CN114153005A - Rock burst risk level prediction method and system based on big data analysis - Google Patents
Rock burst risk level prediction method and system based on big data analysis Download PDFInfo
- Publication number
- CN114153005A CN114153005A CN202210126127.2A CN202210126127A CN114153005A CN 114153005 A CN114153005 A CN 114153005A CN 202210126127 A CN202210126127 A CN 202210126127A CN 114153005 A CN114153005 A CN 114153005A
- Authority
- CN
- China
- Prior art keywords
- tunneling
- data
- point
- primary
- prediction
- 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
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V11/00—Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH 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
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/16—Receiving elements for seismic signals; Arrangements or adaptations of receiving elements
- G01V1/20—Arrangements of receiving elements, e.g. geophone pattern
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/30—Analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V9/00—Prospecting or detecting by methods not provided for in groups G01V1/00 - G01V8/00
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/10—Aspects of acoustic signal generation or detection
- G01V2210/16—Survey configurations
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/64—Geostructures, e.g. in 3D data cubes
Abstract
The invention relates to the technical field of tunnel danger prediction, and discloses a rock burst danger level prediction method and system based on big data analysis, which comprises the steps of S1, collecting and obtaining rock data, drawing a primary, secondary or n-time tunneling shape on a tunnel face according to the edge shape of the tunnel face in a B proportion, S3, recording vibration data of a prediction detection point which is farthest from a primary tunneling area, and then recording vibration data values of a near point and a far point which are A, A1 respectively according to the distance of the prediction detection point when the primary tunneling area is excavated; and comparing the mining numerical values of the mining surfaces for N times, further comparing the data with the data generated by rock burst, judging the danger level of the tunnel face in the mining process according to the data similarity, achieving the purpose of predicting in advance at the later stage, and aiming at the prediction content, comparing the numerical values by adopting a plurality of methods when the mining surfaces for N times are mined, and further selecting the optimal mining method for mining.
Description
Technical Field
The invention relates to the technical field of tunnel danger prediction, in particular to a rockburst danger level prediction method and system based on big data analysis.
Background
The reason that the rock burst takes place is that the strain energy that faces empty rock volume and gathers suddenly nevertheless violently releases totally, causes the rock mass to take place the brittle fracture like the explosion, advances along with the continuous construction of tunnel face, makes the area of facing the empty face increase, because face on the empty face if set up some hole bodies, then the hole body must influence face the structural strength of empty face, can bring out the condition emergence of rock burst in advance, if do not open the hole, then face in the closure state, be not convenient for detect.
The existing rock burst prediction is mainly to open a plurality of hole bodies on a tunnel face, and place a micro-vibration probe inside the hole bodies to detect the vibration of rocks, so that the occurrence of rock bursts can be accurately predicted, but the mode can not evaluate the danger of rock bursts, because rock bursts can be generated soon after the vibration occurs, at the moment, the abnormal vibration is used for notifying that the time is too tight, and along with the advance of the tunnel face, in fact, if the traditional mining mode is adopted, because the effects generated by different area positions are different, for example, in the first progress, the stability of rocks is mined by the method, so that the strength between rocks is less due to cracks generated by the processing mode, when the rear section is tunneled, more cracks are generated, and the potential danger of rock bursts is likely to exist, but when micro-vibration occurs, the risk precursor of the outbreak is generated, therefore, if the danger of the rock burst can be predicted and further remedied aiming at the danger, a plurality of potential problems can be avoided, and the safety of tunnel excavation is further improved.
The rock burst is a result of a large amount of cracks generated and then subsequently expanded, and the danger of the rock burst cannot be estimated through the cracks of the rock because the rock in the wall around the tunnel face cannot be detected.
