CN118897010B

CN118897010B - Acoustic emission-based steel structure critical energy release rate detection method, apparatus, equipment, storage medium and product

Info

Publication number: CN118897010B
Application number: CN202411378471.6A
Authority: CN
Inventors: 赵晓青; 杜晨阳
Original assignee: University of Science and Technology Beijing USTB
Current assignee: University of Science and Technology Beijing USTB
Priority date: 2024-09-30
Filing date: 2024-09-30
Publication date: 2024-12-13
Anticipated expiration: 2044-09-30
Also published as: CN118897010A

Abstract

The application relates to the technical field of steel structure fatigue failure detection, in particular to a method, a device, equipment, a storage medium and a product for detecting the critical energy release rate of a steel structure based on acoustic emission. The method comprises the steps of obtaining stress waves of a to-be-detected test piece at crack tip in each crack expansion period to obtain a plurality of stress wave signals, respectively extracting the stress wave signals to obtain a plurality of crack expansion effective signals, carrying out energy analysis on the plurality of crack expansion effective signals to determine energy release amount of each crack expansion period, and determining a target critical energy release rate according to the energy release amount of each crack expansion period and the crack expansion length of each crack expansion period. According to the method, the stress wave signals at the crack tip are obtained and analyzed, so that the energy released in the crack expansion process is directly measured, the detection precision and the real-time performance are improved, the detection flow is simplified, the application range is enlarged, and the effect and the applicability of the detection of the critical energy release rate of the steel structure are improved.

Description

Acoustic emission-based steel structure critical energy release rate detection method, apparatus, equipment, storage medium and product

Technical Field

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

Background

With the acceleration of the industrialization process in China and the continuous increase of steel yield, the steel structure is widely applied to various fields. However, in recent years, fatigue problems of steel structures, such as fatigue fracture of steel crane beams of industrial plants, fatigue fracture of bridges of steel structures of common iron, fatigue fracture of offshore oil platforms and the like, frequently occur. These engineering fatigue failure problems exhibit extremely strong burstiness and are extremely prone to catastrophic engineering accidents. The fatigue fracture mechanics theory lays a theoretical foundation for the analysis of the engineering failure problem. Because the actual engineering structure geometry is generally complex, the fatigue failure problem of the engineering structure is mostly analyzed by adopting a numerical simulation method in engineering. Common numerical simulation analysis methods include a grid overlapping method, a VCCT method, an XFEM method, a grid-free method, a phase field method and the like. These numerical analysis methods all require input of fracture mechanics parameters of the material, wherein critical energy release rateIt is a very critical parameter. Critical energy release rateThe fatigue crack growth is directly controlled. In the recently emerging phase field fracture model,And more particularly, the only fatigue damage evolution control variable. In summary, it can be seen that the accuracy is obtained by means of material testingAnalysis of fatigue failure problems of steel structures is very important.

Whereas conventionalThe test method of (2) has two main factors, namely crack lengthAnd firstly, a calculation method of stress intensity factors. Many studies have been conducted on improvement of these two key factors, such as direct acquisition of stress intensity factor of crack tip by DIC technique, improvement of crack length by potentiometry or image recognitionMeasurement accuracy of (c) and the like. However, the basis for any improvement of these methods is to obtain the material directly or indirectly based on the load P-displacement v curveValues. Obviously, this method is generally only suitable for static loading and is relatively inefficient. Typically only one can be obtained at a timeValues. However, the high cycle fatigue problem of steel structures is typical cyclic loading, which is difficult to apply. Moreover, during high cycle fatigue loading, not every loading will produce a new measurable crack growth, but every loading will produce some material degradation in the localized area of the crack tip. This degradation necessarily results in a materialIs difficult to obtain materials by the conventional testing methodWhich in turn enables the conventionalThe detection method has poor detection effect and applicability. Therefore, how to improve the effect and applicability of the detection of the critical energy release rate of the steel structure becomes a technical problem to be solved.

Disclosure of Invention

The application mainly aims to provide a method, a device, equipment, a storage medium and a product for detecting the critical energy release rate of a steel structure based on acoustic emission, and aims to solve the technical problem of improving the effect and applicability of the detection of the critical energy release rate of the steel structure.

In order to achieve the above object, the present application provides a method for detecting a critical energy release rate of a steel structure based on acoustic emission, the method comprising the steps of:

the method comprises the steps of obtaining stress waves of crack tips of a test piece to be detected in each crack expansion period, and obtaining a plurality of stress wave signals;

respectively extracting the signals of each stress wave signal to obtain a plurality of crack expansion effective signals;

Performing energy analysis on the plurality of crack growth effective signals to determine the energy release amount of each crack growth period;

And determining a target critical energy release rate according to the energy release amount of each crack extension period and the crack extension length of each crack extension period.

In an embodiment, before the step of obtaining stress waves of the test piece to be detected in each crack extension period to obtain a plurality of stress wave signals, the method further includes:

taking a preset standard test piece prefabricated with an initial crack as the test piece to be detected;

and (3) adopting a preset fatigue testing machine to circularly load the test piece to be detected.

In one embodiment, the step of extracting signals of each stress wave signal to obtain a plurality of crack propagation effective signals includes:

Based on a preset filter and a preset signal amplifier, respectively preprocessing each stress wave signal to obtain a plurality of preprocessed signals;

dividing each preprocessing signal into a plurality of acoustic emission event signals based on a preset signal processing algorithm;

extracting time domain features and frequency domain features from each acoustic emission event signal;

and extracting effective signals of the stress wave signals based on the time domain features and the frequency domain features to obtain a plurality of crack extension effective signals.

In one embodiment, the step of performing energy analysis on the plurality of crack growth effective signals to determine an energy release amount for each crack growth period comprises:

Determining a starting point and an ending point of each acoustic emission event in each crack growth period based on the preset signal processing algorithm;

And determining the energy release amount of each crack expansion period according to the starting point and the ending point of each acoustic emission event and the amplitude of the corresponding crack expansion effective signal.

In one embodiment, after the step of determining the target critical energy release rate according to the energy release amount of each crack growth period and the crack growth length of each crack growth period, the method further comprises:

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 initial crack length and preset material parameters;

determining the fatigue cycle number according to the crack propagation simulation data and the fatigue life prediction model;

and based on the fatigue cycle times, evaluating the fatigue life of the test piece to be detected.

In one embodiment, the step of determining the target critical energy release rate according to the energy release amount of each crack growth period and the crack growth length of each crack growth period includes:

accumulating the energy release amounts of the crack extension periods to obtain total energy release amounts;

accumulating the crack extension lengths to obtain a total extension length;

and determining the target critical energy release rate according to the total energy release amount and the total expansion length.

