Rock failure mechanism discrimination method based on three-dimensional digital image and acoustic emission

Abstract

The invention relates to the technical field of rock failure mechanism identification, in particular to a rock failure mechanism identification method based on three-dimensional digital images and acoustic emission, which comprises the following steps: constructing a multi-source collaborative test platform based on a test rock test piece, acquiring an axial loading data set, a speckle strain meter set and an acoustic emission waveform signal set by using the multi-source collaborative test platform, and determining the rock failure mechanism according to the axial loading data set, the speckle strain meter set and the acoustic emission waveform signal set; calculating a shear modulus according to the speckle strain meter set and the axial loading data set, constructing a global plastic shear strain energy curve and a local plastic shear strain energy curve based on the speckle strain meter set and the shear modulus, and performing signal analysis on the acoustic emission waveform signal set to obtain an RA change curve, an AF change curve and an AE energy curve. According to the method, the calculation precision of rock energy evolution and the reliability of damage mechanism judgment can be improved, and the engineering safety risk is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rock failure mechanism identification, and particularly relates to a rock failure mechanism identification method based on three-dimensional digital images and acoustic emission. BACKGROUND

[0002] With the sustained growth of China's national economy, many geotechnical engineering such as mine, highway, water conservancy and hydropower infrastructure can be constructed on a large scale. However, these facilities have the risk of causing engineering disasters such as fault, ground subsidence and the like under the action of external environmental factors and load, among which the deformation and failure of rock mass is a key factor affecting the engineering safety. The failure of rock mass is a complex process from local to global, and the local deformation evolution is of great significance to the macro mechanical behavior of rock.

[0003] The traditional technology mainly adopts point measurement means such as extensometer, resistance strain gauge and the like combined with acoustic emission parameters for qualitative analysis, and its implementation mode is to simply correlate local strain monitoring and acoustic emission counting and the like characteristic parameters. However, this technology has obvious defects: it cannot finely monitor the full-field displacement of rock surface and local crack evolution, resulting in inaccurate energy calculation, at the same time, it lacks a unified energy characterization method, and it is difficult to quantitatively correlate acoustic emission energy and deformation energy, thereby limiting the accurate identification of failure mechanism. SUMMARY

[0004] The present application provides a rock failure mechanism identification method based on three-dimensional digital images and acoustic emission, which mainly aims to improve the calculation accuracy of rock energy evolution and the reliability of failure mechanism identification, and reduce the engineering safety risk.

[0005] To achieve the above-mentioned purpose, the present application provides a rock failure mechanism identification method based on three-dimensional digital images and acoustic emission, comprising: An original rock specimen is obtained, and speckle pretreatment is performed on the original rock specimen to obtain a test rock specimen, wherein the surface of the test rock specimen comprises a plurality of speckle marks; A multi-source collaborative test platform is constructed based on the test rock specimen, wherein the multi-source collaborative test platform comprises a triaxial test system, a 3D-DIC system and an acoustic emission monitoring system; The triaxial compression experiment of the test rock specimen is performed by using the multi-source collaborative test platform to obtain an axial loading data set, a speckle strain table set and an acoustic emission waveform signal set; The shear modulus is calculated according to the speckle strain table set and the axial loading data set; The global plastic shear strain energy curve and the local plastic shear strain energy curve are constructed based on the speckle strain table set and the shear modulus; Based on the acoustic emission waveform signal set, the global plastic shear strain energy curve and the local plastic shear strain energy curve, rock failure mechanism discrimination based on three-dimensional digital image and acoustic emission is completed.

[0006] Optionally, the triaxial compression experiment of the test rock sample by using the multi-source collaborative test platform obtains an axial loading data set, a speckle strain table set and an acoustic emission waveform signal set, and includes the following steps. Synchronously starting the multi-source collaborative test platform to obtain a starting test platform and record a compression time; Based on the compression time and the starting test platform, experimental data of the test rock sample are collected to obtain axial loading data, a speckle strain table and acoustic emission waveform signals; Based on a preset detection interval and the compression time, an update time is calculated; The update time is taken as the compression time, and the step of collecting experimental data of the test rock sample based on the compression time and the starting test platform is returned until the test rock sample is damaged; The axial loading data, the speckle strain table and the acoustic emission waveform signals are respectively summarized to obtain an axial loading data set, a speckle strain table set and an acoustic emission waveform signal set.

[0007] Optionally, the experimental data of the test rock sample collected based on the compression time and the starting test platform include the following steps. At the compression time, the test rock sample is compressed by using a triaxial experiment system in the starting test platform and a preset test confining pressure to obtain axial loading data, wherein the axial loading data is axial stress; In the compression process, the speckle strain table is obtained by using a 3D-DIC system in the starting test platform to detect the strain of multiple speckle markers in the test rock sample, wherein the speckle strain table includes multiple speckle strain data, and each speckle strain data corresponds to a speckle marker, and the speckle strain data includes transverse axis strain and longitudinal axis strain; In the compression process, the acoustic emission waveform signals of the test rock sample are obtained according to an acoustic emission monitoring system in the starting test platform.

[0008] Optionally, the calculation of the shear modulus according to the speckle strain table set and the axial loading data set includes the following steps. In the speckle strain table set, the speckle strain tables are sequentially extracted, and the same time loading data is confirmed according to the speckle strain table in the axial loading data set; Based on the speckle strain table, the transverse axis displacement change rate and the longitudinal axis displacement change rate are calculated; Based on the transverse axis displacement change rate and the longitudinal axis displacement change rate, the overall shear strain is calculated; determining a current confining pressure corresponding to the speckle strain table, calculating the overall shear stress according to the axial stress in the loading data at the same time and the current confining pressure; respectively aggregating the overall shear stress and the overall shear strain to obtain an overall shear stress set and an overall shear strain set; constructing an overall stress-strain curve according to the overall shear stress set and the overall shear strain set, and fitting the overall stress-strain curve to obtain a shear modulus.

[0009] Optionally, the constructing the global plastic shear strain energy curve and the local plastic shear strain energy curve based on the speckle strain table set and the shear modulus comprises: directionally dividing the speckle strain table set to obtain a horizontal axis strain data set and a vertical axis strain data set, wherein the horizontal axis strain data set includes a plurality of horizontal axis strain data, the vertical axis strain data set includes a plurality of vertical axis strain data, and the horizontal axis strain data and the vertical axis strain data each correspond to a compression time; constructing a horizontal axis strain matrix based on the horizontal axis strain data set, wherein each row of the horizontal axis strain matrix represents one horizontal axis strain data in the horizontal axis strain data set; constructing a vertical axis strain matrix based on the vertical axis strain data set; calculating a speckle shear strain matrix according to the vertical axis strain matrix and the horizontal axis strain matrix, wherein the speckle shear strain matrix includes a plurality of speckle shear strains; constructing an elastic shear strain matrix using the shear modulus and the overall shear stress set, wherein the elastic shear strain matrix is of the same type as the speckle shear strain matrix; calculating a plastic shear strain matrix according to the elastic shear strain matrix and the speckle shear strain matrix; confirming a plastic shear strain column set in the plastic shear strain matrix, wherein the plastic shear strain column set includes a plurality of plastic shear strain columns, and the plastic shear strain columns in the plastic shear strain column set are arranged in order of time from early to late; obtaining a global plastic shear strain energy curve based on the plastic shear strain column set; constructing a local plastic shear strain energy curve according to a preset final failure time and the speckle strain table set.

[0010] Optionally, the constructing the elastic shear strain matrix using the shear modulus and the overall shear stress set comprises: sequentially extracting speckle shear strains in the speckle shear strain matrix, and confirming detection time stamps of the speckle shear strains; identifying a simultaneous time shear stress in the overall shear stress set according to the detection time stamps; calculating an elastic shear strain based on the simultaneous time shear stress and the shear modulus; aggregating the elastic shear strains to obtain an elastic shear strain set, and constructing the elastic shear strain matrix based on the elastic shear strain set.

