A multi-point accurate two-dimensional imaging method based on impact echo data

Abstract

The application discloses a kind of multi-point accurate two-dimensional imaging methods based on impact echo data, applied to nondestructive testing technical field, comprising: according to the principle of impact echo method acquisition multi-section impact echo signal;The cross section of the component to be measured is divided into uniform grid unit, and the theoretical resonance frequency of impact echo signal in each grid unit is calculated;The impact echo signal is subjected to fast Fourier transform, a frequency spectrum is generated, and is matched with the theoretical resonance frequency, to generate a plurality of defect intensity matrix same as the number of impact echo signal;Intensity superposition is carried out on the plurality of defect intensity matrix, to synthesize composite imaging matrix, and can be visualized as defect detection image, and defect detection evaluation is carried out.The application introduces multi-point superposition matrix array, enhances the traditional single-point imaging method.This optimization can reconstruct the shape of defect and improve the accuracy of depth detection, and enhances the defect positioning ability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of non-destructive testing, and particularly relates to a multi-point accurate two-dimensional imaging method based on impact echo data. BACKGROUND

[0002] Hidden defects in concrete members or joints can locally destabilize or prematurely deteriorate under adverse weather conditions or earthquakes, which can lead to significant casualties and economic losses. Non-destructive testing (NDT) is widely used to assess the material properties of existing or historical structures, minimizing or eliminating the need for destructive testing to obtain information about internal defects. The impact echo (IE) method provides a non-invasive, single-sided testing method that balances ease of operation, wide applicability, and short testing periods. It uses low-frequency stress waves to detect deeper defects, which is particularly valuable for on-site assessments of bridges, tunnels, or walls. However, the detection accuracy of this method is limited and needs to be further improved.

[0003] Impact echo signals are typically analyzed by Fourier transform (FFT) to extract frequency domain information, which determines defect depth by identifying peaks in the frequency domain image. However, the propagation of stress waves is influenced by various factors, with complex variability. For example, the multi-layer structure and geometric effects of the component can produce multiple peaks in the frequency domain image, making it difficult to isolate relevant defect frequency information. A common method to reduce interference is to combine wavelet transform with Fourier transform to extract time-frequency information. This method introduces a time factor to filter out interfering waves, enhancing the identification of characteristic frequencies and is widely used to analyze transient and unstable signals. Although these techniques improve the extraction of characteristic frequencies, improving accuracy and identification, the final representation of defect information is still not intuitive and usually requires interpretation by experts with specialized knowledge.

[0004] Early researchers proposed a single-point imaging method based on impact echo signals, which visually detects the location of defects through planar imaging. However, single-point imaging typically depicts defects as ring-shaped dark blocks with different curvatures. This phenomenon makes it difficult to accurately and effectively depict the shape of the interface defect or the width of the ring.

[0005] Therefore, how to provide a multi-point accurate two-dimensional imaging method based on impact echo data that can effectively solve the problems existing in the above-mentioned single-point imaging method, optimize the shape of the reconstructed defect, improve the accuracy of depth detection, and enhance the defect positioning capability is a problem that those skilled in the art need to solve. SUMMARY

[0006] In view of this, the present application provides a multi-point accurate two-dimensional imaging method based on impact echo data, aiming to solve the problems of low positioning accuracy and inaccurate imaging of concrete structure defects. By introducing a multi-point superposition matrix array, the traditional single-point imaging method is enhanced. This optimization can reconstruct the shape of the defect and improve the depth detection accuracy, and enhance the defect positioning capability.

[0007] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0008] A multi-point accurate two-dimensional imaging method based on impact echo data, comprising:

[0009] Step 1: Collecting multiple impact echo signals according to the principle of impact echo method;

[0010] Step 2: Dividing the cross section of the measured member into uniform grid cells, and calculating the theoretical resonance frequency of the impact echo signal in each grid cell;

[0011] Step 3: Performing fast Fourier transform on the impact echo signal to generate a frequency spectrum, and matching it with the theoretical resonance frequency to generate multiple defect intensity matrices equal in number to the impact echo signals;

[0012] Step 4: Superimposing the multiple defect intensity matrices to synthesize a composite imaging matrix, and visualizing it as a defect detection image for defect detection evaluation.

