Method, device, equipment, storage medium and product for detecting critical energy release rate of steel structure based on acoustic emission

Abstract

The present application relates to the technical field of fatigue failure detection of steel structures, and in particular to a method, device, equipment, storage medium and product for detecting the critical energy release rate of steel structures based on acoustic emission. The method obtains multiple stress wave signals by acquiring the stress wave at the crack tip of the test piece to be inspected in each crack extension period; extracts each stress wave signal respectively to obtain multiple effective crack extension signals; performs energy analysis on multiple effective crack extension signals to determine the amount of energy released in each crack extension period; and determines the target critical energy release rate based on the amount of energy released in each crack extension period and the crack extension length in each crack extension period. The method directly measures the energy released during the crack extension process by acquiring and analyzing the stress wave signal at the crack tip, thereby improving the detection accuracy and real-time performance, while simplifying the detection process and expanding the scope of application, thereby improving the effect and applicability of critical energy release rate detection of steel structures.

Description

Technical Field

[0001] The present application relates to the technical field of fatigue failure detection of steel structures, and in particular to a method, device, equipment, storage medium and product for detecting the critical energy release rate of steel structures based on acoustic emission. Background Art

[0002] With the acceleration of my country's industrialization process and the continuous growth of steel production, steel structures have been widely used in various fields. However, in recent years, fatigue problems of steel structures have occurred frequently, such as fatigue fracture problems of steel crane beams in industrial plants, fatigue fracture problems of steel structure bridges on highways and railways, and fatigue fractures of offshore oil platforms. These engineering fatigue failure problems show extremely strong suddenness and can easily lead to catastrophic engineering accidents. The theory of fatigue fracture mechanics has laid a theoretical foundation for the analysis of such engineering failure problems. Since the actual geometric shape of engineering structures is generally more complex, numerical simulation methods are often used in engineering to analyze their fatigue failure problems. Common numerical simulation analysis methods include grid coincidence method, VCCT method, XFEM method, meshless method and phase field method. These numerical analysis methods all require the input of the fracture mechanics parameters of the material, among which the critical energy release rate It is a very critical parameter. Critical energy release rate directly controls fatigue crack growth. In the recently developed phase field fracture model, It is the only control variable of fatigue damage evolution. It is very important to analyze the fatigue failure problem of steel structures.

[0003] The traditional There are two key factors in the test method. One is the crack length. , the first is the calculation method of stress intensity factor. Many studies have been conducted to improve these two key factors, such as directly obtaining the stress intensity factor of the crack tip through DIC technology, and improving the crack length through potential method or image recognition method. However, no matter how these methods are improved, the basis is to obtain the material directly or indirectly based on the load P-displacement v curve. Obviously, this method is generally only applicable to static loading and is inefficient. Generally, only one However, the high cycle fatigue problem of steel structures is a typical cyclic loading problem, and this static loading is difficult to apply. Moreover, during the high cycle fatigue cyclic loading process, not every loading will produce a new measurable crack extension, but each loading process will produce a certain amount of material degradation in the local area of ​​the crack tip. This degradation will inevitably lead to material The above traditional testing methods are also difficult to obtain material This evolution has led to the traditional The detection effect and applicability of the detection method are poor. Therefore, how to improve the effect and applicability of the critical energy release rate detection of steel structures has become a technical problem that needs to be solved urgently. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, equipment, storage medium and product for detecting the critical energy release rate of steel structures based on acoustic emission, aiming to solve the technical problem of how to improve the effect and applicability of critical energy release rate detection of steel structures.

[0005] To achieve the above objectives, the present application provides a method for detecting the critical energy release rate of steel structures based on acoustic emission, the method comprising the following steps:

[0006] Acquire stress waves at the crack tip of the test piece to be inspected at each crack propagation stage to obtain multiple stress wave signals;

[0007] Extract each stress wave signal separately to obtain multiple effective crack extension signals;

[0008] Performing energy analysis on the multiple crack extension effective signals to determine the energy release amount during each crack extension period;

[0009] The target critical energy release rate is determined according to the energy release amount in each crack propagation period and the crack propagation length in each crack propagation period.

[0010] In one embodiment, before the step of acquiring stress waves of the test piece at each crack propagation stage to obtain a plurality of stress wave signals, the method further includes:

[0011] Using a preset standard specimen with an initial crack as the test piece to be tested;

[0012] A preset fatigue testing machine is used to perform cyclic loading on the test piece to be tested.

[0013] In one embodiment, the step of extracting the stress wave signals separately to obtain multiple effective crack extension signals includes:

[0014] Based on a preset filter and a preset signal amplifier, preprocessing each of the stress wave signals is performed to obtain a plurality of preprocessed signals;

[0015] Based on a preset signal processing algorithm, each preprocessed signal is divided into multiple acoustic emission event signals;

[0016] Extracting time domain features and frequency domain features from each acoustic emission event signal;

[0017] Based on the time domain characteristics and the frequency domain characteristics, effective signal extraction is performed on each stress wave signal to obtain the multiple crack extension effective signals.

[0018] In one embodiment, the step of performing energy analysis on the multiple crack extension effective signals to determine the energy release amount in each crack extension period includes:

[0019] Determining the starting point and ending point of each acoustic emission event during each crack propagation period based on the preset signal processing algorithm;

[0020] The energy release amount in each crack propagation period is determined according to the starting point and the ending point of each acoustic emission event and the amplitude of the corresponding crack propagation effective signal.

[0021] In one embodiment, after the step of determining the target critical energy release rate based on the energy release amount in each crack propagation period and the crack propagation length in each crack propagation period, the method further includes:

[0022] converting the target critical energy release rate into a stress intensity factor range;

[0023] Determining a fatigue life prediction model according to the stress intensity factor range, the length of the initial crack, and preset material parameters;

[0024] determining the number of fatigue cycles based on the crack growth simulation data and the fatigue life prediction model;

[0025] The fatigue life of the test piece to be inspected is evaluated based on the number of fatigue cycles.

