Bridge construction risk assessment method combining finite element method and cloud entropy weight method

Abstract

The invention discloses a bridge construction risk assessment method combining a finite element method and a cloud entropy weight method, which relates to the technical field of bridge construction risk assessment and comprises mechanical risk assessment based on the finite element method and overall risk assessment based on the cloud entropy weight method. The mechanical failure risk in the construction stage is quantified through finite element simulation, a multi-factor weight calculation model is established in combination with a cloud entropy weight method, and the influence of mechanical and non-mechanical factors is comprehensively considered. According to the invention, comprehensive and accurate assessment of the risk in the construction stage can be realized, and reliable technical support is provided for bridge construction safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge construction risk assessment, in particular to a bridge construction risk assessment method combining finite element method and cloud entropy weight method. BACKGROUND

[0002] At present, the bridge construction stage is a key period of high risk in the bridge construction process. According to statistics, about 35% of bridge collapse events occur in this stage. Since the bridge has not formed a stable stress system during construction, it is easily affected by various factors such as environment, human, equipment, etc., thereby inducing various risk events. The research on risk assessment method for bridge construction stage can provide important support for formulating targeted risk prevention and control measures, and has significant practical significance.

[0003] Therefore, the risk assessment problem of bridge construction period has been widely studied at home and abroad. From the perspective of mechanics, finite element method or other mechanical analysis methods are used to quantitatively analyze the potential failure risks in the construction stage. For example, the causes of bridge collapse in the construction stage are discussed by using reliability analysis method, and the uncertainty caused by insufficient data is processed by using Bayesian method; the probability risk assessment method of suspension bridge construction stage is studied based on finite element method, and the main cable wire fracture is taken as the ultimate state of bearing capacity for analysis. Although the finite element method can quantitatively characterize the potential risks in the mechanical aspect, it is difficult to consider other risk factors other than mechanics, such as human factors, environmental factors, etc., so it has limitations in comprehensive evaluation.

[0004] At the same time, more scholars try to consider various factors affecting bridge safety production and establish bridge construction risk assessment model based on traditional risk assessment method. For example, the safety risk assessment model of high-speed rail bridge construction stage is constructed by combining analytic hierarchy process and BP neural network; for the problem of complex sea area bridge construction, an index system covering construction environment risk and construction safety risk is proposed. Although these studies consider more risk factors during construction period, the evaluation results are more comprehensive, but it is often difficult to accurately quantify the evaluation results, and the influence of mechanical behavior on risk is rarely explored.

[0005] In summary, the existing bridge construction risk assessment methods can be mainly divided into two categories:

[0006] One type is represented by the finite element method, focusing on the risk quantification analysis of the mechanical level, but it is difficult to include other non-mechanical factors; the other type is mainly based on the traditional risk assessment method, although it can consider various risk factors comprehensively, but there are deficiencies in the quantitative precision and mechanical behavior analysis. Both methods have their own characteristics and limitations, and neither can fully reflect the risk characteristics of the bridge construction stage. Therefore, a new type of risk assessment method that combines the advantages of both methods is needed, which can not only clearly specify the specific risk situation from the mechanical level, but also consider the influence of various factors comprehensively, so as to realize more scientific and comprehensive risk assessment. SUMMARY

[0007] Therefore, the present application provides a bridge construction risk assessment method combining finite element method and cloud entropy weight method, which realizes the quantitative analysis of mechanical failure risk in the construction stage through finite element simulation, and establishes a multi-factor risk factor weight calculation model combining cloud entropy weight method, so as to achieve comprehensive and accurate assessment of mechanical level and non-mechanical factors, thereby solving the technical problem of the existing bridge construction stage risk assessment method in the comprehensive analysis of mechanical level and non-mechanical factors.

[0008] In order to achieve the above purpose, the present application provides the following technical scheme:

[0009] A bridge construction risk assessment method combining finite element method and cloud entropy weight method, comprising the following steps:

[0010] Simulate the whole process of bridge construction based on finite element method, and output the mechanical risk assessment results of the construction stage through simulated mechanical analysis;

[0011] According to the construction characteristics, identify the risk factors in the whole construction process;

[0012] According to the identified risk factors, construct a risk factor weight calculation model based on cloud entropy weight method, and output the overall risk assessment results;

[0013] Combine the mechanical risk assessment results and the overall risk assessment results to generate a comprehensive risk assessment matrix.

