A method, device and storage medium for locating cracks in a concrete structure

Abstract

This application relates to the field of concrete crack detection technology, and particularly to a method, device, and storage medium for locating cracks in concrete structures. The method includes: arranging an array of acoustic emission sensors on the surface of a concrete component; performing independent component analysis on the acquired multi-channel acoustic emission signals to separate independent acoustic emission events; extracting intrinsic mode functions (EMFs) from each separated independent acoustic emission event; filtering the EMFs to obtain denoised acoustic emission signals; extracting the arrival times of the signals received by each sensor based on the denoised acoustic emission signals; constructing a residual function based on the sensor location, arrival time, and wave velocity, and solving it using an optimization algorithm to obtain the spatial location of the crack source; and performing cluster analysis on the spatial locations of multiple acoustic emission events to identify the crack distribution area. This application solves the problem that traditional acoustic emission location methods are easily affected by noise pollution in complex engineering sites, leading to inaccurate location.

Description

Technical Field

[0001] This application relates to the field of concrete crack detection technology, and in particular to a method, equipment and storage medium for locating cracks in concrete structures. Background Technology

[0002] In the field of concrete crack monitoring, the current challenge lies in the significant impact of strong background noise interference, material heterogeneity, and structural boundary effects on the reliable acquisition and accurate analysis of acoustic emission signals. Environmental vibrations and equipment noise can easily mask the weak acoustic emission signals of microcracks, while the uneven distribution of aggregates, pores, and existing micro-damage within concrete cause spatial heterogeneity in sound wave propagation speed, leading to systematic biases in traditional positioning methods based on fixed wave velocity models. Simultaneously, acoustic wave reflections and mode transitions caused by abrupt changes in structural geometry further distort waveform characteristics, increasing the difficulty of first arrival time identification. Existing positioning algorithms are insufficiently adaptable to these factors, often requiring dense sensor deployment to compensate for accuracy losses. The interplay of these problems results in a high false alarm rate and positioning error in monitoring systems, making it difficult to meet the needs of accurate structural health diagnosis. Summary of the Invention

[0003] This application aims to at least partially address one of the aforementioned technical problems in the prior art. To this end, embodiments of this application provide a method, device, and storage medium for locating cracks in concrete structures, effectively solving the technical problems of high false alarm rates and large location errors in the prior art for locating cracks in concrete structures.

[0004] The first aspect of this application provides a method for locating cracks in a concrete structure, including: S1: Arrange an array of acoustic emission sensors on the surface of the concrete component and synchronously collect the acoustic emission signals of each acoustic emission sensor; S2: Perform independent component analysis on the acquired multi-channel acoustic emission signals to separate independent acoustic emission events; S3: Perform empirical mode decomposition on each isolated independent acoustic emission event and extract the intrinsic mode functions; S4: Based on the energy characteristics, the intrinsic mode functions are filtered to remove the noise-dominated components and reconstruct the denoised acoustic emission signal; S5: Based on the denoised acoustic emission signal, the first arrival time of the signal received by each sensor is extracted using the Akaike information criterion; S6: Based on the sensor location, arrival time, and wave velocity, construct the residual function and solve it using an optimization algorithm to obtain the spatial location of the crack source; S7: Cluster analysis was performed on the spatial locations of multiple acoustic emission events to identify the crack distribution areas.

[0005] According to an embodiment of the first aspect of this application, in step S1: the acoustic emission sensor array includes a main acoustic emission sensor and regional acoustic emission sensors, wherein the main acoustic emission sensors are uniformly distributed throughout the structure, and the regional acoustic emission sensors are densely distributed in the stress concentration areas of the structure.

[0006] According to an embodiment of the first aspect of this application, the independent component analysis in step S2 includes the following sub-steps: Perform mean removal and whitening preprocessing on multi-channel signals; The FastICA algorithm, which maximizes negative entropy, is used to estimate the unmixing matrix and separate the independent components.

[0007] According to an embodiment of the first aspect of this application, the empirical mode decomposition in step S3 includes: Extreme point extraction and envelope fitting of the signal; The difference between the envelope mean and the original signal is calculated as the preliminary intrinsic mode function; Determine whether the proposed intrinsic mode function simultaneously satisfies energy stability and instantaneous frequency stability; if so, it is confirmed as an intrinsic mode function.

