CN111472840A - Mining surrounding rock ground pressure disaster intelligent prediction method and system - Google Patents
Mining surrounding rock ground pressure disaster intelligent prediction method and system Download PDFInfo
- Publication number
- CN111472840A CN111472840A CN202010265209.6A CN202010265209A CN111472840A CN 111472840 A CN111472840 A CN 111472840A CN 202010265209 A CN202010265209 A CN 202010265209A CN 111472840 A CN111472840 A CN 111472840A
- Authority
- CN
- China
- Prior art keywords
- surrounding rock
- data
- stress
- disaster
- sensor
- 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.)
- Granted
Links
- 239000011435 rock Substances 0.000 title claims abstract description 177
- 238000005065 mining Methods 0.000 title claims abstract description 46
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000011156 evaluation Methods 0.000 claims abstract description 29
- 238000010801 machine learning Methods 0.000 claims abstract description 22
- 238000013459 approach Methods 0.000 claims abstract description 4
- 238000007781 pre-processing Methods 0.000 claims description 15
- 238000012549 training Methods 0.000 claims description 15
- 238000012544 monitoring process Methods 0.000 claims description 9
- 230000005540 biological transmission Effects 0.000 claims description 6
- 230000007774 longterm Effects 0.000 claims description 6
- 230000007246 mechanism Effects 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims 1
- 230000002265 prevention Effects 0.000 abstract 1
- 230000035882 stress Effects 0.000 description 37
- 239000010438 granite Substances 0.000 description 11
- 229910052500 inorganic mineral Inorganic materials 0.000 description 9
- 239000011707 mineral Substances 0.000 description 9
- 235000010755 mineral Nutrition 0.000 description 9
- 239000002245 particle Substances 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 230000006835 compression Effects 0.000 description 7
- 238000007906 compression Methods 0.000 description 7
- 230000006378 damage Effects 0.000 description 7
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 6
- 230000004308 accommodation Effects 0.000 description 6
- 230000009471 action Effects 0.000 description 6
- 238000005553 drilling Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 6
- 238000012360 testing method Methods 0.000 description 5
- VTYYLEPIZMXCLO-UHFFFAOYSA-L Calcium carbonate Chemical compound [Ca+2].[O-]C([O-])=O VTYYLEPIZMXCLO-UHFFFAOYSA-L 0.000 description 4
- 229910052626 biotite Inorganic materials 0.000 description 4
- 230000008859 change Effects 0.000 description 4
- 238000010276 construction Methods 0.000 description 4
- 239000013078 crystal Substances 0.000 description 4
- 239000000463 material Substances 0.000 description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
- 238000005056 compaction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 239000012071 phase Substances 0.000 description 3
- 230000000630 rising effect Effects 0.000 description 3
- 235000012239 silicon dioxide Nutrition 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 230000004913 activation Effects 0.000 description 2
- 238000001994 activation Methods 0.000 description 2
- 238000005452 bending Methods 0.000 description 2
- 235000010216 calcium carbonate Nutrition 0.000 description 2
- 229910000019 calcium carbonate Inorganic materials 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000005489 elastic deformation Effects 0.000 description 2
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 description 2
- 239000010453 quartz Substances 0.000 description 2
- 238000004227 thermal cracking Methods 0.000 description 2
- 238000005979 thermal decomposition reaction Methods 0.000 description 2
- KWYUFKZDYYNOTN-UHFFFAOYSA-M Potassium hydroxide Chemical compound [OH-].[K+] KWYUFKZDYYNOTN-UHFFFAOYSA-M 0.000 description 1
- 230000001174 ascending effect Effects 0.000 description 1
- WETINTNJFLGREW-UHFFFAOYSA-N calcium;iron;tetrahydrate Chemical compound O.O.O.O.[Ca].[Fe].[Fe] WETINTNJFLGREW-UHFFFAOYSA-N 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 229910052681 coesite Inorganic materials 0.000 description 1
- 239000000470 constituent Substances 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000005336 cracking Methods 0.000 description 1
- 229910052906 cristobalite Inorganic materials 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 229910052840 fayalite Inorganic materials 0.000 description 1
- 239000010433 feldspar Substances 0.000 description 1
- 238000013277 forecasting method Methods 0.000 description 1
- XEEYBQQBJWHFJM-UHFFFAOYSA-N iron Substances [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- MHKWSJBPFXBFMX-UHFFFAOYSA-N iron magnesium Chemical compound [Mg].[Fe] MHKWSJBPFXBFMX-UHFFFAOYSA-N 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 229940072033 potash Drugs 0.000 description 1
- BWHMMNNQKKPAPP-UHFFFAOYSA-L potassium carbonate Substances [K+].[K+].[O-]C([O-])=O BWHMMNNQKKPAPP-UHFFFAOYSA-L 0.000 description 1
- 235000015320 potassium carbonate Nutrition 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 239000011034 rock crystal Substances 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 239000000377 silicon dioxide Substances 0.000 description 1
- 239000007790 solid phase Substances 0.000 description 1
- 229910052682 stishovite Inorganic materials 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000007725 thermal activation Methods 0.000 description 1
- 230000008646 thermal stress Effects 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 229910052905 tridymite Inorganic materials 0.000 description 1
- 229910052882 wollastonite Inorganic materials 0.000 description 1
Images
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Landscapes
- Engineering & Computer Science (AREA)
- Mining & Mineral Resources (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Geology (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Medical Informatics (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Pit Excavations, Shoring, Fill Or Stabilisation Of Slopes (AREA)
- Geophysics And Detection Of Objects (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
An intelligent prediction method and system for mining surrounding rock earth pressure disasters relate to the technical field of deep mining earth pressure disaster prediction and prevention. The method comprises the following steps: s1: arranging a sensor in the mining surrounding rock to acquire surrounding rock data; s2: constructing a stress-strain curve by using the collected surrounding rock data, extracting data before the surrounding rock stress reaches the limit stress as pre-peak data, and extracting data after the surrounding rock stress reaches the limit stress as post-peak data; s3: substituting continuous pre-peak data or post-peak data in a period of time into a machine learning model to perform disaster approach early warning to obtain a preliminary prediction result; s4: evaluating the preliminary prediction result based on the surrounding rock instability theory and external environment information to obtain an evaluation result; s5: and finishing final disaster decision according to the evaluation result. The method can complete the preliminary prediction of the ground pressure disaster through the machine learning model, verify the prediction result through a certain standard and model, and have excellent accuracy and practicability.
