CN117250259B - Rock mass instability mutation early warning method, system and electronic equipment - Google Patents
Rock mass instability mutation early warning method, system and electronic equipment Download PDFInfo
- Publication number
- CN117250259B CN117250259B CN202311473458.4A CN202311473458A CN117250259B CN 117250259 B CN117250259 B CN 117250259B CN 202311473458 A CN202311473458 A CN 202311473458A CN 117250259 B CN117250259 B CN 117250259B
- Authority
- CN
- China
- Prior art keywords
- rock mass
- moment
- predicted
- acoustic emission
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000011435 rock Substances 0.000 title claims abstract description 190
- 230000035772 mutation Effects 0.000 title claims abstract description 115
- 238000000034 method Methods 0.000 title claims abstract description 69
- 238000012544 monitoring process Methods 0.000 claims abstract description 56
- 230000008569 process Effects 0.000 claims abstract description 27
- 230000001687 destabilization Effects 0.000 claims description 44
- 238000009825 accumulation Methods 0.000 claims description 25
- 230000006870 function Effects 0.000 claims description 24
- 230000009467 reduction Effects 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 6
- 238000006467 substitution reaction Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 abstract description 12
- 239000002956 ash Substances 0.000 description 6
- 230000001186 cumulative effect Effects 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 239000000463 material Substances 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000000368 destabilizing effect Effects 0.000 description 3
- 239000002689 soil Substances 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000009412 basement excavation Methods 0.000 description 2
- 238000005336 cracking Methods 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 235000002918 Fraxinus excelsior Nutrition 0.000 description 1
- 241000282414 Homo sapiens Species 0.000 description 1
- 206010057175 Mass conditions Diseases 0.000 description 1
- 241000377350 Puan Species 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000013173 literature analysis Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 230000000869 mutational effect Effects 0.000 description 1
- 239000002243 precursor Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000005316 response function Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 230000002087 whitening effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/14—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object using acoustic emission techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4409—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison
- G01N29/4418—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison with a model, e.g. best-fit, regression analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/023—Solids
- G01N2291/0232—Glass, ceramics, concrete or stone
Landscapes
- Physics & Mathematics (AREA)
- Biochemistry (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Pathology (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Immunology (AREA)
- Acoustics & Sound (AREA)
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
Abstract
The invention discloses a rock mass instability mutation early warning method, a rock mass instability mutation early warning system and electronic equipment, and relates to the technical field of geotechnical engineering, wherein the method comprises the following steps: acquiring acoustic emission monitoring data of a target rock mass at the current moment; the target rock mass is the rock mass to be subjected to rock mass instability mutation early warning; determining the instability condition of the target rock mass at each moment to be predicted based on the acoustic emission monitoring data, the acoustic emission gray prediction model and the rock mass instability gray-fold mutation model at the current moment; the time to be predicted is the time after the current time, and the instability condition is a stable state, a critical state or an unstable state; the acoustic emission gray prediction model and the rock mass instability gray-fold abrupt change model are determined based on acoustic emission monitoring data at each historical moment in the historical instability process. The method improves the accuracy of rock mass instability mutation prediction and realizes real-time accurate early warning of rock mass instability mutation.
Description
Technical Field
The invention relates to the technical field of geotechnical engineering, in particular to a rock mass instability mutation early warning method, a rock mass instability mutation early warning system and electronic equipment.
Background
Along with the increase of the demand of human beings for natural resources, the excavation project of the rock mass underground engineering emerges, and the phenomenon of rock mass instability caused by excavation unloading is layered endlessly, so that the life safety of constructors is seriously threatened. Therefore, accurate prediction of rock mass instability is of great importance to the security of engineering.
Traditional rock mass instability prediction methods mainly comprise geomechanical analysis, geostatistical analysis, finite element analysis and the like, and the methods depend on expert experience and priori knowledge to a great extent, and have high requirements on the quantity and quality of data. Moreover, these methods are generally only suitable for specific rock mass conditions and engineering scenarios, and are poorly versatile, making it difficult to achieve accurate predictions of different geological conditions and different types of rock mass destabilization.
In recent years, acoustic emission technology has been widely used as an effective means for monitoring deformation and destruction of rock mass. The acoustic emission technology can monitor the crack propagation process in the rock mass in real time and continuously, and provides a new thought for rock mass instability prediction. However, the acoustic emission signal has the characteristics of nonlinearity, non-stationarity, complexity and the like, and is difficult to directly analyze and determine rock mass instability precursor information from the appearance data, and the existing acoustic emission technology can monitor a large amount of rock mass deformation data, but cannot realize accurate prediction of rock mass instability, so that deep application of the acoustic emission technology is limited.
Disclosure of Invention
The invention aims to provide a rock mass instability mutation early warning method, a rock mass instability mutation early warning system and electronic equipment, which improve the accuracy of rock mass instability mutation prediction and realize real-time accurate early warning of rock mass instability mutation.
In order to achieve the above object, the present invention provides the following.
A rock mass instability mutation early warning method comprises the following steps: acquiring acoustic emission monitoring data of a target rock mass at the current moment; the target rock mass is the rock mass to be subjected to rock mass instability mutation early warning.
Determining the instability condition of the target rock mass at each moment to be predicted based on the acoustic emission monitoring data, the acoustic emission gray prediction model and the rock mass instability gray-fold mutation model at the current moment; the time to be predicted is the time after the current time, and the instability condition is a stable state, a critical state or an unstable state; the acoustic emission gray prediction model and the rock mass instability gray-fold abrupt change model are determined based on acoustic emission monitoring data at each historical moment in the historical instability process.
Optionally, the acoustic emission monitoring data is an acoustic emission ringing count.
Optionally, the determining process of the acoustic emission gray prediction model and the rock mass instability gray-fold abrupt model includes: and acquiring acoustic emission monitoring data of the rock mass for training at each historical moment in the historical destabilization process, and obtaining an original data sequence.