[0008] According to an embodiment of the first aspect of this application, the energy stability is determined by calculating whether the ratio of the standard deviation to the mean of the envelope energy is less than an adaptive threshold; the instantaneous frequency stability is determined by calculating whether the standard deviation of the instantaneous frequency is less than an adaptive threshold related to the frequency offset.

[0009] According to an embodiment of the first aspect of this application, the method for screening intrinsic mode functions based on energy characteristics in step S4 is as follows: Calculate the Hilbert spectrum of each component; Set an adaptive energy threshold to retain components whose energy percentage exceeds the set proportion and remove noise-dominated components.

[0010] According to an embodiment of the first aspect of this application, the method for extracting the first arrival time using the Akaike information criterion in step S5 is as follows: The waveform is segmented and the AIC value is calculated segment by segment; The peak point where the AIC value exceeds the set threshold is determined as the first arrival time; The set threshold is dynamically adjusted based on the structural dimensions and material attenuation coefficient.

[0011] According to an embodiment of the first aspect of this application, the wave velocity in step S6 is obtained through automatic sensor testing and calibration, specifically by actively exciting and recording the propagation time between sensors under a healthy structural condition, calculating the wave velocity of each path, and constructing a wave velocity field model; the optimization algorithm in step S6 is a particle swarm optimization algorithm, which solves for the crack source coordinates by minimizing the residual function.

[0012] A second aspect of this application provides a computer device, comprising: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a concrete structure crack location method as described above.

[0013] A third aspect of this application provides a computer-readable storage medium, wherein a processor-executable program, when executed by the processor, is used to implement a method for locating cracks in a concrete structure as described in any of the preceding claims.

[0014] Based on the above technical solution, the embodiments of this application have at least the following beneficial effects: in a strong noise interference environment, it achieves high robustness and high precision in locating the source of micro-crack initiation and propagation in concrete, solves the problem that traditional acoustic emission positioning methods are easily affected by noise pollution in complex engineering sites, and effectively solves the technical problems of large false alarm rate and large positioning error in the positioning of cracks in concrete structures in the prior art. Attached Figure Description

[0015] The present application will be further described below with reference to the accompanying drawings and embodiments; Figure 1 This is a flowchart of a method for locating cracks in a concrete structure according to an embodiment of this application; Figure 2 This is a schematic diagram of the array arrangement of acoustic emission sensors on a concrete component in one embodiment of this application. Detailed Implementation

[0016] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0017] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0018] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0019] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0020] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0021] It should be noted that when an element is referred to as being "fixed to" or "set on" another element, it can be directly on the other element or there may be an intervening element. When an element is considered to be "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0022] In the field of concrete crack monitoring, the current challenge lies in the significant impact of strong background noise interference, material heterogeneity, and structural boundary effects on the reliable acquisition and accurate analysis of acoustic emission signals. Environmental vibrations and equipment noise can easily mask the weak acoustic emission signals of microcracks, while the uneven distribution of aggregates, pores, and existing micro-damage within concrete cause spatial heterogeneity in sound wave propagation speed, leading to systematic biases in traditional positioning methods based on fixed wave velocity models. Simultaneously, acoustic wave reflections and mode transitions caused by abrupt changes in structural geometry further distort waveform characteristics, increasing the difficulty of first arrival time identification. Existing positioning algorithms are insufficiently adaptable to these factors, often requiring dense sensor deployment to compensate for accuracy losses. The interplay of these problems results in a high false alarm rate and positioning error in monitoring systems, making it difficult to meet the needs of accurate structural health diagnosis.

[0023] Reference Figure 1 This application provides a method for locating cracks in concrete structures, comprising the following steps: S1. An array of acoustic emission sensors 200 is arranged on the surface of the concrete member 100 to synchronously collect the acoustic emission signals of each sensor. S2. Perform independent component analysis on the acquired multi-channel acoustic emission signals to separate independent acoustic emission events; S3. Perform empirical mode decomposition on each isolated independent acoustic emission event and extract the intrinsic mode functions; S4. Based on the energy characteristics, the intrinsic mode functions are screened to remove the noise-dominated components and reconstruct the denoised acoustic emission signal. S5. Based on the denoised acoustic emission signal, the first arrival time of the signal received by each sensor is extracted using the Akaike information criterion. S6. Based on the sensor location, arrival time, and wave velocity, construct the residual function and solve it using an optimization algorithm to obtain the spatial location of the crack source; S7. Cluster analysis of the spatial locations of multiple acoustic emission events is performed to identify the crack distribution areas.

