CN112100851A - Method for evaluating tunnel water inrush disaster risk based on set pair analysis - Google Patents
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Abstract
The invention provides a method for evaluating the risk of tunnel inrush water disaster based on set pair analysis. The method selects 4 first-level parameters and 12 second-level parameters, constructs an evaluation system for the risk of the tunnel inrush water disaster, and determines the weight of each level parameter by adopting an AHP (analytic hierarchy process). On the basis, a set pair analysis theory is introduced, a set pair analysis method for evaluating the risk of the tunnel inrush water based on improved contact degree calculation is provided, the comprehensive contact degree of the risk of the tunnel inrush water disaster is calculated, and the risk grade with the maximum comprehensive contact degree is selected as the grade of the risk of the tunnel inrush water disaster, so that the construction process of the tunnel is guided. The method has the advantages that the value of each level parameter can be determined according to the preliminary excavation result of the tunnel, so that the level of the sudden water burst risk of the tunnel is calculated, and the method has theoretical guiding significance for engineering practice and the like.
Description
Technical Field
The invention relates to a method for evaluating the risk of a tunnel inrush water disaster based on set pair analysis, and belongs to the technical field of tunnel construction.
Background
The gushing water disaster is one of the most dangerous geological disasters in tunnel construction. In order to realize effective management and control on the sudden water inrush risk, a method for systematically identifying and evaluating the tunnel sudden water inrush risk level based on set pair analysis is provided. The method is based on the analytic hierarchy process, 4 primary level parameters and 12 secondary level parameters are selected, a tunnel inrush water disaster risk assessment system is constructed, and the weight of each evaluation level parameter is determined by the AHP (analytic hierarchy process). On the basis, a set pair analysis theory is introduced, a tunnel water inrush risk set pair analysis method based on improved contact degree calculation is provided, a section K7+ 940-K8 +160 of a tunnel outlet is taken as an example to specifically explain the specific application process of the method, and finally the reliability of the method is verified based on the actual excavation result, so that the method has certain reference significance for decision making of engineering technicians.
Disclosure of Invention
The present invention aims to address at least one of the above-mentioned deficiencies of the prior art. For example, the invention aims to provide a method for systematically identifying and evaluating the risk level of the tunnel inrush water based on set pair analysis.
In order to achieve the purpose, the invention provides a method for evaluating the risk of the tunnel inrush water disaster based on set pair analysis. The method calculates the comprehensive degree of relation of the risk of the tunnel water inrush disaster through the formula 1,
formula 1 is:
wherein, muThe comprehensive degree of connection mu for tunnel water burst disasteri,For each secondary level parameter, the degree of association, w, to levels I, II, III and IV riskiThe weight value vector corresponding to each secondary level parameter is 1, 2, … …, m.
In an exemplary embodiment of the invention, the relationship μ of each secondary level parameter to the level I, II, III and IV risk levelsi,Calculated by the following method:
the hierarchical domain set of the level I, level II, level III and level IV risk levels corresponding to any one secondary level parameter i is as follows:
{(S(i,1),S(i,2)),(S(i,2),S(i,3)),.......,(.S(i,m),S(i,m+1)) And e, calculating the relationship of any secondary level parameter i as follows:
in the traditional set-pair contact degree calculation, the set pairs consisting of individual measured values and separated hierarchical domains are regarded as opposite set pairs, and the contact degree values are all-1, so that the evaluation result is distorted. Actually, the actually measured level parameters and all the hierarchical domains have different degrees of association, and the association degree decreases with the distance of the interval, namely, the set has a gradual change trend to the association degree in the evaluation system. Thus, the oppositivity of the set pairs is cancelled, and the identity and diversity are retained. Then the process of the first step is carried out,
(1) the value lambda of the parameter i at any two-level hierarchy is positioned in a hierarchical discourse domain (S)(i,k),S(i,k+1)) In the inner case, the value λ of any two-level hierarchical parameter i and the hierarchical discourse domain (S)(i,k),S(i,k+1)) Pair of compositions { lambda, (S)(i,k),S(i,k+1)) The degree of association mu of any two-level hierarchical parameter i with the same propertyi,k＝1；
(2) The value lambda of the parameter i at any two-level hierarchy is positioned in a hierarchical discourse domain (S)(i,k),S(i,k+1)) Otherwise, the set pair consisting of the value λ of any one secondary hierarchical parameter i and other hierarchical discourse domains is regarded as a difference set pair, and the calculation of the degree of association of any one secondary hierarchical parameter i is divided into the following three cases:
(S) other hierarchical discourse domain(i,k),S(i,k+1)) Before, the contact degree mu of any secondary level parameter ii,As calculated by the formula 2, the following formula is given,
the formula 2 is:
wherein, 1, 2, … …, k-1;
② other hierarchical discourse is located at (S)(i,k),S(i,k+1)) Then, the contact degree mu of any one secondary level parameter ii,As calculated by the formula 3, the following equation is given,
formula 3 is:
wherein, k +1, k +2, … …, m;
the value lambda of any secondary level parameter i is equal to the boundary value of two level discourse domains, namely, the value lambda is equal to S(i,k)The calculation was performed for the following two cases, respectively:
a. the value lambda of any one secondary level parameter i and two adjacent hierarchical discourse domains form a set pair respectively, and the contact degree mu of any one secondary level parameter ii,kOr mui,k-1As calculated by the equation 4 or 5,
formula 4 is:
formula 5 is:
b. the value lambda of any one secondary level parameter i and two other separated hierarchical discourse domains form a set pair respectively, and the contact degree mu of any one secondary level parameter ii,As calculated by the equations 6 and 7,
formula 6 is:
wherein, 1, 2, … …, k-2;
formula 7 is:
where k +1, k +2, … …, m.