[0011] Optionally, the obtaining the global plastic shear strain energy curve based on the plastic shear strain column set comprises: extracting a plastic shear strain column from the plastic shear strain column set in sequence; identifying a previous shear strain column of the extracted plastic shear strain column in the plastic shear strain column set, and calculating a plastic shear strain increment column according to the previous shear strain column and the extracted plastic shear strain column; calculating a plastic shear strain energy increment column based on the plastic shear strain increment column and a shear stress corresponding to the plastic shear strain column at the same time; performing summation operation on the plastic shear strain energy increment column to obtain a global plastic shear strain energy; obtaining a global plastic shear strain energy set by aggregating the global plastic shear strain energy, wherein the global plastic shear strain energy set comprises a plurality of global plastic shear strain energies, and each global plastic shear strain energy represents a different compression time; performing curve fitting based on the global plastic shear strain energy set to obtain a global plastic shear strain energy curve.

[0012] Optionally, the constructing the local plastic shear strain energy curve based on the preset final failure time and the speckle strain table set comprises: extracting a failure strain table from the speckle strain table set based on the final failure time, wherein the failure strain table is a speckle strain table arranged at the last position in the speckle strain table set; constructing a multi-modal strain verification model, wherein the multi-modal strain verification model is a neural network model; identifying a failure waveform signal from the acoustic emission waveform signal set according to the final failure time, performing feature extraction on the failure waveform signal to obtain a waveform signal feature set; inputting the waveform signal feature set and the failure strain table into the multi-modal strain verification model to obtain a strain table accurate value; if the strain table accurate value is not less than a preset accurate threshold, then filtering a plurality of speckle strain data in the failure strain table according to a preset maximum strain threshold to obtain a plurality of failure strain data; constructing a local plastic shear strain energy curve based on the plurality of failure strain data and the speckle strain table set.

[0013] Optionally, the constructing the local plastic shear strain energy curve based on the plurality of failure strain data and the speckle strain table set comprises: confirming a plurality of failure markers corresponding to the plurality of failure strain data from the plurality of speckle markers; performing data filtering on the speckle strain table set based on the plurality of failure markers to obtain a failure speckle strain table set; obtaining a local plastic shear strain energy curve by using the failure speckle strain table set.

[0014] To achieve the above object, the application further provides a rock failure mechanism discrimination system based on three-dimensional digital images and acoustic emission, comprising: A rock sample marking module is configured to obtain an original rock sample, pre-process the original rock sample by speckle, and obtain a test rock sample, wherein the surface of the test rock sample comprises a plurality of speckle marks. A compression data acquisition module is configured to construct a multi-source collaborative test platform based on the test rock sample, wherein the multi-source collaborative test platform comprises a triaxial test system, a 3D-DIC system and an acoustic emission monitoring system, and the triaxial compression experiment of the test rock sample is performed by using the multi-source collaborative test platform to obtain an axial loading data set, a speckle strain table set and an acoustic emission waveform signal set. A shear strain energy analysis module is configured to calculate the shear modulus according to the speckle strain table set and the axial loading data set. A shear energy analysis module is configured to construct a global plastic shear strain energy curve and a local plastic shear strain energy curve based on the speckle strain table set and the shear modulus.

[0015] To solve the above problems, the application further provides an electronic device, comprising: A memory is configured to store at least one instruction; A processor is configured to execute the instruction stored in the memory to implement the rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emission.

[0016] To solve the above problems, the application further provides a computer readable storage medium, wherein the computer readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emission.

[0017] The present application is to solve the problems described in the background art, first based on the test rock sample to build a multi-source collaborative test platform, this step through the integration of triaxial loading, optical measurement and acoustic emission monitoring system, realizes the data synchronous acquisition, overcomes the inconsistency of time sequence caused by independent operation of each system in the prior art, provides a basic platform support for subsequent energy correlation analysis, further, the present scheme is based on speckle strain table set and shear modulus to build global plastic shear strain energy curve and local plastic shear strain energy curve, this step first based on 3D-DIC full-field displacement data to calculate the plastic shear strain energy, realizes the energy evolution characterization from local to global, solves the problem that the traditional method such as extensometer or resistance strain gauge cannot monitor the local crack energy, and can distinguish the failure mechanism through the consistency of local curve and AE energy, the present scheme qualitatively distinguishes the tensile-shear failure mode through RA-AF analysis, and combines with the AE energy curve, and 3D-DIC energy coupling, reveals the internal relationship between acoustic emission parameters and deformation energy, makes up for the deficiency of the existing acoustic emission technology that only pays attention to counting or amplitude and lacks energy quantitative inference, through the establishment of the above curve, realizes the synchronous quantitative analysis of 3D-DIC plastic shear strain energy and AE energy, and the energy distribution law under different confining pressures is clear, which provides a more scientific and accurate evaluation basis for rock engineering safety, and breaks through the limitation of the prior art that lacks unified energy characterization means. Therefore, the present application first quantitatively calculates the deformation energy based on the full-field displacement data of rock, so as to couple with the acoustic emission energy, monitors the whole process of local to global deformation and failure of rock from the energy point of view, establishes a unified fine energy characterization means, and then distinguishes the tensile-shear failure mode of rock, thereby providing a basic theoretical basis for engineering safety. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A flowchart of a rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emission provided by an embodiment of the present application is shown in the figure. Figure 2 A function module diagram of a rock failure mechanism discrimination system based on three-dimensional digital images and acoustic emission provided by an embodiment of the present application is shown in the figure. Figure 3 A structural schematic diagram of an electronic device for implementing the rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emission provided by an embodiment of the present application is shown in the figure.

[0019] Explanation of reference signs: 1, electronic device; 10, processor; 11, memory; 12, bus.

[0020] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0021] It is to be understood that the specific embodiments described herein are merely illustrative of the present application and should not be used to limit the scope of the present application.

[0022] The embodiment of the present application provides a rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emission. The execution subject of the rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emission includes but is not limited to at least one of electronic devices such as a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the present application. In other words, the rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emission can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to a single server, a server cluster, a cloud server or a cloud server cluster and the like.

[0023] Referring to Figure 1 Fig. 1 shows a flowchart of the rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emission provided by an embodiment of the present application. In the embodiment, the rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emission includes the following steps. S1, obtaining an original rock sample, performing speckle pretreatment on the original rock sample to obtain a test rock sample, wherein the surface of the test rock sample includes a plurality of speckle marks.

[0024] It can be understood that the original rock sample refers to a rock used for subsequent triaxial compression test. The test rock sample refers to the original rock sample after speckle pretreatment, wherein the speckle pretreatment refers to uniformly applying black waterproof glue on the surface of the original rock sample, spraying white primer after air drying, and then forming a uniform speckle pattern (the uniform speckle pattern is the plurality of speckle marks) by spraying black matte paint points. Optionally, the spraying density in the above speckle pretreatment process is 5-10 spots / mm2, and the spot diameter is 0.1-0.3 mm. The role of the speckle pretreatment is to form a high-contrast speckle domain, to ensure that the 3D-DIC system can effectively track the surface displacement and strain of the sample through image discrimination algorithm, and to ensure the accuracy and reliability of the experimental data. The speckle mark refers to the black spot sprayed on the surface of the sample, which is used as a reference point for image discrimination in 3D-DIC monitoring.

[0025] Exemplarily, the test rock sample is prepared as follows: a sandstone to be damaged is cut into a 25mm x 50mm cylindrical sample, which is the original rock sample, and then the two end surfaces of the original rock sample are finely ground using sandpaper to ensure that the size accuracy and flatness of the two ends of the original rock sample meet the standards of the International Society for Rock Mechanics (ISRM) and that the geometric shape and surface quality of the original rock sample meet the experimental requirements, thereby reducing experimental errors. Further, the finely ground original rock sample is pretreated as follows: first, evenly apply a thin layer of black waterproof glue on the surface of the cut cylindrical sample, and after natural air drying, then spray a layer of white primer on the surface of the cylindrical sample, and then spray a layer of uniform black spots with black matte paint, thereby forming a speckle field, wherein the black matte paint spraying density is 5-10 spots / mm2, and the spot diameter is 0.1-0.3mm, ensuring that the speckle pattern meets the gray scale contrast requirements of the 3D-DIC image discrimination algorithm, and after the sprayed speckle field is naturally air dried, the sprayed cylindrical sample is placed in a vacuum pump for vacuum pumping, so that the speckle field is firmly attached to the surface of the sample, thereby obtaining the test rock sample.