[0026] In one embodiment, the step of determining the target critical energy release rate based on the energy release amount in each crack propagation period and the crack propagation length in each crack propagation period includes:

[0027] Accumulating the energy release during each crack extension period to obtain a total energy release;

[0028] Accumulating the crack extension lengths to obtain a total extension length;

[0029] The target critical energy release rate is determined according to the total energy release amount and the total expansion length.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes a steel structure critical energy release rate detection device based on acoustic emission, the steel structure critical energy release rate detection device based on acoustic emission comprising:

[0031] The stress wave module is used to obtain stress waves at the crack tip of the test piece to be tested at each crack propagation stage to obtain multiple stress wave signals;

[0032] A signal extraction module is used to extract each stress wave signal to obtain multiple effective crack extension signals;

[0033] An energy analysis module, configured to perform energy analysis on the plurality of crack extension effective signals to determine the energy release amount during each crack extension period;

[0034] The target module is used to determine the target critical energy release rate according to the energy release amount in each crack propagation period and the crack propagation length in each crack propagation period.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes a steel structure critical energy release rate detection device based on acoustic emission, the device including: a memory, a processor and a steel structure critical energy release rate detection program based on acoustic emission stored on the memory and runnable on the processor, the steel structure critical energy release rate detection program based on acoustic emission configured to implement the steps of the steel structure critical energy release rate detection method based on acoustic emission as described above.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, on which a steel structure critical energy release rate detection program based on acoustic emission is stored. When the steel structure critical energy release rate detection program based on acoustic emission is executed by a processor, the steps of the steel structure critical energy release rate detection method based on acoustic emission as described above are implemented.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the steel structure critical energy release rate detection method based on acoustic emission as described above.

[0038] The present application obtains multiple stress wave signals by acquiring stress waves at the crack tip of the test piece to be tested during each crack extension period; performs signal extraction on each stress wave signal to obtain multiple effective crack extension signals; performs energy analysis on the multiple effective crack extension signals to determine the amount of energy released during each crack extension period; and determines the target critical energy release rate based on the amount of energy released during each crack extension period and the crack extension length during each crack extension period. The present application directly measures the energy released during crack extension by acquiring and analyzing stress wave signals at the crack tip, thereby improving detection accuracy and real-time performance; at the same time, the method does not rely on complex loading and unloading curves and calculations of stress intensity factors, simplifying the detection process; in addition, the present application is applicable to steel structure specimens of various shapes and different loading conditions, which expands the scope of application and thereby improves the effect and applicability of critical energy release rate detection of steel structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of a first embodiment of a method for detecting critical energy release rate of steel structures based on acoustic emission of the present application;

[0040] Figure 2 This is a schematic diagram of a sub-flow in the second embodiment of the method for detecting critical energy release rate of steel structure based on acoustic emission of this application;

[0041] Figure 3 This is a schematic diagram of a sub-flow in the third embodiment of the method for detecting critical energy release rate of steel structure based on acoustic emission of the present application;

[0042] Figure 4 This is a schematic diagram of the sensor arrangement in an embodiment of a method for detecting critical energy release rate of steel structures based on acoustic emission of this application;

[0043] Figure 5 This is a schematic diagram of the module structure of a device for detecting critical energy release rate of steel structures based on acoustic emission according to an embodiment of the present application;

[0044] Figure 6 Schematic diagram of the equipment structure of the hardware operating environment involved in the steel structure critical energy release rate detection method based on acoustic emission in the embodiment of the present application.

[0045] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0046] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0047] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0048] It should be noted that with the acceleration of my country's industrialization process and the continuous growth of steel production, steel structures have been widely used in various fields. However, in recent years, fatigue problems of steel structures have occurred frequently, such as fatigue fracture problems of steel crane beams in industrial plants, fatigue fracture problems of steel structure bridges on highways and railways, and fatigue fractures of offshore oil platforms. These engineering fatigue failure problems show extremely strong suddenness and can easily lead to catastrophic engineering accidents. The theory of fatigue fracture mechanics has laid a theoretical foundation for the analysis of such engineering failure problems. Since the actual geometric shape of engineering structures is generally more complex, numerical simulation methods are often used in engineering to analyze their fatigue failure problems. Common numerical simulation analysis methods include grid coincidence method, VCCT method, XFEM method, meshless method and phase field method. These numerical analysis methods all require the input of the fracture mechanics parameters of the material, among which the critical energy release rate It is a very critical parameter. Critical energy release rate directly controls fatigue crack growth. In the recently developed phase field fracture model, It is the only control variable of fatigue damage evolution. It is very important to analyze the fatigue failure of steel structures. There are two key factors in the test method. One is the crack length. , the first is the calculation method of stress intensity factor. Many studies have been conducted to improve these two key factors, such as directly obtaining the stress intensity factor of the crack tip through DIC technology, and improving the crack length through potential method or image recognition method. However, no matter how these methods are improved, the basis is to obtain the material directly or indirectly based on the load P-displacement v curve. Obviously, this method is generally only applicable to static loading and is inefficient. Generally, only one However, the high cycle fatigue problem of steel structures is a typical cyclic loading problem, and this static loading is difficult to apply. Moreover, during the high cycle fatigue cyclic loading process, not every loading will produce a new measurable crack extension, but each loading process will produce a certain amount of material degradation in the local area of ​​the crack tip. This degradation will inevitably lead to material The above traditional testing methods are also difficult to obtain material This evolution has led to the traditional The detection effect and applicability of the detection method are poor. Therefore, how to improve the effect and applicability of the critical energy release rate detection of steel structures has become a technical problem that needs to be solved urgently.

[0049] The main solution of this application is: by obtaining the stress waves at the crack tip of the test piece to be tested in each crack extension period, multiple stress wave signals are obtained; signal extraction is performed on each stress wave signal to obtain multiple effective crack extension signals; energy analysis is performed on the multiple effective crack extension signals to determine the energy release amount in each crack extension period; based on the energy release amount in each crack extension period and the crack extension length in each crack extension period, the target critical energy release rate is determined.

[0050] This application directly measures the energy released during crack propagation by acquiring and analyzing the stress wave signal at the crack tip, thereby improving detection accuracy and real-time performance; at the same time, this method does not rely on complex loading and unloading curves and the calculation of stress intensity factors, which simplifies the detection process; in addition, this application is applicable to steel structure specimens of various shapes and different loading conditions, expanding the scope of application, thereby improving the effect and applicability of critical energy release rate detection of steel structures.

[0051] It should be noted that the execution entity of the method of this embodiment can be a computing service device with data processing, network communication, and program execution functions, or it can be the above-mentioned steel structure critical energy release rate detection device based on acoustic emission with the same or similar functions. This embodiment and the following embodiments will be described using the steel structure critical energy release rate detection device based on acoustic emission as an example.

[0052] Based on this, the first embodiment of the steel structure critical energy release rate detection method based on acoustic emission is proposed. Please refer to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for detecting the critical energy release rate of steel structures based on acoustic emission in this application.

[0053] In this embodiment, the method for detecting the critical energy release rate of a steel structure based on acoustic emission includes the following steps:

[0054] S1: Acquire stress waves at the crack tip of the test piece at each crack propagation stage to obtain multiple stress wave signals;

[0055] It should be noted that the test piece to be tested refers to the steel structure specimen that needs to be tested, which has an initial crack prefabricated on it and is ready for testing in the fatigue test. The crack tip refers to the front end area of ​​the crack, which is the part with the highest stress concentration and the starting point of crack expansion. The crack extension period is the time period or stage in which the crack gradually expands during the loading process. Each expansion period corresponds to a small expansion length of the crack. Stress wave refers to the energy released and stress wave generated when the crack expands. These stress waves are caused by the damage evolution process at the crack tip. The stress wave signal is the electrical signal converted after the stress wave is captured by the acoustic emission sensor, reflecting the change in energy released during the crack expansion process.

