CN115115240A - Urban subway shield tunnel construction risk evaluation method based on two-dimensional cloud model - Google Patents
Urban subway shield tunnel construction risk evaluation method based on two-dimensional cloud model Download PDFInfo
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Abstract
The invention discloses a two-dimensional cloud model-based urban subway shield tunnel construction risk evaluation method, which comprises the steps of generating cloud droplets through a forward cloud generator, and drawing a two-dimensional standard cloud picture corresponding to a risk evaluation criterion; and calculating the characteristic value of the evaluation cloud of each evaluation index by using the actual data of the tunnel to be evaluated and the expert scoring result, performing weight combination on the characteristic value to calculate a two-dimensional comprehensive cloud picture capable of reflecting the evaluation result of the system, comparing the relationship between the two-dimensional standard cloud picture and the comprehensive cloud picture, and determining the membership degree of the construction risk of a certain shield tunnel to each risk level so as to judge the construction risk level of the shield tunnel. According to the method, the influence of risk loss and possibility of accidents on the risk is comprehensively considered, the accuracy of the evaluation result is improved, meanwhile, the evaluation process is visualized, and a more accurate and rapid evaluation result is provided for the construction risk of the shield tunnel of the urban subway.
Description
Technical Field
The invention relates to the field of urban subway shield tunnel construction risk evaluation, in particular to an urban subway shield tunnel construction risk evaluation method based on a two-dimensional cloud model.
Background
With the rapid development of economy and the acceleration of urbanization process, subways become effective means for relieving the huge traffic pressure of cities. In urban underground tunnel engineering, shield construction is a mature and widely adopted construction technology at present. Compared with the traditional tunnel construction method, the shield method can effectively control the ground surface settlement, has high construction speed, and becomes the first choice of urban underground tunnel engineering.
Due to the characteristics of high construction cost, more related specialties, long early-stage time, complex construction and the like, the phenomena of out-of-control progress, no quality passing, excessive expense and the like are frequently generated and tend to be serious in the construction process of the subway project. Meanwhile, urban subways have short development history, are not deep enough in understanding of tunnel construction technology and management, are incomplete in theoretical knowledge and lack in practical experience, field technicians lack necessary cognition on potential hazard factors existing in the subway construction process, construction managers lack scientific identification, analysis and demonstration capabilities, a risk management system is incomplete, and the risk early warning research and judgment, prevention and remedy modes are unreasonable, so that safety accidents in rail transit construction engineering are frequently caused. Therefore, how to quickly and accurately evaluate the construction risk level of the urban subway shield tunnel, achieve timely and effective safe maintenance, reduce the safety risk of tunnel construction, and become the important and difficult problem of the safety management of the current urban subway shield tunnel construction.
At present, the shield tunnel construction risk evaluation is mostly based on one-dimensional cloud model to single-dimensional evaluation analysis, evaluation results are limited, double dimensions of risk possibility and loss are not combined well, a single main and objective empowerment method is strong in dependency on experts, and the possibility that the evaluation results are contrary to reality exists.
In conclusion, the urban subway shield tunnel construction risk level evaluation is carried out based on the two-dimensional cloud model, the possibility of accident occurrence and the damage degree of the accident to the engineering are analyzed in two dimensions, and the possible risk is predicted more comprehensively and more accurately. Therefore, it is necessary to provide a rapid and accurate construction risk level evaluation method based on a two-dimensional cloud model for the construction process of the shield tunnel of the urban subway, so as to control the construction risk of the subway engineering to be minimum.
Disclosure of Invention
Aiming at the problem that the evaluation result of shield tunnel construction risk evaluation in the prior art is not combined with the double dimensionality of risk possibility and loss, the urban subway shield tunnel construction risk evaluation method based on the two-dimensional cloud model is provided, the two-dimensional cloud model is an extension of the one-dimensional cloud model, the influence of multiple factors of the urban subway shield tunnel construction risk can be considered more comprehensively while the description advantage of the cloud model on the fuzzy concept is kept, the two-dimensional cloud model is adopted as the evaluation model, the fuzzy risk and random uncertainty can be quantized, the accuracy of the evaluation result is improved, the visualization of the evaluation process is realized, and a more accurate and rapid evaluation result is provided for the urban subway shield tunnel construction risk.