In addition, in order to achieve the above object, the present application also provides a device for detecting a critical energy release rate of a steel structure based on acoustic emission, the device for detecting a critical energy release rate of a steel structure based on acoustic emission includes:

The stress wave module is used for acquiring stress waves of the crack tip of the test piece to be detected in each crack expansion period to obtain a plurality of stress wave signals;

the signal extraction module is used for respectively extracting the signals of the stress waves to obtain a plurality of crack expansion effective signals;

The energy analysis module is used for carrying out energy analysis on the plurality of crack extension effective signals and determining the energy release amount of each crack extension period;

And the target module is used for determining a target critical energy release rate according to the energy release amount of each crack extension period and the crack extension length of each crack extension period.

In addition, in order to achieve the aim, the application also provides an acoustic emission-based steel structure critical energy release rate detection device, which comprises a memory, a processor and an acoustic emission-based steel structure critical energy release rate detection program stored on the memory and capable of running on the processor, wherein the acoustic emission-based steel structure critical energy release rate detection program is configured to achieve the steps of the acoustic emission-based steel structure critical energy release rate detection method.

In addition, in order to achieve the above object, the present application also proposes a storage medium having stored thereon an acoustic emission-based steel structure critical energy release rate detection program, which when executed by a processor, implements the steps of the acoustic emission-based steel structure critical energy release rate detection method as described above.

Furthermore, to achieve the above object, the present application also proposes a computer program product comprising a computer program which, when executed by a processor, implements the steps of the acoustic emission based steel structure critical energy release rate detection method as described above.

The method comprises the steps of obtaining stress waves of a to-be-detected test piece at each crack expansion period at the crack tip, obtaining a plurality of stress wave signals, respectively extracting the stress wave signals to obtain a plurality of crack expansion effective signals, carrying out energy analysis on the plurality of crack expansion effective signals to determine the energy release amount of each crack expansion period, and determining the target critical energy release rate according to the energy release amount of each crack expansion period and the crack expansion length of each crack expansion period. According to the application, the stress wave signal at the crack tip is obtained and analyzed, and the energy released in the crack expansion process is directly measured, so that the detection precision and the real-time performance are improved; in addition, the method is suitable for steel structure test pieces with various shapes and different loading conditions, the application range is enlarged, and the effect and the applicability of the detection of the critical energy release rate of the steel structure are further improved.

Drawings

FIG. 1 is a schematic flow chart of a first embodiment of a method for detecting critical energy release rate of a steel structure based on acoustic emission;

FIG. 2 is a schematic view of a sub-process in a second embodiment of the method for detecting critical energy release rate of a steel structure based on acoustic emission according to the present application;

FIG. 3 is a schematic view of a sub-process in a third embodiment of the method for detecting critical energy release rate of a steel structure based on acoustic emission according to the present application;

FIG. 4 is a schematic diagram showing the sensor arrangement in an embodiment of the method for detecting the critical energy release rate of a steel structure based on acoustic emission according to the present application;

FIG. 5 is a schematic diagram of a module structure of a device for detecting critical energy release rate of a steel structure based on acoustic emission according to an embodiment of the present application;

Fig. 6 is a schematic diagram of an equipment structure of a hardware operating environment related to a method for detecting a critical energy release rate of a steel structure based on acoustic emission in an embodiment of the present application.

The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

For a better understanding of the technical solution of the present application, the following detailed description will be given with reference to the drawings and the specific embodiments.

It should be noted that, with the acceleration of the industrial process in China and the continuous increase of steel yield, the steel structure is widely used in various fields. However, in recent years, fatigue problems of steel structures, such as fatigue fracture of steel crane beams of industrial plants, fatigue fracture of bridges of steel structures of common iron, fatigue fracture of offshore oil platforms and the like, frequently occur. These engineering fatigue failure problems exhibit extremely strong burstiness and are extremely prone to catastrophic engineering accidents. The fatigue fracture mechanics theory lays a theoretical foundation for the analysis of the engineering failure problem. Because the actual engineering structure geometry is generally complex, the fatigue failure problem of the engineering structure is mostly analyzed by adopting a numerical simulation method in engineering. Common numerical simulation analysis methods include a grid overlapping method, a VCCT method, an XFEM method, a grid-free method, a phase field method and the like. These numerical analysis methods all require input of fracture mechanics parameters of the material, wherein critical energy release rateIt is a very critical parameter. Critical energy release rateThe fatigue crack growth is directly controlled. In the recently emerging phase field fracture model,And more particularly, the only fatigue damage evolution control variable. In summary, it can be seen that the accuracy is obtained by means of material testingAnalysis of fatigue failure problems of steel structures is very important. Whereas conventionalThe test method of (2) has two main factors, namely crack lengthAnd firstly, a calculation method of stress intensity factors. Many studies have been conducted on improvement of these two key factors, such as direct acquisition of stress intensity factor of crack tip by DIC technique, improvement of crack length by potentiometry or image recognitionMeasurement accuracy of (c) and the like. However, the basis for any improvement of these methods is to obtain the material directly or indirectly based on the load P-displacement v curveValues. Obviously, this method is generally only suitable for static loading and is relatively inefficient. Typically only one can be obtained at a timeValues. However, the high cycle fatigue problem of steel structures is typical cyclic loading, which is difficult to apply. Moreover, during high cycle fatigue loading, not every loading will produce a new measurable crack growth, but every loading will produce some material degradation in the localized area of the crack tip. This degradation necessarily results in a materialIs difficult to obtain materials by the conventional testing methodWhich in turn enables the conventionalThe detection method has poor detection effect and applicability. Therefore, how to improve the effect and applicability of the detection of the critical energy release rate of the steel structure becomes a technical problem to be solved.

The main solution of the application is that a plurality of stress wave signals are obtained by obtaining stress waves of the crack tip of a test piece to be detected in each crack extension period, the stress wave signals are respectively subjected to signal extraction to obtain a plurality of crack extension effective signals, the energy analysis is carried out on the plurality of crack extension effective signals to determine the energy release amount of each crack extension period, and the target critical energy release rate is determined according to the energy release amount of each crack extension period and the crack extension length of each crack extension period.

According to the application, the stress wave signal at the crack tip is obtained and analyzed, and the energy released in the crack expansion process is directly measured, so that the detection precision and the real-time performance are improved; in addition, the method is suitable for steel structure test pieces with various shapes and different loading conditions, the application range is enlarged, and the effect and the applicability of the detection of the critical energy release rate of the steel structure are further improved.

