acoustic emission waves propagating in the concrete slab and the structural symmetry of the composite beam, the coverage area of the acoustic emission sensor array is only half of the entire beam. Establish a coordinate system with the center of the bottom surface of the concrete slab as the origin. The coordinate unit is cm. The coordinates of the eight sensors are: S1 (25, 0, 3), S2 (25, 100, 8), S3 (-25, 0, 8), S4 (-25, 100, 3), S5 (15, 20, 0), S6 (-15, 80, 0), S7 (15, 80, 11), S8 (-15, 20, 11). Load the settings as follows Figure 4 The loading process is divided into 7 stages. The initial load is applied from 0kN to 40kN, and then increases by 20kN in each subsequent stage. This experiment only focuses on the damage in the negative bending moment zone of the steel-concrete composite beam. A four-point bending non-destructive test is conducted to determine the final load of 160kN. The structural stiffness degradation is caused by the cracking of the concrete slab. The steel beam is far from reaching the yield stress. The actual damage distribution in the negative bending moment zone is shown in the figure. Figure 5 As shown, Figure 5 (a) Figure 5 (b) Macroscopic dense cracks appeared in the negative bending moment zone of specimens 1 and 2. In addition, specimen 1 with a stud diameter of 10 mm exhibited interfacial debonding damage at the end, while specimen 2 with a stud diameter of 13 mm produced short cracks at the end.

[0113] (3) Figure 6 Taking the acoustic emission signal sample collected as an example, the empirical wavelet transform is used to adaptively extract the characteristic modal components of the typical acoustic emission signal. The results are shown in Figure 7 As shown in the figure, the acoustic emission signal is adaptively decomposed into four modal components through empirical wavelet transform. The frequency bands of the four components are 0-90kHz, 90-256kHz, 256-478kHz, and 478-1000kHz. The modal component with concentrated energy distribution is selected and the arrival time is calculated using the Akaike Information Criterion. Figure 8 Pick up for Akaike Information Criterion Figure 7The arrival time of the second component of the empirical wavelet transform of the acoustic emission signal example, the dotted line corresponds to the minimum value of the AIC value, and the horizontal axis corresponding to the dotted line is the arrival time of the acoustic emission signal. After picking up the precise arrival time, the error function based on the time difference is optimized by the genetic algorithm to calculate the position coordinates of the acoustic emission source. The acoustic emission source positioning process is as follows: Figure 2 shown.

[0114] (4) Based on the acoustic emission positioning results, the hybrid hierarchical-K-means clustering method is adopted. Hierarchical clustering is first used to identify different damage areas. The clustering tree of specimen 1 is as follows: Figure 9 (a) shows the clustering tree of specimen 2. Figure 9 (b) As shown; K-means clustering is then used to finely identify the densely distributed damage area, which can effectively avoid the interference of local damage area data on the cluster analysis of dense damage area data, thereby accurately identifying and quantifying the damage. The number of K-means clusters is determined by the elbow method. Specimen 1 is shown in Figure 10 (a) is shown. Figure 10 (b) is shown. The mixed cluster analysis results of specimen 1 are shown in Figure 11 As shown in (a), the mixed cluster analysis results of specimen 2 are as follows Figure 11 (b) The final identification and quantification results of specimen 1 are shown in the three-dimensional model. Figure 12 As shown in (a), the final identification and quantification results of specimen 2 are shown in the three-dimensional model. Figure 12 As shown in (b), the scattered points are the locations of the acoustic emission sources, the black lines are the actual distribution of cracks on the structure surface, and the gray translucent surface is the quantitative identification result of cracks. This verifies that the method proposed in the present invention can effectively and quantitatively identify different damaged areas of the structure, including the dense cracking area in the negative bending moment zone of the composite beam and the debonding area of the end interface, and can quantitatively identify the number and location of cracks in the densely damaged area. The quantitative identification results are then intuitively displayed through three-dimensional modeling, providing a scientific basis for timely maintenance and repair of the structure.

