CN110837701A - Mining area bottom plate fault water inrush quantitative evaluation method based on full-coupling analysis - Google Patents
Mining area bottom plate fault water inrush quantitative evaluation method based on full-coupling analysis Download PDFInfo
- Publication number
- CN110837701A CN110837701A CN201911100790.XA CN201911100790A CN110837701A CN 110837701 A CN110837701 A CN 110837701A CN 201911100790 A CN201911100790 A CN 201911100790A CN 110837701 A CN110837701 A CN 110837701A
- Authority
- CN
- China
- Prior art keywords
- main control
- water inrush
- fault
- mining area
- water
- 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.)
- Pending
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 77
- 238000005065 mining Methods 0.000 title claims abstract description 58
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000004458 analytical method Methods 0.000 title claims abstract description 30
- 238000010168 coupling process Methods 0.000 title claims abstract description 27
- 238000005859 coupling reaction Methods 0.000 title claims abstract description 27
- 238000011158 quantitative evaluation Methods 0.000 title claims description 14
- 230000003993 interaction Effects 0.000 claims abstract description 30
- 239000011159 matrix material Substances 0.000 claims abstract description 30
- 239000003245 coal Substances 0.000 claims abstract description 15
- 238000004364 calculation method Methods 0.000 claims abstract description 9
- 238000010586 diagram Methods 0.000 claims abstract description 6
- 230000008878 coupling Effects 0.000 claims description 13
- 239000011435 rock Substances 0.000 claims description 5
- 230000000903 blocking effect Effects 0.000 claims description 2
- 238000011156 evaluation Methods 0.000 abstract description 5
- 230000008569 process Effects 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000013210 evaluation model Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Agronomy & Crop Science (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Husbandry (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for quantitatively evaluating the water inrush risk of a fault of a bottom plate of a mining area, which comprises the following specific steps of: extracting corresponding secondary main control factors of water inrush during mining of the fault of the mine floor according to the primary main control factors of water inrush of the fault of the coal seam floor; constructing a corresponding interaction matrix according to the secondary main control factors, and performing full-coupling analysis on each extracted secondary main control factor by using the constructed interaction matrix; carrying out numerical coding on the interaction matrix according to the interaction strength of each main control factor in the full-coupling analysis, and obtaining the weight of each secondary main control factor according to a coding result; establishing a local floor fault water inrush coefficient calculation method in the mining area according to the weight of each secondary master control factor; spline interpolation is carried out on the fault water inrush coefficient of each local bottom plate, and a bottom plate fault water inrush risk contour distribution diagram of the whole mining area is drawn. The method has deep theoretical and practical values and high evaluation accuracy, and provides a new method for quantitative risk evaluation of water inrush in mining areas.
Description
Technical Field
The invention belongs to the technical field of mine safety, and particularly relates to a mining area bottom plate fault water inrush quantitative evaluation method based on full-coupling analysis.
Background
In the underground mining process, the bottom plate and the fault in the surrounding rock of the bottom plate are influenced by mining stress redistribution to generate stress state change, the fault which is not water-conducting per se is changed into a water-conducting fault or the fault with low water-conducting property is changed into a strong water-conducting fault due to the sudden change of the permeability of the bottom plate or the fault with the low water-conducting property caused by the stress state change, and if the water-conducting fault is connected with the water-containing layer of the bottom plate to generate hydraulic connection, a water-inrush channel can be formed to cause a sudden water inrush (inrush) accident of a working face or a roadway.
Water inrush (inrush) caused by faults is a serious coal mine geological disaster, and from the history of coal mining in China, a large number of underground water inrush accidents occur in mine mining in various regions every year, so that a large number of casualties and equipment inundation are caused, and huge loss is caused to the national economy. From the current mining situation, along with the annual increase of the mining depth of mines in China, the hydrogeological conditions of the mines are more complicated, the disturbance effect of mining on surrounding rocks is more and more large, and the probability of flood disasters in the mine mining in China is more and more large in future. Therefore, accurate quantitative assessment of the water inrush potential of the mine well prior to mining will become increasingly important.
At present, in China, a plurality of methods for evaluating mine water inrush are provided, the simplest and most direct water inrush index method is provided, the vulnerability index method based on an analytic hierarchy process is provided, and many other evaluation methods are established based on a neural network model, fuzzy mathematical analysis and geographic information system technology. In general, although there are many methods for evaluating mine water inrush, the methods are basically constructed by regarding a floor or a roof as an integral object, and no method for constructing the mine floor water inrush risk evaluation by taking a fault as an object exists at present. In addition, when the method is used for building the water inrush evaluation model, each main control factor is considered as an independent individual, and the interaction and the mutual influence among the main control factors are not considered, so that the method is unreasonable, and the built evaluation model cannot accurately evaluate the true level of the water inrush risk of the mining area.
Disclosure of Invention
Aiming at the problems existing in the current mine floor water inrush risk evaluation model, the invention aims to provide a brand-new mine floor fault water inrush evaluation method based on full coupling analysis, which has the characteristics of quantifying the floor fault water inrush risk and visualizing the floor fault water inrush risk.
In order to achieve the purpose, the invention provides the following technical scheme: a mining area bottom plate fault water inrush quantitative evaluation method based on full coupling analysis comprises the following steps:
(1) according to the primary main control factor of water inrush of a coal seam floor fault: extracting corresponding mine floor fault mining water inrush secondary main control factors according to the hydraulic characteristics of a floor aquifer, the water blocking capacity of the floor aquifer, the occurrence conditions of a coal bed, the geological fault distribution complexity and the mining disturbance intensity;
(2) constructing a corresponding interaction matrix according to the two-stage main control factors extracted in the step (1), and performing full-coupling analysis on each extracted two-stage main control factor by using the constructed interaction matrix;
(3) carrying out numerical coding on the interaction matrix according to the interaction strength of each main control factor in the full-coupling analysis, and obtaining the weight of each secondary main control factor according to a coding result;
(4) establishing a local floor fault water inrush coefficient calculation method in the mining area according to the weight of each secondary master control factor;
(5) and (4) carrying out spline interpolation on the fault water inrush coefficient of each local bottom plate by using a discrete point interpolation method, and drawing a bottom plate fault water inrush risk contour distribution diagram of the whole mining area.
Preferably, in the step (1), the secondary master factors include: water-rich water of the aquifer, water pressure of the aquifer, thickness of the water-resisting layer, lithology of the water-resisting layer, mining depth of the coal bed, complexity of fault distribution and water filling property of the aquifer.
Preferably, in the step (2), the specific configuration form of the interaction matrix is as follows: the main control factors of each secondary are respectively listed on the main diagonal line of the matrix, the non-main diagonal elements of the matrix represent the mutual influence among the main control factors of each secondary, the rows in the matrix represent the influence of the main control factors of the rows on other main control factors, and the columns in the matrix represent the influence of the other main control factors on the main control factors corresponding to the columns.
Preferably, in the step (2), the full coupling analysis mode is as follows: and respectively carrying out one-to-one mutual influence analysis on the secondary main control elements on any two main diagonals, and analyzing whether the two elements have interaction and correlation relationship.
Preferably, in the step (3), the coding mode of the matrix adopts a semi-expert quantitative coding method, and the coding rule is as follows:
A. if the two main control factors do not have mutual influence, the code is 0;
B. if the two main control factors have weak mutual influence, the code is 1;
C. if the two main control factors have medium mutual influence, the code is 2;
D. if the two main control factors have strong mutual influence, the code is 3;
E. if there is a critically crucial interaction between two master factors, the code is 4.
Preferably, in the step (3), the weight obtaining manner of the secondary master factors is as follows: firstly, the coded values of the rows and columns of each main control element in the matrix are added, and the larger the addition result is, the larger the influence degree of the main control factor on other main control factors or by other main control factors is, the more important the main control factor is, and the larger the weight is.
Preferably, in the step (4), the method for calculating the water inrush coefficient of the local mining area comprises the following steps: the method comprises the steps of firstly grading each secondary main control factor according to the overall hydrogeological conditions, rock lithology, mining depth, coal seam occurrence conditions and the like of each mining area in China, carrying out comparison rating on each secondary main control factor of the mining area to be actually evaluated on the basis, and multiplying the comparison rating result by the weight of each main control factor to obtain a corresponding water inrush coefficient.
The mining area floor fault water inrush quantitative evaluation method based on the full coupling analysis has the following advantages:
1) the model provided by the invention considers the water-rich property, the water filling property, the mining stress disturbance intensity and the water resistance of the coal seam floor, also considers the complexity of fault distribution, and reflects the complexity of the mine hydrogeological environment more truly and objectively.
2) The method abandons the method of independently considering each main control factor in the prior method in the process of evaluating and modeling, provides a method of reflecting the interaction relation among the main control factors by an interaction matrix coding mode, and obtains the weight of each main control factor by the interaction strength of each main control factor and other main control factors, thereby more objectively reflecting the action of each main control factor in the water inrush model.
3) The model constructed by the method can finally obtain the contour map of the water inrush risk distribution of the fault of the whole mining area, and can visually and vividly partition the water inrush risk of the mining area.
Drawings
FIG. 1 is a flow chart of a mining area floor fault water inrush quantitative evaluation method based on full-coupling analysis.
FIG. 2 shows the interaction matrix constructed in this example and the corresponding full coupling analysis.
Fig. 3 is a floor fault water inrush risk contour distribution diagram in the present embodiment.
Detailed Description
The invention will be further illustrated with reference to the following specific examples and the accompanying drawings:
taking a certain mining area as an example, the mining area floor fault water inrush quantitative evaluation method based on the full-coupling analysis of the invention is adopted, and as shown in fig. 1, the method comprises the following steps:
selecting seven corresponding secondary main control factors which are respectively numbered as P1-P7 according to hydrogeological conditions, coal seam occurrence conditions, floor lithology and fault distribution conditions of the evaluated mining area: aquifer water-rich (P1), aquifer water pressure (P2), aquifer thickness (P3), aquifer lithology (P4), coal seam mining depth (P5), fault distribution complexity (P6), aquifer water-filling property (P7);
step two, constructing an interaction matrix on the basis of seven secondary main control factors (P1-P7) extracted in the step one, and performing full coupling analysis on each extracted secondary main control factor by using the constructed interaction matrix, wherein the constructed interaction matrix and the corresponding full coupling analysis are shown in figure 2;
step three, performing numerical coding on the interaction matrix according to the interaction strength of each main control factor in the full coupling analysis, wherein a semi-expert numerical coding method is used in the embodiment, and the coding rule is as follows: if the two main control factors do not have mutual influence, the code is 0; if the two main control factors have weak mutual influence, the code is 1; if the two main control factors have medium mutual influence, the code is 2; if the two main control factors have strong mutual influence, the code is 3; if there is a critical interplay between the two master factors, the code is 4 (see table 1 for specific numerical codes);
TABLE 1 encoding of values of water inrush interaction matrix in floor fault semi-expert method
And obtaining the weight of each secondary main control factor according to the coding result, wherein the specific calculation formula is as follows:
w in formula (1)iRepresents the calculated weight of the ith master factor, CiShowing the influence of the ith master factor on the other master factors, EiRepresenting the influence of other main control factors on the ith main control factor;
in addition, in the process of specifically calculating the water inrush index, the weight needs to be normalized, and the normalization processing formula is as follows:
s in formula (2)iExpressing the normalized factor weight, the denominator in the formula represents the hierarchical level of the secondary main control factor, the specific hierarchical condition of the main control factor is shown in table 2, and the weight calculation results of seven main control factors in the embodiment are shown in table 3;
TABLE 2 two-stage master control factor grading table
TABLE 3 calculation of weights of secondary master control factors
Step four, carrying out comparison rating on each secondary main control factor according to specific hydrogeological conditions, rock lithology, mining depth, coal seam occurrence conditions and the like of the mining area (referring to a secondary main control factor grading table in the table 2 for comparison rating), multiplying the comparison rating result by the weight of each main control factor to obtain a corresponding water inrush coefficient, wherein a table 4 shows the comparison rating result and the water inrush coefficient calculation result of 12 hydrogeological drill holes selected for the mining area in the embodiment;
TABLE 4 control rating and Water inrush coefficient calculation for hydrogeological drilling in mining area
And fifthly, spline interpolation is carried out on the fault water inrush coefficient of each local bottom plate by using a discrete point interpolation method, a bottom plate fault water inrush risk contour distribution diagram of the whole mining area is drawn, and the fault water inrush risk contour distribution diagram in the embodiment is shown in figure 3.
The above-mentioned application examples are only illustrative and the present invention is described in detail by examples, which are only used for further illustration of the present invention and are not intended to limit the scope of the present invention, and those skilled in the art can make some insubstantial modifications and adaptations of the present invention.
Claims (7)
1. A mining area bottom plate fault water inrush quantitative evaluation method based on full coupling analysis is characterized by comprising the following steps:
(1) according to the primary main control factor of water inrush of a coal seam floor fault: extracting corresponding mine floor fault mining water inrush secondary main control factors according to the hydraulic characteristics of a floor aquifer, the water blocking capacity of the floor aquifer, the occurrence conditions of a coal bed, the geological fault distribution complexity and the mining disturbance intensity;
(2) constructing a corresponding interaction matrix according to the two-stage main control factors extracted in the step (1), and performing full-coupling analysis on each extracted two-stage main control factor by using the constructed interaction matrix;
(3) carrying out numerical coding on the interaction matrix according to the interaction strength of each main control factor in the full-coupling analysis, and obtaining the weight of each secondary main control factor according to a coding result;
(4) establishing a local floor fault water inrush coefficient calculation method in the mining area according to the weight of each secondary master control factor;
(5) and (4) carrying out spline interpolation on the fault water inrush coefficient of each local bottom plate by using a discrete point interpolation method, and drawing a bottom plate fault water inrush risk contour distribution diagram of the whole mining area.
2. The mining area floor fault water inrush quantitative evaluation method based on the full-coupling analysis of claim 1, wherein in the step (1), the secondary main control factors comprise: water-rich water of the aquifer, water pressure of the aquifer, thickness of the water-resisting layer, lithology of the water-resisting layer, mining depth of the coal bed, complexity of fault distribution and water filling property of the aquifer.
3. The mining area floor fault water inrush quantitative evaluation method based on the full coupling analysis of claim 1, characterized in that in the step (2), the specific configuration form of the interaction matrix is as follows: the main control factors of each secondary are respectively listed on the main diagonal line of the matrix, the non-main diagonal elements of the matrix represent the mutual influence among the main control factors of each secondary, the rows in the matrix represent the influence of the main control factors of the rows on other main control factors, and the columns in the matrix represent the influence of the other main control factors on the main control factors corresponding to the columns.
4. The mining area floor fault water inrush quantitative evaluation method based on the full coupling analysis according to claim 1 or 3, characterized in that in the step (2), the full coupling analysis is performed in a manner that: and respectively carrying out one-to-one mutual influence analysis on the secondary main control elements on any two main diagonals, and analyzing whether the two elements have interaction and correlation relationship.
5. The mining area floor fault water inrush quantitative evaluation method based on the full-coupling analysis of claim 1, wherein in the step (3), a matrix coding mode adopts a semi-expert quantitative coding method, and a coding rule is as follows:
A. if the two main control factors do not have mutual influence, the code is 0;
B. if the two main control factors have weak mutual influence, the code is 1;
C. if the two main control factors have medium mutual influence, the code is 2;
D. if the two main control factors have strong mutual influence, the code is 3;
E. if there is a critically crucial interaction between two master factors, the code is 4.
6. The mining area floor fault water inrush quantitative evaluation method based on the full coupling analysis according to claim 1 or 5, characterized in that in the step (3), the weight obtaining mode of the secondary main control factors is as follows: firstly, the coded values of the rows and columns of each main control element in the matrix are added, and the larger the addition result is, the larger the influence degree of the main control factor on other main control factors or by other main control factors is, the more important the main control factor is, and the larger the weight is.
7. The mining area floor fault water inrush quantitative evaluation method based on the full-coupling analysis of claim 1, wherein in the step (4), the calculation method of the local mining area water inrush coefficient comprises the following steps: the method comprises the steps of firstly grading each secondary main control factor according to the overall hydrogeological conditions, rock lithology, mining depth, coal seam occurrence conditions and the like of each mining area in China, carrying out comparison rating on each secondary main control factor of the mining area to be actually evaluated on the basis, and multiplying the comparison rating result by the weight of each main control factor to obtain a corresponding water inrush coefficient.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911100790.XA CN110837701A (en) | 2019-11-12 | 2019-11-12 | Mining area bottom plate fault water inrush quantitative evaluation method based on full-coupling analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911100790.XA CN110837701A (en) | 2019-11-12 | 2019-11-12 | Mining area bottom plate fault water inrush quantitative evaluation method based on full-coupling analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110837701A true CN110837701A (en) | 2020-02-25 |
Family
ID=69576471
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911100790.XA Pending CN110837701A (en) | 2019-11-12 | 2019-11-12 | Mining area bottom plate fault water inrush quantitative evaluation method based on full-coupling analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110837701A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111695303A (en) * | 2020-06-17 | 2020-09-22 | 中煤能源研究院有限责任公司 | Method for evaluating water filling strength of coal seam roof sandstone aquifer |
| CN112879093A (en) * | 2021-01-28 | 2021-06-01 | 中国矿业大学 | Fault water inrush risk quantitative evaluation method |
| CN113932877A (en) * | 2021-09-30 | 2022-01-14 | 深圳市中金岭南有色金属股份有限公司凡口铅锌矿 | Karst water level prediction method for mining area and terminal equipment |
| CN114757508A (en) * | 2022-03-29 | 2022-07-15 | 江西省地质局第七地质大队(江西省地质局稀土应用研究所) | Ion adsorption type rare earth ore in-situ leaching applicability evaluation method and model |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000057200A (en) * | 1998-08-12 | 2000-02-25 | Trinity Ind Corp | Hydraulic transfer print simulation software |
| CN104766242A (en) * | 2015-03-25 | 2015-07-08 | 山东科技大学 | Method for evaluating dangerousness of water inrush from coal floor |
-
2019
- 2019-11-12 CN CN201911100790.XA patent/CN110837701A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000057200A (en) * | 1998-08-12 | 2000-02-25 | Trinity Ind Corp | Hydraulic transfer print simulation software |
| CN104766242A (en) * | 2015-03-25 | 2015-07-08 | 山东科技大学 | Method for evaluating dangerousness of water inrush from coal floor |
Non-Patent Citations (2)
| Title |
|---|
| 常俊杰: "多含水层条件下煤层底板突水危险性评价方法研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》 * |
| 谢志华: "赣南钨矿地压评价方法及地压区回采研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111695303A (en) * | 2020-06-17 | 2020-09-22 | 中煤能源研究院有限责任公司 | Method for evaluating water filling strength of coal seam roof sandstone aquifer |
| CN111695303B (en) * | 2020-06-17 | 2023-08-18 | 中煤能源研究院有限责任公司 | Method for evaluating water filling strength of sandstone aquifer of coal seam roof |
| CN112879093A (en) * | 2021-01-28 | 2021-06-01 | 中国矿业大学 | Fault water inrush risk quantitative evaluation method |
| CN112879093B (en) * | 2021-01-28 | 2022-02-18 | 中国矿业大学 | Fault water inrush risk quantitative evaluation method |
| CN113932877A (en) * | 2021-09-30 | 2022-01-14 | 深圳市中金岭南有色金属股份有限公司凡口铅锌矿 | Karst water level prediction method for mining area and terminal equipment |
| CN113932877B (en) * | 2021-09-30 | 2023-12-22 | 深圳市中金岭南有色金属股份有限公司凡口铅锌矿 | Karst water level prediction method for mining area and terminal equipment |
| CN114757508A (en) * | 2022-03-29 | 2022-07-15 | 江西省地质局第七地质大队(江西省地质局稀土应用研究所) | Ion adsorption type rare earth ore in-situ leaching applicability evaluation method and model |
| CN114757508B (en) * | 2022-03-29 | 2024-06-07 | 江西省地质局第七地质大队(江西省地质局稀土应用研究所) | Ion adsorption type rare earth ore in-situ leaching applicability evaluation method and model |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110837701A (en) | Mining area bottom plate fault water inrush quantitative evaluation method based on full-coupling analysis | |
| Wang et al. | Risk assessment of water inrush in karst tunnels excavation based on normal cloud model | |
| CN108710984B (en) | Comprehensive evaluation method and system for mine geological environment | |
| CN109447493B (en) | Post-earthquake debris flow risk evaluation method based on source activity intensity | |
| CN104636612A (en) | Karst tunnel water outburst and mud outburst overall process gradual dynamic risk assessment method | |
| Wang et al. | Risk Assessment of Water Inrush in Karst Tunnels Based on the Efficacy Coefficient Method. | |
| CN113570226A (en) | Method for evaluating occurrence probability grade of tunnel water inrush disaster in fault fracture zone | |
| CN112610277B (en) | Method for rapidly predicting geological disaster risk of water-rich weak surrounding rock tunnel | |
| Liu et al. | Application on floor water inrush evaluation based on AHP variation coefficient method with GIS | |
| CN112100851A (en) | Method for evaluating tunnel water inrush disaster risk based on set pair analysis | |
| CN109345119A (en) | A kind of roof Dynamical Division evaluation method based on analytic hierarchy process (AHP) | |
| Zhang et al. | Risk assessment of floor water inrush in coal mines based on MFIM-TOPSIS variable weight model | |
| CN115689387A (en) | Comprehensive evaluation method and system for collapse disaster risk of karst tunnel | |
| Liang et al. | Optimization of mining method in subsea deep gold mines: A case study | |
| CN116050877A (en) | Metal mine aquifer water enrichment evaluation method and evaluation system | |
| Tosun et al. | Total risk analyses for large dams in Kizilirmak basin, Turkey | |
| Wang et al. | Exploration and improvement of fuzzy evaluation model for rockburst | |
| CN112241601B (en) | Shield tunnel seismic restorability analysis method | |
| CN114493295B (en) | Method for determining tunnel monitoring project under salt rock geology and monitoring system | |
| CN107463718A (en) | A kind of determination method of the linear structures estimation of stability index in goaf ground of underliing | |
| CN116050708A (en) | Regional geological disaster risk evaluation method | |
| CN112241835B (en) | Multi-source information evaluation method for flood disaster in deep shaft engineering | |
| CN104268785A (en) | Analysis method for deep well floor water inrush risk | |
| Yu et al. | Dynamic Risk Assessment of Karst Tunnel Collapse Based on Fuzzy‐AHP: A Case Study of the LianHuaShan Tunnel, China | |
| Rossini et al. | Combined geostatistical and dynamic simulations for developing a reservoir management strategy: A case history |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200225 |