It should be noted that, the execution body of the method of this embodiment may be a computing service device with functions of data processing, network communication and program running, or may be the above-mentioned acoustic emission-based steel structure critical energy release rate detection device with the same or similar functions. This embodiment and the following embodiments will be described by taking a steel structure critical energy release rate detection apparatus based on acoustic emission as an example.

Based on this, a first embodiment of the method for detecting the critical energy release rate of the steel structure based on acoustic emission according to the present application is presented, and referring to fig. 1, fig. 1 is a schematic flow chart of the first embodiment of the method for detecting the critical energy release rate of the steel structure based on acoustic emission according to the present application.

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

s1, obtaining stress waves of crack tips of a test piece to be detected in each crack expansion period, and obtaining a plurality of stress wave signals;

the test piece to be detected is a steel structure test piece to be detected, and initial cracks are prefabricated on the steel structure test piece to be detected in a fatigue test. The crack tip refers to the foremost area of the crack, and is the location where stress concentration is highest, and is also the starting point of crack propagation. The crack growth period is a period or stage in which the crack is gradually grown during loading, each growth period corresponding to a minute growth length of the crack. Stress waves refer to the release of energy as a crack propagates, producing stress waves that are caused by the evolution of damage at the crack tip. The stress wave signal is an electric signal converted after the stress wave is captured by the acoustic emission sensor, and reflects the energy change released in the crack propagation process.

Specifically, an initial crack is prefabricated on the test piece to be detected, so that the initial crack becomes a starting point of a fatigue test. A plurality of acoustic emission sensors are symmetrically arranged near a crack path of a test piece to be detected to capture stress waves generated when the crack propagates. And (3) carrying out cyclic loading on the test piece to be detected by using a fatigue testing machine, and applying an axial cyclic load. During loading, the crack will gradually propagate.

Further, at each stage of crack propagation, the acoustic emission sensor captures the stress wave generated at the crack tip in real time, converting it into a stress wave signal. And recording stress wave signals captured by the acoustic emission sensor to obtain a plurality of stress wave signals for subsequent analysis.

By acquiring the stress wave signal at the crack tip in real time, the dynamic monitoring of the crack propagation process is realized, each detail of crack propagation can be captured in time, and the real-time performance of detection is improved. The acoustic emission sensor can accurately capture stress wave signals of crack tips, provide high-precision crack propagation data and ensure the accuracy of subsequent analysis. The stress wave signals are directly obtained, complex analysis depending on loading and unloading curves is not needed, experimental steps are simplified, and experimental complexity is reduced. The method directly detects stress wave signals, is not limited by the geometric shape and loading mode of the test piece, is suitable for steel structure test pieces with different shapes and loading conditions, and expands the application range of the method. By acquiring stress wave signals of the crack tip of the test piece to be detected in each crack expansion period, real-time and high-precision monitoring of the crack expansion process is realized, experimental steps are simplified, and the application range of the detection method is expanded. The beneficial effects obviously improve the efficiency and accuracy of the detection of the critical energy release rate of the steel structure.

S2, respectively extracting signals of each stress wave signal to obtain a plurality of crack expansion effective signals;

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 evaluation. The effective crack propagation signal is a signal which can accurately reflect the crack propagation process after signal extraction and processing.

Specifically, a preset filter is used for filtering the original stress wave signal, so that noise and interference signals are removed, and a purer signal is obtained. The filtered signals are amplified, so that the signal strength is enhanced, the weak stress wave signals are more remarkable, and the subsequent analysis is facilitated. Based on a preset signal processing algorithm, each acoustic emission event in the original stress wave signal is identified. Each acoustic emission event corresponds to a primary energy released during crack propagation. Dividing the identified acoustic emission event signals according to time windows, wherein the signals in each time window are used as independent acoustic emission event signals.

Further, time domain features such as signal amplitude, duration, energy, etc. are extracted from each acoustic emission event signal. And carrying out frequency domain analysis on each acoustic emission event signal, and extracting characteristics such as frequency components, spectrum energy and the like. And screening each acoustic emission event signal based on the extracted time domain and frequency domain characteristics to remove irrelevant or interference signals. And judging which signals are effective signals in the crack propagation process according to the feature screening result, and obtaining a plurality of crack propagation effective signals.

Noise and interference in the original stress wave signal are removed through filtering and signal amplification processing, and the signal strength is enhanced, so that the subsequent analysis is more accurate and reliable. Event identification and time window segmentation are carried out based on a preset signal processing algorithm, so that each acoustic emission event in the crack propagation process can be accurately distinguished, and the accuracy of signal segmentation is ensured. The time domain and frequency domain characteristics are extracted, the stress wave signals are comprehensively analyzed, the accuracy and the effectiveness of signal extraction are improved, and only effective signals really reflecting the crack propagation process are ensured to be extracted. Through feature screening and effective signal judgment, uncorrelated or interference signals are eliminated, the possibility of misjudgment is reduced, and the reliability and accuracy of a detection result are improved. The signal extraction is carried out on each stress wave signal to obtain a plurality of crack extension effective signals, the process improves the signal quality, accurately identifies each acoustic emission event in the crack extension process, ensures the accuracy and the effectiveness of the extracted signals through comprehensive feature analysis and effective signal screening, and further improves the accuracy and the reliability of the detection of the critical energy release rate of the steel structure.

S3, carrying out energy analysis on the plurality of crack extension effective signals to determine the energy release amount of each crack extension period;

It should be noted that energy analysis refers to processing and calculating a signal to determine an amount of energy released in a physical process represented by the signal. The energy release refers to the energy released during crack propagation and reflects the energy consumed by the material during the process.

Specifically, each crack growth effective signal is accumulated on a time axis to obtain the total signal intensity of each acoustic emission event. During the accumulation, the total intensity of the signal during each time period is calculated using a numerical integration method. The cumulative signal intensity for each acoustic emission event is integrated to calculate an energy value for each crack growth effective signal. The signal strength is converted into an energy value by an integration method.

Further, the calculated energy value is corrected in consideration of the sensitivity and response characteristics of the sensor. The correction factor is obtained through experimental calibration and is used for correcting errors caused by sensor characteristics. And summarizing the energy of all acoustic emission events in each crack extension period to obtain the total energy release amount of the crack extension period. By accumulating the energy of each event, the total amount of energy released during crack growth is obtained.

