Subway shield method construction risk evaluation method based on combination weighting

Abstract

The invention discloses a subway shield method construction risk evaluation method based on combined weighting, and the method comprises the steps: building a risk index system, constructing a risk factor list, and optimizing the risk factor list; a G1 method subjective weighting method is adopted for the optimized risk factor list to obtain a subjective weight value; a COWA objective weighting method is adopted for the optimized risk factor list to obtain an objective weight value; according to the subjective weight value and the objective weight value, the weight of the subway tunnel shield construction safety risk evaluation index is determined in a combined weighting mode; objectively displaying a risk index weight by adopting a cloud model; according to the method, the problem of low index accuracy in existing subway shield risk evaluation is solved, and meanwhile, the defect of one-sidedness of single index weighting is avoided.

Description

Technical Field

[0001] The invention relates to the technical field of subway shield construction risk, in particular to a subway shield construction risk assessment method based on combined weighting. Background Art

[0002] During the construction process, subways face complex and changeable construction environments and geological conditions, long construction lines, large diameters, and high water pressure. Therefore, subway construction has certain risks. Compared with other construction methods, shield construction is safer and can achieve automated operations. It has become the main method for urban subway construction. How to effectively evaluate the risk weight and determine the risk level when subways are constructed using the shield method is a difficult problem that needs to be solved urgently.

[0003] There are three main methods for determining risk weights: subjective weighting method, objective weighting method and combined weighting method. Commonly used subjective weighting methods include AHP (Analytical Hierarchy Process) and G1 (Order Relationship Hierarchy Process). These methods are based on the subjective consciousness and personal experience of experts and are highly subjective. Objective weighting methods include entropy weight method and COWA (Improved Ordered Weighted Average Operator). They use mathematical methods to analyze data, avoid the influence of subjective consciousness, and the results obtained are more objective. However, if the data used is not reasonable enough, the results may deviate greatly from the actual situation. It can be seen that the weighting of a single indicator has the disadvantage of one-sidedness, so the combined weighting method is now generally used to calculate the weight of risk indicators. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the purpose of the present invention is to provide a subway shield construction risk assessment method based on combined weighting. The present invention establishes an initial risk indicator system by adopting HHM (hierarchical holographic method), and optimizes the risk list by using Vague set theory to obtain an optimized risk indicator system. Then, the risk indicators are combined and weighted by G1 method and COWA method to determine the weights of subway tunnel shield construction safety risk assessment indicators; while considering the uncertainty, randomness and fuzziness of subway shield construction, cloud theory is used to objectively display the risk weights of each indicator. The problem of low accuracy of indicators in the existing subway shield risk assessment is solved, and the disadvantage of one-sidedness of single indicator weighting is avoided.

[0005] To achieve the above object, the technical solution adopted by the present invention is: a subway shield construction risk assessment method based on combined weighting, comprising the following steps:

[0006] Step 1: Establish a risk indicator system, build a risk factor list, and optimize the risk factor list;

[0007] Step 2: Use the G1 method to subjectively assign weights to the optimized risk factor list to obtain subjective weight values;

[0008] Step 3: Use COWA objective weighting method to obtain objective weight values ​​for the optimized risk factor list;

[0009] Step 4: Determine the weight of the subway tunnel shield construction safety risk assessment index by combining weights according to the subjective weight value and the objective weight value;

[0010] Step 5: Use the cloud model to objectively display the risk indicator weights.

[0011] As a further improvement of the present invention, the step 1 specifically comprises the following steps:

[0012] Step 1.1: Split the primary indicators of construction risks into secondary indicators and construct the HHM framework for identifying safety risks in subway tunnel construction. The primary indicators include personnel, machinery and equipment, construction materials, construction management, environment and technology.

[0013] Step 1.2: Use the trajectory intersection theory to conduct a cross-analysis of the HHM framework for safety risk identification in subway tunnel construction and construct a risk factor list;

[0014] Step 1.3: Conduct a preliminary elimination of risks, removing the list of factors that are repeated or not closely related;

[0015] Step 1.4: Use Vague set theory to further optimize the risk list and obtain an optimized risk factor list.