[0026] S2, constructing a multi-source collaborative test platform based on the test rock sample, wherein the multi-source collaborative test platform comprises: a triaxial test system, a 3D-DIC system and an acoustic emission monitoring system.

[0027] It should be explained that the multi-source collaborative test platform refers to a collaborative test system integrating triaxial loading, optical measurement and acoustic emission monitoring, which is used for synchronously collecting the mechanical response, surface deformation and internal damage data of the rock sample during the stress process, wherein the triaxial test system refers to a mechanical servo device capable of applying axial pressure and confining pressure, which is used for simulating the triaxial stress state of the rock under different geological conditions, and by controlling the loading rate (such as 10⁻ 4achieve progressive damage. The 3D-DIC system refers to a non-contact optical measurement system based on three-dimensional digital image correlation technology, which can obtain speckle images on the surface of a test piece from different angles through multiple cameras, and calculate a full-field displacement and strain field by using an image discrimination algorithm. The acoustic emission monitoring system refers to an acoustic emission sensor capable of monitoring elastic wave signals generated by rock rupture in real time. The multi-source collaborative test platform is built in the following manner when testing a rock test piece: the rock test piece is placed in a cavity device of a triaxial test system, and an acoustic emission probe is attached to the surface of the cavity device, then the cavity device with the acoustic emission probe is placed on a triaxial test equipment table of the triaxial test system (the acoustic emission monitoring system receives acoustic emission signals through the acoustic emission probe), and the position of the cavity device is adjusted so that the cavity device is located in the middle of three high-speed cameras (i.e., the 3D-DIC system), wherein each high-speed camera is a binocular camera, which facilitates monitoring of three-dimensional deformation images of the rock test piece, then static water pressure is loaded on the triaxial test system, and the channels of the acoustic emission monitoring system are adjusted, after the static water pressure reaches a preset target value and the acoustic emission monitoring system receives no noise points, the triaxial test system is loaded, the 3D-DIC system records images, and the acoustic emission monitoring system records collision events and keeps monitoring data, thus completing the building of the multi-source collaborative test platform.

[0028] Further, the above-mentioned calculating full-field displacement and strain field by using image discrimination algorithm is: through the strain field calculation of the collected surface speckle image of the rock specimen by the image processing analysis software XTDIC-three-dimensional digital speckle strain measurement and analysis system, specifically: first, determine the analysis calculation area in the rock specimen which needs to be analyzed and calculated, then select a pair of binocular cameras (the two cameras in the binocular camera are respectively recorded as high-speed camera 1 and high-speed camera 2) which can shoot the analysis calculation area in the three high-speed cameras of the above-mentioned 3D-DIC system, demarcate the analysis calculation area in the surface speckle image collected by the high-speed camera 1, select a seed point in the analysis calculation area, then use the speckle image matching algorithm to complete the matching of the above-mentioned seed point in the surface speckle image collected by the high-speed camera 2, thereby establishing the corresponding relationship between the seed points with the same position in the images shot by the binocular camera, based on the above-mentioned corresponding relationship, sequentially carry out relevant matching on the multiple surface speckle images in different states in the analysis calculation area of the high-speed camera 1, and synchronously complete the matching operation of the multiple surface speckle images in different states corresponding to the high-speed camera 2, finally realize the accurate matching of the full-state speckle images of the binocular camera, through the above-mentioned matching operation, combined with the binocular stereo vision principle and image matching technology, the strain (i.e. Dis-X, Dis-Y, Dis-Z) in three directions of XYZ of the surface of the analysis calculation area shot by the high-speed camera 1 and the overall displacement field (Dis-E) and multiple strain fields can be calculated by the software XTDIC.

[0029] For example, when constructing a multi-source collaborative test platform, first, install the test rock specimen on the test bench of the triaxial test system, paste the acoustic emission probe on the surface of the test rock specimen and ensure good coupling, then set the confining pressure and loading parameters of the triaxial test system, the acquisition frequency (such as 1 frame / s) of the 3D-DIC system and the acoustic emission monitoring parameters, make each system start synchronously until the specimen is destroyed, thereby realizing the collaborative acquisition of data.

[0030] Importantly, in addition to the 3D-DIC system described above, the present scheme can also introduce strain gauges and fiber Bragg grating and other means to detect the deformation of each speckle mark on the surface of the rock specimen. The purpose of introducing multiple strain detection methods here is that the 3D-DIC system can only detect the deformation of the surface of the rock specimen, but cannot detect the damage inside the rock specimen. During the strain detection using the 3D-DIC system, the detected strain data (i.e., the speckle strain table) may not match the actual speckle strain due to factors such as lighting. Therefore, the present scheme constructs a multi-modal strain verification model based on multiple detection methods (i.e., strain gauges and fiber Bragg grating and other means) in the subsequent step. This model fits multiple different strain detection methods (including the 3D-DIC system), thereby greatly reducing the deviation of the 3D-DIC system in strain detection from the actual situation. The specific construction method of the multi-modal strain verification model will be described in detail later.

[0031] S3, performing a triaxial compression experiment on the test rock specimen using the multi-source collaborative test platform to obtain an axial loading data set, a speckle strain table set, and an acoustic emission waveform signal set.

[0032] It can be understood that the axial loading data set refers to a collection of mechanical data of the test rock specimen recorded by the triaxial experiment system during the triaxial compression experiment. The speckle strain table set refers to a collection of strain data of the test rock specimen recorded by the 3D-DIC system during the triaxial compression experiment. The acoustic emission waveform signal set refers to a collection of acoustic wave data of the test rock specimen recorded by the acoustic emission monitoring system during the triaxial compression experiment.

[0033] In detail, the step of performing a triaxial compression experiment on the test rock specimen using the multi-source collaborative test platform to obtain an axial loading data set, a speckle strain table set, and an acoustic emission waveform signal set includes: starting the multi-source collaborative test platform synchronously to obtain a started test platform and record a compression time; collecting experimental data of the test rock specimen based on the compression time and the started test platform to obtain axial loading data, a speckle strain table, and an acoustic emission waveform signal; calculating an update time based on a preset detection interval and the compression time; taking the update time as the compression time and returning to the step of collecting experimental data of the test rock specimen based on the compression time and the started test platform until the test rock specimen is damaged; respectively collecting the axial loading data, the speckle strain table, and the acoustic emission waveform signal to obtain an axial loading data set, a speckle strain table set, and an acoustic emission waveform signal set.

[0034] It can be understood that the starting test platform refers to the multi-source collaborative test platform after starting, wherein the triaxial test system, the 3D-DIC system and the acoustic emission monitoring system need to be started synchronously when starting the multi-source collaborative test platform. The compression time refers to the time when subsequent experimental data collection on the test rock sample is performed, which is set by a person, for example, if the compression time is set as time A, then at time A, the triaxial test system is used to compress the test rock sample. The detection interval refers to the time interval for collecting experimental data set by a person. The update time refers to the time after a detection interval from the compression time. When the test rock sample is not damaged, it means that the triaxial compression experiment has not ended, and at this time, the experimental data collection needs to be repeated, that is, the step of collecting experimental data on the test rock sample based on the compression time and the starting test platform needs to be returned until the test rock sample is damaged.

[0035] Further, since the acoustic emission monitoring system can detect the damage inside the rock sample, the strain data detected by the 3D-DIC system is further detected by the acoustic emission waveform signal, so that the strain data detected by the 3D-DIC system can accurately represent the actual strain condition of the rock sample.