The energy of each acoustic emission event is accurately measured by both the integration and the integration calculations. This ensures accurate assessment of the amount of energy released during crack propagation, improving the reliability of the detection results. By applying the correction factor, the sensitivity and response characteristics of the sensor are considered, the accuracy of energy calculation is further improved, and the influence of the sensor characteristics on energy measurement is avoided. Summarizing the energy of all acoustic emission events during each crack growth period provides a comprehensive energy release assessment. This can more accurately reflect the actual energy release per crack growth period. By energy analysis of the plurality of crack growth effective signals, the energy release amount of each crack growth period is determined, and the process ensures the accuracy and the comprehensiveness of energy measurement. Accurate energy measurement, consideration of sensor characteristics and comprehensive energy evaluation jointly promote the accuracy and reliability of steel structure critical energy release rate detection.

And S4, determining a target critical energy release rate according to the energy release amount of each crack extension period and the crack extension length of each crack extension period.

It should be noted that the crack growth length refers to the length of the crack increased in each growth period. The target critical energy release rate is a parameter used for representing the critical energy release amount of the material in the crack propagation process, and is an important control variable for crack propagation.

Specifically, first, the energy release amount during each crack growth period is recorded. The energy release per crack growth period was obtained from the energy analysis in the previous step. And accumulating the energy release amount of each crack extension period to obtain the total energy release amount in the whole crack extension process. This process is needed to ensure that all recorded data is accurate and error free, avoiding missing any crack growth period data during the accumulation process.

Further, the crack growth length during each crack growth period was recorded. Crack growth length is the amount of crack growth per period of growth, typically by high precision measurement equipment or calculations. And accumulating the crack extension lengths of each crack extension period to obtain the total crack extension length in the whole crack extension process. This step requires a calibration of the crack length measurements and recordings to ensure accuracy of the accumulated data. After the total energy release amount and the total crack growth length are accumulated, dividing the total energy release amount by the total crack growth length, and calculating to obtain the target critical energy release rate. This calculation reflects the average energy consumption rate of the material throughout the crack growth process. Recording and verifying the calculation result, ensuring that no calculation errors exist, and carrying out necessary checking and confirmation on the result.

By accumulating the energy release amounts of the crack growth periods, the total energy consumption in the crack growth process is comprehensively estimated, and the accurate description of the crack growth behavior is ensured. By accumulating the crack growth lengths of the individual crack growth periods, the total crack growth in the entire crack growth process is accurately measured, providing detailed crack growth data. And the target critical energy release rate is accurately determined through the calculation of the ratio of the total energy release amount to the total crack propagation length. The calculation method simplifies the complex energy analysis process and improves the calculation efficiency and accuracy. The data of each crack extension period are recorded and accumulated in detail, so that the total error caused by the single crack extension period error is avoided, and the accuracy and reliability of the detection result are improved. The method can be suitable for fatigue analysis of various different types of steel structures by accurately calculating the target critical energy release rate, and improves the applicability and universality of the method. This process can provide reliable underlying data for steel structure fatigue life prediction and structural safety assessment. The target critical energy release rate is obtained through calculation by recording and accumulating the energy release amount and the crack extension length in the crack extension process in detail, so that the accuracy and the reliability of the fatigue analysis of the steel structure are ensured. The accurate energy and length accumulation method simplifies the calculation process and improves the detection precision and applicability.

The method comprises the steps of obtaining stress waves of a to-be-detected test piece at crack tip in each crack expansion period, obtaining a plurality of stress wave signals, respectively extracting the stress wave signals to obtain a plurality of crack expansion effective signals, carrying out energy analysis on the plurality of crack expansion effective signals to determine energy release amount of each crack expansion period, and determining target critical energy release rate according to the energy release amount of each crack expansion period and crack expansion length of each crack expansion period. According to the embodiment, the stress wave signal at the crack tip is obtained and analyzed, so that the energy released in the crack expansion process is directly measured, and the detection precision and the instantaneity are improved; in addition, the method is suitable for steel structure test pieces with various shapes and different loading conditions, the application range is enlarged, and the effect and the applicability of the detection of the critical energy release rate of the steel structure are further improved.

Based on the first embodiment, a second embodiment of the method for detecting the critical energy release rate of the steel structure based on acoustic emission is provided. Referring to fig. 2, fig. 2 is a schematic flow chart of a second embodiment of the method for detecting the critical energy release rate of the steel structure based on acoustic emission according to the present application.

As shown in fig. 2, in this embodiment, before step S1, the method further includes:

S1a, taking a preset standard test piece prefabricated with an initial crack as the test piece to be detected;

s1b, adopting a preset fatigue testing machine to circularly load the test piece to be detected.

It should be noted that the pre-fabricated initial crack is a crack manufactured on the test piece by man, and is used for simulating the propagation condition of a natural crack, so as to facilitate the start and control of the experiment. The preset standard test piece is a test sample which is designed and manufactured in advance according to the experimental requirements, and has uniform size, shape and material characteristics. The test piece to be detected is a test piece to be subjected to fatigue test, and the initial crack is prefabricated. The fatigue testing machine is a device specially used for applying cyclic loading, and is used for testing the fatigue performance of materials or structures by simulating stress and strain conditions in actual use environments. Cyclic loading refers to alternating stress or strain applied repeatedly on a tester to simulate the repeated loading experienced by a material or structure under actual operating conditions.

Specifically, a proper preset standard test piece is selected according to the experimental requirement (detection requirement). The test piece is selected to meet the relevant standards (such as ASTME399 or GB/T6398-2017) and the initial crack is prefabricated according to the experimental requirements. And ensuring that the preset standard test piece has consistent material characteristics and geometric dimensions so as to ensure the repeatability and reliability of experimental results. And taking the preset standard test piece prefabricated with the initial crack as a test piece to be detected, and placing the test piece on a fatigue testing machine. And the test piece is fixed by using the matched clamp and the pin shaft, so that the test piece is ensured not to move or deform in the loading process. The positions of the clamp and the test piece are adjusted so that the crack tip is located on a preset loading path, and the loading stress can be ensured to accurately act on the crack tip area.

Furthermore, cyclic loading parameters including loading frequency, loading amplitude and loading mode are set on the fatigue testing machine. The loading frequency is generally set at 1-3 Hz to avoid overlarge noise of the testing machine and ensure the accuracy of the experimental process. The displacement control parameters for crack opening are set to ensure that the crack propagates in the intended manner during loading. Starting the fatigue testing machine, and circularly loading the test piece to be tested. During loading, the test is repeatedly subjected to alternating stress, so that cracks gradually propagate. And monitoring stress and strain conditions in the loading process in real time, recording related data, and providing basic data for subsequent analysis.

