A method and system for defining precursors of fracture rock failure and predicting failure

Abstract

This invention relates to the field of signal processing and analysis technology, specifically disclosing a method and system for identifying precursors and predicting the failure of fractured rocks. The method includes acquiring acoustic emission energy parameters of the rock sample and constructing an energy time series; obtaining the time-varying spectral width through MF-DFA processing, defining the turning point where it first abruptly enters a continuous high-value region as the initial precursor point; obtaining the time-varying acoustic emission energy variance through CSD processing, defining the characteristic point where the variance explodes near macroscopic failure as a failure precursor point. The initial precursor point is used to determine the predicted value of fracture closure time and the preliminary predicted value of failure time; the failure precursor point is used to determine the secondary predicted value of failure time, ultimately obtaining the predicted failure time of the fractured rock. This achieves precise, step-by-step monitoring and prediction of the entire process of rock failure, from localized damage and compaction to macroscopic crack penetration and fracture.

Description

Technical Field

[0001] This invention relates to the field of signal processing and analysis technology, and in particular to a method and system for identifying precursors of fractured rock failure and predicting its failure. Background Technology

[0002] Underground caverns, tunnels, and slopes commonly contain inherent defects such as joints and fissures within the rock mass. Accurately identifying and defining the precursory signals of macroscopic instability in fractured rocks remains a core challenge in the fields of engineering fracture mechanics and geological hazard prediction. Currently, research on rock failure precursor prediction largely focuses on the anomalous evolution characteristics of multidimensional parameters in the lead-up to failure.

[0003] To address the shortcomings of traditional prediction methods, Multifractal Detrended Fluctuation Analysis (MF-DFA) has been introduced as a method to characterize the multi-scale features of complex non-stationary time series. This method has the advantages of eliminating the interference of long-term trend terms through local detrending processing, exhibiting strong adaptability to non-stationary signals; it possesses earlier prediction and warning capabilities, and can keenly capture the increased complexity of crack activity and system correlation in the early stages of instability, during the local microcrack propagation phase; and because it is based on scale statistical analysis, it is insensitive to local anomalies and still exhibits high robustness when facing multiple interferences such as environmental noise, equipment errors, and sampling discontinuities. However, MF-DFA also has inherent limitations: its analysis results are relatively sensitive to parameter settings such as data length, scale interval, and order; and while multifractal parameters can reflect changes in system complexity, their physical correspondence with the evolution of microcracks within rocks is still unclear, limiting their physical interpretability as a single instability criterion.

[0004] Practical applications reveal that single indicators exhibit significant advantages and limitations at different stages of rock failure. Therefore, to achieve accurate monitoring of the entire process from localized damage to macroscopic fracture, it is urgent to construct a multi-parameter, step-by-step prediction scheme. Summary of the Invention

[0005] To address the issues of low accuracy and susceptibility to environmental interference in existing fractured rock identification methods, this invention provides a multi-parameter combined stepwise prediction scheme: it uses abrupt changes in MF-DFA characteristic parameters to define initial precursor points, and combines this with the explosive surge in the variance of the AE parameter in CSD to accurately pinpoint failure precursor points. This provides more accurate, stepwise, and reliable prediction technology support for engineering fractured rock mass instability disasters.

[0006] On the one hand, a method for defining precursors and predicting failure in fractured rocks is provided, including: Acoustic emission energy parameters of the rock sample to be tested are collected, and acoustic emission energy time series are obtained based on the acoustic emission energy parameters; The acoustic emission energy time series was processed by MF-DFA to obtain the spectral width calculated over time; the turning point where the spectral width first abruptly enters the continuous high value region was defined as the initial precursor point of the rock sample to be tested. Meanwhile, the acoustic emission energy time series was processed by CSD to obtain the acoustic emission energy variance calculated over time; the characteristic point where the acoustic emission energy variance exploded near the final macroscopic failure was defined as the failure precursor point of the rock sample to be tested. Based on the initial precursor points of the rock sample to be tested, the predicted values ​​of fracture closure time and final failure time are determined; based on the failure precursor points of the rock sample to be tested, the secondary predicted value of final failure time is determined; and an early warning is issued based on the preliminary predicted value of final failure time, the predicted value of fracture closure time, and the secondary predicted value of final failure time.

[0007] Furthermore, the acoustic emission energy time series is processed by MF-DFA to obtain the spectral width calculated over time. The specific process is as follows: Based on the acoustic emission energy time series and its average value, a cumulative deviation sequence is obtained; using a two-way truncation method, the cumulative deviation sequence is divided into non-overlapping equal-length sub-intervals of a certain length; the local trend function of the equal-length sub-intervals is fitted using the least squares method to eliminate the local trend and calculate the detrending variance of each interval; the detrending variance of all sub-intervals is averaged by order q to obtain a fluctuation function based on the time scale and order; based on the fluctuation function, the generalized scaling index is determined; through Legendre transformation, the generalized scaling index is converted into a singularity index; the maximum value of the singularity index is subtracted from the minimum value of the singularity index to obtain the spectral width.