In an exemplary embodiment of the invention, the weight vector wiCan be obtained by the following method:
taking four parameters of unfavorable geology, stratum lithology, hydraulic conditions and human factors as first-level parameters for evaluating the risk of the tunnel suffering from the sudden gushing water disaster, wherein the unfavorable geology is divided into three types of faults, folds and interlaminar cracks, and the faults, the folds and the interlaminar cracks are taken as second-level parameters corresponding to the unfavorable geology; the stratum lithology is divided into three types of surrounding rock grade, rock stratum attitude and rock stratum combination, and the surrounding rock grade, the rock stratum attitude and the rock stratum combination are used as second-level hierarchical parameters corresponding to the stratum lithology; the hydraulic conditions are divided into three types of atmospheric precipitation, landform and water head pressure, and the atmospheric precipitation, the landform and the water head pressure are used as secondary level parameters corresponding to the hydraulic conditions; the human factors are divided into three types, namely a construction method, an advance forecast and an advance support, and the construction method, the advance forecast and the advance support are used as secondary level parameters corresponding to the human factors; the four primary level parameters correspond to twelve secondary level parameters, and each secondary level parameter in the twelve secondary level parameters respectively corresponds to I, II, III and IV level hierarchical discourse domains according to the severity; generating a first-level discrimination matrix and four second-level discrimination matrices respectively corresponding to the four first-level hierarchical parameters, calculating characteristic values and characteristic vectors of the first-level discrimination matrix and the second-level discrimination matrix to obtain twelve weight values corresponding to the second-level hierarchical parametersQuantity wi。
In an exemplary embodiment of the invention, the weight vector wiThe calculation of (c) may comprise the steps of:
scaling the four first-level layer parameters by adopting a 1-9-level scaling method to obtain twelve first-level assignments; scaling three secondary level parameters corresponding to unfavorable geology by adopting a 1-9-level scaling method to obtain nine first-second-level assignments; scaling three secondary level parameters corresponding to the lithology of the stratum by adopting a 1-9-level scaling method to obtain nine second-level assignments; scaling three secondary level parameters corresponding to the hydraulic conditions by adopting a 1-9-level scaling method to obtain nine third-level assignments; scaling three secondary level parameters corresponding to the artificial factors by adopting a 1-9 level scaling method to obtain nine fourth secondary assignments; arranging the twelve first-level assignments according to rows and columns to obtain a first-level discrimination matrix; arranging the nine first secondary assignments according to rows and columns to obtain a first secondary discrimination matrix; arranging the nine second-level assignments according to rows and columns to obtain a second-level discrimination matrix; arranging the nine second-level assignments according to rows and columns to obtain a second-level discrimination matrix; arranging the nine fourth secondary assignments according to rows and columns to obtain a fourth secondary discrimination matrix; respectively calculating the eigenvalues and eigenvectors of a first-level discrimination matrix, a first second-level discrimination matrix, a second-level discrimination matrix, a third second-level discrimination matrix and a fourth second-level discrimination matrix, carrying out consistency check, carrying out normalization processing on the eigenvectors of the first second-level discrimination matrix, the second-level discrimination matrix, the third second-level discrimination matrix and the fourth second-level discrimination matrix to obtain weight value vectors w of all second-level parameters relative to respective first-level parametersi。
In an exemplary embodiment of the present invention, the class i, ii, iii and iv risk classes may correspond to low risk, medium risk, high risk and very high risk water inrush disasters, respectively, occurring in a tunnel.
