Ultrasonic phased array nondestructive testing method for building steel structure strength

Abstract

The application relates to the technical field of ultrasonic detection, in particular to a building steel structure strength ultrasonic phased array nondestructive detection method, which comprises the following steps: acquiring topographic data of a building steel structure surface; analyzing coating thickness distribution and weld surface roughness of the building steel structure surface, and constructing a surface information matrix; automatically adjusting ultrasonic wave beam parameters of the ultrasonic phased array by using a self-adaptive parameter regulation model, and performing ultrasonic phased array detection to acquire preliminary ultrasonic echo signals; adopting an echo signal compensation model to perform secondary compensation on the preliminary ultrasonic echo signals by referring to the difference between a reference signal and the preliminary ultrasonic echo signals, so that optimized ultrasonic echo signals are obtained; fusing the optimized ultrasonic echo signals, the topographic data and the surface information matrix, and detecting defects of the building steel structure by using a building steel structure detection model; evaluating the detection results, and dividing confidence levels; performing local re-inspection and parameter adjustment on the regions with low confidence, so that closed-loop optimization is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ultrasonic detection, in particular to a building steel structure strength ultrasonic phased array nondestructive testing method. BACKGROUND

[0002] Building steel structures are widely used in modern buildings due to their good strength, durability and seismic performance. However, as time goes by, steel structures may be damaged due to long-term exposure to harsh environments or external impacts. Damage to steel structures, such as cracks, corrosion, fatigue and welding defects, may affect the load-bearing capacity and safety of the structure. Therefore, regular nondestructive testing of building steel structures to ensure structural safety and reliability is an important part of modern building maintenance and management.

[0003] As an advanced nondestructive testing method, ultrasonic phased array technology uses a phased array probe to emit and receive ultrasonic signals, and uses multiple independent acoustic sensor arrays to achieve electronic control of the sound beam, allowing real-time detection at multiple angles. Compared with traditional ultrasonic detection methods, ultrasonic phased array technology has higher detection accuracy, faster scanning speed and stronger signal processing capability, and can effectively detect defects in complex structures, especially for large-scale, efficient and high-precision nondestructive testing tasks. The application of ultrasonic phased array in building steel structures can obtain real-time and comprehensive internal defect information of the structure, providing a scientific basis for structural safety evaluation.

[0004] Although ultrasonic phased array technology has obvious advantages in building steel structure detection, it still faces some challenges in practical application. First, the steel structure surface often has corrosion-resistant coatings (such as paint, hot-dip galvanizing, aluminum spraying) or rust, and the thickness and uniformity of these coatings may affect the coupling effect of ultrasonic waves, leading to poor coupling, energy loss and signal attenuation. Second, the surface of the field weld may have welding slag, scale or uneven roughness, which will affect the fit of the probe and the detection surface, resulting in fluctuations and errors in the detection signal. Therefore, the existing ultrasonic phased array technology still has the problem of unstable ultrasonic signals caused by defects on the surface of the steel structure in actual detection, and an improved and optimized ultrasonic phased array technology is needed to reduce the impact of defects on the surface of the steel structure on ultrasonic detection and improve the stability of the ultrasonic signal, thereby improving the accuracy and reliability of building steel structure detection.

[0005] Therefore, a building steel structure strength ultrasonic phased array nondestructive testing method is proposed. SUMMARY

[0006] The application aims to provide a building steel structure strength ultrasonic phased array nondestructive testing method to reduce the influence of steel structure surface defects on ultrasonic detection, improve the stability of ultrasonic signals, and thus improve the accuracy and reliability of building steel structure detection. The method comprises the following steps: obtaining the topography data of the surface of a building steel structure by using a high-precision laser scanner and electromagnetic induction technology; analyzing the coating thickness distribution and weld surface roughness of the surface of the building steel structure according to the topography data, and constructing a surface information matrix; automatically adjusting the ultrasonic wave beam parameters of the ultrasonic phased array by using an adaptive parameter regulation model according to the surface information matrix, and performing ultrasonic phased array detection according to the ultrasonic wave beam parameters to obtain preliminary ultrasonic echo signals; calculating the time-frequency domain difference between the preliminary ultrasonic echo signals and reference signals by using an echo signal compensation model, and performing secondary compensation on the preliminary ultrasonic echo signals according to the time-frequency domain difference to obtain optimized ultrasonic echo signals; fusing the optimized ultrasonic echo signals, the topography data and the surface information matrix by using a building steel structure detection model constructed and trained in advance to construct a detection data set; detecting defects of the building steel structure through the detection data set; evaluating the results of the defect detection, and dividing the confidence level according to a pre-set confidence threshold; and performing local re-inspection and parameter adjustment according to the confidence level to realize closed-loop optimization.

[0007] To achieve the above-mentioned purpose, the application provides the following technical scheme:

[0008] A building steel structure strength ultrasonic phased array nondestructive testing method comprises the following steps:

[0009] Obtaining the topography data of the surface of a building steel structure by using a high-precision laser scanner and electromagnetic induction technology; analyzing the coating thickness distribution and weld surface roughness of the surface of the building steel structure according to the topography data, and constructing a surface information matrix;

[0010] Automatically adjusting the ultrasonic wave beam parameters of the ultrasonic phased array by using an adaptive parameter regulation model according to the surface information matrix, and performing ultrasonic phased array detection according to the ultrasonic wave beam parameters to obtain preliminary ultrasonic echo signals;

[0011] Calculating the time-frequency domain difference between the preliminary ultrasonic echo signals and reference signals by using an echo signal compensation model, and performing secondary compensation on the preliminary ultrasonic echo signals according to the time-frequency domain difference to obtain optimized ultrasonic echo signals;

[0012] Fusing the optimized ultrasonic echo signals, the topography data and the surface information matrix by using a building steel structure detection model constructed and trained in advance to construct a detection data set; detecting defects of the building steel structure through the detection data set; evaluating the results of the defect detection, and dividing the confidence level according to a pre-set confidence threshold;

[0013] According to the confidence level, local re-inspection and parameter adjustment are performed to realize closed-loop optimization.

