Methods, devices and storage media for detecting voids in the inner layer of concrete

Abstract

This invention provides a method, apparatus, and storage medium for detecting voids in the inner layer of concrete. The method includes: acquiring echo signals from the area of ​​concrete to be inspected; calculating the quadratic penalty factor α and the number of decomposition layers k of a variational mode decomposition algorithm based on the echo signals and a sparrow search algorithm, with the objective function being the minimum fitness function value, to obtain a parameter combination (k, α); wherein the fitness function is constructed based on power spectral entropy, error severity index, and center frequency evaluation index; decomposing the echo signals using the variational mode decomposition algorithm according to the parameter combination (k, α), obtaining k echo signal modal components; calculating the autocorrelation function of each echo signal modal component in the k echo signal modal components; and determining the detection result of voids in the area of ​​concrete to be inspected based on the autocorrelation function of each echo signal modal component. This invention offers high detection accuracy and speed, meeting the needs of concrete defect detection.

Description

Technical Field

[0001] This invention relates to the field of concrete quality testing technology, and in particular to a method, apparatus and storage medium for detecting voids in the inner layer of concrete. Background Technology

[0002] my country has a vast territory, complex geological conditions, and significant environmental differences. Due to variations in construction conditions, geographical environment, and construction standards, multi-story concrete structures are prone to irreversible structural defects such as cracks and voids. The occurrence and development of these defects lead to rapid deterioration of the concrete structure's performance, shortening its service life and often causing train operation accidents such as concrete falling and support collapse, seriously affecting operational safety. Among concrete structural defects, internal voids are the most common and most harmful. Void defects are often located in the inner layers of multi-story concrete structures, are concealed, and are difficult to detect using conventional surface inspection methods. Furthermore, the appearance of void defects alters the internal stress transmission structure of the concrete, disrupts its normal stress distribution, and easily induces other concrete structural defects. Therefore, research on concrete void defect detection technology is of great engineering significance for maintaining the operational safety of high-speed railways.

[0003] Existing concrete testing methods include manual inspection, ground-penetrating radar (GPR), ultrasonic testing, and infrared thermal imaging. However, manual inspection is limited by the operator's experience and has poor accuracy; GPR is sensitive to the influence of internal steel reinforcement and is subject to significant interference during testing; ultrasonic testing has a fast attenuation rate and limited detection distance; and infrared thermal imaging is greatly affected by the thermal conductivity of concrete, making it difficult to meet the requirements for convenient and rapid detection of defects in concrete structures in service. Summary of the Invention

[0004] This invention provides a method, apparatus, and storage medium for detecting voids in the inner layer of concrete, in order to solve the problems of poor accuracy, limited detection distance, and slow detection speed in current concrete testing methods.

[0005] In a first aspect, embodiments of the present invention provide a method for detecting voids in the inner layer of concrete, comprising:

[0006] The echo signal of the concrete to be tested area is collected; wherein, the echo signal is the echo signal generated by the concrete when the excitation device strikes the concrete surface;

[0007] Based on echo signals and the sparrow search algorithm, the quadratic penalty factor α and the number of decomposition layers of the variational mode decomposition algorithm are calculated with the goal of minimizing the fitness function. k , to obtain parameter combinations ( k , α); where the fitness function is constructed based on the power spectral entropy, error degree index and center frequency evaluation index;

[0008] According to the parameter combination ( k The echo signal is decomposed using a variational mode decomposition algorithm (α), resulting in... k One echo signal modal component; among which... k Integers greater than zero;

[0009] calculate k The autocorrelation function of each echo signal modal component is determined; and the detection result of void defects in the concrete testing area is determined based on the autocorrelation function of each echo signal modal component.

[0010] In one possible implementation, the power spectral entropy is:

[0011]

[0012] in, This represents the power spectral density of the echo signal's modal components.

[0013] In one possible implementation, the error level index is obtained according to the symmetric mean absolute percentage error formula, which is used to calculate the error between the synthesized echo signal and the echo signal; wherein, the synthesized echo signal is synthesized from the modal components of each echo signal;

[0014] The error level index is:

[0015]

[0016] in, Synthesize echo signal sequences for each echo signal mode component. It is an echo signal sequence. This is the signal length.

