CN114580940A - Grouting effect fuzzy comprehensive evaluation method based on grey correlation degree analysis method - Google Patents
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Abstract
The invention discloses a fuzzy comprehensive evaluation method for grouting effect based on grey correlation analysis, which analyzes main influence factors of grouting effect through specific examples, decomposes elements related to grouting effect evaluation into levels such as target, standard, index layer and the like by using a hierarchical analysis method, constructs a judgment matrix, and obtains the priority weight of each element of each level to a certain element of the previous level. And determining an evaluation set of the evaluation object, calculating the actual values of the influence factors and the grey correlation coefficients corresponding to the evaluation grades, and constructing a membership matrix. And calculating an evaluation vector by combining a fuzzy comprehensive evaluation principle, and finally obtaining an evaluation result of the grouting effect according to a maximum membership principle. The method reduces the influence of human subjective factors, solves the problem of small data volume, and enables the evaluation result to be more objective and reasonable.
Description
Technical Field
The invention relates to the field of grouting effect evaluation, in particular to a comprehensive evaluation method for grouting effect.
Background
As shallow coal resources in China are gradually depleted, the trend of turning to deep coal seam mining becomes a necessary trend, and the deep coal seam mining process is threatened by the Ordovician ash at the lower part of the deep coal seam mining process, so that the treatment of a bottom plate grouting area is an important prevention and control measure. The impermeabilization effect after grouting is difficult to guarantee due to the concealment of grouting engineering, and therefore the grouting effect needs to be accurately evaluated to guarantee normal use and safe operation of a mine.
At present, methods for evaluating the treatment effect of a bottom plate grouting area are various, and methods such as tests, instrument detection, numerical simulation and the like are commonly adopted, but the methods have certain limitations. The test cannot completely simulate the actual situation, and the result is not accurate. The detection of the instrument is easily affected by the error of the instrument and external factors, so that the result is deviated. The numerical simulation greatly reduces the applicability in the case of insufficient samples.
Disclosure of Invention
In view of the defects of the evaluation method, the invention provides a fuzzy comprehensive evaluation method for grouting effect based on a grey correlation degree analysis method, which can solve the technical problems.
In order to achieve the purpose, the invention adopts the technical scheme that:
the invention provides a grouting effect comprehensive evaluation method, which comprises the following steps:
s1, determining the influence factors of the grouting effect and establishing a hierarchical structure model.
S2 establishes an evaluation level of the evaluation object.
S3, establishing a judgment matrix by using an analytic hierarchy process and calculating the weight.
S4, calculating the grey correlation coefficient of the actual value of the influence factor and the corresponding evaluation grade, and constructing a membership matrix.
And S5, carrying out fuzzy comprehensive judgment on the grouting effect to obtain a final judgment result.
1. The fuzzy comprehensive evaluation method for grouting effect based on grey correlation analysis method according to claim 1, wherein the step S1 of determining the influence factors of grouting effect includes:
the top layer is a target layer (A) grouting effect evaluation, the second layer is a standard layer (B), the influence factors are divided into 3 types, and the drilling quality, the grouting finishing standard and the grouting effect are checked. The third layer is an index layer and comprises 11 grouting effect influence factors such as a bedding ratio (C1), plane trajectory deviation (C2), well cementation quality (C3), branch hole spacing (C4), grouting ending pump capacity (C5), grouting ending hole pressure (C6), grouting ending water absorption (C7), grouting ending stable time (C8), slurry diffusion radius (C9), pressurized water experiment permeability (C10), abnormal area range comparison (C11) and the like.
2. The fuzzy comprehensive evaluation method for grouting effect based on grey correlation analysis method according to claim 1, wherein the step S2 of establishing the evaluation level of the evaluation object comprises:
the panel of comments V ═ { V1, V2, V3, V4}, and the four grades included are V1 ═ good, V2 ═ good, V3 ═ good, and V4 ═ not good, respectively.