In order to achieve the purpose, the invention adopts the following technical scheme:
the urban subway shield tunnel construction risk evaluation method based on the two-dimensional cloud model comprises the following steps of:
(S11) determining a risk evaluation index: determining risk evaluation indexes suitable for the shield tunnel of the urban subway;
(S12) determining the risk and likelihood rating of the indicator: determining the possibility and the hazard level of each evaluation index according to the specification;
(S13) constructing a judgment matrix, determining an index weight: comparing the importance of the indexes pairwise according to an analytic hierarchy process, constructing a judgment matrix, and determining the weight of the indexes;
(S14) drawing a two-dimensional standard cloud picture: calculating a standard cloud characteristic value, generating cloud drop data by using a forward cloud generator, and drawing a two-dimensional standard cloud picture;
(S15) calculating an evaluation cloud feature value of the evaluation index: calculating the evaluation cloud characteristic value of each evaluation index by using the actual data of the evaluation object tunnel and the expert scoring result;
(S16) carrying out weight combination on the characteristic values of the evaluation cloud, constructing a comprehensive cloud, and drawing a comprehensive cloud picture: performing weight combination by using the characteristic values obtained in the step (S15), constructing a comprehensive cloud capable of reflecting the evaluation result of the whole system, generating the comprehensive cloud by using a forward cloud generator, and drawing a two-dimensional evaluation comprehensive cloud picture;
(S17) comparing the two-dimensional standard cloud picture with the two-dimensional comprehensive cloud picture, and determining the membership degree of the shield tunnel in construction to each evaluation level: and determining the membership degree of the shield tunnel to each evaluation grade T in construction by comparing the two-dimensional standard cloud with the comprehensive cloud.
The two-dimensional cloud model comprises a forward cloud generator and a reverse cloud generator, and the digital characteristics of the two directions of the two-dimensional standard cloud are respectively expressed as (Ex) 1v ,Ex 2v )，(En 1v ,En 2v )，(H 1v ,H 2v ) The numerical characteristics of two-dimensional synthetic clouds in two directions are respectively expressed as (Ex) 1 ,Ex 2 )，(En 1 ,En 2 )，(H 1 ,H 2 )。
Digital signature (Ex) of said two-dimensional standard cloud 1v ,Ex 2v )，(En 1v ,En 2v )，(H 1v ,H 2v ) Calculated by the following method:
H 1v ＝H 2v ＝i
in the formula: ex 1v Expected value of grading standard; en 1v Entropy as a grading criterion; x is the number of max ,x min A limit value of a qualitative concept domain; h e Hyper-entropy as an analytical standard; i is a constant that reflects the comment blur threshold.
The forward cloud generator is calculated by the following method:
(S21) with (En) 1v ,En 2v ) To expect, (H) 1v 2 ,H 2v 2 ) For variance, a two-dimensional normal random number conforming to a two-dimensional normal distribution is generated (Enn) 1 ,Enn 2 )；
(S22) with (Ex) 1v ,Ex 2v ) As desired, (Enn) 12 ,Enn 22 ) For variance, a two-dimensional normal random number (x) conforming to a two-dimensional normal distribution is generated 1 ,x 2 )；
(S23) utilizing (S21) generated (Enn) 1 ,Enn 2 ) The degree of membership μ is calculated by the following method T (x 1 ,x 2 )：
The reverse cloud generator is calculated according to the following method:
(S31) using the known cloud droplet data, the sample mean X and the sample variance S for a certain set of data are calculated by:
(S32) calculating expectation, entropy and hyper-entropy of the evaluation cloud according to the sample mean and the sample variance found in (S31) as follows:
in the formula: f represents the f-th number (f is from 1 to n) in each group of data, g represents the g-th group of data (g is from 1 to m), and n represents the number of data in each group.
The characteristic value weight combination of the evaluation cloud is carried out by the two-dimensional comprehensive cloud through the following method, and the two-dimensional comprehensive cloud formula is as follows:
in the formula: m is the number of evaluation indexes, (Ex) 1 ,Ex 2 )(En 1 ,En 2 )(H 1 ,H 2 ) Is the eigenvalue of the synthetic cloud.
