Disturbance-induced lag-type rock burst prediction method and device, system and storage medium
By collecting disturbance data and performing damage coefficient fitting and iterative adjustment, the high cost and low real-time performance of delayed rockburst prediction in existing technologies have been solved, and effective prediction of delayed rockburst under low amplitude disturbance has been achieved, which is applicable to engineering surrounding rock monitoring.
Patent Information
- Application Number
- CN202510658606.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-21
- Publication Date
- 2025-11-21
- Estimated Expiration
- 2045-05-21
AI Technical Summary
Existing technologies are costly, have poor real-time performance, and are highly complex when predicting delayed rockbursts, making it difficult to effectively predict delayed rockbursts under low-amplitude disturbances.
By collecting disturbance data, calculating and normalizing the damage value, fitting the relationship between the damage coefficient and the number of disturbances using the least squares method, and adjusting the N and εu values using an iterative method, the rockburst time is predicted.
It achieves low-cost and simple delayed rockburst prediction, is suitable for monitoring surrounding rock in engineering projects, has high adaptability and practicality, and can effectively predict rockburst risk under low-amplitude disturbance conditions.
Smart Images
Figure CN120579310B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of geotechnical engineering, and particularly relates to a disturbance-induced lag-type rock burst prediction method and device, system and storage medium. BACKGROUND
[0002] The lag-type rock burst refers to a rock burst phenomenon that occurs in a rock mass under sustained disturbance after excavation is completed. The existing technology is mostly based on stress change, acoustic emission, microseismic monitoring and other methods for prediction, but has the disadvantages of high cost, poor real-time performance, high complexity and the like. SUMMARY
[0003] The technical problem to be solved by the application is to provide a disturbance-induced lag-type rock burst prediction method and device, system and storage medium, which have the characteristics of being capable of realizing early prediction of lag-type rock burst under low-amplitude disturbance, requiring only a strain measurement device, being simple and low in cost, and being capable of being applied to engineering surrounding rock monitoring scenarios.
[0004] To achieve the above object, the application adopts the following technical solution:
[0005] A disturbance-induced lag-type rock burst prediction method comprises the following steps:
[0006] Collecting disturbance data;
[0007] According to the disturbance data, damage values under different disturbance amplitudes are calculated, and the number of disturbances is normalized to obtain a relationship between a damage coefficient and a normalized disturbance number;
[0008] The relationship between the damage coefficient and the relative disturbance number of the sample that has occurred rock burst is fitted, and a least square method is used to find a unified fitting curve that satisfies all rock burst samples;
[0009] For the sample that has not produced lag-type rock burst, an iterative method is used to adjust N and ε u values; wherein N is the number of disturbances that have occurred rock burst, and ε n is the vertical strain corresponding to the number of disturbances n.
[0010] According to the number of disturbances that have occurred rock burst, the rock burst time is calculated.
[0011] As an optimization, lag-type rock burst tests under different disturbance amplitudes are carried out, each test is repeated three times, the rock sample is subjected to disturbance loading test, and the number of disturbances n and the corresponding vertical strain ε n are recorded.
[0012] As an optimization, for the sample that has not produced lag-type rock burst, an iterative method is used to adjust N and ε u values, wherein if ε u is greater than the critical strain ε u0Then, based on the existing strain values of the sample without rockburst, the trend of strain values is predicted, and the predicted strain values are fitted until the goodness of fit R is achieved. 2 When the value is greater than 0.9, the iteration ends and the value of N is determined.
[0013] The present invention also provides a disturbance-induced delayed rockburst prediction device, comprising:
[0014] The first processing module is used to collect disturbance data;
[0015] The second processing module is used to calculate the damage value under different disturbance amplitude conditions based on the disturbance data, and at the same time normalize the number of disturbances to obtain the relationship between the damage coefficient and the normalized number of disturbances.
[0016] The third processing module is used to fit the relationship between the damage coefficient of the rockburst sample and the relative number of disturbances. The least squares method is used to try to find a uniform fitting curve that satisfies all rockburst samples.
[0017] The fourth processing module is used to adjust N and ε using an iterative method for samples that did not exhibit delayed rockburst. u Value; where N is the number of disturbances that cause rockbursts, ε n The vertical strain corresponding to the number of disturbances n;
[0018] The fifth processing module is used to calculate the rockburst time based on the number of disturbances that cause rockbursts.
[0019] Preferably, the first processing module conducts delayed rockburst tests under different disturbance amplitude conditions, with each test repeated three times. The rock samples are subjected to disturbance loading experiments, and the number of disturbances n and the corresponding vertical strain ε are recorded. n .