Disclosure of Invention
The invention aims to provide a rock burst risk level prediction method and system based on big data analysis, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a rock burst risk level prediction method based on big data analysis comprises the following steps:
step S1: acquiring rock data, and drawing a primary, secondary or n-time tunneling shape on the face according to the edge shape of the face in a B proportion;
step S2: arranging a plurality of prediction probe points along the edge of a primary tunneling area;
step S21: the predicted probe points are divided into far points and near points from the edge contour of the primary excavation area, and the number of the predicted probe points is G1;
step S3: recording vibration data of the farthest detection point of the prediction detection point from the primary tunneling area;
step S31: secondly, when a primary tunneling area is excavated, A, A1 vibration data values of a near point and a far point are recorded according to the distance of the predicted detection point;
step S4: after the excavation of the primary tunneling area is finished, pressurized liquid is injected into the hole of the predicted detection point, the outflow of the liquid exposed from the gap in the hole is detected, and the number of the detected gaps at the far point and the near point is X, X1;
step S5: according to the secondary tunneling shape, a plurality of the prediction detection points are arranged;
step S51: dividing the predicted probe point and the edge outline of the primary excavation region into a far point and a near point, wherein the number of G2 is G1B;
step S6: predicting the number of gaps inside the side wall surface, the vault surface or other top surfaces after N times of tunneling according to the vibration value and the combined number of the gap conditions of the secondary tunneling, and counting the data according to the rock type, the excavation area, the number of the gaps and the vibration condition;
step S61: and (4) as the tunnel goes deep, the tunnel face is continuously followed, the changes are recorded and compared with the database at the moment, and the danger of the rock burst is predicted according to the data changes.
As a still further scheme of the invention: in the step S2, the prediction detection points comprise unit far point vibration detection holes far away from the n times of heading faces, deflection gap detection holes located below the unit far point vibration detection holes and close to one side of the n times of heading faces, and vertical forward gap detection holes located below the unit far point vibration detection holes.
As a still further scheme of the invention: and in the steps S5-S6, the area difference between the primary tunneling surface and the secondary tunneling surface is calculated according to the proportion B, meanwhile, the area difference between the secondary tunneling surface and the tertiary tunneling surface is calculated according to the proportion B, and the area difference between the secondary tunneling surface and the tertiary tunneling surface is larger than the area difference between the primary tunneling surface and the secondary tunneling surface.
As a still further scheme of the invention: the farthest distance between the prediction probe point of the n-time heading face and the digging edge is larger than the farthest distance between the n-1-time heading face edge and the prediction probe point.
As a still further scheme of the invention: the liquid in step S4 does not include water, and a pressureless liquid is first injected before the pressurized liquid is injected.
As a still further scheme of the invention: a rock burst risk grade prediction system based on big data analysis comprises an acquisition unit used for detecting rock changes during excavation according to rock conditions, a drawing unit used for drawing the edge shape of a heading face for n times on a tunnel face, and a data processing unit used for processing data of information acquired by the acquisition unit in the heading process of the heading face for n times drawn by the drawing unit.
As a still further scheme of the invention: the acquisition unit comprises a micro-vibration collector and a gap collector, wherein the micro-vibration collector is used for collecting vibration in the hole in the cutting process, and the gap collector is used for detecting gaps.
As still further aspect of the present invention, the data processing unit includes a data collecting unit for storing data inside the database, and a data comparing unit for comparing the data with the database.