[0017] In one possible implementation, the center frequency evaluation index is:

[0018]

[0019] in, For the first i The echo signal modal component and the first i +1 center frequency difference of the initial echo signal modal components The sampling frequency.

[0020] In one possible implementation, the fitness function is: .

[0021] In one possible implementation, the detection result of the concrete area to be inspected is determined based on the autocorrelation function of each echo signal modal component, including:

[0022] The autocorrelation sequence and attenuation rate of each echo signal mode component are determined based on the autocorrelation function of each echo signal mode component.

[0023] The autocorrelation series and attenuation rate corresponding to each echo signal mode component are compared respectively;

[0024] If any echo signal mode component exhibits the largest fluctuation amplitude in its autocorrelation sequence and the slowest decay rate, then the area to be tested in the concrete is determined to have inner-layer void defects.

[0025] In one possible implementation, the sparrow search algorithm is an improved version of the Tent chaotic mapping algorithm and the Cauchy mutation perturbation algorithm.

[0026] In a second aspect, embodiments of the present invention provide a control device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect or any possible implementation thereof.

[0027] Thirdly, embodiments of the present invention provide a detection device, including: a control device and an excitation device as provided in the second aspect;

[0028] The excitation device includes an excitation module, a data acquisition module, a control module, and a support module;

[0029] The excitation module includes a motor, a transmission belt, a cam mechanism, and a housing; the cam mechanism includes a metal hammer, a push rod, a spring, and a cam; the push rod is connected to the cam and the metal hammer at both ends respectively; one end of the spring is connected to the metal hammer, and the other end is fixed to the housing;

[0030] The acquisition module is used to acquire echo signals from the concrete area to be tested.

[0031] The control module is used to control the rotation of the motor. The motor drives the cam to rotate through the transmission belt, which stretches the spring. When the cam reaches its maximum stroke, the spring contracts instantly to strike the concrete area to be tested.

[0032] The support module is used to support the control module, acquisition module, and excitation module.

[0033] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation thereof.

[0034] This invention provides a method, device, and storage medium for detecting voids in the inner layer of concrete. It employs an acoustic vibration detection method, which boasts advantages such as a large detection range, high identification accuracy, and low complexity, making it suitable for concrete structure testing requirements. The method involves acquiring echo signals from the concrete in the area to be tested; using a sparrow algorithm and the echo signals, the quadratic penalty factor α and the number of decomposition layers in the variational mode decomposition algorithm are calculated with the objective function of minimizing the fitness function. k , to obtain parameter combinations ( k The fitness function is constructed based on the power spectral entropy, error index, and center frequency evaluation index. When the fitness function is at its minimum, the echo signal modal components decomposed according to the parameter combination at this time all have small bandwidths and a suitable number of modes, which can achieve effective decomposition of the signal. When the fitness function reaches its optimal value, the echo signal modal components obtained from the solution of the fitness function all have small bandwidths and a suitable number, which can achieve effective decomposition of the echo signal. Finally, the autocorrelation function of each echo signal modal component is calculated; the autocorrelation function can describe the correlation degree of the echo signal at different times. Therefore, the concrete detection result of the area to be detected can be determined according to the autocorrelation function. This invention solves the problems existing in the current detection of concrete structure defects, overcomes the damage to concrete structures caused by the original manual detection methods such as core drilling and sampling, and proposes a variational mode decomposition method to separate the vibration components of defects. The autocorrelation function increases the identification of defects, which is conducive to improving the maintenance level of concrete structures of high-speed railways in my country. It has the characteristics of simple operation, high degree of automation, high detection accuracy, and fast speed, and can meet the needs of concrete defect detection. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart illustrating the implementation of the concrete inner layer void defect detection method provided in this embodiment of the invention;

[0037] Figure 2 This is a schematic diagram of the excitation device structure of the concrete inner layer void defect detection method provided in the embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of the excitation module structure of the concrete inner layer void defect detection method provided in the embodiment of the present invention;

[0039] Figure 4 This is a comparison chart of the parameter combination acquisition results of the concrete inner layer void defect detection method provided in the embodiment of the present invention;

[0040] Figure 5 This is an autocorrelation function graph of the location component of the concrete inner layer void defect detection method provided in the embodiments of the present invention;

[0041] Figure 6 This is an autocorrelation function graph of the compaction location component of the concrete inner layer void defect detection method provided in the embodiments of the present invention;

[0042] Figure 7 This is a spectrum of the location of defects in the concrete inner layer void defect detection method provided in this embodiment of the invention;

[0043] Figure 8 This is a schematic diagram of the control device provided in an embodiment of the present invention. Detailed Implementation

[0044] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0045] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0046] Figure 1 This is a flowchart illustrating the implementation of the concrete inner layer void detection method provided in this embodiment of the invention. Figure 1 As shown:

[0047] Step 110: Collect the echo signal of the concrete area to be tested; wherein, the echo signal is the echo signal generated by the concrete when the excitation device strikes the concrete surface.