Description
Technical Field
The invention relates to the technical field of disaster prediction, in particular to an intelligent prediction method and system for mining surrounding rock ground pressure disasters.
Background
In the process of constructing the roadway (tunnel), the hazard degree of ground pressure disasters in deep mining is very high, and geological disasters such as water burst, mud burst, collapse and the like are important factors influencing the construction safety and cost of the roadway (tunnel). If the occurrence of the disaster can be predicted in advance before the occurrence of the geological disaster, and then corresponding treatment is adopted, the construction risk can be greatly reduced, and the construction cost can be saved.
Although the success rate of forecasting the surrounding rock earth pressure disaster in China is obviously improved, most of the forecasting methods are realized by means of manual experience at present, and the principles, working modes, explanation and application and the like of the methods are not mature and accurate enough, so that the actual engineering requirements are hardly met. In actual construction, accidents such as water burst, sand gushing, debris flow, collapse and the like are also continuously caused, and serious casualties and economic losses are caused. Therefore, the prediction of the mining surrounding rock ground pressure disaster needs to be continuously strengthened, a reasonable coping strategy is made, and the occurrence of the disaster is reduced to the maximum extent.
Disclosure of Invention
In view of the above, the invention aims to provide an intelligent prediction method and system for mining surrounding rock earth pressure disasters, which can complete preliminary prediction of earth pressure disasters through a machine learning model, verify prediction results through certain standards and theoretical models, and have excellent accuracy and practicability.
According to the first aspect of the invention, the invention provides an intelligent prediction method for the mining surrounding rock ground pressure disaster, which comprises the following steps:
and S1, data acquisition: the method comprises the following steps that a plurality of sensor modules are arranged in mining surrounding rocks, the sensor modules comprise stress-strain sensing modules and environment sensing modules, and the sensor modules are used for collecting surrounding rock data and external environment information;
s2 data preprocessing: constructing a stress-strain curve by using surrounding rock data acquired by the stress-strain sensing module, extracting data before the surrounding rock stress reaches the limit stress as pre-peak data, and extracting data after the surrounding rock stress reaches the limit stress as post-peak data;
s3 disaster preliminary prediction: substituting continuous pre-peak data and/or post-peak data in a period of time into a machine learning model to perform disaster approach early warning to obtain a preliminary prediction result;
s4 evaluation of results: evaluating the preliminary prediction result based on a surrounding rock instability theory and external environment information acquired by an environment sensing module to obtain an evaluation result;
s5 final decision: and finishing final disaster decision according to the evaluation result.
Further, dividing the collected data into pre-peak data and post-peak data according to a yield point in a full stress strain curve model of the surrounding rock, using the pre-peak data to complete prediction of the yield point, and judging whether the stress of the surrounding rock reaches the yield point within a certain time; and (4) the post-peak data or the combination of the pre-peak data and the post-peak data is used for completing the prediction of the instability of the surrounding rock and judging whether the surrounding rock is fractured and unstable within a certain time.
Further, the stress-strain sensing module comprises a stress sensor and a strain sensor; the environment sensing module comprises a temperature sensor, an acoustic emission sensor and a microseismic sensor.
Furthermore, the surface of the mining surrounding rock is provided with a drill hole, and the sensor is arranged in the drill hole.
Further, the evaluation of the result of S4 specifically includes:
s41: judging the deformation stage of the surrounding rock according to the full stress-strain curve model of the surrounding rock, and analyzing and predicting the acquired surrounding rock data based on the instability mode of the surrounding rock in the deformation stage and the instability mechanism of different parts of the surrounding rock to obtain an intermediate prediction result;
s42: adjusting the intermediate prediction result by combining the acquired external environment information to obtain a secondary prediction result;
s43: and comparing the secondary prediction result with the primary prediction result to obtain an evaluation result.
Further, the external environment information includes temperature, noise, and vibration.
Further, the higher the temperature is, the surrounding rock is considered to be easy to destabilize, and allowable stress should be properly reduced;
the larger the noise is, the instability of the surrounding rock is considered to be easy, and the allowable stress is properly reduced;
the larger the vibration is, the instability of the surrounding rock is considered to be easy, and the allowable stress should be properly reduced.
Further, the S3 disaster preliminary prediction specifically includes:
s31: preliminarily constructing a machine learning model according to the long-term indexes before the disaster peak and the near index characteristics after the disaster peak;
s32: taking long-term data before a peak and near data after the peak in the existing disaster data as training sets to finish the primary training of a machine learning model;
s33: performing preliminary analysis prediction on the pre-peak data and/or post-peak data in the S2 continuously in a period of time by using the machine learning model;
s34: and when the quantity of the pre-peak data and the post-peak data in the S2 meets the training requirement, taking the pre-peak data and the post-peak data in the S2 as a new training set, and finishing the iterative optimization of the machine learning model.
Further, the evaluation result in step S43 specifically includes:
the secondary prediction result is the same as the primary prediction result: at the moment, the preliminary prediction result is directly used as a final disaster prediction result;
the secondary prediction results are different from the primary prediction results: expanding the time in the S3, repeating the steps S3-S4, and outputting the evaluation result again; and if the re-prediction result obtained again is different from the initial prediction result, taking the re-prediction result as a final disaster prediction result, and training and optimizing the machine learning model in the step S3 by using the re-prediction result and the pre-peak data and/or post-peak data in a period of time after expansion.
According to a second aspect of the present invention, there is provided a mining wall rock burst disaster intelligent prediction system for performing the method according to the first aspect of the present invention, comprising: the device comprises an acquisition unit, a preprocessing unit, a prediction unit, an evaluation unit and a decision unit;
the acquisition unit is used for acquiring surrounding rock data and comprises a stress-strain sensing module and an environment sensing module, wherein the stress-strain sensing module is used for acquiring the stress and strain of the mining surrounding rock in the drill hole in real time, and the environment sensing module is used for acquiring the temperature, noise and vibration of the mining surrounding rock in the drill hole in real time;
the preprocessing unit is used for receiving the surrounding rock data and preprocessing the surrounding rock data;
the prediction unit is used for preliminarily predicting disaster occurrence;
the evaluation unit is used for evaluating the preliminary prediction result based on the surrounding rock instability theory and the external environment information acquired by the environment sensing module;
and the decision unit is used for finishing final disaster decision.