[0008] Furthermore, the acoustic emission energy time series is subjected to CSD processing to obtain the acoustic emission energy variance calculated over time. Specifically, the average value of the acoustic emission energy series within the sliding window is first calculated, and the dispersion between the average value and the acoustic emission energy series is calculated to obtain the acoustic emission energy variance.

[0009] Furthermore, acoustic emission monitoring and uniaxial compression tests were conducted simultaneously on the rock samples to be tested.

[0010] Furthermore, based on the initial precursor point of the rock sample to be tested, the predicted value of the fracture closure time and the preliminary predicted value of the final failure time are determined. Specifically, with the occurrence time of the initial precursor point as the independent variable and the actual closure time of the prefabricated fracture as the dependent variable, an empirical formula is established between the occurrence time of the initial precursor point and the fracture closure time. The occurrence time of the initial precursor point of the rock sample to be tested is substituted into the empirical formula to obtain the corresponding predicted value of the fracture closure time.

[0011] Furthermore, it also includes: using the occurrence time of the initial precursor point as the independent variable and the actual final failure time as the dependent variable, establishing an empirical formula between the occurrence time of the initial precursor point and the final failure time, substituting the occurrence time of the initial precursor point of the rock sample to be tested into the empirical formula to obtain the corresponding preliminary prediction value of the final failure time.

[0012] Furthermore, based on the failure precursor points of the rock sample to be tested, a secondary prediction value of the final failure time is determined. Specifically, an empirical formula is established between the failure precursor point occurrence time and the corresponding actual final macroscopic failure time, with the failure precursor point occurrence time as the independent variable and the corresponding actual final macroscopic failure time as the dependent variable. The failure precursor point occurrence time of the rock sample to be tested is substituted into the empirical formula to obtain the secondary prediction value of the final failure time.

[0013] On the other hand, a system for defining precursors and predicting failure in fractured rocks is provided, including: The acquisition module collects acoustic emission energy parameters of the rock sample to be tested and obtains the acoustic emission energy time series based on the acoustic emission energy parameters; The initial precursor point definition module performs MF-DFA processing on the acoustic emission energy time series to obtain the spectral width calculated over time; the turning point where the spectral width first abruptly enters the continuous high value region is defined as the initial precursor point of the rock sample to be tested. The module for defining precursor points of damage also performs CSD processing on the acoustic emission energy time series to obtain the acoustic emission energy variance calculated over time. The characteristic points where the acoustic emission energy variance explodes near the final macroscopic damage are defined as precursor points of damage to the rock sample under test. The prediction module determines the predicted value of fracture closure time and the preliminary predicted value of final failure time based on the initial precursor points of the rock sample to be tested; it determines the secondary predicted value of final failure time based on the failure precursor points of the rock sample to be tested; and it issues an early warning based on the preliminary predicted value of final failure time, the predicted value of fracture closure time, and the secondary predicted value of final failure time.

[0014] In another aspect, a storage medium is also provided for non-transitory storage of computer-readable instructions, wherein when the non-transitory computer-readable instructions are executed by a computer, the method described in the first aspect is performed.

[0015] Furthermore, an electronic device is also provided, including: Memory, used for non-transitory storage of computer-readable instructions; and Processor, for executing the computer-readable instructions, When the computer-readable instructions are executed by the processor, they perform the method described in the first aspect above.

[0016] The above technical solution has the following advantages or beneficial effects: This invention combines the MF-DFA and CSD methods. It utilizes the extremely strong sensitivity of MF-DFA to early microcrack propagation to accurately define the initial precursor point, and utilizes the high responsiveness of CSD to the energy burst before critical fracture to accurately calibrate the failure precursor point. This enables step-by-step accurate monitoring and prediction of the entire process from local rock damage and compaction to macroscopic crack penetration and fracture. Attached Figure Description