The forward cloud generator qualitatively characterizes the digital characteristics of the two-dimensional comprehensive cloud into quantitative data, generates the two-dimensional comprehensive cloud and draws a two-dimensional comprehensive cloud picture.
The contribution C of the membership degree of T is calculated by the following formula:
in the formula: c T (x 1 ,x 2 ) And (4) representing a membership curve of a normal two-dimensional comprehensive cloud in the domain of discourse.
Compared with the existing evaluation method, the method has the following advantages:
(1) the new method for evaluating the risk of tunnel construction optimizes the process of evaluating the risk of shield tunnel construction, enhances the scientificity and credibility of evaluation criteria, and realizes the visualization of evaluation results.
(2) In past studies, many risk assessment methods predict engineering risk from only one dimension of risk hazard. Although the prediction results of these methods have some confidence, the influence of the risk on the accident probability is ignored, resulting in a coarser evaluation result. In order to solve the problem, the method adopts the two-dimensional cloud model as an evaluation model, comprehensively considers the risk loss and the influence of the possibility of the accident on the risk, and improves the accuracy of the evaluation result.
(3) The process of conversion between qualitative concepts and quantitative discourse domains involves a large amount of fuzzy and random uncertainty information. In order to consider the fuzzy uncertainty and the random uncertainty, the research utilizes the advantage that a two-dimensional cloud model draws natural language, quantitatively converts a risk criterion into a two-dimensional standard cloud model, and quantitatively converts an expert evaluation result into a two-dimensional comprehensive cloud model. The organic combination of the ambiguity of the qualitative concept and the randomness of the membership degree is realized.
(4) At present, membership function in fuzzy mathematics can only reflect the fuzziness of an evaluation system, and probability function can only be used for reflecting the randomness of the evaluation system. In contrast, the research is combined with a two-dimensional cloud model, the contribution degree of the comprehensive cloud droplets in the standard cloud to the comprehensive cloud is calculated, the membership degree of the comprehensive cloud droplets in the three-dimensional space is considered, the project risk level is comprehensively predicted based on the maximum membership principle, and the evaluation accuracy is improved.
Drawings
FIG. 1 is a schematic flow chart of the evaluation method of the present invention;
FIG. 2 is a two-dimensional standard cloud;
FIG. 3 is a two-dimensional evaluation comprehensive cloud chart;
fig. 4 two-dimensionally evaluates the area (domain of discourse) where the integrated cloud overlaps the two-dimensional standard cloud.
Detailed Description
As shown in fig. 1, which is a flow diagram of the urban subway shield tunnel construction risk level evaluation method based on the two-dimensional cloud model, the following takes the south-heading block section shield tunnel engineering as an example to describe the above steps in detail.
(S11) determining a risk assessment index: determining risk evaluation indexes suitable for the subway shield tunnel, determining the first-level risk evaluation indexes of the project according to GB 50652-: (A) 1 ) Instability of the palm surface; (A) 2 ) The tool bit and the tool are worn; (A) 3 ) Shield tail seal failure; (A) 4 ) Floating the tunnel and roof-falling; (A) 5 ) Improper axis control; (A) 6 ) The segment is damaged; (A) 7 ) Water leakage; (A) 8 ) Differential settlement; (A) 9 ) A malfunctioning device. The invention considers the possibility and loss of each first-level index, and the loss comprises 5 sub-indexes of casualties, environmental influence, economic loss, construction period delay and social influence. The scoring criteria are shown in tables 1 and 2.
TABLE 1 loss score criteria
TABLE 2 likelihood score criteria
(S12) determining the risk and likelihood rating of the indicator: determining the possibility and the hazard level of each evaluation index according to the specification; determining the possibility and the hazard level of each evaluation index according to the rule of urban rail transit underground engineering construction risk management (GB50652-2011), wherein the probability and the hazard level are divided into 4 levels in table 3;
TABLE 3 Risk class criteria
(S13) constructing a judgment matrix, determining an index weight: and comparing the importance of the indexes pairwise according to an analytic hierarchy process to construct a judgment matrix and determine the weight of the indexes. In order to reasonably determine the construction risk level of the shield tunnel, the weight of the index of the decision layer needs to be determined, in the research, an analytic hierarchy process is used as a main basis for determining the index weight, and the standard of the analytic hierarchy process is shown in table 4. The importance between every two indexes is judged according to expert experience, a judgment matrix is constructed based on the importance, and the judgment matrix of each risk index is shown in tables 5 to 14.