Further, the preprocessing unit comprises a transmission base station and a ground receiving terminal platform, and the transmission base station can encrypt and transmit the surrounding rock data to the ground receiving terminal platform in batches.
Furthermore, the stress-strain sensing module and the environment sensing module are arranged at the tail end of the drill hole far away from the roadway in a coordinated manner.
Further, the drilling extends to the mining country rock along perpendicular to tunnel lateral wall direction, the drilling includes extension and monitoring portion, the tunnel is connected to extension one end, and the other end passes through monitoring portion and connects the mining country rock.
Furthermore, the monitoring part comprises a first accommodating space, a second accommodating space and a third accommodating space, and the first accommodating space, the second accommodating space and the third accommodating space are sequentially connected in a direction perpendicular to the extending direction of the extending part.
Further, temperature sensor and acoustic emission sensor set gradually along perpendicular to extension extending direction, temperature sensor and acoustic emission sensor all set up in the second accommodation space.
Further, stress sensor and strain sensor all set up in first accommodation space, the second accommodation space is connected to stress sensor and strain sensor's one end, and the other end is connected and is adopted the country rock.
Furthermore, the microseism sensor is arranged in the third accommodating space, one end of the microseism sensor is connected with the second accommodating space, and the other end of the microseism sensor is connected with the mining surrounding rock.
Compared with the prior art, the mining surrounding rock ground pressure disaster intelligent prediction method and system provided by the invention are simple to operate, easy to use, high in precision and accurate in judgment, and have the following outstanding characteristics:
1. dividing the acquired data into pre-peak data and post-peak data according to the yield point in the full stress-strain curve model of the surrounding rock, using the pre-peak data to complete the prediction of the yield point, and judging whether the stress of the surrounding rock reaches the yield point within a certain time; the method can achieve the effect of quickly and accurately predicting in real time by setting the purposes of different data;
2. the method has the advantages that the initial prediction of the disasters is completed in real time through the machine learning model, the initial prediction results are evaluated and judged based on the surrounding rock instability theory and external environment information, the disasters are prevented from being missed and mistakenly reported, and therefore the damage which is difficult to recover is avoided.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of an intelligent prediction method for a surrounding rock burst disaster in mining according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a full stress-strain curve according to an embodiment of the present invention;
fig. 3 is an overall schematic diagram of an intelligent prediction system for a mining wall rock ground pressure disaster according to an embodiment of the invention;
fig. 4 is a schematic diagram of an acquisition unit of the mining surrounding rock ground pressure disaster intelligent prediction system according to the embodiment of the invention;
FIG. 5 is a fracture model of a weak roadway upper part with two soft sides according to embodiment 1 of the present invention;
fig. 6 shows a one-way compression model of a slope shallow rock mass according to embodiment 1 of the invention.
Wherein, in the figure:
1-mining surrounding rock, 2-drilling, 3-a second accommodating space, 4-a first accommodating space and 5-a third accommodating space.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terms "first," "second," and the like in the description and in the claims of the present disclosure are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
A plurality, including two or more.
And/or, it should be understood that, for the term "and/or" as used in this disclosure, it is merely one type of association that describes an associated object, meaning that three types of relationships may exist. For example, a and/or B, may represent: a exists alone, A and B exist simultaneously, and B exists alone.
The method for intelligently predicting the mining surrounding rock earth pressure disaster as shown in fig. 1 comprises the following steps:
and S1, data acquisition: arranging a plurality of sensor modules in the mining surrounding rock 1, wherein each sensor module comprises a stress-strain sensing module and an environment sensing module, and the sensor modules are used for acquiring surrounding rock data and external environment information;
s2 data preprocessing: constructing a stress-strain curve by using surrounding rock data acquired by a stress-strain sensing module, extracting data before the stress of the surrounding rock reaches the limit stress as pre-peak data, and extracting data after the stress of the surrounding rock reaches the limit stress as post-peak data;
s3 disaster preliminary prediction: substituting continuous pre-peak data and/or post-peak data in a period of time into a machine learning model to perform disaster approach early warning to obtain a preliminary prediction result;
s4 evaluation of results: evaluating the preliminary prediction result based on the surrounding rock instability theory and external environment information acquired by the environment sensing module to obtain an evaluation result;
s5 final decision: and finishing final disaster decision according to the evaluation result.
Dividing the acquired data into pre-peak data and post-peak data according to the yield point in the full stress-strain curve model of the surrounding rock, using the pre-peak data to complete the prediction of the yield point, and judging whether the stress of the surrounding rock reaches the yield point within a certain time; and (4) the post-peak data or the combination of the pre-peak data and the post-peak data is used for completing the prediction of the instability of the surrounding rock and judging whether the surrounding rock is fractured and unstable within a certain time.
Wherein, the section O-A in figure 2 is the original crack compaction stage. In the initial loading stage of the surrounding rock, due to the accumulation and increase of the load, the stress-strain curve of the surrounding rock is in an upward bending change trend, the slope of the curve is gradually increased, and the deformation of the surrounding rock mainly comprises the compaction and compaction of fine cracks and micro cavities in the surrounding rock. The deformation is mainly manifested in the form of plastic deformation. In the relatively dense surrounding rock, the duration of the phase is short, and the deformation is small (if the upper and lower loaded ends of the surrounding rock test piece are not completely horizontal due to the error of laboratory processing, the deformation condition of the O-A phase can also occur at the initial loading stage).
The section A-B is the elastic deformation stage of the front line of the peak. During the deformation process of the surrounding rock, the deformation characteristic curve of the surrounding rock also has a linear ascending change trend due to the accumulated increase of the load, and meanwhile, the slope of the curve is kept constant, and the slope is mainly determined by the elastic constant of the surrounding rock material. In this phase, the surrounding rock deformation mainly takes the form of elastic deformation. As the load increases, extremely tiny cracking can occur inside the surrounding rock from a microscopic view; from a macroscopic point of view, the deformation is expressed as a linear deformation stage, and the stress value at the point B in the curve is called the elasticity limit value of the surrounding rock material.