[0048] In this embodiment, when it is necessary to collect echo signals from the concrete area to be tested, the remote control terminal sends a working signal. Upon receiving the signal, the excitation device begins operation. When the excitation device strikes the concrete surface, it collects the echo signals generated by the concrete and sends them back to the remote control terminal for analysis. Because the excitation device has wheels and a simple structure, it can be pushed by a worker to strike the concrete over a wide area, thus enabling large-area testing. Each strike generates a segment of echo signal, and the analysis is performed on this segment of echo signal.

[0049] Specifically, Figure 2 This is a schematic diagram of the excitation device structure of the concrete inner layer void defect detection method provided in the embodiment of the present invention; Figure 3 This is a schematic diagram of the excitation module structure of the concrete inner layer void defect detection method provided in this embodiment of the invention; the following is in conjunction with... Figure 2 and Figure 3 The structure and working principle of the excitation device are described below:

[0050] In this embodiment, the excitation device may include four parts: an excitation module, a data acquisition module, a control module, and a support module.

[0051] The excitation module includes a motor 11, a transmission belt, a cam mechanism, and a housing 12. The cam mechanism is located inside the housing 12, and the transmission belt is located on the camshaft of the cam 124 of the motor 11 and the cam mechanism, and is used to drive the cam 124 to rotate coaxially (not shown in the figure).

[0052] The cam mechanism includes a metal hammer 121, a push rod 122, a spring 123, a cam 124, a nut 125, an upper cover 126, and a lower shell 127. The push rod 122 is connected to the cam 124 and the metal hammer 121 at both ends, respectively; one end of the spring 123 is connected to the metal hammer 121, and the other end is fixed to the lower shell 127.

[0053] The acquisition module may include a microphone sensor 21, a bracket 22, and a connector 23. The bracket 22 supports the microphone sensor 21, which has 8 microphone channels in any combination. It is used to acquire echo signals from the concrete area to be inspected and can also send the acquired echo signals to the control module.

[0054] The control module can be installed at any point on the excitation device as needed, therefore it is not marked in the figure. The control module may include a control chip and a Bluetooth unit. The control chip can be an STM32, and the Bluetooth unit enables information transmission. Upon receiving a working signal, the control module controls the motor 11 to rotate. The motor 11 drives the cam 124 to rotate via a transmission belt, causing the spring 123 to stretch. When the cam 124 reaches its maximum stroke, the spring 123 instantly contracts, causing the metal hammer 121 to fall and strike the concrete area to be tested.

[0055] The support module includes a frame 31 and wheels 32. The frame 31 is made of aluminum alloy tubing and corner brackets, which facilitates loading and unloading and allows for quick assembly to adapt to different working environments. The rods are screwed into the upper connector to keep the metal hammer 121 in close contact with the wall surface, and the wheels 32 allow for easy manual pushing.

[0056] Step 120: Based on the echo signal and the sparrow search algorithm, calculate the quadratic penalty factor α and the number of decomposition levels for the variational mode decomposition algorithm, with the objective function being to minimize the fitness function value. k , to obtain parameter combinations ( k , α); where the fitness function is constructed based on the power spectral entropy, error degree index and center frequency evaluation index.

[0057] In this embodiment, Variational mode decomposition (VMD) is a non-recursive signal processing method with excellent noise robustness and anti-mode aliasing ability. It determines the center frequency and bandwidth of each component by searching for the optimal solution of the variational model, thereby realizing the multi-mode decomposition of complex time-series signals.