In an exemplary embodiment of the present invention, the risk level for evaluating the risk of the tunnel from the inrush water disaster may be obtained according to equation 8,
formula 8 is:
compared with the prior art, the beneficial effects of the invention can comprise at least one of the following:
(1) according to the method, 4 primary level parameters and 12 secondary level parameters are selected, a tunnel water inrush disaster risk assessment system is constructed, the weight of each assessment level parameter is determined by adopting an AHP (analytic hierarchy process), on the basis, a set pair analysis theory is introduced, a tunnel water inrush risk set pair analysis method based on improved contact degree calculation is provided, and the method has theoretical guiding significance on engineering practice;
(2) taking the site construction tunnel exit section as an example, the specific application process of the method is specifically explained, and finally the reliability of the method is verified based on the actual excavation result, so that the method has certain reference significance for the decision of engineering technicians.
Drawings
Fig. 1 shows an evaluation hierarchy parameter system constructed by a method for evaluating a risk of a tunnel inrush water disaster based on set pair analysis according to an exemplary embodiment of the present invention.
Detailed Description
Hereinafter, the method for evaluating the risk of a tunnel inrush water disaster according to the present invention based on set pair analysis will be described in detail with reference to the accompanying drawings and exemplary embodiments.
Fig. 1 shows an evaluation hierarchy parameter system constructed by a method for evaluating a risk of a tunnel inrush water disaster based on set pair analysis according to an exemplary embodiment of the present invention.
In an exemplary embodiment of the present invention, the method for evaluating the risk of the tunnel occurring the water inrush disaster based on the set pair analysis may calculate the comprehensive degree of relation of the risk of the tunnel occurring the water inrush disaster by equation 1,
formula 1 is:
wherein, muThe comprehensive relation degree of the tunnel water burst disaster is shown. Sudden water burst disaster of to-be-evaluated tunnelThe risk rating of a risk can be obtained according to equation 8, where equation 8 is:
specifically, the comprehensive connection degrees corresponding to the I, II, III and IV grade risk grades are respectively calculated through a formula 1, and the risk grade of the tunnel with the water inrush disaster is obtained through a formula 8. That is, by comparing the magnitude of the comprehensive degree of contact of the tunnel with the I, II, III and IV level water inrush disasters, the higher the comprehensive degree of contact is, the more easily the risk of the level occurs, and the first level risk level with the maximum comprehensive degree of contact is the risk level mu of the tunnel with water inrushi,And (4) the degree of relation of each secondary level parameter to the I, II, III and IV level risk levels. The degree of association mu of each secondary level parameter to I, II, III and IV level risk levelsi,Calculated by the following method:
the hierarchical domain set of the level I, level II, level III and level IV risk levels corresponding to any one secondary level parameter i is as follows:
{(S(i,1),S(i,2)),(S(i,2),S(i,3)),.......,(.S(i,m),S(i,m+1)) And e, calculating the relationship of any secondary level parameter i as follows:
(1) the value lambda of the parameter i at any two-level hierarchy is positioned in a hierarchical discourse domain (S)(i,k),S(i,k+1)) In the inner case, the value λ of any two-level hierarchical parameter i and the hierarchical discourse domain (S)(i,k),S(i,k+1)) Pair of compositions { lambda, (S)(i,k),S(i,k+1)) The degree of association mu of any two-level hierarchical parameter i with the same propertyi,k＝1；
(2) The value lambda of the parameter i at any two-level hierarchy is positioned in a hierarchical discourse domain (S)(i,k),S(i,k+1)) Otherwise, the set pair consisting of the value λ of any one secondary hierarchical parameter i and other hierarchical discourse domains is regarded as a difference set pair, and the calculation of the degree of association of any one secondary hierarchical parameter i is divided into the following three cases:
(S) other hierarchical discourse domain(i,k),S(i,k+1)) Before, the contact degree mu of any secondary level parameter ii,As calculated by the formula 2, the following formula is given,
the formula 2 is:
wherein, 1, 2, … …, k-1;
② other hierarchical discourse is located at (S)(i,k),S(i,k+1)) Then, the contact degree mu of any one secondary level parameter ii,As calculated by the formula 3, the following equation is given,
formula 3 is:
wherein, k +1, k +2, … …, m;
the value lambda of any secondary level parameter i is equal to the boundary value of two level discourse domains, namely, the value lambda is equal to S(i,k)The calculation was performed for the following two cases, respectively:
a. the value lambda of any one secondary level parameter i and two adjacent hierarchical discourse domains form a set pair respectively, and the contact degree mu of any one secondary level parameter ii,kOr mui,k-1As calculated by the equation 4 or 5,
formula 4 is:
formula 5 is:
b. the value lambda of any one secondary level parameter i and two other separated hierarchical discourse domains form a set pair respectively, and the contact degree mu of any one secondary level parameter ii,As calculated by the equations 6 and 7,
formula 6 is:
wherein, 1, 2, … …, k-2;
formula 7 is:
where k +1, k +2, … …, m.