[0020] Preferably, the fifth processing module is used to adjust N and ε using an iterative method for samples that have not experienced delayed rockbursts. u Value, where if ε u Greater than the critical strain ε u0 Then, based on the existing strain values of the sample without rockburst, the trend of strain values is predicted, and the predicted strain values are fitted until the goodness of fit R is achieved. 2 When the value is greater than 0.9, the iteration ends and the value of N is determined.
[0021] The present invention also provides a perturbation-induced lag-type rockburst prediction system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a perturbation-induced lag-type rockburst prediction method when executed by the processor.
[0022] The application further provides a storage medium, wherein the storage medium stores a computer program, and the computer program performs the disturbance-induced lag-type rock burst prediction method when running.
[0023] The application obtains sample strain data through a disturbance loading test, defines a damage coefficient and establishes a nonlinear fitting relationship between the damage coefficient and the number of disturbances, and predicts the occurrence time of rock burst in combination with a disturbance frequency. BRIEF DESCRIPTION OF DRAWINGS
[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.
[0025] Figure 1 The disturbance-induced lag-type rock burst prediction method flowchart of the embodiments of the present application;
[0026] Figure 2 The stress path of lag-type rock burst tests for different disturbance amplitudes; wherein (a) is 4Mpa, (b) is 8Mpa, (c) is 12Mpa, and (d) is 16Mpa;
[0027] Figure 3 The strain change with disturbance time in the disturbance process; wherein (a) is a 16Mpa sample, (b) is a 12Mpa sample, (c) is an 8Mpa sample, and (d) is a 4Mpa sample;
[0028] Figure 4 The fitting curve of the damage coefficient change with the relative disturbance number; wherein (a) is a A-12-1 sample, (b) is a A-12-2 sample, (c) is a A-12-3 sample, (d) is a A-16-1 sample, (e) is a A-16-2 sample, and (f) is a A-16-3 sample;
[0029] Figure 5 The prediction flowchart;
[0030] Figure 6 The damage point prediction diagram; wherein (a) is a A-4-1 sample, (b) is a A-4-2 sample, (c) is a A-4-3 sample, (d) is a A-8-1 sample, (e) is a A-8-2 sample, and (f) is a A-8-3 sample. DETAILED DESCRIPTION
[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0032] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0033] Example 1:
[0034] like Figure 1 As shown, this embodiment of the invention provides a method for predicting disturbance-induced delayed rockbursts, including:
[0035] Step S1, Disturbance Data Acquisition: Conduct delayed rockburst tests under different disturbance amplitude conditions. Each test is repeated 3 times. The rock sample is subjected to disturbance loading experiments, and the number of disturbances n and the corresponding vertical strain ε are recorded. n ;
[0036] Step S2, Define the damage coefficient: using the formula Where D c ε represents the damage coefficient. n ε represents the strain value of the specimen after the nth disturbance load. u This represents the strain value at which rockburst failure occurs after the disturbance ends. ε0 represents the strain value before the disturbance begins, and is generally set to 0 initially.
[0037] Step S3, Critical Strain Determination: Statistically analyze the abrupt strain values of rockburst samples and set the critical strain ε. u0 ;
[0038] Step S4: Normalize the number of perturbations: n / N, where N is the number of perturbations that cause rockbursts;
[0039] Step S5: Establishing the fitting formula: Fit the relationship between the damage coefficient and the relative number of disturbances for the rockburst samples (samples with disturbance amplitudes of 12MPa and 16MPa). Try the least squares method to find a uniform fitting curve that satisfies all rockburst samples and has the largest fitting coefficient (satisfying the fitting coefficient is greater than 0.8). Obtain the fitting formula based on the fitting curve.
[0040] Step S6, Predict the number of disturbances: For samples that did not produce delayed rockbursts, adjust N and ε using an iterative method. u Value, where ε u It must be greater than ε u0, based on the existing strain value of the sample without rock burst, to predict the trend of the strain value, according to the fitting formula obtained in step 5, to fit the predicted strain value, until the goodness of fit
[0041] R 2 >0.9, the iteration is ended, and the N value is determined;
[0042] Step S7, calculating the rock burst time: T=N / f, and f is the disturbance frequency (such as 0.5 Hz).