[0014] Preferably, the topographic data includes surface three-dimensional topological information, reflection intensity data and surface texture image data.

[0015] The specific process of analyzing the coating thickness distribution and the weld surface roughness of the building steel structure surface according to the topographic data includes: using point cloud reconstruction and digital image processing algorithms to perform difference and regression analysis on the surface three-dimensional topological information and the reflection intensity data to obtain the coating thickness distribution.

[0016] Based on the surface texture image data, a statistical analysis method is used to extract the weld surface roughness.

[0017] Preferably, the adaptive parameter regulation model includes an ultrasonic wave propagation simulation unit and an ultrasonic beam control unit.

[0018] The ultrasonic wave propagation simulation unit calculates the influence of the coating thickness distribution and the weld surface roughness on the ultrasonic wave propagation path and energy attenuation based on the surface information matrix, to obtain a coating thickness distribution influence factor and a weld surface roughness influence factor.

[0019] The ultrasonic beam control unit dynamically adjusts the ultrasonic wave beam parameters of the ultrasonic phased array probe according to the coating thickness distribution influence factor and the weld surface roughness influence factor; the ultrasonic wave beam parameters include: beam angle, beam frequency, beam focal length, beam width and wave speed.

[0020] Preferably, the echo signal compensation model includes a reference signal acquisition unit, a signal difference analysis unit and a signal correction unit.

[0021] The reference signal acquisition unit extracts a reference echo signal matched with the current detection site and material quality from a standard defect-free steel structure sample library as the reference signal.

[0022] The signal difference analysis unit calculates the time-frequency domain difference between the reference signal and the preliminary ultrasonic echo signal through a wavelet transform algorithm.

[0023] The signal correction unit performs the secondary compensation on the preliminary ultrasonic echo signal according to the time-frequency domain difference and a preset compensation coefficient matrix to obtain the optimized ultrasonic echo signal.

[0024] Preferably, the formula of the time-frequency domain difference is:

[0025] W i (a,b)=w{S i (t)}(a,b);

[0026] W r (a,b)=w{S r (t)}(a,b);

[0027] △W(a,b)=W i (a,b)-W r (a,b);

[0028] Among them, W i (a, b) represent the initial ultrasonic echo signal S. i Wavelet coefficients of (t); W r (a,b) represents the reference signal S. r The wavelet coefficients of (t); △W(a,b) is the time-frequency domain difference; a is the scaling parameter; b is the translation parameter; w{} is the wavelet transform;

[0029] The formula for the secondary compensation is:

[0030] S o (t)=S i (t)+w -1 {K(a,b)·△W(a,b)};

[0031] Among them, S o (t) represents the optimized ultrasonic echo signal; K(a,b) is the preset compensation coefficient matrix; w -1 {} represents the inverse wavelet transform.

[0032] Preferably, the building steel structure inspection model includes: a multimodal feature extraction unit, a defect identification unit, and a confidence assessment unit;

[0033] The multimodal feature extraction unit integrates the multimodal features of the optimized ultrasonic echo signal, the morphology data, and the surface information matrix, and constructs a detection dataset based on the multimodal features; the formula for the detection dataset is:

[0034] F = tanh(ω) S ·S o ☉tanh(ω M ·M)+ω I ·I+b1);

[0035] D=σ(ω F ·F+b2);

[0036] Where F represents the multimodal feature; tanh() is the hyperbolic tangent activation function; ω S To optimize the weighting of the ultrasonic echo signal; S o To optimize the ultrasonic echo signal; ⊙ represents the element and nonlinear interaction; ω Mis the weight of the morphology data; M is the morphology data; ω I is the weight of the surface information matrix; I is the surface information matrix; D is the detection data set; σ() is an activation function; ω F is the weight of the multi-modal feature; b1 and b2 are bias terms;

[0037] The defect recognition unit recognizes defects in the building steel structure based on the detection data set; the defects include: coating defects and weld defects; the coating defects include cracking and peeling of the coating; the weld defects include pores, craters, undercut and cracks on the weld surface;

[0038] The confidence assessment unit assesses the confidence of the defect detection result of the defect recognition unit, and divides the confidence level according to the pre-set confidence threshold; the pre-set confidence threshold is accurately set according to different defects.

[0039] Preferably, according to the confidence level, local re-inspection and parameter adjustment are performed, and the specific process is as follows:

[0040] For the area with a confidence level lower than the pre-set confidence threshold, the area is re-acquired using local high-resolution ultrasonic phased array detection to collect ultrasonic echo signals in the area, and the local re-inspection is performed;

[0041] Based on the results of the local re-inspection, the adaptive parameter regulation model is used to dynamically adjust the ultrasonic wave beam parameters of the ultrasonic phased array probe, realizing the closed-loop optimization.

[0042] Compared with the prior art, the beneficial effects of the present application are:

[0043] 1. The adaptive parameter regulation model uses the surface information matrix to dynamically adjust the beam parameters of the ultrasonic phased array, such as beam angle, frequency, focal length, width and wave speed, which can adapt to various factors such as the thickness of the coating on the surface of the building steel structure and the roughness of the weld. This method can effectively improve the ultrasonic wave incidence and focusing effect, reduce the detection error caused by surface unevenness, and thus improve the quality of the preliminary ultrasonic echo signal and the reliability of the subsequent overall detection.