wiThe weight value vector corresponding to each secondary level parameter is 1, 2, … …, m. The weight vector wiCan be obtained by the following method:
taking four parameters of unfavorable geology, stratum lithology, hydraulic conditions and human factors as first-level parameters for evaluating the risk of the tunnel suffering from the sudden gushing water disaster, wherein the unfavorable geology is divided into three types of faults, folds and interlaminar cracks, and the faults, the folds and the interlaminar cracks are taken as second-level parameters corresponding to the unfavorable geology; the stratum lithology is divided into three types of surrounding rock grade, rock stratum attitude and rock stratum combination, and the surrounding rock grade, the rock stratum attitude and the rock stratum combination are used as second-level hierarchical parameters corresponding to the stratum lithology; the hydraulic conditions are divided into three types of atmospheric precipitation, landform and water head pressure, and the atmospheric precipitation, the landform and the water head pressure are used as secondary level parameters corresponding to the hydraulic conditions; the human factors are divided into three types, namely a construction method, an advance forecast and an advance support, and the construction method, the advance forecast and the advance support are used as secondary level parameters corresponding to the human factors; the four primary level parameters correspond to twelve secondary level parameters, and each secondary level parameter in the twelve secondary level parameters respectively corresponds to I, II, III and IV level hierarchical discourse domains according to the severity; generating a first-level discrimination matrix and four second-level discrimination matrices respectively corresponding to the four first-level hierarchical parameters, calculating the eigenvalues and eigenvectors of the first-level discrimination matrix and the second-level discrimination matrix to obtain twelve weight value vectors w corresponding to the second-level hierarchical parametersi. Specifically, as shown in FIG. 1, the unfavorable geology (A)1) Lithology of the formation (A)2) Hydraulic conditions (A)3) Human factor (A)4) The method is used as a primary level parameter for evaluating the risk of sudden water burst of the tunnel. Among them, unfavorable geology (A)1) And is divided into fault (B)1) Wrinkle (B)2) Interlaminar fracture (B)3) Three secondary level parameters; lithology of the formation (A)2) Is divided intoIs at the grade of surrounding rock (B)4) Formation occurrence (B)5) And rock stratum combination (B)6) Three secondary level parameters; hydraulic Condition (A)3) Is divided into atmospheric precipitation (B)7) Landform (B)8) Head pressure (B)9) Three secondary level parameters; human factor (A)4) Also divided into construction methods (B)10) Advanced prediction (B)11) Advanced support (B)12) Three secondary level parameters. The risk parameter system U for the tunnel inrush water disaster, the four primary level parameters and the twelve secondary level parameters are used for constructing a risk level parameter system for evaluating the tunnel inrush water disaster as shown in figure 1.
Next, a hierarchical discourse domain for each evaluation level parameter is determined
According to the influence degree of the tunnel pregnancy disaster environment characteristics and the parameters of each level on the occurrence of the tunnel water inrush disaster, the development condition of the geological disaster in the tunnel site area and the correlation statistical analysis result are combined, the tunnel water inrush risk grade is divided into four grades of low risk (I grade), medium risk (II grade), high risk (III grade) and high risk (IV grade), and the classification standard for evaluating the tunnel water inrush disaster risk and the corresponding classification domain are compiled as shown in table 1.
TABLE 1 grading Standard for evaluating Risk of tunnel inrush Water disaster and corresponding grading discourse area
The hierarchical domains of qualitative hierarchical parameters such as faults, folds, lithology combinations, construction methods, advanced forecasting, advanced support and the like are given in an assignment mode. For example, the classification domains of fault, fold, lithologic combination, construction method, advance forecast and advance support corresponding to the i, ii, iii and iv level risk levels respectively are assigned as (0,4), (4,6), (6,8) and (8, 10). in this embodiment, the i, ii, iii and iv level risk levels correspond to the tunnel with low-risk, medium-risk, high-risk and extremely high-risk water inrush disasters, respectively.
