CN-121994623-A - Rock fracture prediction method based on acoustic emission and surface strain collaborative monitoring

CN121994623ACN 121994623 ACN121994623 ACN 121994623ACN-121994623-A

Abstract

The invention relates to the technical field of rock mechanics tests and safety monitoring, and discloses a rock fracture prediction method based on acoustic emission and surface strain collaborative monitoring. And synchronously acquiring the acoustic emission signal and the sample surface image sequence by using a synchronous trigger through a load triggering logic, and calculating the three-dimensional coordinates of the acoustic emission source and the evolution characteristics of the surface full-field strain data. On the basis, the variation coefficient of the main strain field is calculated, and the cross-correlation analysis is carried out on the resampled energy rate and the strain rate. And finally, identifying the rock fracture precursor type according to the acoustic emission time sequence parameter, the positioning event and the multi-parameter coupling criterion of the surface strain, and predicting the fracture moment and the fracture area by combining Voight model reverse speed method and seismic source projection technology.

Inventors

YANG LEI
WANG XIAOQING
WANG WANJIE
LIU XIAOMIN
CAO SHUWEN
GAO FUQIANG
LU ZHIGUO
ZHONG LIQUN
LOU JINFU
LI JIANZHONG
WU RUI
LIU WENJU
WEI JIONG

Assignees

中煤科工开采研究院有限公司

Dates

Publication Date: 20260508
Application Date: 20260108

Claims (10)

1. The rock fracture prediction method based on the collaborative monitoring of acoustic emission and surface strain is characterized by comprising the following steps of: S110, selecting a cuboid rock block as a rock sample, constructing a random speckle field suitable for digital image correlation analysis on the surface of the rock sample, and constructing a collaborative monitoring system comprising a loading device, an acoustic emission monitoring system and an industrial camera array; s120, establishing uniform time stamps for the loading device, the acoustic emission monitoring array and the industrial camera array based on a GPS time service module, and setting signal trigger threshold parameters of the loading device, sampling frequency of the acoustic emission monitoring system and image acquisition frame rate parameters of the industrial camera array; S130, applying continuous load to the rock sample through the loading device, triggering the collaborative monitoring system by using a synchronous trigger and the load triggering threshold parameter, acquiring an acoustic emission signal according to the sampling frequency by the acoustic emission monitoring system, and acquiring a sample surface image sequence by using the industrial camera array according to the image acquisition frame rate parameter; S140, collecting and resolving ringing counts and source space coordinates of the acoustic emission signals, tracking pixel subarea position changes of the random speckle field in the sample surface image sequence by utilizing a digital image correlation algorithm, and calculating to obtain sample surface random measuring points and full-field strain data; S150, analyzing the ringing count, the seismic source space coordinate and the evolution characteristics of the strain data in a correlation mode, identifying the rock fracture precursor type according to a multi-parameter coupling criterion, and predicting a fracture area.
2. The method for predicting rock fracture based on collaborative monitoring of acoustic emissions and surface strain according to claim 1, wherein in step S110, the acoustic emission monitoring array comprises at least eight acoustic emission sensors, the acoustic emission sensors are distributed in upper, middle and lower regions of the sides of the rock sample according to spatially non-coplanar and non-collinear principles, and the acoustic emission sensors of adjacent sides are staggered in position; The industrial camera array includes four high resolution industrial cameras arranged at ninety degree angles with their optical axes orthogonal to the corresponding rock sample surface centers.
3. The rock burst prediction method based on the collaborative monitoring of acoustic emission and surface strain according to claim 1, wherein in step S110, the rock sample size satisfies a geometric condition that a ratio of height to length is equal to or greater than two-point zero and a ratio of height to width is equal to or greater than two-point zero; the random speckle field consists of a matte white background layer covered on the surface of the rock sample and matte black spots sprayed on the white background layer, and the coverage rate of the matte black spots is forty to sixty percent.
4. A rock fracture prediction method based on co-monitoring of acoustic emissions and surface strain according to claim 3, wherein in step S120, the specific way of establishing the uniform time stamp is: And connecting the GPS time service module with the loading device, the acoustic emission monitoring system and the control computers of the industrial camera array through 232 interfaces or USB interfaces, installing GpsTimer time setting software on each control computer, and opening GpsTimer time setting before the experiment starts, so as to establish a unified time stamp for the loading device, the acoustic emission monitoring system and the industrial camera array.
5. The rock burst prediction method based on collaborative monitoring of acoustic emissions and surface strain according to claim 4, wherein in step S130, the manner of application of the continuous load includes: The loading device is utilized to load to a preset contact value in a force control mode and then is switched to a displacement control mode; The specific logic for triggering the collaborative monitoring system is that the synchronous trigger is connected with the loading device, the acoustic emission monitoring system and the industrial camera array, when the axial load monitored by the loading device reaches a preset trigger threshold value for the first time, the synchronous trigger generates a pulse signal to trigger the acoustic emission monitoring system to start to acquire acoustic emission signals at the sampling frequency, and the industrial camera array starts to acquire surface images at the image acquisition frame rate.
6. The rock fracture prediction method based on collaborative monitoring of acoustic emission and surface strain according to claim 1 is characterized in that the method for resolving the seismic source space coordinates comprises the steps of constructing a three-dimensional positioning equation set based on arrival time difference data of acoustic emission signals, and adopting a damping least square method to iterate and solve to obtain a three-dimensional space position of an acoustic emission source; The calculation method of the random measuring points and the full-field strain data comprises the step of generating maximum shear strain field data by tracking displacement vectors of each pixel subarea in the random speckle field before and after deformation and utilizing displacement field differential operation.
7. The rock fracture prediction method based on collaborative monitoring of acoustic emissions and surface strain according to claim 1, wherein the specific content of the multi-parameter coupling criteria includes: calculating a main strain field variation coefficient of the full-field strain data, and monitoring the change rate of the main strain field variation coefficient along with time; Performing cross-correlation analysis on the acoustic emission energy rate sequence resampled based on the uniform time step and the surface maximum main strain rate sequence, and calculating a cross-correlation coefficient and a time lag; When the rate of change of the primary strain field coefficient of variation is monitored to exceed a set threshold, the amount of time delay is less than or equal to the uniform time step and the cross-correlation coefficient is greater than 0.85 and indicates a strong coupling characteristic, a fracture precursor is determined to occur.
8. The rock burst prediction method based on acoustic emission and surface strain co-monitoring of claim 1, wherein the identifying a rock burst precursor type comprises: when the ringing count is monitored to be obviously increased and the full-field strain data has no obvious mutation, judging the full-field strain data as an internal crack activation precursor, and judging an internal damage concentration area according to the seismic source space coordinates; When the localized concentration of the full-field strain data and the response delay of the acoustic emission signal are monitored, judging the full-field strain data as a surface layer fracture precursor, and judging a surface layer deformation concentration area according to the full-field strain data; and when the ringing count and the full-field strain data are monitored to be abnormal at the same time, judging the critical instability precursor.
9. The method for predicting a fracture of a rock based on collaborative monitoring of acoustic emissions and surface strain according to claim 1, wherein in step S150, the method for predicting a fracture zone is: Adopting Voight model reverse speed method, using full-field accumulated shear strain as observation physical quantity, carrying out regression analysis on linear relation between reverse strain rate and time to calculate theoretical destruction moment; and carrying out spatial superposition on the projection point of the seismic source spatial coordinate corresponding to the theoretical destruction moment and a high-strain area in the full-field strain data, wherein the overlapping area is the predicted macroscopic fracture position.
10. Computer device, characterized in that it comprises a memory, a processor and a communication module, said processor, when executed, implementing the rock fracture prediction method based on acoustic emission and surface strain co-monitoring according to any one of claims 1 to 9.