[0044] 2. The present application proposes an echo signal compensation model, which calculates the time-frequency domain difference between the preliminary signal and the reference signal through wavelet transform, and performs secondary compensation based on the preliminary ultrasonic echo signal obtained by adjusting the beam parameters through the adaptive parameter regulation model, further correcting the time-frequency deviation caused by the coating and weld surface characteristics. This compensation method can reduce noise and signal distortion, significantly improve the clarity and accuracy of the optimized echo signal, and provide a more reliable data basis for subsequent defect detection.

[0045] 3、The application can comprehensively reflect the multi-dimensional characteristics of the surface of the steel structure by fusing the optimized ultrasonic echo signal with the topographic data and the surface information matrix to construct a detection data set, and using the detection data set for defect detection. This fusion strategy fully utilizes the time-frequency information and surface topography information through data complementation, improves the learning effect and discrimination ability of the building steel structure detection model, thereby realizing accurate positioning and reliable evaluation of defects, and further improving the closed-loop optimization effect of the overall non-destructive testing system. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 A flowchart of a building steel structure strength ultrasonic phased array non-destructive testing method provided for the embodiment of the application is provided.

[0047] Figure 2 A working principle diagram of a building steel structure strength ultrasonic phased array non-destructive testing method provided for the embodiment of the application is provided.

[0048] Figure 3 A working principle diagram of an adaptive parameter regulation model provided for the embodiment of the application is provided.

[0049] Figure 4 A working principle diagram of an echo signal compensation model provided for the embodiment of the application is provided. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0051] Building steel structures are widely used in modern buildings due to their good strength, durability and seismic performance. However, as time goes by, the steel structure may be damaged due to long-term exposure to harsh environments or external impact. Damage to steel structures, such as cracks, corrosion, fatigue and welding defects, may affect the load-carrying capacity and safety of the structure. Therefore, regular non-destructive testing of building steel structures to ensure structural safety and reliability is an indispensable part of modern building maintenance and management.

[0052] The application provides a building steel structure strength ultrasonic phased array nondestructive detection method, which reduces the influence of surface defects of the steel structure on ultrasonic detection, improves the stability of ultrasonic signals, and thus improves the accuracy and reliability of building steel structure detection. In order to illustrate that the method of the application can reduce the influence of surface defects of the steel structure on ultrasonic detection and improve the stability of ultrasonic signals, the effectiveness of the application will be illustrated from two embodiments below.

[0053] Embodiment one

[0054] In the embodiments of the application, the method proposed by the application is used to reduce the influence of surface defects of the steel structure on ultrasonic detection, improve the stability of ultrasonic signals, and thus improve the accuracy and reliability of building steel structure detection. The embodiments of the application are directed to building steel structure detection in the hydrogenation workshop of Company A, which is a two-story steel frame structure building. The building steel structure detection process in the hydrogenation workshop of Company A will be described in detail below according to the content; wherein, Figure 1 Figure 1 is a specific flowchart of the method proposed by the application, which includes: obtaining topographic data of a building steel structure surface by using a high-precision laser scanner and electromagnetic induction technology; analyzing the coating thickness distribution and the weld surface roughness of the building steel structure surface according to the topographic data to construct a surface information matrix; automatically adjusting ultrasonic phased array ultrasonic beam parameters by using a self-adaptive parameter regulation model according to the surface information matrix, and performing ultrasonic phased array detection according to the ultrasonic beam parameters to obtain preliminary ultrasonic echo signals; calculating the time-frequency domain differences between the preliminary ultrasonic echo signals and reference signals by using an echo signal compensation model, and performing secondary compensation on the preliminary ultrasonic echo signals according to the time-frequency domain differences to obtain optimized ultrasonic echo signals; fusing the optimized ultrasonic echo signals, the topographic data and the surface information matrix by using a pre-constructed and trained building steel structure detection model to construct a detection data set; performing defect detection on the building steel structure through the detection data set; evaluating the results of the defect detection, and dividing the confidence level according to a pre-set confidence threshold; performing local re-inspection and parameter adjustment according to the confidence level to realize closed-loop optimization. Figure 2 is a working principle diagram of the method proposed by the application. The following is explained in combination with the content in Figure 1 .

[0055] A building steel structure strength ultrasonic phased array nondestructive detection method, comprising:

[0056] obtaining topographic data of a building steel structure surface by using a high-precision laser scanner and electromagnetic induction technology;

[0057] The topographic data includes surface three-dimensional topological information, reflection intensity data and surface texture image data.​

[0058] According to the topographic data, the coating thickness distribution and the weld surface roughness of the building steel structure surface are analyzed, and a surface information matrix is constructed; the specific process includes:

[0059] The three-dimensional coordinate data and the reflection intensity data are subjected to difference and regression analysis by using point cloud reconstruction and digital image processing algorithm, and the coating thickness distribution is obtained.

[0060] Based on the surface texture image data, a statistical analysis method is used to extract the weld surface roughness.

[0061] Specifically, the height change and the reflection intensity change of each point and the points in its neighborhood are calculated by using a difference operator; according to the difference analysis result, a group of sample data with known coating thickness is selected, and a regression model is obtained by fitting the relationship between the reflection intensity change and the actual thickness; the model is applied to the entire three-dimensional coordinate data to obtain the coating thickness distribution at each detection point.