The B-C section is a fracture stable elastoplasticity transition stage. In the deformation stage of the surrounding rock, as the load is accumulated and increased, the stress-strain curve of the surrounding rock begins to deviate from the linear rising from the point B, although the rising trend is continuously maintained, the stress-strain curve shows a downward bending trend, and the slope of the curve gradually becomes smaller. In the stage, the micro cracks in the surrounding rock expand in a small range, the deformation of the surrounding rock is mainly reflected in that the newly generated cracks expand, and finally the characteristic of plastic deformation is shown. In the deformation stage, with the continuous increase of the load, the tiny cracks of the surrounding rock are continuously increased, the volume of the surrounding rock is changed from the first compression deformation to the expansion deformation under the expansion effect of the surrounding rock, and the C point value is called as the yield limit.
The C-D section is the stage of accelerated fracture plastic deformation. In the deformation stage of the surrounding rock, along with the accumulation and increase of the load, the stress-strain curve of the surrounding rock continuously rises from the point C to the upper right, the rising trend can still be kept, and the slope of the curve gradually becomes smaller. Along with the continuous increase of load, the microcrack inside the surrounding rock further expands and breeds, when being close to peak value summit D department, the speed of the surrounding rock rupture at this moment is accelerated, and the volume continues to accelerate the inflation under the dilatation effect.
The D-E section is a post-peak strength macroscopic destruction stage. After the stress state of the surrounding rock reaches the peak intensity, the mechanical characteristics of rock failure can be divided into two conditions in this stage, as shown in the two types of curves: the first type of curve shows a gradual slow and steady trend descending; the second type of curve shows a "sharp" drop.
The E-F sections are the post-peak residual intensity sections. During the post-peak residual strength process, the accelerated destruction of the surrounding rock is converted from initial macroscopic fracture into macroscopic fracture, the destruction form of the surrounding rock is shown as unstable slippage along the fracture surface of the surrounding rock, the cohesive force inside the surrounding rock reaches zero at the moment, but the surrounding rock still has certain residual strength due to the friction occlusion effect between rock blocks.
The stress-strain sensing module comprises a stress sensor and a strain sensor; the environment sensing module comprises a temperature sensor, an acoustic emission sensor and a microseismic sensor.
The surface of the mining surrounding rock 1 is provided with a drill hole 2, and the sensor is arranged in the drill hole 2.
The evaluation of the result of S4 specifically comprises:
s41: judging the deformation stage of the surrounding rock according to the full stress-strain curve model of the surrounding rock shown in FIG. 2, and analyzing and predicting the acquired surrounding rock data based on the instability mode of the surrounding rock in the deformation stage and the instability mechanism of different parts of the surrounding rock to obtain an intermediate prediction result;
s42: adjusting the intermediate prediction result by combining the acquired external environment information to obtain a secondary prediction result;
s43: and comparing the secondary prediction result with the primary prediction result to obtain an evaluation result.
The external environment information includes temperature, noise, vibration.
The higher the temperature is, the surrounding rock is considered to be easy to destabilize, and the allowable stress is properly reduced;
the larger the noise is, the instability of the surrounding rock is considered to be easy, and the allowable stress is properly reduced;
the larger the vibration is, the instability of the surrounding rock is considered to be easy, and the allowable stress should be properly reduced.
S3 disaster preliminary prediction specifically comprises the following steps:
s31: preliminarily constructing a machine learning model according to the long-term indexes before the disaster peak and the near index characteristics after the disaster peak;
s32: taking long-term data before a peak and near data after the peak in the existing disaster data as training sets to finish the primary training of a machine learning model;
s33: performing preliminary analysis and prediction on the pre-peak data and/or post-peak data in the S2 continuously in a period of time by using a machine learning model;
s34: and when the quantity of the pre-peak data and the post-peak data in the S2 meets the training requirement, taking the pre-peak data and the post-peak data in the S2 as a new training set, and finishing the iterative optimization of the machine learning model.
The evaluation result in step S43 specifically includes:
the secondary prediction result is the same as the primary prediction result: at the moment, the preliminary prediction result is directly used as a final disaster prediction result;
the secondary prediction results are different from the primary prediction results: at the moment, expanding the time in the S3, repeating the steps S3-S4, and outputting the evaluation result again; and if the re-prediction result obtained again is different from the initial prediction result, taking the re-prediction result as a final disaster prediction result, and training and optimizing the machine learning model in the step S3 by using the re-prediction result and the pre-peak data and/or the post-peak data in a period of time after expansion.
An intelligent prediction system for a surrounding rock burst disaster as shown in fig. 3, the system is used for executing the method according to the first aspect of the invention, and comprises: the device comprises an acquisition unit, a preprocessing unit, a prediction unit, an evaluation unit and a decision unit;
the acquisition unit is used for acquiring surrounding rock data and comprises a stress-strain sensing module and an environment sensing module, wherein the stress-strain sensing module is used for acquiring the stress and strain of the surrounding rock in the drill hole 2 in real time, and the environment sensing module is used for acquiring the temperature, noise and vibration of the surrounding rock in the drill hole 2 in real time;
the preprocessing unit is used for receiving the surrounding rock data and preprocessing the surrounding rock data;
a prediction unit for preliminarily predicting occurrence of a disaster;
the evaluation unit is used for evaluating the preliminary prediction result based on the surrounding rock instability theory and the external environment information acquired by the environment sensing module;
and the decision unit is used for finishing final disaster decision.
The preprocessing unit comprises a transmission base station and a ground receiving terminal platform, and the transmission base station can encrypt and transmit surrounding rock data to the ground receiving terminal platform in batches.
The stress strain sensing module and the environment sensing module are arranged at the tail end of the drilling hole 2 far away from the roadway in a coordinated mode.
As shown in fig. 4, the drilling 2 extends to the mining surrounding rock 1 along the direction perpendicular to the side wall of the roadway, the drilling 2 comprises an extension part and a monitoring part, one end of the extension part is connected with the roadway, and the other end of the extension part is connected with the mining surrounding rock 1 through the monitoring part.
The monitoring part comprises a first accommodating space 4, a second accommodating space 3 and a third accommodating space 5, and the first accommodating space 4, the second accommodating space 3 and the third accommodating space 5 are sequentially connected in a direction perpendicular to the extending direction of the extending part.
Temperature sensor and acoustic emission sensor set gradually along perpendicular to extension extending direction, and temperature sensor and acoustic emission sensor all set up in second accommodation space 3.
