Concrete internal defect detection method and device, storage medium and equipment

Abstract

The invention provides a concrete internal defect detection method and device, a storage medium and equipment, and the method comprises the steps: arranging a plurality of acoustic emission sensors on the surface of a to-be-monitored concrete structure in the form of a triangulation network, and obtaining acoustic emission signals collected by the acoustic emission sensors; when target pulse signals exist in the acoustic emission signals collected by the acoustic emission sensors in any partition and belong to the same acoustic emission source, the position of the acoustic emission source is calculated and recorded according to the receiving time difference of the acoustic emission sensors in the partition; and then counting all positions recorded in a preset time and corresponding recording times, and determining the position with the recording times smaller than a target threshold as the position corresponding to the internal defect. Therefore, the concrete structure is monitored for a long time by using the acoustic emission technology, the defect position is judged by automatically recording and identifying signals caused by the environment for a long time and counting the sound source density, and the environment with more internal interference can be identified more accurately.

Description

Technical Field

[0001] This application relates to the field of concrete testing technology, and more specifically, to a method, apparatus, storage medium, and equipment for detecting internal defects in concrete. Background Technology

[0002] Internal defects in concrete structures primarily manifest as concrete cracking caused by steel reinforcement corrosion expansion. Under load and environmental influences, these internal cracks gradually expand, exacerbating concrete damage and reducing its overall integrity. Ultimately, this leads to a decrease in the structure's load-bearing capacity and significantly increases maintenance and repair costs. Therefore, monitoring internal defects in concrete is of great importance.

[0003] Currently, non-destructive testing (NDT) techniques such as elastic wave detection, radar electromagnetic wave detection, and ultrasonic testing are mainly used for detecting internal defects in concrete. Elastic wave detection suffers from multiple reflections when encountering dense steel reinforcement meshes, making defect signal identification difficult. Radar electromagnetic wave detection is sensitive to water-containing media, and electromagnetic waves attenuate significantly in moist concrete, affecting the accuracy of detecting deep defects. Ultrasonic testing is significantly affected by material heterogeneity, and sound wave scattering easily occurs in areas with pores and aggregate interfaces. Furthermore, while infrared thermal imaging technology is widely used for detecting shallow surface defects in concrete, its ability to identify deep internal defects is limited. Existing NDT techniques for detecting internal defects in concrete all have significant limitations under complex working conditions. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, storage medium and equipment for detecting internal defects in concrete, aiming to solve the problem that the existing methods for detecting internal defects in concrete have limitations under complex working conditions.

[0005] In a first aspect, this application provides a method for detecting internal defects in concrete, comprising: acquiring acoustic emission signals collected by multiple acoustic emission sensors; the multiple acoustic emission sensors being arranged in the form of a triangular mesh on the surface of the concrete structure to be monitored; each triangle of the triangular mesh corresponding to a partition; when the acoustic emission signals collected by the acoustic emission sensors in any partition all contain a target pulse signal and belong to the same acoustic emission source, calculating and recording the position of the acoustic emission source based on the receiving time difference of the acoustic emission sensors in the partition; statistically analyzing all positions recorded within a preset time and the corresponding number of records, and determining the position where the number of records is less than a target threshold as the position corresponding to the internal defect of the concrete structure to be monitored.

[0006] In the above implementation process, multiple acoustic emission sensors are arranged in a triangular mesh on the surface of the concrete structure to be monitored. The sensor network includes multiple zones. The acoustic emission signals collected by these sensors are acquired. When the acoustic emission signals collected by the acoustic emission sensors in any zone all contain a target pulse signal and belong to the same acoustic emission source, the location of the acoustic emission source is calculated and recorded by the receiving time difference of the acoustic emission sensors in that zone. Then, all locations recorded within a preset time and the corresponding number of records are counted. The location corresponding to the internal defect is determined by the number of records less than a target threshold. In this way, long-term monitoring of concrete structures using acoustic emission technology, through long-term automatic recording, identifies signals caused by the environment, and determines the location of defects by statistically analyzing the sound source density. This allows for more accurate identification in environments with a lot of internal interference and enables precise detection even under complex working conditions.