[0062] The contrast, correlation and uniformity of the surface texture image data are extracted using a gray level co-occurrence matrix, and the contrast, correlation and uniformity are fused to obtain the weld surface roughness; the specific formula is:

[0063] R rough =k1·C+k2·(1-Corr)+k3·(1-H);

[0064]

[0065] wherein R rough is the weld surface roughness; k1 is the contrast weight; C is the contrast; k2 is the correlation weight; Corr is the correlation; k2 is the uniformity weight; H is the uniformity; P(i,j) is the gray level co-occurrence matrix; i,j=0,1,...,N-1 is the gray level in the image; μ i and μ j respectively represent the mean of the rows and columns of the gray level co-occurrence matrix; σ i and σ j respectively represent the standard deviation of the rows and columns;

[0066] In the above formula, the greater the contrast, the higher the weld surface roughness; the higher the correlation, the more regular the image structure, so 1-Corr is taken as the structural irregularity, and the greater the value, the higher the weld surface roughness; the greater the uniformity, the more uniform the surface texture, so 1-H is taken as the texture unevenness, and the greater the value, the higher the weld surface roughness.

[0067] The embodiment of the application obtains the topographic data of the surface of the building steel structure by using a high-precision laser scanner and an electromagnetic induction technology, including surface three-dimensional topological information, reflection intensity data and surface texture image data, and can comprehensively and accurately describe the characteristics of the structure surface. The three-dimensional coordinate data and the reflection intensity data are subjected to difference and regression analysis by using point cloud reconstruction and digital image processing algorithms to obtain the coating thickness distribution; based on the surface texture image data, the texture features such as contrast, correlation and uniformity are extracted by using a gray level co-occurrence matrix, and these features are fused to obtain the weld surface roughness. The comprehensive use of the above technical means constructs a multi-dimensional surface information matrix, which provides accurate basic data for subsequent ultrasonic phased array detection, so that the detection system can realize high-precision and high-reliability detection effect in each link of subsequent parameter adaptive adjustment, signal compensation and defect identification, thereby significantly improving the accuracy and overall detection efficiency of the building steel structure strength detection.

[0068] Preferably, according to the surface information matrix, an adaptive parameter regulation model is used to automatically adjust the ultrasonic wave beam parameters of the ultrasonic phased array, and ultrasonic phased array detection is performed according to the ultrasonic wave beam parameters to obtain preliminary ultrasonic echo signals; referring to Figure 3 ;

[0069] The adaptive parameter regulation model includes an ultrasonic wave propagation simulation unit and an ultrasonic beam control unit.

[0070] The ultrasonic wave propagation simulation unit calculates the influence of the coating thickness distribution and the weld surface roughness on the ultrasonic wave propagation path and energy attenuation based on the surface information matrix to obtain a coating thickness distribution influence factor and a weld surface roughness influence factor.

[0071] The ultrasonic beam control unit dynamically adjusts the ultrasonic wave beam parameters of the ultrasonic phased array probe according to the coating thickness distribution influence factor and the weld surface roughness influence factor; the ultrasonic wave beam parameters include beam angle, beam frequency, beam focal length, beam width and wave speed.

[0072] Specifically, the formula of the coating thickness distribution influence factor is:

[0073] α=f(d,v c ,α c );

[0074] Wherein, α is the coating thickness distribution influence factor; f() is the coating thickness distribution influence factor function, which needs to be determined by experiment calibration; d is the coating thickness; v c is the propagation speed of ultrasonic waves in the coating; α c is the energy attenuation and the acoustic attenuation coefficient of the coating.

[0075] The formula of the weld surface roughness influencing factor is:

[0076] β=g(R a ,R q );

[0077] Wherein, β is the weld surface roughness influencing factor; g() is the weld surface roughness influencing factor function, which needs to be determined by experiment calibration; R a is the arithmetic average roughness; R q is the root mean square roughness;

[0078] The formula of the beam angle is:

[0079] θ=θ0+k θ ·(α+β);

[0080] Wherein, θ is the beam angle; θ0 is the initial beam angle; k θ is the beam angle adjustment coefficient;

[0081] The formula of the beam frequency is:

[0082] f=f0·(1+k f ·α)·(1+k' f ·β);

[0083] Wherein, f is the beam angle; f0 is the initial beam angle; k f is the coating thickness distribution adjustment coefficient of the beam frequency; k' f is the weld surface roughness adjustment coefficient of the beam frequency;

[0084] The formula of the beam focal length is:

[0085] d=d0+k d ·α;

[0086] Wherein, d is the beam angle; d0 is the initial beam angle; k d is the beam angle adjustment coefficient;

[0087] The formula of the beam width is:

[0088] ω=ω0·(1+k ω ·β);

[0089] Wherein, ω is the beam width; ω0 is the initial beam width; k ω is the beam width adjustment coefficient;

[0090] The formula of the wave velocity is:

[0091] v=v0·(1+k v ·α);

[0092] where v is the wave speed; v0 is the initial wave speed; k v is the wave speed adjustment coefficient;

[0093] Table 1 is a comparison table of various indicators of ultrasonic echo signals before and after adjusting the ultrasonic wave beam parameters.

[0094] Table 1 is a comparison table of various indicators of ultrasonic echo signals before and after adjusting the ultrasonic wave beam parameters.

[0095] Index Before parameter adjustment After parameter adjustment Beam angle (°) 30.2 35.1 Beam frequency (MHz) 2.0 2.2 Beam focal length (mm) 50 55 Beam width (mm) 10 9 Wave velocity (m / s) 5000 5100 Signal-to-noise ratio (dB) 34 51

[0096] The embodiments of the present application utilize an adaptive parameter control model to dynamically adjust the ultrasonic wave beam parameters, including beam angle, frequency, focal length, width, and wave speed, based on the coating thickness distribution on the surface of the building steel structure and the surface roughness of the weld. This dynamic adjustment can optimize the propagation path and energy distribution of the ultrasonic wave, improve the stability of the ultrasonic wave signal, and thus improve the sensitivity and resolution of the subsequent building steel structure detection. By precisely controlling the characteristics of the ultrasonic beam, it can provide a basis for more effective detection of defects in complex structures, thereby improving the reliability and accuracy of the subsequent steel structure detection results.

