Subway construction safety system toughness evaluation method based on cloud model

Abstract

According to the subway construction safety system toughness evaluation method based on the cloud model provided by the invention, the dynamic process attributes of toughness realization and the functions of the components of the subway construction safety system in the toughness realization process are considered; the absorbing force subsystem, the restoring force subsystem and the adaptability subsystem based on the toughness process are combined with the personnel subsystem, the material subsystem, the technology subsystem, the management subsystem and the environment subsystem of the subway construction safety system, and a scientific and comprehensive subway construction safety system toughness evaluation index system is constructed. Secondly, considering different importance of different indexes, considering fuzziness and randomness of evaluation grade and dimension division and index selection in a subway construction safety system toughness evaluation process, and adopting a CRITIC method to calculate index weight and a cloud model to construct a subway construction safety system toughness evaluation model; and finally, in order to further clarify the toughness grade, calculating the similarity with the standard cloud through the fuzzy closeness, and determining the toughness grade according to the grade characteristic value.

Description

Technical Field

[0001] The present invention relates to the technical field of subway construction safety system assessment, and in particular to a subway construction safety system resilience assessment method based on a cloud model. Background Art

[0002] my country's rapid urbanization has promoted the rapid development of subway construction. However, subway construction conditions are complex, the construction period is long, and natural disasters occur frequently, which can easily lead to construction safety accidents. In order to improve the level of subway construction safety management, the resilience assessment of subway construction safety systems has become an important research field.

[0003] As the most commonly used method in subway construction safety system resilience assessment, subway construction safety system resilience assessment mainly involves the construction of an assessment index system and the selection of assessment methods.

[0004] The existing evaluation index construction mainly includes two static perspectives: one is based on system components and the other is based on system resilience characteristics. However, resilience is a multidimensional dynamic process rather than a static phenomenon. Although the above studies have fully considered multidimensional factors, they focus more on static characteristics and ignore the dynamic process of resilience realization.

[0005] The existing evaluation methods mainly include TOPSIS, fuzzy evaluation and Bayesian network. Due to the randomness and fuzziness of evaluation levels and dimensional divisions, and indicator selection in the resilience assessment of subway construction safety systems, TOPSIS and fuzzy evaluation ignore the fuzziness and randomness in the evaluation process. Although Bayesian network analysis can take into account various uncertainties in the evaluation process, it relies on a large amount of monitoring data and has problems of insufficient data or data errors, which affects the accuracy of the evaluation results.

[0006] Therefore, the accuracy of resilience assessment of subway construction safety systems is low. Summary of the invention

[0007] The purpose of the technical solution of the present invention is to provide a subway construction safety system resilience assessment method based on a cloud model.

[0008] The technical solution of the present invention provides a subway construction safety system resilience assessment method based on a cloud model, comprising the following steps:

[0009] Taking the absorptive capacity, resilience and adaptability dimensions based on resilience process as consideration dimensions, combined with the personnel, material, technology, management and environment of subway construction safety system, a subway construction safety system resilience evaluation index system table is constructed;

[0010] Obtain experts' evaluation of the indicators in the index system table of subway construction safety system resilience to construct a data index matrix, calculate the index comparison strength and the conflict between indicators, and use the CRITIC method to calculate the index weights according to the index comparison strength and the conflict between indicators, and determine the resilience evaluation index weight vector;

[0011] According to the fuzziness and randomness of the evaluation level and dimension division, and the indicator selection, the standard cloud of subway construction safety system resilience evaluation is constructed using the golden section method;

[0012] According to the number of experts, the number of indicators and the variance of expert scores corresponding to the evaluation data indicator matrix, the reverse cloud generator is used to calculate the digital characteristics of the indicator cloud, and the characteristic values ​​of expectation, entropy and super entropy of the comprehensive cloud are obtained by combining the weight vector of the resilience evaluation indicators.