[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0018] Figure 1 A flowchart illustrating the overall process of defining precursors and predicting failure in fractured rocks; Figure 2 This is a schematic diagram of the composition of the uniaxial compression system and the acoustic emission monitoring system in an embodiment of the present invention; Figure 3 The results of identifying initial precursor points and destructive precursor points based on the MF-DFA method are shown in the figure. Figure 4 The results of identifying initial precursor points and destructive precursor points based on the CSD method are shown in the figure. Figure 5 To mark the standard point map of the initial precursor points and the destruction precursor points from the perspective of cumulative damage; Figure 6 The results of identifying precursor points for specimens at different tilt angles using damage variables, MF-DFA, and CSD are shown in the figure. Figure 7 The graph shows the relative errors between the results obtained from MF-DFA and CSD and the standard points; where, Figure 7 (a) shows the relative error diagram for defining precursor points of destruction; Figure 7 (b) shows the relative error diagram for defining the initial precursor point; Figure 8 The figure shows the fitting results between the initial precursor point obtained based on the MF-DFA method and the actual closing time of the pre-fabricated crack. Figure 9 The figure shows the fitting results between the initial precursor point and the final macroscopic failure time obtained based on the MF-DFA method. Figure 10 This is a figure showing the fitting results between the precursor points of destruction obtained based on CSD and the final macroscopic destruction time. Detailed Implementation

[0019] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the invention. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] In this embodiment of the invention, "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of this invention, "multiple" refers to two or more.

[0022] Furthermore, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0023] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0024] Example 1 Terminology Explanation: Near final macroscopic failure: As the loading stress continues to increase, microcracks inside the rock continuously initiate, expand, and gradually connect, eventually forming a macroscopic fracture surface and leading to a complete loss of bearing capacity, i.e., final macroscopic failure. "Near final macroscopic failure" specifically refers to the loading period when the rock approaches this critical point of macroscopic instability and failure.

[0025] Uniaxial compression test: The uniaxial compression test is one of the fundamental mechanical testing methods in rock mechanics. A standard cylindrical rock sample is placed on a press, and a constant rate of pressure is applied along the axial direction without lateral restraint until the rock sample fails.

[0026] Initial precursor point and failure precursor point: Pre-fractured rocks, due to inherent defects, will experience the closure effect of pre-fractured cracks under high stress during loading, as well as the overall failure of the specimen caused by crack penetration in the later stages of loading. We define these two failures as the initial precursor point characterizing fracture compaction and the failure precursor point revealing the initiation of the main crack penetration.

[0027] In the field of acoustic monitoring, the acoustic emission (AE) b-value, as a classic indicator characterizing the scale of microcracks, is widely regarded as a key early warning signal for microcrack penetration and macroscopic failure when it drops sharply. However, failure precursor identification methods based on b-value changes are highly sensitive to the selection of sample size and time window, and are essentially limited to static statistical analysis. Furthermore, in complex engineering site environments, mechanical noise and electromagnetic interference often lead to distortions in magnitude and energy statistics, making it difficult for this method to accurately pinpoint the critical time point of instability under highly heterogeneous stress conditions.

[0028] Furthermore, the Critical Deceleration Theory (CSD) based on nonlinear dynamics has been introduced into instability identification research. This theory aims to characterize the loss of restoring force and the slow response to disturbances when a system approaches a bifurcation point by monitoring the surge in the autocorrelation coefficient and variance of acoustic emission signals. This method has a clear dynamic mechanism and can explain the process of rock evolving from stable damage to instability from the perspective of critical transition. Therefore, it has strong theoretical basis and certain early warning value in identifying precursory information near the instability stage. However, in rock mechanics engineering practice, this theory highly relies on continuous, high-resolution, and low-noise time-series data. In field monitoring, if there are missing measurements, low sampling rates, or strong noise, indicators such as variance and autocorrelation are easily distorted. Secondly, during actual loading, CSD indicators are often accompanied by multiple fluctuations and false signals, resulting in non-unique critical point identification and difficulty in accurately calibrating the true instability moment.

[0029] Example 1 provides a method for defining precursors and predicting failure in fractured rocks, such as... Figure 1-2 As shown, the method includes: S1: Collect acoustic emission energy parameters of the rock sample to be tested, and obtain the acoustic emission energy time series based on the acoustic emission energy parameters.

[0030] After the rock cores retrieved from the engineering site were processed into test samples according to the standards of the International Society for Rock Mechanics, a pre-fabricated fracture was cut at the geometric center of the granite sample using a high-pressure water jet. The sample was made into a cylindrical specimen with a diameter of 50 mm and a height of 100 mm, with dimensional errors of less than 0.5 mm in diameter and height, and parallelism tolerance of less than 0.1 mm at both ends of the specimen. The pre-fabricated fracture was cut at the geometric center of the specimen using a high-pressure water jet, with a fracture length and width of approximately 20 mm and 2 mm, respectively.

[0031] Acoustic emission monitoring and uniaxial compression testing were performed simultaneously on the rock sample to obtain real-time acoustic emission ring counts and energy parameters. The multiple real-time acquired acoustic emission energy parameters were arranged sequentially in chronological order to form an acoustic emission energy time series.