3. The fuzzy comprehensive evaluation method for grouting effect based on grey correlation analysis method according to claim 1, wherein the step S3 is to establish a judgment matrix and calculate the weight by using an analytic hierarchy process, specifically:
determining the priority weight grade of each index through pairwise comparison of each index, establishing a judgment matrix according to the 9-grade scale and the expert scoring opinions, calculating and normalizing the judgment matrix by using a square root method to obtain a weight vector of the judgment matrix, and carrying out consistency check on the judgment matrix. If the consistency check shows that the constructed discrimination matrix has better consistency, the discrimination matrix needs to be readjusted if the constructed discrimination matrix does not pass the consistency check.
4. The grouting effect fuzzy comprehensive evaluation method based on the grey correlation degree analysis method according to claim 1, wherein the step S4 of calculating the grey correlation coefficient between the actual value of the influence factor and the corresponding evaluation grade, and the specific steps of constructing the membership degree matrix are as follows:
and acquiring the indexes and actual values of each influence factor at each evaluation level.
The actual values of the influence factors are recorded as reference sequence x0；
x0＝{x0(j)∣j＝1,2,3......n}
Wherein j is the serial number of the influence factors of the grouting effect.
Labeling the evaluation level index as a comparison sequence xj
Xj＝{xj(k)∣k＝1,2,3......n，i＝1,2,3......m}
In the formula, k is the serial number of the evaluation grade, and j is the serial number of the influence factor;
Δj(k)＝∣x0(j)-xj(k)∣
x is thenjFor x0The gray correlation coefficient at the k-th point is
In the formula, rho is a resolution coefficient and is used for weakening the influence of overlarge maximum value on the distortion of the correlation coefficient and improving the resolution between the correlation coefficients. The value interval of rho is (0, 1), and usually rho is 0.5. The gray correlation coefficient matrix R ═ (R) is thus calculatedij)m×nI.e. membership matrix.
5. The fuzzy comprehensive evaluation method for grouting effect based on grey correlation analysis method according to claim 1, wherein the step S5 performs fuzzy comprehensive evaluation for grouting effect to obtain final judgment result, comprising:
and performing primary fuzzy comprehensive evaluation, and performing secondary fuzzy comprehensive evaluation by taking a primary evaluation result as a single factor of secondary fuzzy evaluation. And according to the maximum membership principle, taking the evaluation grade corresponding to the maximum data as the final grouting effect.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the method combines a grey correlation analysis method and an analytic hierarchy process, calculates a correlation coefficient between an actual value of an influence factor and each evaluation grade standard by using the grey correlation method, uses the correlation coefficient as a membership degree, determines the weight of each influence factor by using the analytic hierarchy process, and finally determines a grouting evaluation result by using a fuzzy evaluation principle. The defect of single-factor evaluation is overcome, the influence of artificial subjective factors on the evaluation model is eliminated, the evaluation model is more objective and reasonable, and a new method is provided for grouting effect evaluation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments 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 without inventive exercise.
FIG. 1 is a flow chart of a fuzzy comprehensive evaluation method for grouting effect based on grey correlation analysis provided by the invention;
fig. 2 is a schematic structural diagram of a grouting effect fuzzy comprehensive evaluation method based on a grey correlation analysis method provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a comprehensive evaluation method of grouting effect, which overcomes the defects of single-factor evaluation and insufficient data quantity, eliminates the influence of artificial subjective factors and improves the accuracy of grouting effect evaluation.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.
As shown in fig. 1, the invention provides a fuzzy comprehensive evaluation method for grouting effect based on grey correlation analysis, which comprises the following steps:
s1, determining the influence factors of the grouting effect and establishing a hierarchical structure model.
S2 establishes an evaluation level of the evaluation object.
S3, establishing a judgment matrix by using an analytic hierarchy process and calculating the weight.
S4, calculating the grey correlation coefficient of the actual value of the influence factor and the corresponding evaluation grade, and constructing a membership matrix.