[0007] Furthermore, in some examples, after acquiring the acoustic emission signals collected by multiple acoustic emission sensors, the process includes: detecting whether the target pulse signal exists in the acoustic emission signal of the main analysis sensor; the main analysis sensor is an acoustic emission sensor shared by multiple partitions; if the detection result is yes, querying whether the target pulse signal exists in the acoustic emission signals of the remaining acoustic emission sensors in all partitions where the main analysis sensor is located, and if yes, determining whether the target pulse signal received by each acoustic emission sensor in the partition belongs to the same acoustic emission source.

[0008] In the above implementation process, a master analysis sensor is set up in the sensor network. When a pulse signal with a significant peak appears in the signal collected by the master analysis sensor in real time, it is checked whether the other signals in each partition where the master analysis sensor is located also have a pulse signal with a significant peak. If so, it is determined whether the signals in that partition belong to the same acoustic emission source. In this way, by continuously monitoring some sensors and then exciting the whole system after a significant pulse is detected, power consumption is effectively reduced and the workload of the equipment is reduced.

[0009] Furthermore, in some examples, detecting whether a target pulse signal exists in the acoustic emission signal of the main analysis sensor includes: preprocessing the acoustic emission signal of the main analysis sensor to obtain a preprocessed signal; calculating the local average value and local standard deviation of the preprocessed signal based on a local window, and determining a detection threshold based on the local average value and local standard deviation; determining that a target pulse signal exists in the acoustic emission signal when at least one data point in the preprocessed signal is greater than the detection threshold.

[0010] In the above implementation process, the signal collected by the acoustic emission sensor is first preprocessed, and then a local window is defined to calculate the local average value and local standard deviation to determine the detection threshold. Then, it is detected whether the preprocessed signal exceeds the detection threshold. If so, it is determined that there is a target pulse signal in the original signal. In this way, the accurate detection of pulse signals with obvious peak values ​​is achieved.

[0011] Furthermore, in some examples, determining the detection threshold based on the local mean and local standard deviation includes: adding the local mean to the product of the local standard deviation and the peak detection coefficient to obtain the detection threshold; the peak detection coefficient is a constant used to adjust the sensitivity of peak detection.

[0012] In the above implementation process, a specific method for setting the detection threshold is provided. Using this detection threshold, all obvious pulse peak time points of the sensor can be quickly determined, laying a good foundation for subsequent acoustic emission source localization.

[0013] Furthermore, in some examples, determining whether the target pulse signals received by each acoustic emission sensor in the partition belong to the same acoustic emission source includes: determining a time threshold based on the propagation speed of sound waves in the concrete structure to be monitored and the maximum sensing distance of the acoustic emission sensor; setting a time window according to the time point corresponding to the target pulse signal of the main analysis sensor and the time threshold; determining whether the remaining acoustic emission sensors in the partition detect only one target pulse signal within the time window, and if so, determining that the acoustic emission signals collected by the acoustic emission sensors in the partition belong to the same acoustic emission source.

[0014] In the above implementation process, a specific method is provided to determine whether the target pulse signals received by each sensor belong to the same acoustic emission source.

[0015] Furthermore, in some examples, calculating the location of the acoustic emission source based on the reception time difference of the acoustic emission sensors in the partition includes: calculating the reception time difference of the remaining acoustic emission sensors in the partition relative to the main analysis sensor; establishing a time difference equation based on the reception time difference and the location of the acoustic emission sensors in the partition; and determining the location of the acoustic emission source by solving the time difference equation.

[0016] In the above implementation process, a specific method for locating acoustic emission sources is provided.

[0017] Furthermore, in some examples, before the step of statistically analyzing all locations recorded within a preset time period and their corresponding number of records, the method includes: calculating the distance between any two locations for all locations recorded within the preset time period; when the distance between any two locations is less than a preset distance threshold, merging the two locations and adding the corresponding number of records.

[0018] In the above implementation process, for multiple recorded sound source locations, the distance between every two sound source locations is calculated. When this distance is less than a preset distance threshold, it is determined that the two sound sources originate from the same source. Coordinate fusion processing is then performed, and the corresponding recording counts for both are added together. This improves the accuracy of detecting internal defects in concrete structures.

