Shield tunnel daily risk coupling analysis method

Abstract

The invention relates to a shield tunnel daily risk coupling analysis method. The method comprises the following steps: S1, identifying risk factors based on historical statistical tunnel disease data; s2, establishing a tunnel daily operation risk coupling model; s3, constructing an N-K model, and calculating a risk coupling degree value; s4, calculating a transition probability matrix; s5, determining BN parameters, and constructing a BN model; and S6, constructing a tunnel daily risk coupling analysis model. According to the method, the whole process of daily disease prevention and management of the shield tunnel can be deeply and systematically researched, a comprehensive management thought and an optimization strategy with high practicability are provided, and a scientific and feasible management mode and decision support are provided for safe operation of a shield tunnel system.

Description

Technical Field

[0001] This invention relates to the field of tunnels and underground engineering in civil engineering, and in particular to a method for daily risk coupling analysis of shield tunnels. Background Technology

[0002] During long-term operation, tunnels are frequently affected by various factors such as geological conditions, equipment aging, and improper maintenance, leading to various risks. These risks are generally divided into low-frequency but high-impact events (such as floods, fires, and earthquakes) and high-frequency but low-impact events (such as cracks, leaks, and lighting failures). Preventing the former is undoubtedly crucial, but in reality, the probability of such disasters occurring during the tunnel's lifespan is extremely low, or even nonexistent. Tunnel operators may be more concerned with the frequent risks related to daily operations, i.e., high-frequency but low-impact risks. Although these risks have relatively small impacts, they can hinder normal tunnel operation, reduce traffic efficiency, and lead to serious safety accidents. The mechanisms of daily tunnel risks are complex, involving risk factors related to personnel, tunnels, management, and the environment. Any human error, tunnel system malfunction, or adverse environmental conditions can lead to daily tunnel risk issues. Numerous studies have shown that daily tunnel risks are often caused by the interaction of multiple risk factors. Ignoring the coupling effects between different risk factors and relying solely on single-factor, linear thinking often leads to biased estimates of the actual risk threat and the extent of loss. Therefore, there is an urgent need for a method for coupled analysis of daily risks in shield tunnels, which can comprehensively evaluate the daily service performance of tunnels, improve operational efficiency, reduce operating costs, and promote the sustainable development of tunnels. Summary of the Invention

[0003] In view of the shortcomings of the prior art, the technical problem to be solved by the present invention is to provide a method for daily risk coupling analysis of shield tunnels. This method can conduct in-depth and systematic research on the entire process of daily defect prevention and management of shield tunnels, provide comprehensive management ideas and highly practical optimization strategies, and provide a scientific and feasible management model and decision support for the safe operation of shield tunnel systems.

[0004] This invention is achieved through the following technical solution: a method for daily risk coupling analysis of shield tunnels, comprising the following steps:

[0005] S1. Identify risk factors based on historical statistical tunnel defect data;

[0006] S2. Based on the attributes and number of risk factors in S1, classify the risk coupling types in daily tunnel operation and establish a risk coupling model for daily tunnel operation;

[0007] S3. Construct the NK model, allocate the probability of risk factors through the NK model, calculate the probability of risk coupling, and then calculate the risk coupling degree value;

[0008] S4. Calculate the transition probability matrix, determine the dynamic factors, then perform dynamic performance evaluation on the selected dynamic factors, construct the evaluated dynamic factors as dynamic nodes, and calculate the state transition probability matrix.

[0009] S5. Determine the BN parameters, which include the prior probability of the BN node and the node state value of the BN node;

[0010] S6. Construct a daily risk coupling analysis model for tunnels, which includes a static Bayesian network and a dynamic Bayesian network for daily risk coupling analysis of tunnels.

[0011] Furthermore, S1. The specific steps for identifying risk factors include:

[0012] S11. Based on historical statistical data on tunnel defects, risk factors are divided into four categories: human factors, tunnel factors, management factors, and environmental factors.

[0013] S12. Identify the basic risk factors for human factors, tunnel factors, management factors, and environmental factors respectively.