[0013] According to the characteristic values ​​of expectation, entropy and super entropy of the comprehensive cloud, a first normal random number with a mean of entropy and a standard deviation of super entropy is generated, a second normal random number with a mean of expectation and a standard deviation of entropy is generated, the membership of the second normal random number to the qualitative concept is solved according to the first normal random number and the second normal random number, and a plurality of cloud droplets constructed with the second normal random number and the membership form a comprehensive cloud map;

[0014] The fuzzy proximity is used to calculate the similarity between the comprehensive cloud map and the cloud map corresponding to the standard cloud for subway construction safety system resilience assessment. The resilience level is determined according to the level eigenvalue to conduct subway construction safety system resilience assessment.

[0015] Preferably, the data indicator matrix is:

[0016]

[0017] Where X is the indicator data matrix composed of p experts on n evaluation indicators; x tj (t=1, 2, ..., p, j=1, 2, ..., n) is the evaluation of the t-th expert on the j-th evaluation index.

[0018] Preferably, the formula for the indicator comparison strength is as follows:

[0019]

[0020] In the formula, σ j is the comparison strength of the jth indicator; μ j is the arithmetic mean of the j-th indicator data.

[0021] Preferably, the formula for the conflict between the indicators is as follows:

[0022]

[0023] In the formula, r jl is the Pearson correlation coefficient between the jth indicator and the lth indicator (j, l = 1, 2, ..., n); y j is the conflict between the jth indicator and other indicators (j=1, 2, ..., n).

[0024] Preferably, the formula of the indicator weight is as follows:

[0025]

[0026] In the formula, w j is the weight of the jth indicator.

[0027] Preferably, the standard cloud formula is as follows:

[0028]

[0029] Where xmin and xmax are the lower and upper limits of the valid domain [xmin, xmax] respectively; (Ex -2 ,En -2 ,He -2 )、(Ex -1 ,En -1 ,He -1 ), (Ex0, En0, He0), (Ex1, En1, He1), and (Ex2, En2, He2) are standard cloud digital eigenvalues.

[0030] Preferably, the indicator cloud digital feature formula is as follows:

[0031]

[0032] Where, Ex j is the expected characteristic value of the indicator cloud, En j is the characteristic value of the entropy of the indicator cloud, He j is the characteristic value of the super entropy of the indicator cloud; p is the number of experts, n is the number of indicators, is the variance of the expert ratings.

[0033] Preferably, the characteristic value formulas of expectation, entropy and super entropy of the comprehensive cloud are as follows:

[0034]

[0035] Where Ex is the expected eigenvalue of the comprehensive cloud, En is the eigenvalue of the entropy of the comprehensive cloud, and He is the eigenvalue of the super entropy of the comprehensive cloud.

[0036] Preferably, the formula of the membership degree is as follows:

[0037]

[0038] Where, E' n is the first normal random number, c i is a second normal random number.

[0039] Preferably, the formula for determining the toughness grade according to the grade characteristic value is as follows:

[0040]

[0041] Where C refers to the similarity of the cloud models of the two groups N1 and N2 to be compared, (E x1 , E n1 , H e1 )、(E x2 , E n2 , H e2 ) are the cloud parameters of the two groups of cloud models. L is the level characteristic value; k = 1, 2, ..., m, m is the total number of levels; C k is the similarity of the evaluation object about the kth level.

[0042] The technical solution of the present invention proposes a subway construction safety system resilience assessment method based on a cloud model, which takes into account the dynamic process attributes of resilience realization and the role played by the components of the subway construction safety system in the resilience realization process, and combines the absorptive capacity, resilience and adaptability based on the resilience process with the five subsystems of personnel, material, technology, management and environment of the subway construction safety system to construct a scientific and comprehensive subway construction safety system resilience evaluation index system. Secondly, considering the different importance of different indicators, the evaluation level and dimension division in the process of subway construction safety system resilience assessment, and the fuzziness and randomness of indicator selection, the CRITIC method is used to calculate the indicator weights and the cloud model to construct a subway construction safety system resilience assessment model. Finally, in order to further clarify the resilience level, the similarity between the standard cloud and the fuzzy closeness calculation is used to determine the resilience level according to the level characteristic value. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flow chart of a subway construction safety system resilience assessment method based on a cloud model is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0045] like Figure 1As shown, an embodiment of the present invention provides a subway construction safety system resilience assessment method based on a cloud model, comprising the following steps:

[0046] S1, based on the absorptive capacity, recovery capacity and adaptability of the resilience process, and taking into account the five subsystems of personnel, material, technology, management and environment of the subway construction safety system, a subway construction safety system resilience assessment index system is constructed.