Compared with the prior art, the invention has the beneficial effects that:
the invention excavates the face according to equal proportion, sets up vibrations and gap detection in the course of excavating, and then can adopt the excavation mode to collect the influence that the rock produces according to face rock situation in the course of excavating, excavate vibrations data and gap data collection that are gathered through the acquisition unit and store, because the face data that record have already been excavated data, predict the probe point while excavating and using, can also be used for according to the microseismic detection system, and then predict the tunnel rock burst, after a section of rock burst incident in the tunnel at this moment, according to the information data situation of face and rock type at that time of rock burst area acquisition, and then obtain the rock burst occurrence number value, this way is used and data acquisition many times, to with the mining vibrations in the rock burst, the method is with, And counting the crack data, comparing the mining numerical values of the mining surfaces for N times when subsequent working faces are mined, comparing the data with the data generated by rock burst, judging the risk level of the working faces in the mining process according to the data similarity, and achieving the purpose of predicting in advance at the later stage.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic diagram of a primary tunneling surface of a rock burst risk level prediction method and system based on big data analysis;
FIG. 2 is a schematic diagram of a secondary tunneling surface of a rock burst risk level prediction method and system based on big data analysis;
FIG. 3 is a schematic diagram illustrating a main view crack of a rock burst risk level prediction method and system based on big data analysis;
FIG. 4 is a schematic flow chart of a rock burst risk level prediction method based on big data analysis;
FIG. 5 is a schematic diagram of a rock burst risk level prediction system based on big data analysis;
in the figure: 1. predicting a probe point; 11. a unit remote point vibration detection hole; 12. deflecting towards the gap detection hole; 13. a vertical forward slit detection hole; 2. a drawing unit; 3. a data processing unit; 4. and a collecting unit.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, 3, 4 and 5, in the embodiment of primary tunneling, a tunnel is firstly excavated to an inner side for a distance, after a tunnel face is generated, a tunnel face is firstly drawn for N times by a drawing unit 2, the tunnel face is drawn by a ratio B, the smallest tunnel face is a primary tunnel face, when the tunnel face is extended to the outer side, an arc-top tunnel is installed as an example, two edges of the primary tunnel face are extended to the outer side for 2 meters, the diameter of an arc-top circle is increased by 4 meters to draw a secondary tunnel face, the extended length of a tertiary tunnel face and the secondary tunnel face is 4 meters, the diameter of an arc-shaped circle is increased by 8 meters to increase the number of the ratio B along with the number N of the tunnel faces, the purpose is to detect the subsequent crack extension condition for infinite extension, at the moment, a predicted vibration detection point 1 is opened on the outer side according to the shape of the primary tunnel face, and the predicted vibration detection point 1 comprises a unit far point detection hole 11, A deviation gap detection hole 12 and a vertical forward gap detection hole 13, wherein the distance between a prediction detection point 1 and the edge of a primary driving face is adjusted, a plurality of prediction detection points 1 are from near to far from the edge of the primary driving face until reaching the boundary of a secondary driving face, the arrangement depth of a unit far point vibration detection hole 11, a deviation gap detection hole 12 and a vertical forward gap detection hole 13 is consistent with the driving depth of the primary driving face, a micro-vibration sensor in an acquisition unit 4 is inserted into the unit far point vibration detection hole 11 after the arrangement is completed, the primary driving face is excavated, when the primary driving face is excavated, the micro-vibration sensor in the unit far point vibration detection hole 11 senses that the primary driving face is excavated, the vibration transmission data A, A1 of boundary rocks are transmitted, after the primary driving face is completely excavated, the micro-vibration detector is taken out, a gap collector is respectively inserted into the deviation gap detection hole 12 and the vertical forward gap detection hole 13, injecting liquid into the unit far point vibration detection hole 11, inserting a plug into the orifice of the unit far point vibration detection hole 11, injecting water into the unit far point vibration detection hole 11, filling the inside of the unit far point vibration detection hole with the water, inserting a deflection gap detection hole 12 and a vertical forward gap detection hole 13 into a gap collector, wherein the gap collector can adopt a rod body, attaching liquid detection test paper on the surface of the rod body, attaching the liquid detection test paper to the inner walls of the deflection gap detection hole 12 and the vertical forward gap detection hole 13, taking out the rod body after inserting the rod body for a period of time, allowing the liquid to permeate into the deflection gap detection hole 12 and the vertical forward gap detection hole 13 through the gap and then contacting the test paper, leaving mark points on the test paper, judging the number of the gaps and the length according to the mark points to obtain the number of X gaps without pressure, and pressurizing the inside of the unit far point vibration detection hole 11 at the moment if no liquid permeates, and then the liquid is affected by the pressure and permeates to the gap to be collected by the gap collector, the number of X gaps with pressure is obtained, the width of the gap can be judged at the moment, and the near point data and the far point data of the rock are inconsistent after the rock is vibrated, so that the number of X1 gaps without pressure and the number of X1 gaps with pressure are obtained by detecting at the far point.