[0019] Secondly, this application provides a concrete internal defect detection device, comprising: an acquisition module for acquiring acoustic emission signals collected by multiple acoustic emission sensors; the multiple acoustic emission sensors are arranged in the form of a triangular mesh on the surface of the concrete structure to be monitored; each triangle of the triangular mesh corresponds to a partition; a positioning module for calculating and recording the position of the acoustic emission source based on the receiving time difference of the acoustic emission sensors in the partition when the acoustic emission signals collected by the acoustic emission sensors in any partition all contain a target pulse signal and belong to the same acoustic emission source; and a statistics module for counting all positions recorded within a preset time and the corresponding number of records, and determining the position where the number of records is less than a target threshold as the position corresponding to the internal defect of the concrete structure to be monitored.

[0020] Thirdly, this application provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method described in any of the first aspects.

[0021] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described in any of the first aspects.

[0022] Fifthly, this application provides a computer program product that, when run on a computer, causes the computer to perform the method described in any of the first aspects.

[0023] Other features and advantages disclosed in this application will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the above-described technology disclosed in this application.

[0024] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart illustrating a method for detecting internal defects in concrete provided in this application embodiment;

[0027] Figure 2 A schematic diagram of the arrangement of acoustic emission sensors in a concrete structure internal defect detection scheme based on acoustic emission sensors provided in an embodiment of this application;

[0028] Figure 3 A schematic diagram illustrating the workflow of a concrete structure internal defect detection scheme based on an acoustic emission sensor, provided in an embodiment of this application;

[0029] Figure 4 A block diagram of a concrete internal defect detection device provided in an embodiment of this application;

[0030] Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0031] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0032] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0033] Currently, the number of civil infrastructure projects using concrete as the primary building material is rapidly increasing. The quality of concrete directly affects the safety and durability of buildings; therefore, detecting internal defects in concrete is particularly important. Common non-destructive testing (NDT) techniques for detecting internal defects in concrete structures include elastic wave testing, radar electromagnetic wave testing, and ultrasonic testing. However, elastic wave testing encounters dense steel reinforcement meshes, resulting in multiple reflections and interference, making defect signal identification difficult. Radar electromagnetic wave testing is sensitive to water-containing media, and electromagnetic waves attenuate significantly in damp concrete, affecting the accuracy of detecting deep defects. Ultrasonic testing is significantly affected by material heterogeneity, and sound wave scattering easily occurs in areas with pores or aggregate interfaces. Furthermore, while infrared thermal imaging technology is widely used for detecting shallow surface defects in concrete, its ability to identify deep internal defects is limited. Therefore, existing NDT techniques for detecting internal defects in concrete all have significant limitations under complex working conditions.

[0034] To address the aforementioned issues, this application provides a concrete internal defect detection scheme that utilizes acoustic emission (AE) technology to detect internal defects in concrete structures. Long-term monitoring of the concrete structure and automatic recording over extended periods identify environmentally induced signals. Locations with low emission source density and acoustic emission ray density are generally areas where sound waves have difficulty penetrating, indicating the presence of internal structural defects. Therefore, determining defect locations by statistically analyzing sound source density allows for more accurate identification in environments with significant internal interference. Precise detection is also possible under complex working conditions, while expanding the detection range and effectively reducing structural safety hazards.

[0035] The embodiments of this application will be described below:

[0036] like Figure 1 As shown, Figure 1 This is a flowchart illustrating a method for detecting internal defects in concrete, as provided in an embodiment of this application. The method can be applied to a centralized control device, which is connected via wired or wireless means to various acoustic emission sensors arranged on the concrete structure to be monitored.

[0037] The method includes:

[0038] Step 101: Acquire acoustic emission signals collected by multiple acoustic emission sensors; the multiple acoustic emission sensors are arranged in the form of a triangular mesh on the surface of the concrete structure to be monitored; each triangle of the triangular mesh corresponds to a partition;

[0039] Acoustic emission technology is a technique that utilizes the phenomenon of strain energy released when a material deforms or cracks under stress. By receiving these acoustic emission signals, dynamic non-destructive testing of materials or components is performed. Acoustic emission sensors are sensor devices used to receive acoustic emission signals from within materials or structures. In this embodiment, the acoustic emission sensors are arranged in a triangular mesh on the surface of the concrete structure to be monitored. In practice, the sensor density can be set according to the thickness of the structure. For example, the sensor spacing is typically 1 meter; when the structure thickness is large, the sensor spacing can be reduced to 0.8 meters or 0.5 meters.

