CN113780763A - Hydraulic engineering safety evaluation method based on improved fuzzy analytic hierarchy process - Google Patents
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Abstract
The invention discloses a hydraulic engineering safety evaluation method based on an improved fuzzy analytic hierarchy process, which mainly solves the problem that the evaluation result of the conventional hydraulic engineering safety evaluation method is inaccurate. The method comprises the following steps: (S1) determining a factor set of the evaluation target; (S2) determining a set of evaluation ratings for the evaluation object; (S3) performing single-factor evaluation on the evaluation object to form a membership matrix; (S4) determining the factor weight vector of the evaluation object to obtain a judgment matrix, and carrying out digitization or normalization on the judgment grade set; (S5) obtaining a comprehensive evaluation result vector matrix; (S6) judging according to the maximum membership rule, or calculating the membership grade by using weighted average to obtain a comprehensive judgment value. The invention adopts a method based on natural index e0/4～e8/4Improved fuzzy comprehensive evaluation method of scaleAnd (4) safety identification is carried out on the hydraulic engineering, the subjective weight and the objective weight of the bottom layer evaluation index are optimized, and the subjective weight is corrected, so that the weight distribution of the index is more reasonable.
Description
Technical Field
The invention relates to a hydraulic engineering safety evaluation method, in particular to a hydraulic engineering safety evaluation method based on an improved fuzzy analytic hierarchy process.
Background
Hydraulic engineering is an engineering built for controlling and allocating surface water and underground water in the nature to achieve the purpose of removing harm and benefiting, also called water engineering, water is a valuable resource essential for human production and life, but the naturally existing state of the water does not completely meet the needs of human beings, and water flow can be controlled only by building the hydraulic engineering to prevent flood disasters and adjust and distribute water quantity so as to meet the needs of people life and production on water resources.
The safety evaluation of the hydraulic engineering is a long-term and important work, and is used for safety monitoring of the hydraulic engineering structure and guaranteeing the safe operation of the hydraulic engineering structure. Although certain achievements are made on the safety evaluation work of hydraulic engineering at present, the following problems still exist: (1) monitoring models and evaluation methods for hydraulic engineering safety analysis and evaluation are not mature enough; (2) the existing evaluation method is slightly reluctant to use under certain conditions, much information is lost, and even unreasonable evaluation results are obtained.
Disclosure of Invention
The invention aims to provide a hydraulic engineering safety evaluation method based on an improved fuzzy analytic hierarchy process, and mainly solves the problems that the existing hydraulic engineering safety evaluation method is not mature enough and the evaluation result is inaccurate.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a hydraulic engineering safety evaluation method based on an improved fuzzy analytic hierarchy process is characterized by comprising the following steps:
(S1) determining a factor set of the evaluation target;
(S2) determining a set of evaluation ratings for the evaluation object;
(S3) performing single-factor evaluation on the evaluation object to obtain a membership vector, and forming a membership matrix according to the membership vectors of all elements of the factor set;
(S4) determining the factor weight vector of the evaluation object to obtain a judgment matrix, and carrying out digitization or normalization on the judgment grade set;
(S5) multiplying the judgment matrix and the membership matrix to obtain a comprehensive evaluation result vector matrix;
(S6) judging according to the maximum membership rule, or calculating the membership grade by using weighted average to obtain a comprehensive judgment value.
Further, in the step (S1), P evaluation factor forming factor sets U:
u＝{u1,u2,……,up}。
further, in the step (S2), each of the evaluation level sets corresponds to a fuzzy subset.
Further, in the step (S3), the degree of membership (R | u) of the evaluated object to the fuzzy subset is obtained from the one-factor evaluationi) (ii) a Wherein R is a membership matrix:
further, in the step (S4), the natural index e is adopted0/4～e8/4The scale of (a) determines the subjective weight, e1/4，e3/4，e5/4，e7/4The influence of the ith factor relative to the jth factor is between the two adjacent levels, and a judgment matrix A is formedij)n*nBy aijRepresenting the result of comparing the ith factor with the jth factor, wherein:
maximum eigenvalue λ of matrix AmaxThe corresponding feature vectors are normalized and then are recorded as:
W＝(ω1,ω2,…，ωn) Wherein
Compared with the prior art, the invention has the following beneficial effects:
the invention adopts a method based on natural index e0/4～e8/4The scale improved fuzzy comprehensive evaluation method is used for carrying out safety identification on hydraulic engineering, optimizing the subjective weight and the objective weight of the bottom-layer evaluation index and correcting the subjective weight, so that the weight distribution of the index is more reasonable. Taking the aqueduct as an example, compared with the existing safety evaluation standard of the aqueduct, the safety category comprehensive evaluation result of the aqueduct obtained by adopting the improved fuzzy comprehensive evaluation method can better reflect the actual situation of the aqueduct, is consistent with the actual safety evaluation result of the engineering, and provides a scientific and reasonable solution for the safety evaluation of the hydraulic engineering.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
Detailed Description
The present invention will be further described with reference to the following description and examples, which include but are not limited to the following examples.
