Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide an evaluation method for the pile slipping of the ocean platform by considering risk grade division, and the pile slipping condition of the ocean platform determined by the method is more consistent with the actual condition.
The purpose of the invention is realized by the following technical scheme.
The invention relates to an evaluation method for a pile slipping of an ocean platform considering risk grade division, which comprises the following steps of:
(1) dividing risk levels of pile slipping;
(2) establishing an evaluation index system, and grading the risk of each evaluation index;
(3) determining a comparison matrix, constructing a judgment matrix by adopting a pole difference method, and determining the weight of each evaluation index in the step (2);
(4) determining the membership degree of each evaluation index belonging to each risk grade in the step (2);
(5) and establishing a fuzzy mathematical model, solving a fuzzy vector of the pile slipping risk level of the ocean platform, and evaluating the pile slipping risk level of the ocean platform according to the maximum membership principle.
And (3) the number of the pile slipping risk grades divided in the step (1) is equal to the number of the risk grades divided by each evaluation index in the step (2).
The weight values of the evaluation indexes determined in the step (3) are subjected to consistency check by adopting the following formula:
CR=Ci/Ri
wherein, CiTo determine the general consistency index of the matrix, Ci=(λmax-n)/(n-1),λmaxJudging the maximum characteristic root of the matrix; riJudging the average random consistency index of the matrix; when C is presentR<When the evaluation index is 0.1, the judgment matrix has satisfactory consistency, the weight distribution of each evaluation index is reasonable, and when the evaluation index is CRWhen the weight is more than or equal to 0.1, the weight needs to be distributed again.
The membership degree of each evaluation index in the step (4) is calculated according to the following formula:
wherein S isiAnd Si-1X is a grading limit value when the evaluation index is graded, and is a specific numerical value of the evaluation index in the actual engineering.
And (5) calculating the fuzzy vector of the pile slipping risk level of the ocean platform according to the following formula:
B=A*R
wherein A is a characteristic vector of the judgment matrix; and R is a fuzzy relation matrix formed by the membership degrees of all the evaluation indexes.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
(1) the method is based on AHP and fuzzy mathematical theory, determines weight and membership degree by establishing an evaluation index system, and constructs a fuzzy mathematical model for evaluating the risk level of pile slipping;
(2) the result of the model analysis is the basis for effective ocean platform pile foundation project management, and can help to realize effective control of the risk of pile slipping during ocean platform pile driving;
(3) the establishment of the pile slipping risk assessment model enriches the pile slipping prediction system of the ocean platform and provides a new idea for predicting the pile slipping of the ocean platform.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following examples are given in conjunction with the accompanying tables for detailed description.
The invention relates to an evaluation method for a pile slipping of an ocean platform considering risk grade division, which comprises the following steps of:
firstly, the risk grade of pile slipping is divided into 3-5 grades from good grade to bad grade, and the grades are divided into 3 grades of high grade, medium grade and low grade.
And then, establishing an evaluation index system, and dividing risk grades for each evaluation index, wherein the number of the risk grades divided by each evaluation index is equal to the number of the divided risk grades of the pile slipping. Here, a total of 9 evaluation indexes belonging to five major categories of criteria are determined, and each evaluation index is further classified into 3 levels, high, medium, and low, by range according to its different environmental conditions. The five major types of standards include pile foundations, pile hammers, geological conditions, the piling process, and surrounding experience.
Secondly, determining the weight of each evaluation index. The weight is a quantity reflecting the contribution of each evaluation index to quality grading, and the specific determination principle has the following points:
a, the contribution of each evaluation index to pile slipping is a general principle of weighting, and the weight of the person with the greater contribution is larger;
b correlation of each evaluation index. When one factor is restricted by another factor to a certain extent, the two factors have certain correlation, the weight of the restricted factor is smaller, and the weight of the restricted factor is larger;
c data reliability. Individual indicators are qualitative or semi-empirical, and are weighted less heavily when less reliable to avoid producing unrealistic results.
The design method adopts an improved three-scale comparison matrix to judge the weight, and the form of the comparison matrix is shown in table 1.
TABLE 1 general form of the three-Scale comparison matrix
Wherein:
after the comparison matrix is determined, a judgment matrix is constructed by adopting a pole difference method: by transformation
Obtained B ═ B
ij)
n×nDetermining a matrix for consistency, wherein b
cIs a constant, and is the relative importance of the range element pair (b is often selected in practical applications)
c9); n is the number of evaluation indexes, wherein n is 9; r ═ R
max-r
minKnown as range. The three-scale matrix is converted to a nine-scale decision matrix, see table 2, where R is 15.