[0040] Furthermore, the triangular network composed of acoustic emission sensors, i.e., the sensor network, includes multiple partitions. Each triangle in the network corresponds to one partition, meaning each partition contains three acoustic emission sensors, and a single acoustic emission sensor can be added to multiple partitions. After the sensors are deployed, a topology table of the survey area can be established to facilitate subsequent signal processing.

[0041] Step 102: When the acoustic emission signals collected by the acoustic emission sensors in any partition all contain target pulse signals and belong to the same acoustic emission source, calculate and record the position of the acoustic emission source based on the receiving time difference of the acoustic emission sensors in the partition.

[0042] The target pulse signal mentioned in this step can refer to a pulse signal with a significant peak value. When a pulse signal with a significant peak value appears in the signals collected by the sensors in a certain area, and the pulses received by all sensors in that area belong to the same source, the signal of that area is intercepted for acoustic emission positioning, and the specific location is determined by the time difference of the pulse signals received by the three sensors.

[0043] In some embodiments, after acquiring acoustic emission signals collected by multiple acoustic emission sensors, the process may include: detecting whether the target pulse signal exists in the acoustic emission signal of a main analysis sensor; the main analysis sensor is an acoustic emission sensor shared by multiple partitions; if the detection result is yes, querying whether the target pulse signal exists in the acoustic emission signals of the remaining acoustic emission sensors in all partitions where the main analysis sensor is located; if yes, determining whether the target pulse signals received by each acoustic emission sensor in the partition belong to the same acoustic emission source. That is, in a sensor network, several main analysis sensors are set up, each shared by multiple partitions. When a pulse signal with a significant peak appears in the signal collected in real time by the main analysis sensor, the centralized control device queries whether the remaining signals in each partition where the main analysis sensor is located also show a pulse signal with a significant peak. When a pulse signal with a significant peak also appears in the remaining signals of a partition where the main analysis sensor is located, the centralized control device determines whether the signals in that partition belong to the same acoustic emission source. Thus, by continuously monitoring some sensors and then exciting the whole system only after a significant pulse is detected, power consumption is effectively reduced and the workload of the equipment is lessened.

[0044] Furthermore, the aforementioned detection of the presence of a target pulse signal in the acoustic emission signal of the main analysis sensor may include: preprocessing the acoustic emission signal of the main analysis sensor to obtain a preprocessed signal; calculating the local average and local standard deviation of the preprocessed signal based on a local window, and determining a detection threshold based on the local average and local standard deviation; determining the presence of a target pulse signal in the acoustic emission signal when at least one data point in the preprocessed signal is greater than the detection threshold. In other words, the discrimination of a target pulse signal may include two steps: signal preprocessing and peak detection. Specifically, using x... uh (t) represents the signal collected by the i-th acoustic emission sensor in the j-th partition at time t. The centralized control device can process the raw signal x collected by the sensor. ij (t) Preprocessing is performed, including noise reduction, filtering, and normalization. During peak detection, in addition to setting a peak threshold, it is necessary to ensure that the signal value is relatively small outside of the pulse. Therefore, a local window ω is defined to calculate the local average μ(t) and local standard deviation σ(t), thereby determining the detection threshold. Then, the preprocessed signal is detected. If the detection threshold is exceeded, the original signal is determined to contain the target pulse signal. In this way, accurate detection of pulse signals with obvious peak values ​​is achieved.

[0045] Optionally, determining the detection threshold based on the local average and local standard deviation mentioned above may include: adding the product of the local average, the local standard deviation, and the peak detection coefficient to obtain the detection threshold; the peak detection coefficient is a constant used to adjust the sensitivity of peak detection. That is, in peak detection, in addition to using the local average and local standard deviation of the signal as references, a constant coefficient k can be set to adjust the sensitivity of peak detection. In this case, the detection threshold can be expressed as μ(t) + kσ(t). Thus, for each sensor i, the signal detected in partition j... It can quickly determine all the obvious pulse peak time points. This lays a solid foundation for subsequent acoustic emission source localization.