Through the use of the preset standard test piece and the preset initial crack, the standardization and consistency of the experiment are ensured, and the experiment error caused by inconsistent test pieces is reduced. The fatigue testing machine is adopted for cyclic loading, so that the crack expansion process can be accurately controlled, the crack expansion path and speed meet the expectations, and a reliable data base is provided for subsequent energy analysis. Through cyclic loading, repeated loads applied to materials or structures under actual working conditions are simulated, so that experimental results have more practical significance and engineering application value. In the loading process, the fatigue testing machine can monitor and record stress, strain, displacement and other data in real time, and provides comprehensive data support for subsequent energy release calculation and crack propagation analysis. Through accurate pre-fabricated initial cracks and cyclic loading parameter setting, controllability and repeatability of a crack propagation process are ensured, and accuracy and reliability of experiments are improved. The method has the advantages that the preset standard test piece prefabricated with the initial crack is used as a test piece to be detected, the preset fatigue testing machine is adopted to carry out cyclic loading on the test piece, the test process can be standardized, the crack expansion can be accurately controlled, the working condition is truly simulated, the data are efficiently collected, and the accuracy and the reliability of the detection of the critical energy release rate of the steel structure are improved. The process lays a solid foundation for subsequent crack growth energy analysis and critical energy release rate determination.

Based on the above-described first embodiment, in the present embodiment, step S2 includes:

s21, respectively preprocessing each stress wave signal based on a preset filter and a preset signal amplifier to obtain a plurality of preprocessed signals;

s22, respectively dividing each preprocessing signal into a plurality of acoustic emission event signals based on a preset signal processing algorithm;

s23, extracting time domain features and frequency domain features from each acoustic emission event signal;

and S24, extracting effective signals of the stress wave signals based on the time domain features and the frequency domain features to obtain a plurality of crack extension effective signals.

The preset filter is a preset filter, which is used for filtering noise and interference in the stress wave signal, and retaining useful signal components. The preset signal amplifier refers to a preset signal amplifier, and is used for amplifying stress wave signals to enable the amplitude of the stress wave signals to reach a level suitable for subsequent processing. Preprocessing refers to the preliminary processing of the original signal, including filtering, amplifying, etc., for subsequent signal processing. The preprocessing signal refers to a signal subjected to preprocessing steps such as filtering, amplifying and the like, contains effective stress wave information and is ready for further analysis. The preset signal processing algorithm refers to a preset signal processing algorithm for dividing, analyzing and extracting useful information in the signal. An acoustic emission event signal refers to a stress wave signal generated by a single acoustic emission event reflecting the instantaneous information of energy release during crack propagation. Time domain features refer to features that analyze signals in the time domain, such as amplitude, duration, and event interval. Frequency domain features refer to features that analyze signals in the frequency domain, such as spectral distribution, frequency content, and power spectrum. Effective signal extraction refers to extracting an effective signal reflecting crack propagation from the pre-processed signal, removing extraneous or noise signals.

Specifically, each stress wave signal is filtered using a preset filter to remove noise and interference, leaving a useful signal component. The design of the filter needs to be optimized according to the characteristics of the stress wave signals so as to improve the definition of the signals to the greatest extent. And amplifying the filtered signal by using a preset signal amplifier 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, a plurality of pre-processed signals are obtained, which signals contain effective stress wave information. Each pre-processed signal is segmented using a preset signal processing algorithm, dividing the continuous signal into a plurality of independent acoustic emission event signals. The segmentation process requires precise identification of the start and end points of the signal to ensure independence of the event signals. And obtaining a plurality of acoustic emission event signals, wherein each signal corresponds to a crack propagation event and reflects instantaneous information of energy release.

Further, time domain features, including amplitude, duration, event interval, etc., are extracted from each acoustic emission event signal. These features reflect the law of variation of the signal in the time domain. Frequency domain features, including spectral distribution, frequency components, power spectrum, and the like, are extracted from each acoustic emission event signal. These features reflect the energy distribution of the signal in the frequency domain. The time domain characteristics and the frequency domain characteristics of each acoustic emission event signal are obtained, and a basis is provided for subsequent effective signal extraction. Based on the extracted time domain features and frequency domain features, each stress wave signal is analyzed to identify an effective signal reflecting crack propagation. The effective signal has obvious characteristic change and can accurately reflect the energy release in the crack growth process. And extracting an effective signal from the preprocessed signal according to an analysis result, removing irrelevant or noise signals, and ensuring that the extracted signal can accurately reflect the actual situation of crack expansion. And obtaining a plurality of crack extension effective signals, and providing basic data for subsequent energy analysis and critical energy release rate calculation.

Noise and interference in stress wave signals are removed through pretreatment steps such as filtering and amplifying, the quality and definition of the signals are improved, and a reliable basis is provided for subsequent analysis. Through signal segmentation and feature extraction, each crack expansion event can be accurately identified, and an effective signal reflecting the crack expansion process is extracted, so that the accuracy and reliability of signal analysis are ensured. By extracting the time domain features and the frequency domain features, the change rule of stress wave signals in the crack propagation process is comprehensively reflected, and rich signal feature information is provided. 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 has wide applicability, can be suitable for different types of steel structures and crack propagation conditions, and improves the universality and applicability of the method. The stress wave signals are preprocessed through a preset filter and a signal amplifier, signal segmentation and feature extraction are performed based on a preset signal processing algorithm, and finally a plurality of crack expansion effective signals are extracted. The process not only improves the signal quality and the recognition precision, but also comprehensively reflects the actual situation of crack propagation, provides a reliable data base for energy analysis and critical energy release rate calculation, and remarkably improves the effect and the applicability of the detection method for the critical energy release rate of the steel structure.

Based on the above-described first embodiment, in the present embodiment, step S3 includes:

S31, determining a starting point and an ending point of each acoustic emission event in each crack growth period based on the preset signal processing algorithm;

S32, determining the energy release amount of each crack expansion period according to the starting point and the ending point of each acoustic emission event and the amplitude of the corresponding crack expansion effective signal.

The start point and the end point refer to the start and end time points of the acoustic emission event signal, and are used to determine the duration of each event. The amplitude represents the peak magnitude of the acoustic emission event signal, reflecting the intensity and energy of the signal.