The weight vector wiThe calculation of (c) may comprise the steps of: scaling the four first-level layer parameters by adopting a 1-9-level scaling method to obtain twelve first-level assignments; scaling three secondary level parameters corresponding to unfavorable geology by adopting a 1-9-level scaling method to obtain nine first-second-level assignments; scaling three secondary level parameters corresponding to the lithology of the stratum by adopting a 1-9-level scaling method to obtain nine second-level assignments; scaling three secondary level parameters corresponding to the hydraulic conditions by adopting a 1-9-level scaling method to obtain nine third-level assignments; scaling three secondary level parameters corresponding to the artificial factors by adopting a 1-9 level scaling method to obtain nine fourth secondary assignments; arranging the twelve first-level assignments according to rows and columns to obtain a first-level discrimination matrix; arranging the nine first secondary assignments according to rows and columns to obtain a first secondary discrimination matrix; arranging the nine second-level assignments according to rows and columns to obtain a second-level discrimination matrix; arranging the nine second-level assignments according to rows and columns to obtain a second-level discrimination matrix; arranging the nine fourth secondary assignments according to rows and columns to obtain a fourth secondary discrimination matrix; respectively calculating the eigenvalues and eigenvectors of a first-level discrimination matrix, a first second-level discrimination matrix, a second-level discrimination matrix, a third second-level discrimination matrix and a fourth second-level discrimination matrix, carrying out consistency check, carrying out normalization processing on the eigenvectors of the first second-level discrimination matrix, the second-level discrimination matrix, the third second-level discrimination matrix and the fourth second-level discrimination matrix to obtain weight value vectors w of all second-level parameters relative to respective first-level parametersi. Specifically, the determining the weight of each evaluation level parameter includes the steps of:
(1) determining a decision matrix
Judging by 1-9 level scale method, comparing pairwise, and using aijRepresenting evaluation level parameter CiAnd CjThe ratio of the degree of impact on the target (i.e., the tunnel is experiencing a sudden water burst).
TABLE 21-9 definition of the Scale
| Assignment aij | Means of |
| 1 | CiAnd CjIn contrast, the two are equally important |
| 3 | CiAnd CjIn contrast, the former is of slight importance |
| 5 | CiAnd CjIn contrast, the former is of significant importance |
| 7 | CiRatio CjThe former is more important than the latter |
| 9 | CiRatio CjIn contrast, the former is extremely important |
| 2,4,6,8 | Between two adjacent levels |
| Reciprocal of the | 1/aijIs CjAnd CiCompare |
Wherein i and j represent the serial numbers of rows and columns in the hierarchical parameter composition matrix.
According to the above method, a primary decision matrix and four secondary decision matrices are obtained as shown in tables 3 to 7:
TABLE 3 first-level discrimination matrix Q1
| U–A | A1 | A2 | A3 | A4 |
| A1 | a11 | a12 | a13 | a14 |
| A2 | a21 | a22 | a23 | a24 |
| A3 | a31 | a32 | a33 | a34 |
| A4 | a41 | a42 | a43 | a44 |
TABLE 4 first and second-level decision matrices Q2
| A1-B | B1 | B2 | B3 |
| B1 | b11 | b12 | b13 |
| B2 | b21 | b22 | b23 |
| B3 | b31 | b32 | b33 |
TABLE 5 second level of discrimination matrix Q3
| A2-B | B4 | B5 | B6 |
| B4 | b44 | b45 | b46 |
| B5 | b54 | b55 | b56 |
| B6 | b64 | b65 | b66 |
TABLE 6 third two-level decision matrix Q4
| A3-B | B7 | B8 | B9 |
| B7 | b77 | b78 | b79 |
| B8 | b87 | b88 | b89 |
| B9 | b97 | b98 | b99 |
TABLE 7 fourth two-level decision matrix Q5
| A4-B | B10 | B11 | B12 |
| B10 | b1010 | b1011 | b1012 |
| B11 | b1110 | b1111 | b1112 |
| B12 | b1210 | b1211 | b1212 |
(2) Computing eigenvalues and eigenvectors
Respectively calculating the above judgment matrixes Q1～Q5And carrying out consistency check on the characteristic value and the characteristic vector. The results are shown in Table 8:
TABLE 8 determination matrix eigenvectors and eigenvalues
| Judgment matrix | Feature vector | Characteristic value |
| Q1 | [u11,u12,u13,u14] | λ1 |
| Q2 | [u21,u22,u23] | λ2 |
| Q3 | [u31,u32,u33] | λ3 |
| Q4 | [u41,u42,u43] | λ4 |
| Q5 | [u51,u52,u53] | λ5 |
(3) Calculating the weight of each level parameter
Normalizing the characteristic vector u to obtain a vector w, and taking the vector w as a relative weight value vector w of each factor relative to an upper-layer criterioniThe results are shown in Table 9.