[0058] The VMD algorithm defines each eigenmode as revolving around a center frequency. Given a finite bandwidth signal, the bandwidth of each modal component is estimated using Gaussian smoothness, thus constructing a constrained variational problem. The VMD algorithm can be described as follows:

[0059]

[0060] in, and These are the sets of each mode and its center frequency. The function to be decomposed, It is a unit impulse function. denoted as the number of modes in the decomposition.

[0061] By introducing a secondary penalty factor With Lagrange multiplication operators The constrained variational problem is transformed into the unconstrained problem shown in the following equation:

[0062]

[0063] The alternating direction multiplier method is used to solve the unconstrained problem. The update formulas for each mode and the center frequency are as follows:

[0064]

[0065]

[0066] Using the VMD algorithm to decompose complex time-series signals requires pre-setting relevant parameters, including the number of modes (i.e., the number of decomposition layers). k With secondary penalty factor The number of modes has a significant impact on the decomposition effect. k When the setting is too small, the original signal cannot be completely decomposed, and frequency component aliasing still exists, resulting in a low number of modes. k Setting it too high will cause the original signal to be over-decomposed, resulting in spurious components. (Secondary penalty factor) Controlling the bandwidth of each mode, The larger the value, the smaller the bandwidth.

[0067] To obtain the optimal parameter combination for the VMD algorithm ( k In this embodiment, the Sparrow Search Algorithm (SSA) is used. The quadratic penalty factor α and the number of decomposition levels of the variational mode decomposition algorithm are calculated iteratively. k Each iteration yields a corresponding combination of parameters ( k The variational mode decomposition algorithm can be based on the parameter combination obtained in each iteration (α) k The echo signal is decomposed into multiple echo signal mode components (α, 0.05), and the fitness function is calculated based on the echo signal. When the fitness function value is minimized, the sparrow search algorithm stops iterating and outputs the corresponding parameter combination at this time. k α) can also be called the optimal parameter combination ( k , α). The number of echo signal mode components is determined by k It is determined that the sparrow search algorithm can be an improved sparrow search algorithm based on the Tent chaotic mapping algorithm and the Cauchy mutation perturbation algorithm (optimized sparrow search algorithm); the fitness function can be constructed based on the power spectral entropy, error degree index and center frequency evaluation index.

[0068] The Sparrow Search Algorithm (SSA) and how to obtain parameter combinations are described below through a specific example. k , α):

[0069] The Sparrow Search Algorithm, proposed by Xue and Shen, is a swarm intelligence optimization algorithm based on the foraging and anti-predation behaviors of sparrows. This algorithm divides the sparrow population into three categories: discoverers, joiners, and watchers. During the algorithm's operation, the sparrows with high fitness initially act as searchers, finding food and providing foraging directions. The position update process for the discoverers is as follows:

[0070]

[0071] in, This represents the current iteration number. The maximum number of iterations, These are random numbers that follow a normal distribution. For all elements equal to 1 matrix, To determine the dimension of the variables, These are the warning value and the safe value, respectively.

[0072] While foraging, newcomers monitor the discoverer. If the discoverer finds better food, the newcomer will leave its current position to compete for it. The formula for updating the newcomer's position is:

[0073]

[0074] In the formula, The optimal position for the current discoverer. This is the current worst-case position globally. for A matrix whose elements are randomly assigned the value 1 or -1.

[0075] The process by which a watchdog monitors the predator's movements and raises an alarm upon spotting a predator is as follows:

[0076]

[0077] Decomposition of mode numbers using SSA and secondary penalty factor Joint optimization is performed, and appropriate parameter combinations are automatically selected to decompose the original aliased signal, resulting in parameter combinations ( k ,α).

[0078] In this embodiment, the SSA algorithm has strong convergence ability and fast convergence speed, but it is still prone to getting trapped in local optima. To address this, the initialization and position update process of the SSA algorithm is optimized based on the Tent chaotic mapping algorithm and the Cauchy mutation perturbation algorithm, and the Optimized SparrowSearch Algorithm (OSSA) is proposed to improve the global search capability of the algorithm.

[0079] Specifically: The initialization process of SSA randomly assigns each component to the solution space, which cannot guarantee the uniformity of the initial positions in the solution space. To improve the initialization effect, the Tent chaotic mapping is introduced in the initialization process of various groups to make the distribution of various groups uniform. The Tent mapping expression is:

[0080]

[0081] in, .