[0024] In some embodiments, in step S1: the acoustic emission sensor 200 array includes a main acoustic emission sensor 200 and regional acoustic emission sensors 200, wherein the main acoustic emission sensors 200 are uniformly distributed throughout the structure, and the regional acoustic emission sensors 200 are densely distributed in the stress concentration areas of the structure.

[0025] Specifically, a hierarchical "trunk-region" sensor network is arranged on the concrete component 100. Trunk acoustic emission sensors 200 are symmetrically arranged in the middle of the left and right sides. Additional regional acoustic emission sensors 200 are arranged at stress concentration points according to the component's stress state, for a total of N acoustic emission sensors 200. The acoustic emission sensors 200 are connected to the concrete component 100 using double-sided adhesive tape. The acoustic emission sensors 200 are connected to a data acquisition device. When the concrete component 100 cracks, the generated acoustic signal is received by the acoustic emission sensors 200. The parameters of the acoustic emission sensors 200 and the acquisition device are set as follows: the operating frequency of the acoustic emission sensors 200 is set between 35kHz and 65kHz, the resonant frequency is 55kHz, and the peak sensitivity is 106dB. The sampling rate of the acquisition device is 5 Msamples / s. Figure 2 In the illustrated embodiment, acoustic emission sensors 200 are arranged on the top, bottom, left, and right sides of the concrete component 100. This acoustic emission sensor array, which employs oblique symmetry on the top and bottom sides and central symmetry on the left and right sides, requires only four sensors to form an effective spatial constraint, significantly reducing cost and complexity compared to traditional dense arrays.

[0026] In some embodiments, the independent component analysis in step S2 includes the following sub-steps: Perform mean removal and whitening preprocessing on multi-channel signals; The FastICA algorithm, which maximizes negative entropy, is used to estimate the unmixing matrix and separate the independent components.

[0027] The specific steps are as follows: Step S2.1: Synchronously acquire signals from all N acoustic emission sensors (200 channels) to form an N-dimensional observation signal vector. X (t)=[ x 1(t), x 2(t), ..., x N (t)] T .

[0028] Step S2.2: Calculate the observed signal vector X The mean vector of (t) m X(t) is preprocessed by removing the mean and whitening.

[0029] Step S2.3: Input the preprocessed multi-channel signal X(t) into the ICA algorithm model to separate the independent components of the multi-channel signal as independent acoustic emission events. This specifically includes the following operations: The ICA algorithm model is based on the linear mixture assumption. X The algorithm aims to find a demixing matrix W such that the output signal is equal to A × S(t), where S(t) is an unknown statistically independent source signal vector and A is an unknown mixing matrix. Y (t)=W× X (t) is the best approximation of the source signal S(t). This maximizes the output signal components. y i The statistical independence between (t) is taken as the optimization objective. The FastICA algorithm based on maximizing negative entropy is adopted. The unmixing matrix W is quickly estimated by fixed-point iteration method, and the output is a series of independent components. y i (t).

[0030] In some embodiments, the empirical mode decomposition in step S3 includes: Extreme point extraction and envelope fitting of the signal; The difference between the envelope mean and the original signal is calculated as the preliminary intrinsic mode function; Determine whether the proposed intrinsic mode function simultaneously satisfies energy stability and instantaneous frequency stability; if so, it is confirmed as an intrinsic mode function.

[0031] The specific steps are as follows: Step S3.1: For each individual component separated by ICA y i (t) Improved adaptive EMD noise reduction processing. Specifically, this includes: Step S3.1.1: Locate the acoustic emission signal ( y i Find all the local maxima and minima of (t) and then fit them together as upper and lower envelopes using a cubic spline function.

[0032] Step S3.1.2: Calculate the average value of the upper and lower envelopes ( µ i (t)).