[0014] On the basis of the above technical scheme, the present application is further described as follows:

[0015] As a further scheme of the present application,

[0016] The simulation of the whole process of bridge construction based on finite element method, and the output of the mechanical risk assessment results of the construction stage through simulated mechanical analysis, specifically includes:

[0017] Carrying out finite element simulation on the whole construction process of the construction bridge, extracting the key mechanical indexes of each construction stage, including material stress, strain and deformation;

[0018] In the mechanical analysis process, the tensile redundancy is defined as the ratio of the actual tensile strength to the design value, and the compressive redundancy is defined as the ratio of the actual compressive strength to the design value by the change trend of the concrete tensile redundancy and the compressive redundancy, which are used to reflect the safety margin of material performance, and the mechanical risk assessment results of the construction stage output by the mechanical analysis are used as the basic data for the subsequent comprehensive assessment.

[0019] As a further scheme of the present application,

[0020] The risk factors of the whole construction process are identified according to the construction characteristics, and specifically include:

[0021] The risk factors of the whole construction process are identified according to the bridge construction characteristics;

[0022] The risk factors include environmental factors such as wind speed, temperature change, human factors and equipment factors.

[0023] As a further scheme of the present application,

[0024] The risk factor weight calculation model based on the cloud entropy weight method is constructed according to the identified risk factors, and the overall risk assessment result is output, and specifically includes:

[0025] The original data is standardized and normalized;

[0026] The entropy value and weight of each risk factor are calculated.

[0027] As a further scheme of the present application,

[0028] The original data is standardized and normalized, and specifically includes:

[0029] The original data is standardized and normalized to ensure the comparability of data with different dimensions; m evaluation schemes and n index judgment matrix R=xij m×n , xij represents the original value of the jth index of the ith scheme; for easy comparison and calculation, xij is standardized and normalized:

[0030]

[0031] p ij The proportion of the ith scheme in the jth index.

[0032] As a further scheme of the present application,

[0033] The entropy value and weight of each risk factor are calculated, and specifically includes:

[0034] The entropy value e j of the jth index is calculated, such as formula:

[0035]

[0036] Calculate the jth index weight ω j , as formula:

[0037]

[0038] Calculate the available index weight W.

[0039] According to the characteristics of the three parameters of the cloud model, the parameters are corrected step by step, and after improvement, the information such as the expected value, variability and randomness of the data is integrated into the calculation formula, and the improved weight calculation formula is determined as:

[0040]

[0041] Parameter En j and He j , the greater the divergence, the less accurate the weight, and the index weight is reduced; in the ideal case, all indexes reach complete agreement, that is, parameters En j and He j are 0, at this time, there is no need to correct the entropy En j .

[0042] As a further scheme of the present application,

[0043] The risk factor weight calculation model based on the cloud entropy weight method is constructed, and the overall risk assessment result is output, and specifically further includes:

[0044] Overall risk assessment is carried out based on cloud similarity.

[0045] As a further scheme of the present application,

[0046] The overall risk assessment based on cloud similarity specifically includes:

[0047] In order to further clarify the risk grade division, the standard cloud model is generated by a forward cloud generator, and the evaluation cloud model is generated by a reverse cloud generator;

[0048] The specific algorithm of the forward cloud generator is input with three numerical characteristics, that is, the expected value Ex, the entropy En and the hyper entropy He, and the cloud droplet quantity, and the output is the maximum value and the certainty degree of each cloud droplet;

[0049] The normal cloud model is used to calculate the standard cloud, first the evaluation score is normalized to obtain the effective domain U=[X min ,X max ], X min and X maxrespectively represent the upper and lower limit values of the score to which the grade belongs, and the risk grade corresponding to the overall risk assessment can be represented by a normal model; to reduce subjectivity, the boundary value of the risk grade division score is taken as the transition value of the two grades, and at the same time, the degree of belonging to the two grades is the same, that is:

[0050]

[0051] Rewrite as:

[0052] En≈(X max -X min ) / 2.355

[0053] Further complete the calculation of the standard cloud:

[0054]

[0055] Where, X i min , X i max respectively represent the upper and lower limit values of the score interval corresponding to the i risk grade k is a coefficient, which is adjusted according to the fuzzy degree of the concept, and the specific value is 0.1.