[0013] Optionally, in step 1, multiple impact echo signals are collected according to the principle of impact echo method, specifically:

[0014] Randomly arranging a measuring line on the surface of the concrete above the defect, and arranging equidistant measuring points on the side line, taking each measuring point as an impact point and the adjacent measuring point as a collection point in turn, obtaining n-1 signal segments using n measuring points according to the principle of impact echo method; wherein n is an odd number, and the signal segment is obtained by multiple continuous impacts at each measuring point and taking the mathematical expectation of multiple signals.

[0015] Optionally, after collecting multiple impact echo signals in step 1, it further comprises:

[0016] According to the steel ball impact time filtering surface wave, the multiple impact echo signals are subjected to signal denoising treatment, specifically:

[0017] Calculating the impact time and filtering out the time domain signal before the impact time; the calculation formula of the impact time is as follows:

[0018]

[0019] Wherein, is the impact time; is the diameter of the steel ball; is an empirical coefficient.

[0020] Optionally, in step 2, the maximum size of the grid cell satisfies the Nyquist sampling criterion, as follows:

[0021]

[0022] wherein, is the size of the grid cell; is the longitudinal wave velocity; is the sampling time interval.

[0023] Optionally, in step 2, the theoretical resonance frequency of the impact-echo signal in each grid cell is calculated, as follows:

[0024]

[0025] wherein, is the theoretical resonance frequency of the grid cell; is the longitudinal wave velocity; is the distance from the center of the grid cell to the impact point of the impact-echo signal; is the distance from the center of the grid cell to the collection point of the impact-echo signal.

[0026] Optionally, in step 4, the intensity superposition is performed on the plurality of defect intensity matrices to synthesize a composite imaging matrix, as follows:

[0027]

[0028] wherein, is the composite imaging matrix; is the number of impact-echo signal segments; is the defect intensity matrix of the i-th impact-echo signal segment.

[0029] Optionally, in step 4, the composite imaging matrix is visualized as a defect detection image by the jet color mapping method and the contour imaging technique.

[0030] Optionally, in step 4, the defect detection evaluation includes color threshold determination, as follows:

[0031]

[0032] wherein, is the color threshold; is a scaling factor for adjusting the color level range, ; is the number of contour levels when the initial image is generated; is a floor function for ensuring that the color level is an integer.

[0033] Optionally, in step 4, the defect detection evaluation further comprises: depth and width calculation, specifically:

[0034] For the kth color level, identify the corresponding contour in the image, detect the depth, as follows:

[0035]

[0036] wherein, is the detected depth of the kth color level; , are the maximum and minimum y coordinates of the contour corresponding to the kth color level, respectively;

[0037] The relative error of the depth is as follows:

[0038]

[0039] wherein, is the relative error of ; is the actual depth; the overall detected depth takes ; ;

[0040] Based on the same contour, estimate the defect width within the imaging range, as follows:

[0041]

[0042] wherein, is the detected width of the kth color level; , are the maximum and minimum x coordinates of the contour corresponding to the kth color level, respectively;

[0043] The relative error formula of the width is as follows:

[0044]

[0045] wherein, is the relative error of ; is the actual width; the overall detected width takes ; .

[0046] Optionally, in step 4, the defect detection evaluation further comprises: boundary processing, specifically: for a concrete slab member, if the imaging area exceeds the slab thickness, only consider the imaging results within the slab thickness range.

[0047] Compared with the prior art, the application provides a multi-point accurate two-dimensional imaging method based on impact echo data. Through multi-point detection and intensity matrix superposition technology, the efficiency of concrete structure defect detection is significantly improved. Multi-point measurement and iterative repositioning of the imaging center enhance the reflection intensity of the defect position, significantly improve the spatial resolution and imaging accuracy of the defect geometric characteristics. The intensity matrix superposition and jet color mapping technology are combined to generate high-contrast defect images, which intuitively highlight the defect boundary and provide a reliable visual basis for engineering detection. In addition, through color threshold and depth width error evaluation, quantitative analysis of the defect is realized, noise and abnormal value interference is reduced, and the robustness of the evaluation result is ensured. Overall, the application performs excellently in terms of operation simplicity and result reliability, and is suitable for non-destructive detection of various concrete structures, providing an efficient and accurate technical solution for defect detection in the engineering field. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only a part of the embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0049] Figure 1 The method flowchart of the present application.

[0050] Figure 2 The theoretical resonance frequency calculation schematic diagram of the present application.

[0051] Figure 3 The multi-point superposition principle schematic diagram of the present application.