And establishing an acoustic emission gray prediction model according to the original data sequence by utilizing a gray theory.
And establishing a rock mass instability gray-fold mutation model based on the acoustic emission gray prediction model.
Optionally, according to the original data sequence, an acoustic emission gray prediction model is established by using a gray theory, including: performing primary accumulation on the original data sequence to obtain a primary accumulation generation sequence; the primary accumulation generation sequence comprises actual values of primary accumulation data of the training rock mass at each historical moment in the historical destabilization process.
Establishing an initial gray prediction model containing parameters to be estimated; the parameters to be estimated include: developing ash number and endogenous control ash number; the initial gray prediction model is a function of one-time accumulated data at any time in the historical destabilization process, one-time accumulated data at the initial time and time difference; the time difference is the time difference between any time and the initial time.
And generating a number column based on the one-time accumulation, fitting by a Levenberg-Marquardt method, determining the value of the parameter to be estimated, and substituting the value of the parameter to be estimated into the initial gray prediction model to obtain the acoustic emission gray prediction model.
Optionally, establishing a rock mass destabilizing gray-fold mutation model based on the acoustic emission gray prediction model includes: and determining a power series form equation corresponding to the primary accumulated data based on the primary accumulated generated sequence by utilizing a multiple regression method.
And deriving the power series form equation about time to obtain a reduction solution of the power series form equation.
And carrying out variable substitution on the constant coefficient in the reduction solution by using the constant coefficient of the power series form equation to obtain the rock mass instability gray-fold mutation model.
Optionally, determining the destabilization condition of the target rock mass at any time to be predicted based on the acoustic emission monitoring data, the acoustic emission gray prediction model and the rock mass destabilization gray-fold abrupt model at the current time includes: and determining the current time as an initial time.
And accumulating the acoustic emission monitoring data at the initial moment for one time to obtain one-time accumulated data at the initial moment.
And inputting the acoustic emission monitoring data at the initial moment and the time difference to be predicted into the acoustic emission gray prediction model to obtain a predicted value of the primary accumulated data at the moment to be predicted. The time difference to be predicted is the time difference between the time to be predicted and the initial time.
And determining a standard potential function form of the folding mutation model at the moment to be predicted based on the predicted value of the primary accumulated data at the moment to be predicted and the rock mass instability gray-folding mutation model.
And (5) obtaining a first derivative in the form of a standard potential function of the folding mutation model at the moment to be predicted, and obtaining a balanced curved surface equation at the moment to be predicted.
And solving a second derivative in the form of a standard potential function of the folding mutation model at the moment to be predicted to obtain a singular point set equation at the moment to be predicted.
And (3) combining a balanced curved surface equation and a singular point set equation at the moment to be predicted, and calculating the control quantity of the fold mutation model standard potential function form at the moment to be predicted.
And determining the destabilization condition of the target rock mass at the moment to be predicted based on the control quantity corresponding to the moment to be predicted.
Optionally, determining the destabilization condition of the target rock mass at the time to be predicted based on the control quantity corresponding to the time to be predicted includes: and when the control quantity corresponding to the moment to be predicted is greater than 0, the target rock mass is in a stable state at the moment to be predicted.
And when the control quantity corresponding to the moment to be predicted is equal to 0, the target rock mass is in a critical state at the moment to be predicted.
And when the control quantity corresponding to the moment to be predicted is smaller than 0, the target rock mass is in an unstable state at the moment to be predicted.
A rock mass destabilization mutation early warning system, comprising: the acoustic emission monitoring module is used for acquiring acoustic emission monitoring data of the target rock mass at the current moment; the target rock mass is the rock mass to be subjected to rock mass instability mutation early warning.
The destabilization prediction module is used for determining the destabilization condition of the target rock mass at each time to be predicted based on the acoustic emission monitoring data, the acoustic emission gray prediction model and the rock mass destabilization gray-fold mutation model at the current time; the time to be predicted is the time after the current time, and the instability condition is a stable state, a critical state or an unstable state; the acoustic emission gray prediction model and the rock mass instability gray-fold abrupt change model are determined based on acoustic emission monitoring data at each historical moment in the historical instability process.
An electronic device comprising a memory and a processor, the memory being configured to store a computer program, the processor being configured to run the computer program to cause the electronic device to perform the rock mass destabilization mutation warning method described above.
Optionally, the memory is a readable storage medium.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention discloses a rock mass instability mutation early warning method, a rock mass instability mutation early warning system and electronic equipment, wherein firstly, acoustic emission monitoring data of a target rock mass at the current moment are obtained; then, determining the instability condition of the target rock mass at each moment to be predicted based on the acoustic emission monitoring data, the acoustic emission gray prediction model and the rock mass instability gray-fold mutation model at the current moment; the acoustic emission gray prediction model and the rock mass instability gray-fold mutation model are determined based on acoustic emission monitoring data at each historical moment in the historical instability process. According to the invention, through combining a gray system theory, a folding mutation theory and a sound emission signal processing technology, the accuracy of rock mass instability mutation prediction is improved, and real-time accurate early warning of rock mass instability mutation is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a rock mass instability mutation early warning method provided in embodiment 1 of the present invention.
Fig. 2 is a flow chart of a rock mass instability sudden change early warning method based on acoustic emission time sequence in a specific embodiment.
FIG. 3 is a schematic diagram of a balanced surface of a folded mutant model.
Fig. 4 is a graph of stress curve, ringing count rate curve and cumulative ringing count for soft rock at a confining pressure of 0 MPa.
Fig. 5 is a graph of stress curve, ringing count rate curve and cumulative ringing count for soft rock at 4MPa confining pressure.
Fig. 6 is a graph of stress curve, ringing count rate curve and cumulative ringing count for soft rock at a confining pressure of 7 MPa.
Fig. 7 is a graph of stress curve, ringing count rate curve and cumulative ringing count for soft rock at a confining pressure of 10 MPa.