[0056] As a further scheme of the present application,

[0057] The mechanical risk assessment result and the overall risk assessment result are combined to generate a comprehensive risk assessment matrix, which specifically comprises:

[0058] The mechanical risk assessment result and the overall risk assessment result are combined to generate a comprehensive risk assessment matrix of the construction stage, and the comprehensive risk assessment matrix is composed of occurrence possibility scores and occurrence consequence scores, and the horizontal coordinate in the corresponding coordinate system represents the occurrence possibility score, and the vertical coordinate represents the occurrence consequence score.

[0059] According to the ALARP criterion, the risk is divided into five grades: "negligible", "tolerable", "ALARP lower limit", "ALARP upper limit" and "unacceptable";

[0060] For the risk of the "ALARP lower limit" grade, the risk is controlled by optimizing the construction process or increasing the monitoring frequency, and the dynamic monitoring of the change trend of the tensile and compressive properties of concrete is realized through the mechanical redundancy analysis of the whole construction process, so as to provide early warning information for the risk prevention and control in the construction stage.

[0061] As a further scheme of the present application,

[0062] The mechanical risk assessment result and the overall risk assessment result are combined to generate a comprehensive risk assessment matrix, which specifically further comprises:

[0063] Based on the evaluation of the possibility and consequence of the risk factors in the construction process, one-dimensional cloud is generated by reverse cloud generation algorithm, the two-dimensional cloud model of each risk factor is obtained by calculating the two-dimensional cloud membership degree and synthesizing the one-dimensional cloud digital features of the possibility and consequence, and the calculation method of the two-dimensional cloud membership degree is:

[0064]

[0065] Wherein, μ is the membership degree, x and y are the scores generated by the one-dimensional cloud digital features of the possibility and consequence respectively, Ex1 and Ex2 are the corresponding expected values respectively, En1 and En2 are the corresponding entropy values respectively, for the construction process risk with multiple levels of indexes, the two-dimensional cloud model of the upper level index can be calculated by the one-dimensional cloud digital features of the lower level indexes, and the calculation method is:

[0066]

[0067] In order to evaluate the risk level, the standard cloud and the evaluation cloud are quantified by similarity, and the construction safety risk level is measured by the similarity, that is, the risk level corresponding to the standard cloud with the maximum similarity is the final ALARP evaluation result, and the similarity calculation method is:

[0068]

[0069] The value range of R is [0, 1], Ex is the expected value of the risk possibility evaluation cloud, Ex is the expected value of the risk possibility standard cloud, Ey is the expected value of the risk consequence evaluation cloud, Ey is the expected value of the risk consequence standard cloud.

[0070] The application has the following beneficial effects:

[0071] The method realizes the quantitative analysis of the mechanical failure risk in the construction stage by combining the finite element method and the cloud entropy weight method, comprehensively considers the influence of non-mechanical factors such as human and environment, improves the comprehensiveness and accuracy of the evaluation result, at the same time, the cloud model theory is introduced, the uncertainty of the risk factor is described by the expected value, entropy and hyper entropy three digital features, and the fuzziness and randomness of the risk factor are intuitively reflected, and further by improving the traditional entropy weight method, the expected value, variability and randomness of the data are comprehensively considered, the influence of subjective factors is reduced, and the scientificity and reliability of the weight calculation are improved. BRIEF DESCRIPTION OF DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. The structure, proportion, size, etc. shown in the specification are only used to cooperate with the content disclosed in the specification, so as to be understood and read by those skilled in the art. Any modification, change of proportion relationship or adjustment of size, which does not affect the effects and purposes that can be achieved by the present application, should still fall within the scope of the technical content disclosed by the present application.

[0073] Figure 1 The bridge construction risk assessment flowchart combined with the finite element method and the cloud entropy weight method includes failure risk analysis based on finite element simulation and overall risk assessment steps based on the cloud entropy weight method.

[0074] Figure 2 The concrete tensile and compressive redundancy change curve in the construction process shows the relationship between the construction stage number and the material strength redundancy.

[0075] Figure 3 The evaluation cloud diagram shows the relationship between the possible score, the consequence score after occurrence and the membership in the three-dimensional coordinate system.