[0052] Figure 4 The defect detection evaluation schematic diagram of the present application.

[0053] Figure 5 The defect imaging result schematic diagram of the present application embodiment 2. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0055] Embodiment 1:

[0056] Embodiment 1 of the present application discloses a multi-point accurate two-dimensional imaging method based on impact echo data, as shown in Figure 1 , comprising:

[0057] Step 1: Collecting multi-segment impact echo signals according to the principle of impact echo method.

[0058] According to the principle of impact echo method, multi-segment impact echo signals are collected, specifically:

[0059] A measuring line is randomly arranged on the surface of the concrete above the defect, and equidistant measuring points are arranged on the side line. Each measuring point is taken as an impact point in turn, and adjacent measuring points are taken as acquisition points. According to the principle of impact echo method, n-1 signal segments are obtained by using n measuring points; wherein n is an odd number, which can ensure the symmetrical distribution of signals and improve the defect positioning accuracy, and the signal segment is obtained by multiple continuous impacts at each measuring point to ensure the waveform stability, and the mathematical expectation is taken for multiple groups of signals to reduce noise interference.

[0060] Specifically, the impactor is used to generate elastic stress waves at the measuring point , and the piezoelectric sensor is placed at the measuring point . The impactor and the sensor are repositioned to the subsequent measuring points (for example, the impactor is placed at , the sensor is placed at , and so on) in turn, until the sensor reaches the measuring point , and signal segments are obtained.

[0061] The present application can effectively improve the data quality, reduce the influence of non-stable time domain signals, and ensure the reliability of subsequent signal processing and imaging by using systematic data acquisition method, multiple point continuous impact and mathematical expectation processing.

[0062] After collecting multi-segment impact echo signals, it further comprises:

[0063] According to the steel ball impact time filtering surface wave, the signal noise reduction processing is performed on the multi-segment impact echo signals, specifically:

[0064] The impact time is calculated, and the time domain signal before the impact time is filtered out; the calculation formula of the impact time is as follows:

[0065]

[0066] Wherein, is the impact time; is the diameter of the steel ball; is an empirical coefficient, which is about 0.0043 s / m.

[0067] Step 2: Divide the cross-section of the component under test into uniform grid cells and calculate the theoretical resonant frequency of the impact echo signal in each grid cell.

[0068] The maximum size of the grid cells satisfies the Nyquist sampling criterion, ensuring that the imaging resolution meets the theoretical requirements of signal sampling, avoiding aliasing, and improving the characterization accuracy of defect spatial features, as follows:

[0069]

[0070] in, The size of the grid cell; For longitudinal wave velocity; This is the sampling time interval to ensure accurate imaging.

[0071] like Figure 2 As shown, the theoretical resonant frequency of the impact echo signal in each grid cell is calculated as follows:

[0072]

[0073] in, The theoretical resonant frequency of the grid cell; For longitudinal wave velocity; This is the distance from the center of the grid cell to the impact point of the impact echo signal; This is the distance from the center of the grid cell to the acquisition point of the impact echo signal.

[0074] Step 3: Perform a Fast Fourier Transform (FFT) on the shock echo signal to generate a spectrum, and match it with the theoretical resonant frequency to generate multiple defect intensity matrices with the same number as the number of shock echo signals. .

[0075] This invention improves imaging accuracy by introducing spatial multi-point location information, transforming traditional single-point detection into multi-point detection. In the Fast Fourier Transform (FFT) spectrum generated by traditional single-point detection, the maximum amplitude... Corresponding to defect frequency This results in high-intensity mesh elements along the circular arc path, whose intensity distribution can be expressed as:

[0076]

[0077] in, For imaging center The radial distance; The corresponding arc radius; The Dirac function is used to approximate a circular arc path; although this method can be used... The depth of the defect can be determined, but information on the complete spatial characteristics of the defect is limited.

[0078] By extending to multipoint measurement (total number of measurement points n), the imaging "center" related to the high-amplitude annular region is iteratively relocated. Let the imaging center of the i-th impact echo signal segment be , the defect position be , then the radial distance of the defect to is as follows:

[0079]

[0080] Each measurement point produces a unique FFT spectrum with a characteristic frequency , while maintaining a strong reflection at the defect position. The reflection intensity of the defect position under the i-th impact echo signal segment is modeled as follows:

[0081]

[0082] Step 4: Intensity superposition is performed on multiple defect intensity matrices to synthesize a composite imaging matrix, which can be visualized as a defect detection image for defect detection evaluation.