[0016] As a further improvement of the present invention, the step 1.4 specifically includes the following steps:

[0017] Step 1.4.1, record m A (x i ) is the xth i The Vague value of a risk factor, assuming that the number of people who agree with the risk factor and those who oppose the risk factor are different, that is, t A (x i )≠f A (x i ),but:

[0018] m A (x i )=[t A (x i )-f A (x i )]×[2-t A (x i )-f A (x i )]

[0019] t A (x i ), f A (x i ),[1-t A (x i )-f A (x i )]∈[0,1]

[0020] Step 1.4.2: Invite n experts to conduct a questionnaire survey, and then count and organize the survey results. Among them, a experts agree with the indicator, b experts oppose, and c experts abstain, where n = a + b + c. Calculate t A (x i ), f A (x i ),[1-t A (x i )-f A (x i )]:

[0021] Then calculate the Vague value of the risk factor: m A (x i )=[t A (x i )-f A (x i )]×[2-t A (x i )-f A (x i )];

[0022] Repeat steps 1.4.1 and 1.4.2 to obtain the t of the initially established risk indicator system. A (x i ), f A (x i ),[1-t A (x i )-f A (x i )];

[0023] Step 1.4.3: Calculate the Vague set m of each risk factor A (x i ), and determine the risk indicator parameters, retain the Vague set values ​​that are greater than or equal to the risk indicator parameters, discard the values ​​that are less than the risk indicator parameters, obtain the optimized risk factor list, and establish a risk indicator evaluation system.

[0024] As a further improvement of the present invention, the step 2 specifically comprises the following steps:

[0025] Step 2.1, determine the order relationship: experts make judgments based on the recognition of each risk factor, and then determine the importance of each indicator and rank them; record x i >x j For the xth i The importance of an evaluation index based on a certain criterion is greater than that of index x j , remember x1>x2>x3>...>x m The order relationship is determined for the m evaluation indicators according to their importance;

[0026] Step 2.2: Experts judge the relative importance of adjacent evaluation indicators based on their own experience and project characteristics, and obtain the ratio of the relative importance of the indicators, denoted as r. k :

[0027]

[0028] Step 2.3: Determine the evaluation index weights according to the calculation formula of the order relationship hierarchy method:

[0029]

[0030] m k-1 * =r k m k (k=n,n-1,...2)

[0031] Where: m k ,m k-1 is the subjective weight of the kth and k-1th risk indicators calculated by the G1 method; n is the total number of indicators.

[0032] As a further improvement of the present invention, the step 3 specifically comprises the following steps:

[0033] Step 3.1, determine the order of indicators: determine the original decision set of indicator A as {a1, a2, a3...a n}, sort the subsets in the decision set in descending order from large to small, and the decision set after sorting is {b1,b2,b3...b n}, and b1>b2>b3...>b n ;

[0034] Step 3.2, determine the weighted vector: Use the combination number method to weight the data and get the weighted vector value as follows:

[0035]

[0036] Step 3.3, determine the absolute weight and relative weight value: calculate the absolute weight value of the indicator for the sorted data:

[0037]

[0038] In the formula is the relative weight value, w i is the absolute weight value.

[0039] As a further improvement of the present invention, in step 4, the weight coefficients of the subjective weight value and the objective weight value are both 0.5.

[0040] As a further improvement of the present invention, the step 5 specifically comprises the following steps:

[0041] Step 5.1, determine the review level;

[0042] Step 5.2, determine the evaluation criteria: divide the domain in [0,1] into multiple risk levels, and then construct a cloud model scale for the safety risk evaluation index of subway shield construction;

[0043] Step 5.3, invite several experts to score the secondary risk indicators of shield construction within the range of [0,1], and obtain the index evaluation cloud model value of the secondary indicator based on the results. Use the formula to calculate the comprehensive evaluation cloud of subway shield construction risk. The formula is as follows:

[0044]

[0045] Where n is the number of secondary indicators, w i Assign weights to the combination of each indicator;

[0046] Step 5.4: Compare the results to get the risk level of each indicator.