[0047] In order to consider the dynamic process properties of resilience realization and the role of the components of the subway construction safety system in the process of resilience realization, a scientific and comprehensive subway construction safety system resilience evaluation index system is constructed by combining the absorptive capacity, recovery capacity and adaptability based on the resilience process with the five subsystems of personnel, material, technology, management and environment of the subway construction safety system.

[0048] Based on the resilience process, the conceptual model of subway construction safety resilience is divided into three dimensions: absorption dimension, resilience dimension and adaptability dimension. The absorption dimension refers to the ability of the five subsystems of the subway construction safety system, namely personnel, material, technology, management and environment, to absorb disturbance shocks and minimize losses when disasters or emergencies occur. The resilience dimension refers to the fact that after a disaster or emergency occurs, the five subsystems of the subway construction safety system, namely personnel, material, technology, management and environment, successively take emergency recovery and post-disaster repair measures, first suppress the expansion of the disaster in a short period of time after the disaster and gradually recover, and then take a series of effective measures to restore the system to the pre-disaster safety state level. The adaptability dimension refers to the fact that after the system is restored to the pre-disaster safety state, the four subsystems of the subway construction safety system, namely personnel, technology, management and environment, learn from successful experiences and failure lessons, continuously adjust and optimize their own safety systems, and promote the improvement of the subway construction safety system to better adapt to future uncertain disasters or emergencies.

[0049] Table 1. Index system of resilience of subway construction safety system

[0050]

[0051]

[0052] S2, considering the different importance of different indicators, the CRITIC method is used to calculate the weights and determine the weight vector of resilience assessment indicators. The CRITIC method considers the comparative strength of indicators and the conflict between indicators to comprehensively measure the weight of indicators. It can take into account the variability of indicators and the correlation between indicators at the same time. Compared with the entropy weight method that only considers the degree of data dispersion, CRITIC is better.

[0053] The specific steps for calculating the CRITIC method to determine the weight vector of resilience assessment indicators are as follows:

[0054] S21, construct the data indicator matrix, the formula is as follows:

[0055]

[0056] Where X is the indicator data matrix composed of p experts on n evaluation indicators; x tj (t=1, 2, ..., p, j=1, 2, ..., n) is the evaluation of the t-th expert on the j-th evaluation index.

[0057] S22, calculate the index comparison strength, the formula is as follows:

[0058]

[0059] In the formula, σ j is the comparison strength of the jth indicator; μ j is the arithmetic mean of the j-th indicator data.

[0060] S23, calculate the conflict between indicators, the formula is as follows:

[0061]

[0062] In the formula, r jl is the Pearson correlation coefficient between the jth indicator and the lth indicator (j, l = 1, 2, ..., n); y j is the conflict between the jth indicator and other indicators (j=1, 2, ..., n).

[0063] S24, calculate the indicator weight, the formula is as follows:

[0064]

[0065] In the formula, w j is the weight of the jth indicator.

[0066] S3, using cloud model to build subway construction safety system resilience assessment model. Taking into full consideration the fuzziness and randomness of evaluation level and dimension division and indicator selection in the process of subway construction safety system resilience assessment, cloud model is used to build subway construction safety system resilience assessment model. Cloud model can fully reflect the randomness and fuzziness of language concepts and has strong qualitative and quantitative analysis characteristics. Cloud model can well solve the randomness and fuzziness problems in the process of subway construction safety system resilience assessment.