Fig. 8 is a graph of acoustic emission ringing count rate and cumulative ringing count during a roadway surrounding rock burst.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a rock mass instability mutation early warning method, a rock mass instability mutation early warning system and electronic equipment, aiming at improving the accuracy of rock mass instability mutation prediction and realizing real-time accurate early warning of rock mass instability mutation.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Embodiment 1 provides a rock mass instability mutation early warning method.
Fig. 1 is a schematic flow chart of a rock mass instability mutation early warning method provided in embodiment 1 of the present invention. As shown in fig. 1, the rock mass instability mutation early warning method in this embodiment includes steps 101 to 102.
Step 101: acquiring acoustic emission monitoring data of a target rock mass at the current moment; the target rock mass is the rock mass to be subjected to rock mass instability mutation early warning.
As an alternative embodiment, the acoustic emission monitoring data is an acoustic emission ringing count.
Step 102: and determining the instability condition of the target rock mass at each moment to be predicted based on the acoustic emission monitoring data, the acoustic emission gray prediction model and the rock mass instability gray-fold mutation model at the current moment.
Wherein, each time to be predicted is a time after the current time, and the instability condition is a stable state, a critical state or an unstable state; the acoustic emission gray prediction model and the rock mass instability gray-fold abrupt change model are determined based on acoustic emission monitoring data at each historical moment in the historical instability process.
As an alternative embodiment, the determining process of the acoustic emission gray prediction model and the rock mass instability gray-fold abrupt model comprises: and acquiring acoustic emission monitoring data of the rock mass for training at each historical moment in the historical destabilization process, and obtaining an original data sequence.
And according to the original data sequence, establishing an acoustic emission gray prediction model by utilizing a gray theory.
And establishing a rock mass instability gray-fold mutation model based on the acoustic emission gray prediction model.
As an alternative embodiment, the method for creating the acoustic emission gray prediction model according to the original data sequence by using gray theory includes: performing primary accumulation on the original data sequence to obtain primary accumulation generated sequence; the primary accumulation generating sequence comprises actual values of primary accumulation data of the training rock mass at each historical moment in the historical instability process.
Establishing an initial gray prediction model containing parameters to be estimated; parameters to be estimated include: developing ash number and endogenous control ash number; the initial gray prediction model is a function of primary accumulated data at any moment in the historical destabilization process, primary accumulated data at the initial moment and time difference; the time difference is the time difference between any one time instant and the initial time instant.
And generating a series based on one-time accumulation, fitting by a Levenberg-Marquardt method, determining the value of the parameter to be estimated, and substituting the value of the parameter to be estimated into an initial gray prediction model to obtain the acoustic emission gray prediction model.
As an alternative embodiment, the rock mass destabilizing gray-fold mutation model is built based on an acoustic emission gray prediction model, comprising: and determining a power series form equation corresponding to the primary accumulated data based on the primary accumulated generated sequence by utilizing a multiple regression method.
And deriving the power series form equation about the moment to obtain a reduction solution of the power series form equation.
And carrying out variable substitution on the constant coefficient in the reduction solution by using the constant coefficient of the power series form equation to obtain the rock mass instability gray-fold mutation model.
As an alternative embodiment, determining the destabilization condition of the target rock mass at any time to be predicted based on the acoustic emission monitoring data, the acoustic emission gray prediction model, and the rock mass destabilizing gray-fold abrupt model at the current time includes: the current time is determined as the initial time.
And accumulating the acoustic emission monitoring data at the initial moment for one time to obtain one-time accumulated data at the initial moment.
Inputting the acoustic emission monitoring data at the initial moment and the time difference to be predicted into an acoustic emission gray prediction model to obtain a predicted value of the primary accumulated data at the moment to be predicted; the time difference to be predicted is the time difference between the time to be predicted and the initial time.
And determining a standard potential function form of the folding mutation model at the moment to be predicted based on the predicted value of the primary accumulated data at the moment to be predicted and the rock mass instability gray-folding mutation model.
And (5) obtaining a first derivative in the form of a standard potential function of the folding mutation model at the moment to be predicted, and obtaining a balanced curved surface equation at the moment to be predicted.
And solving a second derivative in the form of a standard potential function of the folding mutation model at the moment to be predicted to obtain a singular point set equation at the moment to be predicted.
And (3) combining a balanced curved surface equation and a singular point set equation at the moment to be predicted, and calculating the control quantity of the fold mutation model standard potential function form at the moment to be predicted.
And determining the destabilization condition of the target rock mass at the moment to be predicted based on the control quantity corresponding to the moment to be predicted.
As an optional implementation manner, determining the destabilization condition of the target rock mass at the moment to be predicted based on the control quantity corresponding to the moment to be predicted includes: when the control quantity corresponding to the moment to be predicted is larger than 0, the target rock mass is in a stable state at the moment to be predicted.
When the control quantity corresponding to the moment to be predicted is equal to 0, the target rock mass is in a critical state at the moment to be predicted.
When the control quantity corresponding to the moment to be predicted is smaller than 0, the target rock mass is in an unstable state at the moment to be predicted.
Example 1
To verify the accuracy and rationality of the method in example 1, specific example 1 provides mountain acoustic emission monitoring results of 320 national channels, guizhou Puan environment, K2403+500.
As shown in FIG. 2, the K2403+500 mountain instability mutation early warning method based on the acoustic emission time sequence comprises the following steps 1-4.
Step 1: acoustic emission monitoring data in the rock mass destabilization process are collected.
And (3) in the step (1), the acoustic emission monitoring data is acoustic emission ringing count.
Step 2: and according to the acoustic emission monitoring data, establishing an acoustic emission gray prediction model by adopting a gray theory.