Stress sensor and strain sensor all set up in first accommodation space 4, and second accommodation space 3 is connected to stress sensor and strain sensor's one end, and mining country rock 1 is connected to the other end.
The microseism sensor is arranged in the third containing space 5, one end of the microseism sensor is connected with the second containing space 3, and the other end of the microseism sensor is connected with the mining surrounding rock 1.
Example 1
This embodiment will take the instability of two sides of the surrounding rock as an example to illustrate how to obtain the intermediate prediction result in step S41.
The two sides of the surrounding rock are soft rock layers generally, and due to the low strength of the surrounding rock, after the surrounding rock is excavated, if no supporting force is applied, the surrounding rock side parts are subjected to compression shear damage in a compression stress field with the vertical stress as the maximum main stress, and the stress deformation state can be similar to a one-way compression state. Fig. 5 shows a two-side fracture model of two-side weak surrounding rock, and fig. 6 shows a one-way compression model of the surrounding rock.
Under the action of unidirectional compression, the main failure mode of the rock mass is embodied as compression-shear fracture, and the instability condition of the rock mass in brittle shear-expansion fracture can be considered that the rock mass is unstable under the action of vertical pressure after the applied load exceeds the ultimate strength of the rock mass. And regarding the compression-shear fracture of the rock mass, the instability criterion is judged according to the shear strength form.
Expressed in terms of shear strength:
in the formula:
a-empirical constant;
b-coefficient of compressibility of the material;
τf-rock mass shear strength;
σC-uniaxial compressive strength of the rock mass;
σ — shear fracture surface normal stress;
β — shear slip angle;
τ -shear slope shear stress;
and m and s are dimensionless test constants, s represents the integrity of the rock mass, the complete rock s is 1, m is related to factors such as lithology and the like, and the parameters can be determined through tests and engineering classification of the rock mass.
And (3) rock mass destruction instability: if | τ | ≧ τfThe rock mass generates sliding instability, the rock mass failure instability can be judged, and the sliding angle can be obtained by the formula 4-3.
Example 2
The allowable intensity of the surrounding rock is affected by temperature, noise and vibration, and the influence of the temperature on the granite is taken as an example in the embodiment to explain the influence of the external environment information on the allowable intensity.
The damage mechanism of granite under the action of high temperature is as follows:
(1) thermal cracking. Under the action of temperature, due to the difference of particle size, thermal expansion coefficient and thermal elasticity of various mineral particles in the granite, thermal expansion of particle boundaries is inconsistent, tensile and compressive stresses, namely structural thermal stress, are generated among the mineral particles or inside the particles, so that microcracks are generated inside the granite, and then primary and secondary cracks are expanded and communicated, thus macroscopically representing the deterioration of the physical and mechanical properties of the granite.
In addition, certain minerals undergo a metamorphic polytropic change at high temperature, accompanied by a volume change, which further exacerbates thermal cracking and changes in the physical and mechanical properties of granite. For example, quartz is the most important constituent mineral of granite, and the following changes occur in the quartz particles with changes in temperature, and the volume changes:
(2) and (4) thermal activation. Thermal motion or stress of rock crystal particles can cause defects (e.g. dislocations, local lattice slippage along atomic planes, crystal defects up to the interior of the lattice, particle disorganization at the interface of slipped and non-slipped parts of the lattice) in rock crystals, thus making the material susceptible to fracture. When the rock is subjected to high temperature of about 400 ℃, after-0H atomic groups in the test piece are heated and activated, the original silicon-oxygen bonds are replaced by hydroxyl bonds, so that the increase of dislocation in rock crystals is promoted, and the test piece is weakened. In this context, granite, subjected to 400 ℃, after rapid cooling in water, has a sudden decrease in the axial peak strain and a rapid decrease in the radial peak strain above 400 ℃ with an increase in temperature. It is presumed that after the action of the temperature of 400 ℃, the heat activation effect is remarkable when the granite is cooled in water, more silicon-oxygen bonds are replaced by hydroxyl bonds, the axial strain is rapidly reduced by the sudden strong heat activation effect, and the radial peak strain is also rapidly reduced after the temperature exceeds 400 ℃.
(3) Thermochemical action. The mineral composition of rock determines to a large extent the physico-mechanical properties of rock. Under the high-temperature environment, the granite mineral undergoes chemical reaction to convert the mineral components. SiO2 reacts with CaO at about 300 ℃ to generate CaSiO3, CaO reacts with Fe2O at 400-550 ℃ to generate calcium ferrite, CaO reacts with CO2 to generate CaCO3, and CaCO3 is decomposed into CaO and CO2 after the temperature exceeds 800 ℃. When the temperature reaches 600 ℃, MgO and Fe2O3 react in a solid phase to generate magnesium-iron mixed crystals. Fe2O3 reacts with CaO at about 700 ℃ to generate fayalite. The chemical composition of the biotite is K (Mg, Fe)3AISI3O1O (OH)2, and because of containing (OH), the biotite has a tendency of thermal decomposition from 200 ℃, starts thermal decomposition at the temperature higher than 450 ℃, expands in thickness, is accelerated when the temperature reaches above 600 ℃, and almost completely decomposes when the temperature reaches 900 ℃, and has different temperatures, so that the mineral components generated by the decomposition of the biotite are obviously different, and the biotite, the potash feldspar and the like are mainly used.
Therefore, under the high-temperature environment, mineral components in the rock can be changed to different degrees, so that the physical and mechanical properties of granite are influenced, and the allowable strength of the rock is reduced.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the above implementation method can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation method. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (15)
1. An intelligent prediction method for mining surrounding rock ground pressure disasters is characterized by comprising the following steps:
and S1, data acquisition: the method comprises the following steps that a plurality of sensor modules are arranged in mining surrounding rocks, the sensor modules comprise stress-strain sensing modules and environment sensing modules, and the sensor modules are used for collecting surrounding rock data and external environment information;
s2 data preprocessing: constructing a stress-strain curve by using surrounding rock data acquired by the stress-strain sensing module, extracting data before the surrounding rock stress reaches the limit stress as pre-peak data, and extracting data after the surrounding rock stress reaches the limit stress as post-peak data;
s3 disaster preliminary prediction: substituting continuous pre-peak data and/or post-peak data in a period of time into a machine learning model to perform disaster approach early warning to obtain a preliminary prediction result;
s4 evaluation of results: evaluating the preliminary prediction result based on a surrounding rock instability theory and external environment information acquired by an environment sensing module to obtain an evaluation result;
s5 final decision: and finishing final disaster decision according to the evaluation result.