Referring to fig. 2, 3, 4 and 5, in the embodiment of secondary excavation, since the area of the secondary excavation surface covers the primary excavation surface, the earlier-stage arrangement of the remote-point vibration detection hole 11, the deviation gap detection hole 12 and the vertical forward gap detection hole 13 on the primary excavation surface provides convenience for the arrangement of the secondary excavation surface, so that the arrangement of the secondary excavation surface is faster and more convenient, thereby avoiding the time waste caused by the boring detection, then the predicted probe points 1 are arranged along the secondary excavation surface, at this time, the number of the predicted probe points 1 needs to be increased due to the increase of the area of the secondary excavation surface, so that the number of the predicted probe points 1 of the primary excavation surface is G1, the number of the predicted probe points 1 of the secondary excavation surface is G1 a, at this time, the excavation unit 2 is detected according to the above-described manner, and vibration data a2, a, B, c, a3, the number of the gaps is X2 and X3, and the farthest distance between the predicted probe point 1 of the excavation face at n times and the excavation edge is larger than the farthest distance between the edge of the excavation face at n-1 times and the predicted probe point 1, so that when the excavation face is excavated at the second time, the vibration change at a farther distance from the cutting surface is obtained, and the gap number condition at the farthest distance is obtained at the same time.
Referring to fig. 3 and 4, in the embodiment of predicting the temporary empty face after N times of excavation, vibration data A, A1, the number of near points and far points of the gap X, X1 are obtained according to one excavation, vibration data a2 and A3 are obtained according to the second excavation, and the number of near points and far points of the gap X2 and X3 are obtained according to the second excavation, because the influence of the crack propagation is larger when the excavation is actually performed as the cutting area is increased, at this time, the number of near points of the gap X on the first excavation face and the number of near points of the gap X2 on the second excavation face are obtained in the processes of one excavation and the second excavation, and the number of near points of the gap a can be obtained through multiple measurements, which is represented as follows:
n is the drawing number of the tunneling surface, the number of n is larger than 3, although the areas of the n tunneling surfaces are not consistent with the areas of the n-1 tunneling surfaces, the number of the prediction detection points 1 on the n tunneling surfaces is larger than the number of the prediction detection points 1 on the n-1 tunneling surfaces, therefore, the number of the near point prediction detection points 1 on the n tunneling surfaces can be the same as the number of the near points on the n-1 tunneling surfaces, the data of the rock stratum are subjected to multiple verification experiments along with the tunneling, a more accurate value a can be obtained, and further the rock type can be calculated.
Because the prediction detection point 1 arranged on the tunneling surface is divided into a near point and a far point, and the distance between the subsequent middle and far point prediction detection point 1 in the tunneling surface and the tunneling surface is greater than the distance between the previous middle and far point prediction detection point 1 in the tunneling surface and the edge of the tunneling surface, the influence depth of splitting can be judged.
In the embodiment of fig. 1, 2, 3, 4, 5 of the method for predicting the rock burst risk, when the tunnel is excavated, the tunnel face moves forward, and due to the particularity of the landform, the situation of the surrounding rock changes continuously, n times of excavation is adopted during mining, and the number of gaps and the situation of vibration are measured continuously during excavation, so that by detection, the rock change after the tunnel face penetrates inwards can be predicted, the number of the gaps a obtained on each tunnel face changes, while under the normal rock state, the number of the gaps a obtained on each tunnel face does not differ greatly, when the tunnel face excavates, the value of a changes greatly, thereby explaining that the rock situation of the tunnel face changes, and the change mainly includes the change of the vibration value and the change of the number of the gaps, at the moment, the area is just mined, the tunnel face is just tunneled once, the free area of the area is small, and the tunneling mode can be changed according to the situation, so that the risk of the terrain of the area is estimated and judged, the problem is prevented from being serious, and the construction safety is prevented from being influenced.