Table 2 weight distribution judgment matrix table
|
Index one
|
Index two
|
Index three
|
Index four
|
Index five
|
Index six
|
Index seven
|
Index eight
|
Index nine
|
Index one
|
1.00
|
0.56
|
0.20
|
0.27
|
0.17
|
0.42
|
0.75
|
0.56
|
0.11
|
Index two
|
1.80
|
1.00
|
0.36
|
0.48
|
0.31
|
0.75
|
1.34
|
1.00
|
0.20
|
Index three
|
5.01
|
2.79
|
1.00
|
1.34
|
0.86
|
2.08
|
3.74
|
2.79
|
0.56
|
Index four
|
3.74
|
2.08
|
0.75
|
1.00
|
0.64
|
1.55
|
2.79
|
2.08
|
0.42
|
Index five
|
5.80
|
3.23
|
1.16
|
1.55
|
1.00
|
2.41
|
4.33
|
3.23
|
0.64
|
Index six
|
2.41
|
1.34
|
0.48
|
0.64
|
0.42
|
1.00
|
1.80
|
1.34
|
0.27
|
Index seven
|
1.34
|
0.75
|
0.27
|
0.36
|
0.23
|
0.56
|
1.00
|
0.75
|
0.15
|
Index eight
|
1.80
|
1.00
|
0.36
|
0.48
|
0.31
|
0.75
|
1.34
|
1.00
|
0.20
|
Index nine
|
9.00
|
5.01
|
1.80
|
2.41
|
1.55
|
3.74
|
6.71
|
5.01
|
1.00 |
Calculating the characteristic vector A and the maximum characteristic root lambda of the judgment matrix by using MATLABmaxHere, a is (0.03, 0.06, 0.16, 0.12, 0.17, 0.08, 0.04, 0.06, 0.28). Then from RiValue assignment table (Table 3), consistency check, using equation CR=Ci/RiTo test, wherein, CiTo determine the general consistency index of the matrix, the formula Ci=(λmax-n)/(n-1); riThe average random consistency index of the judgment matrix is obtained. When C is presentR<When the evaluation index is 0.1, the judgment matrix has satisfactory consistency, the weight distribution of each evaluation index is reasonable, and when the evaluation index is CRWhen the weight is more than or equal to 0.1, the weight needs to be distributed again.
TABLE 3RiValue allocation table
n
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
Ri |
0.00
|
0.00
|
0.58
|
0.90
|
1.12
|
1.24
|
1.32
|
1.41
|
1.45 |
Note: when n is>At time 9, n is uniformly and randomly extracted from 17 of 1, 2, …, 9, 1/2, 1/3, … and 1/9 according to the average probability of 1/17
2The number of the comparison matrixes AA form k-order pairwise comparison matrixes AA; 2) consistency index C of calculation matrix AA
i(ii) a 3) Repeating the operation for multiple times to generate m k-order random judgment matrixes A
kCalculating the consistency index each time, and averaging, i.e.
Then, the membership degree of each evaluation index belonging to each risk level is determined. The membership degree reflects the evaluation index belonging to various ocean engineering geologyDegree of environmental stability level. According to the property of the related evaluation index, the membership degree of the continuous intercourse quantitative index is determined by adopting a normal distribution function, and the general form is as follows:
when X is ═ S
i+S
i-1) At/2, μ (X) ═ 1; x is S
iWhen μ (X) ═ 0.5, parameters S and σ can be determined, and the final membership function is
Wherein S is
iAnd S
i-1In order to determine the classification threshold value when classifying the evaluation indexes, X is a specific numerical value of the evaluation index in the actual project, and the assignment of the membership degree of each evaluation index is determined as shown in table 4.
TABLE 4 membership degree distribution Table
|
Height of
|
In
|
Is low in
|
Index one
|
0
|
0.2
|
0.8
|
Index two
|
0.8
|
0.2
|
0
|
Index three
|
1.0
|
0
|
0
|
Index four
|
0.8
|
0
|
0.2
|
Index five
|
1.0
|
0
|
0
|
Index six
|
1.0
|
0
|
0
|
Index seven
|
0
|
0
|
1.0
|
Index eight
|
0.3
|
0.6
|
0.1
|
Index nine
|
0.1
|
0.3
|
0.6 |
And finally, establishing a fuzzy mathematical model. And solving the membership degree of each evaluation index to each risk grade in the quality grading evaluation set to form a fuzzy relation matrix R. The risk classification of pile slipping of the ocean platform in a certain area can be evaluated by a fuzzy vector B of quality classification, wherein the B is A and R is (0.6000, 0.1380 and 0.2620), A is an eigenvector of a judgment matrix, and R is a fuzzy relation matrix formed by membership degrees of evaluation indexes, and the fuzzy relation matrix is shown in a table 4.
And (3) sequentially representing the membership degree of the judged unit belonging to each ocean platform risk level by the numerical value in the fuzzy vector B calculated by the formula, and evaluating the pile slipping risk level of the ocean platform according to the maximum membership degree principle. Here, the maximum value of b is 0.6000, which is the quality level, that is, the estimated result of the pile slipping of the ocean platform: the pile slipping risk level is high.
In conclusion, the invention provides a risk assessment method based on AHP and fuzzy mathematical theory, and a risk grade division method is adopted to evaluate the pile slipping of the ocean platform, so that the assessment result is closer to the actual situation.
Although the present invention has been described in terms of its functions and operations, it is to be understood that the invention is not limited to the specific functions and operations described above, and that the described embodiments are merely illustrative and not restrictive, since various modifications may be made by those skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.