CN112100851A - Method for evaluating tunnel water inrush disaster risk based on set pair analysis - Google Patents

Abstract

The invention provides a method for evaluating the risk of water inrush disaster in a tunnel based on set pair analysis. The method selects 4 first-level parameters and 12 second-level parameters, constructs an assessment system for the risk of water inrush disaster risk in tunnels, and uses AHP (Analytical Hierarchy Process) method to determine the weights of parameters at each level. On this basis, the set-pair analysis theory is introduced, and a set-pair analysis method for evaluating the risk of water inrush in a tunnel based on the improved connection degree calculation is proposed. The risk level is used as the level of water inrush risk disaster in the tunnel, so as to guide the construction process of the tunnel. The invention has the advantages of being able to determine the values of parameters of each level according to the preliminary excavation results of the tunnel, thereby calculating the level of the risk of water inrush in the tunnel, and having theoretical guiding significance for engineering practice and the like.

Description

A method for evaluating the risk of water inrush disasters in tunnels based on set pair analysis

technical field

The invention relates to a method for evaluating the risk of water inrush disaster in a tunnel based on set pair analysis, and belongs to the technical field of tunnel construction.

Background technique

Water inrush is one of the most dangerous geological disasters in tunnel construction. In order to realize the effective management and control of water inrush risk, a method based on set pair analysis to systematically identify and evaluate the risk level of tunnel water inrush is proposed. Based on the Analytic Hierarchy Process, the present invention selects 4 first-level parameters and 12 second-level parameters, constructs a risk assessment system for tunnel water inrush disaster, and adopts AHP (Analytic Hierarchy Process) method to determine the parameters of each evaluation level the weight of. On this basis, the set-pair analysis theory is introduced, and a set-pair analysis method for tunnel water inrush risk based on improved connection degree calculation is proposed. In the specific application process, the reliability of the method is finally verified based on the actual excavation results, which has certain reference significance for the decision-making of engineering and technical personnel.

SUMMARY OF THE INVENTION

The purpose of the present invention is to solve at least one of the above deficiencies of the prior art. For example, the purpose of the present invention is to provide a method for systematically identifying and evaluating the risk level of water inrush in a tunnel based on set pair analysis.

In order to achieve the above object, the present invention provides a method for evaluating the risk of water inrush disaster in a tunnel based on set pair analysis. The method calculates the comprehensive connection degree of the water inrush disaster risk in the tunnel through Equation 1,

Formula 1 is:

Among them, μ _δ is the comprehensive connection degree of water inrush disaster in the tunnel, μ _{i, δ} is the connection degree of each second-level parameter to the risk levels I, II, III and IV, and _wi is the corresponding level of each second-level parameter. Weight value vector, δ=1, 2, ..., m.

In an exemplary embodiment of the present invention, the degree of connection μ _i,δ of the respective second-level parameters to the I, II, III and IV risk levels is calculated by the following method:

For any second-level parameter i corresponding to the I, II, III and IV risk levels, the hierarchical universe set is:

{(S _(i,1) ,S _(i,2) ),(S _(i,2) ,S _(i,3) ),.......,(.S _(i,m) , S _(i,m+1) )}, the calculation method of the connection degree of any second-level parameter i is as follows:

The traditional set-pair connection degree calculation is to treat the set pair composed of the individual measured value and the separated hierarchical universe as an opposite set pair, and the connection degree value is -1, which distorts the evaluation result. In fact, the measured hierarchical parameters have different degrees of correlation with all the hierarchical universes, and this correlation decreases with the distance of the interval, that is, in the evaluation system, the set-pair connection degree shows a gradual trend. Therefore, the opposition of the set pair is cancelled, and its identity and difference are preserved. but,

(1) In the case that the value λ of any second-level parameter i is within the hierarchical universe (S _(i,k) , S _(i,k+1) ), the value of any second-level parameter i is The set pair {λ,(S _(i,k) ,S _(i,k+1) )} composed of the value λ and the hierarchical universe (S _(i,k) ,S ₍ i,k+1)) has Identity, the connection degree of any second-level parameter i _i,k =1;

(2) When the value λ of any second-level parameter i is outside the hierarchical universe (S _(i,k) , S _(i,k+1) ), the value of any second-level parameter i is The set pairs composed of the value λ and other hierarchical universes are regarded as different set pairs, and the calculation of the connection degree of any second-level parameter i is divided into the following three cases:

①Other hierarchical universes are located before (S _(i,k) , S _(i,k+1) ), then the connection degree μ _i,δ of any second-level parameter i is calculated by formula 2,

Formula 2 is:

Among them, δ=1, 2, ..., k-1;

②Other hierarchical universes are located after (S _(i,k) , S _(i,k+1) ), then the connection degree μ _i,δ of any second-level parameter i is calculated by formula 3,

Formula 3 is:

Among them, δ=k+1, k+2, ..., m;

③The value λ of any two-level parameter i is equal to the boundary value of the two-level universe of discourse, that is, λ=S _(i,k) , and the calculation is performed for the following two cases:

a. The value λ of any second-level parameter i and two adjacent hierarchical universes respectively form a set pair, then the connection degree μ _i,k or μ _i,k-1 of any second-level parameter i is calculated by formula 4 or 5 calculations,

Formula 4 is:

Formula 5 is:

b. The value λ of any second-level parameter i and two other separated hierarchical universes respectively form a set pair, then the connection degree μ _i,δ of any second-level parameter i is calculated by formulas 6 and 7,

Formula 6 is:

Among them, δ=1, 2, ..., k-2;

Formula 7 is:

Among them, δ=k+1, k+2, ..., m.

In an exemplary embodiment of the present invention, the weight vector w _i can be obtained by the following method:

The four parameters of unfavorable geology, stratigraphic lithology, hydraulic conditions and human factors are used as the first-level parameters for evaluating the risk of water inrush disaster in tunnels. Among them, unfavorable geology is further divided into three categories: faults, folds and interlayer fissures. Folds and interlayer fissures are the secondary level parameters corresponding to poor geology; stratum lithology is further divided into three types: surrounding rock grade, rock formation occurrence and rock formation combination, and the surrounding rock grade, rock formation occurrence and rock formation combination are used as the corresponding formation lithology. The hydraulic conditions are further divided into three categories: atmospheric precipitation, topography and head pressure, and atmospheric precipitation, topography and head pressure are taken as the second-level parameters corresponding to hydraulic conditions; human factors are further divided into construction methods, advanced There are three types of forecasting and advanced support, with construction methods, advanced forecasting and advanced support as the second-level parameters corresponding to human factors; four first-level parameters correspond to twelve second-level parameters, and twelve second-level parameters Each second-level parameter corresponds to the I, II, III, and IV-level classification universes according to the severity; the first-level discriminant matrix and four second-level discriminant matrices corresponding to the four first-level parameters are generated, and the first-level discriminant is calculated. The eigenvalues and eigenvectors of the matrix and the second-level discriminant matrix are used to obtain the weight value vector w _i corresponding to the twelve second-level parameters.

In an exemplary embodiment of the present invention, the calculation of the weight vector w _i may include the steps:

The four first-level parameters are scaled by the 1-9 scale method to obtain twelve first-level assignments; the 1-9 scale method is used to scale the three second-level parameters corresponding to the poor geology, and nine first-level assignments are obtained. The first and second-level assignments are obtained; the 1-9 scale method is used to scale the three second-level parameters corresponding to the formation lithology to obtain nine second-level assignments; the 1-9 scale method is used to evaluate the hydraulic conditions The corresponding three second-level parameters are scaled to obtain nine third-level assignments; the 1-9 scale method is used to scale the three second-level parameters corresponding to human factors to obtain nine fourth-level assignments ; Arrange the twelve first-level assignments in rows and columns to obtain the first-level discrimination matrix; arrange the nine first-level assignments in rows and columns to obtain the first-level discrimination matrix; arrange the nine second-level assignments according to Arrange the rows and columns to obtain the second-level discrimination matrix; arrange the nine third-level assignments in rows and columns to obtain the third-level discrimination matrix; arrange the nine fourth-level assignments in rows and columns to obtain the fourth-level discrimination matrix Level discriminant matrix; calculate the eigenvalues and eigenvectors of the first level discriminant matrix, the first level discriminant matrix, the second level discriminant matrix, the third level discriminant matrix and the fourth level discriminant matrix respectively and perform consistency check, Normalize the eigenvectors of the first-level discriminant matrix, the second-level discriminant matrix, the third-level discriminant matrix, and the fourth-level discriminant matrix to obtain the weight of each level-two-level parameter relative to the respective level-one parameter value vector w _i .