In the embodiment of fig. 1, 2, 3, 4, and 5, in which the method combines the rock burst risk with big data prediction, since the tunnel face is continuously advanced, the tunnel face is deep at this time, the excavation vibration data and the gap acquisition data acquired by the acquisition unit 4 are stored, since the recorded tunnel face data are already data after excavation is completed, and the prediction probe 1 is used for mining, it can also be used for predicting tunnel rock bursts according to the microseismic detection system, at this time, after a certain section of the tunnel has a rock burst event, the information data condition of the tunnel face at this time and the rock type are acquired according to the rock burst area, and then the rock burst occurrence value is acquired, and this way, through multiple uses and data acquisition, statistics is performed on the mining vibration and crack data in the rock bursts, and further during subsequent tunnel face excavation, the mining numerical values of the mining surfaces of N times are compared, and then the data processing unit 3 is used for comparing the data with the data of rock burst occurrence, so that the danger of the tunnel face in the mining process is judged, the purpose of forecasting in advance is achieved at the later stage, aiming at the forecasting content, the numerical values can be compared by adopting a plurality of methods when the mining surfaces of N times are mined, and then the optimal mining method is selected for mining.
The working process of the invention is as follows: firstly, drawing N times of heading faces on a tunnel face through a drawing unit 2, wherein the drawing unit 2 can draw by adopting laser, mainly adopting a laser beam formed by matching to irradiate on the tunnel face according to the shape for operation, the heading faces are drawn by adopting a B proportion, the smallest heading face is a primary heading face, when the smallest heading face is extended outwards, an arc-top tunnel is installed as an example, two edges of the primary heading face are extended outwards for 2 meters, the diameter of an arc-top circle is increased for 4 meters to draw a secondary heading face, the extended length of a tertiary heading face and the secondary heading face is 4 meters, the diameter of the arc-top circle is increased for 8 meters to draw, the quantity of the B proportion is increased along with the number N of the heading faces, wherein the distance between a predicted probe point 1 and the edge of the primary heading face is adjusted, a plurality of predicted probe points 1 are close to the edge of the primary heading face from far to the edge of the secondary heading face, and a unit far point detection hole 11, The depth of the deviated gap detecting hole 12 and the vertical forward gap detecting hole 13 is the same as the tunneling depth of the primary tunneling surface, after the completion of the digging, the micro-vibration sensor inside the collecting unit 4 is inserted into the unit remote point vibration detecting hole 11, the primary tunneling surface is excavated, when the excavation is performed, the micro-vibration sensor in the unit remote point vibration detecting hole 11 senses the primary tunneling surface, the vibration transmission data A, A1 of the boundary rock is generated, after the primary tunneling surface is completely excavated, the micro-vibration detector is taken out, the gap collectors are respectively inserted into the deviated gap detecting hole 12 and the vertical forward gap detecting hole 13, the liquid is injected into the unit remote point vibration detecting hole 11, the opening of the unit remote point vibration detecting hole 11 is plugged, the unit remote point vibration detecting hole 11 is filled with water, the deviated gap detecting hole 12 and the vertical forward gap detecting hole 13 are inserted into the gap collectors, the gap collector can adopt a rod body, liquid detection test paper is attached to the surface of the rod body, the liquid detection test paper is attached to the inner walls of the deviation gap detection hole 12 and the vertical forward gap detection hole 13, the rod body is inserted and waits for a period of time to be taken out, at the moment, liquid penetrates into the deviation gap detection hole 12 and the vertical forward gap detection hole 13 through the gap and then contacts with the test paper, mark points are left on the test paper, the number and the length of the gap are judged according to the mark points to obtain the number of the X gaps without pressure, if no liquid penetrates, the inside of the unit far point vibration detection hole 11 is pressurized, then the liquid penetrates to the gap under the influence of the pressure and is collected by