[0115] While the present invention has been described above with reference to preferred embodiments, this is not intended to limit the present invention. Persons skilled in the art will readily appreciate that various modifications and variations can be made without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for quantitative identification of structural damage by acoustic emission, characterized in that: The steps include: S1. Collecting acoustic emission signals when the target structure is damaged; S2. Calculate the arrival time of the acoustic emission signal, extract the acoustic emission event, construct and optimize the objective function of the acoustic emission source position coordinates, and obtain the accurate positioning of the acoustic emission source when the target structure is damaged. This specifically includes the following sub-steps: S201, according to the acoustic emission signal x(t) when the target structure is damaged, N natural mode functions x i (t)(i=1,2,...,N), the Fourier spectrum of the signal is divided into N parts containing independent intrinsic mode functions, according to the empirical scaling function and empirical wavelet function The empirical wavelet is constructed as follows: Among them, ω n is the boundary of each Fourier spectrum, and each part is [ω n-1 ,ω n ] interval separation, where ω0=0, ω n =π, T n =2τ n =2γ×ω n is the transition phase, where γ is the coefficient to ensure that there is no overlap between transition phases, and its value range is [0, min n ((ω n+1 -ω n ) / (ω n+1 +ω n ))], the arbitrary function β(x) adopts a common form, which is expressed as: β(x) = x 4 (35-84x+70x 2 -20x 3 )x∈[0,1]; the acoustic emission signal of the target structure under test is decomposed into N amplitude-frequency modulation single-component signal modal functions x i (t), the decomposed signal is reconstructed to obtain the characteristic mode component with a preset type of energy distribution as follows: Among them, * represents the convolution operation, the detail coefficient W x (i, t) and the approximation coefficient W x (0, t) is obtained by the inner product operation of the signal with the empirical scaling function and the empirical wavelet function, as follows: Among them, F -1 is the inverse Fourier transform, is the Fourier transform, represents complex conjugate; S202: For the characteristic mode component obtained in step S201, the accurate arrival time is extracted using the Akaike information criterion function, and the accurate arrival time of the acoustic emission signal is as follows: AIC(t)=tlog 10 (R(1,t))+(T-t-1)log 10 (R(t+1,T)), Among them, R(1,t) is the variance of the time series from 1 to t. When the value of the Akaike information criterion function reaches the minimum value, it is considered to be the accurate arrival time of the corresponding acoustic emission signal; S203, calculating the acoustic emission arrival time difference based on the time when the acoustic emission signal accurately reaches its corresponding sensor, and extracting the acoustic emission event based on the geometric relationship of the acoustic emission sensor array corresponding to the acoustic emission signal, as shown in the following formula: in, is an acoustic emission signal occurring at time t and the wave propagation speed v p , where (x0, y0, z0) is the position coordinate of the acoustic emission source, t i (i=1,2,…,n) is the time when the acoustic emission signal reaches the i-th sensor, d j is the distance from the acoustic emission source to the jth sensor, (x i ,y i ,z i ) The position coordinates of the i-th sensor; S204. Based on the time difference positioning principle, an objective function of the acoustic emission source position coordinates is constructed, and a genetic algorithm with global search capability is introduced to optimize the objective function. The objective function E(x0, y0, z0) is as follows: E(x0,y0,z0)=[(t i -t j )-(d i -d j ) / v p ] 2 , Among them, the value of E(x0, y0, z0) is only related to the coordinates of the acoustic emission source position. The estimated coordinates that make E(x0, y0, z0) obtain the minimum value are the coordinates of the acoustic emission source. j , is the distance from the acoustic emission source to the jth sensor, t j is the time when the acoustic emission signal reaches the jth sensor; S3. Based on the precise location of the acoustic emission source when the target structure is damaged, a hybrid hierarchical-K-means cluster analysis is performed to obtain the locations of dense cracks in each damaged area. This specifically includes the following sub-steps: S301. Use hierarchical clustering to accurately locate and identify the acoustic emission of the target structure when it is damaged, and obtain its shortest distance as the corresponding damage area, as shown in the following formula: Among them, u and v represent different data sets, dist(u i -v j ) represents data u i With v j the distance between them; S302, using K-means clustering to perform fine identification of dense cracks in the damaged area, and obtain the dense crack positions of each damaged area, as shown in the following function, until the following function reaches a minimum: in For the data set, The Kth center, w is the iteration step; S4. Based on the results of cluster analysis, regression analysis is performed to obtain the size of dense cracks in each damage area.

2. A method for quantitative identification of structural damage by acoustic emission according to claim 1, characterized in that: In step S1, an acoustic emission sensor array arranged in three dimensions is used to collect acoustic emission signals from the damaged area of the target structure to be measured.