[0047] As a further improvement of the present invention, the cloud model includes three digital features: expected value Ex, entropy En and super entropy He.

[0048] As a further improvement of the present invention, in step 5.1, by consulting engineering literature and asking for expert advice, the rating levels of the indicators are divided into: high risk, relatively high risk, medium risk, relatively low risk, and low risk.

[0049] The beneficial effects of the present invention are:

[0050] 1. The present invention uses the HHM hierarchical holographic method and track crossing theory to obtain the initial risk index, uses the Vague set theory to optimize the initial risk list, sets the risk index parameters, retains the risk index greater than or equal to the index parameters, and then quickly establishes the risk index system for subway shield construction. This solves the problems of risk index duplication, risk system redundancy and ambiguity in the actual risk system.

[0051] 2. The present invention adopts combined weighting to determine the weight value of risk indicators. The G1 method determines the subjective weight, and the COWA method determines the objective weight. The weight coefficients are all set to 0.5. Finally, the combined weight of risk indicators is determined by the combined weighting method, which weakens the single influence of subjective and objective weights. At the same time, cloud theory and cloud model diagrams are used to objectively display the weight values. Using normal cloud distribution, the risk weight value of each indicator is more intuitively displayed. This provides a methodological basis for evaluating the construction risks of subway shield method in advance before construction of actual projects, provides a reference for formulating construction plans, saves construction costs, improves construction efficiency, and can effectively reduce construction risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flow chart of an embodiment of the present invention;

[0053] Figure 2 This is a standard cloud model diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0055] Example

[0056] This embodiment uses the HHM hierarchical holographic method and track intersection theory to obtain the initial risk index, uses the Vague set theory to optimize the initial risk list, sets the risk index parameters, retains the risk index that is greater than or equal to the index parameters, and then quickly establishes a risk index system for subway shield construction. It solves the problems of risk index duplication, risk system redundancy and ambiguity in the actual risk system. The combined weighting is used to determine the weight value of the risk index, and the cloud theory and cloud model diagram are used to objectively display the weight value. This provides a methodological basis for evaluating the risks of subway shield construction in advance before construction of actual projects, provides a reference for formulating construction plans, saves construction costs, improves construction efficiency, and can effectively reduce construction risks.

[0057] like Figure 1 As shown, a subway shield construction risk assessment method based on combined weighting includes the following steps:

[0058] Step 1: Establish and optimize the risk indicator system:

[0059] Adopting the 4M1E theory and taking into account the important factor of technology, the construction risks are divided into six factors: personnel, machinery and equipment, construction materials, construction management, environment and technology; based on the HHM method, the safety risk factors of subway tunnel shield construction are identified and an initial risk factor list is established; in order to avoid unreasonable risk factors, redundant and repeated risks, etc., Vague set theory is selected to optimize the risk factors and obtain an optimized risk list. In a specific embodiment, the optimized risk list is shown in Table 1:

[0060]

[0061] Table 1 Optimized risk list

[0062] Step 1 The specific steps to establish and optimize the risk indicator system are:

[0063] S11: The first-level indicators of construction risks, including personnel, machinery and equipment, construction materials, construction management, environment and technology, are divided into second-level indicators to construct the HHM framework for safety risk identification in subway tunnel construction;

[0064] S12: Use the “trajectory intersection theory” to conduct cross-analysis on the HHM framework for safety risk identification in subway tunnel construction and construct a risk factor list;

[0065] S13: Conduct a preliminary elimination of risks, removing the list of factors that are duplicated and not closely connected;

[0066] S14: The risk list is further optimized using Vague set theory to obtain an optimized risk factor list. The specific steps include:

[0067] S141: Remember m A (x i ) is the xth i The Vague value of a risk factor, assuming that the number of people who agree with the risk factor and those who oppose the risk factor are different, that is, t A (x i )≠f A (x i ),but:

[0068] m A (x i )=[t A (x i )-f A (x i )]×[2-t A (x i )-f A (x i )]

[0069] t A (xi ), f A (x i ),[1-t A (x i )-f A (x i )]∈[0,1]

[0070] S142: Invite n experts to conduct a questionnaire survey, count and organize the survey results, among which a experts agree with the indicator, b experts oppose, and c experts abstain, where n = a + b + c, calculate t A (x i ), f A (x i ),[1-t A (x i )-f A (x i )]:

[0071] Then calculate the Vague value of the risk factor: m A (x i )=[t A (x i )-f A (x i )]×[2-t A (x i )-f A (x i )].

[0072] Repeat the above steps to obtain the risk indicator system that is initially established. A (x i ), f A (x i ),[1-t A (x i )-f A (x i )];

[0073] S143: Calculate the Vague set m of each risk factor A (x i ), and determine the risk indicator parameters, retain the Vague set values ​​that are greater than or equal to the risk indicator parameters, discard the values ​​that are less than the risk indicator parameters, obtain the optimized risk factor list, and establish a risk indicator evaluation system.

[0074] Step 2: Use the G1 method to subjectively assign weights to the optimized risk factor list to obtain subjective weight values. The specific steps include:

[0075] S21: Determine the order relationship. Experts make judgments based on the recognition of each risk factor, and then determine the importance of each indicator and rank them. i >x j For the xth i The importance of an evaluation index based on a certain criterion is greater than that of index x j , remember x1>x2>x3>...>x m The order relationship is determined for the m evaluation indicators according to their importance.

[0076] S22: Determine the relative importance of adjacent evaluation indicators, and the importance ratio is recorded as r k :

[0077]

[0078] r k The value of is: index x k-1 With x k Equally important (1.0), the indicator x k-1 X k Slightly important (1.2), index x k-1 X k Obviously important (1.4), index x k-1 X k Very important (1.6), indicator x k-1 X k Strongly important (1.8).

[0079] S23: Determine the evaluation index weights according to the calculation formula of the order relationship hierarchy method:

[0080]

[0081] m k-1 * =r k m k (k=n,n-1,...2)

[0082] Where: m k ,m k-1 is the subjective weight of the kth and k-1th risk indicators calculated by the G1 method; n is the total number of indicators. In a specific embodiment, the subjective weight values ​​are shown in Table 2.

[0083]

[0084] Table 2 Weight values ​​of subjective weighting using the G1 method

[0085] Step 3: Use the COWA objective weighting method to obtain objective weight values ​​for the optimized risk factor list. The specific steps are as follows:

[0086] S31: Determine the order of indexes: Determine the original decision set of index A as {a1, a2, a3...a n}, sort the subsets in the decision set in descending order from large to small, and the decision set after sorting is {b1,b2,b3...b n}, and b1>b2>b3...>b n ;

[0087] S32: Determine the weighted vector: Use the combination number method to weight the data, and the weighted vector value can be obtained as follows:

[0088]

[0089] S33: Determine the absolute weight and relative weight using the weighted vector value: Calculate the absolute weight value of the indicator for the sorted data:

[0090]

[0091] In the formula is the relative weight value, w i is the absolute weight value.

[0092] Step 4: In order to avoid the influence of subjective and objective weights, after obtaining the subjective weight value in step 2 and the objective weight value in step 3, the weight of the subway tunnel shield construction safety risk assessment index is determined by a combined weighting method.

[0093] When the weights of the subway tunnel shield construction safety risk assessment indicators are determined by combined weighting in step 4, the weight coefficients of the subjective weighting value and the objective weighting value are both 0.5.