[0033] Step S3.1.3: Calculate the difference between the acoustic emission signal and the envelope average value ( I i (t) serves as the first preparatory intrinsic mode function.

[0034] Step S3.1.4: Confirm I i(t) Whether it satisfies both energy stability and instantaneous frequency stability; if it satisfies both, then... I i (t) is considered an intrinsic mode function; otherwise, it is considered as y i (t) and repeat steps S3.1.1-S3.1.4 until it satisfies the key characteristics of the intrinsic mode function, i.e., satisfies the stopping criterion.

[0035] In some embodiments, the energy stability is determined by calculating whether the ratio of the standard deviation to the mean of the envelope energy is less than an adaptive threshold; the instantaneous frequency stability is determined by calculating whether the standard deviation of the instantaneous frequency is less than an adaptive threshold related to the frequency offset.

[0036] The calculation of the two characteristics, energy stability and instantaneous frequency stability, is as follows: Define the instantaneous envelope energy as: ; in, E env ( t () represents the instantaneous envelope energy. e upper ( t )and e lower ( t ) are the upper and lower envelopes, respectively.

[0037] Calculate the standard deviation of the instantaneous envelope energy over the entire time range. s E with the mean m E The ratio of is defined as the energy stability index. c ; calculate E env The ratio of the median absolute deviation (MAD) of (t) to its median is used as the volatility benchmark. R : ; in, i γ,auto For adaptive energy stability threshold, k γ This is a relaxation factor with a value between 2 and 4.

[0038] like c < i γ,auto Then it is believed I i(t) satisfies the energy stationarity condition, which is one of the conditions for being an eigenmode function.

[0039] Next, for I i (t) is subjected to Hilbert transform to obtain the analytic signal. z (t) ; Extracting instantaneous frequency from an analytical signal f inst (t) (The derivative of the instantaneous phase with respect to time) Calculate the standard deviation of instantaneous frequency s f Then calculate the adaptive frequency stability threshold. i f,auto Adaptive frequency stability threshold i f,auto The calculation is as follows: ; Among them, IQR is f inst The interquartile range of (t), k f A preset scaling factor with values ​​between 1.5 and 2. α (Δ f ) is the frequency offset Δ f The relevant modulation factor function.

[0040] ; Where, Δ f This refers to the relative frequency offset. α min The minimum modulation factor has a value range of [0.5, 1.0], with a preferred value of 0.8; α max is the maximum modulation factor, with a value range of [1.5, 3.0], and a preferred value of 2.0; k is the curve steepness factor, with a value range of [5, 20], and a preferred value of 10; Δ f 0 is the center point of the function, and its calculation formula is Δ f 0 = ζ•s, where ζ is the damping ratio of the concrete structure obtained through active testing and calibration, and s is the scaling factor, with a value range of [1.0, 3.0], and a preferred value of 2.0.

[0041] like s f < i f,auto Then it is believed I i(t) satisfies the frequency stability condition, which is another condition for it to be an eigenmode function.

[0042] like I i If (t) simultaneously satisfies energy stability and frequency stability, then it is considered that I i (t) is an eigenmode function.

[0043] Step 3.2: Upon successful calculation I i (t) after which, from the original signal y i Subtract the signal from (t) I i (t), and repeat steps S3.1.1-S3.1.4 to find other intrinsic mode functions.

[0044] The calculation is described as follows: ; in I i (t) (i=1, 2, ..., n) represents the recorded signal y The eigenmode functions of (t), R n (t) is y The residual of (t).

[0045] In some embodiments, the method for screening intrinsic mode functions based on energy characteristics in step S4 is as follows: Calculate the Hilbert spectrum of each component; Set an adaptive energy threshold to retain components whose energy percentage exceeds the set proportion and remove noise-dominated components.

[0046] Specifically, the Hilbert spectrum of each intrinsic mode function (EMF) component is calculated to obtain its time-frequency-energy distribution. An adaptive energy threshold is set, and the proportion of energy in the Hilbert spectrum exceeding the threshold is calculated. If the proportion is greater than 80%, the EMF is considered to be dominated by effective signals and is retained; if the proportion is less than 20%, the EMF is considered to be dominated by noise and is removed; if it is between the two, wavelet thresholding denoising is performed on the EMF before retention.