[0032] Acoustic emission monitoring is a dynamic non-destructive testing technique that is performed simultaneously during uniaxial compression tests to capture elastic wave signals generated by micro-fractures inside rock samples. The specific process is as follows: 1. Determine the probe installation position on the rock sample surface, apply coupling agent, and fix the acoustic emission probe on the rock sample.

[0033] 2. Place the sample in the testing machine for mechanical testing. Simultaneously with the loading of the testing machine, activate the acoustic emission acquisition system. During the compression process, the initiation and propagation of internal microcracks in the rock sample release elastic waves, which are received by the sensor and converted into electrical signals. Simultaneously record the stress, deformation, and acoustic emission signals of the rock sample.

[0034] 3. After the signal is amplified and converted by the preamplifier, the system automatically extracts key parameters such as acoustic emission ring count, energy, and number of events.

[0035] like Figure 2 The diagram shows the composition of a single-axis compression system and an acoustic emission monitoring system. Figure 2 The uniaxial compression system transmits the physical feedback signal generated by the uniaxial compressor to the test control cabinet, which then connects to the uniaxial test control computer to control the mechanical loading and record data. The acoustic emission monitoring system collects acoustic wave signals through an acoustic emission probe fixed on the specimen. The signal is transmitted unidirectionally to the preamplifier for amplification, then to the acoustic emission instrument for data acquisition and conversion, and finally connected to the acoustic emission control computer to complete the recording and processing of the audio signal.

[0036] It is important to note that the uniaxial compression test and acoustic emission monitoring process must be started and stopped simultaneously. This ensures the consistency of the time reference between the acoustic emission data and the stress-strain curve obtained from the uniaxial compression test. If the two are not started synchronously, the time of the acoustic emission data will be misaligned with the loading curve, making it impossible to correlate the signal characteristics of the quiescent and active phases with the different stages of stress-strain (compaction, elasticity, plasticity, and failure).

[0037] Before the uniaxial compression test began, the contact between the acoustic emission probe and the core sample was coupled with Vaseline.

[0038] There should be at least four acoustic emission probes, and it should be ensured that the four probes are not coplanar.

[0039] S2: Perform MF-DFA processing on the acoustic emission energy time series to obtain the spectral width calculated over time; define the turning point where the spectral width first abruptly enters the continuous high value region as the initial precursor point of the rock sample to be tested; Based on the sliding window and sliding step size, each part of the time series is obtained; the acoustic emission energy parameters of each part of the time series are processed by MF-DFA and CSD respectively to obtain the spectral width and the energy variance evolving over time.

[0040] The acoustic emission energy time series is processed by MF-DFA to obtain the spectral width calculated over time. Specifically, the following steps are taken: Based on the acoustic emission energy time series and its average value, a cumulative deviation sequence is obtained; using a two-way truncation method, the cumulative deviation sequence is divided into non-overlapping equal-length sub-intervals of a certain length; the local trend function of the equal-length sub-intervals is fitted using the least squares method to eliminate the local trend and calculate the detrended variance of each interval; the detrended variance of all sub-intervals is averaged by order q to obtain a fluctuation function based on the time scale and order; based on the fluctuation function, the generalized scaling index is determined; the generalized scaling index is converted into a singularity index using Legendre transformation; the maximum value of the singularity index is subtracted from the minimum value of the singularity index to obtain the spectral width.

[0041] Define a sliding window of length L and a sliding step size S. Each time the window slides, a small segment of the local time series is extracted. For the acoustic emission energy parameters of each segment, the spectral width is calculated using MF-DFA and CSD, respectively. And the evolution of energy variance over time.

[0042] The specific calculation process for calculating the evolution of spectral width over time based on the MF-DFA method is as follows: Assume the acoustic emission energy time series is ,in, Let N be the acoustic emission energy parameter; N be the total sequence length. Calculate the average value of this time series and construct the cumulative deviation sequence. : (1) Cumulative deviation sequence The sequence is divided into non-overlapping sub-intervals of length *s*. Considering that the total sequence length *N* is usually not an integer multiple of the time scale *s*, a bidirectional truncation method is adopted to fully utilize the data at the end of the sequence, dividing it from both ends. The number of sub-intervals resulting from a unidirectional division is denoted as... A total of 2Ns sub-intervals can be obtained by combining the forward and reverse directions.

[0043] For the 2Ns subintervals (denoted by v, where v = 1, 2, ..., 2Ns), the local trend function of the equal-length subintervals is fitted using the least squares method. : (2) in, The coefficients of the fitted polynomial, , where k is the highest order of the polynomial fitting.