[0082] During the sparrow position update process, the characteristics of the SSA update process give the algorithm excellent convergence speed, but it also makes it prone to getting trapped in local optima. Therefore, Cauchy mutation perturbation is introduced during the sparrow position update process and the global optimal position update process. The situation where the global optimal position remains unchanged is defined as being trapped in a local optimum. The Cauchy mutation operator jumps out of the current local optimum with a large step size, and the Cauchy mutation operator mutates the sparrow positions of various groups, thereby enhancing the global search capability of SSA. The Cauchy mutation perturbation is as follows:

[0083]

[0084] in, The new position after the disturbance. For Cauchy operators.

[0085] The sparrow search algorithm provided in this embodiment has the following global search parameters: sparrow population size can be 30, maximum number of iterations can be 5000, optimization function dimension can be 30, and parameter optimization solution space can be [missing information]. The proportion of discoverers can be set to 50%, and the proportion of vigilants can be set to 30%. The algorithm is defined as being trapped in a local optimum when the same optimal position appears 80 times.

[0086] Figure 4 This is a comparison chart of the parameter combination acquisition results of the concrete inner layer void defect detection method provided in the embodiments of the present invention, as shown in the figure. Figure 4 As shown:

[0087] To verify the performance of the optimized Sparrow Search Algorithm (OSSA), the global search capabilities of the original SSA, OSSA, Artificial Bee Colony (ABC), and Image-based Lighting (IBL) algorithms were compared. A high-dimensional multimodal function was used as the fitness function to verify the convergence speed and convergence ability of each algorithm. for:

[0088]

[0089] from Figure 4 As can be seen from this, the optimized sparrow search algorithm provided in this embodiment has the best performance, can quickly obtain the value of the fitness function, and can maintain the value of the fitness function stable even if the number of iterations increases after the number of iterations reaches 200.

[0090] In some specific embodiments, to ensure that each decomposed modal component has a small bandwidth and an appropriate number of modes, thereby achieving effective signal decomposition, the fitness function of the sparrow search algorithm can be constructed based on power spectral entropy, error severity index, and center frequency evaluation index. The fitness function can be:

[0091] in, For power spectral entropy, As an indicator of the degree of error, Evaluation of center frequency.

[0092] In this embodiment, since it is necessary to limit the bandwidth of each echo signal mode component, and considering that the power spectral density of the signal can reflect the power distribution of the signal on the frequency axis, the power spectral entropy can be used as an optimization index to limit the bandwidth of each decomposed component.

[0093] Specifically, power spectral entropy is a type of information entropy. Information entropy is a parameter that measures the complexity of a system or time-series signal. The greater the complexity of the system or signal, the greater the entropy value. The definition of information entropy is:

[0094]

[0095] in, For random events for The probability of.

[0096] The power spectral density of a signal reflects the distribution of its power along the frequency axis. Power spectral entropy is used as an optimization metric to limit the bandwidth of each decomposed component. When the frequency bandwidth is wide, the distribution of the power spectral density is more complex, resulting in a higher power spectrum complexity and thus an increased entropy value. Conversely, when the bandwidth decreases, the distribution of the power spectrum becomes less complex, and the entropy value decreases. Therefore, finding the minimum value of the power spectral entropy can effectively limit the bandwidth. The corresponding definition of power spectral entropy can be expressed as:

[0097]

[0098] in, This represents the power spectral density of the echo signal's modal components.

[0099] In this embodiment, in order to ensure the accuracy of the decomposition of each echo signal mode component, it is necessary to synthesize each echo signal mode component obtained in each iteration to obtain a synthesized echo signal. Then, the synthesized echo signal and the echo signal are subjected to error analysis by the symmetric mean absolute percentage error (SMAPE) formula to obtain the error degree index.

[0100] Specifically, the error level index can be:

[0101]

[0102] in, Synthesize echo signal sequences for each echo signal mode component. It is an echo signal sequence. This is the signal length.

[0103] Since the VMD algorithm can decompose the original signal into multiple narrowband signals with certain center frequencies, when over-decomposition occurs, the center frequencies of adjacent components become close, and the difference significantly decreases. The reciprocal of the normalized center frequency difference has the characteristic of being inversely proportional to the frequency difference. By establishing a center frequency evaluation index, the number of decomposition layers can be controlled. k Using this index can significantly inhibit over-decomposition.