[0076] Figure 4 The cloud generator flowchart includes a forward cloud generator and a reverse cloud generator. DETAILED DESCRIPTION

[0077] The embodiments of the present application are described below by specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in the specification. Obviously, the described examples are part of the examples of the present application, but not all the examples. Based on the examples in the present application, all other examples obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0078] The terms such as "upper", "lower", "left", "right", "middle" and the like cited in the specification are only for the convenience of clear description, and are not used to limit the scope of implementation of the present application. The change or adjustment of the relative relationship without substantial change of the technical content is also regarded as the implementation scope of the present application.

[0079] The present application provides a bridge construction stage risk assessment method combined with the finite element method and the cloud entropy weight method, and the technical solutions of the present application will be described in detail below in combination with the drawings Figures 1 to 4 and specific examples.

[0080] S1: Perform mechanical risk assessment of the whole construction process based on the finite element method.

[0081] In S1 step, the whole construction process of the construction bridge is simulated by using MIDAS Civil software, and the mechanical response of the material in the strength growth process is reflected in real time through the finite element simulation. In the process of mechanical response analysis, the tensile redundancy is defined as the ratio of the actual tensile strength to the design value, and the compressive redundancy is defined as the ratio of the actual compressive strength to the design value, which are used to reflect the safety margin of the material performance. Considering that the material strength has not fully reached the design value during the construction period, the tensile and compressive strength standard values of all construction stages are reduced by 70%.

[0082] As shown in Figure 2 , the square solid line represents the concrete tensile redundancy curve, and the circular dotted line represents the concrete compressive redundancy curve. As can be seen from the figure, with the advancement of the construction process, the concrete compressive strength redundancy gradually decreases to 27.6% at the edge of the closure, while the tensile redundancy remains at a high level throughout the construction process. This indicates that attention should be paid to the concrete compressive performance in the later stage of construction. After the completion of S1 step, the mechanical risk assessment results of the construction stage are output as the basis data for subsequent comprehensive assessment.

[0083] S2: Calculate the weight of the risk factors in the construction process based on the cloud entropy weight method.

[0084] In S2 step, according to the characteristics of bridge construction, combined with literature research, accident cause analysis and historical accident data, the risk factors in the whole construction process are identified. The risk factors include environmental factors such as wind speed, temperature change, human factors such as operation error, management oversight, equipment factors such as mechanical failure, support system instability, and other factors that may affect construction safety. In order to scientifically calculate the weight of each risk factor, a risk factor weight calculation model based on the cloud entropy weight method is constructed.

[0085] In information theory, the randomness and disorder degree of an event can be judged by calculating the entropy value, and the entropy becomes a measure of uncertainty. The greater the uncertainty, the greater the entropy, and the more information extracted from the target. Therefore, the entropy value can be used to judge the dispersion degree of a certain index, so as to determine the index weight. The greater the dispersion degree of the index, the greater the influence (weight) of the index on the comprehensive evaluation.

[0086] The specific steps of calculating the weight by cloud entropy weight method are as follows:

[0087] S201: Standardize and normalize the original data to ensure the comparability of data with different dimensions. Let m be the number of evaluation schemes, and n be the number of indexes. The judgment matrix R = xij m×n , xij represents the original value of the jth index of the ith scheme. In order to facilitate comparison and calculation, xij is standardized and normalized:

[0088]

[0089] p ij is the proportion of the i-th option for the j-th indicator.

[0090] S202: Calculate the entropy value and weight of each risk factor;

[0091] Calculate the entropy value e of the jth indicator j , such as:

[0092]

[0093] Calculate the jth indicator weight ω j , such as:

[0094]

[0095] The indicator weight W can be obtained by calculation.

[0096] Compared with the traditional weight calculation method to solve the j-th indicator weight w j Such as:

[0097]

[0098] While traditional methods offer simple calculations, they underutilize data. Unlike the entropy weight method, they prioritize numerical values ​​over fluctuations, failing to guarantee comprehensive and reliable results. If the mean scores for each indicator are the same, the weights are also the same, failing to objectively reflect the actual situation. Based on this, this paper gradually refines the three parameters of the cloud model based on their characteristics. This improvement incorporates information such as the expected value, variability, and randomness of the data into the calculation formula, increasing the comprehensiveness of the results while reducing the influence of subjective factors.