2. The method for intelligently predicting the mining surrounding rock earth pressure disaster according to claim 1, wherein the stress-strain sensing module comprises a stress sensor and a strain sensor; the environment sensing module comprises a temperature sensor, an acoustic emission sensor and a microseismic sensor.
3. The method as claimed in claim 2, wherein the mining surrounding rock earth pressure disaster intelligent prediction method is characterized in that a drill hole is formed in the surface of the mining surrounding rock, and the sensor is arranged in the drill hole.
4. The method for intelligently predicting the disaster of the mining surrounding rock earth pressure as claimed in claim 1, wherein the evaluation of the result of S4 specifically comprises:
s41: judging the deformation stage of the surrounding rock according to the full stress-strain curve model of the surrounding rock, and analyzing and predicting the acquired surrounding rock data based on the instability mode of the surrounding rock in the deformation stage and the instability mechanism of different parts of the surrounding rock to obtain an intermediate prediction result;
s42: adjusting the intermediate prediction result by combining the acquired external environment information to obtain a secondary prediction result;
s43: and comparing the secondary prediction result with the primary prediction result to obtain an evaluation result.
5. The method as claimed in claim 4, wherein the external environment information includes temperature, noise and vibration.
6. The method for intelligently predicting the mining surrounding rock earth pressure disaster according to claim 1, wherein the S3 disaster preliminary prediction specifically comprises:
s31: preliminarily constructing a machine learning model according to the long-term indexes before the disaster peak and the near index characteristics after the disaster peak;
s32: taking long-term data before a peak and near data after the peak in the existing disaster data as training sets to finish the primary training of a machine learning model;
s33: performing preliminary analysis prediction on the pre-peak data and/or post-peak data in the S2 continuously in a period of time by using the machine learning model;
s34: and when the quantity of the pre-peak data and the post-peak data in the S2 meets the training requirement, taking the pre-peak data and the post-peak data in the S2 as a new training set, and finishing the iterative optimization of the machine learning model.
7. The method for intelligently predicting the disaster of the mining surrounding rock earth pressure as claimed in claim 4, wherein the evaluation result in the step S43 specifically comprises:
the secondary prediction result is the same as the primary prediction result: at the moment, the preliminary prediction result is directly used as a final disaster prediction result;
the secondary prediction results are different from the primary prediction results: expanding the time in the S3, repeating the steps S3-S4, and outputting the evaluation result again; and if the re-prediction result obtained again is different from the initial prediction result, taking the re-prediction result as a final disaster prediction result, and training and optimizing the machine learning model in the step S3 by using the re-prediction result and the pre-peak data and/or post-peak data in a period of time after expansion.
8. An intelligent prediction system for a mining wall rock burst disaster, the system being configured to perform the method of any one of claims 1-7, comprising: the device comprises an acquisition unit, a preprocessing unit, a prediction unit, an evaluation unit and a decision unit;
the acquisition unit is used for acquiring surrounding rock data and comprises a stress-strain sensing module and an environment sensing module, wherein the stress-strain sensing module is used for acquiring the stress and strain of the mining surrounding rock in the drill hole in real time, and the environment sensing module is used for acquiring the temperature, noise and vibration of the mining surrounding rock in the drill hole in real time;
the preprocessing unit is used for receiving the surrounding rock data and preprocessing the surrounding rock data;
the prediction unit is used for preliminarily predicting disaster occurrence;
the evaluation unit is used for evaluating the preliminary prediction result based on the surrounding rock instability theory and the external environment information acquired by the environment sensing module;
and the decision unit is used for finishing final disaster decision.
9. The system of claim 8, wherein the preprocessing unit comprises a transmission base station and a ground receiving terminal platform, and the transmission base station can encrypt and transmit the surrounding rock data to the ground receiving terminal platform in batch.
10. The system of claim 8, wherein the stress-strain sensing module and the environmental sensing module are arranged at the tail end of the drill hole in a position away from the roadway.
11. The system of claim 10, wherein the borehole extends towards the mining surrounding rock in a direction perpendicular to a side wall of the roadway, the borehole comprises an extension portion and a monitoring portion, one end of the extension portion is connected with the roadway, and the other end of the extension portion is connected with the mining surrounding rock through the monitoring portion.
12. The system of claim 11, wherein the monitoring portion comprises a first accommodating space, a second accommodating space and a third accommodating space, and the first accommodating space, the second accommodating space and the third accommodating space are sequentially connected perpendicular to an extending direction of the extending portion.
13. The system of claim 12, wherein the temperature sensor and the acoustic emission sensor are sequentially arranged in a direction perpendicular to the extension direction of the extension portion, and the temperature sensor and the acoustic emission sensor are both arranged in the second accommodating space.
14. The system of claim 12, wherein the stress sensor and the strain sensor are both disposed in the first accommodating space, one end of the stress sensor and one end of the strain sensor are connected to the second accommodating space, and the other end of the stress sensor and the other end of the strain sensor are connected to the mining surrounding rock.