Table 9 hierarchical Combined Total ordering
Wherein, the level A represents a primary level parameter, and the level B represents a secondary level parameter. Specific values of the matrices in tables 3 to 9 are as follows:
TABLE 3 first-level discrimination matrix Q1
| U-A | A1 | A2 | A3 | A4 |
| A1 | 1 | 3 | 1/3 | 6 |
| A2 | 1/3 | 1 | 1/5 | 4 |
| A3 | 3 | 5 | 1 | 7 |
| A4 | 1/6 | 1/4 | 1/7 | 1 |
TABLE 4 first and second-level decision matrices Q2
| A1-B | B1 | B2 | B3 |
| B1 | 1 | 4 | 3 |
| B2 | 1/4 | 1 | 1/2 |
| B3 | 1/3 | 2 | 1 |
TABLE 5 second level of discrimination matrix Q3
| A2-B | B4 | B5 | B6 |
| B4 | 1 | 1/4 | 1/3 |
| B5 | 4 | 1 | 1/2 |
| B6 | 3 | 2 | 1 |
TABLE 6 third two-level decision matrix Q4
| A3-B | B7 | B8 | B9 |
| B7 | 1 | 1/3 | 1/5 |
| B8 | 3 | 1 | 1/3 |
| B9 | 5 | 3 | 1 |
TABLE 7 fourth two-level decision matrix Q5
| A4-B | B10 | B11 | B12 |
| B10 | 1 | 1/3 | 1/4 |
| B11 | 3 | 1 | 1 |
| B12 | 4 | 1 | 1 |
TABLE 8 determination matrix eigenvectors and eigenvalues
| Judgment matrix | Feature vector | Characteristic value |
| Q1 | [0.2687,0.1248,0.5576,0.0489] | 4.1793 |
| Q2 | [0.6252,0.1365,0.2383] | 3.0183 |
| Q3 | [0.1242,0.3586,0.5172] | 3.1078 |
| Q4 | [0.1047,0.2584,0.6369] | 3.0385 |
| Q5 | [0.126,0.4161,0.4579] | 3.0092 |
Table 9 hierarchical Combined Total ordering
Exemplary embodiments of the present invention and the effects thereof are further illustrated and described below with reference to specific examples.
Taking a certain tunnel outlet section K7+ 940-K8 +160 as an example, the accuracy of an evaluation result is verified by utilizing the specific application of a method for evaluating the risk of the tunnel inrush water disaster based on set pair analysis and through the comparative analysis with the site construction condition.
(1) Determination of values of parameters of each layer
The tunnel of the section is initially excavated, the designed surrounding rock of the section is obtained to be V-grade, the basic quality level parameter BQ of the surrounding rock is 230, the average buried depth of the tunnel of the section is 230m, the surrounding rock is composed of heterogeneous cerebellar sandy slate and a small amount of metamorphic sandstone sandwiched by slate in a three-layer system, the rock is broken to extremely broken, belongs to soft rock, is influenced by a fault traction structure, is a rock breaking zone, joint cracks develop relatively, most joint intervals are about 0.3m, and are mostly seen in a sheet-like dense zone, and the type of underground water is mainly bedrock fracture water. The construction method adopts a method of annular excavation and core soil retention, adopts an advanced anchor rod to control the deformation of surrounding rocks, and gives advance predictions mainly based on tunnel face geological sketch, geological radar and advanced horizontal drilling. According to the construction drawing design file, the risk level evaluation factors of the tunnel exits K7+ 940-K8 +160 with the inrush water disaster are obtained and are shown in Table 10.
TABLE 10 Risk level evaluation factor for water inrush disaster in a tunnel
Wherein, λ is the measured value or assignment of each secondary level parameter.