[0043] Example verification:
[0044] The lag-type rock burst tests with disturbance amplitudes of 4 MPa, 8 MPa, 12 MPa and 16 MPa are carried out, and the test loading path is as shown in Figure 2
[0045] The size of the vertical strain during the loading process is obtained, and the damage of the sample in the embodiment of the application mainly occurs in the disturbance stage. The initial moment of disturbance is set to 0. The curve of the strain of the sample with the disturbance time is drawn, as shown in Figure 3 From the figure, it can be seen that the strain presents a fluctuating rising trend, and the strain suddenly increases at the rock burst. For the sample with the disturbance amplitude of 16 (a) of Figure 3 , the maximum strain values before the strain mutation (i.e. the rock burst) are 0.429%, 0.394% and 0.451% respectively. When the strain value exceeds this value, the rock burst will occur again. The critical strain value can be used as the critical strain value of the rock burst. For the sample with the disturbance amplitude of 12 (b) of Figure 3 , the critical strain values are 0.396%, 0.406% and 0.429% respectively. The average of the six values is 0.418%, that is, the critical strain ε u0 is 0.418%. For the samples with the disturbance amplitudes of 8 MPa (c) of Figure 3 and 4 MPa (d) of Figure 3 , the strain values do not exceed 0.418%, and the rock burst does not occur. Therefore, the rock burst generation time is predicted.
[0046] The damage values of the samples with the disturbance amplitudes of 12 and 16 are calculated by using the formula of step S2 respectively, and then the disturbance numbers are normalized. The relationship between the damage coefficient and the normalized disturbance number is drawn. On this basis, the samples with the disturbance amplitudes of 12 and 16 are fitted. The least square method is used for multiple attempts to find a unified fitting curve that satisfies all the samples, and the fitting coefficient is as large as possible. The final fitting result is shown in Figure 4 The final fitting equation is
[0047] y=0.4489+0.5394x 38.7493 +0.0811ln(x+10-6 ) (1)
[0048] y represents the damage coefficient D in the fitting equation c , and x represents the relative disturbance number n / N.
[0049] The lag-type rock burst time of the samples with disturbance amplitudes of 4 and 8 is predicted according to step S6. The prediction flowchart is shown in FIG. 5. First, the acquired strain data points of the samples with disturbance amplitudes of 4 and 8 are input, and then the iterative method is used to continuously select appropriate N values and ε u values. As known from the foregoing, when ε u is greater than 0.418%, the sample produces rock burst damage, and this condition is set as a limiting condition. Subsequently, the prediction points are generated based on the known data points and the discrimination condition, and all the points are normalized. Then, the given fitting formula (1) is used to fit and match the known points and the prediction points. If the fitting coefficient is less than 0.9, the values of N and ε u are reselected for continuous calculation, and after the fitting coefficient is greater than 0.9, the calculation is stopped and the data is saved.
[0050] Figure 6 The damage prediction points generated by using the flowchart have a higher matching degree with the given curve. According to the prediction results, the N and ε u of the samples with disturbance amplitudes of 4 and 8 when rock burst occurs can be obtained. Knowing that the disturbance frequency f is 0.5 Hz, the time (N / f) from the start of disturbance to the rock burst damage can be calculated, as shown in Table 1.
[0051] Table 1
[0052] Sample No. N u (%)]]> Rock burst time (s) A-4-1 4668 0.4561 9336 A-4-2 4390 0.4561 8780 A-4-3 4514 0.4347 9028 A-8-1 1402 0.4561 2804 A-8-2 1800 0.4561 3600 A-8-3 1670 0.4561 3340
[0053] Embodiment 2:
[0054] The embodiment of the present application also provides a disturbance-induced lag-type rock burst prediction device, comprising:
[0055] A first processing module is configured to collect disturbance data.
[0056] A second processing module is configured to calculate damage values under different disturbance amplitude conditions according to the disturbance data, and simultaneously normalize the disturbance numbers to obtain a relationship between the damage coefficient and the normalized disturbance number.
[0057] A third processing module is configured to fit the relationship between the damage coefficient of the sample that produces rock burst and the relative disturbance number, and use the least square method to find a unified fitting curve that satisfies all the rock burst samples.
[0058] A fourth processing module is configured to adjust N and ε uN is the number of disturbances that cause rock burst, and ε is the corresponding vertical strain n N is the number of disturbances that cause rock burst, and ε is the corresponding vertical strain
[0059] The fifth processing module is configured to calculate the rock burst time according to the number of disturbances that cause rock burst.
[0060] As an embodiment of the present application, the first processing module carries out a hysteresis rock burst test under different disturbance amplitudes, each test is repeated three times, the rock sample is subjected to disturbance loading test, and the number of disturbances n and the corresponding vertical strain ε n are recorded.