15. The mining surrounding rock earth pressure disaster intelligent prediction system as claimed in claim 12, wherein the microseismic sensor is arranged in the third accommodating space, one end of the microseismic sensor is connected with the second accommodating space, and the other end of the microseismic sensor is connected with the mining surrounding rock.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010265209.6A CN111472840B (en) | 2020-04-07 | 2020-04-07 | Mining surrounding rock ground pressure disaster intelligent prediction method and system |
| LU502697A LU502697B1 (en) | 2020-04-07 | 2020-04-23 | Intelligent prediction method and system for ground pressure disasters of wall rock affected by mining |
| PCT/CN2020/086431 WO2021203491A1 (en) | 2020-04-07 | 2020-04-23 | Intelligent prediction method and system for ground pressure disasters of wall rock affected by mining |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010265209.6A CN111472840B (en) | 2020-04-07 | 2020-04-07 | Mining surrounding rock ground pressure disaster intelligent prediction method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111472840A true CN111472840A (en) | 2020-07-31 |
| CN111472840B CN111472840B (en) | 2020-11-13 |
Family
ID=71749860
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010265209.6A Active CN111472840B (en) | 2020-04-07 | 2020-04-07 | Mining surrounding rock ground pressure disaster intelligent prediction method and system |
Country Status (3)
| Country | Link |
|---|---|
| CN (1) | CN111472840B (en) |
| LU (1) | LU502697B1 (en) |
| WO (1) | WO2021203491A1 (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112215438A (en) * | 2020-11-09 | 2021-01-12 | 广东新禾道信息科技有限公司 | Emergency disaster early warning analysis data processing method and system |
| CN113756869A (en) * | 2021-08-16 | 2021-12-07 | 中煤科工开采研究院有限公司 | Rock burst local monitoring and early warning system and method |
| CN114396312A (en) * | 2022-01-15 | 2022-04-26 | 安徽铜冠（庐江）矿业有限公司 | Multi-source monitoring and coupling early warning method suitable for large goaf ground pressure activity |
Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113868897B (en) * | 2021-11-19 | 2024-11-01 | 大连海事大学 | Layered rock statistical damage calculation method under thermal-force coupling condition |
| CN114383717B (en) * | 2021-12-10 | 2023-11-17 | 中煤科工开采研究院有限公司 | Rock burst roadway safety monitoring method and device |
| CN114324610A (en) * | 2021-12-24 | 2022-04-12 | 广西大学 | Multi-band acoustic signal monitoring method, device and equipment for multi-scale fracture of hard rock and storage medium |
| CN114611835B (en) * | 2022-03-31 | 2024-05-31 | 安徽理工大学 | Method for predicting and analyzing disaster risk of rock burst of mine with hard roof |
| CN114994746B (en) * | 2022-06-20 | 2024-06-11 | 广西大学 | Microseismic sensor mounting device based on tunnel steel arch and use method |
| CN115408927B (en) * | 2022-07-22 | 2024-06-28 | 北京交通大学 | Data processing method and device for predicting rock mass parameters |
| CN115508548B (en) * | 2022-11-22 | 2023-04-07 | 山东科技大学 | Method and system for determining supporting structure combining stress environment and surrounding rock structure |
| CN116792155B (en) * | 2023-06-26 | 2024-06-07 | 华南理工大学 | Tunnel health state monitoring and early warning method based on distributed optical fiber sensing |
| CN117074171B (en) * | 2023-07-21 | 2024-03-08 | 中国矿业大学(北京) | Deep coal rock stress field coupling induced coal rock instability early warning method |
| CN116777085B (en) * | 2023-08-23 | 2023-11-10 | 北京联创高科信息技术有限公司 | Coal mine water damage prediction system based on data analysis and machine learning technology |
| CN117967405B (en) * | 2024-03-28 | 2024-06-07 | 山东金恒力建工有限公司 | Intelligent monitoring and control system based on coal mine filling |
| CN118257630B (en) * | 2024-05-29 | 2024-10-15 | 陕西延长石油矿业有限责任公司 | Intelligent monitoring method for surrounding rock of coal mine shaft |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110015907A1 (en) * | 2009-07-20 | 2011-01-20 | Crawford Brian R | Petrophysical Method For Predicting Plastic Mechanical Properties In Rock Formations |
| CN102155231A (en) * | 2011-03-18 | 2011-08-17 | 大连海事大学 | Quick feedback analyzing system in tunnel constructing process |
| CN102505965A (en) * | 2011-11-11 | 2012-06-20 | 中国矿业大学(北京) | Method for identifying rock mass failure instability early warning |
| CN103883352A (en) * | 2014-04-08 | 2014-06-25 | 中煤科工集团重庆研究院有限公司 | Acoustic emission early warning method for dynamic disaster of underground coal instability |
| CN104198679A (en) * | 2014-09-17 | 2014-12-10 | 辽宁工程技术大学 | Full-waveform synchronous integrated monitoring system and method for deformation and fracture process of coal rock |
| CN105866835A (en) * | 2016-03-28 | 2016-08-17 | 中国石油大学(华东) | Fault 3D sealing quantitative evaluating method based on geostress distribution |
| CN106959748A (en) * | 2017-02-20 | 2017-07-18 | 西安科技大学 | A kind of roadway surrounding rock calamity emergency prediction scheme system and method based on virtual reality |
| CN107590357A (en) * | 2017-10-31 | 2018-01-16 | 石家庄铁道大学 | A kind of different construction stage Tunnel Stabilities sentence method for distinguishing |
| CN109632016A (en) * | 2019-02-20 | 2019-04-16 | 湖北理工学院 | Rock And Soil adit digging and surrouding rock stress, strain monitoring experimental rig and its method |
| CN109918696A (en) * | 2018-11-13 | 2019-06-21 | 山西潞安环保能源开发股份有限公司常村煤矿 | A kind of classification method and device of bump strength grade |
| CN110390800A (en) * | 2019-06-06 | 2019-10-29 | 北京市地质研究所 | Net formula disaster monitoring and early-warning system |
| CN210071003U (en) * | 2019-07-22 | 2020-02-14 | 北京市地质研究所 | Ground body surface strain stress acquisition equipment and net type disaster monitoring and early warning system |
| CN110928181A (en) * | 2019-12-13 | 2020-03-27 | 东北大学 | Intelligent control method for hard rock post-peak damage process under true triaxial surface disturbance |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SU1171676A1 (en) * | 1984-05-14 | 1985-08-07 | Новосибирский государственный университет им.Ленинского комсомола | Device for measuring rock pressure in well |
| CN105260575B (en) * | 2015-11-17 | 2018-12-04 | 中国矿业大学 | A kind of deformation of the surrounding rock in tunnel prediction technique neural network based |
| CN105759010B (en) * | 2016-02-04 | 2017-11-07 | 山东大学 | A kind of dynamic monitoring of mining influence tunnel and Stability Assessment method |