The specific steps of the step 2 comprise: suppose that in time series t 1 ，t 2 ，…，t n The number of the acoustic emission ringing counts measured in the internal is a number series X (0) (1)，X (0) (2)，…，X (0) (n) and arranging the number X (0) (1)，X (0) (2)，…，X (0) (n) denoted as original data sequence X (0) . Original data sequence X (0) Expressed by formula (1). In order to weaken randomness of original data sequence, the original data sequence is accumulated once, acoustic emission ringing count at corresponding moment is generated, a series (1-AGO sequence) is generated by once accumulating, and the series generated by once accumulating is marked as X (1) 。X (1) Expressed by formula (2). And establishing a gray prediction model, namely a GM (1, 1) model, wherein a whitening differential equation corresponding to the gray prediction model is expressed by a formula (3). Substituting the initial value (i.e., t=0) into equation (3) and solving to obtain equation (4). And generating a sequence according to one accumulation, and fitting by using a Levenberg-Marquardt method by using a formula (4) to obtain the values of p and q. When the measurement time interval of the acoustic emission ringing count is l, the discrete form of the formula (4) is represented by the formula (5). Deriving l in the formula (5) results in a reduction solution (time response function) represented by the formula (6). Formulas (1) - (6) are shown below.
X (0) =(X (0) (1)，X (0) (2)，…，X (0) (n))，n≥2 （1）。
X (1) =(X (1) (1)，X (1) (2)，…，X (1) (n))，n≥2 （2）。
dX (1) /dt+pX (1) =q （3）。
X (1) (t)=[X (1) (1)-q/p]exp(-pt)+q/p （4）。
X (1) (l+1)=[X (0) (1)-q/p]exp(-pl)+q/p （5）。
X (0) (l+1)=-p[X (0) (1)-q/p]exp(-pl) （6）。
Wherein X is (0) (1) Counting acoustic emission ringing measured at the 1 st moment; x is X (0) (2) Counting acoustic emission ringing measured at the 2 nd moment; x is X (0) (n) is an acoustic emission ringing count measured at time n. X is X (1) (1) Generating a series of values for one accumulation at time 1; x is X (1) (2) Generating a series of values for one accumulation at time 2; x is X (1) (n) generating a series of values for one accumulation at time n. t is the t moment; p is the number of development ashes to be estimated; q is the endogenous control ash number to be estimated.
When simulation verification is carried out, the K2403+500 mountain acoustic emission monitoring data and the prediction result comprise the following conditions: (1) 5 months 23 days: the measured value is 3.6 times/min, the predicted value is 3.8 times/min, and the relative error of the measured value-the predicted value I/the measured value is 0.083; (2) 5 months 24 days: the measured value is 1.2 times/min, the predicted value is 0.7 times/min, and the relative error of the measured value-predicted value/measured value is 0.417; (3) 5 months 25 days: the measured value is 1.0 times/min, the predicted value is 1.1 times/min, and the relative error of the measured value-the predicted value I/the measured value is 0.100; (4) 5 months 26 days: the measured value is 1.6 times/min, the predicted value is 1.9 times/min, and the relative error of the measured value-the predicted value I/the measured value is 0.186; (5) 5 months 27 days: the measured value is 3.4 times/min, the predicted value is 3.4 times/min, and the relative error of the measured value-predicted value/measured value is 0.000; (6) 5 months 28 days: the measured value was 6.2 times/min, the predicted value was 5.9 times/min, and the relative error of i measured value-predicted value/measured value was 0.048. Therefore, the relative error of the data predicted by the acoustic emission gray prediction model and the monitoring data is smaller than 0.5, the average relative error is smaller than 0.02, the prediction precision is qualified, and the data predicted by the acoustic emission gray prediction model is effective.
Step 3: and establishing a folding mutation model according to the acoustic emission gray prediction model.
The specific steps of the step 3 comprise: the equation (4) is expanded into a power series form according to the Taylor series, and the equation (7) is obtained. Has a certain trend ofAnd in the sequence process of the potential rules, the accuracy requirement can be met by intercepting the 4 th item. The acoustic emission cumulative ringing count curve has a clear monotonically increasing trend, so equation (7) can be expressed approximately as equation (8). The solution of the taylor expansion form obtained by deriving t in the expression (8) is expressed by the expression (9). Let t=x-a 3 /(4A 4 ) The formula (9) is subjected to variable substitution to obtain a formula (10). The constant term does not affect X (0) The mutational nature of (t) so v may be omitted, giving formula (12). The formula (12) is a standard potential function form of a folding mutation model in the formula (9), so that a rock mass instability gray-folding mutation early warning model based on acoustic emission monitoring data is established. The formulae (7) to (12) are shown below.
X (1) (t)=A 0 +A 1 t+A 2 t 2 +A 3 t 3 +…+A m t m （7）。
X (1) (t)=A 0 +A 1 t+A 2 t 2 +A 3 t 3 +A 4 t 4 （8）。
X (0) (t)=A 1 +2A 2 t+3A 3 t 2 +4A 4 t 3 （9）。
X (0) (t)=x 3 +ux+v （10）。
（11）。
X (0) (t)=x 3 +ux （12）。
Wherein A is 0~ A m As a constant coefficient, determining a generated sequence according to one accumulation by utilizing a multiple regression method; m is the number of stages.
Step 4: and calculating a control variable u of the acoustic emission gray-folding mutation model, and judging whether the rock mass is in a stable state according to a mutation rule.
The specific steps of the step 4 comprise: obtaining a balanced curved surface equation and a balanced curved surface square of the rock mass instability gray-fold mutation early warning model by solving a first derivative of x in the step (12) and enabling the value of the first derivative to be 0The equation (13) is shown. And (3) obtaining the second derivative of x in the formula (12) and enabling the value of the second derivative to be 0, and obtaining an odd point set of the rock mass instability gray-fold mutation early warning model, wherein the odd point set is represented by the formula (14). Combining the formula (13) and the formula (14) to obtain a relation of u, wherein u=0 is a bifurcation setSolving to obtain a bifurcation setEquation, bifurcation set->The equation is expressed by equation (15). The formulae (13) to (15) are shown below.