the gap collector to obtain the number of the X gaps with pressure, the excavation vibration data and the gap collection data collected by the collection unit 4 are stored, and the recorded palm surface data are already the data after excavation, meanwhile, the prediction probe point 1 can be used for predicting tunnel rock burst according to a micro-seismic detection system when mining, and at the moment, when a rock burst event occurs in a certain section of the tunnel, acquiring the information data condition of the face and the rock type according to the rock burst area to further acquire the rock burst occurrence value, the method is used for a plurality of times and data acquisition, and is used for counting the mining vibration and crack data in the rock burst, and then comparing the mining numerical values of the mining surfaces for N times during the subsequent face mining, and further comparing the data with the data of rock burst occurrence so as to judge the danger of the face in the mining process, the purpose of prediction in advance is achieved in the later period, and aiming at the prediction content, numerical values can be compared by adopting various methods when the mining surfaces are mined for N times, and then the optimal mining method is selected for mining.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention are equivalent to or changed within the technical scope of the present invention.
Claims (8)
1. A rock burst risk level prediction method based on big data analysis is characterized by comprising the following steps: the method comprises the following steps:
step S1: acquiring rock data, and drawing a primary, secondary or n-time tunneling shape on the face according to the edge shape of the face in a B proportion;
step S2: a plurality of prediction probe points (1) are arranged along the edge of a primary tunneling area;
step S21: the predicted probe points (1) and the edge contour of the primary excavation area are divided into far points and near points, and the number of the predicted probe points (1) is G1;
step S3: recording vibration data of the farthest detection point of the prediction detection point (1) away from the primary tunneling area;
step S31: secondly, when a primary tunneling area is excavated, A, A1 vibration data values of a near point and a far point are recorded according to the distance of the prediction detection point (1);
step S4: after the excavation of the primary tunneling area is finished, pressurized liquid is injected into the hole of the prediction detection point (1), the outflow of the liquid exposed from the gap in the hole is detected, and the number of the detected gaps at the far point and the near point is X, X1;
step S5: according to the secondary tunneling shape, a plurality of the prediction detection points (1) are arranged;
step S51: predicting that the detection point (1) and the edge profile of the primary excavation region are divided into a far point and a near point, and the number of G2 is G1B;
step S6: predicting the number of gaps inside the side wall surface, the vault surface or other top surfaces after N times of tunneling according to the vibration value and the combined number of the gap conditions of the secondary tunneling, and counting the data according to the rock type, the excavation area, the number of the gaps and the vibration condition;
step S61: and (4) as the tunnel goes deep, the tunnel face is continuously followed, the changes are recorded and compared with the database at the moment, and the danger of the rock burst is predicted according to the data changes.
2. The rock burst risk level prediction method based on big data analysis according to claim 1, characterized in that: in the step S2, the prediction detection point (1) comprises a unit far point vibration detection hole (11) far away from the tunneling surface for n times, a deviation gap detection hole (12) located below the unit far point vibration detection hole (11) and close to one side of the tunneling surface for n times, and a vertical forward gap detection hole (13) located below the unit far point vibration detection hole (11).
3. The rock burst risk level prediction method based on big data analysis according to claim 1, characterized in that: and in the steps S5-S6, the area difference between the primary tunneling surface and the secondary tunneling surface is calculated according to the proportion B, meanwhile, the area difference between the secondary tunneling surface and the tertiary tunneling surface is calculated according to the proportion B, and the area difference between the secondary tunneling surface and the tertiary tunneling surface is larger than the area difference between the primary tunneling surface and the secondary tunneling surface.
4. The rock burst risk level prediction method based on big data analysis according to claim 1, characterized in that: the farthest distance between the prediction probe point (1) positioned on the n times of heading faces and the digging edge is larger than the farthest distance between the edge of the n-1 times of heading faces and the prediction probe point (1).
5. The rock burst risk level prediction method based on big data analysis according to claim 1, characterized in that: the liquid in step S4 does not include water, and a pressureless liquid is first injected before the pressurized liquid is injected.
6. A rock burst danger level prediction system based on big data analysis is characterized in that: the device comprises an acquisition unit (4) for detecting rock changes during excavation according to rock conditions, a drawing unit (2) for drawing the edge shape of the excavation face for n times on a tunnel face, and a data processing unit (3) for processing data of information acquired by the acquisition unit (4) in the process of n times of excavation face drawing by the drawing unit (2).
7. The rock burst risk level prediction system based on big data analysis as claimed in claim 6, wherein: the acquisition unit (4) comprises a micro-vibration acquisition device for acquiring vibration in the hole in the cutting process and a gap acquisition device for detecting the gap.
8. The rock burst risk level prediction system based on big data analysis as claimed in claim 6, wherein: the data processing unit (3) comprises a data collection unit for storing data inside the database and a data comparison unit for comparing the data with the database.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210126127.2A CN114153005B (en) | 2022-02-10 | 2022-02-10 | Rock burst risk level evaluation method based on big data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210126127.2A CN114153005B (en) | 2022-02-10 | 2022-02-10 | Rock burst risk level evaluation method based on big data analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114153005A true CN114153005A (en) | 2022-03-08 |
| CN114153005B CN114153005B (en) | 2022-04-19 |
Family
ID=80450373
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210126127.2A Active CN114153005B (en) | 2022-02-10 | 2022-02-10 | Rock burst risk level evaluation method based on big data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114153005B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114964469A (en) * | 2022-04-21 | 2022-08-30 | 东北大学 | Asymmetric high-stress tunnel blasting vibration testing method and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103670516A (en) * | 2013-12-27 | 2014-03-26 | 中国矿业大学(北京) | Identification method for rockburst disaster micro-seismic monitoring and early-warning key points |
| CN103678901A (en) * | 2013-12-05 | 2014-03-26 | 上海理工大学 | Rock burst intensity grade evaluating method |
| WO2014176952A1 (en) * | 2013-04-28 | 2014-11-06 | 中国矿业大学 | Method for partitioning, grading and forecasting rock bursts in underground coal mine |
| CN107748103A (en) * | 2017-09-01 | 2018-03-02 | 中国科学院武汉岩土力学研究所 | A kind of tunnel Rockburst Prediction Method, equipment, storage medium and system |
| CN110318808A (en) * | 2019-06-25 | 2019-10-11 | 武汉工程大学 | A kind of Rockburst Prediction Method introducing gradient stress |
| CN111577390A (en) * | 2020-04-29 | 2020-08-25 | 中铁十二局集团有限公司 | Tunnel rock burst micro-seismic monitoring, judging and early warning construction method |
| CN113094641A (en) * | 2021-06-04 | 2021-07-09 | 北京建筑大学 | K-nearest neighbor rock burst prediction method and device based on big data visualization analysis |
-
2022
- 2022-02-10 CN CN202210126127.2A patent/CN114153005B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014176952A1 (en) * | 2013-04-28 | 2014-11-06 | 中国矿业大学 | Method for partitioning, grading and forecasting rock bursts in underground coal mine |
| CN103678901A (en) * | 2013-12-05 | 2014-03-26 | 上海理工大学 | Rock burst intensity grade evaluating method |
| CN103670516A (en) * | 2013-12-27 | 2014-03-26 | 中国矿业大学(北京) | Identification method for rockburst disaster micro-seismic monitoring and early-warning key points |
| CN107748103A (en) * | 2017-09-01 | 2018-03-02 | 中国科学院武汉岩土力学研究所 | A kind of tunnel Rockburst Prediction Method, equipment, storage medium and system |
| CN110318808A (en) * | 2019-06-25 | 2019-10-11 | 武汉工程大学 | A kind of Rockburst Prediction Method introducing gradient stress |
| CN111577390A (en) * | 2020-04-29 | 2020-08-25 | 中铁十二局集团有限公司 | Tunnel rock burst micro-seismic monitoring, judging and early warning construction method |
| CN113094641A (en) * | 2021-06-04 | 2021-07-09 | 北京建筑大学 | K-nearest neighbor rock burst prediction method and device based on big data visualization analysis |
Non-Patent Citations (1)
| Title |
|---|
| 吴世勇 等: "锦屏二级水电站长引水隧洞高地应力下开敞式硬岩隧道掘进机安全快速掘进技术研究", 《岩石力学与工程学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114964469A (en) * | 2022-04-21 | 2022-08-30 | 东北大学 | Asymmetric high-stress tunnel blasting vibration testing method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114153005B (en) | 2022-04-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104390537B (en) | A kind of side slope pre split Blasting Excavation damage control method based on blasting vibration test | |
| CN101251605B (en) | Method for forecasting advanced geology for tunnel construction | |
| CN110889532B (en) | Intelligent selection and optimization method and system for tunnel excavation and support parameters | |
| CN101270666B (en) | Tunnel influence geological condition section-expanding horizontal drilling estimation and prediction method | |
| CN111691872B (en) | Dynamic and accurate detection method for development range and degree of coal seam overlying strata fracture | |
| CN107085235A (en) | A kind of earthquake forward probe system and method applied to development machine | |
| CN114153005B (en) | Rock burst risk level evaluation method based on big data analysis | |
| CN103308946A (en) | Tunnel advance geology forecast method based on blast hole drilling information | |
| CN110056342B (en) | Gas injection device and method for judging feasibility of open-seam mining by drilling gas injection | |
| CN105719433A (en) | In-hole seismic wave based advanced prediction method | |
| CN104880729A (en) | Coal roadway anomalous structure advanced detection method based on continuous channel wave signal tracking | |
| CN106529762A (en) | New mine coal and gas outburst risk assessment method | |
| CN106149770A (en) | Bored concrete pile pile foundation construction period synchronizes the large-section in-situ concrete pile hole wall Rock Mass Integrality detection method carried out | |
| CN109239779B (en) | Testing method for loose circle of tunnel surrounding rock and surrounding rock damage grading method | |
| CN109555502B (en) | Industrial test method for presplitting permeability increase of high-gas coal roadway | |
| CN106032750B (en) | Geological logging instrument based on drilling energy spectrum | |
| CN108868777B (en) | Comprehensive detection and treatment construction method for tunnel unfavorable geological surrounding rock | |
| CN112948924B (en) | Method for determining height of near-unconsolidated formation mining water-guiding fracture zone based on overburden structure | |
| CN112819195B (en) | Tunnel advanced drilling geology refined forecasting method | |
| CN111828033A (en) | Advanced horizontal drilling method applied to tunnel construction | |
| CN106761804B (en) | One kind being equipped on TBM advanced hydraulic pressure detection device and method in real time | |
| CN110794039B (en) | Method for calculating crack filling rate of curtain grouting rock mass by using rock mass wave velocity | |
| CN105756709B (en) | A kind of face roof is pressed and disrumpent feelings monitoring method | |
| CN112505746A (en) | Detection system based on tunnel drilling and blasting construction and corresponding method thereof | |
| CN105257307B (en) | It is a kind of to reduce the construction method that shield machine cutter is damaged in upper-soft lower-hard ground |
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 |