[0056] Specifically, an initial crack is pre-introduced into the test piece, serving as the starting point for the fatigue test. Multiple acoustic emission sensors are symmetrically positioned near the crack path to capture the stress waves generated by the crack's propagation. Cyclic loading is then applied to the test piece using a fatigue testing machine, applying axial cyclic loads. During this loading process, the crack gradually expands.

[0057] Furthermore, at each stage of crack growth, the acoustic emission sensor captures the stress waves generated at the crack tip in real time, converting them into stress wave signals. These signals are then recorded to generate multiple stress wave signals for subsequent analysis.

[0058] By acquiring the stress wave signal at the crack tip in real time, dynamic monitoring of the crack propagation process is achieved, and every detail of the crack propagation can be captured in time, improving the real-time performance of the detection. The acoustic emission sensor can accurately capture the stress wave signal at the crack tip, providing high-precision crack propagation data to ensure the accuracy of subsequent analysis. Directly acquiring the stress wave signal does not require complex analysis of the loading and unloading curves, which simplifies the experimental steps and reduces the complexity of the experiment. Since this method directly detects the stress wave signal, it is not limited by the specimen geometry and loading method. It is applicable to steel structure specimens of various shapes and loading conditions, expanding the scope of application of the method. By acquiring the stress wave signal at the crack tip of the test piece to be tested at each crack propagation stage, real-time and high-precision monitoring of the crack propagation process is achieved, simplifying the experimental steps and expanding the scope of application of the detection method. These beneficial effects significantly improve the efficiency and accuracy of the critical energy release rate detection of steel structures.

[0059] S2: Extract each stress wave signal to obtain multiple effective crack extension signals;

[0060] It should be noted that signal extraction refers to extracting useful information from the original stress wave signal for subsequent energy analysis and crack growth assessment. The effective crack growth signal is a signal that can accurately reflect the crack growth process after signal extraction and processing.

[0061] Specifically, a preset filter is used to filter the original stress wave signal, removing noise and interference signals to obtain a relatively pure signal. The filtered signal is amplified to enhance signal strength, making the weak stress wave signal more prominent and facilitating subsequent analysis. Based on a preset signal processing algorithm, individual acoustic emission events within the original stress wave signal are identified. Each acoustic emission event corresponds to a single energy release during crack propagation. The identified acoustic emission event signals are segmented into time windows, with the signal within each time window being treated as an independent acoustic emission event signal.

[0062] Furthermore, time-domain features, such as signal amplitude, duration, and energy, are extracted from each acoustic emission event signal. Frequency-domain analysis is then performed on each acoustic emission event signal to extract features such as frequency content and spectral energy. Based on the extracted time-domain and frequency-domain features, each acoustic emission event signal is screened to remove irrelevant or interfering signals. Based on the feature screening results, the signals that are valid for crack growth are determined, resulting in multiple valid crack growth signals.

[0063] Through filtering and signal amplification, noise and interference are removed from the original stress wave signal, signal strength is enhanced, and subsequent analysis is more accurate and reliable. Event identification and time window segmentation based on a preset signal processing algorithm can accurately distinguish between individual acoustic emission events during the crack propagation process, ensuring the accuracy of signal segmentation. Time and frequency domain features are extracted, and the stress wave signal is comprehensively analyzed, improving the accuracy and effectiveness of signal extraction and ensuring that only valid signals that truly reflect the crack propagation process are extracted. Feature screening and valid signal determination eliminate irrelevant or interfering signals, reducing the possibility of misjudgment and improving the reliability and accuracy of the detection results. By extracting each stress wave signal separately, multiple valid crack propagation signals are obtained. This process improves signal quality and accurately identifies each acoustic emission event during the crack propagation process. Comprehensive feature analysis and valid signal screening ensure the accuracy and effectiveness of the extracted signals, thereby improving the accuracy and reliability of critical energy release rate detection of steel structures.

[0064] S3: performing energy analysis on the multiple crack extension effective signals to determine the energy release amount during each crack extension period;

[0065] It should be noted that energy analysis refers to the processing and calculation of signals to determine the amount of energy released during the physical process represented by the signal. Energy release refers to the energy released during crack growth and reflects the energy consumed by the material during this process.

[0066] Specifically, each effective crack growth signal is accumulated over time to obtain the total signal intensity of each acoustic emission event. During this accumulation process, a numerical integration method is used to calculate the total signal intensity within each time period. The accumulated signal intensity of each acoustic emission event is integrated to calculate the energy value of each effective crack growth signal. This integration method converts the signal intensity into energy.

[0067] Furthermore, the calculated energy values ​​are corrected to account for the sensor's sensitivity and response characteristics. This correction factor, derived through experimental calibration, corrects for errors caused by sensor characteristics. The energy of all acoustic emission events within each crack propagation period is summed to obtain the total energy released during that period. By summing the energy of each event, the total energy released during the crack propagation period is obtained.

[0068] Through cumulative and integral calculations, the energy of each acoustic emission event is accurately measured. This ensures a precise assessment of the energy released during crack propagation and improves the reliability of the test results. By applying a correction factor, the sensitivity and response characteristics of the sensor are taken into account, further improving the accuracy of the energy calculation and avoiding the influence of sensor characteristics on the energy measurement. The energy of all acoustic emission events within each crack propagation period is aggregated to provide a comprehensive assessment of the energy release. This more accurately reflects the actual energy release during each crack propagation period. By performing energy analysis on multiple effective crack propagation signals and determining the energy release for each crack propagation period, this process ensures the accuracy and comprehensiveness of the energy measurement. Accurate energy measurement, consideration of sensor characteristics, and comprehensive energy assessment collectively improve the precision and reliability of critical energy release rate detection in steel structures.

[0069] S4: Determine a target critical energy release rate according to the energy release amount in each crack propagation period and the crack propagation length in each crack propagation period.

[0070] It should be noted that the crack propagation length refers to the length of the crack that increases during each propagation period. The target critical energy release rate is a parameter used to characterize the critical energy release of a material during crack propagation and is an important control variable for crack propagation.

[0071] Specifically, first, record the energy released during each crack growth period. This energy release is obtained from the energy analysis in the previous step. The energy release during each crack growth period is accumulated to obtain the total energy released during the entire crack growth process. This process requires ensuring that all recorded data is accurate and that no data from any crack growth period is missed during the accumulation process.