[0097] Preferably, an echo signal compensation model is used to calculate the time-frequency domain difference between the preliminary ultrasonic echo signal and the reference signal, and the preliminary ultrasonic echo signal is compensated again according to the time-frequency domain difference to obtain an optimized ultrasonic echo signal; see Figure 4 ;

[0098] The echo signal compensation model includes a reference signal acquisition unit, a signal difference analysis unit, and a signal correction unit.

[0099] The reference signal acquisition unit extracts a reference echo signal matching the current detection site and material quality from a standard defect-free steel structure sample library as the reference signal;

[0100] The signal difference analysis unit calculates the time-frequency domain difference between the reference signal and the preliminary ultrasonic echo signal through a wavelet transform algorithm;

[0101] The signal correction unit compensates the preliminary ultrasonic echo signal again according to the time-frequency domain difference combined with a pre-set compensation coefficient matrix to obtain the optimized ultrasonic echo signal.

[0102] The echo signal compensation model proposed in the embodiment of the application fundamentally improves the quality and stability of the ultrasonic echo signal by accurately calculating the difference between the preliminary ultrasonic echo signal and the reference signal in the time-frequency domain and using a preset compensation coefficient matrix to perform secondary compensation on the preliminary signal. By extracting a reference signal matched with the current detection part and material from a standard defect-free steel structure sample library and using a wavelet transform algorithm to analyze the signal in the time-frequency domain, the method can accurately capture signal distortion and energy attenuation caused by uneven coating and weld defects, thereby realizing fine correction of the preliminary signal. The scheme not only effectively reduces noise and interference in the detection process, but also significantly improves the resolution and detection accuracy of ultrasonic phased array detection, providing more reliable signal data support for subsequent defect judgment.

[0103] Preferably, the formula of the time-frequency domain difference is:

[0104] W i (a,b)=w{S i (t)}(a,b);

[0105] W r (a,b)=w{S r (t)}(a,b);

[0106] △W(a,b)=W i (a,b)-W r (a,b);

[0107] Wherein, W i (a,b) is the wavelet coefficient of the preliminary ultrasonic echo signal S i (t); W r (a,b) is the wavelet coefficient of the reference signal S r (t); △W(a,b) is the time-frequency domain difference; a is a scale parameter; b is a translation parameter; w{} is a wavelet transform;

[0108] The formula of the secondary compensation is:

[0109] S o (t)=S i (t)+w -1 {K(a,b)·△W(a,b)};

[0110] Wherein, S o (t) is the optimized ultrasonic echo signal; K(a,b) is a preset compensation coefficient matrix; w -1 {} is an inverse wavelet transform;

[0111] The preset compensation coefficient matrix is obtained by the following steps: collecting echo signals of a large number of standard steel structure samples without defects and with known defects through ultrasonic detection, and performing wavelet transform on the signals to extract time-frequency domain features under different scale and translation parameters; comparing the experimental data with reference signals, and solving compensation coefficients under each scale parameter and translation parameter through regression analysis, least square fitting or machine learning optimization method, so that the error between the compensated signal and the true signal is minimized after inverse wavelet transform is applied to reconstruct the signal; and the coefficient set obtained through the above steps constitutes the preset compensation coefficient matrix.

[0112] Table 2 is a comparison table of the stability of ultrasonic echo signals before and after secondary compensation.

[0113] Table 2 is a comparison table of the stability of ultrasonic echo signals before and after secondary compensation.

[0114] Stability index Before secondary compensation After secondary compensation Signal-to-noise ratio (db) 51 63 Signal amplitude standard deviation (V) 0.8 0.3 Stability index 0.85 0.92

[0115] The embodiment of the present application compares the coefficients of the preliminary ultrasonic echo signal and the reference signal under different scales and translation parameters by using wavelet transform through the time-frequency domain difference formula, and accurately quantifies the subtle differences of the signals in time domain and frequency domain; the preset compensation coefficient matrix and the secondary compensation formula of inverse wavelet transform can finely correct these differences and generate optimized ultrasonic echo signals, thereby effectively eliminating signal distortion and energy attenuation caused by structural surface state and material characteristics, significantly improving the clarity and stability of the signals, and further improving the reliability and overall detection capability of ultrasonic phased array nondestructive testing in defect identification of building steel structures.

[0116] Preferably, the optimized ultrasonic echo signal, the topographic data and the surface information matrix are fused by using a pre-constructed and trained building steel structure detection model to construct a detection data set; the building steel structure is detected for defects through the detection data set; the result of the defect detection is evaluated, and a confidence level is divided according to a preset confidence threshold;

[0117] The building steel structure detection model comprises a multi-modal feature extraction unit, a defect identification unit and a confidence evaluation unit.

[0118] The multi-modal feature extraction unit fuses multi-modal features of the optimized ultrasonic echo signal, the topographic data and the surface information matrix, and constructs a detection data set according to the multi-modal features; the formula of the detection data set is:

[0119] F=tanh(ω S ·S o ☉tanh(ω M ·M)+ω I ·I+b1);

[0120] D = σ(ω F · F + b2);

[0121] wherein, F is a multi-modal feature; tanh() is a hyperbolic tangent activation function; ω S is a weight of an optimized ultrasound echo signal; S o is an optimized ultrasound echo signal; is an element and nonlinear interaction; ω M is a weight of topography data; M is topography data; ω I is a weight of surface information matrix; I is a surface information matrix; D is a detection data set; σ() is an activation function; ω F is a multi-modal feature weight; b1 and b2 are bias terms;

[0122] The defect recognition unit recognizes defects in the building steel structure based on the detection data set; the defects include: coating defects and weld defects; the coating defects include cracking and peeling of the coating; the weld defects include pores, craters, undercut and cracks on the weld surface;

[0123] The confidence assessment unit assesses the confidence of the defect detection result of the defect recognition unit, and divides the confidence level according to the pre-set confidence threshold; the pre-set confidence threshold is accurately set according to different defects.