[0099] The improved weight calculation formula is determined as follows:

[0100]

[0101] Parameter En j and He j The larger the value, the greater the difference, the less accurate the weight of the solution, and the weight of this indicator should be reduced. In an ideal situation, all indicators are completely consistent, that is, the parameter En j and He j are all 0, and there is no need to adjust the entropy En j Make corrections and improve the formula to be consistent with the traditional weight calculation formula.

[0102] S3: Perform overall risk assessment based on cloud similarity.

[0103] In step S3, in order to further clarify the risk level classification,Figure 4 As shown, the standard cloud model is generated by a forward cloud generator, and the evaluation cloud model is generated by a reverse cloud generator. The specific algorithm of the forward cloud generator is as shown in the formula (1) below, and the input is three numerical characteristics, i.e. the expected value Ex, the entropy En and the hyper-entropy He and the number of cloud droplets, and the output is the maximum value and the certainty degree of each cloud droplet. Figure 4 As shown, the input is three numerical characteristics, i.e. the expected value Ex, the entropy En and the hyper-entropy He and the number of cloud droplets, and the output is the maximum value and the certainty degree of each cloud droplet.

[0104] The normal cloud model is used to calculate the standard cloud. Firstly, the evaluation score is normalized to obtain the effective domain U = [X min , X max ], X min and X max represent the upper and lower limit values of the normalized score of the grade, and the risk grade corresponding to the overall risk assessment can be represented by the normal model. In order to reduce subjectivity, the boundary value of the risk grade division score is taken as the transition value of the two grades, and the degree of belonging to the two grades is the same, i.e.

[0105]

[0106] which can be rewritten as:

[0107] En≈(X max -X min ) / 2.355

[0108] Further calculation of the standard cloud is completed:

[0109]

[0110] wherein, X i min , X i max represent the upper and lower limit values of the score interval corresponding to the i risk grade, and k is a coefficient, which is adjusted according to the fuzzy degree of the concept, and the specific value is 0.1.

[0111] S4: generating a comprehensive risk assessment matrix based on the ALARP criterion.

[0112] In step S4, the mechanical risk assessment result and the overall risk assessment result are combined to generate a comprehensive risk assessment matrix of the construction stage. As shown in the formula (2) below: Figure 3As shown, the comprehensive risk assessment matrix is composed of occurrence possibility score and occurrence consequence score, the horizontal coordinate represents occurrence possibility score, the vertical coordinate represents occurrence consequence score, and the membership degree is represented by color depth. According to the ALARP criterion, the risk is divided into five levels: “negligible”, “tolerable”, “ALARP lower limit”, “ALARP upper limit” and “unacceptable”. For different risk levels, corresponding risk control measures are developed. For example, for the risk of “unacceptable” level, measures such as shutdown for rectification or redesign scheme are taken, please refer to Table 1 and Table 2.

[0113] For the risk of “ALARP lower limit” level, control is performed by optimizing construction process or increasing monitoring frequency, etc. In addition, through mechanical redundancy analysis throughout the construction process, the changing trend of concrete tensile and compressive performance is dynamically monitored, providing timely early warning information for risk prevention and control in the construction phase.

[0114]

[0115]

[0116] Table 1 Risk assessment criteria and standard cloud characteristic values

[0117]

[0118] Table 2 ALARP risk assessment matrix

[0119] Based on the evaluation of occurrence possibility and consequence of construction process risk factors, one-dimensional clouds are generated through reverse cloud generation algorithm, the two-dimensional cloud model of each risk factor is obtained by calculating the two-dimensional cloud membership degree based on the digital characteristics of one-dimensional clouds of occurrence possibility and occurrence consequence. The calculation method of two-dimensional cloud membership degree is:

[0120]

[0121] Where μ is the membership degree, x and y are the scores generated according to the one-dimensional cloud digital characteristics of occurrence possibility and occurrence consequence respectively, Ex1 and Ex2 are the corresponding expected values, and En1 and En2 are the corresponding entropy values. For construction process risks with multiple levels of indicators, the two-dimensional cloud model of the upper level indicator can be calculated based on the cloud digital characteristics of the lower level indicators, and the calculation method is:

[0122]

[0123] To evaluate the risk level, the similarity between the standard cloud and the evaluation cloud is quantified, and the construction safety risk level is determined by the similarity, that is, the risk level corresponding to the standard cloud with the maximum similarity is the final ALARP evaluation result. The calculation method of similarity is:

[0124]

[0125] R is in the interval [0,1], Ex is the risk possibility evaluation cloud expectation value, is the risk possibility standard cloud expectation value, Ey is the risk consequence evaluation cloud expectation value, is the risk consequence standard cloud expectation value.