Description

Rock fracture prediction method based on acoustic emission and surface strain collaborative monitoring Technical Field The invention relates to the technical field of rock mechanics tests and safety monitoring, in particular to a rock fracture prediction method based on acoustic emission and surface strain collaborative monitoring. Background In deep underground engineering and geotechnical engineering, destabilization and rupture of rock materials are often accompanied by severe release of energy, and accurate prediction of the damage characteristics of the rock materials is of great significance to engineering disaster prevention and reduction. At present, an indoor rock mechanical test is a main means for researching a rock damage evolution mechanism, and a common non-contact monitoring technology mainly comprises an acoustic emission monitoring technology and a digital image related technology. The acoustic emission technology focuses on capturing elastic wave signals generated by the expansion of micro cracks in the rock, so that the evolution process of internal damage can be reflected, and the digital image related technology focuses on full-field non-contact deformation measurement, so that the strain localized belt characteristics of the rock surface can be visually presented. However, the prior art has certain limitations in utilizing the two means for joint monitoring. The acoustic emission system and DIC optical system typically operate as separate devices, each having an independent data acquisition clock and trigger mechanism. In practical experiments, the alignment of data of the two is often dependent on the starting time of manual recording or the later fitting of a software layer, and a unified microsecond time base of a hardware level is lacked. Because of the start response delay and the internal clock drift of different devices, the high-frequency acoustic emission signal and the low-frequency image data cannot be accurately corresponding on a time axis, and reliable time sequence association is difficult to establish in the rock transient fracture process from microsecond to millisecond. In addition, existing fracture prediction methods are often limited to threshold alarms for a single physical quantity. For example, the danger is judged only according to the sudden increase of the acoustic emission ringing count rate or the accelerated change of strain at a certain point, and the physical mapping relation between the internal damage accumulation and the macroscopic deformation of the surface is ignored. Such single view monitoring is difficult to quantify the driving process of internal micro-fracture transmission to the surface, and is prone to false positives due to interference from ambient noise or local non-destructive deformation. Meanwhile, the traditional damage time prediction model is mostly based on regression analysis of single-point data, focuses on prediction of time dimension, and cannot effectively perform geometric projection and superposition analysis on spatial distribution of internal seismic sources and a surface high-strain area, so that synchronous and accurate prediction on macroscopic fracture occurrence positions and specific moments is difficult to realize. Disclosure of Invention Aiming at the defects of the prior art, the invention provides a rock fracture prediction method based on acoustic emission and surface strain collaborative monitoring, which solves the problem that single physical criterion is difficult to effectively correlate internal damage and surface deformation to cause misjudgment due to multi-source data time sequence deviation caused by lack of unified hardware time base of heterogeneous monitoring equipment in the existing rock mechanical test. To achieve the above object, a first aspect of the present invention provides a rock fracture prediction method based on acoustic emission and surface strain co-monitoring, comprising the steps of: Constructing a collaborative monitoring system and preparing a sample: And selecting a cuboid rock standard sample, and constructing a random speckle field consisting of a matte white background layer and uniformly distributed matte black speckles on the surface of the rock sample, so as to be used as a characteristic carrier for digital image correlation analysis. A hardware system is constructed that includes a loading device, an acoustic emission monitoring array, and an industrial camera array. The acoustic emission sensors are arranged on the side of the sample in a spatial non-coplanar and non-collinear distribution to acquire seismic source data containing depth information, and the high-resolution industrial camera with an optical axis orthogonal to the sample surface is arranged to record the full-field deformation process of the sample surface. Establishing a unified time base of the multi-source system: And connecting the GPS time service module with each subset control computer of the colla