(2) Calculation of degree of association
By the level parameter fault (B)1) "for example, the calculation process of the contact degree is described in detail. From Table 10 and Table 1, the hierarchy parameter B1The set of hierarchical discourse domains corresponding to each risk level is: {5,(0,4)},{5,(4,6)},{5,(6,8)},{5,(8,10)}. 5 is located between the domain theories (4,6) then mu1,2The degree of association is calculated according to equations 6 and 7 in turn, 1:
similarly, according to the corresponding relation between other secondary level parameters and the corresponding hierarchical discourse domains, a corresponding calculation formula is selected from the formulas 2 to 7, and the association between other 11 level parameters and each risk level can be calculated, so that the association matrix of the comprehensive state evaluation of the tunnel in the section is obtained. And then, according to the formula 1, the comprehensive degree of association between the tunnel and each risk level can be calculated.
From the above calculation results, the comprehensive degree of association between the evaluation tunnel segment and the high risk level is 0.633, which is the maximum value among the four comprehensive degrees of association. Therefore, the level of the water inrush risk in the tunnel section is judged to be III level, namely high risk. Surrounding rocks of a tunnel face (mile pile number K8+067.4) after boundary excavation mainly comprise sandy slate and carbonaceous sericite slate, are in a thin-medium-thick layered shape, develop joint cracks, and form a fracture zone between the middle development layers of the tunnel face, wherein the thickness of the fracture zone is about 0.8-1.5 m, a rock mass of the fracture zone is in a small-fragment scattered body structure, underground water flows out of the fracture zone in a strand-shaped manner, the stability is poor, and the rock mass drops from time to time, so the rock mass is a water inrush high-risk section.
In summary, the beneficial effects of the invention include at least one of the following:
(1) the method selects 4 primary level parameters and 12 secondary level parameters, constructs a tunnel water inrush disaster risk assessment system, determines the weight of each assessment level parameter by adopting an AHP (analytic hierarchy process), introduces a set pair analysis theory on the basis, provides a tunnel water inrush risk set pair analysis method based on improved contact degree calculation, and has theoretical guiding significance on engineering practice;
(2) taking the site construction tunnel exit section as an example, the specific application process of the method is specifically explained, and finally the reliability of the method is verified based on the actual excavation result, so that the method has certain reference significance for the decision of engineering technicians.
Although the present invention has been described above in connection with the exemplary embodiments and the accompanying drawings, it will be apparent to those of ordinary skill in the art that various modifications may be made to the above-described embodiments without departing from the spirit and scope of the claims.
Claims (6)
1. A method for evaluating the risk of tunnel water inrush disaster based on set pair analysis is characterized in that the method calculates the comprehensive degree of relation of the risk of tunnel water inrush disaster by formula 1,
formula 1 is:
wherein, muThe comprehensive degree of connection mu for tunnel water burst disasteri,For each secondary level parameter, the degree of association, w, to levels I, II, III and IV riskiThe weight value vector corresponding to each secondary level parameter is 1, 2, … …, m.
2. The method for evaluating the risk of a tunnel inrush water disaster according to claim 1, wherein the relationship degree μ between each secondary level parameter and the level I, II, III and IV risk leveli,Calculated by the following method:
the hierarchical domain set of the level I, level II, level III and level IV risk levels corresponding to any one secondary level parameter i is as follows:
{(S(i,1),S(i,2)),(S(i,2),S(i,3)),.......,(.S(i,m),S(i,m+1)) And e, calculating the relationship of any secondary level parameter i as follows:
(1) the value lambda of the parameter i at any two-level hierarchy is positioned in a hierarchical discourse domain (S)(i,k),S(i,k+1)) In the inner case, the value λ of any two-level hierarchical parameter i and the hierarchical discourse domain (S)(i,k),S(i,k+1)) Pair of compositions { lambda, (S)(i,k),S(i,k+1)) The degree of association mu of any two-level hierarchical parameter i with the same propertyi,k＝1；
(2) The value lambda of the parameter i at any two-level hierarchy is positioned in a hierarchical discourse domain (S)(i,k),S(i,k+1)) Otherwise, the set pair consisting of the value λ of any one secondary hierarchical parameter i and other hierarchical discourse domains is regarded as a difference set pair, and the calculation of the degree of association of any one secondary hierarchical parameter i is divided into the following three cases:
(S) other hierarchical discourse domain(i,k),S(i,k+1)) Before, the contact degree mu of any secondary level parameter ii,As calculated by the formula 2, the following formula is given,
the formula 2 is:
wherein, 1, 2, … …, k-1;
② other hierarchical discourse is located at (S)(i,k),S(i,k+1)) Then, the contact degree mu of any one secondary level parameter ii,As calculated by the formula 3, the following equation is given,
formula 3 is:
wherein, k +1, k +2, … …, m;
the value lambda of any secondary level parameter i is equal to the boundary value of two level discourse domains, namely, the value lambda is equal to S(i,k)The calculation was performed for the following two cases, respectively:
a. the value lambda of any one secondary level parameter i and two adjacent hierarchical discourse domains form a set pair respectively, and the contact degree mu of any one secondary level parameter ii,kOr mui,k-1As calculated by the equation 4 or 5,
formula 4 is:
formula 5 is:
b. the value lambda of any one secondary level parameter i and two other separated hierarchical discourse domains form a set pair respectively, and the contact degree mu of any one secondary level parameter ii,As calculated by the equations 6 and 7,
formula 6 is:
wherein, 1, 2, … …, k-2;
formula 7 is:
where k +1, k +2, … …, m.