[0044] Eliminate this local trend and calculate the detrending variance for each interval: (3) The mean of the detrended variances of all 2Ns subintervals is calculated by order q, yielding a fluctuation function that depends on the time scale s and the order q. : (4) In the formula, q represents the order of the multifractal analysis, and its value ranges from a non-zero real number. This invention defines the range of q as -10 to 10. When q = 0, it can be obtained through geometric mean... The definition is as follows: (5) Calculate the scaling exponent h(q): For different q values, the fluctuation function... The following scaling relationship exists between the step size s and the step size s: (6) Where h(q) is the generalized Hurst exponent, which can be obtained by linear fitting. The slope of lns is obtained.

[0045] By using the Legendre transformation, the generalized Hurst exponent h(q) is converted into singularity intensity (singularity exponent). : (7) Spectrum width for: (8) in, This represents the maximum value of the singularity index; It is the minimum value of the singularity index.

[0046] Spectrum width As a multifractal parameter The wider the crack, the stronger the non-uniformity of the acoustic emission signal distribution, reflecting a higher degree of complexity in the development of microcracks and damage evolution within the rock, and a more significant local fluctuation in energy release. The process of pre-fabricated crack closure is inevitably accompanied by a large local release of energy and increased damage complexity.

[0047] Therefore, this invention uses MF-DFA to increase the spectral width. The turning point where the first mutation enters the continuous high-value zone is determined as the initial precursor point of the rock sample to be tested.

[0048] S3: Simultaneously, CSD processing is performed on the acoustic emission energy time series to obtain the acoustic emission energy variance calculated over time; the characteristic point where the acoustic emission energy variance explodes near the final macroscopic failure is defined as the failure precursor point of the rock sample to be tested.

[0049] The acoustic emission energy time series is processed by CSD to obtain the acoustic emission energy variance calculated over time. Specifically, the average value of the acoustic emission energy series within the sliding window is first calculated, and the dispersion between the average value and the acoustic emission energy series is calculated to obtain the acoustic emission energy variance.

[0050] In this embodiment, the sliding window L is set to 300, and the sliding step size S is set to 200.

[0051] The energy variance evolution over time is calculated based on the CSD method. The specific calculation process is as follows: The acoustic emission energy sequence within this window is denoted as: First, calculate the average value of the acoustic emission energy sequence within the sliding window. : (9) Calculate the degree of dispersion of the sequence from the mean within the sliding window, i.e., the sample variance. : (10) In the analysis based on the Critical Deceleration Theory (CSD), the sample variance of the acoustic emission energy sequence within each sliding time window is calculated segment by segment, and a variance-time curve of the sample variance evolution over time is constructed. As loading progresses into the middle and later stages, and the sample approaches final macroscopic failure, the acoustic emission energy variance exhibits an explosive surge on the variance-time curve, which can accurately determine the precursor point of failure in the tested rock sample.

[0052] S4: Based on the initial precursor points of the rock sample to be tested, determine the predicted value of the fracture closure time and the preliminary predicted value of the final failure time; based on the failure precursor points of the rock sample to be tested, determine the secondary predicted value of the final failure time; and issue an early warning based on the preliminary predicted value of the final failure time, the predicted value of the fracture closure time, and the secondary predicted value of the final failure time.

[0053] Based on the initial precursor point of the rock sample to be tested, the predicted value of the fracture closure time and the preliminary predicted value of the final failure time are determined, including: taking the occurrence time of the initial precursor point as the independent variable and the actual closure time of the prefabricated fracture as the dependent variable, establishing an empirical formula between the occurrence time of the initial precursor point and the fracture closure time, and substituting the occurrence time of the initial precursor point of the rock sample to be tested into the empirical formula to obtain the corresponding predicted value of the fracture closure time. Using the occurrence time of the initial precursor point as the independent variable and the actual final failure time as the dependent variable, an empirical formula is established between the occurrence time of the initial precursor point and the final failure time. Substituting the occurrence time of the initial precursor point of the rock sample to be tested into the empirical formula, the corresponding preliminary prediction value of the final failure time is obtained.

[0054] Based on the precursor points of the rock sample to be tested, the secondary prediction value of the final failure time is determined, including: taking the time of occurrence of the precursor points as the independent variable and the corresponding actual final macroscopic failure time as the dependent variable, establishing an empirical formula between the time of occurrence of the precursor points and the final macroscopic failure time, and substituting the time of occurrence of the precursor points of the rock sample to be tested into the empirical formula to obtain the secondary prediction value of the final failure time.

[0055] Stress-strain curves were obtained based on a large amount of uniaxial compression test data.