Examples
As shown in the figure, the invention discloses a hydraulic engineering safety evaluation method based on an improved fuzzy analytic hierarchy process, which is characterized by comprising the following steps:
(S1) a factor set u of P evaluation factors is specified, and the factor set u is { u ═ u { (S1)1,u2,……,up}。
(S2) determining a set of evaluation ratings for the evaluation object, one for each evaluation factor, i.e., a set of evaluation ratings v ═ { v ═ v { (S)1,v2,……,vp}. And each grade in the evaluation grade set corresponds to a fuzzy subset, and each evaluation index can be divided into a plurality of grades according to the actual condition of the hydraulic engineering.
(S3) performing single-factor evaluation on the evaluation object to obtain a membership vector which is marked as r, ri＝(ri1,ri2,…,rim) And forming a membership matrix R according to the membership vectors of all the elements of the factor set. Obtaining the membership (R | u) of the evaluated object to the fuzzy subset according to the single factor evaluationi) (ii) a The membership matrix R is then:
(S4) determining a weight vector of the evaluation target, using a natural index e0/4～e8/4The subjective weight is determined by the scale of the judgment matrix, and the judgment grade set can be digitalized or normalized. The meaning of the different scales is shown in table 1.
TABLE 1 meanings on different scales
Wherein e is1/4，e3/4，e5/4，e7/4Indicating that the influence of the ith factor relative to the jth factor lies between the two adjacent levels. Form a judgment matrix A＝(aij)n*nBy aijRepresenting the result of comparing the ith factor with the jth factor, wherein:
maximum eigenvalue λ of matrix AmaxThe corresponding feature vectors are normalized and then are recorded as:
W＝(ω1,ω2,…，ωn) Wherein
(S5) multiplying the judgment matrix and the membership matrix to obtain a comprehensive evaluation result vector matrix; namely, it is
(S6) calculating the membership grade by using the weighted average to obtain a comprehensive judgment value.
Taking the safety evaluation of the aqueduct of the hydraulic engineering as an example, the corresponding safety level of the aqueduct engineering is determined, and the operation condition and the safety state of the engineering can be mastered in time. The method comprises the steps of collecting operation state indexes of a project by carrying out deformation monitoring, seepage monitoring, stress strain monitoring, environment quantity monitoring and the like on the aqueduct project, predicting the safety level of the project by using a fuzzy hierarchical analysis algorithm improved by the text, finding out problems in time and taking effective measures in time due to missing report, and ensuring the safe operation of the aqueduct project by adopting the mode of safety accidents.
The Li's ditch aqueduct is evaluated for safety by combining the method, the Li's ditch aqueduct consists of a left aqueduct and a right aqueduct, the interval between the aqueducts is 3.1m, an inlet and an outlet are connected by a water diversion fish mouth, the height of the inlet fish mouth is 3.2m, and the height of the outlet fish mouth is 3.2 m. The right side is an old aqueduct, the number of the start-stop piles is 22+ 751-22 +838(km + m), the height of the bottom of the inlet section is 495.070m, the height of the bottom of the outlet section is 495.058m, and the flow rate is 22.1m3Water depth 2.712 m/s, aqueduct type is grouted stripThe whole aqueduct is 8 holes, 12m big span 4 holes and 3m small span 4 holes. The length of the groove body is 87 meters, the specific drop of the bottom of the groove is 1/1300, and the construction time is 1966; the left side is a new aqueduct, the number of the start-stop piles is 22+ 751-22 +838(km + m), the height of the bottom of the inlet section is 495.327m, the height of the bottom of the outlet section is 495.269m, and the flow rate is 27.9m3And/s, the water depth is 2.98m, the aqueduct type is a grouted stone arch aqueduct, the whole aqueduct has 8 holes, 4 holes with a large span of 12m and 4 holes with a small span of 3 m. The length of the groove body is 87 meters, the specific drop of the bottom of the groove is 1/1300, and the construction time is 1977.
The aqueduct engineering risk categories are divided into 4 categories. Class I engineering: the application index reaches the design standard, and the project can run normally; and II, engineering: the application indexes basically reach the design standard, the defect of influencing the normal operation of the project is avoided, and the normal operation can be realized through the conventional maintenance; and III engineering: the application indexes can not reach the design standard, the defect of influencing the normal operation of the project exists, and the normal operation can be realized through reinforcement and modification; and IV engineering: the operation index can not reach the design standard, the function of the project is lost or obviously reduced, and the standard operation, reconstruction or scrapping reconstruction needs to be reduced. Specific evaluation factors are shown in table 2.