[0126] The application realizes the quantitative analysis of the mechanical failure risk in the construction stage by combining the finite element method and the cloud entropy weight method, simultaneously considers the influence of non-mechanical factors such as human and environment, and improves the comprehensiveness and accuracy of the evaluation result.

[0127] Through the improvement of the traditional entropy weight method, the expectation value, variability and randomness of the data are comprehensively considered, the influence of the subjective factors is reduced, and the scientificity and reliability of the weight calculation are improved.

[0128] Although the application has been fully described in the foregoing description with reference to the embodiments, it is apparent that modifications and changes can be made to the application by those skilled in the art without departing from the spirit of the application. Therefore, these modifications or improvements made on the basis of the application without departing from the spirit of the application shall fall within the scope of the application.

Claims

1. A bridge construction risk assessment method combining finite element method and cloud entropy weight method, characterized in that: The steps include: The entire bridge construction process is simulated using the finite element method, and through simulated mechanical analysis, the mechanical risk assessment results of the construction phase are output; Identify risk factors throughout the construction process based on construction characteristics; According to the identified risk factors, a risk factor weight calculation model based on the cloud entropy weight method is constructed to output the overall risk assessment results; The mechanical risk assessment results and the overall risk assessment results are combined to generate a comprehensive risk assessment matrix.

2. The bridge construction risk assessment method combining the finite element method and the cloud entropy weight method according to claim 1 is characterized in that: The finite element method is used to simulate the entire bridge construction process, and through simulated mechanical analysis, the mechanical risk assessment results of the construction phase are output, specifically including: Conduct finite element simulation of the entire construction process of the bridge and extract key mechanical indicators of each construction stage, including material stress, strain and deformation; During the mechanical analysis process, the tensile redundancy is defined as the ratio of the actual tensile strength to the design value, and the compressive redundancy is defined as the ratio of the actual compressive strength to the design value based on the changing trends of the tensile redundancy and compressive redundancy of concrete. These are used to reflect the safety margin of material properties. The mechanical risk assessment results of the construction phase are output through mechanical analysis as the basic data for subsequent comprehensive evaluation.

3. The bridge construction risk assessment method combining the finite element method and the cloud entropy weight method according to claim 2 is characterized in that: The risk factors of the entire construction process identified based on construction characteristics specifically include: Identify risk factors throughout the entire construction process based on the characteristics of bridge construction; The risk factors include environmental factors such as wind speed, temperature changes, human factors and equipment factors.

4. The bridge construction risk assessment method combining the finite element method and the cloud entropy weight method according to claim 3 is characterized in that: The risk factor weight calculation model based on the cloud entropy weight method is constructed based on the identified risk factors to output the overall risk assessment results, specifically including: Standardize and normalize the raw data; Calculate the entropy value and weight of each risk factor.

5. The bridge construction risk assessment method combining finite element method and cloud entropy weight method according to claim 4 is characterized in that: The standardization and normalization of the raw data specifically includes: Standardize and normalize the original data to ensure that data of different dimensions are comparable; suppose there are m evaluation schemes and n indicators, and the judgment matrix R = xij m×n , xij represents the original value of the jth indicator of the i-th solution; for the convenience of comparison and calculation, xij is standardized and normalized: p ij is the proportion of the i-th option for the j-th indicator.

6. The bridge construction risk assessment method combining finite element method and cloud entropy weight method according to claim 5 is characterized in that: The calculation of the entropy value and weight of each risk factor specifically includes: Calculate the entropy value e of the jth indicator j , such as: Calculate the jth indicator weight ω j , such as: Calculate the available indicator weight W; According to the characteristics of the three parameters of the cloud model, the parameters are modified step by step. After the improvement, the expected value, variability and randomness of the data are integrated into the calculation formula, and the improved weight calculation formula is determined as follows: Parameter En j and He j The larger the value, the greater the divergence, the less accurate the weight of the solution, and the lower the weight of the indicator. In an ideal situation, all indicators are completely consistent, that is, the parameter En j and He j are all 0, and there is no need to adjust the entropy En j Make corrections.