[0124] Specifically, the defect recognition unit identifies the probability value of each defect type in the steel structure through a pre-trained CNN model;

[0125] The confidence assessment unit calculates the confidence according to the probability value of each defect type; the formula of the confidence is:

[0126] C = max(P1, P2,..., P i ,..., P n );

[0127] wherein, C is the confidence; P i is the probability value of the i-th defect; n is the total number of defect types;

[0128] The pre-set confidence threshold is: for critical structural parts, such as welds, a higher confidence threshold is set; in this embodiment, the weld confidence threshold is set to 0.8; for steel frame coating parts, the coating confidence threshold is set to 0.6.

[0129] The embodiment of the application can accurately identify various defects in the coating and the weld by fusing and optimizing the ultrasonic echo signal, the topographic data and the surface information matrix to construct a detection data set, and combining a pre-trained deep learning model to detect defects of the building steel structure. The CNN model is used to extract the probability value of each defect type, and the confidence evaluation unit is used to perform probability calculation and threshold division on the detection result, so that differential detection of key parts (such as welds) and secondary parts (such as coatings) is realized. A higher confidence threshold is set for the weld area to ensure the strictness of the detection, and the threshold is set to 0.6 for the coating area to balance the detection accuracy and error tolerance. This method not only improves the accuracy and reliability of the detection, reduces the false detection and missed detection phenomenon, but also provides a scientific basis for subsequent defect repair and safety evaluation, and significantly improves the overall efficiency and application value of the non-destructive testing of the building steel structure.

[0130] Preferably, local re-inspection and parameter adjustment are performed according to the confidence level to realize closed-loop optimization. The specific process is as follows:

[0131] For the area with a confidence level lower than the preset confidence threshold, the ultrasonic echo signal of the area is re-acquired by using local high-resolution ultrasonic phased array detection, and the local re-inspection is performed.

[0132] Based on the result of the local re-inspection, the ultrasonic wave beam parameters of the ultrasonic phased array probe are dynamically adjusted by using the adaptive parameter regulation model to realize the closed-loop optimization.

[0133] Table 3 is a comparison table of detection performance before and after local re-inspection and parameter adjustment.

[0134] Table 3 is a comparison table of detection performance before and after local re-inspection and parameter adjustment.

[0135] Detection performance index Before local recheck After local recheck Defect detection accuracy rate (%) 92.9 96.8 Missed detection rate (%) 15.7 7.2 False detection rate (%) 6.3 4.9 Mean confidence 0.65 0.76

[0136] The embodiment of the application realizes further compensation for the insufficient part of the preliminary detection result by performing local re-inspection on the area with a confidence level lower than the preset threshold. The echo signal of the target area is re-acquired by using high-resolution ultrasonic phased array detection, so that the system can capture more detailed information. In combination with the dynamic adjustment of the ultrasonic wave beam parameters by the adaptive parameter regulation model, the signal deviation caused by local environmental changes and / or insufficient detection conditions is compensated. This process not only improves the accuracy and stability of the detection signal, but also realizes real-time feedback and parameter optimization, further reduces the risk of missed detection, ensures the efficiency and reliability of the defect detection of the building steel structure, and provides solid data support for engineering safety evaluation.

[0137] The embodiments of the present application significantly improve the accuracy and reliability of non-destructive testing of building steel structures by accurately obtaining surface information, adaptively adjusting detection parameters, compensating signal distortion, and optimizing the detection process in a closed loop. First, the topography data of the building steel structure surface, including the coating thickness distribution and the weld surface roughness, is obtained using a high-precision laser scanner and electromagnetic induction technology, and a surface information matrix is constructed. This enables the detection system to accurately grasp the surface characteristics of the measured structure, providing accurate input parameters for subsequent ultrasonic testing. Second, based on the surface information matrix, an adaptive parameter control model is used to automatically adjust the ultrasonic beam parameters of the ultrasonic phased array, such as beam angle, frequency, focal length, width, and wave speed. This dynamic adjustment ensures the optimal propagation path and energy distribution of ultrasonic waves under complex surface conditions, improving the quality of the preliminary ultrasonic echo signal. Then, through the echo signal compensation model, the time-frequency domain differences between the preliminary ultrasonic echo signal and the reference signal are calculated, and these differences are analyzed using wavelet transform. Combined with the pre-set compensation coefficient matrix, the preliminary echo signal is compensated again to obtain the optimized ultrasonic echo signal. This process effectively corrects the signal distortion caused by surface characteristics, enhancing the accuracy of the signal. Finally, the optimized ultrasonic echo signal, topography data, and surface information matrix are fused to construct a detection dataset, and a pre-trained building steel structure detection model is used for defect detection. The detection results are evaluated, and local re-inspection and parameter adjustment are performed on low confidence areas to achieve closed-loop optimization of the detection process.

[0138] Embodiment Two

[0139] In Embodiment One, the method proposed by the present application successfully reduces the influence of surface defects on ultrasonic detection of steel structures, improves the stability of ultrasonic signals, and thus improves the accuracy and reliability of building steel structure detection. To further verify the effectiveness of the present application, another steel frame structure building B is also subjected to steel structure detection in the embodiments of the present application.