TABLE 4 criteria for analytic hierarchy process
TABLE 5 Risk estimation index likelihood comparison rank scores
| Risk factors | A 1 | A 2 | A 3 | A 4 | A 5 | A 6 | A 7 | A 8 | A 9 |
| A 1 | 1 | 1/7 | 1 | 1/3 | 1/3 | 1/7 | 1/7 | 1/3 | 1/4 |
| A 2 | 7 | 1 | 4 | 1/2 | 7 | 1/2 | 1/2 | 5 | 2 |
| A 3 | 1 | 1/4 | 1 | 1/3 | 2 | 1/5 | 1/5 | 1/3 | 1/3 |
| A 4 | 3 | 2 | 3 | 1 | 3 | 1/4 | 1/4 | 1/2 | 2 |
| A 5 | 3 | 1/7 | 1/2 | 1/3 | 1 | 1/8 | 1/7 | 1/3 | 1/2 |
| A 6 | 7 | 2 | 5 | 4 | 8 | 1 | 1 | 4 | 3 |
| A 7 | 7 | 2 | 5 | 4 | 7 | 1 | 1 | 3 | 5 |
| A 8 | 3 | 1/5 | 3 | 2 | 3 | 1/4 | 1/3 | 1 | 5 |
| A 9 | 4 | 1/2 | 3 | 1/2 | 2 | 1/3 | 1/5 | 1/5 | 1 |
TABLE 6 Risk estimation loss comparative grade score (first-order index)
| Risk factors | A 1 | A 2 | A 3 | A 4 | A 5 | A 6 | A 7 | A 8 | A 9 |
| A 1 | 1 | 8 | 8 | 3 | 7 | 8 | 8 | 7 | 6 |
| A 2 | 1/8 | 1 | 1/3 | 1/5 | 1/2 | 1/3 | 1/3 | 1/3 | 2 |
| A 3 | 1/8 | 3 | 1 | 1 | 3 | 2 | 2 | 2 | 4 |
| A 4 | 1/3 | 5 | 1 | 1 | 3 | 7 | 7 | 8 | 8 |
| A 5 | 1/7 | 2 | 1/3 | 1/3 | 1 | 3 | 3 | 3 | 4 |
| A 6 | 1/8 | 3 | 1/2 | 1/7 | 1/3 | 1 | 1/2 | 1/2 | 3 |
| A 7 | 1/8 | 3 | 1/2 | 1/7 | 1/3 | 2 | 1 | 1/3 | 3 |
| A 8 | 1/7 | 3 | 1/2 | 1/8 | 1/3 | 2 | 3 | 1 | 4 |
| A 9 | 1/6 | 1/2 | 1/4 | 1/8 | 1/4 | 1/3 | 1/3 | 1/4 | 1 |
TABLE 7 Risk estimation loss comparative grade score (second level index-palm instability)
| Instability of palm surface | A 11 | A 12 | A 13 | A 14 | A 15 |
| A 11 | 1 | 3 | 2 | 3 | 4 |
| A 12 | 1/3 | 1 | 1/3 | 3 | 1/4 |
| |
1/2 | 3 | 1 | 3 | 1 |
| A 14 | 1/3 | 1/3 | 1/3 | 1 | 1/3 |
| |
1/4 | 4 | 1 | 3 | 1 |
TABLE 8 Risk estimation loss comparative grade score (second level index-tool bit and tool wear)
| Risk factors | A 21 | A 22 | A 23 | A 24 | A 25 |
| A 21 | 1 | 8 | 6 | 4 | 4 |
| A 22 | 1/8 | 1 | 1/3 | 1/4 | 1/2 |
| |
1/6 | 3 | 1 | 1/3 | 3 |
| A 24 | 1/4 | 4 | 3 | 1 | 6 |
| A 25 | 1/4 | 2 | 1/3 | 1/6 | 1 |
TABLE 9 Risk estimation loss comparative grade score (second level index-Shield tail seal failure)
| Risk factors | A 31 | A 32 | A 33 | A 34 | A 35 |
| A 31 | 1 | 6 | 6 | 4 | 3 |
| A 32 | 1/6 | 1 | 1/3 | 1/3 | 1/3 |
| |
1/6 | 3 | 1 | 1/3 | 2 |
| A 34 | 1/4 | 3 | 3 | 1 | 3 |
| A 35 | 1/3 | 3 | 1/2 | 1/3 | 1 |
TABLE 10 Risk estimation loss comparative grade score (second level index-Floating on tunnel, roof fall)
TABLE 11 Risk estimation loss comparison grade score (second level index-axis improper control)
| Risk factors | A 51 | A 52 | A 53 | A 54 | A 55 |
| A 51 | 1 | 5 | 3 | 4 | 4 |
| A 52 | 1/5 | 1 | 1/5 | 1/5 | 1/2 |
| |
1/3 | 5 | 1 | 2 | 4 |