[0104] Specifically, the center frequency evaluation index can be:

[0105]

[0106] in, For the first i The echo signal modal component and the first i +1 center frequency difference of the initial echo signal modal components The sampling frequency.

[0107] A fitness function is constructed using power spectral entropy, error severity index, and center frequency evaluation index. The minimum value of the fitness function is then iteratively sought to find the optimal parameter combination. k Since the maximum value of the power spectral entropy, the symmetric average absolute percentage error (SMAPE), and the reciprocal of the maximum normalized center frequency difference are all positive, when searching for the minimum value downwards, all three need to be satisfied to reach their minimum values. At this time, each echo signal modal component (i.e., each intrinsic mode function (IMF)) has a small bandwidth and the number of modes is appropriate, thus enabling effective decomposition of the echo signal.

[0108] Step 130: Based on parameter combinations ( kThe echo signal is decomposed using a variational mode decomposition algorithm (α), resulting in... k One echo signal modal component; among which... k It is an integer greater than zero.

[0109] In this embodiment, when the fitness function is at its minimum value, the parameter combination calculated by the sparrow search algorithm ( k α) is the optimal parameter combination for the variational mode decomposition algorithm. k , α). Based on the parameter combination at this time ( k α) Using variational mode decomposition algorithm to decompose the echo signal yields the best decomposition results. k Each echo signal modal component.

[0110] Step 140: Calculation k The autocorrelation function of each echo signal modal component is determined; and the detection result of void defects in the concrete testing area is determined based on the autocorrelation function of each echo signal modal component.

[0111] In this embodiment, due to the presence of voids in the concrete, the stiffness of the concrete structure above the voids decreases. After transient excitation, the mechanical vibration attenuation rate of the slab-like concrete structure above the voids decreases, exhibiting a longer attenuation process compared to the vibration components in the denser concrete areas. To concretize this attenuation rate, this embodiment employs an autocorrelation function. The autocorrelation function describes the sum of the product of the signal itself and its time-shifted signal, and its expression is:

[0112]

[0113] The autocorrelation function can be used to describe the correlation between the modal components of each echo signal at different times. Due to the slower decay rate of the faulty vibration component, the values ​​in its autocorrelation function series are larger over a longer period of time, and the rate of decrease is slower, which can clearly characterize its decay process. In contrast, the values ​​in the autocorrelation function series of the non-faulty dense concrete location decrease faster due to its faster decay rate.

[0114] Accordingly, in some specific embodiments, determining the detection result of the concrete area to be inspected based on the autocorrelation function of each echo signal modal component may include:

[0115] The autocorrelation sequence and decay rate of each echo signal mode component are determined based on the autocorrelation function of each echo signal mode component.

[0116] The autocorrelation series and decay rate corresponding to each echo signal mode component are compared respectively.

[0117] If any echo signal mode component exhibits the largest fluctuation amplitude in its autocorrelation sequence and the slowest decay rate, then the area to be tested in the concrete is determined to have inner-layer void defects.

[0118] Figure 5 This is an autocorrelation function graph of the location component of the concrete inner layer void defect detection method provided in the embodiments of the present invention; Figure 6 This is an autocorrelation function graph of the compaction location component of the concrete inner layer void defect detection method provided in this embodiment of the invention. The following is in conjunction with... Figure 5 and Figure 6 This embodiment will be described as follows:

[0119] In this embodiment, Figure 5 Demonstrates the use of optimal parameter combinations in a concrete void defect model. The autocorrelation function after decomposing the disease location signal. Figure 6 Demonstrates the use of the optimal parameter combination The autocorrelation function of the location signal of dense concrete without defects. IMF1-IMF5 correspond to the five echo signal modal components, with the horizontal axis representing the attenuation rate and the vertical axis representing the peak value.

[0120] Figure 5 The autocorrelation peaks of IMF1-IMF4 are relatively small, with smaller numerical fluctuations and faster decay rates, all concentrated near the 0 coordinate. In contrast, the peak value of IMF5 is close to 1, exhibiting drastic fluctuations and a decay rate also close to 1. Therefore, it can be determined that... Figure 5 The corresponding concrete area to be tested is a defect location, with internal voids present.