[0056] Uniaxial compression test data of multiple sets of related experimental samples were obtained to generate stress-strain curves. The initial precursor point occurrence time of each sample, determined by multifractal detrended fluctuation analysis (MF-DFA), was then read from the stress-strain curves. t i And record the actual closing time of the prefabricated crack corresponding to each sample. T i And the actual final destruction time; the time of occurrence of the initial precursor point. t i The independent variable is the actual closing time of the pre-fabricated fracture. T i Using the variable as the dependent variable, a linear fit was performed on a large amount of experimental data to establish an empirical formula between the time of occurrence of the initial precursor point and the time of fracture closure; the empirical formula is: T i = 1.08403 t i - 11.37075 (11) Based on this empirical formula, in the actual prediction process, the time of occurrence of the initial precursor point obtained by the current rock sample based on the MF-DFA method is substituted into the empirical formula to calculate the corresponding predicted value of the fracture closure time.

[0057] Furthermore, using the same fitting method as described above, the occurrence time of the initial precursor point is determined. t i As the independent variable, the actual final failure time is used. T f To fit the data to the dependent variable, the relationship model between the initial precursor point occurrence time and the final destruction time is established as follows: T f = 1.13066 t i +71.16134(12) By substituting the initial precursor point occurrence time obtained from the current rock sample using the MF-DFA method into the above empirical formula, the preliminary prediction of the final failure time can be calculated.

[0058] Since there is still a relatively long time interval between the initial precursor point and the final macroscopic failure of the rock, the preliminary prediction of the final failure time can still serve as an early warning and is only used to indicate the approximate time range of rock failure.

[0059] In addition, monitoring signals from multiple sets of related experimental samples were acquired, and features were extracted and analyzed using a CSD-based method to accurately identify the precursor points of failure for each experimental sample. The occurrence times of these precursor points were then read from the stress-strain curves. t f Simultaneously, record the actual final macroscopic destruction moment of each sample. T f ; based on the timing of the precursor point of destruction t f As the independent variable, with the corresponding actual final macroscopic destruction time. T f Using the experimental sample data as the dependent variable, a mathematical fitting analysis was performed to establish an empirical equation between the time of occurrence of the precursor point of destruction and the time of final macroscopic destruction: T f = 1.25942 t f - 80.43431 (13) By substituting the time of occurrence of the precursor point of failure obtained from the CSD method for the current rock sample under test into the above empirical formula, the secondary prediction value of the final failure time can be calculated.

[0060] Based on the predicted values ​​of fracture closure time, preliminary prediction of final failure time, and secondary prediction of final failure time, a multi-parameter joint early warning of rock failure time is achieved, ultimately realizing the accurate identification of failure precursor points and the stepwise accurate prediction of failure time.

[0061] Take a sample with a pre-fabricated crack angle of 60° to the horizontal as an example. Figure 3 The results of identifying initial precursor points and destructive precursor points based on the MF-DFA method; Figure 4 The results of identifying initial precursor points and destructive precursor points based on the CSD method; Figure 5 To extract significant step points from the evolution curve of damage variable D from the perspective of damage accumulation, these points are defined as standard points representing the initial precursor points of crack compaction and the failure precursor points revealing the incubation of the main crack.

[0062] Figure 6 The results of precursor point identification for specimens with different tilt angles using damage variables, MF-DFA, and CSD; Figure 7 A graph showing the relative errors between the results obtained from MF-DFA and CSD and the standard points; Figure 7 (a) shows the relative error diagram for defining precursor points of destruction; Figure 7 (b) shows the relative error diagram for defining the initial precursor point; Figure 7 based on Figure 6 The results obtained from the damage variable D are defined as the standard value, and the results obtained from both MF-DFA and CSD methods are used as the predicted values. The calculation formula is as follows: (14) in, This is relative error; This is the standard value; This represents the predicted value. The smaller the relative error, the higher the accuracy of the prediction method.

[0063] Figure 7 (a) is a relative error diagram for identifying precursor points of destruction. The results show that the relative error of energy variance is less than 4% in identifying "precursor points of destruction", which shows a significantly better early warning capability. Figure 7 (b) shows the relative error in defining the initial precursor point. The results indicate that the absolute values ​​of the relative errors of both are less than 6% in identifying the "initial precursor point," with the average error being lower than the energy variance. This demonstrates that both the multifractal parameter and the energy variance have good early warning capabilities, with the predictive performance being more outstanding.

[0064] Figure 8 The fitting results are based on the MF-DFA method, showing the initial precursor point and the actual closure time of the pre-fabricated fracture; the fitting result R... 2 The value is 0.961. After obtaining the "initial precursor point" using the MF-DFA method, the actual closure time of the pre-fabricated fracture can be predicted relatively accurately using empirical formulas.

[0065] Figure 9 The fitting results are the initial precursor points and the final macroscopic failure time obtained based on the MF-DFA method; the fitting result R2 The value is 0.796, slightly lower than the actual closure time of the pre-existing fracture. This indicates that after obtaining the initial precursor point using the MF-DFA method, it is possible to make a preliminary prediction of the final macroscopic failure time using empirical formulas, but the prediction accuracy is slightly lower than the closure time.