| A 54 | 1/4 | 5 | 1/2 | 1 | 4 |
| A 55 | 1/4 | 2 | 1/4 | 1/4 | 1 |
TABLE 12 Risk estimation loss comparative grade score (second level index-segment breakage)
| Risk factors | A 61 | A 62 | A 63 | A 64 | A 65 |
| A 61 | 1 | 6 | 6 | 5 | 7 |
| A 62 | 1/6 | 1 | 1/4 | 1/5 | 1/3 |
| |
1/6 | 4 | 1 | 1/2 | 1/2 |
| |
1/5 | 5 | 2 | 1 | 3 |
| A 65 | 1/7 | 3 | 2 | 1/3 | 1 |
TABLE 13 Risk estimation loss comparison grade score (second level index-Water leakage)
| Risk factors | A 71 | A 72 | A 73 | A 74 | A 75 |
| A 71 | 1 | 4 | 5 | 4 | 5 |
| A 72 | 1/4 | 1 | 1/3 | 1/3 | 1/2 |
| |
1/5 | 3 | 1 | 1/3 | 3 |
| A 74 | 1/4 | 3 | 3 | 1 | 2 |
| A 75 | 1/5 | 2 | 1/3 | 1/2 | 1 |
TABLE 14 Risk estimation loss comparative grade score (second level index-differential Settlement)
| Risk factors | A 81 | A 82 | A 83 | A 84 | A 85 |
| A 81 | 1 | 4 | 5 | 5 | 6 |
| A 82 | 1/4 | 1 | 1/2 | 1/2 | 1/3 |
| |
1/5 | 2 | 1 | 1/3 | 1/2 |
| |
1/5 | 2 | 3 | 1 | 2 |
| A 85 | 1/6 | 3 | 2 | 1/2 | 1 |
TABLE 15 Risk estimation loss comparative grade score (second level index-faulty device)
| Risk factors | A 91 | A 92 | A 93 | A 94 | A 95 |
| A 91 | 1 | 6 | 5 | 5 | 7 |
| A 92 | 1/6 | 1 | 1/4 | 1/5 | 1/2 |
| |
1/5 | 4 | 1 | 1/3 | 3 |
| A 94 | 1/5 | 5 | 3 | 1 | 4 |
| A 95 | 1/7 | 2 | 1/3 | 1/4 | 1 |
(S14) drawing a two-dimensional standard cloud picture: and calculating a standard cloud characteristic value, generating cloud drop data by using a forward cloud generator, and drawing a two-dimensional standard cloud picture. Through the characteristic values of the two-dimensional cloud model standard cloud and the calculation method of the two-dimensional standard cloud characteristic parameters, as shown in formulas (1) to (3), cloud droplets are generated by using a forward algorithm, then a two-dimensional standard cloud picture corresponding to a risk grade division table (table 3) in a one-to-one mode is drawn by using cloud droplet data through a forward cloud generator, and 3000 groups of comprehensive cloud droplets are generated in the research aiming at the risk evaluation of the project;
H 1v ＝H 2v 0.01 (3) ═ i
In the formula: ex 1v Expected value of grading standard; en 1v Entropy, which is a grading criterion; x is the number of max ，x min A limit value of a qualitative concept domain; h e Hyper-entropy as an analytical standard; and i is a constant for reflecting the fuzzy domain degree of the comment, different values are set, the thicknesses of the clouds are inconsistent, and i in the standard cloud in the embodiment is 0.01.