[0047] In some embodiments, the method for extracting the first arrival time using the Akaike information criterion in step S5 is as follows: The waveform is segmented and the AIC value is calculated segment by segment; The peak point where the AIC value exceeds the set threshold is determined as the first arrival time; The set threshold is dynamically adjusted based on the structural dimensions and material attenuation coefficient.

[0048] The specific steps are as follows: Step S5.1: Extract the arrival time of the acoustic emission signal from the waveform information using the Akaike Information Criterion (AIC) to identify the moment when the signal first deviates from the noise band. The implementation process is as follows: 1. Waveform segmentation: Divide the measured acoustic emission waveform into several time segments; 2. AIC value calculation: Calculate the AIC value segment by segment according to the following formula; ; in j Each data point in the waveform represents a data point. t j For data points j The corresponding time, t 0 represents the waveform start time. var( R j ( t 0, t j )) is from t 0 to t j The variance of all voltage values T j This represents the time of the last data point in the waveform. (var() R j ( t j+1 , T j )) is from t j+1 arrive T j The variance of all voltage values.

[0049] Step S5.2: In each segment, when the calculated AIC value exceeds the set threshold, the peak point is identified as the first arrival time of the sound wave.

[0050] After calculating the spatial coordinates of a large number of acoustic emission events ( x a , y a After time t, the K-Means algorithm is used to cluster these data points to distinguish which locations belong to the same crack and which are irrelevant noise points or other cracks.

[0051] Finally, box plots were used to quantify the accuracy of local acoustic emission events caused by crack initiation and propagation, and the propagation of these events within the component.

[0052] In some embodiments, the wave velocity in step S6 is obtained through automatic sensor testing and calibration, specifically by actively exciting and recording the propagation time between sensors under a healthy structural condition, calculating the wave velocity of each path, and constructing a wave velocity field model; the optimization algorithm in step S6 is a particle swarm optimization algorithm, which solves for the crack source coordinates by minimizing the residual function.

[0053] The specific steps are as follows: 1. The wave velocity of sound waves in concrete member 100 is calibrated using the Automatic Sensor Test (AST) function. v The wave propagation time between sensors is measured using the AST function, and the wave velocity is calculated as the ratio of the distance between paired sensors to the corresponding propagation time. ; Where, Δ x Representing paired sensors x Axial spacing, Δ x Representing paired sensors y Axial spacing, Δ t This represents the time interval between the reception of acoustic signals by the paired sensors.

[0054] 2. Obtain the time when the sound signal is received by the sensor, and regard the process of obtaining the location of concrete cracking as searching in space for the location that minimizes the residual function of the following formula ( x a , y a ): ; in, t j It is a sensor j Arrival time, x a and y a These are the expected coordinates of the acoustic emission source (i.e., the source of concrete crack initiation). x j and y j These are the coordinates of the sensor. v It is measured by the wave velocity of the material being monitored. It can provide the lowest residual value ( α The estimated coordinates of ) x a , y a This is the optimal location for the acoustic emission source.

[0055] The location and time of occurrence of the acoustic emission source (crack) are retrieved from the acoustic emission signal. The specific operation consists of the following sub-steps: 1. The wave velocity of sound waves in concrete member 100 is calibrated using the Automatic Sensor Test (AST) function. v The wave propagation time between sensors is measured using the AST function, and the wave velocity is calculated as the ratio of the distance between paired sensors to the corresponding propagation time. ; 2. Obtain the time when the acoustic signals are received by the four sensors. The process of obtaining the location of concrete cracking is regarded as searching in space for the location that minimizes the residual function in the following formula. x a , y a This is the unknown variable that needs to be optimized, and the goal is to minimize it. α Find its optimal value: ; in, t j It is a sensor j The actual measured arrival time of the acoustic signal (in seconds). This is known input data, recorded by the sensors. The acoustic signal is generated by the concrete cracking event (acoustic emission source) and propagates to each sensor.

[0056] x a and y a These are the expected coordinates of the acoustic emission source (i.e., the source of concrete crack initiation).

[0057] x j and y j It is a sensor j The known coordinates (unit: meters). The sensor position is fixed and precisely measured before the experiment.