[0094] The combined weight values ​​obtained in step 4 are shown in Table 3:

[0095]

[0096] Table 3 Weight values ​​of combined weighting

[0097] Step 5: The cloud model theory combines fuzzy mathematics theory with probability theory, which can handle the uncertainty between qualitative values. In order to more intuitively display the risk level of each risk indicator, the cloud model is used to objectively display the weight of the risk indicator.

[0098] The cloud model mainly includes three digital features: expected value Ex, entropy En and super entropy He. The expected value Ex is the value that reflects the classification of the safety risk level of shield construction; entropy En mainly reflects the degree of aggregation of cloud droplets in the cloud map. If it is too small, the cloud droplets will scatter, and if it is too large, it will cause excessive aggregation and overlap; super entropy He reflects the thickness and discreteness of the cloud in the cloud map. The standard cloud model is shown in the figure Figure 2 As shown; the specific steps of this step are:

[0099] S51: Determine the rating level. By consulting similar project literature and asking for expert advice, the rating level of the indicator can be divided into: high risk, relatively high risk, medium risk, relatively low risk, and low risk;

[0100] S52: Determine the evaluation criteria. Divide the domain [0,1] into five risk levels, and then construct a cloud model scale for the safety risk evaluation index of subway shield construction;

[0101] S53: Invite several experts to score the secondary risk indicators of shield construction within the range of [0,1]. According to the results, the index evaluation cloud model value of the secondary indicator is obtained, and the formula is used to calculate the comprehensive evaluation cloud of subway shield construction risk. The formula is as follows:

[0102]

[0103] Where n is the number of secondary indicators, w i Assign weights to the combination of each indicator.

[0104] S54: The risk level of each indicator can be obtained by comparing the obtained results.

[0105] In a specific embodiment, the numerical characteristics of the cloud model theory are shown in Table 4:

[0106]

[0107] Table 4 Numerical characteristics of cloud model theory

[0108] Moreover, the subway shield construction risk assessment method described in this embodiment is not limited to subway tunnels in its application scope, but can also be used for risk assessment in the same or similar fields. It can provide a methodological basis for evaluating construction risks in advance before construction of actual projects, provide a reference for formulating construction plans, save construction costs, improve construction efficiency, and effectively reduce construction risks.

[0109] The above-mentioned embodiments only express the specific implementation of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A risk assessment method for subway shield construction based on combined weighting, characterized in that: The following steps are involved: Step 1: Establish a risk indicator system, build a risk factor list, and optimize the risk factor list; Step 2: Use the G1 method to subjectively assign weights to the optimized risk factor list to obtain subjective weight values; Step 3: Use COWA objective weighting method to obtain objective weight values ​​for the optimized risk factor list; Step 4: Determine the weight of the subway tunnel shield construction safety risk assessment index by combining weights according to the subjective weight value and the objective weight value; Step 5: Use the cloud model to objectively display the risk indicator weights.

2. The subway shield construction risk assessment method based on combined weighting according to claim 1 is characterized in that: The step 1 specifically comprises the following steps: Step 1.1: Split the primary indicators of construction risks into secondary indicators and construct the HHM framework for identifying safety risks in subway tunnel construction. The primary indicators include personnel, machinery and equipment, construction materials, construction management, environment and technology. Step 1.2: Use the trajectory intersection theory to conduct a cross-analysis of the HHM framework for safety risk identification in subway tunnel construction and construct a risk factor list; Step 1.3: Conduct a preliminary elimination of risks, removing the list of factors that are repeated or not closely related; Step 1.4: Use Vague set theory to further optimize the risk list and obtain an optimized risk factor list.