[0083] Intensity superposition is performed on multiple defect intensity matrices to synthesize a composite imaging matrix as follows:

[0084]

[0085] wherein, is the composite imaging matrix; is the number of impact echo signal segments; is the defect intensity matrix of the i-th impact echo signal segment.

[0086] As shown in Figure 3 , by n-1 times transformation of the circular center position , the reflection intensity of the intersection region of the circular pattern and the defect position is superimposed, and the cumulative intensity of the defect position is approximately:

[0087]

[0088] This makes the reflection intensity of the defect position obtain enhancement, generating an image that more accurately reflects the actual defect geometry. By adjusting the center coordinates of the system, the grid reflection intensity relative to the surrounding low-amplitude region is enhanced, and the intensity of the non-defect region satisfies:

[0089]

[0090] Thus, the intensity reflection of non-defect area is effectively reduced, the resolution of defect spatial representation is improved, and the effectiveness of intensity superposition is proved.

[0091] The composite imaging matrix is visualized as a high-contrast defect detection image by jet color mapping and contour imaging technology. The superposition process not only enhances the signal intensity of the defect area, but also highlights the geometric boundary of the defect through contour visualization technology, providing intuitive basis for subsequent defect evaluation.

[0092] As shown in Figure 4 , the defect detection evaluation includes color threshold determination, as follows:

[0093]

[0094] wherein, is the color threshold; is a scaling factor for adjusting the color level range, ; is the number of contour levels when generating the initial image, generally 25, which can meet the needs of most working conditions; is a lower limit function to ensure that the color level is an integer. Take each value in the set in turn, and the calculation result is .

[0095] The defect detection evaluation also includes depth and width calculation, specifically:

[0096] For the kth color level, identify the corresponding contour in the image, and detect the depth as follows:

[0097]

[0098] wherein, is the detection depth of the kth color level; , are the maximum and minimum y coordinates of the contour corresponding to the kth color level, respectively;

[0099] The relative error of the depth is as follows:

[0100]

[0101] wherein, is the relative error of ; is the actual depth; the overall detection depth is taken when ; ;

[0102] Based on the same contour, estimate the defect width within the imaging range as follows:

[0103]

[0104] wherein, is the detection width of the kth color level; 、 are the maximum and minimum x-coordinates of the contour corresponding to the kth color level, respectively;

[0105] The relative error formula of the width is as follows:

[0106]

[0107] wherein, is the relative error of is the actual width; the overall detection width takes when .

[0108] The defect detection evaluation also includes boundary processing, specifically: for a concrete slab component, if the imaging area exceeds the slab thickness, only the imaging results within the slab thickness range are considered.

[0109] The present application realizes quantitative analysis of defect detection by introducing color threshold, depth and width relative error evaluation method. The median estimation method of depth and width effectively reduces the influence of noise and abnormal values, ensuring the robustness of the evaluation results. In addition, the boundary processing rules for concrete slab components further improve the applicability of the method.

[0110] Example 2:

[0111] The embodiment 2 of the present application discloses a specific application of the multi-point accurate two-dimensional imaging method based on impact echo data disclosed in embodiment 1, as follows:

[0112] In order to verify the effectiveness of the multi-point accurate two-dimensional imaging method based on impact echo data, a defect detection experiment was conducted on a reinforced concrete specimen (sample A) with a size of 1050mm×1000mm×240mm. A rectangular artificial defect A13 was embedded in sample A, with a size of 100mm×50mm×20mm and a buried depth of 170mm. The following describes the detection process and results in four steps.

[0113] Step 1: Collect multiple impact echo signals according to the principle of impact echo method.

[0114] ​Measurement point setup: According to the size and defect configuration of specimen A, the distance between adjacent measurement points is determined to be 10 mm, n = 7 (odd number to ensure symmetric distribution of signals), which will generate n-1 = 6 signal segments. The measurement points are arranged along the surface axis of the test piece, covering the area above the defect.

[0115] Signal generation and collection: A steel ball impactor with a diameter of 8 mm is used to impact the measurement points A sinusoidal force impact (peak force 30 N, duration 34.4 μs) is applied to generate elastic stress waves. A piezoelectric sensor is placed at the measurement point , with a distance of 10 mm between the two. The impact is repeated 5 times continuously at each measurement point, and the mathematical expectation of the signals is taken to reduce noise interference and ensure waveform stability.