In an exemplary embodiment of the present invention, the risk levels I, II, III and IV may correspond to low risk, medium risk, high risk and extremely high risk of sudden water inrush disasters occurring in the tunnel, respectively.

In an exemplary embodiment of the present invention, the risk level for evaluating the risk of water inrush disaster in a tunnel can be obtained according to Equation 8,

Formula 8 is:

Compared with the prior art, the beneficial effects of the present invention may include at least one of the following:

(1) The present invention selects 4 first-level level parameters and 12 second-level level parameters to construct a risk assessment system for tunnel water inrush disasters, and uses AHP (Analytical Hierarchy Process) method to determine the weight of each evaluation level parameter. On this basis, the set-pair analysis theory is introduced, and a set-pair analysis method for tunnel water inrush risk based on the calculation of improved connection degree is proposed, which has theoretical guiding significance for engineering practice.

(2) Taking the tunnel exit section of on-site construction as an example, the specific application process of the method is explained in detail, and finally the reliability of the method is verified based on the actual excavation results, which has certain reference significance for engineering and technical personnel to make decisions.

Description of drawings

FIG. 1 shows an evaluation hierarchical parameter system constructed by a method for evaluating the risk of water inrush disaster in a tunnel based on set pair analysis according to an exemplary embodiment of the present invention.

Detailed ways

Hereinafter, the method for evaluating the risk of water inrush disaster in a tunnel based on the set pair analysis of the present invention will be described in detail with reference to the accompanying drawings and exemplary embodiments.

FIG. 1 shows an evaluation hierarchical parameter system constructed by a method for evaluating the risk of water inrush disaster in a tunnel 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 water inrush disaster in a tunnel based on set pair analysis can calculate the comprehensive connection degree of the risk of water inrush disaster in a tunnel through Equation 1,

其中，μ_δ为隧道发生突涌水灾害的综合联系度。所待评价隧道发生突涌水灾害风险的风险等级可根据式8得到，式8为：

具体来讲，通过式1分别计算出对应Ⅰ、Ⅱ、Ⅲ和Ⅳ级风险等级的综合联系度，通过式8得到隧道发生突涌水灾害的风险等级。也就是说，通过比较隧道发生Ⅰ、Ⅱ、Ⅲ和Ⅳ级突涌水灾害的综合联系度大小，综合联系度越大说明越容易发生该等级的风险，综合联系度值最大的一级风险等级即为隧道发生突涌水的风险等级μ_i,δ为各个二级层次参数对Ⅰ、Ⅱ、Ⅲ和Ⅳ级风险等级的联系度。所述各个二级层次参数对Ⅰ、Ⅱ、Ⅲ和Ⅳ级风险等级的联系度μ_i,δ通过以下方法计算：Formula 1 is:

Among them, μ _δ is the comprehensive connection degree of the water inrush disaster in the tunnel. The risk level of the water inrush disaster risk of the tunnel to be evaluated can be obtained according to Equation 8, and Equation 8 is:

Specifically, the comprehensive connection degree corresponding to the risk levels of I, II, III and IV is calculated respectively by Equation 1, and the risk level of the tunnel water inrush disaster is obtained by Equation 8. That is to say, by comparing the comprehensive connection degree of grade I, II, III and IV water inrush disasters in tunnels, the greater the comprehensive connection degree, the more likely the risk of this level will occur. The first-level risk level with the largest comprehensive connection degree value is the is the risk level of water inrush in the tunnel, μ _{i, δ} is the connection degree of each second-level parameter to the I, II, III and IV risk levels. The degree of connection μ _i,δ of each of the second-level parameters to the I, II, III and IV risk levels is calculated by the following method:

For any second-level parameter i corresponding to the I, II, III and IV risk levels, the hierarchical universe set is:

{(S _(i,1) ,S _(i,2) ),(S _(i,2) ,S _(i,3) ),.......,(.S _(i,m) , S _(i,m+1) )}, the calculation method of the connection degree of any second-level parameter i is as follows:

(1) In the case that the value λ of any second-level parameter i is within the hierarchical universe (S _(i,k) , S _(i,k+1) ), the value of any second-level parameter i is The set pair {λ,(S _(i,k) ,S _(i,k+1) )} composed of the value λ and the hierarchical universe (S _(i,k) ,S ₍ i,k+1)) has Identity, the connection degree of any second-level parameter i _i,k =1;