And S5, carrying out fuzzy comprehensive judgment on the grouting effect to obtain a final judgment result.
Each of the above steps is explained below with reference to specific examples.
In step S1, determining the influence factors of the grouting effect, and establishing a hierarchical structure model, specifically:
the top layer is a target layer (A) grouting effect evaluation, the second layer is a standard layer (B), the influence factors are divided into 3 types, and the drilling quality, the grouting finishing standard and the grouting effect are checked. The third layer is an index layer and comprises 11 grouting effect influence factors such as a layer following rate (C1), plane trajectory deviation (C2), well cementation quality (C3), branch hole spacing (C4), grouting ending pump capacity (C5), grouting ending hole pressure (C6), grouting ending water absorption (C7), grouting ending stable time (C8), slurry diffusion radius (C9), pressurized water experiment permeability (C10), abnormal area range comparison (C11) and the like, and particularly shown in figure 2.
In step S2, establishing an evaluation level of the evaluation object, specifically:
the panel of comments V ═ { V1, V2, V3, V4}, and the four grades included are V1 ═ good, V2 ═ good, V3 ═ good, and V4 ═ not good, respectively.
In step S3, an analytic hierarchy process is used to establish a judgment matrix and calculate weights, specifically:
the judgment matrix is used for showing the comparison of the relative importance of the elements of the layer to a certain factor of the previous layer, and the elements of the previous layer are compared pairwise in the next layer according to the elements of the previous layer from top to bottom, so that the priority weight grade of each factor is determined. The elements of the decision matrix are given by a 1-9 scale method, the scale meaning is shown in table 1.
TABLE 1 evaluation criteria
In the analytic hierarchy process, many common methods are used for calculating the maximum eigenvalue and the eigenvector of the judgment matrix, and the square root method is adopted in the example.
The geometric mean value of all elements in each row of the judgment matrix is calculated.
And then normalization processing is carried out to obtain the weight of the judgment matrix.
And solving the maximum characteristic value.
And (3) checking the consistency, wherein the checking formula is as follows:
in the formula, CR is a consistency ratio; CI is a consistency index; RI is the average random consistency index, and the RI values are shown in Table 2. The larger the value of CI, the greater the degree to which the matrix deviates from consistency, and vice versa. When the structure matrix CR is less than 0.1, the judgment matrix is considered to have better consistency, the result after normalization processing is the weight of the factor, otherwise, the original judgment matrix needs to be adjusted and corrected.
TABLE 2 table for obtaining RI values of random consistency indexes of matrices with different orders
| Order of matrix | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 |
| RI | 0.58 | 0.9 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | 1.51 | 1.54 | 1.56 | 1.58 |
In this embodiment, the judgment matrix is constructed, the weight is calculated, and the results of the consistency check are shown in tables 3 to 7.
TABLE 3 criterion layer decision matrix and calculation results
TABLE 4 matrix of drilling quality decisions and calculations
TABLE 5 judgment matrix of grouting ending criteria and calculation results
TABLE 6 grouting effect check judgment matrix and calculation results
In step S4, calculating a gray correlation coefficient between the actual value and the corresponding evaluation level, and constructing a membership matrix, specifically:
and acquiring the index of each influence factor at each judgment level and the actual value of the influence factor. And taking the actual value of the influence factor as a reference sequence, and taking the evaluation grade index as a comparison sequence. Since the gray correlation coefficient is the correlation degree between the reference sequence and the comparison sequence, the larger the gray correlation value is, the stronger the correlation is, and the stronger the influence is. It is feasible to use it as a membership degree in the fuzzy comprehensive evaluation.
And acquiring the indexes and actual values of each influence factor at each evaluation level.
The actual values of the influence factors are recorded as reference sequence x0；
x0＝{x0(j)∣j＝1,2,3......n}
Wherein j is the serial number of the influence factors of the grouting effect.