[0036] In detail, the experimental data collection on the test rock sample based on the compression time and the starting test platform obtains axial loading data, speckle strain table and acoustic emission waveform signal, which comprises: At the compression time, the triaxial test system in the starting test platform and the preset test confining pressure are used to compress the test rock sample to obtain axial loading data, wherein the axial loading data is axial stress; In the compression process, the 3D-DIC system in the starting test platform is used to detect the strain of the plurality of speckle marks in the test rock sample to obtain a speckle strain table, wherein the speckle strain table comprises a plurality of speckle strain data, and each speckle strain data corresponds to a speckle mark, and the speckle strain data comprises transverse axis strain and longitudinal axis strain; In the compression process, the acoustic emission waveform signal of the test rock sample is obtained according to the acoustic emission monitoring system in the starting test platform.

[0037] It is explained that the test confining pressure refers to the constant lateral pressure applied in the triaxial experiment for simulating the stress state of rock at different geological depths or environments, the value of which is preset by the experiment (for example, 0 MPa, 5 MPa or 10 MPa), and the test confining pressure can be changed to obtain experimental data under different confining pressures. The axial loading data refers to the axial stress of the test rock sample detected at the compression moment, and in addition to the axial stress, the axial loading data also includes the axial strain. The speckle strain table refers to a structured data set for storing strain information of each speckle marker during the entire compression process, which is a collection of multiple speckle strain data, wherein one speckle strain data corresponds to one speckle marker, and the speckle strain data refers to the strain value of a certain speckle marker detected by the 3D-DIC system during the compression process, wherein the 3D-DIC system detects in the following manner: synchronously capturing speckle images by multiple cameras, comparing the gray distribution of the images before and after deformation by using a digital image correlation algorithm, calculating a displacement field and deriving a strain field, and thus obtaining the strain value of each speckle marker. The above process of obtaining the strain value from the speckle image is a prior art and will not be described here. The horizontal axis strain refers to the strain component of the speckle marker in the horizontal direction (radial direction or x-axis). The vertical axis strain refers to the strain component of the speckle marker in the vertical direction (axial direction or y-axis). The acoustic emission waveform signal refers to the elastic wave signal released when the internal cracks of the rock produce and expand during the compression process, and the waveform thereof contains characteristic parameters such as rise time, amplitude, frequency and duration. The acoustic emission waveform signal is collected by an acoustic emission monitoring system.

[0038] S4. Calculate the shear modulus according to the speckle strain table set and the axial loading data set.

[0039] It can be understood that the shear modulus refers to a material constant fitted by the ratio of shear stress to shear strain in the elastic stage, which can represent the ability of the material to resist shear deformation.

[0040] In detail, the calculation of the shear modulus according to the speckle strain table set and the axial loading data set includes: Extract the speckle strain table from the speckle strain table set in sequence, and determine the simultaneous time loading data according to the speckle strain table in the axial loading data set; Calculate the horizontal axis displacement change rate and the vertical axis displacement change rate based on the speckle strain table; Calculate the overall shear strain according to the horizontal axis displacement change rate and the vertical axis displacement change rate; Determine the current confining pressure corresponding to the speckle strain table, and calculate the overall shear stress according to the axial stress in the simultaneous time loading data and the current confining pressure; Respectively, the overall shear stress and the overall shear strain are summarized to obtain the overall shear stress set and the overall shear strain set; According to the whole shear stress set and the whole shear strain set, a whole stress-strain curve is constructed, and the whole stress-strain curve is fitted to obtain a shear modulus.

[0041] To be explained, the simultaneous loading data refer to the axial loading data in the compression process corresponding to the speckle strain table at the same compression moment. The lateral axis displacement rate refers to the rate of change of all lateral axis strains in the speckle strain table, and is used to represent the deformation of the test rock sample in the lateral axis direction (radial direction). The calculation method is as follows: all lateral axis strains in the speckle strain table are extracted, and the average value of all lateral axis strains is calculated. The average value is the lateral axis displacement rate. The longitudinal axis displacement rate refers to the rate of change of all longitudinal axis strains in the speckle strain table, and is used to represent the deformation of the test rock sample in the longitudinal axis direction (axial direction). The calculation method is the same as that of the lateral axis displacement rate, and will not be described here. The whole shear strain refers to the shear strain of the whole test rock sample, and is used to represent the degree of shear deformation of the sample. The calculation method of the whole shear strain is as follows: the absolute difference between the lateral axis displacement rate and the longitudinal axis displacement rate. The current confining pressure refers to the test confining pressure when the speckle strain table is obtained.

[0042] Further, the whole shear stress refers to the shear stress of the sample, and is used to represent the shear load. The calculation method of the whole shear stress is as follows: the absolute difference between the axial stress and the current confining pressure. The whole stress-strain curve refers to the change curve between the whole shear stress set and the whole shear strain set. The horizontal axis of the curve is the whole shear strain, and the vertical axis is the whole shear stress. The fitting of the whole stress-strain curve refers to the linear fitting of the linear elastic stage in the curve. The slope is calculated by linear regression or least squares method. The slope is the shear modulus.

[0043] S5, based on the speckle strain table set and the shear modulus, a global plastic shear strain energy curve and a local plastic shear strain energy curve are constructed.

[0044] As can be understood, the global plastic shear strain energy curve refers to the energy curve obtained by accumulating the plastic shear strain energy increments of all speckle points in the whole field. The global plastic shear strain energy curve is used to represent the plastic shear deformation energy dissipation evolution process of the sample in the whole range. The local plastic shear strain energy curve refers to the energy curve obtained by accumulating the plastic shear strain energy increments of only the local areas of the speckle points that reach the critical strain value at the final moment. The local plastic shear strain energy curve is used to represent the energy concentration and evolution behavior of the sample in the local failure area (such as the macroscopic crack through area). By introducing the local plastic shear strain energy curve, the tensile-shear failure mode of the rock can be more accurately determined.

[0045] In detail, the global plastic shear strain energy curve and the local plastic shear strain energy curve are constructed based on the speckle strain table set and the shear modulus, including: directions, to obtain a horizontal-axis strain data set and a vertical-axis strain data set, wherein the horizontal-axis strain data set comprises a plurality of horizontal-axis strain data, the vertical-axis strain data set comprises a plurality of vertical-axis strain data, and the horizontal-axis strain data and the vertical-axis strain data correspond to a compression time; constructing a horizontal-axis strain matrix based on the horizontal-axis strain data set, wherein each row of the horizontal-axis strain matrix represents a horizontal-axis strain data in the horizontal-axis strain data set; constructing a vertical-axis strain matrix based on the vertical-axis strain data set; calculating a speckle shear strain matrix according to the vertical-axis strain matrix and the horizontal-axis strain matrix, wherein the shear strain matrix comprises a plurality of speckle shear strains; constructing an elastic shear strain matrix using the shear modulus and the overall shear stress set, wherein the elastic shear strain matrix is of the same type as the speckle shear strain matrix; calculating a plastic shear strain matrix according to the elastic shear strain matrix and the speckle shear strain matrix; identifying a plastic shear strain column set in the plastic shear strain matrix, wherein the plastic shear strain column set comprises a plurality of plastic shear strain columns, and the plastic shear strain columns in the plastic shear strain column set are arranged in order of time from early to late; obtaining a global plastic shear strain energy curve based on the plastic shear strain column set; constructing a local plastic shear strain energy curve according to a preset final failure time and the speckle strain table set.

[0046] It should be explained that the horizontal-axis strain data set refers to a collection of a plurality of horizontal-axis strain data, wherein the horizontal-axis strain data refers to a collection of all horizontal-axis strains of a certain speckle strain table in the speckle strain table set. The vertical-axis strain data set refers to a collection of a plurality of vertical-axis strain data, wherein the vertical-axis strain data refers to a collection of all vertical-axis strains of a certain speckle strain table in the speckle strain table set. The horizontal-axis strain matrix refers to a matrix composed of the horizontal-axis strain data set, wherein a row of the horizontal-axis strain matrix represents all horizontal-axis strains in a horizontal-axis strain data, and a column of the horizontal-axis strain matrix represents the horizontal-axis strains of all speckle markers at the same compression time. The vertical-axis strain matrix refers to a matrix composed of the vertical-axis strain data set, which is of the same type as the horizontal-axis strain matrix and will not be described here.