[0061] As an embodiment of the present application, the fifth processing module is configured to adjust N and ε u value of the sample that does not produce hysteresis rock burst by using an iterative method. u If ε u0 is greater than the critical strain ε 2 , the trend of the predicted strain value is predicted based on the existing strain value of the sample that does not produce rock burst, and the predicted strain value is fitted until the fitting goodness R >0.9, the iteration is ended, and the N value is determined.
[0062] Embodiment 3:
[0063] The present application also provides a disturbance-induced hysteresis rock burst prediction system, comprising a memory and a processor, the memory stores a computer program run by the processor, and the computer program runs the disturbance-induced hysteresis rock burst prediction method when the processor is run.
[0064] Embodiment 4:
[0065] The present application also provides a storage medium, which stores a computer program that executes the disturbance-induced hysteresis rock burst prediction method when run.
[0066] The above-described embodiments are only descriptions of the preferred modes of the present application, and do not limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements to the technical solutions of the present application made by those skilled in the art shall fall within the protection scope of the present application as defined by the claims.
Claims
1. A method of predicting a disturbance-induced lag-type rockburst, characterized by, Comprising: Step S1, disturbance data collection: carry out the hysteresis rockburst test under different disturbance amplitude conditions, repeat 3 times for each test, carry out the disturbance loading experiment on the rock sample, record the disturbance times n and the corresponding vertical strain ε n ; Step S2, defining damage coefficient: using formula wherein D c represents damage coefficient, εn represents strain value of the sample after the nth disturbance load, ε u represents strain value at the end of disturbance and the strain value at the occurrence of rock burst damage, and ε0 represents strain value before the disturbance. Step S3, critical strain determination: statistics the mutation strain value of the sample of rock burst, set the critical strain ε u0 ; Step S4, normalizing disturbance times: n / N, N is the disturbance times of rockburst; Step S5, fitting formula establishment: fitting the relationship between the damage coefficient of the sample of rockburst and the relative disturbance times, trying to find a unified fitting curve that satisfies all rockburst samples by using the least square method, and the fitting coefficient is maximum, and a fitting formula is obtained according to the fitting curve; Step S6, predicting the number of disturbance: for the sample without hysteresis rock burst, the iteration method is used to adjust N and ε u , where ε u > 0.9, the iteration is ended, and the N value is determined. u0 Based on the existing strain value of the sample without rock burst, the trend of the strain value is predicted, the fitting formula obtained in step 5 is used to fit the predicted strain value, until the goodness of fit R 2 > 0.9, the iteration is ended, and the N value is determined.
2. A disturbance-induced hysteresis-type rockburst prediction device, characterized by, Comprising: The first processing module is used for disturbance data collection: developing the lag-type rock burst test under different disturbance amplitudes, repeating each test for 3 times, performing the disturbance loading experiment on the rock sample, and recording the disturbance times n and the corresponding vertical strain ε n ; The second processing module is used for defining the damage coefficient by using the formula wherein D c represents the damage coefficient, ε n represents the strain value of the sample after the nth disturbance load, ε u represents the strain value at the end of the disturbance and the occurrence of rock burst damage, and ε0 represents the strain value before the disturbance. The third processing module is used for critical strain determination: the mutation strain value of the sample of rock burst is counted, and the critical strain ε u0 is set A fourth processing module configured to normalize disturbance times: n / N, N is the disturbance times of rockburst; A fifth processing module configured to fit formula establishment: fitting the relationship between the damage coefficient of the sample of rockburst and the relative disturbance times, trying to find a unified fitting curve that satisfies all rockburst samples by using the least square method, and the fitting coefficient is maximum, and a fitting formula is obtained according to the fitting curve; The sixth processing module is used for predicting the number of disturbances: for the sample without generating the lag-type rock burst, the iteration method is used to adjust N and ε u value, wherein ε u is greater than ε u0 , based on the existing strain value of the sample without generating the rock burst, the trend of the strain value is predicted, the predicted strain value is fitted according to the obtained fitting formula, and when the fitting goodness R 2 > 0.9, the iteration is ended, and the N value is determined.
3. A perturbation-induced hysteresis-type rockburst prediction system, characterized in that, Comprising: A memory and a processor, the memory has a computer program run by the processor stored thereon, and the computer program performs the disturbance-induced lag-type rockburst prediction method according to claim 1 when run by the processor.
4. A storage medium, characterized by The storage medium has a computer program stored thereon, and the computer program performs the disturbance-induced lag-type rockburst prediction method according to claim 1 when run.