[0140] A building steel structure strength ultrasonic phased array non-destructive testing method, comprising:

[0141] High-precision laser scanners and electromagnetic induction technology are used to obtain topography data of the building steel structure surface;

[0142] The topography data includes surface three-dimensional topological information, reflection intensity data, and surface texture image data;

[0143] According to the topography data, the coating thickness distribution and the weld surface roughness of the building steel structure surface are analyzed, and a surface information matrix is constructed; the specific process includes:

[0144] Point cloud reconstruction and digital image processing algorithms are used to perform difference and regression analysis on the three-dimensional coordinate data and the reflection intensity data to obtain the coating thickness distribution;

[0145] extracting the weld surface roughness based on the surface texture image data by using a statistical analysis method.

[0146] Preferably, according to the surface information matrix, the ultrasonic phased array detection is performed based on the ultrasonic wave beam parameters, and a preliminary ultrasonic echo signal is obtained;

[0147] The adaptive parameter regulation model comprises an ultrasonic wave propagation simulation unit and an ultrasonic beam control unit.

[0148] The ultrasonic wave propagation simulation unit calculates the influence of the coating thickness distribution and the weld surface roughness on the ultrasonic wave propagation path and energy attenuation based on the surface information matrix, and obtains a coating thickness distribution influence factor and a weld surface roughness influence factor.

[0149] The ultrasonic beam control unit dynamically adjusts the ultrasonic wave beam parameters of the ultrasonic phased array probe according to the coating thickness distribution influence factor and the weld surface roughness influence factor; the ultrasonic wave beam parameters include beam angle, beam frequency, beam focal length, beam width and wave speed.

[0150] Preferably, an echo signal compensation model is used to calculate the time-frequency domain difference between the preliminary ultrasonic echo signal and a reference signal, and the preliminary ultrasonic echo signal is compensated again according to the time-frequency domain difference, so as to obtain an optimized ultrasonic echo signal.

[0151] The echo signal compensation model comprises a reference signal acquisition unit, a signal difference analysis unit and a signal correction unit.

[0152] The reference signal acquisition unit extracts a reference echo signal matched with the current detection position and material from a standard defect-free steel structure sample library as the reference signal.

[0153] The signal difference analysis unit calculates the time-frequency domain difference between the reference signal and the preliminary ultrasonic echo signal by using a wavelet transform algorithm.

[0154] The signal correction unit compensates the preliminary ultrasonic echo signal again according to the time-frequency domain difference and a preset compensation coefficient matrix to obtain the optimized ultrasonic echo signal.

[0155] Preferably, the formula of the time-frequency domain difference is:

[0156] W i (a,b)=w{S i (t)}(a,b);

[0157] Wr (a,b) = w{S r (t)}(a,b);

[0158] △W(a,b) = W i (a,b) - W r (a,b);

[0159] wherein, W i (a,b) is the wavelet coefficient of the preliminary ultrasonic echo signal S i (t); W r (a,b) is the wavelet coefficient of the reference signal S r (t); △W(a,b) is the time-frequency domain difference; a is a scale parameter; b is a translation parameter; w{} is a wavelet transform;

[0160] The formula of the secondary compensation is:

[0161] S o (t) = S i (t) + w -1 {K(a,b)·△W(a,b)};

[0162] wherein, S o (t) is an optimized ultrasonic echo signal; K(a,b) is a preset compensation coefficient matrix; w -1 {} is an inverse wavelet transform.

[0163] Preferably, the optimized ultrasonic echo signal, the topographic data and the surface information matrix are fused by using a pre-constructed and trained building steel structure detection model to construct a detection data set, and the building steel structure is detected for defects through the detection data set; the result of the defect detection is evaluated, and a confidence level is divided according to a preset confidence threshold;

[0164] The building steel structure detection model comprises a multi-modal feature extraction unit, a defect recognition unit and a confidence evaluation unit.

[0165] The multi-modal feature extraction unit fuses multi-modal features of the optimized ultrasonic echo signal, the topographic data and the surface information matrix, and constructs a detection data set according to the multi-modal features; the formula of the detection data set is:

[0166] F = tanh(ω S ·S o ☉ tanh(ω M ·M) + ω I ·I + b1);

[0167] D = σ(ω F ·F + b2);

[0168] wherein F is the multi-modal feature; tanh() is the hyperbolic tangent activation function; ω S is the weight of the optimized ultrasound echo signal; S o is the optimized ultrasound echo signal; is the element-wise and nonlinear interaction; ω M is the weight of the topography data; M is the topography data; ω I is the weight of the surface information matrix; I is the surface information matrix; D is the detection dataset; σ() is the activation function; ω F is the multi-modal feature weight; b1 and b2 are the bias terms;

[0169] The defect recognition unit recognizes defects in the building steel structure based on the detection dataset; the defects include coating defects and weld defects; the coating defects include cracking and peeling of the coating; the weld defects include pores, craters, undercut, and cracks on the weld surface;

[0170] The confidence assessment unit assesses the confidence of the defect detection result of the defect recognition unit, and divides the confidence level according to a pre-set confidence threshold; the pre-set confidence threshold is accurately set according to different defects.

[0171] Preferably, local re-inspection and parameter adjustment are performed according to the confidence level to realize closed-loop optimization; the specific process is as follows:

[0172] For the area in the confidence level that is lower than the pre-set confidence threshold, the area is re-acquired using local high-resolution ultrasonic phased array detection to collect the ultrasonic echo signal of the area, and the local re-inspection is performed;

[0173] Based on the result of the local re-inspection, the ultrasonic phased array probe is dynamically adjusted using the adaptive parameter control model to realize the closed-loop optimization.

[0174] Table 4 shows the overall performance optimization result table.