[0121] Figure 6 The autocorrelation peaks of IMF1-IMF5 are all relatively small, with small amplitude of numerical fluctuations and rapid decay rates, all concentrated near the 0 coordinate, indicating that... Figure 6 The corresponding concrete testing area is a dense location and does not have internal void defects.

[0122] Figure 7 This is a spectrum diagram of the location of voids in the inner layer of concrete provided in this embodiment of the invention; such as... Figure 7 As shown:

[0123] In this embodiment, to verify the analysis results of the autocorrelation function, the frequency components of each echo signal modal component are analyzed to obtain the spectrum diagram of each echo signal modal component at the location of the defect. Figure 7 The five spectrograms in the image correspond to the following from top to bottom: Figure 5 China's IMF1-IMF5. From Figure 7As can be seen, the last spectrum exhibits a narrow-band vibration pattern and is less affected by other components. Accordingly, it can be determined that the analysis results of the autocorrelation function are accurate and effective.

[0124] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0125] Figure 8 This is a schematic diagram of the control device provided in an embodiment of the present invention. Figure 8 As shown, the control device 8 in this embodiment includes a processor 80, a memory 81, and a computer program 82 stored in the memory 81 and executable on the processor 80. When the processor 80 executes the computer program 82, it implements the steps in the various concrete inner layer void detection method embodiments described above, for example... Figure 1 Steps 110 to 140 are shown.

[0126] The control device 8 can be a desktop computer, laptop, handheld computer, or cloud server, etc. The control device 8 may include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will understand that... Figure 8 This is merely an example of control device 8 and does not constitute a limitation on control device 8. It may include more or fewer components than shown, or combine certain components, or different components. For example, the control device may also include input / output devices, network access devices, buses, etc.

[0127] The processor 80 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0128] The memory 81 can be an internal storage unit of the control device 8, such as a hard disk or RAM of the control device 8. The memory 81 can also be an external storage device of the control device 8, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the control device 8. Furthermore, the memory 81 can include both internal storage units and external storage devices of the control device 8. The memory 81 is used to store the computer program and other programs and data required by the control device. The memory 81 can also be used to temporarily store data that has been output or will be output.

[0129] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0130] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0131] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0132] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / control device and method can be implemented in other ways. For example, the apparatus / control device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0133] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0134] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0135] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various concrete inner layer void defect detection method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0136] In some embodiments, the present invention also provides a detection device, which includes: the control device and the excitation device provided in the above embodiments;

[0137] The excitation device includes an excitation module, a data acquisition module, a control module, and a support module.

[0138] The excitation module includes a motor 11, a transmission belt, a cam mechanism, and a housing 12; the cam mechanism includes a metal hammer 121, a push rod 122, a spring 123, and a cam 124; the push rod 122 is connected to the cam 124 and the metal hammer 121 at both ends respectively; one end of the spring 123 is connected to the metal hammer 121, and the other end is fixed to the housing.

[0139] The acquisition module is used to acquire echo signals from the concrete area to be tested.

[0140] The control module is used to control the rotation of the motor 11. The motor 11 drives the cam 124 to rotate through the transmission belt, which stretches the spring 123. When the cam 124 reaches its maximum stroke, the spring 123 contracts instantly to strike the concrete area to be tested.

[0141] The support module is used to support the control module, acquisition module, and excitation module.

[0142] The following is combined Figure 2 and Figure 3 This embodiment will be described as follows:

[0143] In this embodiment, the excitation device includes an excitation module, a data acquisition module, a control module, and a support module.

[0144] The excitation module includes a motor 11, a transmission belt, a cam mechanism, and a housing 12. The cam mechanism is located inside the housing 12, and the transmission belt is located on the camshaft of the cam 124 of the motor 11 and the cam mechanism, and is used to drive the cam 124 to rotate coaxially (not shown in the figure).

[0145] The cam mechanism includes a metal hammer 121, a push rod 122, a spring 123, a cam 124, a nut 125, an upper cover 126, and a lower shell 127. The push rod 122 is connected to the cam 124 and the metal hammer 121 at both ends, respectively; one end of the spring 123 is connected to the metal hammer 121, and the other end is fixed to the lower shell 127.

[0146] The acquisition module may include a microphone sensor 21, a bracket 22, and a connector 23. The bracket 22 supports the microphone sensor 21, which has 8 microphone channels in any combination. It is used to acquire echo signals from the concrete area to be inspected and can also send the acquired echo signals to the control module.