[0066] Figure 10 This represents the fitting results between the precursor points of destruction obtained based on CSD and the final macroscopic destruction time. The fitting result R... 2 The value was 0.952, indicating a good fit. This shows that after obtaining the precursor points of failure using the CSD method, the final macroscopic failure time of the pre-fabricated fractured rock sample can be predicted relatively accurately using empirical formulas.

[0067] This invention combines the MF-DFA and CSD methods. It utilizes the extremely strong sensitivity of MF-DFA to early microcrack propagation to accurately define the initial precursor point, and utilizes the high responsiveness of CSD to the energy burst before critical fracture to accurately calibrate the failure precursor point. This enables step-by-step accurate monitoring and prediction of the entire process from local rock damage and compaction to macroscopic crack penetration and fracture.

[0068] This invention introduces multifractal detrended fluctuation analysis (MF-DFA), which effectively eliminates the interference of long-term trend terms and environmental noise through local detrending processing. It overcomes the shortcomings of traditional CSD theory, which is prone to distortion under strong mechanical noise and electromagnetic interference, and significantly improves the anti-interference ability and robustness in complex engineering field environments.

[0069] This invention employs a dual-insurance prediction mechanism. In the early stages, fluctuations are filtered and trends are locked in by MF-DFA. When the final macroscopic failure is approaching, the explosive surge of CSD energy variance is used to lock in the precursor point of failure. This effectively filters out false signals in the early stages of loading, clarifies the uniqueness of the true instability moment, and effectively reduces the false alarm rate and missed alarm rate of predicting engineering fractured rock mass instability disasters.

[0070] Example 2 This embodiment provides a system for identifying precursors to fractured rock failure and predicting its failure, including: The acquisition module collects acoustic emission energy parameters of the rock sample to be tested and obtains the acoustic emission energy time series based on the acoustic emission energy parameters; The initial precursor point definition module performs MF-DFA processing on the acoustic emission energy time series to obtain the spectral width calculated over time; the turning point where the spectral width first abruptly enters the continuous high value region is defined as the initial precursor point of the rock sample to be tested. The module for defining precursor points of damage also performs CSD processing on the acoustic emission energy time series to obtain the acoustic emission energy variance calculated over time. The characteristic points where the acoustic emission energy variance explodes near the final macroscopic damage are defined as precursor points of damage to the rock sample under test. The prediction module determines the predicted value of fracture closure time and the preliminary predicted value of final failure time based on the initial precursor points of the rock sample to be tested; it determines the secondary predicted value of final failure time based on the failure precursor points of the rock sample to be tested; and it issues an early warning based on the preliminary predicted value of final failure time, the predicted value of fracture closure time, and the secondary predicted value of final failure time.

[0071] It should be noted that the aforementioned acquisition module, initial precursor point definition module, destructive precursor point definition module, and prediction module correspond to steps 1 to 4 in Embodiment 1. The examples and application scenarios implemented by these modules and their corresponding steps are the same, but they are not limited to the content disclosed in Embodiment 1. It should also be noted that these modules, as part of the system, can be executed in a computer system, such as a set of computer-executable instructions.

[0072] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0073] The proposed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and the division of modules described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed.

[0074] Example 3 This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform the method described in Embodiment 1.

[0075] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0076] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0077] In the implementation process, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by software instructions.

[0078] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0079] Those skilled in the art will recognize that the units and algorithm steps described in connection with the various examples of this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0080] Example 4 This embodiment also provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the method described in Embodiment 1.

[0081] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for defining precursors and predicting failure in fractured rocks, characterized in that, include: Acoustic emission energy parameters of the rock sample to be tested are collected, and acoustic emission energy time series are obtained based on the acoustic emission energy parameters; The acoustic emission energy time series is processed by MF-DFA to obtain the spectral width calculated over time. The turning point where the spectral width first abruptly enters the continuous high-value region is defined as the initial precursor point of the rock sample to be tested. The specific process is as follows: Based on the acoustic emission energy time series and its average value, a cumulative deviation sequence is obtained; using a two-way truncation method, the cumulative deviation sequence is divided into non-overlapping equal-length sub-intervals of a certain length; the local trend function of the equal-length sub-intervals is fitted using the least squares method to eliminate the local trend and calculate the detrended variance of each interval; the detrended variance of all sub-intervals is averaged by order q to obtain a fluctuation function based on the time scale and order; based on the fluctuation function, the generalized scaling index is determined; the generalized scaling index is converted into a singularity index through Legendre transformation; the maximum value of the singularity index is subtracted from the minimum value of the singularity index to obtain the spectral width. Simultaneously, the acoustic emission energy time series is subjected to CSD processing to obtain the acoustic emission energy variance calculated over time; the characteristic point where the acoustic emission energy variance explodes near the final macroscopic failure is defined as the failure precursor point of the rock sample to be tested; specifically: first, the average value of the acoustic emission energy sequence within the sliding window is calculated, and the dispersion between the average value and the acoustic emission energy sequence is calculated to obtain the acoustic emission energy variance; Based on the initial precursor points of the rock sample to be tested, the predicted values ​​of fracture closure time and final failure time are determined. Based on the precursor points of the rock sample to be tested, a secondary predicted value of the final failure time is determined; Early warning is issued based on the preliminary prediction of the final failure time, the prediction of the fracture closure time, and the secondary prediction of the final failure time.