TABLE 2 first and second evaluation factors
Dividing aqueduct monitoring into 4 primary classifications of deformation monitoring, seepage monitoring, stress strain and temperature and environmental quantity monitoring, and constructing a fuzzy judgment matrix A of the primary classification1. As shown in table 3:
TABLE 3 first-level classification fuzzy judgment matrix
Setting a first class weight vector W1＝(ω1,ω2,ω3,ω4) Constructing an auxiliary matrix:
TABLE 4 auxiliary matrix
Solving the feature vector as:
the normalized weight vector is: w1＝(0.082,0.142,0.340,0.436)。
Similarly, the weight of the underlying monitoring index, A, is obtained from the improved fuzzy analytic hierarchy process1～A4The optimization weights are 0.313, 0.304, 0.201 and 0.182 respectively; b is1、B2The optimization weights are 0.582 and 0.418 respectively; c1～C5The optimization weights are 0.289, 0.232, 0.260, 0.073 and 0.146 respectively; d1～D4The optimization weights are 0.301, 0.282, 0.134, 0.283, respectively.
According to various monitoring indexes of the aqueduct and the actual running condition of the aqueduct, inviting 10 experts to evaluate the state of the bottom indexes, and calculating the membership degree of the indexes according to the statistical result. The 4 security levels are denoted by V ═ { a, B, C, D }. A membership matrix as shown in table 5 is obtained:
TABLE 5 base level index membership matrix
According to the weight vector of each index and the index membership degree matrix, a target matrix R can be obtained:
B＝W1·R＝(0.151 0.476 0.337 0.036)
according to the principle of maximum membership degree, the aqueduct belongs to II-class engineering. The safety classification of the aqueduct in 2018 is evaluated as two types, and the two conclusions are consistent.
By the method, the invention adopts the method based on the natural index e0/4～e8/4The scale improved fuzzy comprehensive evaluation method is used for carrying out safety identification on hydraulic engineering, optimizing the subjective weight and the objective weight of the bottom-layer evaluation index and correcting the subjective weight, so that the weight distribution of the index is more reasonable. Taking the aqueduct as an example, compared with the existing safety evaluation standard of the aqueduct, the safety category comprehensive evaluation result of the aqueduct obtained by adopting the improved fuzzy comprehensive evaluation method can better reflect the actual situation of the aqueduct, is consistent with the actual safety evaluation result of the engineering, and provides a scientific and reasonable solution for the safety evaluation of the hydraulic engineering. Therefore, the method has high use value and popularization value.
The above-mentioned embodiment is only one of the preferred embodiments of the present invention, and should not be used to limit the scope of the present invention, but all the insubstantial modifications or changes made within the spirit and scope of the main design of the present invention, which still solve the technical problems consistent with the present invention, should be included in the scope of the present invention.
Claims (5)
1. A hydraulic engineering safety evaluation method based on an improved fuzzy analytic hierarchy process is characterized by comprising the following steps:
(S1) determining a factor set of the evaluation target;
(S2) determining a set of evaluation ratings for the evaluation object;
(S3) performing single-factor evaluation on the evaluation object to obtain a membership vector, and forming a membership matrix according to the membership vectors of all elements of the factor set;
(S4) determining the factor weight vector of the evaluation object to obtain a judgment matrix, and carrying out digitization or normalization on the judgment grade set;
(S5) multiplying the judgment matrix and the membership matrix to obtain a comprehensive evaluation result vector matrix;
(S6) judging according to the maximum membership rule, or calculating the membership grade by using weighted average to obtain a comprehensive judgment value.
2. The hydraulic engineering safety evaluation method based on the improved fuzzy analytic hierarchy process of claim 1, wherein in the step (S1), P evaluation factors form a factor set U:
u＝{u1,u2,……,up}。
3. the hydraulic engineering safety evaluation method based on the improved fuzzy analytic hierarchy process of claim 2, wherein in the step (S2), each grade in the evaluation grade set corresponds to a fuzzy subset.
4. The hydraulic engineering safety evaluation method based on the improved fuzzy analytic hierarchy process of claim 3, wherein in the step (S3), the degree of membership (R | u) of the evaluated object to the fuzzy subset is obtained from single factor evaluationi) (ii) a Wherein R is a membership matrix:
5. the hydraulic engineering safety evaluation method based on the improved fuzzy analytic hierarchy process of claim 4, wherein in the step (S4), the natural index e is used0/4～e8/4The scale of (a) determines the subjective weight, e1/4，e3 /4，e5/4，e7/4The influence of the ith factor relative to the jth factor is between the two adjacent levels, and a judgment matrix A is formedij)n*nBy aijRepresenting the result of comparing the ith factor with the jth factor, wherein:
maximum eigenvalue λ of matrix AmaxThe corresponding feature vectors are normalized and then are recorded as:
W＝(ω1,ω2,…，ωn) Wherein
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