(2) When the value λ of any second-level parameter i is outside the hierarchical universe (S _(i,k) , S _(i,k+1) ), the value of any second-level parameter i is The set pairs composed of the value λ and other hierarchical universes are regarded as different set pairs, and the calculation of the connection degree of any second-level parameter i is divided into the following three cases:

①Other hierarchical universes are located before (S _(i,k) , S _(i,k+1) ), then the connection degree μ _i,δ of any second-level parameter i is calculated by formula 2,

Formula 2 is:

Among them, δ=1, 2, ..., k-1;

②Other hierarchical universes are located after (S _(i,k) , S _(i,k+1) ), then the connection degree μ _i,δ of any second-level parameter i is calculated by formula 3,

Formula 3 is:

Among them, δ=k+1, k+2, ..., m;

③The value λ of any two-level parameter i is equal to the boundary value of the two-level universe of discourse, that is, λ=S _(i,k) , and the calculation is performed for the following two cases:

a. The value λ of any second-level parameter i and two adjacent hierarchical universes respectively form a set pair, then the connection degree μ _i,k or μ _i,k-1 of any second-level parameter i is calculated by formula 4 or 5 calculations,

Formula 4 is:

Formula 5 is:

b. The value λ of any second-level parameter i and two other separated hierarchical universes respectively form a set pair, then the connection degree μ _i,δ of any second-level parameter i is calculated by formulas 6 and 7,

Formula 6 is:

Among them, δ=1, 2, ..., k-2;

Formula 7 is:

Among them, δ=k+1, k+2, ..., m.

w _i is the weight value vector corresponding to each secondary level parameter, δ=1, 2, ..., m. The weight vector w _i can be obtained by the following methods:

The four parameters of unfavorable geology, stratigraphic lithology, hydraulic conditions and human factors are used as the first-level parameters for evaluating the risk of water inrush disaster in tunnels. Among them, unfavorable geology is further divided into three categories: faults, folds and interlayer fissures. Folds and interlayer fissures are the secondary level parameters corresponding to poor geology; stratum lithology is further divided into three types: surrounding rock grade, rock formation occurrence and rock formation combination, and the surrounding rock grade, rock formation occurrence and rock formation combination are used as the corresponding formation lithology. The hydraulic conditions are further divided into three categories: atmospheric precipitation, topography and head pressure, and atmospheric precipitation, topography and head pressure are taken as the second-level parameters corresponding to hydraulic conditions; human factors are further divided into construction methods, advanced There are three types of forecasting and advanced support, with construction methods, advanced forecasting and advanced support as the second-level parameters corresponding to human factors; four first-level parameters correspond to twelve second-level parameters, and twelve second-level parameters Each second-level parameter corresponds to the I, II, III, and IV-level classification universes according to the severity; the first-level discriminant matrix and four second-level discriminant matrices corresponding to the four first-level parameters are generated, and the first-level discriminant is calculated. The eigenvalues and eigenvectors of the matrix and the second-level discriminant matrix are used to obtain the weight value vector w _i corresponding to the twelve second-level level parameters. Specifically, as shown in Fig. 1, poor geology (A ₁ ), formation lithology (A ₂ ), hydraulic conditions (A ₃ ), and human factors (A ₄ ) are used as the first-level layers for evaluating the risk of water inrush in tunnels parameter. Among them, unfavorable geology (A ₁ ) is further divided into three second-level parameters: faults (B ₁ ), folds (B ₂ ) and interlayer fissures (B ₃ ); stratigraphic lithology (A ₂ ) is further divided into surrounding rock levels (B ₄ ), rock formation occurrence (B ₅ ), and rock formation assemblages (B ₆ ) three second-level parameters; hydraulic conditions (A ₃ ) are further divided into atmospheric precipitation (B ₇ ), topography (B ₈ ), water head Pressure (B ₉ ) three second-level parameters; human factors (A ₄ ) are further divided into three second-level parameters of construction method (B ₁₀ ), advance forecast (B ₁₁ ) and advance support (B ₁₂ ). Based on the risk parameter system U, four first-level parameters and twelve second-level parameters, the parameter system for evaluating the risk of tunnel water inrush disaster risk is constructed as shown in Figure 1.

Next, determine the hierarchical universe of parameters for each evaluation level

According to the characteristics of the disaster-pregnancy environment of the tunnel and the influence of the parameters of each level on the occurrence of water inrush disasters in the tunnel, combined with the development status of geological disasters in the tunnel site and the results of statistical analysis of correlation, the risk levels of water inrush in the tunnel are classified as low risk ( Grade I), medium risk (grade II), high risk (grade III) and extremely high risk (grade IV), the classification standards for evaluating the risk of water inrush disasters in tunnels and their corresponding gradation domain.

Table 1. The classification standards for evaluating the risk of water inrush disasters in tunnels and their corresponding classification domains

Here, the hierarchical universe of qualitative hierarchical parameters such as faults, folds, lithologic combinations, construction methods, advanced forecasting and advanced support is given by means of assignment. For example, assign the classification universes of faults, folds, lithologic combinations, construction methods, advanced forecasting and advanced support corresponding to I, II, III and IV risk levels as (0, 4), (4, 6), (6, 8) and (8, 10) In the present embodiment, the I, II, III and IV risk levels may respectively correspond to low risk, medium risk, high risk and extremely high risk water inrush disaster in the tunnel.

The calculation of the weight vector w _i may include the steps of: using the 1-9 scale method to scale the four first-level parameters to obtain twelve first-level assignments; using the 1-9 scale method to correspond to the bad geological The three second-level parameters are scaled to obtain nine first-level assignments; the three second-level parameters corresponding to the formation lithology are scaled by the 1-9 scale method to obtain nine second-level assignments ; Using the 1-9 scale method to scale the three second-level parameters corresponding to hydraulic conditions to obtain nine third-level assignments; using the 1-9 scale method to scale the three second-level parameters corresponding to human factors The parameters are scaled to obtain nine fourth-level assignments; the twelve first-level assignments are arranged in rows and columns to obtain a first-level discriminant matrix; the nine first-level assignments are arranged in rows and columns to obtain the first and second levels Discriminant matrix; arrange nine second-level assignments in rows and columns to obtain a second-level discrimination matrix; arrange nine third-level assignments in rows and columns to obtain a third-level discrimination matrix; arrange nine fourth-level assignments in rows and columns to obtain a third-level discrimination matrix; The second-level assignment is arranged in rows and columns to obtain the fourth-level discriminant matrix; the first-level discriminant matrix, the first-level discriminant matrix, the second-level discriminant matrix, the third-level discriminant matrix, and the fourth-level discriminant matrix are calculated respectively. The eigenvalues and eigenvectors of the The weight value vector w _i of the level-level parameters relative to the respective level-level parameters. Specifically, determining the weight of each evaluation level parameter includes the following steps:

(1) Determine the judgment matrix

The 1-9 scale method is used to judge, and the pairwise comparison is adopted, and a _ij is used to represent the ratio of the influence degree of the evaluation level parameters C _i and C _j on the target (that is, the sudden water gushing disaster in the tunnel).

Table 2 Definition of scales 1 to 9

Assign a_ij meaning 1 C_i is equally important compared to C_j 3 C_i is slightly more important than C_j 5 C_i is obviously more important than C_j 7 C_i is more important than C_j 9 C_i is extremely important compared to C_j 2,4,6,8 between two adjacent levels reciprocal 1/a_ij is C_j compared to C_i

Among them, i and j represent the serial numbers of the rows and columns in the matrix of the hierarchical parameters.

According to the above method, the first-level discriminant matrix and four second-level discriminant matrices are obtained as shown in Tables 3 to 7:

Table 3 First-level discriminant matrix Q ₁

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 The first and second level discriminant matrix Q ₂

A₁-B B₁ B₂ B₃ B₁ b₁₁ b₁₂ b₁₃ B₂ b₂₁ b₂₂ b₂₃ B₃ b₃₁ b₃₂ b₃₃

Table 5 Second-level discriminant matrix Q ₃

A₂-B B₄ B₅ B₆ B₄ b₄₄ b₄₅ b₄₆ B₅ b₅₄ b₅₅ b₅₆ B₆ b₆₄ b₆₅ b₆₆

Table 6 The third-level discriminant matrix Q ₄

A₃-B B₇ B₈ B₉ B₇ b₇₇ b₇₈ b₇₉ B₈ b₈₇ b₈₈ b₈₉ B₉ b₉₇ b₉₈ b₉₉

Table 7 Fourth level discriminant matrix Q ₅

A₄-B B₁₀ B₁₁ B₁₂ B₁₀ b₁₀₁₀ b₁₀₁₁ b₁₀₁₂ B₁₁ b₁₁₁₀ b₁₁₁₁ b₁₁₁₂ B₁₂ b₁₂₁₀ b₁₂₁₁ b₁₂₁₂

(2) Calculate eigenvalues and eigenvectors

The eigenvalues and eigenvectors of the above judgment matrices Q ₁ to Q ₅ are calculated respectively, and the consistency check is carried out. The results are shown in Table 8:

Table 8 Eigenvectors and eigenvalues of each judgment matrix

Judgment Matrix Feature vector Eigenvalues 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) Calculate the weights of parameters at each level

The eigenvector u is normalized, and the obtained vector w is used as the relative weight value vector w _i of each factor relative to the upper-level criterion. The results are shown in Table 9.

Table 9 Total Ranking of Hierarchical Combinations

Among them, level A represents the first-level level parameter, and level B represents the second-level level parameter. The specific values of the matrices in Tables 3 to 9 are as follows:

Table 3 First-level discriminant matrix Q ₁

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 The first and second level discriminant matrix Q ₂

A₁-B B₁ B₂ B₃ B₁ 1 4 3 B₂ 1/4 1 1/2 B₃ 1/3 2 1

Table 5 Second-level discriminant matrix Q ₃

A₂-B B₄ B₅ B₆ B₄ 1 1/4 1/3 B₅ 4 1 1/2 B₆ 3 2 1

Table 6 The third-level discriminant matrix Q ₄

A₃-B B₇ B₈ B₉ B₇ 1 1/3 1/5 B₈ 3 1 1/3 B₉ 5 3 1

Table 7 Fourth level discriminant matrix Q ₅

A₄-B B₁₀ B₁₁ B₁₂ B₁₀ 1 1/3 1/4 B₁₁ 3 1 1 B₁₂ 4 1 1

Table 8 Eigenvectors and eigenvalues of each judgment matrix

Judgment Matrix Feature vector Eigenvalues 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 Total Ranking of Hierarchical Combinations

The exemplary embodiments of the present invention and their effects will be further described and explained below in conjunction with specific examples.

Taking the section K7+940～K8+160 at the exit of a tunnel as an example, the specific application of the method for evaluating the risk of water inrush disaster in a tunnel based on set pair analysis is used, and the correctness of the evaluation results is verified through the comparative analysis with the on-site construction situation. .

(1) Determination of the values of parameters at each level

Preliminary excavation of this section of the tunnel shows that the designed surrounding rock of this section is V-grade, the basic quality layer parameter BQ of the surrounding rock is 230, the average burial depth of this section of the tunnel is 230m, and the surrounding rock is composed of the middle Triassic Zagunao sand. It is composed of slate and slate with a small amount of metamorphic sandstone. The rock mass is relatively fragmented to extremely fragmented. It is a soft rock and is affected by the fault traction structure. The schistose dense zone is more common, and the groundwater type is mainly bedrock fissure water. The construction method adopts the method of annular excavation to retain the core soil, and adopts the advanced bolt to control the deformation of the surrounding rock. According to the design documents of the construction drawing, the risk level evaluation factors of water inrush disaster at the exit of the tunnel K7+940～K8+160 are obtained as shown in Table 10.

Table 10 Evaluation factors for the risk level of water inrush disaster in a tunnel

Among them, λ is the measured value or assignment of each second-level parameter.

(2) Calculation of connection degree

Taking the hierarchical parameter "fault (B ₁ )" as an example, the calculation process of the connection degree is described in detail. It can be obtained from Table 10 and Table 1. The set pairs composed of the hierarchical parameter B ₁ and the hierarchical universe corresponding to each risk level are: {5,(0,4)}, {5,(4,6)}, {5 ,(6,8)}, {5,(8,10)}. 5 is located between the domain theory (4, 6), then μ 1, ₂ =1, and the connection degree is calculated according to formulas 6 and 7 in turn:

In the same way, according to the correspondence between the other two-level parameters and their corresponding hierarchical universes, select the corresponding calculation formulas in Equations 2 to 7, and the relationship between the other 11 level parameters and each risk level can be calculated. degree, so as to obtain the connection degree matrix of the comprehensive state assessment of this section of the tunnel. Then according to formula 1, the comprehensive connection degree between this section of tunnel and each risk level can be calculated.

It can be seen from the above calculation results that the comprehensive connection degree between the evaluation tunnel section and the high risk level is 0.633, which is the maximum value among the four comprehensive connection degrees. Therefore, the risk level of water inrush in this section of the tunnel is determined to be level III, that is, "high risk". After the excavation, the surrounding rocks of the tunnel face (mileage pile number K8+067.4) are mainly sandy slate and carbonaceous sericite slate, which are thin to medium-thick layers, with developed joints and fissures, and developed in the middle of the tunnel face. The interlayer fractured zone is about 0.8-1.5m thick. The rock mass in the fractured zone has a small fragmentary structure, and the groundwater flows out of the fractured zone in the form of strands. The stability is poor, and the rock mass falls from time to time, so this section is a surge Water inrush high risk paragraph.

To sum up, the beneficial effects of the present invention include at least one of the following:

(1) The present invention selects 4 first-level level parameters and 12 second-level level parameters to construct a risk assessment system for tunnel water inrush disasters, and uses AHP (Analytical Hierarchy Process) method to determine the weight of each evaluation level parameter. On this basis, the set-pair analysis theory is introduced, and a set-pair analysis method for tunnel water inrush risk based on the calculation of improved connection degree is proposed, which has theoretical guiding significance for engineering practice.

(2) Taking the tunnel exit section of on-site construction as an example, the specific application process of the method is explained in detail, and finally the reliability of the method is verified based on the actual excavation results, which has certain reference significance for engineering and technical personnel to make decisions.

While the present invention has been described above in conjunction with the exemplary embodiments and accompanying drawings, it will be apparent to those skilled 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 sudden water gushing in a tunnel based on set-to-analysis, is characterized in that, the method calculates the comprehensive connection degree of the sudden gushing water disaster risk in tunnel by formula 1, Formula 1 is:

Among them, μ _δ is the comprehensive connection degree of water inrush disaster in the tunnel, μ _{i, δ} is the connection degree of each second-level parameter to the risk levels I, II, III and IV, and _wi is the corresponding level of each second-level parameter. Weight value vector, δ=1, 2, ..., m. 2. The method for evaluating the risk of water inrush disaster risk in a tunnel based on set pair analysis according to claim 1, wherein the degree of connection μ of each second-level parameter to the I, II, III and IV risk levels _i,δ are calculated by: For any second-level parameter i corresponding to the I, II, III and IV risk levels, the hierarchical universe set is: {(S _(i,1) ,S _(i,2) ),(S _(i,2) ,S _(i,3) ),.......,(.S _(i,m) , S _(i,m+1) )}, the calculation method of the connection degree of any second-level parameter i is as follows: (1) In the case that the value λ of any second-level parameter i is within the hierarchical universe (S _(i,k) , S _(i,k+1) ), the value of any second-level parameter i is The set pair {λ,(S _(i,k) ,S _(i,k+1) )} composed of the value λ and the hierarchical universe (S _(i,k) ,S ₍ i,k+1)) has Identity, the connection degree of any second-level parameter i _i,k =1; (2) When the value λ of any second-level parameter i is outside the hierarchical universe (S _(i,k) , S _(i,k+1) ), the value of any second-level parameter i is The set pairs composed of the value λ and other hierarchical universes are regarded as different set pairs, and the calculation of the connection degree of any second-level parameter i is divided into the following three cases: ①Other hierarchical universes are located before (S _(i,k) , S _(i,k+1) ), then the connection degree μ _i,δ of any second-level parameter i is calculated by formula 2, Formula 2 is:

Among them, δ=1, 2, ..., k-1; ②Other hierarchical universes are located after (S _(i,k) , S _(i,k+1) ), then the connection degree μ _i,δ of any second-level parameter i is calculated by formula 3, Formula 3 is:

Among them, δ=k+1, k+2, ..., m; ③The value λ of any two-level parameter i is equal to the boundary value of the two-level universe of discourse, that is, λ=S _(i,k) , and the calculation is performed for the following two cases: a. The value λ of any second-level parameter i and two adjacent hierarchical universes respectively form a set pair, then the connection degree μ _i,k or μ _i,k-1 of any second-level parameter i is calculated by formula 4 or 5 calculations, Formula 4 is:

Formula 5 is:

b. The value λ of any second-level parameter i and two other separated hierarchical universes respectively form a set pair, then the connection degree μ _i,δ of any second-level parameter i is calculated by formulas 6 and 7, Formula 6 is:

Among them, δ=1, 2, ..., k-2; Formula 7 is:

Among them, δ=k+1, k+2, ..., m. 3. The method for evaluating the risk of sudden water gushing in a tunnel based on set pair analysis according to claim 1, wherein the weight vector w _i can be obtained by the following methods: The four parameters of unfavorable geology, stratum lithology, hydraulic conditions and human factors are used as the first-level parameters for evaluating the risk of water inrush disaster in tunnels. Among them, Unfavorable geology is further divided into three categories: faults, folds and interlayer fissures, and faults, folds and interlayer fissures are regarded as the secondary level parameters corresponding to unfavorable geology; stratigraphic lithology is further divided into three types: surrounding rock grade, rock layer occurrence and rock layer combination. The surrounding rock grade, rock formation occurrence and rock formation combination are used as the secondary level parameters corresponding to the stratum lithology; the hydraulic conditions are further divided into three categories: atmospheric precipitation, topography and head pressure, and atmospheric precipitation, topography and head pressure are used as the The second-level parameters corresponding to hydraulic conditions; human factors are further divided into three categories: construction method, advance forecast and advance support, and construction method, advance forecast and advance support are taken as the second-level parameters corresponding to human factors; Four first-level parameters correspond to twelve second-level parameters, and each second-level parameter in the twelve second-level parameters corresponds to I, II, III and IV hierarchical universes according to the severity; Generate the first-level discriminant matrix and four second-level discriminant matrices corresponding to the four first-level parameters, calculate the eigenvalues and eigenvectors of the first-level discriminant matrix and the second-level discriminant matrix, and obtain the weights corresponding to the twelve second-level parameters. value vector w _i . 4. the method for evaluating the risk of sudden water gushing disaster in tunnel based on set pair analysis according to claim 3, is characterized in that, the calculation of described weight vector _wi comprises the steps: The four first-level parameters are scaled by the 1-9 scale method to obtain twelve first-level assignments; the 1-9 scale method is used to scale the three second-level parameters corresponding to the poor geology, and nine first-level assignments are obtained. The first and second-level assignments are obtained; the 1-9 scale method is used to scale the three second-level parameters corresponding to the formation lithology to obtain nine second-level assignments; the 1-9 scale method is used to evaluate the hydraulic conditions The corresponding three second-level parameters are scaled to obtain nine third-level assignments; the 1-9 scale method is used to scale the three second-level parameters corresponding to human factors to obtain nine fourth-level assignments ; Arrange the twelve first-level assignments in rows and columns to obtain the first-level discrimination matrix; arrange the nine first-level assignments in rows and columns to obtain the first-level discrimination matrix; arrange the nine second-level assignments in rows Arrange the second-level discriminant matrix with the columns; arrange the nine third-level assignments in rows and columns to obtain the third-level discrimination matrix; arrange the nine fourth-level assignments in rows and columns to obtain the fourth level discriminant matrix; Calculate the eigenvalues and eigenvectors of the first-level discriminant matrix, the first-level discriminant matrix, the second-level discriminant matrix, the third-level discriminant matrix, and the fourth-level discriminant matrix, respectively, and perform consistency checks. The eigenvectors of the level discriminant matrix, the second level discriminant matrix, the third level discriminant matrix and the fourth level discriminant matrix are normalized to obtain the weight value vector w _i of each level two level parameter relative to the respective level one level parameter . 5. The method for evaluating the risk of water inrush disaster in a tunnel based on set pair analysis according to claim 1, wherein the I, II, III and IV risk levels correspond to low risk, medium risk, High and very high risk water inrush hazards. 6. The method for evaluating the risk of sudden water gushing disaster in a tunnel based on set pair analysis according to claim 1, wherein the risk level of the risk of water gushing disaster in the evaluation tunnel is obtained according to formula 8, Formula 8 is:

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