Labeling the evaluation level index as a comparison sequence xj；
Xj＝{xj(k)∣k＝1,2,3......n，j＝1,2,3......m}
In the formula, k is the serial number of the evaluation grade, and j is the serial number of the influence factor;
Δj(k)＝∣x0(j)-xj(k)∣
x is thenjFor x0The gray correlation coefficient at the k-th point is
In the formula, rho is a resolution coefficient and is used for weakening the influence of overlarge maximum value on the distortion of the correlation coefficient and improving the resolution between the correlation coefficients. The value interval of rho is (0, 1), and usually rho is 0.5. The gray correlation coefficient matrix R ═ (R) is thus calculatedij)m×nI.e. membership matrix:
the membership degree matrices calculated by the above-described method are shown in tables 7 to 9.
TABLE 7 drilling quality membership matrix
| B1 | C1 | C2 | C3 | C4 |
| Superior food | 1 | 1 | 1 | 0.7837 |
| Good effect | 0.931 | 0.8947 | 1 | 1 |
| Qualified | 0.551 | 0.5862 | 0.6364 | 0.5918 |
| Fail to be qualified | 0.3991 | 0.4359 | 0.4667 | 0.4203 |
TABLE 8 end of grouting criteria membership matrix
| B2 | C5 | C6 | C7 | C8 |
| Youyou (an instant noodle) | 1 | 1 | 1 | 1 |
| Good effect | 1 | 0.6429 | 0.7541 | 0.9 |
| Qualified | 0.6364 | 0.4737 | 0.5349 | 0.8182 |
| Fail to be qualified | 0.4667 | 0.375 | 0.4144 | 0.75 |
TABLE 9 evaluation membership matrix for grouting effect
| B3 | C9 | C10 | C11 |
| Youyou (an instant noodle) | 1 | 1 | 1 |
| Good effect | 0.6883 | 0.875 | 0.75 |
| Qualified | 0.5247 | 0.5385 | 0.6 |
| Fail to be qualified | 0.424 | 0.3889 | 0.5 |
In step S5, performing fuzzy comprehensive evaluation on the grouting effect to obtain a final judgment result, specifically:
first, a first-level fuzzy comprehensive evaluation is performed, and the weight ω between the second-level indexes calculated in step S3 is calculatedBiThe membership degree matrix R obtained in step S4BiThe fuzzy operation can obtain 3 first-order indexes of evaluation vector Bi ═ bi1,bi2,bi3) I is 1,2,3, 4, and the formula of the fuzzy operation is:
Bi＝RBi*ωBi
first-level fuzzy evaluation result
Then, performing a second-level fuzzy comprehensive evaluation, and comparing the first-level index evaluation vector with the weight vectors ω of the three first-level indexes calculated in step S3AAnd performing fuzzy operation again to obtain a final evaluation vector B.
Secondary fuzzy judging result
According to the maximum membership principle, the grade corresponding to the maximum value in the evaluation vectors is taken as an evaluation result, so that the grouting effect evaluation grade of the embodiment is excellent.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (6)
1. A grouting effect comprehensive evaluation method is characterized by comprising the following steps:
s1, determining the influence factors of the grouting effect and establishing a hierarchical structure model.
S2 establishes an evaluation level of the evaluation object.
S3, establishing a judgment matrix by using an analytic hierarchy process and calculating the weight.
S4, calculating the grey correlation coefficient of the actual value of the influence factor and the corresponding evaluation grade, and constructing a membership matrix.
And S5, carrying out fuzzy comprehensive judgment on the grouting effect to obtain a final judgment result.
2. The fuzzy comprehensive evaluation method for grouting effect based on grey correlation analysis method according to claim 1, wherein the step S1 of determining the influence factors of grouting effect includes:
the top layer is a target layer (A) grouting effect evaluation, the second layer is a standard layer (B), the influence factors are divided into 3 types, and the drilling quality, the grouting finishing standard and the grouting effect are checked. The third layer is an index layer and comprises 11 grouting effect influence factors such as a bedding ratio (C1), plane trajectory deviation (C2), well cementation quality (C3), branch hole spacing (C4), grouting ending pump capacity (C5), grouting ending hole pressure (C6), grouting ending water absorption (C7), grouting ending stable time (C8), slurry diffusion radius (C9), pressurized water experiment permeability (C10), abnormal area range comparison (C11) and the like.