[0047] For example, the time The horizontal-axis strains of the three speckle markers (ID1, ID2, ID3) are 0.0008, 0.0009, and 0.0007 respectively at the time , and are 0.0010, 0.0011, and 0.0009 respectively at the time , so the horizontal-axis strain data set is {time {0.0010, 0.0011, 0.0009}, the first row of the transverse strain matrix formed by this transverse strain data set is: [0.0008, 0.0009, 0.0007] (representing the transverse strain of all speckle markers at the first compression time {0.0008, 0.0010} (representing the transverse strain of speckle marker ID1 at compression times {0.0008, 0.0010} (representing the transverse strain of speckle marker ID1 at compression times

[0048] It can be understood that the speckle shear strain matrix refers to a matrix composed of a plurality of speckle shear strains, wherein the plurality of speckle shear strains refer to the shear strains of different speckle markers at different compression times after calculation of the longitudinal strain matrix and the transverse strain matrix, one speckle shear strain corresponds to one speckle marker and one compression time, and the speckle shear strain matrix is constructed in the following manner: the longitudinal strain and the transverse strain are extracted in sequence from the longitudinal strain matrix and the transverse strain matrix, respectively, wherein the extracted longitudinal strain and the extracted transverse strain have the same position in the respective matrix, the difference between the extracted longitudinal strain and the extracted transverse strain is calculated, and the difference is the speckle shear strain. All speckle shear strains are obtained in this way to obtain a plurality of speckle shear strains, and the speckle shear strain matrix is constructed according to the matrix structure of the longitudinal strain matrix or the transverse strain matrix and based on the plurality of speckle shear strains.

[0049] Further, the elastic shear strain matrix refers to a matrix composed of a plurality of elastic shear strains, wherein the elastic shear strain refers to the elastic strain of a certain speckle marker at a certain compression time. The plastic shear strain matrix refers to a matrix composed of a plurality of plastic shear strains, wherein the plastic shear strain refers to the plastic strain of a certain speckle marker at a certain compression time. The plastic shear strain matrix is constructed in the following manner: the speckle shear strain in the speckle shear strain matrix is subtracted by the elastic shear strain in the corresponding position of the elastic shear strain matrix to obtain the plastic shear strain, a plurality of plastic shear strains are obtained by repeating the above steps, and the plastic shear strain matrix is constructed according to the plurality of plastic shear strains.

[0050] It should be explained that the plastic shear strain column set refers to a set of a plurality of plastic shear strain columns, wherein the plastic shear strain column refers to a column in the plastic shear strain matrix, and different plastic shear strain columns represent a set of plastic shear strains of each speckle marker at different compression times. The final failure time refers to the compression time when the test rock sample is destroyed.

[0051] In detail, the elastic shear strain matrix is constructed by using the shear modulus and the overall shear stress set, which includes: extracting the speckle shear strain in sequence from the speckle shear strain matrix, and confirming the detection timestamp of the speckle shear strain; ​According to the detection timestamp, the same time shear stress is identified in the overall shear stress concentration; Based on the same time shear stress and the shear modulus, the elastic shear strain is calculated; The elastic shear strain is summarized to obtain an elastic shear strain set, and an elastic shear strain matrix is constructed based on the elastic shear strain set.

[0052] It can be understood that the detection timestamp refers to the compression time when the speckle shear strain is detected. The same time shear stress refers to the overall shear stress corresponding to the detection timestamp. The elastic shear strain is calculated based on the same time shear stress and the shear modulus. The result obtained by dividing the same time shear stress by the shear modulus is the elastic shear strain. The above construction of the elastic shear strain matrix based on the elastic shear strain set refers to the construction of the elastic shear strain set into a matrix form according to the structure of the speckle shear strain matrix. The matrix is the elastic shear strain matrix.

[0053] In detail, the global plastic shear strain energy curve is obtained based on the plastic shear strain column set, including: The plastic shear strain column is extracted from the plastic shear strain column set; The previous shear strain column of the extracted plastic shear strain column is identified in the plastic shear strain column set, and the plastic shear strain increment column is calculated according to the previous shear strain column and the extracted plastic shear strain column; Based on the plastic shear strain increment column and the same time shear stress corresponding to the plastic shear strain column, the plastic shear strain energy increment column is calculated; The plastic shear strain energy increment column is summed to obtain the global plastic shear strain energy; The global plastic shear strain energy is summarized to obtain a global plastic shear strain energy set, wherein the global plastic shear strain energy set includes multiple global plastic shear strain energies, and each global plastic shear strain energy represents a different compression time; Curve fitting is performed based on the global plastic shear strain energy set to obtain the global plastic shear strain energy curve.

[0054] It needs to be explained that the previous shear strain column refers to the plastic shear strain column adjacent to the extracted plastic shear strain column in time and earlier than the plastic shear strain column, for example: the extracted plastic shear strain column is the plastic shear strain column arranged in the third position in the plastic shear strain column set, and the previous shear strain column corresponding to the plastic shear strain column is the plastic shear strain column arranged in the second position in the plastic shear strain column set. If the extracted plastic shear strain column is the first plastic shear strain column in the plastic shear strain column set, skip the subsequent operation steps of the plastic shear strain column, and extract the second plastic shear strain column.

[0055] Further, the plastic shear strain increment column refers to a set of plastic shear strain increments, wherein the plastic shear strain increment refers to a value obtained by subtracting the plastic shear strain at the corresponding position of the previous shear strain column from the extracted plastic shear strain column, for example, the extracted plastic shear strain column is ], the previous shear strain column is ], and the plastic shear strain increment column is , , ], wherein , and are plastic shear strain increments in the plastic shear strain increment column. The plastic shear strain energy increment column refers to a set of plastic shear strain energy increments, which are in one-to-one correspondence with the plastic shear strain increments, wherein the plastic shear strain energy increment represents the energy change amount of a speckle marker due to plastic shear deformation within a certain time interval, and the plastic shear strain energy increment is calculated by multiplying the shear stress at the corresponding time of the plastic shear strain column by the corresponding plastic shear strain increment in the plastic shear strain increment column, and the result is the plastic shear strain energy increment, for example, the plastic shear strain increment column is , , ], the shear stress at the corresponding time of the plastic shear strain increment column is , and the plastic shear strain energy increment column is , , ], wherein , and all represent plastic shear strain energy increments in the plastic shear strain energy increment column. The summation operation refers to adding all plastic shear strain energy increments in the plastic shear strain energy increment column, and the result is the global plastic shear strain energy, which represents the cumulative value of the plastic shear strain energy increment of all speckle markers in a certain period of triaxial compression experiment. The above curve fitting refers to fitting discrete global plastic shear strain energy data points into a continuous curve to analyze the energy evolution trend by mathematical algorithm, and the curve fitting can be performed by least squares method, linear regression, etc.

[0056] In detail, the local plastic shear strain energy curve is constructed according to the preset final failure time and the speckle strain table set, comprising: extracting a failure strain table from the speckle strain table set based on the final failure time, wherein the failure strain table is the speckle strain table arranged at the last position in the speckle strain table set; constructing a multi-modal strain verification model, wherein the multi-modal strain verification model is a neural network model; According to the final destruction moment, a destruction waveform signal is identified from the acoustic emission waveform signal set, a waveform signal feature set is obtained by feature extraction on the destruction waveform signal; The waveform signal feature set and the destruction strain table are input into a multi-modal strain verification model to obtain a strain table accurate value; If the strain table accurate value is not less than a preset accurate threshold, a plurality of speckle strain data in the destruction strain table are screened according to a preset maximum strain threshold to obtain a plurality of destruction strain data; Based on the plurality of destruction strain data and the speckle strain table set, a local plastic shear strain energy curve is constructed.