7. The bridge construction risk assessment method combining finite element method and cloud entropy weight method according to claim 6 is characterized in that: The risk factor weight calculation model based on the cloud entropy weight method is constructed to output the overall risk assessment results, which specifically includes: Perform an overall risk assessment based on cloud similarity.

8. The bridge construction risk assessment method combining finite element method and cloud entropy weight method according to claim 7 is characterized in that: The overall risk assessment based on cloud similarity specifically includes: To further clarify the risk level classification, the standard cloud model is generated through the forward cloud generator, and the evaluation cloud model is generated through the reverse cloud generator; The specific algorithm input of the forward cloud generator is three digital features, namely the expected value Ex, entropy En and super entropy He, as well as the number of cloud droplets, and the output is the maximum value and certainty of each cloud droplet; The normal cloud model is used to calculate the standard cloud. First, the evaluation score is normalized to obtain the effective domain U = [X min ,X max ], X min and X max They represent the upper and lower limits of the normalized scores of the levels. The risk level corresponding to the overall risk assessment can be represented by a normal model. To reduce subjectivity, the boundary value of the risk level division score is taken as the transition value of the two levels, and the degree of belonging to the two levels is the same, that is: Rewritten as: En≈(X max -X min ) / 2.355 Further complete the calculation of the standard cloud: Among them, X i min , X i max k is a coefficient representing the upper and lower limits of the score range corresponding to risk level i. The value is adjusted according to the degree of fuzziness of the concept, and the specific value is 0.

1.

9. The bridge construction risk assessment method combining the finite element method and the cloud entropy weight method according to claim 8 is characterized in that: The combined mechanical risk assessment results and the overall risk assessment results generate a comprehensive risk assessment matrix, specifically including: Combine the mechanical risk assessment results and the overall risk assessment results to generate a comprehensive risk assessment matrix for the construction phase. The comprehensive risk assessment matrix consists of the probability of occurrence score and the consequence of occurrence score. The horizontal axis of the corresponding coordinate system represents the probability of occurrence score, and the vertical axis represents the consequence of occurrence score. According to the ALARP criteria, risks are divided into five levels: "negligible", "tolerable", "ALARP lower limit", "ALARP upper limit" and "unacceptable"; Risks at the "ALARP lower limit" level are controlled by optimizing construction processes or increasing monitoring frequency. Mechanical redundancy analysis throughout the construction process dynamically monitors the changing trends in concrete's tensile and compressive properties, providing early warning information for risk prevention and control during the construction phase.

10. The bridge construction risk assessment method combining finite element method and cloud entropy weight method according to claim 9 is characterized in that: The combined mechanical risk assessment results and the overall risk assessment results generate a comprehensive risk assessment matrix, which specifically includes: Based on the evaluation of the occurrence possibility and consequences of the risk factors in the construction process, the reverse cloud generation algorithm is used to generate the respective one-dimensional clouds. The digital features of the one-dimensional clouds of the occurrence possibility and consequences are integrated to calculate the two-dimensional cloud membership and obtain the two-dimensional cloud model of each risk factor. The two-dimensional cloud membership calculation method is as follows: Among them, μ is the degree of membership, x and y are the scores generated by the one-dimensional cloud digital features of the probability of occurrence and the consequence of occurrence, respectively, Ex1 and Ex2 are their corresponding expected values, En1 and En2 are their corresponding entropy values. For construction process risks with multi-level indicators, the digital features of the upper-level indicator cloud can be calculated from the lower-level indicators to determine its two-dimensional cloud model. The calculation method is: To evaluate the risk level, the similarity between the standard cloud and the evaluation cloud is quantified, and the construction safety risk level is measured accordingly. That is, the risk level corresponding to the standard cloud with the greatest similarity is taken as the final ALARP evaluation result. The similarity calculation method is: The value range of R is [0,1], Ex is the expected value of risk probability evaluation cloud, is the expected value of the risk possibility standard cloud, Ey is the expected value of the risk consequence evaluation cloud, Risk consequence standard cloud expected value.

Images