3. The subway shield construction risk assessment method based on combined weighting according to claim 2 is characterized in that: The step 1.4 specifically includes the following steps: Step 1.4.1, record m A (x i ) is the xth i Vague value of a risk factor, assuming that the number of people who agree with the risk factor and those who oppose the risk factor are different, that is, t A (x i )≠f A (x i ),but: m A (x i )=[t A (x i )-f A (x i )]×[2-t A (x i )-f A (x i )] t A (x i )、f A (x i )、[1-t A (x i )-f A (x i )]∈[0,1] Step 1.4.2: Invite n experts to conduct a questionnaire survey, and then count and organize the survey results. Among them, a experts agree with the indicator, b experts oppose, and c experts abstain, where n = a + b + c. Calculate t A (x i ), f A (x i ),[1-t A (x i )-f A (x i )]: Then calculate the Vague value of the risk factor: m A (x i )=[t A (x i )-f A (x i )]×[2-t A (x i )-f A (x i )]; Repeat steps 1.4.1 and 1.4.2 to obtain the t of the initially established risk indicator system. A (x i ), f A (x i ),[1-t A (x i )-f A (x i )]; Step 1.4.3: Calculate the Vague set m of each risk factor A (x i ), and determine the risk indicator parameters, retain the Vague set values ​​that are greater than or equal to the risk indicator parameters, discard the values ​​that are less than the risk indicator parameters, obtain the optimized risk factor list, and establish a risk indicator evaluation system.

4. The subway shield construction risk assessment method based on combined weighting according to claim 1, 2 or 3, characterized in that: The step 2 specifically includes the following steps: Step 2.1, determine the order relationship: experts make judgments based on the recognition of each risk factor, and then determine the importance of each indicator and rank them; record x i >x j For the xth i The importance of an evaluation index based on a certain criterion is greater than that of index x j , remember x1>x2>x3>...>x m The order relationship is determined for the m evaluation indicators according to their importance; Step 2.2: Experts judge the relative importance of adjacent evaluation indicators based on their own experience and project characteristics, and obtain the ratio of the relative importance of the indicators, denoted as r. k : Step 2.3: Determine the evaluation index weights according to the calculation formula of the order relationship hierarchy method: m k-1 * =r k m k (k=n,n-1,...2) Where: m k ,m k-1 is the subjective weight of the kth and k-1th risk indicators calculated by the G1 method; n is the total number of indicators.

5. The subway shield construction risk assessment method based on combined weighting according to claim 4 is characterized in that: The step 3 specifically comprises the following steps: Step 3.1, determine the order of indicators: determine the original decision set of indicator A as {a1, a2, a3...a n }, and arrange the subsets in the decision set in descending order from large to small. The decision set after arrangement is {b1,b2,b3...b n }, and b1>b2>b3...>b n ; Step 3.2, determine the weighted vector: Use the combination number method to weight the data and get the weighted vector value as follows: Step 3.3, determine the absolute weight and relative weight value: calculate the absolute weight value of the indicator for the sorted data: In the formula is the relative weight value, w i is the absolute weight value.

6. The subway shield construction risk assessment method based on combined weighting according to claim 1 is characterized in that: In step 4, the weight coefficients of the subjective weight value and the objective weight value are both 0.

5.

7. The risk assessment method for subway shield construction based on combined weighting according to claim 1 is characterized in that: The step 5 specifically comprises the following steps: Step 5.1, determine the review level; Step 5.2, determine the evaluation criteria: divide the domain in [0,1] into multiple risk levels, and then construct a cloud model scale for the safety risk evaluation index of subway shield construction; Step 5.3, invite several experts to score the secondary risk indicators of shield construction within the range of [0,1], and obtain the index evaluation cloud model value of the secondary indicator based on the results. Use the formula to calculate the comprehensive evaluation cloud of subway shield construction risk. The formula is as follows: Where n is the number of secondary indicators, w i Assign weights to the combination of each indicator; Step 5.4: Compare the results to get the risk level of each indicator.

8. The subway shield construction risk assessment method based on combined weighting according to claim 7 is characterized in that: The cloud model includes three digital features: expected value Ex, entropy En and super entropy He.

9. The subway shield construction risk assessment method based on combined weighting according to claim 7 is characterized in that: In step 5.1, by consulting engineering literature and seeking expert advice, the rating levels of the indicators are divided into: high risk, relatively high risk, medium risk, relatively low risk, and low risk.

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