[0057] It can be understood that the destruction strain table refers to the speckle strain table corresponding to the final destruction moment. The maximum strain threshold refers to a strain critical value set by a person, which is used to distinguish between destruction and non-destruction regions. If the horizontal axis strain or the vertical axis strain of the speckle marker is greater than the maximum strain threshold, it means that the speckle marker has undergone significant plastic deformation or destruction at the final destruction moment, and the speckle marker can be marked as a destruction marker. The plurality of speckle strain data in the destruction strain table are screened according to the preset maximum strain threshold: the speckle strain data are extracted in turn from the plurality of speckle strain data. If the horizontal axis strain or the vertical axis strain in the speckle strain data is greater than the maximum strain threshold, the speckle strain data is marked as destruction strain data, and the destruction strain data is collected to obtain a plurality of destruction strain data.

[0058] Further, the multi-modal strain verification model refers to a neural network model previously constructed for the accuracy of the destruction strain table. Optionally, a long short-term memory network is used as the multi-modal strain verification model. If the strain data in the destruction strain table has a large error, it will lead to a large error in the subsequent local plastic shear strain energy curve. The strain table accurate value refers to the probability that the model training label corresponding to the destruction strain table is accurate in DIC detection. If the probability is not less than the minimum probability set by a person (i.e., the accurate threshold), it means that the strain data in the destruction strain table is accurate and can be used for subsequent construction of the local plastic shear strain energy curve. Otherwise, the strain table accurate value needs to be uploaded to the relevant operating personnel for decision-making (such as changing the light environment and re-experimenting). The above waveform signal feature set refers to a set of waveform features that can represent the destruction waveform signal. The waveform signal feature set includes but is not limited to: time domain features (such as skewness, kurtosis, waveform factor, pulse factor, etc.), frequency domain features (such as center frequency, peak frequency, etc.), time-frequency domain features (such as wavelet energy coefficients extracted by wavelet transform, wavelet packet node energy, etc.), etc.

[0059] Importantly, the multi-modal strain verification model is constructed in the following manner: multi-modal strain detection is performed on the rock specimen, wherein the multi-modal detection methods include but are not limited to strain gauges, DIC, fiber Bragg gratings, etc., thereby obtaining strain data corresponding to different detection methods. In the detection process, the acoustic emission waveform signals of the rock specimen are collected in real time, and the acoustic emission waveform signals are subjected to feature extraction to obtain a waveform feature set for subsequent training (the extraction method is the same as that of the waveform signal feature set). The average strain data is obtained by averaging the strain data corresponding to different detection methods. Then, the strain data deviation between the average strain data and the strain data collected by the DIC method is calculated (which can be calculated by using the root mean square error, etc.). If the strain data deviation is greater than the maximum deviation set by humans, the DIC detection is inaccurate and is recorded as a model training label. Otherwise, the DIC detection is accurate and is recorded as a model training label. The strain data corresponding to the DIC, the model training label, and the waveform feature set for training are combined to obtain training data. The above steps are repeated to obtain a large amount of training data, and the selected neural network is supervised trained by using the large amount of training data, thereby obtaining the multi-modal strain verification model.

[0060] In detail, the local plastic shear strain energy curve is constructed based on the plurality of damage strain data and the speckle strain table set, comprising: Confirming a plurality of damage markers corresponding to the plurality of damage strain data in the plurality of speckle markers; Data screening of the speckle strain table set based on the plurality of damage markers to obtain a damage speckle strain table set; Obtaining the local plastic shear strain energy curve by using the damage speckle strain table set.

[0061] Understandably, the damage marker refers to the speckle marker corresponding to the damage strain data. The data screening of the speckle strain table set based on the plurality of damage markers refers to retaining only the data about the damage markers in the speckle strain table set, and the retained speckle strain table set is the damage speckle strain table set. The damage speckle strain table set refers to the speckle strain table set after data screening. The method of obtaining the local plastic shear strain energy curve is the same as that of obtaining the global plastic shear strain energy curve, which is not described here again.

[0062] Importantly, in addition to obtaining the global plastic shear strain energy curve and the local plastic shear strain energy curve, the scheme also performs signal analysis on the acoustic emission waveform signal set to obtain an RA change curve, an AF change curve, and an AE energy curve. The RA change curve refers to a curve representing the change of the RA value (ratio of rise time to amplitude) with time, which is constructed based on the RA value set. The AF change curve refers to a curve representing the change of the AF value (ratio of ringing count to duration) with time, which is constructed based on the AF value set. The AE energy curve refers to a curve representing the change of the cumulative acoustic emission energy with time, which is constructed based on the AE energy set.

[0063] In detail, the signal analysis on the acoustic emission waveform signal set to obtain the RA change curve, the AF change curve, and the AE energy curve includes: extracting acoustic emission waveform signals in the acoustic emission waveform signal set in sequence; analyzing the acoustic emission waveform signals to obtain the rise time, the signal amplitude, the ringing count, and the duration; obtaining the AE energy based on the acoustic emission waveform signals; calculating the RA value based on the rise time and the signal amplitude, and calculating the AF value based on the ringing count and the duration; respectively collecting the RA value, the AF value, and the AE energy to obtain the RA value set, the AF value set, and the AE energy set, and respectively performing curve fitting based on the RA value set, the AF value set, and the AE energy set to obtain the RA change curve, the AF change curve, and the AE energy curve.

[0064] Understandably, the AE energy refers to the elastic wave energy released by rock rupture in the acoustic emission signal. The RA value refers to the ratio of the rise time to the signal amplitude. The AF value refers to the ratio of the ringing count to the duration. Since there are obvious differences in the acoustic emission signals released by tensile and shear ruptures during rock rupture: the longitudinal wave energy released by tensile rupture is larger, the rise time of the acoustic emission waveform is short, and the frequency is high; while the transverse wave energy released by shear rupture is larger, the rise time of the acoustic emission waveform is long, and the frequency is low, so the RA value and the AF value are introduced to qualitatively analyze the rock rupture mechanism: if the RA value is larger and the AF value is smaller during the rupture process, then shear failure dominates; otherwise, tensile failure dominates.

[0065] S6, based on the acoustic emission waveform signal set, the global plastic shear strain energy curve, and the local plastic shear strain energy curve, complete rock failure mechanism discrimination based on three-dimensional digital images and acoustic emission.

[0066] If need to be explained, the RA change curve and the AF change curve can reflect the distribution of the acoustic emission RA-AF value in the process from the start of loading to the final destruction, and relevant personnel can identify the rock failure mechanism, such as tensile failure or shear failure, through the RA change curve and the AF change curve. And relevant personnel can make more accurate analysis on the failure mechanism of the rock by comparing the AE energy curve, the global plastic shear strain energy curve and the local plastic shear strain energy curve.

[0067] To solve the problems in the background art, first, a multi-source collaborative test platform is constructed based on the test rock specimen, which overcomes the inconsistency in time sequence caused by independent operation of each system in the prior art by integrating a triaxial loading system, an optical measurement system and an acoustic emission monitoring system, and provides a basic platform support for subsequent energy correlation analysis. Further, the global plastic shear strain energy curve and the local plastic shear strain energy curve are constructed based on the speckle strain table set and the shear modulus, which first finely calculates the plastic shear strain energy based on 3D-DIC full-field displacement data, realizes the energy evolution characterization from the local to the global, solves the problem that the traditional method such as the extensometer or the resistance strain gauge cannot monitor the local crack energy, and can identify the failure mechanism through the consistency of the local curve and the AE energy. The present scheme qualitatively identifies the tensile-shear failure mode through RA-AF analysis, and combines the AE energy curve and the 3D-DIC energy coupling to reveal the internal relationship between the acoustic emission parameters and the deformation energy, which makes up for the lack of energy quantitative inference of the existing acoustic emission technology which only focuses on counting or amplitude. Through the establishment of the above curves, the 3D-DIC plastic shear strain energy and the AE energy are synchronously quantitatively analyzed, the energy distribution law under different confining pressures is determined, more scientific and accurate evaluation basis for rock engineering safety is provided, and the limitation of the prior art that lacks a unified energy characterization means is broken through. Therefore, the present application first finely and quantitatively calculates the deformation energy based on the full-field displacement data of the rock to couple with the acoustic emission energy, monitors the whole process of local to global deformation and failure of the rock from the energy point of view, establishes a unified fine energy characterization means, and further identifies the tensile-shear failure mode of the rock, thereby providing a basic theoretical basis for engineering safety.