(S15) calculating an evaluation cloud feature value of the evaluation index: calculating the evaluation cloud characteristic value of each evaluation index by using the actual data of the evaluation object tunnel and the expert scoring result; the method comprises the following steps of respectively calculating evaluation cloud characteristic values of evaluation indexes by using actual data of a tunnel and through expert scoring results, wherein the calculation method of the evaluation cloud characteristic parameters comprises the following steps:
calculating the mean value of a certain group of data samples by the formulas (4) and (5) by using the known cloud droplet data
And sample variance S:
and (3) further calculating and evaluating the expectation, entropy and super-entropy of the cloud by using the formulas (6) to (8) through an inverse cloud algorithm according to the obtained sample mean value and sample variance:
in the formula: f represents the f-th number (f is from 1 to n) in each group of data, g represents the g-th group of data (g is from 1 to m), and n represents the number of data in each group.
(S16) carrying out weight combination on the characteristic values of the evaluation cloud, constructing a comprehensive cloud, and drawing a comprehensive cloud picture: performing weight combination by using the characteristic values obtained in the step (S15), constructing a comprehensive cloud capable of reflecting the evaluation result of the whole system, generating the comprehensive cloud by using a forward cloud generator, and drawing a two-dimensional evaluation comprehensive cloud picture; the eigenvalue obtained in step (S15) is expressed by the following formula(9) Carrying out weight combination, qualitatively representing the digital characteristics of the two-dimensional comprehensive cloud into quantitative data (cloud droplets) by a forward cloud generator, generating the two-dimensional comprehensive cloud and drawing a cloud picture (generating 1000 groups of cloud droplet drops (x) 1 ,x 2 ,μ T (x 1 ,x 2 )。
In the formula: m is the number of evaluation indexes, (Ex) 1 ,Ex 2 )(En 1 ,En 2 )(H 1 ,H 2 ) Is the eigenvalue of the synthetic cloud.
(S17) comparing the two-dimensional standard cloud picture with the two-dimensional comprehensive cloud picture, and determining the membership degree of the shield tunnel in construction to each evaluation level: and determining the membership degree of the shield tunnel to each evaluation grade T in construction by comparing the two-dimensional standard cloud with the comprehensive cloud. And finally, according to the membership degree of elements in a theoretical domain (namely an overlapped region of the generated standard cloud and the comprehensive cloud) in the two-dimensional cloud model to a qualitative concept T of the risk level, based on the total contribution degree of cloud drop elements on the theoretical domain in the one-dimensional cloud model to the qualitative concept T, taking the formula (12) as the contribution degree C of cloud drops on each standard cloud theoretical domain in the two-dimensional cloud model to the concept T, and determining the membership degree of the shield tunnel in construction to each evaluation level T (T is I level, II level, III level and IV level):
in the formula: c T (x 1 ,x 2 ) And (4) representing a membership curve of a normal two-dimensional comprehensive cloud in the domain of discourse.
The distribution probability of the comprehensive evaluation cloud in each grade weight is p (N4) ═ 0.628804601, p (N3) ═ 0.235541417, p (N2) ═ 0.064610302, and p (N1) ═ 0.07104368. (the number of each grade is output from the codes, and then the proportion is calculated) according to the membership degree analysis result, the project engineering construction risk grade belongs to the IV grade, and the safety is good. According to the standard (GB50652-2011), the prevention and control requirements can be well met by implementing a reasonable risk management scheme only by carrying out daily examination and inspection on time, and the occurrence of disasters is avoided.