[0175] Table 4 overall performance optimization result table

[0176] Optimization step Signal-to-noise ratio (dB) Stability index Detection accuracy rate (%) Original ultrasonic echo signal 29 0.64 76.3％ Preliminary ultrasonic echo signal 53 0.81 87.5％ Optimized ultrasonic echo signal 68 0.90 93.7％ Signal after closed-loop optimization 72 0.93 97.1％

[0177] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for non-destructive testing of the strength of building steel structures using ultrasonic phased array, characterized in that, include: High-precision laser scanners and electromagnetic induction technology are used to acquire morphological data of the surface of building steel structures; Based on the morphological data, the coating thickness distribution and weld surface roughness of the building steel structure surface are analyzed to construct a surface information matrix; Based on the surface information matrix, the ultrasonic beam parameters of the ultrasonic phased array are automatically adjusted using an adaptive parameter control model, and ultrasonic phased array detection is performed based on the ultrasonic beam parameters to obtain preliminary ultrasonic echo signals. The time-frequency domain difference between the preliminary ultrasonic echo signal and the reference signal is calculated using an echo signal compensation model, and the preliminary ultrasonic echo signal is compensated a second time based on the time-frequency domain difference to obtain an optimized ultrasonic echo signal. The formula for the time-frequency domain difference is: ； ； ； in, Preliminary ultrasound echo signal wavelet coefficients; Reference signal wavelet coefficients; Differences in the time and frequency domains; For scale parameters; These are translation parameters; Wavelet transform; The formula for the secondary compensation is: ； in, To optimize the ultrasonic echo signal; This is a preset compensation coefficient matrix; This is the inverse wavelet transform; A pre-built and trained building steel structure inspection model is used to fuse the optimized ultrasonic echo signal, the morphology data, and the surface information matrix to construct an inspection dataset; the building steel structure is then inspected for defects using the inspection dataset; the results of the defect inspection are evaluated, and confidence levels are assigned based on a pre-set confidence threshold. The building steel structure inspection model includes: a multimodal feature extraction unit, a defect identification unit, and a confidence assessment unit; the multimodal feature extraction unit integrates the multimodal features of the optimized ultrasonic echo signal, the morphology data, and the surface information matrix, and constructs an inspection dataset based on the multimodal features; the formula for the inspection dataset is: ； ； in, It is a multimodal feature; It is the hyperbolic tangent activation function; To optimize the weighting of ultrasonic echo signals; To optimize the ultrasonic echo signal; ⊙ represents elements and nonlinear interactions; Weights for topographic data; For morphological data; The weights of the surface information matrix; For surface information matrix; For the detection dataset; For activation functions; For multimodal feature weights; and For bias terms; The defect identification unit identifies defects in the building steel structure based on the detection dataset; the defects include: coating defects and weld defects; the coating defects include coating cracking and peeling; the weld defects include porosity, arc crater, undercut, and cracks on the weld surface; The confidence assessment unit evaluates the confidence level of the defect detection result of the defect identification unit and classifies the confidence level according to the preset confidence threshold; the preset confidence threshold is precisely set according to different defects. Local re-examination and parameter adjustment are performed based on the confidence level to achieve closed-loop optimization.

2. The method for ultrasonic phased array nondestructive testing of the strength of building steel structures according to claim 1, characterized in that: The topographic data includes three-dimensional surface topological information, reflection intensity data, and surface texture image data; The specific process of analyzing the coating thickness distribution and weld surface roughness of the building steel structure surface based on the morphology data includes: using point cloud reconstruction and digital image processing algorithms to perform differential and regression analysis on the three-dimensional topological information of the surface and the reflection intensity data to obtain the coating thickness distribution; The surface roughness of the weld is extracted using statistical analysis methods based on the surface texture image data.

3. The ultrasonic phased array nondestructive testing method for the strength of building steel structures according to claim 1, characterized in that: The adaptive parameter control model includes an ultrasonic propagation simulation unit and an ultrasonic beam control unit. Based on the surface information matrix, the ultrasonic propagation simulation unit calculates the influence of the coating thickness distribution and the weld surface roughness on the ultrasonic propagation path and energy attenuation, obtaining the coating thickness distribution influence factor and the weld surface roughness influence factor. The ultrasonic beam control unit dynamically adjusts the ultrasonic beam parameters of the ultrasonic phased array probe according to the coating thickness distribution influence factor and the weld surface roughness influence factor. The ultrasonic beam parameters include: beam angle, beam frequency, beam focal length, beamwidth, and beam velocity.

4. The ultrasonic phased array nondestructive testing method for the strength of building steel structures according to claim 1, characterized in that: The echo signal compensation model includes a reference signal acquisition unit, a signal difference analysis unit, and a signal correction unit. The reference signal acquisition unit extracts a reference echo signal that matches the current detection location and material from a standard defect-free steel structure sample library, which serves as the reference signal. The signal difference analysis unit calculates the time-frequency domain difference between the reference signal and the preliminary ultrasonic echo signal using a wavelet transform algorithm. The signal correction unit performs secondary compensation on the preliminary ultrasonic echo signal based on the time-frequency domain difference and a preset compensation coefficient matrix to obtain the optimized ultrasonic echo signal.

5. The ultrasonic phased array nondestructive testing method for the strength of building steel structures according to claim 1, characterized in that: For the low-confidence regions within the stated confidence levels, local re-examination and parameter adjustment are performed. The specific process is as follows: For regions with confidence levels below the preset confidence threshold, the ultrasonic echo signal of the region is re-acquired using local high-resolution ultrasonic phased array detection for local re-detection. Based on the results of the local re-detection, the ultrasonic beam parameters of the ultrasonic phased array probe are dynamically adjusted using the adaptive parameter control model to achieve closed-loop optimization.

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