[0068] As Figure 2 shown is a functional module diagram of a rock failure mechanism identification system based on three-dimensional digital images and acoustic emission according to an embodiment of the present application.

[0069] The rock failure mechanism discrimination system 100 based on three-dimensional digital images and acoustic emission can be installed in an electronic device. According to the functions to be implemented, the rock failure mechanism discrimination system 100 based on three-dimensional digital images and acoustic emission can include a rock specimen marking module 101, a compression data acquisition module 102, a shear strain energy analysis module 103, and a shear energy analysis module 104. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete a fixed function, which are stored in the memory of the electronic device.

[0070] The rock specimen marking module 101 is used to obtain an original rock specimen, pre-process the original rock specimen by speckle, and obtain a test rock specimen, wherein the surface of the test rock specimen includes a plurality of speckle marks. The compression data acquisition module 102 is used to construct a multi-source collaborative test platform based on the test rock specimen, wherein the multi-source collaborative test platform includes a triaxial test system, a 3D-DIC system, and an acoustic emission monitoring system. The triaxial compression experiment of the test rock specimen is performed by using the multi-source collaborative test platform to obtain an axial loading data set, a speckle strain table set, and an acoustic emission waveform signal set. The shear strain energy analysis module 103 is used to calculate the shear modulus according to the speckle strain table set and the axial loading data set. The shear energy analysis module 104 is used to construct a global plastic shear strain energy curve and a local plastic shear strain energy curve based on the speckle strain table set and the shear modulus.

[0071] In detail, the modules in the rock failure mechanism discrimination system 100 based on three-dimensional digital images and acoustic emission in the embodiments of the present application use the same technical means as the rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emission in the above Figure 1 , and can produce the same technical effects, which will not be described here.

[0072] As shown in Figure 3 , it is a structural schematic diagram of an electronic device for implementing the rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emission according to an embodiment of the present application.

[0073] The electronic device 1 can include a processor 10, a memory 11, and a bus 12, and can further include a computer program stored in the memory 11 and executable on the processor 10, such as a rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emission program.

[0074] The memory 11 includes at least one type of readable storage medium, such as flash memory, mobile hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the electronic device 1. Further, the memory 11 includes both the internal storage unit and the external storage device of the electronic device 1. The memory 11 can be used not only to store application software and various data installed on the electronic device 1, such as the code of the rock failure mechanism discrimination method program based on three-dimensional digital images and acoustic emission, but also to temporarily store data that has been output or will be output.

[0075] The processor 10 can be composed of an integrated circuit in some embodiments, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including one or more combinations of central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips, etc. The processor 10 is the control core of the electronic device, which connects various components of the entire electronic device through various interfaces and lines, executes programs or modules stored in the memory 11 (such as the rock failure mechanism discrimination method program based on three-dimensional digital images and acoustic emission, etc.), and calls data stored in the memory 11 to perform various functions and process data of the electronic device 1.

[0076] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0077] Figure 3 Only the electronic device with components is shown, and those skilled in the art can understand that, Figure 3The illustrated structure does not constitute a limitation on the electronic device 1, and can include fewer or more components than illustrated, or combine certain components, or different component arrangements.

[0078] For example, although not shown, the electronic device 1 can also include a power source (such as a battery) to power the various components. Preferably, the power source can be logically connected to the at least one processor 10 through a power management system, so that functions such as charge management, discharge management, and power consumption management can be achieved through the power management system. The power source can also include one or more DC or AC power sources, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and any other components. The electronic device 1 can also include various sensors, Bluetooth modules, Wi-Fi modules, and the like, which are not described here.

[0079] Further, the electronic device 1 can also include a network interface, which can optionally include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), and is typically used to establish a communication connection between the electronic device 1 and other electronic devices.

[0080] Optionally, the electronic device 1 can also include a user interface, which can be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally a standard wired interface, a wireless interface. Optionally, in some embodiments, the display can be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch, etc. The display can also be appropriately referred to as a display screen or a display unit, and is used to display information processed in the electronic device 1 and to display a visualized user interface.

[0081] The program stored in the memory 11 of the electronic device 1 for identifying rock failure mechanism based on three-dimensional digital image and acoustic emission is a combination of multiple instructions, which, when executed in the processor 10, can achieve: An original rock sample is obtained, and speckle preprocessing is performed on the original rock sample to obtain a test rock sample, wherein the surface of the test rock sample includes a plurality of speckle marks; A multi-source collaborative test platform is constructed based on the test rock sample, wherein the multi-source collaborative test platform includes a triaxial test system, a 3D-DIC system, and an acoustic emission monitoring system; The multi-source collaborative test platform is used to perform a triaxial compression experiment on the test rock sample to obtain an axial loading data set, a speckle strain table set, and an acoustic emission waveform signal set; The shear modulus is calculated according to the speckle strain table set and the axial loading data set; constructing a global plastic shear strain energy curve and a local plastic shear strain energy curve based on the speckle strain table set and the shear modulus; judging the rock failure mechanism based on three-dimensional digital images and acoustic emission based on the acoustic emission waveform signal set, the global plastic shear strain energy curve and the local plastic shear strain energy curve.

[0082] Specifically, the specific implementation method of the processor 10 to the above instructions can refer to Figures 1 to 3 The description of related steps in the corresponding embodiments will not be repeated here.

[0083] Further, the modules / units integrated in the electronic device 1 are stored in a computer readable storage medium if they are realized in the form of software function units and sold or used as independent products. The computer readable storage medium can be volatile or non-volatile. For example, the computer readable medium can include any entity or system capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory).

[0084] The application further provides a computer readable storage medium, which stores a computer program, and the computer program can realize the following when executed by a processor of an electronic device: An original rock sample is obtained, and speckle preprocessing is performed on the original rock sample to obtain a test rock sample, wherein the surface of the test rock sample comprises a plurality of speckle marks; A multi-source collaborative test platform is constructed based on the test rock sample, wherein the multi-source collaborative test platform comprises a triaxial test system, a 3D-DIC system and an acoustic emission monitoring system; The triaxial compression experiment is performed on the test rock sample by using the multi-source collaborative test platform to obtain an axial loading data set, a speckle strain table set and an acoustic emission waveform signal set; The shear modulus is calculated according to the speckle strain table set and the axial loading data set; A global plastic shear strain energy curve and a local plastic shear strain energy curve are constructed based on the speckle strain table set and the shear modulus; Judging the rock failure mechanism based on three-dimensional digital images and acoustic emission based on the acoustic emission waveform signal set, the global plastic shear strain energy curve and the local plastic shear strain energy curve.

[0085] In the several embodiments of the application, it should be understood that the disclosed device, system and method can be implemented in other ways. For example, the system embodiments described above are only schematic. Actual implementation can have another division way.

[0086] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, i.e., may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs.

[0087] In addition, each functional module in various embodiments of the present application can be integrated in one processing unit, or each unit can exist physically independently, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software functional modules.

[0088] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emission, characterized in that, The method comprises: An original rock sample is obtained, and speckle pretreatment is performed on the original rock sample to obtain a test rock sample, wherein the surface of the test rock sample comprises a plurality of speckle marks; A multi-source collaborative test platform is constructed based on the test rock sample, wherein the multi-source collaborative test platform comprises a triaxial test system, a 3D-DIC system, and an acoustic emission monitoring system; A triaxial compression experiment is performed on the test rock sample by using the multi-source collaborative test platform to obtain an axial loading data set, a speckle strain table set, and an acoustic emission waveform signal set; Shear modulus is calculated according to the speckle strain table set and the axial loading data set; A global plastic shear strain energy curve and a local plastic shear strain energy curve are constructed based on the speckle strain table set and the shear modulus; Rock failure mechanism discrimination based on three-dimensional digital images and acoustic emission is completed based on the acoustic emission waveform signal set, the global plastic shear strain energy curve, and the local plastic shear strain energy curve.