[0046] The indicator that the pulses received by each sensor belong to the same source can be that the pulse occurrence times of each sensor are close, and there is only one pulse within a certain time segment. Based on this, the aforementioned determination of whether the target pulse signals received by each acoustic emission sensor in the partition belong to the same acoustic emission source can include: determining a time threshold based on the propagation speed of sound waves in the concrete structure to be monitored and the maximum sensing distance of the acoustic emission sensor; setting a time window according to the time point corresponding to the target pulse signal of the main analysis sensor and the time threshold; determining whether the remaining acoustic emission sensors in the partition detect only one target pulse signal within the time window, and if so, determining that the acoustic emission signals collected by the acoustic emission sensors in the partition belong to the same acoustic emission source. That is, assuming the main analysis sensor is numbered 1, a time window [t] is set. k -Δt,t k +Δt], where t k It is the set of obvious pulse peak time points P of the main analysis sensor. 1j The time point Δt is a time threshold calculated based on the propagation speed v of the sound wave in the structure and the maximum sensing distance d of the sensor, where Δt = d / v. Thus, the P values ​​of the remaining sensors in partition j are checked. ij Does a time point t exist in the data? l , so that |t k -t l |≤Δt and within the time window [t] k -Δt,t k If there is only one target pulse signal within [+Δt], then the signals in partition j are determined to belong to the same acoustic emission source. The signal propagation speed v can be obtained by pre-testing components of similar materials based on the spectral characteristics of the pulse signal.

[0047] When the signals from each sensor in partition j exhibit pulse signals with significant peaks, and these pulse signals originate from the same source, the centralized control device can use the signals from partition j to identify the location of an acoustic emission source. The time difference between the pulses received by each sensor indicates that the acoustic emission source is not equidistant from each sensor; this can be used to locate the emission position of the acoustic pulses. In some embodiments, calculating the location of the acoustic emission source based on the receiving time difference of the acoustic emission sensors in the partition, as mentioned in this step, may include: calculating the receiving time difference of the remaining acoustic emission sensors in the partition relative to the main analysis sensor; establishing a time difference equation based on the receiving time difference and the positions of the acoustic emission sensors in the partition; and determining the location of the acoustic emission source by solving the time difference equation.

[0048] In other words, when locating the acoustic emission source, the main analysis sensor is selected as the reference sensor, the time difference between other sensors and the reference sensor is calculated, and the position of the sensor numbered i is denoted as (x... i ,y i ,z i The location of the sound source is (c s ,y s ,z s If the main analytical sensor is numbered 1, then the time difference equation can be:

[0049]

[0050] Where i is 2 and 3, Δt i1 The time difference between other sensors and the reference sensor is represented by the time difference. Numerical methods, such as Newton's iteration method and the least squares method, can be used to solve this time difference equation to obtain the location of the sound source in three-dimensional space (x). s ,y s ,z s ).

[0051] Step 103: Statistically analyze all locations and their corresponding number of records within a preset time period, and determine the locations where the number of records is less than the target threshold as the locations corresponding to the internal defects of the concrete structure to be monitored.

[0052] In this embodiment, when the concrete structure has internal defects, the coordinates of several acoustic emission sources can be located within each detection cycle. These locations are recorded. After N detection cycles, the positions of all acoustic emission sources and their corresponding recording counts are statistically analyzed. Because of obstruction, defective areas have fewer opportunities to act as emission sources, resulting in lower density. Therefore, locations with fewer recording counts than the target threshold, i.e., locations with low density, can be identified as the locations corresponding to the internal defects in the concrete structure to be monitored. The preset time and target threshold mentioned in this step can be set according to the specific needs of the scenario, and this application does not impose any restrictions on them.