2. The method for defining precursors and predicting failure of fractured rock according to claim 1, characterized in that, Acoustic emission monitoring and uniaxial compression testing of the rock sample to be tested were carried out simultaneously.

3. The method for defining precursors and predicting failure of fractured rock according to claim 1, characterized in that, Based on the initial precursor point of the rock sample to be tested, the predicted value of the fracture closure time and the preliminary predicted value of the final failure time are determined. Specifically, the occurrence time of the initial precursor point is taken as the independent variable and the actual closure time of the prefabricated fracture is taken as the dependent variable. An empirical formula is established between the occurrence time of the initial precursor point and the closure time of the fracture. The occurrence time of the initial precursor point of the rock sample to be tested is substituted into the empirical formula to obtain the corresponding predicted value of the fracture closure time.

4. The method for defining precursors and predicting failure of fractured rock according to claim 3, characterized in that, Also includes: Using the occurrence time of the initial precursor point as the independent variable and the actual final failure time as the dependent variable, an empirical formula is established between the occurrence time of the initial precursor point and the final failure time. Substituting the occurrence time of the initial precursor point of the rock sample to be tested into the empirical formula, the corresponding preliminary prediction value of the final failure time is obtained.

5. The method for identifying precursors and predicting failure of fractured rock according to claim 1, characterized in that, Based on the precursor points of the rock sample to be tested, the secondary predicted value of the final failure time is determined. Specifically, an empirical formula is established with the occurrence time of the precursor points as the independent variable and the corresponding actual final macroscopic failure time as the dependent variable. The occurrence time of the precursor points of the rock sample to be tested is substituted into the empirical formula to obtain the secondary predicted value of the final failure time.

6. A system for defining precursors and predicting failure in fractured rocks, characterized in that, include: The acquisition module collects acoustic emission energy parameters of the rock sample to be tested and obtains the acoustic emission energy time series based on the acoustic emission energy parameters; The initial precursor point definition module performs MF-DFA processing on the acoustic emission energy time series to obtain the spectral width calculated over time. The turning point where the spectral width first abruptly enters the continuous high-value region is defined as the initial precursor point of the rock sample to be tested. The specific process is as follows: Based on the acoustic emission energy time series and its average value, a cumulative deviation sequence is obtained; using a two-way truncation method, the cumulative deviation sequence is divided into non-overlapping equal-length sub-intervals of a certain length; the local trend function of the equal-length sub-intervals is fitted using the least squares method to eliminate the local trend and calculate the detrended variance of each interval; the detrended variance of all sub-intervals is averaged by order q to obtain a fluctuation function based on the time scale and order; based on the fluctuation function, the generalized scaling index is determined; through Legendre transformation, the generalized scaling index is converted into a singularity index; the maximum value of the singularity index is subtracted from the minimum value of the singularity index to obtain the spectral width. The module for defining precursor points of damage includes CSD processing of the acoustic emission energy time series to obtain the acoustic emission energy variance calculated over time. The characteristic points where the acoustic emission energy variance explodes near the final macroscopic damage are defined as precursor points of damage to the rock sample under test. Specifically, the average value of the acoustic emission energy sequence within the sliding window is first calculated, and the dispersion between the average value and the acoustic emission energy sequence is calculated to obtain the acoustic emission energy variance. The prediction module determines the predicted value of fracture closure time and the preliminary predicted value of final failure time based on the initial precursor points of the rock sample to be tested. Based on the precursor points of the rock sample to be tested, determine the secondary predicted value of the final failure time; Early warning is issued based on the preliminary prediction of the final failure time, the prediction of the fracture closure time, and the secondary prediction of the final failure time.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the method for identifying precursors of fractured rock failure and predicting failure as described in any one of claims 1 to 5.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the method for identifying precursors of fractured rock failure and predicting failure as described in any one of claims 1 to 5.

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