[0116] Point-by-point collection: The impactor and sensor are repositioned to the subsequent measurement points in turn (e.g., the impactor is placed at , the sensor is placed at , and so on), until the sensor reaches the measurement point . A total of 6 segments of impact echo signal data are collected.

[0117] Signal denoising: The 6 segments of signal data collected are subjected to surface wave filtering. For C30 concrete, the empirical coefficient is approximately 0.0043 s / m, and the diameter of the steel ball is 0.008 m, so the calculation is:

[0118]

[0119] The time-domain signal is filtered with as the boundary, removing surface wave (Rayleigh wave) interference and retaining the longitudinal wave (P-wave) signal carrying defect information.

[0120] Step 2: Divide the cross-section of the component to be tested into uniform grid cells and calculate the theoretical resonance frequency of the impact echo signal in each grid cell.

[0121] The cross-section of specimen A (imaging area 300 mm x 200 mm) is divided into uniform grid cells. The Nyquist sampling criterion must be met:

[0122]

[0123] Given , the sampling time interval , the calculation is:

[0124]

[0125] To ensure accurate imaging, the grid size The imaging area is divided into 75x50 grid cells (300mm ÷ 4mm = 75, 200mm ÷ 4mm = 50). For each grid cell, the theoretical resonance frequency of the impact echo signal is calculated: where is the distance from the grid cell center to the impact point (e.g. ), is the distance from the grid cell center to the collection point (e.g. ). Taking the grid cell center coordinate (x, y) = (50, 170) (close to the defect center) as an example, is located at (40, 0), is located at (50, 0), then:

[0126]

[0127]

[0128]

[0129]

[0130] Repeat this calculation for all grid cells and 6 groups of collection points to generate a theoretical frequency distribution.

[0131] Step 3: Perform a Fast Fourier Transform (FFT) on the impact echo signal to generate a frequency spectrum, and match it with the theoretical resonance frequency to generate multiple defect intensity matrices equal to the number of impact echo signals.

[0132] Perform a Fast Fourier Transform (FFT) on the filtered signal to generate a frequency spectrum. Match the theoretical resonance frequency of the impact echo signal calculated in Step 2 at each grid cell with the frequency in the FFT frequency spectrum to extract the corresponding amplitude, generating 6 intensity matrices. The amplitude is higher at the defect location, reflecting a strong reflection surface; the amplitude is lower in the non-defect area, reflecting structural integrity.

[0133] Step 4: Superimpose multiple defect intensity matrices to synthesize a composite imaging matrix, and visualize it as a defect detection image for defect detection evaluation.

[0134] Superimpose the 6 intensity matrices generated in Step 3 to synthesize a composite imaging matrix. Use jet color mapping (deep red for high amplitude, deep blue for low amplitude) and contour imaging technology to visualize the composite matrix as a defect detection image. As shown in Figure 5 , the imaging result shows that the defect A13 location presents a deep red area, clearly highlighting the defect boundary, and the surrounding non-defect area is deep blue.

[0135] Defect detection evaluation:

[0136] (1) Color threshold determination: Calculate the color threshold according to the contour level number and the scaling factor, determine the color level for identifying defects.

[0137] (2) Depth calculation: In the contour line of the selected color threshold, identify the maximum and minimum longitudinal coordinates of the defect area, calculate the estimated depth, compare with the ground truth depth, and obtain the depth error.

[0138] (3) Width calculation: Based on the same contour line, calculate the transverse width of the defect area, compare with the ground truth width, and obtain the width error.

[0139] (4) Boundary processing: The thickness of the test piece is 240mm, the imaging area depth does not exceed the plate thickness, and the plate bottom reflection is not significantly disturbed, so there is no need to adjust.

[0140] The embodiment of the application discloses a multi-point accurate two-dimensional imaging method based on impact echo data. Through multi-point detection and intensity matrix superposition technology, the efficiency of concrete structure defect detection is significantly improved. Multi-point measurement and iterative repositioning of imaging center enhance the reflection intensity of defect position, significantly improve the spatial resolution and imaging accuracy of defect geometric characteristics. The combination of intensity matrix superposition and jet color mapping technology generates high-contrast defect images, which intuitively highlights the defect boundary and provides a reliable visual basis for engineering detection. In addition, through color threshold and depth width error evaluation, quantitative analysis of defects is realized, noise and abnormal value interference is reduced, and the robustness of the evaluation result is ensured. Overall, the application performs excellently in terms of operation simplicity and result reliability, and is suitable for non-destructive detection of various concrete structures, providing an efficient and accurate technical solution for defect detection in the engineering field.