3. The method for evaluating the risk of a tunnel inrush water disaster according to claim 1, wherein the weight vector w isiCan be obtained by the following method:
taking four parameters of unfavorable geology, stratum lithology, hydraulic conditions and human factors as first-level parameters for evaluating the risk of sudden water inrush disaster in the tunnel,
the unfavorable geology is divided into three categories of fault, fold and interlaminar fracture, and the fault, the fold and the interlaminar fracture are used as second-level hierarchical parameters corresponding to the unfavorable geology; the stratum lithology is divided into three types of surrounding rock grade, rock stratum attitude and rock stratum combination, and the surrounding rock grade, the rock stratum attitude and the rock stratum combination are used as second-level hierarchical parameters corresponding to the stratum lithology; the hydraulic conditions are divided into three types of atmospheric precipitation, landform and water head pressure, and the atmospheric precipitation, the landform and the water head pressure are used as secondary level parameters corresponding to the hydraulic conditions; the human factors are divided into three types, namely a construction method, an advance forecast and an advance support, and the construction method, the advance forecast and the advance support are used as secondary level parameters corresponding to the human factors;
the four primary level parameters correspond to twelve secondary level parameters, and each secondary level parameter in the twelve secondary level parameters respectively corresponds to I, II, III and IV level hierarchical discourse domains according to the severity;
generating a first-level discrimination matrix and four second-level discrimination matrices respectively corresponding to the four first-level hierarchical parameters, calculating the eigenvalues and eigenvectors of the first-level discrimination matrix and the second-level discrimination matrix to obtain twelve weight value vectors w corresponding to the second-level hierarchical parametersi。
4. The method for evaluating the risk of a tunnel inrush water disaster according to claim 3, wherein the weight vector w isiThe calculation of (a) comprises the steps of:
scaling the four first-level layer parameters by adopting a 1-9-level scaling method to obtain twelve first-level assignments; scaling three secondary level parameters corresponding to unfavorable geology by adopting a 1-9-level scaling method to obtain nine first-second-level assignments; scaling three secondary level parameters corresponding to the lithology of the stratum by adopting a 1-9-level scaling method to obtain nine second-level assignments; scaling three secondary level parameters corresponding to the hydraulic conditions by adopting a 1-9-level scaling method to obtain nine third-level assignments; scaling three secondary level parameters corresponding to the artificial factors by adopting a 1-9 level scaling method to obtain nine fourth secondary assignments;
arranging the twelve first-level assignments according to rows and columns to obtain a first-level discrimination matrix; arranging the nine first secondary assignments according to rows and columns to obtain a first secondary discrimination matrix; arranging the nine second-level assignments according to rows and columns to obtain a second-level discrimination matrix; arranging the nine second-level assignments according to rows and columns to obtain a second-level discrimination matrix; arranging the nine fourth secondary assignments according to rows and columns to obtain a fourth secondary discrimination matrix;
respectively calculating the eigenvalues and eigenvectors of a first-level discrimination matrix, a first second-level discrimination matrix, a second-level discrimination matrix, a third second-level discrimination matrix and a fourth second-level discrimination matrix, carrying out consistency check, carrying out normalization processing on the eigenvectors of the first second-level discrimination matrix, the second-level discrimination matrix, the third second-level discrimination matrix and the fourth second-level discrimination matrix to obtain weight value vectors w of all second-level parameters relative to respective first-level parametersi。
5. The method for evaluating the risk of a tunnel inrush water disaster according to claim 1, wherein the levels I, II, III and IV risk correspond to the tunnel inrush water disaster with low risk, medium risk, high risk and very high risk, respectively.
6. The method for evaluating the risk of the tunnel inrush water disaster based on the set pair analysis as claimed in claim 1, wherein the risk level for evaluating the risk of the tunnel inrush water disaster is obtained according to equation 8,
formula 8 is:
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