[X (0) (t)] ’ =3x 2 +u=0 （13）。
[X (0) (t)] ’’ =6x=0 （14）。
=u=0（15）。
In the folding abrupt model shown in fig. 3, when u > 0 (corresponding to the positive half axis of the u axis), equation (12) has no real solution (i.e., x does not exist), and the critical point of the system potential function does not exist, so that the rock is in a stable state at this time; when u=0 (corresponding to the origin of the u-x coordinate system), equation (12) has only one real solution (i.e., x=0), and the rock mass is in a critical state; when u is smaller than 0 (corresponding to the negative half axis of the u axis), the equation (12) has two unequal real roots which are respectively minimum and maximum points of the potential function, and at the moment, the system potential function has two different extremum at the same time, which represents that the rock mass is in an unstable state. Specifically, when u < 0, in FIG. 3, there are curve OA (i.e., branch I) and curve OB (i.e., branch II), and when u has a value of u-axis coordinates parallel to the intersection of the dashed line of the x-axis and the u-axis, two unequal real roots correspond to x respectively i And x j ，x i Is the x-axis coordinate of the intersection C of the broken line parallel to the x-axis and branch I, x j Is the x-axis sitting at the intersection point D of the broken line parallel to the x-axis and branch IIAnd (5) marking. Therefore, according to the control variable u, it can be judged when the acoustic emission generated by the rock mass is suddenly changed, namely, the moment corresponding to u=0 is the suddenly changed moment.
Parameter identification is carried out according to the acoustic emission parameter predicted value, and A in the gray-fold mutation early warning model is obtained 0 、A 1 、A 2 、A 3 And A 4 Further, the value of the control variable u is obtained from the expression (11), and the prediction result includes: (1) 5 months 28 days: u is 7.01, and the forecasting condition is stable; (2) 5 months 29 days: u is 2.81, and the forecasting condition is stable; (3) 5 months 30 days: u is-0.27, and the prediction is unstable.
The value of the control variable u in the prediction result indicates that u < 0 is 5 months and 30 days, and it is indicated that mountain instability may occur on 5 months and 30 days. The actual situation is that the local collapse occurs near the monitoring point of about 10 am in 5 months and 30 days near the national road side, so that the prediction result is consistent with the actual situation, and the prediction is proved to be feasible by applying the gray-fold mutation model.
Example 2
Taking soft rock material as a test piece to perform different confining pressures sigma 3 The soft rock instability gray-fold mutation early warning model based on the acoustic emission time sequence is tested by the following acoustic emission test, and the soft rock instability damage prediction results under different confining pressures are as follows: 1. sigma (sigma) 3 Soft rock acoustic emission parameters at=0mpa are shown in fig. 4, and the prediction result includes: (1) When the time is 463s, u is 29.35, and the forecasting condition is stable; (2) When the time is 464s, u is 154.63, and the forecasting condition is stable; (3) When the time is 465s, u is-205.05, and the forecasting condition is unstable; (4) At 470s (i.e., peak stress point), u is-2699.01 and the predicted condition is unstable. 2. Sigma (sigma) 3 Soft rock acoustic emission parameters at=4mpa are shown in fig. 5, and the prediction results include: (1) When the time is 824s, u is 1372.08, and the forecasting condition is stable; (2) When the time is 825s, u is 426.178, and the forecasting condition is stable; (3) When the time is 826s, u is-586.78, and the forecasting condition is unstable; (4) At a time of 870s (i.e., peak stress point), u is-26725.17 and the predicted condition is unstable. 3. Sigma (sigma) 3 Soft rock acoustic emission parameters at 7MPa are shown in fig. 6, predictive junctionThe fruit comprises: (1) When the time is 612s, u is 1395687.65, and the forecasting condition is stable; (2) When the time is 613s, u is 1561111.16, and the forecasting condition is stable; (3) When the time is 614s, u is-1382835.57, and the forecasting condition is unstable; (4) At time 714s (i.e., peak stress point), u is-67788.68 and the predicted condition is unstable. 4. Sigma (sigma) 3 Soft rock acoustic emission parameters at =7mpa are shown in fig. 7, and the prediction results include: (1) When the time is 900s, u is 360.79, and the forecasting condition is stable; (2) When the time is 901s, u is 68.11, and the forecasting condition is stable; (3) When the time is 902s, u is-356.27, and the forecasting condition is unstable; (4) At a time of 1172s (i.e., peak stress point), u is-95128.55 and the predicted condition is unstable.
According to the prediction results, when the confining pressures are respectively 0, 4MPa, 7MPa and 10MPa, the soft rock is in a unsteady state at 465s, 826s, 614s and 902s respectively, which shows that the acoustic emission parameters are in a sudden change phenomenon at the point, and the corresponding stress levels are about 99.7%, 98.8%, 90.3% and 83.5% of the peak stress, and are all in a plastic yield deformation stage before the peak stress. The mutation early warning modes of the soft rock under different confining pressures have higher consistency, which indicates that the early warning method provided by the invention has better universality.
Example 3
Gray mutation theory and application of acoustic emissions in rock burst prediction [ J ]]Chinese mining, 2008, 17 (8): for example, the method comprises the following steps of (1) carrying out an acoustic emission test on surrounding rock of a roadway, wherein the acoustic emission ringing count rate monitoring result over time comprises: (1) when the time is 729s, the ringing count rate is 933Hz; (2) when the time is 730s, the ringing count rate is 1757Hz; (3) when the time is 731s, the ringing count rate is 2074Hz; (4) when the time is 732s, the ringing count rate is 3424Hz; (5) at 733s, the ringing count rate is 4158Hz; (6) when the time is 734s, the ringing count rate is 7469Hz; (7) when the time is 735s, the ringing count rate is 5450Hz; (8) at a time of 736s, the ringing count rate was 3180Hz. The gray-sharp mutation model and the prediction result of the invention related in the literature comprise: (1) At a time of 734sGray-sharp point mutation modelFor 1209585.8, gray-fold mutation model +.>7.55, the forecast situation of the gray-sharp point mutation model is that no rock burst occurs, and the forecast situation of the gray-folding mutation model is that no rock burst occurs; (2) At time 735s, grey-sharp point mutation model +.>For 7907560.0, gray-fold mutation model +.>The prediction situation of the gray-sharp point mutation model is that no rock burst occurs, and the prediction situation of the gray-folding mutation model is that rock burst occurs; (3) At time 736s, the gray-sharp point mutation model +.>For-16381737.0, gray-fold mutation model +.>The prediction of the gray-sharp point mutation model was-8.26, and the prediction of the gray-fold mutation model was that no rock burst occurred.