[0072] Furthermore, the crack extension length during each crack extension period is recorded. The crack extension length is the amount of growth of the crack during each extension period, which is usually obtained by high-precision measuring equipment or calculation. The crack extension length of each crack extension period is accumulated to obtain the total crack extension length during the entire crack extension process. This step requires proofreading the measurement and recording of the crack length to ensure the accuracy of the accumulated data. After accumulating the total energy release and the total crack extension length, the total energy release is divided by the total crack extension length to calculate the target critical energy release rate. This calculation reflects the average energy consumption rate of the material during the entire crack extension process. Record and verify the calculation results to ensure that there are no calculation errors, and perform necessary proofreading and confirmation of the results.

[0073] By accumulating the energy released during each crack growth phase, the total energy dissipation during crack growth is comprehensively assessed, ensuring an accurate description of crack growth behavior. By accumulating the crack extension length during each phase, the total crack growth during the entire crack growth process is precisely measured, providing detailed crack growth data. By calculating the ratio of total energy release to total crack extension length, the target critical energy release rate is accurately determined. This calculation method simplifies the complex energy analysis process, improving computational efficiency and accuracy. Detailed recording and accumulation of data for each crack growth phase avoids overall errors caused by errors in a single crack growth phase, enhancing the accuracy and reliability of the test results. The precise calculation of the target critical energy release rate makes it applicable to various types of steel structure fatigue analysis, enhancing the method's applicability and versatility. This process provides reliable basic data for fatigue life prediction and structural safety assessment of steel structures. By carefully recording and accumulating the energy released and crack extension length during the crack growth process to calculate the target critical energy release rate, it ensures the accuracy and reliability of steel structure fatigue analysis. The accurate energy and length accumulation method simplifies the calculation process and improves the detection accuracy and applicability.

[0074] This embodiment obtains multiple stress wave signals by acquiring stress waves at the crack tip of the test piece to be tested during each crack propagation period; performs signal extraction on each stress wave signal to obtain multiple effective crack propagation signals; performs energy analysis on the multiple effective crack propagation signals to determine the amount of energy released during each crack propagation period; and determines the target critical energy release rate based on the amount of energy released during each crack propagation period and the crack propagation length during each crack propagation period. This embodiment directly measures the energy released during crack propagation by acquiring and analyzing stress wave signals at the crack tip, thereby improving detection accuracy and real-time performance; at the same time, this method does not rely on complex loading and unloading curves and the calculation of stress intensity factors, simplifying the detection process; in addition, this embodiment is applicable to steel structure specimens of various shapes and different loading conditions, which expands the scope of application and thereby improves the effectiveness and applicability of critical energy release rate detection of steel structures.

[0075] Based on the above first embodiment, a second embodiment of the steel structure critical energy release rate detection method based on acoustic emission is proposed. Figure 2 , Figure 2 This is a schematic diagram of a sub-process in the second embodiment of the method for detecting the critical energy release rate of steel structures based on acoustic emission of this application.

[0076] like Figure 2 As shown, in this embodiment, before step S1, the following steps are further included:

[0077] S1a: using a preset standard specimen with an initial crack as the test specimen to be tested;

[0078] S1b: Using a preset fatigue testing machine, cyclically load the test piece to be tested.

[0079] It should be noted that the prefabricated initial crack is a crack artificially created on the specimen to simulate the expansion of a natural crack, which is convenient for the start and control of the experiment. The preset standard specimen is a test sample designed and manufactured in advance according to the experimental requirements, with uniform size, shape and material properties. The test piece to be tested is the specimen that is about to undergo fatigue testing, and the initial crack has been prefabricated. A fatigue testing machine is a device specially designed to apply cyclic loading, which tests the fatigue properties of materials or structures by simulating the stress and strain conditions in the actual use environment. Cyclic loading refers to the repeated application of alternating stress or strain on the testing machine to simulate the repeated loads to which the material or structure is subjected under actual working conditions.

[0080] Specifically, select appropriate preset standard specimens according to the experimental requirements (testing requirements). The selection of specimens should comply with relevant standards (such as ASTME399 or GB / T6398-2017), and the initial cracks should be prefabricated according to the experimental requirements. Ensure that the preset standard specimens have consistent material properties and geometric dimensions to ensure the repeatability and reliability of the experimental results. Place the preset standard specimens with initial cracks as the test pieces to be tested on the fatigue testing machine. Use matching fixtures and pins to fix the specimens to ensure that they do not move or deform during loading. Adjust the position of the fixture and specimen so that the crack tip is located on the predetermined loading path to ensure that the loading stress can accurately act on the crack tip area.

[0081] Furthermore, the cyclic loading parameters are set on the fatigue testing machine, including the loading frequency, loading amplitude and loading mode. The loading frequency is usually set at 1~3Hz to avoid excessive noise from the testing machine while ensuring the accuracy of the experimental process. The displacement control parameters for the crack gap opening are set to ensure that the crack can expand in the expected manner during the loading process. Start the fatigue testing machine and perform cyclic loading on the test piece to be tested. During the loading process, the testing machine will repeatedly apply alternating stress to cause the crack to gradually expand. Monitor the stress and strain conditions during the loading process in real time, and record the relevant data to provide basic data for subsequent analysis.

[0082] The use of pre-set standard specimens and prefabricated initial cracks ensures standardized and consistent testing, reducing experimental errors caused by specimen inconsistencies. Cyclic loading using a fatigue testing machine allows precise control of crack propagation, ensuring the expected crack propagation path and velocity, providing reliable data for subsequent energy analysis. Cyclic loading simulates the repeated loading experienced by materials or structures under actual operating conditions, making the experimental results more relevant and valuable for engineering applications. During the loading process, the fatigue testing machine monitors and records stress, strain, and displacement data in real time, providing comprehensive data support for subsequent energy release calculations and crack growth analysis. Precise pre-set initial cracks and cyclic loading parameter settings ensure controllable and repeatable crack propagation, improving experimental accuracy and reliability. Using pre-set standard specimens with pre-fabricated initial cracks as the test piece and subjecting them to cyclic loading using a pre-set fatigue testing machine standardizes the testing process, precisely controls crack propagation, realistically simulates operating conditions, and efficiently collects data, thereby improving the accuracy and reliability of critical energy release rate testing for steel structures. This process lays a solid foundation for the subsequent crack growth energy analysis and critical energy release rate determination.

[0083] Based on the above first embodiment, in this embodiment, step S2 includes:

[0084] S21: pre-processing each stress wave signal based on a preset filter and a preset signal amplifier to obtain a plurality of pre-processed signals;

[0085] S22: Based on a preset signal processing algorithm, each preprocessed signal is divided into a plurality of acoustic emission event signals;

[0086] S23: extracting time domain features and frequency domain features from each acoustic emission event signal;

[0087] S24: Based on the time domain characteristics and the frequency domain characteristics, extract effective signals from the stress wave signals to obtain the multiple crack extension effective signals.