[0058] From the estimated source location ( x a , y a ) to sensor j Euclidean distance (unit: meter). This represents the straight-line distance a signal travels through a material.

[0059] v It is the speed of sound waves propagating in the monitored material (such as concrete) (unit: meters per second). This is a known constant, usually determined in advance through material calibration experiments.

[0060] The predicted time (in seconds) for a signal to travel from the estimated source location to the sensor is based on the fundamental physical law that propagation time = distance / wave speed.

[0061] Represents a single sensor j The residual (in seconds). It represents the error between the actual arrival time and the predicted arrival time. If the residual is positive ( t j >Predicted time indicates that the signal actually arrived later than expected, the predicted location may be too close or the wave velocity estimation may be incorrect.

[0062] If the residual is negative ( t j <Predicted time> indicates that the signal actually arrived earlier than expected, and the estimated location may be too far away.

[0063] For all sensors ( j =1 to m The residuals of are summed. In this practical case, m =4, therefore α It is the sum of the residuals of the four sensors.

[0064] Particle swarm optimization provides the lowest residual value. α The estimated coordinates of ) x a , y a The specific steps are as follows: 1. Initialize the particle swarm. Define a two-dimensional search space within the structure monitoring plane: x min ≤ x a ≤ x max , y min ≤ y a ≤ y max In this project case, the bottom left corner of the front of the concrete cuboid is defined as the origin (0,0), with the length of the cuboid as the positive x-axis and the width as the positive y-axis. Therefore, x min and y min It is 0. x max and y max Given the maximum width and length of the concrete cuboid. Randomly generate N particles, where N must be greater than 30, and each particle's position vector P. i=( x a,i , y a,i The particles are uniformly distributed in the search space. Initialize particle velocities. V i =( v a,i , v y,i ).

[0065] 2. Set the algorithm parameters. Set the inertia weight range to [0.4, 0.9], with an initial value of 0.8; set the individual learning factor range to [1.0, 2.0], with an initial value of 1.5; set the group learning factor range to [1.5, 2.5], with an initial value of 2.0. The maximum number of iterations needs to be greater than 500.

[0066] Iterative optimization process. First, calculate the residual value of each particle's current position according to the following formula: ; in S j =( x j , y j ) represents the sensor coordinates, and ||●|| represents the Euclidean distance.

[0067] like α i < α best , i Then update the optimal solution P for the individual particle. best , i =P i If min( α i )< α global Then update the global optimal solution g. best =argmin Pi α i Update particle positions .

[0068] Termination and output. When reached... T max or the rate of change of the optimal solution Set to 10 -6 The iteration terminates when the time limit is reached. Output the global optimal solution and its residual value.

[0069] Post-processing verification. Calculate the theoretical arrival time of each sensor and verify whether the residual distribution meets the expected error range.

[0070] This application also provides a computer device, including: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a concrete structure crack location method as described above.

[0071] A concrete structure crack location device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) or a program loaded from a storage device into random access memory (RAM). The RAM also stores various programs and data required for the operation of the concrete structure crack location device. The processing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus. Typically, the following systems can be connected to the I / O interface: input devices including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices including, for example, magnetic tapes, hard disks, etc.; and communication devices. The communication device allows the wall identification device to communicate wirelessly or wiredly with other devices to exchange data. However, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0072] This application also discloses a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to implement an embodiment of a concrete structure crack location method of this application.

[0073] Various implementations of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SOCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof.

[0074] These various implementations may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0075] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0076] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0077] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0078] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0079] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0080] This invention is based on an adaptive strong noise suppression technique using intrinsic mode function (IMF) decomposition, and a precise spatiotemporal positioning algorithm that integrates wave velocity calibration, residual minimization, and the Akaike Information Criterion (AIC). Under strong noise interference, it achieves highly robust and accurate positioning of the initiation and propagation sources of microcracks within concrete, solving the core problem of traditional acoustic emission positioning methods being susceptible to noise pollution and resulting in inaccurate positioning in complex engineering sites.