As can be seen from fig. 8, the number of acoustic emission ringing count rates reaches a maximum value at 734s and then gradually decreases, and the literature analysis shows that "the accumulated elastic strain energy is largely and rapidly released, and the rock burst occurs at the moment of energy release", which means that the rock burst is generated between [ 284 s, 730s ] in the actual engineering (the rock burst is a dynamic instability form of the rock mass). The grey-sharp point mutation model provided in the literature predicts that the time interval of rock burst occurrence is [ 730s, 736s ], the grey-fold mutation early warning model provided by the invention predicts that the time interval of rock burst occurrence is [ 284 s,735s ], and the time interval is consistent with the time interval of actual rock burst occurrence, so that the rock burst time can be predicted more accurately through the invention.
After the rock-soil material is stressed, cracks randomly appear and develop from microscopic cracking to macroscopic cracking, the internal cracks of the rock-soil material change from variable quantity to variable quality, and from gradual change to abrupt change, the abrupt change of acoustic emission parameters is the visual representation of the leap and unstable expansion, and the abrupt change points of the acoustic emission parameters are the distinguishing characteristics of the critical state of the rock-soil material.
Embodiment 2 provides a rock mass destabilization mutation warning system, comprising: the acoustic emission monitoring module is used for acquiring acoustic emission monitoring data of the target rock mass at the current moment; the target rock mass is the rock mass to be subjected to rock mass instability mutation early warning.
The destabilization prediction module is used for determining the destabilization condition of the target rock mass at each time to be predicted based on the acoustic emission monitoring data, the acoustic emission gray prediction model and the rock mass destabilization gray-fold mutation model at the current time; the time to be predicted is the time after the current time, and the instability condition is a stable state, a critical state or an unstable state; the acoustic emission gray prediction model and the rock mass instability gray-fold abrupt change model are determined based on acoustic emission monitoring data at each historical moment in the historical instability process.
Embodiment 3 provides an electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform the rock mass destabilization mutation warning method of embodiment 1.
As an alternative embodiment, the memory is a readable storage medium.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (6)
1. The rock mass instability mutation early warning method is characterized by comprising the following steps of:
acquiring acoustic emission monitoring data of a target rock mass at the current moment; the target rock mass is the rock mass to be subjected to rock mass instability mutation early warning;
determining the instability condition of the target rock mass at each moment to be predicted based on the acoustic emission monitoring data, the acoustic emission gray prediction model and the rock mass instability gray-fold mutation model at the current moment; the time to be predicted is the time after the current time, and the instability condition is a stable state, a critical state or an unstable state; the acoustic emission gray prediction model and the rock mass instability gray-fold mutation model are determined based on acoustic emission monitoring data at each historical moment in the historical instability process;
the determining process of the acoustic emission gray prediction model and the rock mass instability gray-fold mutation model comprises the following steps:
acquiring acoustic emission monitoring data of a rock mass for training at each historical moment in a historical destabilization process to obtain an original data sequence;
according to the original data sequence, an acoustic emission gray prediction model is established by utilizing a gray theory;
establishing a rock mass instability gray-fold mutation model based on the acoustic emission gray prediction model;
according to the original data sequence, an acoustic emission gray prediction model is established by utilizing a gray theory, and the acoustic emission gray prediction model comprises the following steps:
performing primary accumulation on the original data sequence to obtain a primary accumulation generation sequence; the primary accumulation generation sequence comprises actual values of primary accumulation data of each historical moment of the rock mass for training in the historical destabilization process;
establishing an initial gray prediction model containing parameters to be estimated; the parameters to be estimated include: developing ash number and endogenous control ash number; the initial gray prediction model is a function of one-time accumulated data at any time in the historical destabilization process, one-time accumulated data at the initial time and time difference; the time difference is the time difference between any moment and the initial moment;
generating a number column based on the one-time accumulation, fitting by a Levenberg-Marquardt method, determining the value of the parameter to be estimated, and substituting the value of the parameter to be estimated into the initial gray prediction model to obtain the acoustic emission gray prediction model;
establishing a rock mass instability gray-fold mutation model based on the acoustic emission gray prediction model, comprising:
determining a power series form equation corresponding to the primary accumulated data based on the primary accumulated generated sequence by utilizing a multiple regression method;
deriving the power series form equation about time to obtain a reduction solution of the power series form equation;
performing variable substitution on the constant coefficients in the reduction solution by using the constant coefficients of the power series form equation to obtain the rock mass instability gray-fold mutation model;
determining a destabilization condition of the target rock mass at any time to be predicted based on the acoustic emission monitoring data, the acoustic emission gray prediction model and the rock mass destabilization gray-fold abrupt model at the current time, including:
determining the current time as an initial time;
accumulating acoustic emission monitoring data at the initial moment for one time to obtain primary accumulated data at the initial moment;
inputting acoustic emission monitoring data at an initial moment and a time difference to be predicted into the acoustic emission gray prediction model to obtain a predicted value of primary accumulated data at the moment to be predicted; the time difference to be predicted is the time difference between the time to be predicted and the initial time;
determining a standard potential function form of a folding mutation model at the moment to be predicted based on a predicted value of one-time accumulated data at the moment to be predicted and the rock mass instability gray-folding mutation model;
obtaining a first derivative in a standard potential function form of a folding mutation model at a moment to be predicted, and obtaining a balanced curved surface equation at the moment to be predicted;
obtaining a second derivative in a standard potential function form of a folding mutation model at a moment to be predicted, and obtaining a singular point set equation at the moment to be predicted;
the equilibrium curved surface equation and the singular point set equation at the moment to be predicted are combined, and the control quantity of the standard potential function form of the folding mutation model at the moment to be predicted is calculated;
and determining the destabilization condition of the target rock mass at the moment to be predicted based on the control quantity corresponding to the moment to be predicted.