Specifically, each crack growth effective signal is analyzed based on a preset signal processing algorithm. The algorithm determines the starting and ending points for each acoustic emission event based on characteristics of the signal (e.g., amplitude, frequency variation, etc.). When the algorithm detects that the signal amplitude changes significantly or reaches a certain preset threshold, the time point is marked as the starting point of the acoustic emission event. When the signal amplitude falls below a preset threshold or the change tends to be smooth, the time point is marked as the ending point of the acoustic emission event. And obtaining a starting point and an ending point of each acoustic emission event in each crack extension effective signal, and providing basic data for energy calculation.

Further, amplitude data of the corresponding signal segment is extracted according to the determined starting point and ending point of the acoustic emission event. The amplitude reflects the energy intensity of each event. The energy release per acoustic emission event is calculated from the extracted amplitude data and the duration of the event. Generally, energy is proportional to the square of amplitude, and thus the energy release of an event is obtained by integrating the square of amplitude. And summarizing the energy release amount of each acoustic emission event to obtain the total energy release amount of each crack extension period.

By accurately identifying the starting point and the ending point of each acoustic emission event, the energy calculation is ensured to be based on an accurate event range, and the accuracy of energy release calculation is improved. The amplitude reflects the intensity of each acoustic emission event, and the energy release condition in the crack growth process can be accurately estimated by analyzing the amplitude to reflect the actual condition of crack growth. By calculating the energy release per crack extension period, detailed energy data is provided, which facilitates further analysis of crack extension laws and assessment of fatigue life of the steel structure. The signal processing and energy calculating method in the step has universality, is suitable for different types of steel structures and crack propagation conditions, and improves the wide applicability of the method. The starting point and the ending point of each acoustic emission event in each crack expansion period are determined based on a preset signal processing algorithm, and the energy release amount is calculated according to the points and the amplitudes of the corresponding crack expansion effective signals, so that the accuracy of energy calculation is effectively improved, the energy release condition in the crack expansion process is accurately reflected, detailed crack expansion data are provided, and the applicability and the universality of the steel structure critical energy release rate detection method are enhanced.

Based on the second embodiment, a third embodiment of the method for detecting the critical energy release rate of the steel structure based on acoustic emission according to the present application is presented. Referring to fig. 3, fig. 3 is a schematic flow chart of a third embodiment of a method for detecting a critical energy release rate of a steel structure based on acoustic emission according to the present application.

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

S4a, converting the target critical energy release rate into a stress intensity factor range;

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

s4c, determining the fatigue cycle times according to the crack extension simulation data and the fatigue life prediction model;

and S4d, based on the fatigue cycle times, evaluating the fatigue life of the test piece to be detected.

The stress intensity factor range is a parameter range describing the intensity of the crack tip stress field, and is generally used for predicting crack growth and fatigue life. The initial crack length is the length of an initial crack prefabricated in a test piece to be detected as an initial condition for fatigue life prediction. The preset material parameters refer to physical and mechanical parameters of the steel structure material, such as elastic modulus, poisson ratio, material toughness and the like. The fatigue life prediction model is a mathematical model based on material characteristics, crack propagation rules and stress intensity factors and is used for predicting the fatigue life of the steel structure under cyclic load. The crack propagation simulation data are crack propagation data under different load conditions, which are obtained through numerical simulation or experiments. The fatigue cycle number is the number of load cycles that the steel structure experiences under cyclic loading from initial crack propagation to failure. Fatigue life is the total working time or number of cycles a steel structure has from a crack-free state to failure under certain load conditions.

Specifically, according to fracture mechanics theory, the calculated target critical energy release rate is converted into a corresponding stress intensity factor range. This conversion relationship can be obtained by a formula or an experimental calibration. And obtaining a stress intensity factor range reflecting the stress field intensity of the crack tip, and providing input parameters for the subsequent fatigue life prediction. And establishing a fatigue life prediction model according to the stress intensity factor range, the initial crack length and the preset material parameters. The model is typically based on the Paris formula or other fatigue crack growth theory. The parameters are input into a fatigue life prediction model, and initial conditions and calculation parameters of the model are determined. And a fatigue life prediction model suitable for specific steel structures and materials is established, so that a crack propagation process can be simulated.

Further, the crack growth simulation data is substituted into the fatigue life prediction model, and the number of fatigue cycles required from the initial crack length to the failure state is calculated. And according to the simulation result, the fatigue cycle data are arranged and analyzed, and the key fatigue cycle in the crack growth process is determined. And obtaining the fatigue cycle times required by the crack to propagate to failure, and providing a basis for fatigue life assessment. Based on the determined fatigue cycle times, the fatigue life of the test piece to be detected is calculated in combination with actual working conditions (such as load frequency, working environment and the like). And (3) evaluating the fatigue life of the test piece to be detected under the actual working condition by analyzing the calculation result, and providing corresponding use suggestions or improvement measures. And obtaining a fatigue life evaluation report of the test piece to be detected, and providing scientific basis for design, use and maintenance of the steel structure.

By converting the target critical energy release rate into the stress intensity factor range and combining the fatigue life prediction model, the fatigue performance of the steel structure can be more accurately estimated, and the life of the steel structure under the actual use condition can be predicted. Based on the fatigue life evaluation result, the design of the steel structure can be optimized, and the fatigue resistance can be improved by selecting proper materials and structural forms. Meanwhile, reasonable maintenance and overhaul plans can be formulated, and the service life of the structure is prolonged. Through accurately predicting the fatigue life, the possible fatigue failure problem is discovered and prevented in advance, the catastrophic engineering accident is avoided, and the safety and the reliability of the steel structure are improved. The fatigue life assessment method based on the scientific method and the data analysis is provided, scientific guidance is provided for the design, construction and use of the steel structure in the actual engineering, and the engineering quality and efficiency are improved.

Based on the above-described second embodiment, in the present embodiment, step S4 includes:

S41, accumulating the energy release amounts of the crack extension periods to obtain total energy release amounts;

s42, accumulating the crack extension lengths to obtain a total extension length;

s43, determining the target critical energy release rate according to the total energy release amount and the total expansion length.

The total energy release amount is the accumulated value of the energy release amount in all crack growth periods, and reflects the total energy change in the whole crack growth process. Total propagation length refers to the cumulative value of crack propagation length over all crack propagation periods, reflecting the total length change throughout the crack propagation process.

Specifically, the energy release amount of each crack extension period is accumulated to obtain the total energy release amount in the whole crack extension process. And processing the experimental data by adopting a data accumulation algorithm. The total energy released reflects the total energy released by the material during crack propagation. And accumulating the crack extension length of each crack extension period to obtain the total extension length in the whole crack extension process. And adopting measurement and data accumulation technology to carry out accumulation calculation on the crack propagation length. The total propagation length reflects the total increase in crack growth throughout the propagation process. And determining the target critical energy release rate according to the ratio of the total energy release amount to the total expansion length. And calculating the energy release rate by adopting a formula calculation and data processing technology. The target critical energy release rate is used as an important index of the fatigue performance of the material and reflects the energy release rate in the crack growth process.