[0147] The control module can be installed at any point on the excitation device as needed, therefore it is not marked in the figure. The control module may include a control chip and a Bluetooth unit. The control chip can be an STM32, and the Bluetooth unit enables information transmission. Upon receiving a working signal, the control module controls the motor 11 to rotate. The motor 11 drives the cam 124 to rotate via a transmission belt, causing the spring 123 to stretch. When the cam 124 reaches its maximum stroke, the spring 123 instantly contracts, causing the metal hammer 121 to fall and strike the concrete area to be tested.

[0148] The support module includes a frame 31 and wheels 32. The frame 31 is made of aluminum alloy tubing and corner brackets, which facilitates loading and unloading and allows for quick assembly to adapt to different working environments. The rods are screwed into the upper connector to keep the metal hammer 121 in close contact with the wall surface, and the wheels 32 allow for easy manual pushing.

[0149] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for detecting voids in the inner layer of concrete, characterized in that, include: The echo signal of the concrete to be tested area is collected; wherein, the echo signal is the echo signal generated by the concrete when the excitation device taps the concrete surface; Based on the echo signal and the sparrow search algorithm, the quadratic penalty factor α and the number of decomposition layers of the variational mode decomposition algorithm are calculated with the goal of minimizing the fitness function. k , to obtain parameter combinations ( k , α); where the fitness function is constructed based on the power spectral entropy, error degree index and center frequency evaluation index; According to the parameter combination ( k The echo signal is decomposed using a variational mode decomposition algorithm to obtain... k One echo signal modal component; among which... k Integers greater than zero; Calculate the k The autocorrelation function of each echo signal modal component in the echo signal modal component; and the detection result of the void defect in the concrete to be tested is determined based on the autocorrelation function of each echo signal modal component; The step of determining the detection results of void defects in the concrete testing area based on the autocorrelation function of each echo signal modal component includes: The autocorrelation sequence and attenuation rate of each echo signal mode component are determined based on the autocorrelation function of each echo signal mode component. The autocorrelation series and attenuation rate corresponding to each echo signal mode component are compared respectively; If any echo signal mode component has the largest fluctuation amplitude in its autocorrelation series and the slowest decay rate, then it is determined that the concrete in the area to be tested has inner layer void defects.

2. The method for detecting voids in the inner layer of concrete according to claim 1, characterized in that, The power spectral entropy is: in, This represents the power spectral density of the echo signal's modal components.

3. The method for detecting voids in the inner layer of concrete according to claim 1, characterized in that, The error level index is obtained according to the symmetrical average absolute percentage error formula, which is used to calculate the error between the synthesized echo signal and the echo signal; wherein, the synthesized echo signal is synthesized from the modal components of each echo signal; The error level index is: in, Synthesize echo signal sequences for each echo signal mode component. The echo signal sequence, This is the signal length.

4. The method for detecting voids in the inner layer of concrete according to claim 1, characterized in that, The center frequency evaluation index is: in, For the first i The echo signal modal component and the first i +1 center frequency difference of the initial echo signal modal components The sampling frequency.

5. The method for detecting voids in the inner layer of concrete according to claim 1, characterized in that, The fitness function is: .

6. The method for detecting voids in the inner layer of concrete according to claim 1, characterized in that, The sparrow search algorithm is an improved sparrow search algorithm based on the Tent chaotic mapping algorithm and the Cauchy mutation perturbation algorithm.

7. A control device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 6 above.

8. A detection device, characterized in that, Includes the control device and excitation device as described in claim 7; The excitation device includes an excitation module, a data acquisition module, a control module, and a support module; The excitation module includes a motor, a transmission belt, a cam mechanism, and a housing; the cam mechanism includes a metal hammer, a push rod, a spring, and a cam; the push rod is connected to the cam and the metal hammer at both ends respectively; one end of the spring is connected to the metal hammer, and the other end is fixed to the housing; The acquisition module is used to acquire echo signals from the concrete area to be tested. The control module is used to control the rotation of the motor. The motor drives the cam to rotate through the transmission belt, which stretches the spring. When the cam reaches its maximum stroke, the spring instantly contracts to strike the concrete area to be tested. The support module is used to support the control module, the acquisition module and the excitation module.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6 above.

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