2. The rock mass destabilization mutation warning method according to claim 1, characterized in that the acoustic emission monitoring data is acoustic emission ringing count.
3. The rock mass destabilization mutation warning method according to claim 1, characterized in that determining a destabilization condition of a target rock mass at a time to be predicted based on a control amount corresponding to the time to be predicted includes:
when the control quantity corresponding to the moment to be predicted is greater than 0, the target rock mass is in a stable state at the moment to be predicted;
when the control quantity corresponding to the moment to be predicted is equal to 0, the target rock mass is in a critical state at the moment to be predicted;
and when the control quantity corresponding to the moment to be predicted is smaller than 0, the target rock mass is in an unstable state at the moment to be predicted.
4. A rock mass destabilization mutation warning system, the system comprising:
the acoustic emission monitoring module is used for acquiring acoustic emission monitoring data of the target rock mass at the current moment; the target rock mass is the rock mass to be subjected to rock mass instability mutation early warning;
the destabilization prediction module is used for determining the destabilization condition of the target rock mass at each time to be predicted based on the acoustic emission monitoring data, the acoustic emission gray prediction model and the rock mass destabilization gray-fold mutation model at the current time; the time to be predicted is the time after the current time, and the instability condition is a stable state, a critical state or an unstable state; the acoustic emission gray prediction model and the rock mass instability gray-fold mutation model are determined based on acoustic emission monitoring data at each historical moment in the historical instability process;
the determining process of the acoustic emission gray prediction model and the rock mass instability gray-fold mutation model comprises the following steps:
acquiring acoustic emission monitoring data of a rock mass for training at each historical moment in a historical destabilization process to obtain an original data sequence;
according to the original data sequence, an acoustic emission gray prediction model is established by utilizing a gray theory;
establishing a rock mass instability gray-fold mutation model based on the acoustic emission gray prediction model;
according to the original data sequence, an acoustic emission gray prediction model is established by utilizing a gray theory, and the acoustic emission gray prediction model comprises the following steps:
performing primary accumulation on the original data sequence to obtain a primary accumulation generation sequence; the primary accumulation generation sequence comprises actual values of primary accumulation data of each historical moment of the rock mass for training in the historical destabilization process;
establishing an initial gray prediction model containing parameters to be estimated; the parameters to be estimated include: developing ash number and endogenous control ash number; the initial gray prediction model is a function of one-time accumulated data at any time in the historical destabilization process, one-time accumulated data at the initial time and time difference; the time difference is the time difference between any moment and the initial moment;
generating a number column based on the one-time accumulation, fitting by a Levenberg-Marquardt method, determining the value of the parameter to be estimated, and substituting the value of the parameter to be estimated into the initial gray prediction model to obtain the acoustic emission gray prediction model;
establishing a rock mass instability gray-fold mutation model based on the acoustic emission gray prediction model, comprising:
determining a power series form equation corresponding to the primary accumulated data based on the primary accumulated generated sequence by utilizing a multiple regression method;
deriving the power series form equation about time to obtain a reduction solution of the power series form equation;
performing variable substitution on the constant coefficients in the reduction solution by using the constant coefficients of the power series form equation to obtain the rock mass instability gray-fold mutation model;
determining a destabilization condition of the target rock mass at any time to be predicted based on the acoustic emission monitoring data, the acoustic emission gray prediction model and the rock mass destabilization gray-fold abrupt model at the current time, including:
determining the current time as an initial time;
accumulating acoustic emission monitoring data at the initial moment for one time to obtain primary accumulated data at the initial moment;
inputting acoustic emission monitoring data at an initial moment and a time difference to be predicted into the acoustic emission gray prediction model to obtain a predicted value of primary accumulated data at the moment to be predicted; the time difference to be predicted is the time difference between the time to be predicted and the initial time;
determining a standard potential function form of a folding mutation model at the moment to be predicted based on a predicted value of one-time accumulated data at the moment to be predicted and the rock mass instability gray-folding mutation model;
obtaining a first derivative in a standard potential function form of a folding mutation model at a moment to be predicted, and obtaining a balanced curved surface equation at the moment to be predicted;
obtaining a second derivative in a standard potential function form of a folding mutation model at a moment to be predicted, and obtaining a singular point set equation at the moment to be predicted;
the equilibrium curved surface equation and the singular point set equation at the moment to be predicted are combined, and the control quantity of the standard potential function form of the folding mutation model at the moment to be predicted is calculated;
and determining the destabilization condition of the target rock mass at the moment to be predicted based on the control quantity corresponding to the moment to be predicted.
5. An electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform the rock mass destabilization mutation warning method according to any one of claims 1 to 3.