[0081] The core noise suppression mechanism iteratively filters single-component signals (IMFs) that meet stringent characteristics and removes low-amplitude noise components based on energy thresholds, achieving adaptive removal of strong noise from the original signal and significantly improving the signal-to-noise ratio. The localization process combines material wave velocity calibrated by Automated Sensor Testing (AST), a spatial search algorithm based on residual function minimization, and precise first-arrival time acquisition technology based on the AIC criterion, providing triple assurance of localization accuracy. The AIC threshold is dynamically adjusted based on structural scale (small / medium / large) and material attenuation characteristics to ensure robustness of first-arrival time identification under different operating conditions. Finally, box plot quantification analysis effectively identifies real crack events and their propagation characteristics.

[0082] The embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of this application.

Claims

1. A method for locating cracks in a concrete structure, characterized in that, include: S1: Arrange an array of acoustic emission sensors on the surface of the concrete component and synchronously collect the acoustic emission signals of each acoustic emission sensor; S2: Perform independent component analysis on the acquired multi-channel acoustic emission signals to separate independent acoustic emission events; S3: Perform empirical mode decomposition on each isolated independent acoustic emission event and extract the intrinsic mode functions; S4: Based on the energy characteristics, the intrinsic mode functions are filtered to remove the noise-dominated components and reconstruct the denoised acoustic emission signal; S5: Based on the denoised acoustic emission signal, the first arrival time of the signal received by each sensor is extracted using the Akaike information criterion; S6: Based on the sensor location, arrival time, and wave velocity, construct the residual function and solve it using an optimization algorithm to obtain the spatial location of the crack source; S7: Cluster analysis of the spatial locations of multiple acoustic emission events to identify the crack distribution areas.

2. The method for locating cracks in concrete structures according to claim 1, characterized in that: In step S1: the acoustic emission sensor array includes a main acoustic emission sensor and regional acoustic emission sensors, wherein the main acoustic emission sensors are uniformly distributed throughout the structure, and the regional acoustic emission sensors are densely distributed in the stress concentration areas of the structure.

3. The method for locating cracks in concrete structures according to claim 1, characterized in that: The independent component analysis in step S2 includes the following sub-steps: Perform mean removal and whitening preprocessing on multi-channel signals; The FastICA algorithm, which maximizes negative entropy, is used to estimate the unmixing matrix and separate the independent components.

4. The method for locating cracks in concrete structures according to claim 1, characterized in that: The empirical mode decomposition mentioned in step S3 includes: Extreme point extraction and envelope fitting of the signal; The difference between the envelope mean and the original signal is calculated as the preliminary intrinsic mode function; Determine whether the proposed intrinsic mode function simultaneously satisfies energy stability and instantaneous frequency stability; if so, it is confirmed as an intrinsic mode function.

5. The method for locating cracks in concrete structures according to claim 4, characterized in that: The energy stability is determined by calculating whether the ratio of the standard deviation to the mean of the envelope energy is less than an adaptive threshold; the instantaneous frequency stability is determined by calculating whether the standard deviation of the instantaneous frequency is less than an adaptive threshold related to the frequency offset.

6. The method for locating cracks in concrete structures according to claim 1, characterized in that: The method for selecting intrinsic mode functions based on energy characteristics in step S4 is as follows: Calculate the Hilbert spectrum of each component; Set an adaptive energy threshold to retain components whose energy percentage exceeds the set proportion and remove noise-dominated components.

7. The method for locating cracks in concrete structures according to claim 1, characterized in that: The method for extracting the first arrival time using the Akaike information criterion in step S5 is as follows: The waveform is segmented and the AIC value is calculated segment by segment; The peak point where the AIC value exceeds the set threshold is determined as the first arrival time; The set threshold is dynamically adjusted based on the structural dimensions and material attenuation coefficient.

8. The method for locating cracks in concrete structures according to claim 1, characterized in that: The wave velocity mentioned in step S6 is obtained through automatic sensor testing and calibration. Specifically, it involves actively exciting and recording the propagation time between sensors under a healthy structural condition, calculating the wave velocity of each path, and constructing a wave velocity field model. The optimization algorithm mentioned in step S6 is a particle swarm optimization algorithm, which solves for the crack source coordinates by minimizing the residual function.

9. A computer device, characterized in that: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a method for locating cracks in a concrete structure as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The program executable by the processor is used, when executed by the processor, to implement a method for locating cracks in a concrete structure as described in any one of claims 1 to 8.

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