By accumulating the energy release amount and the crack propagation length, the error in single measurement is eliminated, and the detection precision of the critical energy release rate is improved. The accumulated result of the total energy release amount and the total expansion length provides comprehensive evaluation of the fatigue characteristics of the material and provides accurate basis for design and use. By accumulating the data of each crack extension period, the calculation process is simplified, so that the determination of the target critical energy release rate is more visual and easier to realize. By accurately determining the target critical energy release rate, the fatigue life of the material in actual engineering can be predicted better, and the safety and reliability of the engineering structure are improved. And the analysis and evaluation are carried out according to the accumulated results, so that the material selection and structural design can be guided and optimized, and the engineering quality and the service life are improved. The target critical energy release rate is determined by accumulating the energy release amount and the crack extension length, so that the detection precision and applicability are improved, the problems of error accumulation and complex calculation in the traditional detection method are effectively solved, and reliable data support is provided for material performance evaluation and engineering application.

For an exemplary purpose of understanding the technical concept or principle of the method for detecting the critical energy release rate of the steel structure based on acoustic emission according to the above embodiment, please refer to fig. 4, fig. 4 is a schematic diagram illustrating the sensor arrangement in an embodiment of the method for detecting the critical energy release rate of the steel structure based on acoustic emission according to the present application.

In order to dynamically obtain the critical energy release rate of metal in the test loading process, the existing critical energy release rate obtaining method is limited by geometric dimensions or the loading and unloading test process, and the embodiment provides a structural steel fatigue crack propagation dynamic critical energy release rate testing method based on an acoustic emission technology. The method is based on the characteristic that the acoustic emission sensor can capture stress waves generated by energy release in the crack tip damage evolution process, and a test method for directly obtaining the crack tip release energy is established. The method does not need to pay attention to a load P-displacement v curve in the loading and unloading process or to a stress intensity factor calculation formula of cracks, but directly measures the energy released by crack expansion of the material, namely the critical energy release rate, through the stress wave energy captured by the acoustic emission sensor.

The test (detection) method is as follows:

Preparation of the test sample

Test specimens were prepared with reference to CT specimens or three-point bending specimens in ASTM E399 standard or GB/T6398-2017 "fatigue crack propagation method for metallic Material", but the dimensions and loading positions were not limited by the specifications of these standards. Here, the test procedure is illustrated with CT samples.

(Two) Acoustic emission sensor arrangement

The sensor arrangement is shown in fig. 4. The acoustic emission sensors are symmetrically arranged with the crack propagation path as a central axis. Dividing 2n acoustic emission sensors into n groups, and respectively monitoring n equal-length crack segmentsThe released energy is formed.Perpendicular to the acoustic emission sensor line. Potential difference testing techniques for enhancementThe identification accuracy of the starting point and the ending point.

(III) Loading procedure

And adopting a fatigue testing machine, fixing the sample through a matched clamp and a pin shaft, and applying an axial cyclic load to the sample. In the test process, the loading frequency is controlled to be 1-3 Hz (the loading frequency cannot be too high so as to avoid excessive noise of the tester), and meanwhile, the displacement of crack gap opening is controlled. And loading acoustic emission signals of all sensors acquired in the whole process.

(IV) data processing method

Principle of calculation is toIs arranged at the end of the pre-made initial crack, the test is initiatedAnd have no fatigue damageIn agreement, no degradation effects need to be considered. When (when)The length of (2) is small enough to be inIn the forming processThe variation of (2) is negligible and is recorded as。

Will beThe total energy of the acoustic emission sensor AE11, AE21 signals during formation is recorded as(The superscript represents acoustic emission and the subscript corresponds to the crack segment number), thenAnd (3) withTotal energy released by crack formation,,There must be some correspondence, written as:

。

In the middle of 、、(Specimen plate thickness),Can be determined by testing, thus the relationCan be determined by data fitting.

Maintaining the test conditions of each micro-segment sensorIdentical, then、、The above formula is also necessarily satisfied, and thus the following relation can be established:

thereby can calculate according to the acoustic emission signal ~。

The embodiment of the application also provides a steel structure critical energy release rate detection device based on acoustic emission, please refer to fig. 5, fig. 5 is a schematic block diagram of the 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 comprises:

the stress wave module 501 is used for acquiring stress waves of the crack tip of the test piece to be detected in each crack expansion period to obtain a plurality of stress wave signals;

the signal extraction module 502 is configured to perform signal extraction on each stress wave signal to obtain a plurality of crack propagation effective signals;

An energy analysis module 503, configured to perform energy analysis on the plurality of crack growth effective signals, and determine an energy release amount of each crack growth period;

a target module 504, configured to determine a target critical energy release rate according to the energy release amount of each crack extension period and the crack extension length of each crack extension period.

The steel structure critical energy release rate detection device based on the acoustic emission provided by the embodiment of the application can solve the technical problem of how to improve the effect and applicability of steel structure critical energy release rate detection by adopting the steel structure critical energy release rate detection method based on the acoustic emission. Compared with the prior art, the beneficial effects of the steel structure critical energy release rate detection device based on acoustic emission provided by the embodiment of the application are the same as those of the steel structure critical energy release rate detection method based on acoustic emission provided by the embodiment, and other technical features of the steel structure critical energy release rate detection device based on acoustic emission are the same as those disclosed by the method of the embodiment, so that the description is omitted.

The application provides a steel structure critical energy release rate detection device based on acoustic emission, which comprises at least one processor and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable 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 embodiment.

Referring now to fig. 6, a schematic diagram of an acoustic emission based steel structure critical energy release rate detection apparatus suitable for use in implementing embodiments of the present application is shown. 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, notebook computers, digital broadcast receivers, PDAs (Personal DIGITAL ASSISTANT: personal digital assistants), PADs (Portable Application Description: tablet computers), PMPs (Portable MEDIA PLAYER: portable multimedia players), vehicle-mounted terminals (e.g., vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The acoustic emission based steel structure critical energy release rate detection apparatus shown in fig. 6 is only one example and should not impose any limitation on the functionality and scope of use of the embodiments of the present application.