2. The rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emissions according to claim 1, characterized in that, The triaxial compression experiment performed on the test rock sample by using the multi-source collaborative test platform to obtain the axial loading data set, the speckle strain table set, and the acoustic emission waveform signal set comprises: The multi-source collaborative test platform is started synchronously to obtain a starting test platform, and a compression time is recorded; Experimental data of the test rock sample are collected based on the compression time and the starting test platform to obtain axial loading data, a speckle strain table, and acoustic emission waveform signals; An update time is calculated based on a preset detection interval and the compression time; The update time is taken as the compression time, and the step of collecting experimental data of the test rock sample based on the compression time and the starting test platform is returned until the test rock sample is damaged; The axial loading data, the speckle strain table, and the acoustic emission waveform signals are respectively summarized to obtain an axial loading data set, a speckle strain table set, and an acoustic emission waveform signal set.

3. The rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emissions according to claim 2, characterized in that, The experimental data of the test rock sample are collected based on the compression time and the starting test platform to obtain axial loading data, a speckle strain table, and acoustic emission waveform signals, which comprises: At the compression time, the test rock sample is compressed by using the triaxial test system in the starting test platform and a preset test confining pressure to obtain axial loading data, wherein the axial loading data is axial stress; During the compression process, the 3D-DIC system in the starting test platform is used to detect the strain of the plurality of speckle marks in the test rock sample to obtain a speckle strain table, wherein the speckle strain table comprises a plurality of speckle strain data, and each speckle strain data corresponds to one speckle mark, and the speckle strain data comprises horizontal axis strain and vertical axis strain; During the compression process, the acoustic emission waveform signals of the test rock sample are obtained according to the acoustic emission monitoring system in the starting test platform.

4. The rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emissions according to claim 3, characterized in that, The shear modulus is calculated according to the speckle strain table set and the axial loading data set, which comprises: Speckle strain tables in the speckle strain table set are sequentially extracted, and simultaneous time loading data are confirmed according to the speckle strain tables in the axial loading data set; Horizontal axis displacement change rates and vertical axis displacement change rates are calculated based on the speckle strain tables; Overall shear strain is calculated according to the horizontal axis displacement change rates and the vertical axis displacement change rates; Determine the current confining pressure corresponding to the speckle strain table, calculate the overall shear stress according to the axial stress in the loading data at the same time and the current confining pressure; Respectively aggregate the overall shear stress and the overall shear strain to obtain an overall shear stress set and an overall shear strain set; According to the overall shear stress set and the overall shear strain set, construct the overall stress-strain curve, and fit the overall stress-strain curve to obtain the shear modulus.

5. The rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emissions according to claim 4, characterized in that, The construction of the global plastic shear strain energy curve and the local plastic shear strain energy curve based on the speckle strain table set and the shear modulus comprises: Directionally divide the speckle strain table set to obtain a horizontal axis strain data set and a vertical axis strain data set, wherein the horizontal axis strain data set includes a plurality of horizontal axis strain data, the vertical axis strain data set includes a plurality of vertical axis strain data, and the horizontal axis strain data and the vertical axis strain data each correspond to a compression time; Construct a horizontal axis strain matrix based on the horizontal axis strain data set, wherein each row of the horizontal axis strain matrix represents a horizontal axis strain data in the horizontal axis strain data set; Construct a vertical axis strain matrix based on the vertical axis strain data set; Calculate a speckle shear strain matrix according to the vertical axis strain matrix and the horizontal axis strain matrix, wherein the shear strain matrix includes a plurality of speckle shear strains; Construct an elastic shear strain matrix using the shear modulus and the overall shear stress set, wherein the elastic shear strain matrix is of the same type as the speckle shear strain matrix; Calculate a plastic shear strain matrix according to the elastic shear strain matrix and the speckle shear strain matrix; Confirm the plastic shear strain column set in the plastic shear strain matrix, wherein the plastic shear strain column set includes a plurality of plastic shear strain columns, and the plastic shear strain columns in the plastic shear strain column set are arranged in order of time from early to late; Obtain the global plastic shear strain energy curve based on the plastic shear strain column set; Construct the local plastic shear strain energy curve according to the preset final failure time and the speckle strain table set.

6. The rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emissions according to claim 5, characterized in that, The construction of the elastic shear strain matrix using the shear modulus and the overall shear stress set comprises: Extract the speckle shear strain in the speckle shear strain matrix in sequence, and confirm the detection timestamp of the speckle shear strain; Identify the simultaneous shear stress in the overall shear stress set according to the detection timestamp; Calculate the elastic shear strain based on the simultaneous shear stress and the shear modulus; Aggregate the elastic shear strain to obtain an elastic shear strain set, and construct the elastic shear strain matrix based on the elastic shear strain set.

7. The rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emissions according to claim 6, characterized in that, The obtaining of the global plastic shear strain energy curve based on the plastic shear strain column set comprises: Extract the plastic shear strain column in the plastic shear strain column set in sequence; Identify the previous shear strain column of the extracted plastic shear strain column in the plastic shear strain column set, and calculate the plastic shear strain increment column according to the previous shear strain column and the extracted plastic shear strain column; Calculate the plastic shear strain energy increment column based on the plastic shear strain increment column and the simultaneous shear stress corresponding to the plastic shear strain column; Sum the plastic shear strain energy increment column to obtain the global plastic shear strain energy; Aggregate the global plastic shear strain energy to obtain a global plastic shear strain energy set, wherein the global plastic shear strain energy set includes a plurality of global plastic shear strain energies, and each global plastic shear strain energy represents a different compression time; Perform curve fitting based on the global plastic shear strain energy set to obtain the global plastic shear strain energy curve.

8. The rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emissions according to claim 7, characterized by, The local plastic shear strain energy curve is constructed according to the preset final failure moment and the speckle strain table set, and the method comprises the steps of: extracting a failure strain table from the speckle strain table set based on the final failure moment, wherein the failure strain table is the speckle strain table arranged at the last position in the speckle strain table set; constructing a multi-modal strain verification model, wherein the multi-modal strain verification model is a neural network model; identifying a failure waveform signal from the acoustic emission waveform signal set according to the final failure moment, performing feature extraction on the failure waveform signal, and obtaining a waveform signal feature set; inputting the waveform signal feature set and the failure strain table into the multi-modal strain verification model to obtain a strain table accurate value; if the strain table accurate value is not less than a preset accurate threshold, then filtering a plurality of speckle strain data in the failure strain table according to a preset maximum strain threshold to obtain a plurality of failure strain data; constructing a local plastic shear strain energy curve based on the plurality of failure strain data and the speckle strain table set.

9. The rock failure mechanism discrimination method based on three-dimensional digital images and acoustic emissions according to claim 8, characterized in that, The local plastic shear strain energy curve is constructed based on the plurality of failure strain data and the speckle strain table set, and the method comprises the steps of: confirming a plurality of failure markers corresponding to the plurality of failure strain data from the plurality of speckle markers; performing data filtering on the speckle strain table set based on the plurality of failure markers to obtain a failure speckle strain table set; obtaining the local plastic shear strain energy curve by using the failure speckle strain table set.

10. A rock failure mechanism discrimination system based on three-dimensional digital images and acoustic emissions, characterized by, The system comprises: a rock sample marking module configured to obtain an original rock sample, perform speckle preprocessing on the original rock sample, and obtain a test rock sample, wherein the surface of the test rock sample comprises a plurality of speckle markers; a compression data acquisition module configured to construct a multi-source collaborative test platform based on the test rock sample, wherein the multi-source collaborative test platform comprises a triaxial test system, a 3D-DIC system, and an acoustic emission monitoring system, and the triaxial compression experiment of the test rock sample is performed by using the multi-source collaborative test platform to obtain an axial loading data set, a speckle strain table set, and an acoustic emission waveform signal set; a shear strain energy analysis module configured to calculate a shear modulus according to the speckle strain table set and the axial loading data set; a shear energy analysis module configured to construct a global plastic shear strain energy curve and a local plastic shear strain energy curve based on the speckle strain table set and the shear modulus.

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