[0141] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0142] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method of multi-point accurate 2D imaging based on impact echo data, characterized in that, The method comprises the following steps: Step 1: Collecting multiple impact echo signals according to the principle of impact echo method; Step 2: Dividing the cross section of the component to be tested into uniform grid cells and calculating the theoretical resonance frequency of the impact echo signal in each grid cell; Step 3: Performing fast Fourier transform on the impact echo signal to generate a frequency spectrum and matching the theoretical resonance frequency to generate multiple defect intensity matrices with the same number as the impact echo signals; Step 4: Superimposing the intensities of the multiple defect intensity matrices to synthesize a composite imaging matrix and visualizing it as a defect detection image for defect detection evaluation; In step 1, after collecting the multiple impact echo signals, the method further comprises the following steps: Filtering surface waves according to the steel ball impact time to perform signal denoising processing on the multiple impact echo signals, specifically as follows: Calculating the impact time and filtering out the time domain signal before the impact time; the calculation formula of the impact time is as follows: t c ≈C·D; where t c is the impact time; D is the diameter of the steel ball; and C is an empirical coefficient. In step 4, the intensities of the multiple defect intensity matrices are superimposed to synthesize a composite imaging matrix, as follows: wherein I total (x, y) is a composite imaging matrix; n-1 is the number of impact echo signal segments; I i (x, y) is the defect intensity matrix of the i-th impact echo signal segment; In step 4, the defect detection evaluation includes color threshold determination, as follows: where T c is a color threshold; S is a scaling factor to adjust the color level range, s ∈ (0, 1]; L c is the number of contour levels when generating the initial image; is a floor function to ensure the color level is an integer; In step 4, the defect detection evaluation further includes depth and width calculation, specifically as follows: For the kth color level, identify the corresponding contour line in the image to detect the depth, as follows: where D k is the detection depth of the kth color level; y max,k , y min,k are the maximum and minimum y coordinates of the contour corresponding to the kth color level, respectively. The relative error of the depth is as follows: Among them, E D,k D k The relative error; D true The actual depth; the overall detection depth E D，det Take min{E D,k D at time} k ; Based on the same contour line, estimate the defect width within the imaging range, as follows: W k = x max,k - x min,k ; wherein W k is the detection width of the kth color level; x max，k , x min，k are the maximum and minimum x coordinates of the contour corresponding to the kth color level, respectively. The relative error formula of the width is as follows: where E W，k is the relative error; W k is the actual width; the overall detection width E true is taken as W W，det when min{E W，k} is taken as W k .

2. The method of claim 1, wherein, In step 1, the multiple impact echo signals are collected according to the principle of impact echo method, specifically as follows: Randomly arrange a measuring line on the concrete surface above the defect and arrange equidistant measuring points on the measuring line, then take each measuring point as an impact point and the adjacent measuring point as a collection point, and use n measuring points to obtain n-1 signal segments according to the principle of impact echo method; wherein n is an odd number, and the signal segment is obtained by multiple continuous impacts at each measuring point and taking the mathematical expectation of multiple signal groups.

3. The method of claim 1, wherein, In step 2, the maximum size of the grid cell meets the Nyquist sampling criterion, as follows: where Δx is the size of the grid cell; C p is the longitudinal wave velocity; and Δt is the sampling time interval.

4. The method of claim 1, wherein, In step 2, the theoretical resonance frequency of the impact echo signal in each grid cell is calculated, as follows: where f is the theoretical resonant frequency of the grid cell; C p is the longitudinal wave velocity; r1 is the distance from the center of the grid cell to the impact point of the impact echo signal; and r2 is the distance from the center of the grid cell to the collection point of the impact echo signal.

5. The method of claim 1, wherein, In step 4, the composite imaging matrix is visualized as a defect detection image by jet color mapping method and contour imaging technology.

6. The method of claim 1, wherein, In step 4, the defect detection evaluation further includes boundary processing, specifically as follows: for a concrete slab component, if the imaging area exceeds the slab thickness, only the imaging results within the slab thickness range are considered.

Images