[0014] Furthermore, the specific classification of risk coupling types in the daily operation of tunnel S2 is as follows:

[0015] S21. Single-factor risk coupling refers to tunnel operation risks originating from a single risk factor;

[0016] S22. Two-factor risk coupling refers to the interaction between two different factors that lead to tunnel operation risks, including human-tunnel coupling, human-management coupling, human-environment coupling, tunnel-management coupling, tunnel-environment coupling, and management-environment coupling.

[0017] S23. Multi-factor risk coupling refers to the interaction of three or more factors in operational risk, including human-tunnel-management coupling, human-tunnel-environment coupling, human-management-environment coupling, tunnel-management-environment coupling, and human-tunnel-management-environment coupling.

[0018] Furthermore, the specific steps for calculating the risk coupling degree value in S3 are as follows:

[0019] S31. Statistical coupling frequency of different risk factors

[0020] In the NK model calculation, based on the 15 coupling situations of four risk factors in S2—human, tunnel, management, and environment—the 15 coupling frequencies were statistically analyzed.

[0021] S32. Calculate the probabilities of different forms of risk coupling.

[0022] Based on the statistical coupling frequency of S31, calculate the probabilities of different forms of single-factor risk coupling, different forms of two-factor risk coupling, and different forms of multi-factor risk coupling.

[0023] S33. Calculate the risk coupling degree value.

[0024] The interaction information T between the factors involved in the coupling is calculated using the NK model. The larger the coupling degree T of a certain risk coupling, the greater the risk value of that coupling form, and the greater the probability that the coupling form will cause an accident. The formula for calculating the coupling degree of a two-factor risk is as follows:

[0025]

[0026] The formula for calculating the coupling degree of multi-factor risks is as follows:

[0027]

[0028] Furthermore, in S4, human factors, tunnel factors, and management factors are identified as dynamic factors. The transition probability matrix of the node is calculated from the three perspectives of human factors, tunnel factors, and management factors. The transition probability matrix is ​​used to quantitatively describe the probability distribution of the state transition of the event.

[0029] Furthermore, in S31, 1 in the probability symbol represents the occurrence of the risk, and 0 represents the non-occurrence. The probability of single-factor risk coupling is represented as: "P1000, P0100, P0010, P0001", where "P1000" represents the probability that routine tunnel defects are caused by personnel risk factors, "P0100" represents the probability that routine tunnel defects are caused by tunnel risk factors, "P0010" represents the probability that routine tunnel defects are caused by management risk factors, and "P0001" represents the probability that routine tunnel defects are caused by management environmental factors.

[0030] The probability of two-factor risk coupling occurring is expressed as: "P" 1100 P 1010 P 1001 P 0110 P 0101 P 0011 ”, where “P” 1100 "P" indicates the probability that routine tunnel defects are caused by the combined effects of human risk factors and tunnel risk factors. 1010 "P" indicates the probability that routine tunnel defects are caused by the combined effects of human and management risk factors. 1001"P" indicates the probability that routine tunnel defects are caused by the combined effects of human and environmental risk factors. 0110 "P" indicates the probability that routine tunnel defects are caused by the combined effects of tunnel risk factors and management risk factors. 0101 "P" indicates the probability that routine tunnel defects are caused by the combined effects of tunnel risk factors and environmental risk factors. 0011 This indicates the probability that routine tunnel defects are caused by the combined effects of management risk factors and environmental risk factors.

[0031] The probability of multi-factor risk coupling occurring is expressed as: "P" 1110 P 1101 P 1011 P 0111 P 1111 ”, where “P” 1110 "P" indicates the probability that routine tunnel defects are caused by the combined effects of personnel risk factors, tunnel risk factors, and management risk factors. 1101 "P" indicates the probability that routine tunnel defects are caused by the combined effects of human risk factors, tunnel risk factors, and environmental risk factors. 1011 "P" indicates the probability that routine tunnel defects are caused by the combined effects of human, management, and environmental risk factors. 0111 "P" indicates the probability that routine tunnel defects are caused by the combined effects of tunnel risk factors, management risk factors, and environmental risk factors. 1111 This indicates that the probability of routine tunnel defects occurring is caused by the combined effects of personnel risk factors, tunnel risk factors, management risk factors, and environmental risk factors.