[0053] During sound source localization, due to environmental noise or algorithm limitations, the same sound emission source may generate two independent coordinate estimates. Therefore, to eliminate localization ambiguity, in some embodiments, before the step of statistically analyzing all locations recorded within a preset time and their corresponding recording counts mentioned in this section, the following steps may be included: calculating the distance between every two locations for all locations recorded within the preset time; when the distance between any two locations is less than a preset distance threshold, merging the two locations and adding their corresponding recording counts. In other words, for multiple recorded sound source locations, the distance between every two sound source locations is calculated, such as the Euclidean distance; when this distance is less than a preset distance threshold, it is determined that the two locations originate from the same sound source, coordinate fusion processing is performed, and the corresponding recording counts are added together. This improves the accuracy of detecting internal defects in concrete structures.

[0054] In this embodiment, multiple acoustic emission sensors are arranged in a triangular mesh on the surface of the concrete structure to be monitored. The sensor network includes multiple zones. Acoustic emission signals collected by these sensors are acquired. When the acoustic emission signals collected by the sensors in any zone all contain a target pulse signal and belong to the same acoustic emission source, the location of the acoustic emission source is calculated and recorded using the receiving time difference of the sensors in that zone. Then, all locations recorded within a preset time and the corresponding number of records are statistically analyzed. Locations with a number of records less than a target threshold are identified as the locations corresponding to internal defects. In this way, long-term monitoring of concrete structures using acoustic emission technology, through long-term automatic recording, identifies signals caused by the environment, and determines defect locations by statistically analyzing sound source density. This allows for more accurate identification of environments with significant internal interference.

[0055] To provide a more detailed explanation of the solution in this application, a specific embodiment is described below:

[0056] This embodiment provides a scheme for detecting internal defects in concrete structures based on acoustic emission sensors. This scheme is applied to a centralized control system. In implementation, multiple acoustic emission sensors are arranged on the non-primary load-bearing surfaces of the concrete structure to be monitored. The arrangement of these acoustic emission sensors is as follows: Figure 2 As shown, by Figure 2 As can be seen, the acoustic emission sensors 21 are arranged in the form of a triangular network, with each triangle of the three networks corresponding to a partition. That is, three sensors constitute one acoustic emission sensor partition 22, and a single sensor can be added to multiple partitions. External loading and environmental effects emit sound wave pulses inside the structure, which are eventually transmitted to each acoustic emission sensor. The centralized control device is connected to each sensor to obtain the signals collected by each sensor in real time.

[0057] The workflow of this solution is as follows: Figure 3 As shown, it includes:

[0058] S301. Filter out the signals used for acoustic emission source localization from the acquired sensor signals;

[0059] Specifically, let x be the signal collected in real time by the i-th acoustic emission sensor in partition j. ij (t), let the main analysis sensor, i.e., the acoustic emission sensor shared by multiple partitions, be numbered 1. When a pulse signal with a significant peak appears in its signal, query whether the other signals of that partition also have a pulse signal with a significant peak within a certain time range. If so, the signal of that partition is intercepted and used for acoustic emission source localization.

[0060] The process of determining whether a pulse signal with a significant peak appears in the signal includes: processing the acquired raw signal x... ij (t) Preprocessing is performed, including noise reduction, filtering, and normalization, to obtain the preprocessed signal. Next, a local window ω is defined to calculate the local mean μ(t) and the local standard deviation σ(t). The calculation process is shown below:

[0061]

[0062] Then, the detection signal x ij Whether (t) exceeds μ(t) + kσ(t) is used to obtain all its obvious pulse peak time points. Where k is a constant coefficient used to adjust the sensitivity of peak detection;

[0063] The process of determining whether the pulses received by each sensor belong to the same source includes: for all obvious pulse peak time points P of the main analysis sensor. 1j The time point t in k Check the P of other sensors i ijDoes a time point t exist in the data? l , so that |t k -t l |≤Δt and within the time window [t] k -Δt,t k If there is only one pulse peak signal within the time window, and there is only one pulse peak signal that meets the conditions, then these signals belong to the same acoustic emission source; where Δt is the time threshold calculated based on the propagation speed v of the sound wave in the structure and the maximum sensing distance d of the sensor, i.e., Δt=d / v;

[0064] S302. Use the selected signals to locate the sound source and record the location of the sound emission source;

[0065] Specifically, when the selected signal is the signal of partition j, the time difference between other sensors and the main analysis sensor is calculated based on the following formula:

[0066]

[0067] Among them, t i This represents the time it takes for a sound wave to travel from the sound source to the i-th sensor; (x i ,y i ,z i (x) represents the position of the i-th sensor. s ,y s ,z s () indicates the location of the sound source;

[0068] Using this time difference, the time difference equation can be established as follows:

[0069]

[0070] For this time difference equation, let r = (x s ,y s ,z s Let r be the location of the sound source to be solved. Based on the time difference equation, the objective function f(r) can be set. Where α is the step size, The gradient of the objective function is set to an initial value r0. The optimal solution is gradually approximated through iteration. When the iteration stops, the optimal solution is the location of the sound source.

[0071] S303. Count the acoustic emission source locations recorded within a preset time period and determine the number of times each acoustic emission source location is recorded;

[0072] S304. The location of acoustic emission source with a recording frequency less than the target threshold is identified as a defect location.

[0073] This embodiment utilizes acoustic emission technology to conduct long-term monitoring of concrete structures, obtain the locations of acoustic emission sources throughout the process, and identify the specific locations of internal defects by statistically analyzing the acoustic emission source density. This allows for more accurate identification of environments with significant internal interference, while also expanding the identification range and effectively reducing potential structural safety hazards.

[0074] Corresponding to the embodiments of the aforementioned methods, this application also provides embodiments of a concrete internal defect detection device and its application terminal:

[0075] like Figure 4 As shown, Figure 4 This is a block diagram of a concrete internal defect detection device provided in an embodiment of this application. The device includes:

[0076] Acquisition module 41 is used to acquire acoustic emission signals collected by multiple acoustic emission sensors; the multiple acoustic emission sensors are arranged in the form of a triangular mesh on the surface of the concrete structure to be monitored; each triangle of the triangular mesh corresponds to a partition;

[0077] The positioning module 42 is used to calculate and record the position of the acoustic emission source based on the receiving time difference of the acoustic emission sensors in the partition when the acoustic emission signals collected by the acoustic emission sensors in any partition all contain target pulse signals and belong to the same acoustic emission source.

[0078] The statistics module 43 is used to count all locations and the corresponding number of records within a preset time period, and to determine the locations where the number of records is less than the target threshold as the locations corresponding to the internal defects of the concrete structure to be monitored.

[0079] The specific implementation process of the functions and roles of each module in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0080] This application also provides an electronic device, please refer to [link to application]. Figure 5 , Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of this application. The electronic device may include a processor 510, a communication interface 520, a memory 530, and at least one communication bus 540. The communication bus 540 is used to enable direct communication between these components. In this embodiment, the communication interface 520 of the electronic device is used for signaling or data communication with other node devices. The processor 510 may be an integrated circuit chip with signal processing capabilities.

[0081] The processor 510 described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor, or the processor 510 can be any conventional processor.

[0082] The memory 530 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc. The memory 530 stores computer-readable instructions. When these computer-readable instructions are executed by the processor 510, the electronic device can perform the aforementioned operations. Figure 1 The various steps involved in the method implementation examples.

[0083] Alternatively, the electronic device may also include a storage controller and an input / output unit.

[0084] The memory 530, storage controller, processor 510, peripheral interface, and input / output unit are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses 540. The processor 510 is used to execute executable modules stored in the memory 530, such as software function modules or computer programs included in electronic devices.

[0085] The input / output unit is used to provide users with the ability to create tasks and to set optional start periods or preset execution times for those tasks, thereby enabling user-server interaction. The input / output unit may be, but is not limited to, a mouse and keyboard.

[0086] Understandable. Figure 5 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 5 The more or fewer components shown, or having the same Figure 5 The different configurations shown. Figure 5 The components shown can be implemented using hardware, software, or a combination thereof.

[0087] This application also provides a storage medium storing instructions. When the instructions are run on a computer, the computer program is executed by a processor to implement the method described in the method embodiment. To avoid repetition, the method will not be described again here.

[0088] This application also provides a computer program product that, when run on a computer, causes the computer to perform the method described in the method embodiment.

[0089] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0090] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0091] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0092] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0093] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0094] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for detecting internal defects in concrete, characterized in that, include: Acquire acoustic emission signals collected by multiple acoustic emission sensors; The multiple acoustic emission sensors are arranged in a triangular mesh on the surface of the concrete structure to be monitored. Each triangle in the triangulation corresponds to a partition; When the acoustic emission signals collected by the acoustic emission sensors in any partition all contain target pulse signals and belong to the same acoustic emission source, the position of the acoustic emission source is calculated and recorded based on the receiving time difference of the acoustic emission sensors in the partition. All locations and their corresponding number of records within a preset time period are statistically analyzed, and the locations where the number of records is less than a target threshold are determined as the locations corresponding to the internal defects of the concrete structure to be monitored.

2. The method according to claim 1, characterized in that, After acquiring the acoustic emission signals collected by multiple acoustic emission sensors, the process includes: The detection method checks whether the target pulse signal exists in the acoustic emission signal of the main analysis sensor; the main analysis sensor is an acoustic emission sensor shared by multiple partitions. If the detection result is yes, check whether the target pulse signal exists in the acoustic emission signals of the other acoustic emission sensors in all partitions where the main analysis sensor is located. If yes, determine whether the target pulse signals received by each acoustic emission sensor in the partition belong to the same acoustic emission source.

3. The method according to claim 2, characterized in that, The detection of whether a target pulse signal exists in the acoustic emission signal of the main analysis sensor includes: The acoustic emission signal of the main analysis sensor is preprocessed to obtain the preprocessed signal; Based on a local window, the local mean and local standard deviation of the preprocessed signal are calculated, and the detection threshold is determined based on the local mean and local standard deviation. When at least one data point in the preprocessed signal is greater than the detection threshold, it is determined that a target pulse signal exists in the acoustic emission signal.

4. The method according to claim 3, characterized in that, The step of determining the detection threshold based on the local average value and local standard deviation includes: The detection threshold is obtained by adding the product of the local average value, the local standard deviation, and the peak detection coefficient; the peak detection coefficient is a constant used to adjust the sensitivity of peak detection.

5. The method according to claim 3, characterized in that, The step of determining whether the target pulse signals received by each acoustic emission sensor in the partition belong to the same acoustic emission source includes: The time threshold is determined based on the propagation speed of sound waves in the concrete structure to be monitored and the maximum sensing distance of the acoustic emission sensor. A time window is set based on the time point corresponding to the target pulse signal of the main analysis sensor and the time threshold. If the remaining acoustic emission sensors in the partition detect only one target pulse signal within the time window, then the acoustic emission signals collected by the acoustic emission sensors in the partition belong to the same acoustic emission source.

6. The method according to claim 1, characterized in that, The step of calculating the location of the acoustic emission source based on the reception time difference of the acoustic emission sensors in the partition includes: Calculate the reception time difference between the residual acoustic emission sensors in the partition and the main analysis sensor; Based on the receiving time difference and the location of the acoustic emission sensors in the partition, a time difference equation is established; The location of the acoustic emission source is determined by solving the time difference equation.

7. The method according to claim 1, characterized in that, Before the statistics of all locations and corresponding record counts within a preset time period, the following are included: For all locations recorded within a preset time period, calculate the distance between every two locations; When the distance between any two locations is less than a preset distance threshold, the two locations are merged, and the corresponding number of records is added together.

8. A device for detecting internal defects in concrete, characterized in that, include: The acquisition module is used to acquire acoustic emission signals collected by multiple acoustic emission sensors; The multiple acoustic emission sensors are arranged in a triangular mesh on the surface of the concrete structure to be monitored. Each triangle in the triangulation corresponds to a partition; The positioning module is used to calculate and record the position of the acoustic emission source based on the receiving time difference of the acoustic emission sensors in the partition when the acoustic emission signals collected by the acoustic emission sensors in any partition all contain target pulse signals and belong to the same acoustic emission source; The statistics module is used to count all locations and their corresponding number of records within a preset time period, and to determine the locations where the number of records is less than a target threshold as the locations corresponding to the internal defects of the concrete structure to be monitored.

9. A computer-readable storage medium, characterized in that, It stores a computer program thereon, which, when executed by a processor, implements the method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any one of claims 1 to 7.

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