Claims (8)
1. The urban subway shield tunnel construction risk evaluation method based on the two-dimensional cloud model is characterized by comprising the following steps of:
(S11) determining a risk assessment index: determining risk evaluation indexes suitable for the shield tunnel of the urban subway;
(S12) determining the risk and likelihood rating of the indicator: determining the possibility and the hazard level of each evaluation index according to the specification;
(S13) constructing a judgment matrix, determining an index weight: comparing the importance of the indexes pairwise according to an analytic hierarchy process, constructing a judgment matrix, and determining the weight of the indexes;
(S14) drawing a two-dimensional standard cloud picture: calculating a standard cloud characteristic value, generating cloud drop data by using a forward cloud generator, and drawing a two-dimensional standard cloud picture;
(S15) calculating an evaluation cloud feature value of the evaluation index: calculating the evaluation cloud characteristic value of each evaluation index by using the actual data of the evaluation object tunnel and the expert scoring result;
(S16) carrying out weight combination on the characteristic values of the evaluation cloud, constructing a comprehensive cloud, and drawing a comprehensive cloud picture: performing weight combination by using the characteristic values obtained in the step (S15), constructing a comprehensive cloud capable of reflecting the evaluation result of the whole system, generating the comprehensive cloud by using a forward cloud generator, and drawing a two-dimensional evaluation comprehensive cloud picture;
(S17) comparing the two-dimensional standard cloud picture with the two-dimensional comprehensive cloud picture, and determining the membership degree of the shield tunnel in construction to each evaluation level: and determining the membership degree of the shield tunnel to each evaluation grade T in construction by comparing the two-dimensional standard cloud with the comprehensive cloud.
2. The evaluation method according to claim 1, wherein the two-dimensional cloud model comprises a forward cloud generator and a reverse cloud generator, and the numerical features of the two directions of the two-dimensional standard cloud are respectively represented as (Ex) 1v ,Ex 2v )，(En 1v ,En 2v )，(H 1v ,H 2v ) The numerical characteristics of two-dimensional synthetic clouds in two directions are respectively expressed as (Ex) 1 ,Ex 2 )，(En 1 ,En 2 )，(H 1 ,H 2 )。
3. The evaluation method according to claim 2, wherein the numerical characteristic (Ex) of the two-dimensional standard cloud 1v ,Ex 2v )，(En 1v ,En 2v )，(H 1v ,H 2v ) Calculated by the following method:
H 1v ＝H 2v ＝i
in the formula: ex 1v Expected value of grading standard; en 1v Entropy as a grading criterion; x is the number of max ,x min A limit value of a qualitative concept domain; h e Hyper-entropy as an analytical standard; i is a constant that reflects the comment blur threshold.
4. The evaluation method of claim 2, wherein the forward cloud generator is calculated by:
(S21) with (En) 1v ,En 2v ) To expect, (H) 1v 2 ,H 2v 2 ) Generating a fitting two-dimension for the varianceNormally distributed two-dimensional normal random number (Enn) 1 ,Enn 2 )；
(S22) with (Ex) 1v ,Ex 2v ) As desired, (Enn) 12 ,Enn 22 ) Generating a two-dimensional normal random number (x) conforming to the two-dimensional normal distribution for variance 1 ,x 2 )；
(S23) utilizing (S21) generated (Enn) 1 ,Enn 2 ) The degree of membership μ is calculated by the following method T (x 1 ,x 2 )：
5. The evaluation method according to claim 2, wherein the inverse cloud generator is calculated as follows:
(S31) calculating a sample mean value of a certain set of data by the following method using the known cloud droplet data
And sample variance S:
(S32) calculating expectation, entropy and hyper-entropy of the evaluation cloud according to the sample mean and the sample variance found in (S31) as follows:
in the formula: f represents the f-th number (f is from 1 to n) in each group of data, g represents the g-th group of data (g is from 1 to m), and n represents the number of data in each group.
6. The evaluation method according to claim 1, wherein the two-dimensional integrated cloud combines eigenvalue weights of the evaluation clouds by the following method, and the two-dimensional integrated cloud formula is as follows:
in the formula: m is the number of evaluation indexes, (Ex) 1 ,Ex 2 )(En 1 ,En 2 )(H 1 ,H 2 ) Is the eigenvalue of the synthetic cloud.
7. The evaluation method of claim 1, wherein the forward cloud generator qualitatively characterizes the digital features of the two-dimensional synthetic cloud as quantitative data, generates the two-dimensional synthetic cloud, and plots the two-dimensional synthetic cloud map.
8. The evaluation method according to claim 1, wherein the degree of contribution C of the membership degree of T is calculated by the following formula:
in the formula: c T (x 1 ,x 2 ) And (4) representing a membership curve of a normal two-dimensional comprehensive cloud in the domain of discourse.
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