[0067] The steps for constructing a resilience assessment model for subway construction safety system based on the cloud model are as follows:

[0068] S31, use the golden section method to determine the standard cloud, the formula is as follows:

[0069]

[0070]

[0071] Where xmin and xmax are the lower and upper limits of the valid domain [xmin, xmax] respectively; (Ex -2 ,En -2 ,He -2 )、(Ex -1 ,En -1 ,He -1 ), (Ex0, En0, He0), (Ex1, En1, He1), and (Ex2, En2, He2) are standard cloud digital eigenvalues.

[0072] S32, using the reverse cloud generator to calculate the index cloud digital features are as follows:

[0073]

[0074] Where, Ex j is the expected characteristic value of the indicator cloud, En j is the characteristic value of the entropy of the indicator cloud, He j is the characteristic value of the super entropy of the indicator cloud; p is the number of experts, n is the number of indicators, is the variance of the expert ratings.

[0075] S33, using the index cloud digital features and weights to calculate the comprehensive cloud digital features is as follows:

[0076]

[0077] S34, generating a comprehensive cloud map through a forward cloud generator, and performing the following processing according to the three characteristic values ​​of the comprehensive cloud, namely, expectation (Ex), entropy (En), and hyperentropy (He):

[0078] 1) Generate a normal random number E' with mean En and standard deviation He n ;

[0079] 2) Generate a normal random number c with mean Ex and standard deviation En i ;

[0080] 3) Solve for c i Membership degree μ to qualitative concept i as follows:

[0081]

[0082] 4) Repeat steps 1) to 3) N times to obtain N cloud droplets (c i ,μ i ) form a cloud map.

[0083] S4, using fuzzy closeness to calculate the similarity with the standard cloud, and determine the toughness level according to the level characteristic value. In order to further quantify the evaluation results, the similarity with each standard cloud is calculated based on fuzzy closeness, and the specific toughness value is determined according to the similarity using the level characteristic value. The formula for calculating the toughness level according to the level characteristic value is as follows:

[0084]

[0085] Where C refers to the similarity of the cloud models of the two groups N1 and N2 to be compared, (E x1 , E n1 , H e1 )、(E x2 , E n2 , H e2 ) are the cloud parameters of the two groups of cloud models. L is the level characteristic value; k = 1, 2, ..., m, m is the total number of levels; C k is the similarity of the evaluation object about the kth level.

[0086] The embodiment of the present invention is based on the resilience realization process and takes into account the role played by the components of the subway construction safety system in the resilience realization process. It combines the five subsystems of personnel, material, technology, management and environment of the subway construction safety system based on resilience absorption, recovery and adaptability (ARA) to construct a scientific and comprehensive subway construction safety system resilience evaluation index system. Taking into full consideration the fuzziness and randomness of the evaluation level and dimension division and indicator selection in the process of subway construction safety system resilience evaluation, a cloud model is used to construct a subway construction safety system resilience evaluation model. The cloud model can fully reflect the randomness and fuzziness of language concepts and has strong qualitative and quantitative analysis characteristics. In addition, in order to further quantify the evaluation results, the similarity with each standard cloud is calculated based on fuzzy proximity, and the specific resilience value is determined by the level characteristic value according to the similarity. The proposed model improves the accuracy of the subway construction safety system resilience evaluation method and provides a reference for the improvement and control of the subway construction safety system resilience.