[0088] It should be noted that the term "preset filter" refers to a pre-set filter used to filter out noise and interference from stress wave signals while retaining useful signal components. The term "preset signal amplifier" refers to a pre-set signal amplifier used to amplify stress wave signals to a level suitable for subsequent processing. Preprocessing refers to the initial processing of the raw signal, including steps such as filtering and amplification, to prepare it for subsequent signal processing. The term "preprocessed signal" refers to the signal that has undergone preprocessing steps such as filtering and amplification and contains valid stress wave information, ready for further analysis. The term "preset signal processing algorithm" refers to a pre-set signal processing algorithm used to segment, analyze, and extract useful information from the signal. The term "acoustic emission event signal" refers to the stress wave signal generated by a single acoustic emission event, reflecting the instantaneous energy release during crack growth. Time domain features refer to signal characteristics analyzed in the time domain, such as amplitude, duration, and event interval. Frequency domain features refer to signal characteristics analyzed in the frequency domain, such as spectral distribution, frequency components, and power spectrum. Effective signal extraction refers to extracting the valid signal reflecting crack growth from the preprocessed signal while removing irrelevant or noisy signals.

[0089] Specifically, a preset filter is used to filter each stress wave signal to remove noise and interference and retain useful signal components. The design of the filter needs to be optimized according to the characteristics of the stress wave signal to maximize the clarity of the signal. A preset signal amplifier is used to amplify the filtered signal to ensure that the signal amplitude reaches a level suitable for subsequent processing. The gain setting of the amplifier should be adjusted according to the signal strength and amplification requirements. After filtering and amplification, multiple preprocessed signals are obtained, which contain valid stress wave information. A preset signal processing algorithm is used to segment each preprocessed signal, dividing the continuous signal into multiple independent acoustic emission event signals. The segmentation process requires accurate identification of the starting and ending points of the signal to ensure the independence of the event signal. Multiple acoustic emission event signals are obtained, each signal corresponds to a crack extension event, reflecting the instantaneous information of energy release.

[0090] Furthermore, time domain features are extracted from each acoustic emission event signal, including amplitude, duration, and event interval. These features reflect the signal's variation in the time domain. Frequency domain features are extracted from each acoustic emission event signal, including spectral distribution, frequency components, and power spectrum. These features reflect the signal's energy distribution in the frequency domain. The time and frequency domain features of each acoustic emission event signal are obtained, providing a basis for subsequent effective signal extraction. Based on the extracted time and frequency domain features, each stress wave signal is analyzed to identify effective signals reflecting crack propagation. Effective signals should exhibit significant characteristic changes and accurately reflect the energy released during crack propagation. Based on the analysis results, effective signals are extracted from the preprocessed signals, and irrelevant or noise signals are removed to ensure that the extracted signals accurately reflect the actual crack propagation situation. Multiple effective crack propagation signals are obtained, providing basic data for subsequent energy analysis and critical energy release rate calculation.

[0091] Through preprocessing steps such as filtering and amplification, the noise and interference in the stress wave signal are removed, the quality and clarity of the signal are improved, and a reliable basis is provided for subsequent analysis. Through signal segmentation and feature extraction, each crack propagation event can be accurately identified, and the effective signal reflecting the crack propagation process can be extracted to ensure the accuracy and reliability of the signal analysis. By extracting time domain features and frequency domain features, the changing pattern of the stress wave signal during the crack propagation process is fully reflected, providing rich signal feature information. The effective signal extraction process ensures that the extracted signal can accurately reflect the actual situation of crack propagation, improves the accuracy and reliability of data analysis, and provides a reliable data basis for subsequent energy analysis and critical energy release rate calculation. The signal processing method of this step has a wide range of applicability and can be applied to different types of steel structures and crack propagation conditions, which improves the versatility and applicability of the method. The stress wave signal is preprocessed by a preset filter and signal amplifier, and signal segmentation and feature extraction are performed based on the preset signal processing algorithm, and finally multiple effective crack propagation signals are extracted. This process not only improves signal quality and recognition accuracy, but also comprehensively reflects the actual situation of crack propagation, provides a reliable data basis for energy analysis and critical energy release rate calculation, and significantly improves the effectiveness and applicability of the critical energy release rate detection method for steel structures.

[0092] Based on the above first embodiment, in this embodiment, step S3 includes:

[0093] S31: determining the starting point and ending point of each acoustic emission event in each crack propagation period based on the preset signal processing algorithm;

[0094] S32: determining the energy release amount of each crack propagation period according to the starting point and the ending point of each acoustic emission event and the amplitude of the corresponding crack propagation effective signal.

[0095] It should be noted that the start and end points refer to the start and end time points of the acoustic emission event signal, which are used to determine the duration of each event. The amplitude represents the peak value of the acoustic emission event signal, reflecting the signal strength and energy.

[0096] Specifically, a preset signal processing algorithm analyzes each effective crack growth signal. The algorithm determines the start and end points of each acoustic emission event based on signal characteristics (such as amplitude and frequency changes). When the algorithm detects a significant change in signal amplitude or reaches a preset threshold, it marks that point as the start point of the acoustic emission event. When the signal amplitude drops below the preset threshold or stabilizes, it marks that point as the end point of the acoustic emission event. The start and end points of each acoustic emission event in each effective crack growth signal are determined, providing basic data for energy calculation.

[0097] Furthermore, based on the determined start and end points of the acoustic emission events, the amplitude data of the corresponding signal segments is extracted. The amplitude reflects the energy intensity of each event. Based on the extracted amplitude data and the duration of the event, the energy released for each acoustic emission event is calculated. Generally, energy is proportional to the square of the amplitude, so the energy released by the event is obtained by integrating the square of the amplitude. The energy released by each acoustic emission event is summed to obtain the total energy released during each crack propagation period.

[0098] By accurately identifying the starting and ending points of each acoustic emission event, the energy calculation is ensured to be based on the accurate event range, thereby improving the accuracy of the energy release calculation. The amplitude reflects the intensity of each acoustic emission event. By analyzing the amplitude, the energy release during crack propagation can be accurately assessed, reflecting the actual situation of crack propagation. By calculating the energy release during each crack propagation period, detailed energy data is provided, which helps to further analyze the crack propagation law and evaluate the fatigue life of steel structures. The signal processing and energy calculation methods of this step are universal and applicable to different types of steel structures and crack propagation conditions, which improves the wide applicability of the method. By determining the starting and ending points of each acoustic emission event in each crack propagation period based on a preset signal processing algorithm, and calculating the energy release based on these points and the amplitude of the corresponding crack propagation effective signal, the accuracy of the energy calculation is effectively improved, the energy release during crack propagation is accurately reflected, detailed crack propagation data is provided, and the applicability and breadth of the critical energy release rate detection method for steel structures are enhanced.