As shown in fig. 6, the acoustic emission-based steel structure critical energy release rate detection apparatus may include a processing device 1001 (e.g., a central processor, a graphic processor, etc.), which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access Memory (RAM: random Access Memory) 1004. In the RAM1004, various programs and data required for the operation of the steel structure critical energy release rate detection apparatus based on acoustic emission are also stored. The processing device 1001, the ROM1002, and the RAM1004 are connected to each other by a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus. In general, a system including an input device 1007 such as a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc., an output device 1008 including a Liquid crystal display (LCD: liquid CRYSTAL DISPLAY), a speaker, a vibrator, etc., a storage device 1003 including a magnetic tape, a hard disk, etc., and a communication device 1009 may be connected to the I/O interface 1006. The communication means 1009 may allow the acoustic emission based steel structure critical energy release rate detection device to communicate wirelessly or by wire with other devices to exchange data. While acoustic emission based steel structure critical energy release rate detection apparatus having various systems are shown, it should be understood that not all of the illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. The above-described functions defined in the method of the disclosed embodiment of the application are performed when the computer program is executed by the processing device 1001.

The steel structure critical energy release rate detection equipment based on the acoustic emission provided by the application adopts the steel structure critical energy release rate detection method based on the acoustic emission in the embodiment, so that the technical problem of how to improve the effect and applicability of steel structure critical energy release rate detection can be solved. Compared with the prior art, the acoustic emission-based steel structure critical energy release rate detection device has the same beneficial effects as the acoustic emission-based steel structure critical energy release rate detection method provided by the embodiment, and other technical features in the acoustic emission-based steel structure critical energy release rate detection device are the same as the features disclosed in the method of the previous embodiment, and are not repeated herein.

It is to be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

The present application provides a computer readable storage medium having computer readable program instructions (i.e., a computer program) stored thereon for performing the acoustic emission based steel structure critical energy release rate detection method of the above embodiments.

The computer readable storage medium provided by the present application may be, for example, a U disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of a computer-readable storage medium may include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access Memory (RAM: random Access Memory), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (EPROM: erasable Programmable Read Only Memory or flash Memory), an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this embodiment, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to electrical wiring, fiber optic cable, RF (Radio Frequency) and the like, or any suitable combination of the foregoing.

The computer readable storage medium may be included in the acoustic emission based steel structure critical energy release rate detection apparatus or may exist alone without being assembled into the acoustic emission based steel structure critical energy release rate detection apparatus.

The computer readable storage medium carries one or more programs, when the one or more programs are executed by the steel structure critical energy release rate detection equipment based on acoustic emission, the steel structure critical energy release rate detection equipment based on acoustic emission obtains stress waves of crack tips of a test piece to be detected in each crack expansion period to obtain a plurality of stress wave signals, respectively extracts the stress wave signals to obtain a plurality of crack expansion effective signals, analyzes the energy of the plurality of crack expansion effective signals to determine the energy release amount of each crack expansion period, and determines the target critical energy release rate according to the energy release amount of each crack expansion period and the crack expansion length of each crack expansion period. Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language 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 remote computers, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN: local Area Network) or a wide area network (WAN: wide Area Network), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules involved in the embodiments of the present application may be implemented in software or in hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.

The readable storage medium provided by the application is a computer readable storage medium, and the computer readable storage medium is stored with computer readable program instructions (namely computer program) for executing the method for detecting the critical energy release rate of the steel structure based on acoustic emission, so that the technical problem of how to improve the effect and the applicability of the detection of the critical energy release rate of the steel structure can be solved. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the application are the same as those of the steel structure critical energy release rate detection method based on acoustic emission provided by the embodiment, and are not repeated here.

An embodiment of the application provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the acoustic emission based steel structure critical energy release rate detection method described above.

The computer program product provided by the application can solve the technical problem of how to improve the effect and applicability of the detection of the critical energy release rate of the steel structure. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the application are the same as those of the steel structure critical energy release rate detection method based on acoustic emission provided by the embodiment, and are not repeated here.

The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the application, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein, or any application, directly or indirectly, within the scope of the application.

Claims

1. The method for detecting the critical energy release rate of the steel structure based on acoustic emission is characterized by comprising the following steps of:

taking a preset standard test piece prefabricated with an initial crack as a test piece to be detected;

carrying out cyclic loading on the test piece to be detected by adopting a preset fatigue testing machine;

Acquiring stress waves of the crack tip of the test piece to be detected in each crack expansion period, and obtaining a plurality of stress wave signals;

Determining a target critical energy release rate according to 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 according to the energy release amount of each crack extension period and the crack extension length of each crack extension period comprises the following steps:

accumulating the crack extension lengths to obtain a total extension length;

2. The method of claim 1, wherein the step of separately extracting the respective stress wave signals to obtain a plurality of crack growth effective signals comprises:

3. The method of claim 2, wherein the step of performing an energy analysis on the plurality of crack growth effective signals to determine an energy release per crack growth period comprises:

4. The method of claim 1, further comprising, after the step of determining the target critical energy release rate based on the energy release per crack growth period and the crack growth length per crack growth period:

5. An acoustic emission-based steel structure critical energy release rate detection device, characterized in that the device comprises:

The stress wave module is used for taking a preset standard test piece prefabricated with an initial crack as a test piece to be detected, circularly loading the test piece to be detected by adopting a preset fatigue testing machine, and acquiring stress waves of the crack tip of the test piece to be detected in each crack expansion period to obtain a plurality of stress wave signals;

The target module is used for determining a target critical energy release rate according to the energy release amount of each crack expansion period and the crack expansion length of each crack expansion period, wherein the step of determining the target critical energy release rate according to the energy release amount of each crack expansion period and the crack expansion length of each crack expansion period comprises the steps of accumulating the energy release amounts of the crack expansion periods to obtain total energy release amount, accumulating the crack expansion lengths to obtain total expansion length, and determining the target critical energy release rate according to the total energy release amount and the total expansion length.

6. A computer device, characterized in that the device comprises a memory, a processor and an acoustic emission based steel structure critical energy release rate detection program stored on the memory and executable on the processor, the acoustic emission based steel structure critical energy release rate detection program being configured to implement the steps of the acoustic emission based steel structure critical energy release rate detection method according to any of claims 1 to 4.

7. A storage medium, wherein the storage medium has stored thereon an acoustic emission-based steel structure critical energy release rate detection program, which when executed by a processor, implements the steps of the acoustic emission-based steel structure critical energy release rate detection method according to any one of claims 1 to 4.

8. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, implements the steps of the acoustic emission based steel structure critical energy release rate detection method as claimed in any of claims 1 to 4.