Claims

1. A cloud model-based subway construction safety system resilience assessment method, characterized in that: The following steps are involved: Taking the absorptive capacity, resilience and adaptability dimensions based on resilience process as consideration dimensions, combined with the personnel, material, technology, management and environment of subway construction safety system, a subway construction safety system resilience evaluation index system table is constructed; Obtain experts' evaluation of the indicators in the index system table of subway construction safety system resilience to construct a data index matrix, calculate the index comparison strength and the conflict between indicators, and use the CRITIC method to calculate the index weights according to the index comparison strength and the conflict between indicators, and determine the resilience evaluation index weight vector; According to the fuzziness and randomness of the evaluation level and dimension division, and the indicator selection, the standard cloud of subway construction safety system resilience evaluation is constructed using the golden section method; According to the number of experts, the number of indicators and the variance of expert scores corresponding to the evaluation data indicator matrix, the reverse cloud generator is used to calculate the digital characteristics of the indicator cloud, and the characteristic values ​​of expectation, entropy and super entropy of the comprehensive cloud are obtained by combining the weight vector of the resilience evaluation indicators. According to the characteristic values ​​of expectation, entropy and super entropy of the comprehensive cloud, a first normal random number with a mean of entropy and a standard deviation of super entropy is generated, a second normal random number with a mean of expectation and a standard deviation of entropy is generated, the membership of the second normal random number to the qualitative concept is solved according to the first normal random number and the second normal random number, and a plurality of cloud droplets constructed with the second normal random number and the membership form a comprehensive cloud map; The fuzzy proximity is used to calculate the similarity between the comprehensive cloud map and the cloud map corresponding to the standard cloud for subway construction safety system resilience assessment. The resilience level is determined according to the level eigenvalue to conduct subway construction safety system resilience assessment.

2. A cloud model-based subway construction safety system resilience assessment method as claimed in claim 1, characterized in that: The data indicator matrix is: Where X is the indicator data matrix composed of p experts on n evaluation indicators; x tj (t=1, 2, ..., p, j=1, 2, ..., n) is the evaluation of the t-th expert on the j-th evaluation index.

3. A cloud model-based subway construction safety system resilience assessment method as claimed in claim 1, characterized in that: The formula for comparing the strength of the indicators is as follows: In the formula, σ j is the comparison strength of the jth indicator; μ j is the arithmetic mean of the j-th indicator data.

4. The cloud model-based subway construction safety system resilience assessment method according to claim 1, characterized in that: The formula for the conflict between the indicators is as follows: In the formula, r jl is the Pearson correlation coefficient between the jth indicator and the lth indicator (j, l = 1, 2, ..., n); y j is the conflict between the jth indicator and other indicators (j=1, 2, ..., n).

5. The cloud model-based subway construction safety system resilience assessment method according to claim 1, characterized in that: The formula for the indicator weight is as follows: In the formula, w j is the weight of the jth indicator.

6. A cloud model-based subway construction safety system resilience assessment method as claimed in claim 1, characterized in that: The standard cloud for the resilience assessment of the subway construction safety system is as follows: Where xmin and xmax are the lower and upper limits of the valid domain [xmin, xmax] respectively; (Ex -2 ,En -2 ,He -2 )、(Ex -1 ,En -1 ,He -1 ), (Ex0, En0, He0), (Ex1, En1, He1), and (Ex2, En2, He2) are standard cloud digital eigenvalues.

7. The cloud model-based subway construction safety system resilience assessment method according to claim 1, characterized in that: The digital characteristic formula of the indicator cloud is as follows: Where, Ex j is the expected characteristic value of the indicator cloud, En j is the characteristic value of the entropy of the indicator cloud, He j is the characteristic value of the super entropy of the indicator cloud; p is the number of experts, n is the number of indicators, is the variance of the expert ratings.

8. A cloud model-based subway construction safety system resilience assessment method as claimed in claim 7, characterized in that: The characteristic value formulas of the expectation, entropy and super entropy of the comprehensive cloud are as follows: Where Ex is the expected eigenvalue of the comprehensive cloud, En is the eigenvalue of the entropy of the comprehensive cloud, and He is the eigenvalue of the super entropy of the indicator cloud and the comprehensive cloud.

9. A cloud model-based subway construction safety system resilience assessment method as claimed in claim 8, characterized in that: The formula of the membership degree is as follows: Where, E' n is the first normal random number, c i is a second normal random number.

10. The cloud model-based subway construction safety system resilience assessment method according to claim 1, characterized in that: The formula for determining the toughness grade based on the grade characteristic value is as follows: Where C refers to the similarity of the cloud models of the two groups N1 and N2 to be compared, (E x1 , E n1 , H e1 )、(E x2 , E n2 , H e2 ) are the cloud parameters of the two groups of cloud models. L is the level characteristic value; k = 1, 2, ..., m, m is the total number of levels; C k is the similarity of the evaluation object about the kth level.

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