3. A quantitative identification and visualization system for acoustic emission of structural damage, characterized in that: include: The signal acquisition module is configured to perform the following actions: collecting acoustic emission signals when the target structure to be tested is damaged; The module for positioning the area to be tested is configured to perform the following actions: calculate the arrival time of the acoustic emission signal, extract the acoustic emission event, construct and optimize the objective function of the acoustic emission source position coordinates, and obtain the accurate positioning of the acoustic emission source when the target structure to be tested is damaged; specifically, the following sub-steps are performed: S201, according to the acoustic emission signal x(t) when the target structure to be tested is damaged, N natural mode functions x i (t)(i=1,2,...,N), the Fourier spectrum of the signal is divided into N parts containing independent intrinsic mode functions, according to the empirical scaling function and empirical wavelet function The empirical wavelet is constructed as follows: Among them, ω n is the boundary of each Fourier spectrum, and each part is [ω n-1 ,ω n ] interval separation, where ω0=0, ω n =π, T n =2τ n =2γ×ω n is the transition phase, where γ is the coefficient to ensure that there is no overlap between transition phases, and its value range is [0, min n ((ω n+1 -ω n ) / (ω n+1 +ω n ))], the arbitrary function β(x) adopts a common form, which is expressed as: β(x) = x 4 (35-84x+70x 2 -20x 3 )x∈[0,1]; the acoustic emission signal of the target structure under test is decomposed into N amplitude-frequency modulation single-component signal modal functions x i (t), the decomposed signal is reconstructed to obtain the characteristic mode component with a preset type of energy distribution as follows: Among them, * represents the convolution operation, the detail coefficient W x (i, t) and the approximation coefficient W x (0, t) is obtained by the inner product operation of the signal with the empirical scaling function and the empirical wavelet function, as follows: Among them, F -1 is the inverse Fourier transform, is the Fourier transform, represents complex conjugate; S202: For the characteristic mode component obtained in step S201, the accurate arrival time is extracted using the Akaike information criterion function, and the accurate arrival time of the acoustic emission signal is as follows: AIC(t)=tlog 10 (R(1,t))+(T-t-1)log 10 (R(t+1,T)), Among them, R(1,t) is the variance of the time series from 1 to t. When the value of the Akaike information criterion function reaches the minimum value, it is considered to be the accurate arrival time of the corresponding acoustic emission signal; S203, calculating the acoustic emission arrival time difference based on the time when the acoustic emission signal accurately reaches its corresponding sensor, and extracting the acoustic emission event based on the geometric relationship of the acoustic emission sensor array corresponding to the acoustic emission signal, as shown in the following formula: in, is an acoustic emission signal occurring at time t and the wave propagation speed v p , where (x0, y0, z0) is the position coordinate of the acoustic emission source, t i (i=1,2,…,n) is the time when the acoustic emission signal reaches the i-th sensor, d j is the distance from the acoustic emission source to the jth sensor, (x i ,y i ,z i ) The position coordinates of the i-th sensor; S204. Based on the time difference positioning principle, an objective function of the acoustic emission source position coordinates is constructed, and a genetic algorithm with global search capability is introduced to optimize the objective function. The objective function E(x0, y0, z0) is as follows: E(x0,y0,z0)=[(t i -t j )-(d i -d j ) / v p ] 2 , Among them, the value of E(x0, y0, z0) is only related to the coordinates of the acoustic emission source position. The estimated coordinates that make E(x0, y0, z0) obtain the minimum value are the coordinates of the acoustic emission source. j , is the distance from the acoustic emission source to the jth sensor, t j is the time when the acoustic emission signal reaches the jth sensor; The precise location acquisition module is used to configure and perform the following actions: based on the precise location of the acoustic emission source when the target structure is damaged, a hybrid hierarchical-K-means clustering analysis is performed to obtain the dense crack locations in each damage area; The analysis and display module is used to configure and execute the following actions: based on the results of cluster analysis, regression analysis is performed to obtain the size of dense cracks in each damage area; specifically, the following steps are performed: S301. Use hierarchical clustering to accurately locate and identify the acoustic emission of the target structure when it is damaged, and obtain its shortest distance as the corresponding damage area, as shown in the following formula: Among them, u and v represent different data sets, dist(u i -v j ) represents data u i With v j the distance between them; S302, using K-means clustering to perform fine identification of dense cracks in the damaged area, and obtain the dense crack positions of each damaged area, as shown in the following function, until the following function reaches a minimum: in For the data set, The Kth center, w is the iteration step; The visualization module is based on the size display of dense cracks in each damage area and is used to configure and perform the following actions: using 3D modeling to depict the damage source and damage surface, identifying the damage surface and evaluating its size, and displaying the structural damage.

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