| CN107563092B (en) * | 2017-09-19 | 2020-08-04 | 山东蓝光软件有限公司 | Holographic early warning method for mine dynamic disasters |
| CN110159347B (en) * | 2019-05-05 | 2020-05-08 | 北京科技大学 | Dynamic disaster monitoring and early warning method for deep high-stress hard roof stope |
-
2020
- 2020-04-07 CN CN202010265209.6A patent/CN111472840B/en active Active
- 2020-04-23 WO PCT/CN2020/086431 patent/WO2021203491A1/en active Application Filing
- 2020-04-23 LU LU502697A patent/LU502697B1/en active IP Right Grant
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110015907A1 (en) * | 2009-07-20 | 2011-01-20 | Crawford Brian R | Petrophysical Method For Predicting Plastic Mechanical Properties In Rock Formations |
| CN102155231A (en) * | 2011-03-18 | 2011-08-17 | 大连海事大学 | Quick feedback analyzing system in tunnel constructing process |
| CN102505965A (en) * | 2011-11-11 | 2012-06-20 | 中国矿业大学(北京) | Method for identifying rock mass failure instability early warning |
| CN103883352A (en) * | 2014-04-08 | 2014-06-25 | 中煤科工集团重庆研究院有限公司 | Acoustic emission early warning method for dynamic disaster of underground coal instability |
| CN104198679A (en) * | 2014-09-17 | 2014-12-10 | 辽宁工程技术大学 | Full-waveform synchronous integrated monitoring system and method for deformation and fracture process of coal rock |
| CN105866835A (en) * | 2016-03-28 | 2016-08-17 | 中国石油大学(华东) | Fault 3D sealing quantitative evaluating method based on geostress distribution |
| CN106959748A (en) * | 2017-02-20 | 2017-07-18 | 西安科技大学 | A kind of roadway surrounding rock calamity emergency prediction scheme system and method based on virtual reality |
| CN107590357A (en) * | 2017-10-31 | 2018-01-16 | 石家庄铁道大学 | A kind of different construction stage Tunnel Stabilities sentence method for distinguishing |
| CN109918696A (en) * | 2018-11-13 | 2019-06-21 | 山西潞安环保能源开发股份有限公司常村煤矿 | A kind of classification method and device of bump strength grade |
| CN109632016A (en) * | 2019-02-20 | 2019-04-16 | 湖北理工学院 | Rock And Soil adit digging and surrouding rock stress, strain monitoring experimental rig and its method |
| CN110390800A (en) * | 2019-06-06 | 2019-10-29 | 北京市地质研究所 | Net formula disaster monitoring and early-warning system |
| CN210071003U (en) * | 2019-07-22 | 2020-02-14 | 北京市地质研究所 | Ground body surface strain stress acquisition equipment and net type disaster monitoring and early warning system |
| CN110928181A (en) * | 2019-12-13 | 2020-03-27 | 东北大学 | Intelligent control method for hard rock post-peak damage process under true triaxial surface disturbance |
Non-Patent Citations (3)
| Title |
|---|
| 古德生: "《现代金属矿床开采科学技术》", 31 December 2006, 冶金工业出版社 * |
| 李元松等: "《高等岩土力学》", 30 June 2013, 武汉大学出版社 * |
| 邓绪彪等: "基于声发射时空演化的岩石全应力–应变曲线阶段特征分析", 《岩石力学与工程学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112215438A (en) * | 2020-11-09 | 2021-01-12 | 广东新禾道信息科技有限公司 | Emergency disaster early warning analysis data processing method and system |
| CN112215438B (en) * | 2020-11-09 | 2021-05-14 | 广东新禾道信息科技有限公司 | Emergency disaster early warning analysis data processing method and system |
| CN113756869A (en) * | 2021-08-16 | 2021-12-07 | 中煤科工开采研究院有限公司 | Rock burst local monitoring and early warning system and method |
| CN114396312A (en) * | 2022-01-15 | 2022-04-26 | 安徽铜冠（庐江）矿业有限公司 | Multi-source monitoring and coupling early warning method suitable for large goaf ground pressure activity |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111472840B (en) | 2020-11-13 |
| LU502697B1 (en) | 2022-10-14 |
| WO2021203491A1 (en) | 2021-10-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111472840B (en) | Mining surrounding rock ground pressure disaster intelligent prediction method and system | |
| Brown et al. | Fatigue failure characteristics of some models of jointed rock | |
| Cuss et al. | Experimental observations of the mechanics of borehole failure in porous sandstone | |
| Saadati et al. | On the mechanical behavior of granite material with particular emphasis on the influence from pre-existing cracks and defects | |
| Searle et al. | Compressional metamorphic core complexes, low-angle normal faults and extensional fabrics in compressional tectonic settings | |
| Yang et al. | Numerical study on failure behavior of brittle rock specimen containing pre-existing combined flaws under different confining pressure | |
| Garza-Cruz et al. | Use of 3DEC to study spalling and deformation associated with tunnelling at depth | |
| Hu et al. | A nonlinear rheological damage model of hard rock | |
| CN115017833B (en) | High-ground-stress soft rock body ground stress calculation method based on deep neural network | |
| CN109241651B (en) | Universal stratum fracture pressure prediction method | |
| Yoshinaka et al. | Deformation behaviour of soft rocks during consolidated-undrained cyclic triaxial testing | |
| Tisot et al. | Structural response of rich Green River oil shales to heat and stress and its relation to induced permeability | |
| CN117556496A (en) | Shale mixed type crack extension calculation method based on variable phase-splitting field fracture theory | |
| CN112364489B (en) | Carbon dioxide blasting construction method for controlling damage and vibration effect of bedrock | |
| CN112380484B (en) | Method for measuring and calculating in-situ modulus of rock mass | |
| Souley et al. | Damage around a keyed URL excavation: change in permeability induced by microcracks growth | |
| CN112329206A (en) | Tunnel construction non-blasting area judgment method | |
| MIYAZAKI et al. | Triaxial compressive properties of artificial methane-hydrate-bearing sediments containing fine fraction | |
| CN112883542A (en) | Overburden permeability updating method considering mining stress path and surrounding rock damage degree | |
| Zhou et al. | Degradation mechanism of rock under impact loadings by integrated investigation on crack and damage development | |
| Brenne et al. | Hydraulic Fracturing of a Devonian Slate under confining pressure–with emphasis on cleavage inclination | |
| Abi Aad et al. | Physical modelling of underground excavations in a random fractured rock mass | |
| Lahaie et al. | Impact of epoxy glue curing time on the quality of overcoring stress measurements in low-temperature environments | |
| Wang et al. | Study on Strain Softening Deformation Characteristics and Damage Constitutive Model for Rocks | |
| CN117932951B (en) | Logging data-based depleted sandstone type gas storage injection and production rock stratum compressibility evaluation method |
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 |