[0099] This embodiment obtains multiple stress wave signals by acquiring stress waves at the crack tip of the test piece to be tested during each crack propagation period; performs signal extraction on each stress wave signal to obtain multiple effective crack propagation signals; performs energy analysis on the multiple effective crack propagation signals to determine the amount of energy released during each crack propagation period; and determines the target critical energy release rate based on the amount of energy released during each crack propagation period and the crack propagation length during each crack propagation period. This embodiment directly measures the energy released during crack propagation by acquiring and analyzing stress wave signals at the crack tip, thereby improving detection accuracy and real-time performance; at the same time, this method does not rely on complex loading and unloading curves and the calculation of stress intensity factors, simplifying the detection process; in addition, this embodiment is applicable to steel structure specimens of various shapes and different loading conditions, which expands the scope of application and thereby improves the effectiveness and applicability of critical energy release rate detection of steel structures.

[0100] Based on the above second embodiment, the third embodiment of the steel structure critical energy release rate detection method based on acoustic emission is proposed. Figure 3 , Figure 3 This is a schematic diagram of a sub-process in the third embodiment of the method for detecting critical energy release rate of steel structures based on acoustic emission of this application.

[0101] In this embodiment, after step S4, the method further includes:

[0102] S4a: converting the target critical energy release rate into a stress intensity factor range;

[0103] S4b: determining a fatigue life prediction model according to the stress intensity factor range, the length of the initial crack, and preset material parameters;

[0104] S4c: determining the number of fatigue cycles based on the crack growth simulation data and the fatigue life prediction model;

[0105] S4d: Evaluate the fatigue life of the test piece based on the number of fatigue cycles.

[0106] It should be noted that the stress intensity factor range is a parameter range that describes the strength of the stress field at the crack tip, and is usually used to predict crack propagation and fatigue life. The initial crack length is the length of the initial crack prefabricated in the test piece to be tested, which serves as the initial condition for fatigue life prediction. The preset material parameters refer to the physical and mechanical parameters of the steel structure material, such as elastic modulus, Poisson's ratio, material toughness, etc. The fatigue life prediction model is a mathematical model based on material properties, crack propagation laws and stress intensity factors, which is used to predict the fatigue life of steel structures under cyclic loads. Crack propagation simulation data is the crack propagation data under different load conditions obtained through numerical simulation or experiments. The number of fatigue cycles is the number of load cycles that a steel structure experiences from the initial crack propagation to failure under cyclic load. Fatigue life is the total working time or number of cycles of a steel structure from a crack-free state to failure under specific load conditions.

[0107] Specifically, according to the fracture mechanics theory, the calculated target critical energy release rate is converted into the corresponding stress intensity factor range. This conversion relationship can be obtained through a formula or experimental calibration. The stress intensity factor range reflecting the stress field strength at the crack tip is obtained to provide input parameters for subsequent fatigue life prediction. A fatigue life prediction model is established based on the stress intensity factor range, initial crack length and preset material parameters. This model is usually based on the Paris formula or other fatigue crack propagation theories. The above parameters are input into the fatigue life prediction model to determine the initial conditions and calculation parameters of the model. A fatigue life prediction model suitable for specific steel structures and materials is established, which can simulate the crack propagation process.

[0108] Furthermore, the crack propagation simulation data is substituted into the fatigue life prediction model to calculate the number of fatigue cycles required from the initial crack length to the failure state. Based on the simulation results, the fatigue cycle data is sorted and analyzed to determine the critical fatigue cycle number in the crack propagation process. The number of fatigue cycles required for the crack to propagate to failure is obtained, providing a basis for fatigue life assessment. Based on the determined number of fatigue cycles and combined with actual working conditions (such as load frequency, working environment, etc.), the fatigue life of the test piece to be tested is calculated. By analyzing the calculation results, the fatigue life of the test piece to be tested under actual working conditions is evaluated, and corresponding usage recommendations or improvement measures are proposed. A fatigue life assessment report for the test piece to be tested is obtained, providing a scientific basis for the design, use and maintenance of steel structures.

[0109] By converting the target critical energy release rate into a stress intensity factor range and combining it with a fatigue life prediction model, the fatigue performance of steel structures can be more accurately assessed, and their lifespan under actual service conditions can be predicted. Based on the fatigue life assessment results, the design of steel structures can be optimized, and appropriate materials and structural forms can be selected to improve their fatigue resistance. At the same time, reasonable maintenance and overhaul plans can be formulated to extend the service life of the structure. By accurately predicting fatigue life, potential fatigue failures can be detected and prevented in advance, catastrophic engineering accidents can be avoided, and the safety and reliability of steel structures can be improved. This paper provides a fatigue life assessment method based on scientific methods and data analysis, providing scientific guidance for the design, construction, and use of steel structures in actual projects, thereby improving project quality and efficiency.

[0110] Based on the above second embodiment, in this embodiment, step S4 includes:

[0111] S41: Accumulating the energy release amounts during each crack extension period to obtain a total energy release amount;

[0112] S42: Accumulating the crack extension lengths to obtain a total extension length;

[0113] S43: Determine the target critical energy release rate according to the total energy release amount and the total extension length.

[0114] It should be noted that the total energy release refers to the cumulative value of the energy released during all crack propagation periods, reflecting the total energy change during the entire crack propagation process. The total propagation length refers to the cumulative value of the crack propagation length during all crack propagation periods, reflecting the total length change during the entire crack propagation process.

[0115] Specifically, the energy release in each crack extension period is accumulated to obtain the total energy release in the entire crack extension process. The experimental data is processed using a data accumulation algorithm. The total energy release reflects the total energy released by the material during the crack extension process. The crack extension length in each crack extension period is accumulated to obtain the total extension length in the entire crack extension process. The crack extension length is accumulated and calculated using measurement and data accumulation techniques. The total extension length reflects the total growth of the crack in the entire extension process. The target critical energy release rate is determined based on the ratio of the total energy release to the total extension length. The energy release rate is calculated using formula calculation and data processing techniques. The target critical energy release rate, as an important indicator of material fatigue performance, reflects the rate of energy release during crack extension.

[0116] By accumulating the energy release and crack extension length, errors in single measurements are eliminated, improving the accuracy of critical energy release rate detection. The cumulative results of total energy release and total extension length provide a comprehensive assessment of the material's fatigue properties, providing an accurate basis for design and use. By accumulating data from each crack extension period, the calculation process is simplified, making the determination of the target critical energy release rate more intuitive and easier to implement. By accurately determining the target critical energy release rate, the fatigue life of materials in actual engineering can be better predicted, improving the safety and reliability of engineering structures. Analysis and evaluation based on the accumulated results can guide the optimization of material selection and structural design, improving project quality and service life. By accumulating energy release and crack extension length to determine the target critical energy release rate, the detection accuracy and applicability are improved, effectively solving the error accumulation and complex calculation problems in traditional detection methods, and providing reliable data support for material performance evaluation and engineering applications.

[0117] This embodiment obtains multiple stress wave signals by acquiring stress waves at the crack tip of the test piece to be tested during each crack propagation period; performs signal extraction on each stress wave signal to obtain multiple effective crack propagation signals; performs energy analysis on the multiple effective crack propagation signals to determine the amount of energy released during each crack propagation period; and determines the target critical energy release rate based on the amount of energy released during each crack propagation period and the crack propagation length during each crack propagation period. This embodiment directly measures the energy released during crack propagation by acquiring and analyzing stress wave signals at the crack tip, thereby improving detection accuracy and real-time performance; at the same time, this method does not rely on complex loading and unloading curves and the calculation of stress intensity factors, simplifying the detection process; in addition, this embodiment is applicable to steel structure specimens of various shapes and different loading conditions, which expands the scope of application and thereby improves the effectiveness and applicability of critical energy release rate detection of steel structures.

[0118] For example, in order to help understand the technical concept or technical principle of the steel structure critical energy release rate detection method based on acoustic emission in the above embodiment, please refer to Figure 4 , Figure 4 This is a schematic diagram of the sensor arrangement in one embodiment of the steel structure critical energy release rate detection method based on acoustic emission of this application.

[0119] In order to dynamically obtain the critical energy release rate of the metal during the test loading process and improve the existing critical energy release rate acquisition method which is limited by the geometric size or loading and unloading test process, this embodiment proposes a dynamic critical energy release rate test method for fatigue crack extension of structural steel based on acoustic emission technology. This method is based on the characteristic that the acoustic emission sensor can capture the stress wave generated by the energy release during the evolution of the crack tip damage, and establishes a test method for directly obtaining the energy released from the crack tip. This method does not need to pay attention to the load P-displacement v curve during the loading and unloading process, nor does it need to pay attention to the calculation formula of the stress intensity factor of the crack. Instead, it directly measures the energy released by the material crack extension, that is, the critical energy release rate, through the stress wave energy captured by the acoustic emission sensor.

[0120] The test (detection) method is as follows:

[0121] (1) Sample preparation

[0122] The test specimens are manufactured using CT specimens or three-point bend specimens as specified in ASTM E399 or GB / T6398-2017, "Metallic Materials Fatigue Tests - Fatigue Crack Growth Methods." However, the dimensions and loading positions are not restricted by these standards. Here, the test steps are explained using CT specimens as an example.

[0123] (2) Acoustic emission sensor layout

[0124] Sensor arrangement as Figure 4 As shown. The acoustic emission sensors are arranged symmetrically with the crack propagation path as the central axis. The 2n acoustic emission sensors are divided into n groups, each monitoring n equal-length crack segments. The energy released by formation. The potential difference test technology is used to improve the The recognition accuracy of the starting point and the ending point.

[0125] (3) Loading process

[0126] A fatigue testing machine was used to secure the specimen using a matching fixture and pins, and to apply axial cyclic loading. During the test, the loading frequency was controlled between 1 and 3 Hz (not too high to avoid excessive machine noise), while the crack opening displacement was also controlled. Acoustic emission signals from all sensors were collected throughout the loading process.

[0127] (IV) Data processing methods

[0128] Calculation principle: The starting point is arranged at the end of the prefabricated initial crack, so at the beginning of the test Compared with those without fatigue damage Consistent, no need to consider degradation effects. The length of is small enough, then In the process of formation The change of can be ignored and is recorded as .

[0129] Will The total energy of the acoustic emission sensor AE11 and AE21 signals during the formation process is recorded as (the superscript represents the acoustic emission, and the subscript corresponds to the crack segment number), then and Total energy released during crack formation , , There must be some corresponding relationship, written as:

[0130] .

[0131] In the formula 、 、 (test piece thickness), can be determined by testing, so the relationship It can be determined by data fitting.

[0132] Maintain the test conditions of each subsequent micro-segment sensor Same, then 、 、 It must also satisfy the above formula, so the following relationship can be established:

[0133]

[0134] Therefore, the acoustic emission signal can be used to calculate ~ .

[0135] The present application also provides a device for detecting the critical energy release rate of steel structures based on acoustic emission. Figure 5 , Figure 5 This is a schematic diagram of the module structure of a steel structure critical energy release rate detection device based on acoustic emission according to an embodiment of the present application. The steel structure critical energy release rate detection device based on acoustic emission includes:

[0136] The stress wave module 501 is used to obtain stress waves at the crack tip of the test piece to be inspected at each crack propagation stage to obtain multiple stress wave signals;

[0137] A signal extraction module 502 is used to extract each stress wave signal to obtain multiple effective crack extension signals;

[0138] An energy analysis module 503 is configured to perform energy analysis on the plurality of crack extension effective signals to determine the energy release amount during each crack extension period;

[0139] The target module 504 is configured to determine a target critical energy release rate based on the energy release amount in each crack propagation period and the crack propagation length in each crack propagation period.

[0140] The acoustic emission-based steel structure critical energy release rate detection device provided in the embodiment of the present application adopts the acoustic emission-based steel structure critical energy release rate detection method in the above embodiment, which can solve the technical problem of how to improve the effect and applicability of steel structure critical energy release rate detection. Compared with the prior art, the beneficial effects of the acoustic emission-based steel structure critical energy release rate detection device provided in the embodiment of the present application are the same as the beneficial effects of the acoustic emission-based steel structure critical energy release rate detection method provided in the above embodiment, and the other technical features of the acoustic emission-based steel structure critical energy release rate detection device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0141] The present application provides a steel structure critical energy release rate detection device based on acoustic emission. The steel structure critical energy release rate detection device based on acoustic emission includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steel structure critical energy release rate detection method based on acoustic emission in the above embodiment.

[0142] Reference below Figure 6 , which shows a schematic structural diagram of a steel structure critical energy release rate detection device based on acoustic emission suitable for implementing the embodiment of the present application. The steel structure critical energy release rate detection device based on acoustic emission in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The acoustic emission-based steel structure critical energy release rate detection device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0143] like Figure 6As shown, the acoustic emission-based critical energy release rate detection equipment for steel structures may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the acoustic emission-based critical energy release rate detection equipment for steel structures. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, hard disk, etc.; and communication devices 1009. Communication devices 1009 can allow the acoustic emission-based critical energy release rate detection device for steel structures to communicate wirelessly or wired with other devices to exchange data. While the figures illustrate an acoustic emission-based critical energy release rate detection device for steel structures with various systems, it should be understood that implementation or presence of all illustrated systems is not required. More or fewer systems may alternatively be implemented or present.

[0144] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0145] The acoustic emission-based steel structure critical energy release rate detection device provided in the present application adopts the acoustic emission-based steel structure critical energy release rate detection method in the above-mentioned embodiment, which can solve the technical problem of how to improve the effect and applicability of steel structure critical energy release rate detection. Compared with the prior art, the beneficial effects of the acoustic emission-based steel structure critical energy release rate detection device provided in the present application are the same as the beneficial effects of the acoustic emission-based steel structure critical energy release rate detection method provided in the above-mentioned embodiment, and the other technical features of the acoustic emission-based steel structure critical energy release rate detection device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0146] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0147] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0148] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, the computer-readable program instructions being used to execute the method for detecting the critical energy release rate of a steel structure based on acoustic emission in the above-mentioned embodiment.

[0149] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including, but not limited to, wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0150] The computer-readable storage medium may be included in the steel structure critical energy release rate detection device based on acoustic emission; or it may exist independently without being assembled into the steel structure critical energy release rate detection device based on acoustic emission.

[0151] The computer-readable storage medium carries one or more programs. When executed by an acoustic emission-based critical energy release rate detection device for steel structures, the acoustic emission-based critical energy release rate detection device causes the device to: obtain stress waves at the crack tip of a test piece during each crack propagation phase to obtain multiple stress wave signals; perform signal extraction on each stress wave signal to obtain multiple effective crack propagation signals; perform energy analysis on the multiple effective crack propagation signals to determine the amount of energy released during each crack propagation phase; and determine a target critical energy release rate based on the energy release during each crack propagation phase and the crack propagation length during each crack propagation phase. The computer program code for performing the operations of the present application can be written in one or more programming languages, or a combination thereof. The programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0152] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0153] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0154] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned acoustic emission-based method for detecting the critical energy release rate of steel structures. This computer-readable storage medium can address the technical problem of improving the effectiveness and applicability of critical energy release rate detection for steel structures. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the acoustic emission-based method for detecting the critical energy release rate of steel structures provided in the aforementioned embodiments, and are not further elaborated here.

[0155] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for detecting the critical energy release rate of a steel structure based on acoustic emission.

[0156] The computer program product provided in this application can solve the technical problem of improving the effectiveness and applicability of critical energy release rate detection for steel structures. Compared to the prior art, the beneficial effects of the computer program product provided in the embodiments of this application are the same as those of the acoustic emission-based critical energy release rate detection method for steel structures provided in the aforementioned embodiments, and are not further elaborated here.

[0157] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

Claims

1. A method for detecting critical energy release rate of steel structure based on acoustic emission. It is characterized in that The method comprises: A preset standard specimen with an initial crack is used as a test piece to be inspected; Using a preset fatigue testing machine to perform cyclic loading on the test piece to be tested; Acquire stress waves at the crack tip of the test piece to be inspected at each crack extension stage to obtain multiple stress wave signals; Extract each stress wave signal separately to obtain multiple effective crack extension signals; Performing energy analysis on the multiple crack extension effective signals to determine the energy release amount in each crack extension period; Determining a target critical energy release rate according to the energy release amount in each crack extension period and the crack extension length in each crack extension period; Wherein, the step of determining the target critical energy release rate according to the energy release amount in each crack extension period and the crack extension length in each crack extension period comprises: Accumulating the energy release amounts during each crack extension period to obtain a total energy release amount; Accumulating the crack extension lengths to obtain a total extension length; The target critical energy release rate is determined according to the total energy release amount and the total extension length.

2. The method according to claim 1, It is characterized in that The step of extracting signals from each stress wave signal to obtain multiple effective crack extension signals comprises: Based on a preset filter and a preset signal amplifier, preprocessing each of the stress wave signals respectively to obtain a plurality of preprocessed signals; Based on a preset signal processing algorithm, each preprocessed signal is divided into a plurality of acoustic emission event signals; Extracting time domain features and frequency domain features from each acoustic emission event signal; Based on the time domain characteristics and the frequency domain characteristics, effective signal extraction is performed on each of the stress wave signals to obtain the multiple crack extension effective signals.

3. The method according to claim 2, It is characterized in that The step of performing energy analysis on the multiple crack extension effective signals to determine the energy release amount in each crack extension period comprises: Based on the preset signal processing algorithm, determining the starting point and the ending point of each acoustic emission event in each crack propagation period; The energy release amount of each crack extension period is determined according to the starting point and the ending point of each acoustic emission event and the amplitude of the corresponding crack extension effective signal.

4. The method according to claim 1, It is characterized in that After the step of determining the target critical energy release rate according to the energy release amount in each crack extension period and the crack extension length in each crack extension period, the method further includes: converting the target critical energy release rate into a stress intensity factor range; Determining a fatigue life prediction model according to the stress intensity factor range, the length of the initial crack and preset material parameters; Determining the number of fatigue cycles according to the crack growth simulation data and the fatigue life prediction model; Based on the number of fatigue cycles, the fatigue life of the test piece to be inspected is evaluated.

5. A critical energy release rate detection device for steel structures based on acoustic emission, It is characterized in that The device comprises: A stress wave module is used to use a preset standard specimen with an initial crack as a test piece to be tested; use a preset fatigue testing machine to cyclically load the test piece to be tested; obtain stress waves at the crack tip of the test piece to be tested at each crack extension stage to obtain multiple stress wave signals; A signal extraction module is used to extract each stress wave signal to obtain multiple effective crack extension signals; An energy analysis module, used to perform energy analysis on the multiple crack extension effective signals to determine the energy release amount in each crack extension period; A target module is used to determine a target critical energy release rate based on the energy release amount of each crack extension period and the crack extension length of each crack extension period; wherein the step of determining the target critical energy release rate based on the energy release amount of each crack extension period and the crack extension length of each crack extension period comprises: accumulating the energy release amounts of each crack extension period to obtain a total energy release amount; accumulating the crack extension lengths to obtain a total extension length; and determining the target critical energy release rate based on the total energy release amount and the total extension length.

6. A computer device, It is characterized in that The device includes: a memory, a processor, and a steel structure critical energy release rate detection program based on acoustic emission stored in the memory and executable on the processor, wherein the steel structure critical energy release rate detection program based on acoustic emission is configured to implement the steps of the steel structure critical energy release rate detection method based on acoustic emission as described in any one of claims 1 to 4.

7. A storage medium, It is characterized in that The storage medium stores a steel structure critical energy release rate detection program based on acoustic emission. When the steel structure critical energy release rate detection program based on acoustic emission is executed by the processor, the steps of the steel structure critical energy release rate detection method based on acoustic emission as described in any one of claims 1 to 4 are implemented.

8. A computer program product, It is characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method for detecting critical energy release rate of steel structure based on acoustic emission as claimed in any one of claims 1 to 4 are implemented.

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