6. The electronic device of claim 5, wherein the memory is a readable storage medium.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311473458.4A CN117250259B (en) | 2023-11-08 | 2023-11-08 | Rock mass instability mutation early warning method, system and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311473458.4A CN117250259B (en) | 2023-11-08 | 2023-11-08 | Rock mass instability mutation early warning method, system and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117250259A CN117250259A (en) | 2023-12-19 |
| CN117250259B true CN117250259B (en) | 2024-01-30 |
Family
ID=89129681
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311473458.4A Active CN117250259B (en) | 2023-11-08 | 2023-11-08 | Rock mass instability mutation early warning method, system and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117250259B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3570075A1 (en) * | 2018-05-18 | 2019-11-20 | Repsol Exploración, S.A. | Computer implemented method for generating a subsurface rock and/or fluid model of a determined domain |
| CN111539515A (en) * | 2020-04-21 | 2020-08-14 | 中国电子科技集团公司第三十八研究所 | Complex equipment maintenance decision method based on fault prediction |
| CN111999171A (en) * | 2020-08-21 | 2020-11-27 | 青岛理工大学 | Hard rock joint surface sudden dislocation and instability early warning method based on acoustic emission monitoring |
| WO2021031461A1 (en) * | 2019-08-20 | 2021-02-25 | 沙洲职业工学院 | Method for determining instability of arch dam abutment rock mass comprising structural plane |
| CN113030278A (en) * | 2021-03-09 | 2021-06-25 | 北京科技大学 | Corrosion concrete fracture analysis method based on acoustic emission technology |
| CN114705549A (en) * | 2022-04-06 | 2022-07-05 | 生态环境部固体废物与化学品管理技术中心 | Early warning method for instability and mutation of easily-argillized ore rock |
| WO2023115811A1 (en) * | 2021-12-24 | 2023-06-29 | 广西大学 | Method, apparatus and device for monitoring multi-scale fracture of hard rock by using multi-band acoustic signals, and storage medium |
-
2023
- 2023-11-08 CN CN202311473458.4A patent/CN117250259B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3570075A1 (en) * | 2018-05-18 | 2019-11-20 | Repsol Exploración, S.A. | Computer implemented method for generating a subsurface rock and/or fluid model of a determined domain |
| WO2021031461A1 (en) * | 2019-08-20 | 2021-02-25 | 沙洲职业工学院 | Method for determining instability of arch dam abutment rock mass comprising structural plane |
| CN111539515A (en) * | 2020-04-21 | 2020-08-14 | 中国电子科技集团公司第三十八研究所 | Complex equipment maintenance decision method based on fault prediction |
| CN111999171A (en) * | 2020-08-21 | 2020-11-27 | 青岛理工大学 | Hard rock joint surface sudden dislocation and instability early warning method based on acoustic emission monitoring |
| CN113030278A (en) * | 2021-03-09 | 2021-06-25 | 北京科技大学 | Corrosion concrete fracture analysis method based on acoustic emission technology |
| WO2023115811A1 (en) * | 2021-12-24 | 2023-06-29 | 广西大学 | Method, apparatus and device for monitoring multi-scale fracture of hard rock by using multi-band acoustic signals, and storage medium |
| CN114705549A (en) * | 2022-04-06 | 2022-07-05 | 生态环境部固体废物与化学品管理技术中心 | Early warning method for instability and mutation of easily-argillized ore rock |
Non-Patent Citations (4)
| Title |
|---|
| Fold catastrophe analysis of constitutive model of pick cutting coal with rock;Chenxu Luo 等;Energy Reports;第8卷;9-15 * |
| 参数修正法改进灰色预测模型及其在岩土工程变形信息分析中的应用;史玉峰, 靳奉祥, 马健;有色金属(矿山部分)(03);26-27 * |
| 岩体动力失稳的折迭突变模型;张黎明;王在泉;张晓娟;王志强;;岩土工程学报;第31卷(第04期);552-557 * |
| 灰色突变理论及声发射在岩爆预测中的应用;李长洪;张立新;张磊;张吉良;范丽萍;;中国矿业;第17卷(第08期);87-90 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117250259A (en) | 2023-12-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109165425B (en) | Gear contact fatigue reliability analysis method | |
| CN103616179A (en) | Transmission gear fatigue life assessment method based on defect modeling | |
| CN111539142B (en) | Pipeline crack propagation driving force calculation method and system | |
| CN104453879B (en) | The Forecasting Methodology of pressure before boring | |
| Keramat et al. | Investigation of transient cavitating flow in viscoelastic pipes | |
| CN115983062A (en) | High arch dam earthquake damage assessment method and system based on finite element model correction | |
| CN105138770A (en) | Spaceflight product reliability simulation evaluating method based on indirect reliability characteristic quantity | |
| CN107545110A (en) | A kind of dynamic stress accelerated life test section preparation method | |
| CN110096805A (en) | Based on the quantization of structural parameters uncertainty and transmission method for improving bootstrap under a kind of finite observation data | |
| CN115288650B (en) | Method for parallel calculation and simulated hydraulic fracturing in pore elastic medium | |
| CN106980735B (en) | Numerical simulation method for thermal fracture of brittle material | |
| Li et al. | Automatic modal parameter identification of high arch dams: feasibility verification | |
| CN115526483A (en) | Rapid evaluation method and system for anti-seismic toughness of shield tunnel structure | |
| Liu et al. | Machine learning-based stochastic subspace identification method for structural modal parameters | |
| CN117250259B (en) | Rock mass instability mutation early warning method, system and electronic equipment | |
| CN111695307B (en) | Water hammer finite volume simulation method considering dynamic friction resistance explicitly | |
| CN113177272B (en) | Method for numerical simulation and parameter analysis of fatigue finite element after corrosion of metal material | |
| Nam et al. | A study on mesh size dependency of finite element blast structural analysis induced by non-uniform pressure distribution from high explosive blast wave | |
| CN112784470A (en) | Method for evaluating damage grade of civil engineering target under action of explosive load | |
| CN113791023B (en) | Method for establishing metal surface corrosion prediction model based on corrosion probability | |
| CN115640683A (en) | Rock-soil mass creep prediction method and system | |
| JP2012037305A (en) | Sequential nonlinear earthquake response analysis method for foundation and storage medium with analysis program stored thereon | |
| CN114611820A (en) | Method for predicting deformation of deep foundation pit of unequal-interval gray BP neural network | |
| CN107908596A (en) | A kind of mode Method of determining the optimum based on singular value decomposition | |
| CN115906710B (en) | Simulation method for flow medium stress distribution of high-pressure air pipeline |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |