CN115689387A - Comprehensive evaluation method and system for collapse disaster risk of karst tunnel - Google Patents
Comprehensive evaluation method and system for collapse disaster risk of karst tunnel Download PDFInfo
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Abstract
The invention discloses a comprehensive evaluation method and a comprehensive evaluation system for collapse disaster risks of a karst tunnel, wherein the method comprises the following steps: constructing a collapse risk evaluation system and a corresponding hierarchical structure of the karst tunnel; establishing each level judgment matrix, calculating a comprehensive weight vector, checking the consistency of each judgment matrix, and checking the rationality of the judgment matrices; establishing a collapse disaster risk evaluation set of the karst tunnel; determining the membership degree of the risk assessment index and forming a fuzzy matrix; carrying out fuzzy comprehensive evaluation on the fuzzy matrix and a weight matrix consisting of comprehensive weight vectors to obtain a fuzzy comprehensive evaluation set; and according to the maximum membership principle, taking the risk grade corresponding to the maximum element in the fuzzy evaluation set in the karst tunnel collapse disaster risk evaluation set as an evaluation result to form a karst collapse disaster risk comprehensive evaluation model. The invention has comprehensive evaluation system, easy acquisition of evaluation index parameters and easy operation of evaluation process; the interference of artificial subjective factors is eliminated; the construction safety of the karst tunnel is improved.
Description
Technical Field
The invention relates to the field of collapse disasters, in particular to a comprehensive evaluation method and system for collapse disaster risks of karst tunnels.
Background
The highly soluble rock is widely distributed in China, and the area of the highly soluble rock is as much as 365 km 2 It occupies more than 1/3 of the territory area in China. Under the action of corrosion and erosion of surface water and underground water, karst landscapes such as karst funnels, karst depressions, skylights, karst cave holes and the like are generated in the high-solubility rock area, and the influence on the surrounding tunnel engineering is mainly reflected in the phenomenon of easy karst collapse caused in tunnel construction.
Summarizing a large number of domestic karst tunnel collapse accidents, the formation mechanism can be summarized as follows: a large number of large holes exist in the karst tunnel, and when the tunnel is excavated, stress is redistributed to destroy the stress balance of the original structure, and key rock stratums are damaged in an instable manner. The water in the original holes can be discharged due to the reduction of the underground water level, so that the stress of the structure is changed. The occurrence mechanism of tunnel collapse is known, and the occurrence of tunnel collapse is mainly limited by conditions such as hydrogeological conditions, topography, stratigraphic lithology and geological structure. At present, for the risk assessment of the collapse disaster of the karst tunnel considering the influence factors, the traditional expert scoring method has high subjectivity and often causes the contradiction between weight prediction and actual conditions, and most of other existing risk assessment methods rely on single or individual indexes to assess the risk degree, so that the practicability of assessment results is insufficient.
Therefore, in order to guarantee the construction safety of the karst tunnel practically, make up for the short plates of the existing karst tunnel risk assessment method, scientifically and reasonably formulate a unified risk assessment index and establish a comprehensive risk assessment system, the problem to be solved at present is urgent.
Disclosure of Invention
Aiming at the defects in the prior art, the comprehensive evaluation method and system for the collapse disaster risk of the karst tunnel solve the problems of large subjective factors and insufficient practicability of evaluation results in the prior art.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a comprehensive evaluation method for collapse disaster risks of karst tunnels comprises the following steps:
s1, constructing a collapse risk evaluation system and a corresponding hierarchical structure of the karst tunnel by investigating collapse cases of the karst tunnel;
s2, establishing judgment matrixes of each hierarchy structure, calculating comprehensive weight vectors, checking consistency of the judgment matrixes, and checking the rationality of the judgment matrixes;
s3, carrying out risk classification according to the evaluation indexes, and establishing a karst tunnel collapse disaster risk evaluation set;
s4, determining the membership degree of the risk assessment index according to the collapse disaster risk evaluation set of the karst tunnel, and forming a fuzzy matrix;
s5, carrying out fuzzy comprehensive evaluation on the fuzzy matrix and a weight matrix composed of comprehensive weight vectors to obtain a fuzzy comprehensive evaluation set;
and S6, according to the maximum membership principle, taking the risk level corresponding to the maximum element in the fuzzy evaluation set in the karst tunnel collapse disaster risk evaluation set as an evaluation result, and forming a karst collapse disaster risk comprehensive evaluation model.
Further, the karst tunnel collapse risk assessment system comprises a criterion layer and an index layer; wherein:
obtaining a target of a karst tunnel collapse risk evaluation system classified into karst collapse risk grades in tunnel construction;
the criterion layer comprises four evaluation indexes, namely geological condition indexes, natural condition indexes, karst cave characteristic indexes and design and construction factor indexes;
the index layer comprises specific evaluation objects forming four evaluation indexes of the criterion layer;
the geological condition indexes comprise surrounding rock grade, surrounding rock softening coefficient and weathering degree;
the natural condition indexes comprise rainfall and underground water conditions;
the karst cave characteristic indexes comprise the size of the karst cave, the clear distance between the karst cave and the tunnel, the characteristics of karst fillers and the position relation between the karst cave and the tunnel;
design and construction factor indexes comprise tunnel burial depth, tunnel section, excavation step distance, blasting method suitability, support timeliness and advance geological forecast and monitoring measurement scheme rationality.
Further, the specific implementation manner of step S2 is as follows:
s2-1, comparing evaluation indexes of all levels in a karst tunnel collapse risk evaluation system, and performing relative weight assignment on different evaluation indexes and the importance of a specific evaluation object to obtain a judgment matrix;
s2-3, calculating a matrix characteristic vector according to the judgment matrix, and performing normalization processing to obtain comprehensive weight vectors corresponding to each index and the corresponding layer;
s2-4, multiplying the weight vectors corresponding to the criterion layer and the index layer to obtain a comprehensive weight vector;
s2-5, according to a formula:
CR＝CI/RI
obtaining a judgment matrix consistency check index CI and a check coefficient CR corresponding to different layers; wherein λ is max Judging the maximum eigenvalue of the matrix; RI is an average random consistency index; j is the order of the judgment matrix;
when the matrix consistency check index CI and the check coefficient CR are in a set range, judging that the matrix is reasonable, and entering a step S3; otherwise, the step S2-1 is returned.
Further, the karst tunnel collapse disaster risk level evaluation set comprises four levels of low risk, medium risk, high risk and extremely high risk.
Further, the specific implementation manner of step S4 is as follows:
s4-1, calculating membership functions of different specific evaluation objects with different grades of collapse disaster risks of the karst tunnel;
and S4-2, calculating membership degree vectors according to the membership degree function, and taking the membership degree vectors corresponding to the same risk level as the same column of the matrix to obtain a matrix with 15 rows and 4 columns, namely a fuzzy matrix R.
Further, the specific implementation manner of step S4-1 is as follows:
s4-1-1, judging whether a specific evaluation object is a qualitative index or not; if yes, the membership function adopts a rectangular membership function
Wherein u is i Representing the risk level of the karst tunnel collapse disaster, i =1,2,3,4; respectively representing four grades of low risk, medium risk, high risk and extremely high risk; u represents a specific evaluation object; otherwise, entering a step S4-1-2;
s4-1-2, judging whether the specific evaluation object is the size of the karst cave or not and the clear distance between the karst cave and the tunnel; if so, determining membership functions of different levels of the karst cave size by using matlab software according to the measured data of the karst cave size, the collapse disaster risk level of the karst tunnel and the diameter of the tunnel; determining membership functions of different risk levels of the karst cave and tunnel clear distance by using matlab software according to the karst cave and tunnel clear distance, the tunnel diameter and the collapse disaster risk level of the karst tunnel; otherwise, entering a step S4-1-3;
s4-1-3, determining a membership function under a low risk level:
according to the formula:
obtaining the membership function mu of the softening coefficient of the low-risk grade surrounding rock A1 (x) (ii) a Wherein x is the softening coefficient of the surrounding rock; pi is the circumference ratio;
according to the formula:
obtaining a low risk grade rainfall membership function mu A1 (y); wherein y is rainfall;
according to the formula:
obtaining a tunnel buried depth membership function mu under a low risk level A1 (b) (ii) a Wherein, b represents the length of the tunnel buried depth;
according to the formula:
obtaining a characteristic membership function mu of the karst filler under a low risk level A1 (c) (ii) a Wherein c represents a karst filler characteristic;
determining membership functions at intermediate risk level:
according to the formula:
obtaining the membership function mu of the softening coefficient of the surrounding rock under the medium risk level A2 (x)；
According to the formula:
obtaining the rainfall membership function mu of the medium risk grade A2 (y)；
According to the formula:
obtaining tunnel buried depth membership function mu under medium risk A2 (b)；
According to the formula:
obtaining the characteristic membership function mu of the karst filler under the medium risk A2 (c)；
Determining membership functions at high risk levels:
according to the formula:
obtaining the membership function mu of the softening coefficient of the surrounding rock under the high risk level A3 (x)；
According to the formula:
obtaining a high risk grade rainfall membership function mu A3 (y)；
According to the formula:
obtaining a tunnel buried depth membership function mu under a high risk level A3 (b)；
According to the formula:
obtaining a characteristic membership function mu of the karst filler under a high risk level A3 (c)；
Determining membership functions at extremely high risk levels:
according to the formula:
obtaining the membership function mu of the softening coefficient of the surrounding rock under the extremely high risk level A4 (x)；
According to the formula:
obtaining the membership function mu of the extremely high risk rainfall A4 (y)；
According to the formula:
obtaining the tunnel buried depth membership function mu under extremely high risk A4 (b)；
According to the formula:
obtaining the characteristic membership function mu of the karst filler under extremely high risk A4 (c)。
Further, the specific implementation manner of step S5 is as follows:
s5-1, obtaining a weight matrix W = [ W ] according to the evaluation index comprehensive weight vector 1 ,w 2 ,...w m ]；
S5-2, according to a formula:
obtaining a fuzzy comprehensive judgment set B; wherein m represents the number of weight matrix factors, namely the number of index factors, and m =1,2, …,15; n represents the number of risk classes, n =1,2,3,4; r denotes the element of the blur matrix, b k The kth element of the fuzzy evaluation set is represented.
The comprehensive evaluation system for the collapse disaster risks of the karst tunnel comprises a karst tunnel collapse risk evaluation system construction module, a rationality judgment module, a karst tunnel collapse disaster risk evaluation set construction module, a fuzzy matrix construction module, a fuzzy comprehensive evaluation module and a comprehensive evaluation module; wherein:
the karst tunnel collapse risk assessment system construction module is used for constructing a karst tunnel collapse risk assessment system and a corresponding hierarchical structure through investigation on a karst tunnel collapse case;
the rationality judgment module is used for establishing judgment matrixes of each hierarchy structure, calculating comprehensive weight vectors, checking the consistency of each judgment matrix and checking the rationality of the judgment matrixes;
the karst tunnel collapse disaster risk evaluation set building module is used for carrying out risk classification according to the evaluation indexes and building a karst tunnel collapse disaster risk evaluation set;
the fuzzy matrix construction module is used for determining the membership degree of the risk assessment index according to the collapse disaster risk evaluation set of the karst tunnel and forming a fuzzy matrix;
the fuzzy comprehensive evaluation module is used for carrying out fuzzy comprehensive evaluation on the fuzzy matrix and a weight matrix consisting of comprehensive weight vectors to obtain a fuzzy comprehensive evaluation set;
and the comprehensive evaluation module is used for taking the risk grade corresponding to the maximum element in the fuzzy evaluation set in the karst tunnel collapse disaster risk evaluation set as an evaluation result according to the maximum membership principle to form a karst collapse disaster risk comprehensive evaluation model.
The invention has the beneficial effects that:
(1) The method is based on the analysis of a large number of domestic karst collapse disaster accidents, establishes two-stage evaluation indexes, is a progressive and scientific system, and has the advantages of comprehensive evaluation system, easily-obtained evaluation index parameters and easily-operated evaluation process.
(2) The invention adopts a matrix consistency method to check the rationality of the judgment matrix, eliminates the error interference of artificial subjective factors, and simultaneously adopts a membership function to determine the membership of each index factor to the classification thereof, thereby quantitatively describing the objective attribute of each index.
(3) According to the invention, by using a fuzzy comprehensive evaluation method, the collapse disaster risk level of the karst tunnel can be reasonably judged according to engineering conditions, and the qualitative evaluation on the collapse disaster risk of the karst tunnel is realized. By means of the evaluation result of the invention, the engineering measures corresponding to the related risk levels can be timely and effectively carried out in site construction, and the safety and the economy of karst tunnel construction are greatly improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a hierarchical analysis model of karst collapse risk factors according to the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, a comprehensive evaluation method for collapse disaster risk of karst tunnel includes the following steps:
s1, constructing a karst tunnel collapse risk evaluation system and a corresponding hierarchical structure through investigation on a karst tunnel collapse case;
s2, establishing judgment matrixes of each hierarchy structure, calculating comprehensive weight vectors, checking consistency of the judgment matrixes, and checking the rationality of the judgment matrixes;
s3, carrying out risk classification according to the evaluation indexes, and establishing a karst tunnel collapse disaster risk evaluation set;
s4, determining the membership degree of the risk assessment index according to the collapse disaster risk evaluation set of the karst tunnel, and forming a fuzzy matrix;
s5, carrying out fuzzy comprehensive evaluation on the fuzzy matrix and a weight matrix composed of comprehensive weight vectors to obtain a fuzzy comprehensive evaluation set;
and S6, according to the maximum membership principle, taking the risk level corresponding to the maximum element in the fuzzy evaluation set in the karst tunnel collapse disaster risk evaluation set as an evaluation result, and forming a karst collapse disaster risk comprehensive evaluation model.
The specific implementation manner of step S2 is as follows:
s2-1, comparing evaluation indexes of all levels in a karst tunnel collapse risk evaluation system, and performing relative weight assignment on different evaluation indexes and the importance of a specific evaluation object to obtain a judgment matrix;
s2-3, calculating a matrix characteristic vector according to the judgment matrix, and performing normalization processing to obtain comprehensive weight vectors corresponding to each index and the corresponding layer;
s2-4, multiplying the weight vectors corresponding to the criterion layer and the index layer to obtain a comprehensive weight vector;
s2-5, according to a formula:
CR＝CI/RI
obtaining a judgment matrix consistency check index CI and a check coefficient CR corresponding to different layers; wherein λ is max Judging the maximum eigenvalue of the matrix; RI is an average random consistency index; j is the order of the judgment matrix;
when the matrix consistency check index CI and the check coefficient CR are in a set range, judging that the matrix is reasonable, and entering a step S3; otherwise, the step S2-1 is returned.
The karst tunnel collapse disaster risk level evaluation set comprises four levels of low risk, medium risk, high risk and extremely high risk.
The specific implementation manner of step S4 is as follows:
s4-1, calculating membership functions of different specific evaluation objects with different grades of collapse disaster risks of the karst tunnel;
and S4-2, calculating membership degree vectors according to the membership degree function, and taking the membership degree vectors corresponding to the same risk level as the same column of the matrix to obtain a matrix with 15 rows and 4 columns, namely a fuzzy matrix R.
The specific implementation manner of the step S4-1 is as follows:
s4-1-1, judging whether a specific evaluation object is a qualitative index or not; if yes, the membership function adopts a rectangular membership function
Wherein u is i Representing the collapse disaster risk level of the karst tunnel, i =1,2,3,4; respectively representing four grades of low risk, medium risk, high risk and extremely high risk; u represents a specific evaluation object; otherwise, entering the step S4-1-2;
s4-1-2, judging whether the specific evaluation object is the size of the karst cave or not and the clear distance between the karst cave and the tunnel; if so, determining membership functions of different levels of the karst cave size by using matlab software according to the measured data of the karst cave size, the collapse disaster risk level of the karst tunnel and the diameter of the tunnel; determining membership functions of different risk levels of the karst cave and tunnel clear distance by using matlab software according to the karst cave and tunnel clear distance, the tunnel diameter and the collapse disaster risk level of the karst tunnel; otherwise, entering a step S4-1-3;
s4-1-3, determining a membership function under a low risk level:
according to the formula:
obtaining the membership function mu of the softening coefficient of the low-risk grade surrounding rock A1 (x) (ii) a Wherein x is the softening coefficient of the surrounding rock; pi is the circumference ratio;
according to the formula:
obtaining a low risk grade rainfall membership function mu A1 (y); wherein y is rainfall;
according to the formula:
obtaining a tunnel buried depth membership function mu under a low risk level A1 (b) (ii) a Wherein, b represents the length of the tunnel buried depth;
according to the formula:
obtaining a characteristic membership function mu of the karst filler under a low risk level A1 (c) (ii) a Wherein c represents a karst fill characteristic;
determining membership functions at intermediate risk level:
according to the formula:
obtaining the membership function mu of the softening coefficient of the surrounding rock under the medium risk level A2 (x)；
According to the formula:
obtaining the rainfall membership function mu of the medium risk grade A2 (y)；
According to the formula:
obtaining tunnel buried depth membership function mu under medium risk A2 (b)；
According to the formula:
obtaining the characteristic membership function mu of the karst filler under the medium risk A2 (c) (ii) a Determining a membership function at a high risk level:
according to the formula:
obtaining the membership function mu of the softening coefficient of the surrounding rock under the high risk level A3 (x) (ii) a According to the formula:
obtaining a high risk grade rainfall membership function mu A3 (y)；
According to the formula:
obtaining a tunnel buried depth membership function mu under a high risk level A3 (b)；
According to the formula:
obtaining a characteristic membership function mu of the karst filler under a high risk level A3 (c)；
Determining membership functions at extremely high risk levels:
according to the formula:
obtaining the membership function mu of the softening coefficient of the surrounding rock under the extremely high risk level A4 (x)；
According to the formula:
obtaining the membership function mu of the extremely high risk rainfall A4 (y)；
According to the formula:
obtaining the tunnel buried depth membership function mu under extremely high risk A4 (b)；
According to the formula:
obtaining the characteristic membership function mu of the karst filler under extremely high risk A4 (c)。
The specific implementation manner of step S5 is as follows:
s5-1, obtaining a weight matrix W = [ W ] according to the evaluation index comprehensive weight vector 1 ,w 2 ,...w m ]；
S5-2, according to a formula:
obtaining a fuzzy comprehensive judgment set B; wherein m represents the number of weight matrix factors, namely the number of index factors, and m =1,2, …,15; n represents the number of risk classes n =1,2,3,4; r denotes the element of the blur matrix, b k The kth element of the fuzzy evaluation set is represented.
The comprehensive evaluation system for the collapse disaster risks of the karst tunnel comprises a karst tunnel collapse risk evaluation system construction module, a rationality judgment module, a karst tunnel collapse disaster risk evaluation set construction module, a fuzzy matrix construction module, a fuzzy comprehensive evaluation module and a comprehensive evaluation module; wherein:
the karst tunnel collapse risk assessment system construction module is used for constructing a karst tunnel collapse risk assessment system and a corresponding hierarchical structure through investigation on a karst tunnel collapse case;
the rationality judgment module is used for establishing judgment matrixes of each hierarchical structure, calculating comprehensive weight vectors, checking the consistency of each judgment matrix and checking the rationality of the judgment matrixes;
the karst tunnel collapse disaster risk evaluation set building module is used for carrying out risk classification according to the evaluation indexes and building a karst tunnel collapse disaster risk evaluation set;
the fuzzy matrix construction module is used for determining the membership degree of the risk assessment index according to the collapse disaster risk evaluation set of the karst tunnel and forming a fuzzy matrix;
the fuzzy comprehensive evaluation module is used for carrying out fuzzy comprehensive evaluation on the fuzzy matrix and a weight matrix consisting of comprehensive weight vectors to obtain a fuzzy comprehensive evaluation set;
and the comprehensive evaluation module is used for taking the risk grade corresponding to the maximum element in the fuzzy evaluation set in the karst tunnel collapse disaster risk evaluation set as an evaluation result according to the maximum membership principle to form a karst collapse disaster risk comprehensive evaluation model.
As shown in fig. 2, the collapse risk assessment system for the karst tunnel includes a criterion layer and an index layer; wherein:
obtaining a target of a karst tunnel collapse risk evaluation system classified into karst collapse risk grades in tunnel construction;
the criterion layer comprises four evaluation indexes, namely geological condition indexes, natural condition indexes, karst cave characteristic indexes and design and construction factor indexes;
the index layer comprises specific evaluation objects forming four evaluation indexes of the criterion layer;
the geological condition indexes comprise surrounding rock grade, surrounding rock softening coefficient and weathered degree;
the natural condition indexes comprise rainfall and underground water conditions;
karst cave characteristic indexes comprise the size of the karst cave, the clear distance between the karst cave and the tunnel, the characteristics of karst fillers and the position relation between the karst cave and the tunnel;
design and construction factor indexes comprise tunnel burial depth, tunnel section, excavation step distance, blasting method suitability, support timeliness and advance geological forecast and monitoring measurement scheme rationality.
In one embodiment of the present invention, the impact index classification criteria in the present invention are shown in table 1.
TABLE 1 Master control influence index of collapse risk of karst tunnel
The relative importance among the evaluation indexes is determined according to a 9-scale method, judgment matrixes of the evaluation indexes at all levels are constructed as shown in tables 2 to 6, and the comprehensive weight values of the collapse disaster risk evaluation indexes of the karst tunnel obtained through calculation and summarization are shown in table 7.
TABLE 2 decision matrix A Z
Calculating and judging matrix A Z Has a maximum characteristic value of
Normalized feature vector e Z = (0.2389,0.4337,0.2389,0.0885). Tested CR =0.008<0.1, meeting the requirement of consistency.
TABLE 3 decision matrix
By calculating, judging the matrix
Has a maximum characteristic value of
Normalized feature vector
Examined CR =0.018<0.1, meeting the requirement of consistency.
TABLE 4 decision matrix
By calculating, judging the matrix
Has a maximum eigenvalue of λ =2, normalized eigenvector
Examined CR =0<0.1, meeting the requirement of consistency.
TABLE 5 decision matrix
By calculating, judging the matrix
Has a maximum eigenvalue of
Normalized feature vector
Examined CR =0.01<0.1, meeting the requirement of consistency.
TABLE 6 decision matrix
By calculating, judging the matrix
Has a maximum eigenvalue of
Normalized feature vector
Examined CR =0.01<0.1, meeting the requirement of consistency.
TABLE 7 comprehensive weighted value of index
The weight set shows that the most important factor in the first-level influence factors is the natural condition, and then the geological condition and the karst cave characteristic are respectively, and the least influence is the design and construction factors; among the secondary influencing factors, the most important influencing factor is the underground water condition, and then the most important influencing factors are the surrounding rock softening coefficient, the rainfall, the characteristics of the karst filler, the weathering degree, the size of the karst cave and the like, and the least influencing factor is the rationality of the advanced geological prediction and monitoring measurement scheme.
According to the probability and the severity of the accident, a risk matrix method is utilized to determine the risk level standard of karst collapse risk evaluation in tunnel construction as shown in table 8, and the risk acceptance criterion of the karst disaster in tunnel construction as shown in table 9.
TABLE 8 Risk class criteria
TABLE 9 Risk acceptance criteria
When the diameter of the karst cave is 11m and 10.1m, obtaining a karst cave size membership function and a karst cave and tunnel net distance membership function under different risk levels;
according to the formula:
obtaining a size membership function mu of the karst cave with low risk level A1 (z) and μ A1 (z'); wherein z is the size of the karst cave; d is the diameter of the karst cave and the unit is m; z' is the size of the karst cave when the diameter of the karst cave is 10.1 m;
according to the formula:
obtaining a low risk grade karst cave and tunnel net distance membership function mu A1 (a) (ii) a Wherein a is the diameter of the karst cave of 11 m The clearance between the karst cave and the tunnel; a' represents a karst cave diameter of 10.1 m The clearance between the karst cave and the tunnel;
according to the formula:
obtaining a membership function mu of the size of the karst cave under the medium risk level A2 (z) and μ A2 (z')；
According to the formula:
obtaining a karst cave and tunnel net distance membership function mu under medium risk A2 (a) And mu A1 (a)；
According to the formula:
obtaining a membership function mu of the net distance between the karst cave and the tunnel under the high risk level A3 (a) And mu A3 (a')；
According to the formula:
obtaining a karst cave size membership function mu under a high risk level A3 (z) and μ A3 (z')；
According to the formula:
obtaining the membership function mu of the size of the karst cave under extremely high risk A4 (z) and μ A4 (z')；
According to the formula:
obtaining the net distance membership function mu of the karst cave and the tunnel under extremely high risk A4 (a) And mu A4 (a')；
Taking a section of engineering located in a karst tunnel as an example, the risk evaluation factor is determined by combining the geological survey data, the design file, the advanced geological forecast data and the like, and is shown in table 10.
TABLE 11 Table 0 Table of risk assessment factors
The membership of the karst collapse risk assessment in a certain section of construction can be obtained, see table 11.
TABLE 11 evaluation values of single-factor membership
Obtaining a fuzzy matrix R:
weight matrix W:
W＝{0.0291,0.1334,0.0764,0.1084,0.3253,0.0709,0.0370,0.1081,0.0229,0.0057,0.0081,0.0229,0.0134,0.0349,0.0035}
fuzzy evaluation set B:
B＝W×R＝[0.0138 0 0.4559 0.5303]
according to the maximum membership principle, the result of karst collapse risk evaluation in the construction section is level IV, and the risk is extremely high, namely the possibility of disaster occurrence is extremely high, and the consequence is extremely serious. When the right line of the tunnel is excavated to the engineering position in site construction, karst collapse disasters happen to the tunnel, the tunnel is sealed, the in-tunnel equipment is damaged, construction stagnation and surface collapse also have great influence on the lives of nearby residents, and the construction section performance accords with the karst tunnel collapse disaster risk assessment result.
According to the invention, two-stage evaluation indexes are established, a progressive and scientific system is adopted, and the method has the advantages of comprehensive evaluation system, easily obtained evaluation index parameters and easily operated evaluation process; the matrix consistency method is adopted to check the rationality of the judgment matrix, and the error interference of human subjective factors is eliminated; by using a fuzzy comprehensive evaluation method, the collapse disaster risk level of the karst tunnel can be reasonably judged according to engineering conditions, so that the qualitative evaluation of the collapse disaster risk of the karst tunnel is realized; the construction safety and the economical efficiency of the karst tunnel are improved.
Claims (8)
1. A comprehensive evaluation method for collapse disaster risks of karst tunnels is characterized by comprising the following steps:
s1, constructing a collapse risk evaluation system and a corresponding hierarchical structure of the karst tunnel by investigating collapse cases of the karst tunnel;
s2, establishing judgment matrixes of each hierarchy structure, calculating comprehensive weight vectors, checking consistency of the judgment matrixes, and checking the rationality of the judgment matrixes;
s3, carrying out risk classification according to the evaluation indexes, and establishing a karst tunnel collapse disaster risk evaluation set;
s4, determining the membership degree of the risk assessment index according to the collapse disaster risk evaluation set of the karst tunnel, and forming a fuzzy matrix;
s5, carrying out fuzzy comprehensive evaluation on the fuzzy matrix and a weight matrix composed of comprehensive weight vectors to obtain a fuzzy comprehensive evaluation set;
and S6, according to the maximum membership principle, taking the risk level corresponding to the maximum element in the fuzzy evaluation set in the karst tunnel collapse disaster risk evaluation set as an evaluation result, and forming a karst collapse disaster risk comprehensive evaluation model.
2. The comprehensive evaluation method for the collapse disaster risk of the karst tunnel according to claim 1, wherein the collapse risk evaluation system of the karst tunnel comprises a criterion layer and an index layer; wherein:
acquiring targets of karst tunnel collapse risk evaluation systems classified into karst tunnel collapse risk grades in tunnel construction;
the criterion layer comprises four evaluation indexes, namely geological condition indexes, natural condition indexes, karst cave characteristic indexes and design and construction factor indexes;
the index layer comprises specific evaluation objects forming four evaluation indexes of the criterion layer;
the geological condition indexes comprise surrounding rock grade, surrounding rock softening coefficient and weathered degree;
the natural condition indexes comprise rainfall and underground water conditions;
the karst cave characteristic indexes comprise the size of the karst cave, the clear distance between the karst cave and the tunnel, the characteristics of karst fillers and the position relation between the karst cave and the tunnel;
design and construction factor indexes comprise tunnel burial depth, tunnel section, excavation step distance, blasting method suitability, support timeliness and advance geological forecast and monitoring measurement scheme rationality.
3. The comprehensive evaluation method for the collapse disaster risk of the karst tunnel according to claim 2, wherein the specific implementation manner of the step S2 is as follows:
s2-1, comparing elements of each layer in a collapse risk assessment system of the karst tunnel, and performing relative weight assignment on the importance of different elements to obtain a judgment matrix of each layer;
s2-3, calculating a matrix characteristic vector according to each layer of judgment matrix, and performing normalization processing to obtain a weight vector of each specific evaluation object;
s2-4, multiplying the weight vectors corresponding to the criterion layer and the index layer to obtain a comprehensive weight vector;
s2-5, according to a formula:
CR＝CI/RI
obtaining a judgment matrix consistency check index CI and a check coefficient CR corresponding to different layers; wherein λ is max Judging the maximum eigenvalue of the matrix; RI is an average random consistency index; j is the order of the judgment matrix;
when the matrix consistency test index CI and the test coefficient CR are in a set range, judging that the matrix is reasonable, and entering a step S3; otherwise, the step S2-1 is returned.
4. The comprehensive evaluation method for the collapse disaster risk of the karst tunnel according to claim 3, wherein the evaluation set for the collapse disaster risk level of the karst tunnel comprises four levels of low risk, medium risk, high risk and extremely high risk.
5. The comprehensive evaluation method for the collapse disaster risk of the karst tunnel according to claim 4, wherein the specific implementation manner of the step S4 is as follows:
s4-1, calculating membership functions of different specific evaluation objects with different grades of collapse disaster risks of the karst tunnel;
and S4-2, calculating membership degree vectors according to the membership degree function, and taking the membership degree vectors corresponding to the same risk level as the same column of the matrix to obtain a matrix with 15 rows and 4 columns, namely a fuzzy matrix R.
6. The comprehensive evaluation method for the disaster risk of the karst tunnel collapse according to claim 5, wherein the specific implementation manner of the step S4-1 is as follows:
s4-1-1, judging whether a specific evaluation object is a qualitative index or not; if yes, the membership function adopts a rectangular membership function
Wherein u is i Representing the collapse disaster risk level of the karst tunnel, i =1,2,3,4; respectively representing four grades of low risk, medium risk, high risk and extremely high risk; u represents a specific evaluation object; otherwise, entering a step S4-1-2;
s4-1-2, judging whether the specific evaluation object is the size of the karst cave or not and the clear distance between the karst cave and the tunnel; if so, determining membership functions of different levels of the karst cave size by using matlab software according to the measured data of the karst cave size, the collapse disaster risk level of the karst tunnel and the diameter of the tunnel; determining membership functions of different risk levels of the karst cave and tunnel clear distance by using matlab software according to the karst cave and tunnel clear distance, the tunnel diameter and the collapse disaster risk level of the karst tunnel; otherwise, entering a step S4-1-3;
s4-1-3, determining a membership function under a low risk level:
according to the formula:
obtaining the membership function mu of the softening coefficient of the low-risk grade surrounding rock A1 (x) (ii) a Wherein x is the softening coefficient of the surrounding rock; pi is the circumference ratio;
according to the formula:
obtaining a low risk grade rainfall membership function mu A1 (y); wherein y is rainfall;
according to the formula:
obtaining a tunnel buried depth membership function mu under a low risk level A1 (b) (ii) a Wherein, b represents the length of the tunnel buried depth;
according to the formula:
obtaining a characteristic membership function mu of the karst filler under a low risk level A1 (c) (ii) a Wherein c represents a karst filler characteristic;
determining membership functions at intermediate risk level:
according to the formula:
obtaining the membership function mu of the softening coefficient of the surrounding rock under the medium risk level A2 (x)；
According to the formula:
obtaining the rainfall membership function mu of the medium risk grade A2 (y)；
According to the formula:
obtaining tunnel buried depth membership function mu under medium risk A2 (b)；
According to the formula:
obtaining the characteristic membership function mu of the karst filler under the medium risk A2 (c)；
Determining a membership function at a high risk level:
according to the formula:
obtaining the membership function mu of the softening coefficient of the surrounding rock under the high risk level A3 (x)；
According to the formula:
obtaining a high risk grade rainfall membership function mu A3 (y)；
According to the formula:
obtaining a tunnel buried depth membership function mu under a high risk level A3 (b)；
According to the formula:
obtaining a characteristic membership function mu of the karst filler under a high risk level A3 (c)；
Determining membership functions at extremely high risk levels:
according to the formula:
obtaining the membership function mu of the softening coefficient of the surrounding rock under the extremely high risk level A4 (x)；
According to the formula:
obtaining the membership function mu of the extremely high risk rainfall A4 (y)；
According to the formula:
obtaining the tunnel buried depth membership function mu under extremely high risk A4 (b)；
According to the formula:
obtaining the characteristic membership function mu of the karst filler under extremely high risk A4 (c)。
7. The comprehensive evaluation method for the collapse disaster risk of the karst tunnel according to claim 6, wherein the specific implementation manner of the step S5 is as follows:
s5-1, obtaining a weight matrix W = [ W ] according to the comprehensive weight vector 1 ,w 2 ,...w m ]；
S5-2, according to a formula:
obtaining a fuzzy comprehensive judgment set B; wherein m represents the number of weight matrix factors, namely the number of index factors, and m =1,2, …,15; n represents the number of risk classes n =1,2,3,4; r denotes the element of the blur matrix, b k The kth element of the fuzzy evaluation set is represented.
8. A comprehensive evaluation system for collapse disaster risks of a karst tunnel is characterized by comprising a karst tunnel collapse risk evaluation system construction module, a rationality judgment module, a karst tunnel collapse disaster risk evaluation set construction module, a fuzzy matrix construction module, a fuzzy comprehensive evaluation module and a comprehensive evaluation module; wherein:
the karst tunnel collapse risk assessment system construction module is used for constructing a karst tunnel collapse risk assessment system and a corresponding hierarchical structure through investigation on a karst tunnel collapse case;
the rationality judgment module is used for establishing judgment matrixes of each hierarchy structure, calculating comprehensive weight vectors, checking the consistency of each judgment matrix and checking the rationality of the judgment matrixes;
the karst tunnel collapse disaster risk evaluation set building module is used for carrying out risk classification according to the evaluation indexes and building a karst tunnel collapse disaster risk evaluation set;
the fuzzy matrix construction module is used for determining the membership degree of the risk assessment index according to the collapse disaster risk evaluation set of the karst tunnel and forming a fuzzy matrix;
the fuzzy comprehensive evaluation module is used for carrying out fuzzy comprehensive evaluation on the fuzzy matrix and a weight matrix consisting of comprehensive weight vectors to obtain a fuzzy comprehensive evaluation set;
and the comprehensive evaluation module is used for taking the risk grade corresponding to the maximum element in the fuzzy evaluation set in the karst tunnel collapse disaster risk evaluation set as an evaluation result according to the maximum membership principle to form a karst collapse disaster risk comprehensive evaluation model.
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| CN116629614A (en) * | 2023-06-05 | 2023-08-22 | 北京城建设计发展集团股份有限公司 | Dynamic evaluation method for urban deep karst collapse risk based on Bayesian network |
| CN116797026A (en) * | 2023-06-30 | 2023-09-22 | 西南石油大学 | Risk early warning method under soil landslide effect of buried gas transmission pipeline |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116629614A (en) * | 2023-06-05 | 2023-08-22 | 北京城建设计发展集团股份有限公司 | Dynamic evaluation method for urban deep karst collapse risk based on Bayesian network |
| CN116629614B (en) * | 2023-06-05 | 2024-05-10 | 北京城建设计发展集团股份有限公司 | Dynamic evaluation method for urban deep karst collapse risk based on Bayesian network |
| CN116797026A (en) * | 2023-06-30 | 2023-09-22 | 西南石油大学 | Risk early warning method under soil landslide effect of buried gas transmission pipeline |
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