3. The fuzzy comprehensive evaluation method for grouting effect based on grey correlation analysis method according to claim 1, wherein the step S2 of establishing the evaluation level of the evaluation object comprises:
the panel of comments V ═ { V1, V2, V3, V4}, and the four grades included are V1 ═ good, V2 ═ good, V3 ═ good, and V4 ═ not good, respectively.
4. The fuzzy comprehensive evaluation method for grouting effect based on grey correlation analysis method according to claim 1, wherein the step S3 is to establish a judgment matrix and calculate the weight by using an analytic hierarchy process, specifically:
determining the priority weight grade of each index through pairwise comparison of each index, establishing a discrimination matrix according to 9-grade scale and expert scoring opinions, calculating and normalizing the discrimination matrix by using a square root method to obtain a weight vector of the discrimination matrix, and carrying out consistency check on the discrimination matrix. If the consistency check shows that the constructed discrimination matrix has better consistency, the discrimination matrix needs to be readjusted if the constructed discrimination matrix does not pass the consistency check.
5. The grouting effect fuzzy comprehensive evaluation method based on the grey correlation degree analysis method according to claim 1, wherein the step S4 of calculating the grey correlation coefficient between the actual value of the influence factor and the corresponding evaluation grade, and the specific steps of constructing the membership degree matrix are as follows:
and acquiring the indexes and actual values of each influence factor at each evaluation level.
The actual values of the influence factors are recorded as reference sequence x0
x0＝{x0(j)∣j＝1,2,3......n}
Wherein j is the serial number of the influence factors of the grouting effect.
Labeling the evaluation level index as a comparison sequence xj
xj＝{xj(k)∣k＝1,2,3......n，i＝1,2,3......m}
In the formula, k is the serial number of the evaluation grade, and j is the serial number of the influence factor;
Δj(k)＝∣x0(j)-xj(k)∣
then xjFor x0The gray correlation coefficient at the k-th point is
In the formula, rho is a resolution coefficient and is used for weakening the influence of overlarge maximum value on the distortion of the correlation coefficient and improving the resolution between the correlation coefficients. The value interval of rho is (0, 1), and usually rho is 0.5. The gray correlation coefficient matrix R ═ (R) is thus calculatedij)m×nI.e. membership matrix.
6. The fuzzy comprehensive evaluation method for grouting effect based on grey correlation analysis method according to claim 1, wherein the step S5 performs fuzzy comprehensive evaluation for grouting effect to obtain final judgment result, comprising:
and performing primary fuzzy comprehensive evaluation, and performing secondary fuzzy comprehensive evaluation by taking a primary evaluation result as a single factor of secondary fuzzy evaluation. And according to the maximum membership principle, taking the evaluation grade corresponding to the maximum data as the final grouting effect.
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| CN115218963A (en) * | 2022-08-01 | 2022-10-21 | 沈阳工业大学 | Multivariable built-in panoramic-sensing transformer state comprehensive fuzzy evaluation method |
| CN116884225A (en) * | 2023-09-05 | 2023-10-13 | 天津大学 | Method and device for evaluating influence of proportion of automatic driving vehicles on traffic efficiency |
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| CN115218963A (en) * | 2022-08-01 | 2022-10-21 | 沈阳工业大学 | Multivariable built-in panoramic-sensing transformer state comprehensive fuzzy evaluation method |
| CN116884225A (en) * | 2023-09-05 | 2023-10-13 | 天津大学 | Method and device for evaluating influence of proportion of automatic driving vehicles on traffic efficiency |
| CN116884225B (en) * | 2023-09-05 | 2023-12-26 | 天津大学 | Method and device for evaluating influence of proportion of automatic driving vehicles on traffic efficiency |
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