[0032] If the number of accidents occurring in any of the four risk factors that do not participate in the risk effect is 0, then P 0000 =0.

[0033] Furthermore, S32 calculates the probabilities of different forms of risk coupling, specifically including:

[0034] S321. Single-factor risk coupled with different forms of probability

[0035] Based on the coupling frequency statistically derived in S31, P is calculated under different single-factor risk coupling scenarios. h P s P m P e The value of P, where P h The probability that a person's risk factors are involved in risk coupling, P s P represents the probability that risk factors in the tunnel participate in risk coupling. mThe probability that the managed risk factors participate in risk coupling, P e The probability of environmental risk factors participating in risk coupling;

[0036] S322. Two-factor risk coupling of different forms of probability

[0037] Based on the coupling frequency statistically derived in S31, P is calculated under different conditions of two-factor risk coupling. h,s P h,m P h,e P s,m P s,e P m,e The value of P, where P h,s The probability of daily tunnel risks occurring when human risk factors and tunnel risk factors are involved in risk coupling, P h,m The probability of daily tunnel risks occurring when human and managerial risk factors are involved in risk coupling, P h,e The probability of daily tunnel risks occurring when human and environmental risk factors are involved in risk coupling, P s,m P represents the probability of daily tunnel risks occurring when the tunnel's risk factors and management risk factors are involved in risk coupling. s,e P represents the probability of daily tunnel risks occurring when tunnel risk factors and environmental risk factors are involved in risk coupling. m,e The probability of daily tunnel risks occurring when managed risk factors and environmental risk factors are involved in risk coupling;

[0038] S323. Multi-factor risk coupling of different forms of probability

[0039] Based on the coupling frequency statistically derived in S31, P is calculated under different scenarios of multi-factor risk coupling. h,s,m P h,s,e P h,m,e P s,m,e P h,s,m,e The value of P, where P h,s,m The probability, P, of daily tunnel risks occurring due to the combined risk coupling of human risk factors, tunnel risk factors, and management risk factors. h,s,e The probability, P, of daily tunnel risks occurring due to the combined risk coupling of human risk factors, tunnel risk factors, and environmental risk factors. h,m,e The probability, P, of daily tunnel risks occurring due to the combined risk coupling of human, managerial, and environmental risk factors is considered. s,m,e P represents the probability of daily tunnel risks occurring due to the combined risk coupling of tunnel risk factors, management risk factors, and environmental risk factors. h,s,m,eThe probability of daily tunnel risks occurring is caused by the combined risk coupling of personnel risk factors, tunnel risk factors, management risk factors, and environmental risk factors.

[0040] Furthermore, the specific steps for determining the BN parameter in S5 are as follows:

[0041] S51. Determine the prior probability of a BN node.

[0042] Basic risk factors are used as BN nodes, and the prior probability of the corresponding BN node is determined based on the historical statistical data of the basic risk factors and the ratio of annual maintenance volume to workload.

[0043] S52. Determine the node state value of the BN node.

[0044] The risk coupling degree value calculated by S3 is input into the BN model to obtain the node state value of the BN node.

[0045] Furthermore, the daily risk coupling analysis model for the tunnel is constructed in S6, and the specific steps are as follows:

[0046] S61. Establish a static Bayesian network for coupling analysis of daily risks in tunnels.

[0047] The basic risk factors, four major categories of risk factors, and coupling types are mapped to parameter nodes in a Bayesian network. The prior probabilities of the BN nodes determined by S51 are input. A static Bayesian network for daily risk coupling analysis of tunnels is established based on the causal relationship between the basic risk factors, four major categories of risk factors, and coupling types. The parameter nodes include parent nodes composed of basic risk factors, intermediate nodes composed of four major categories of risk factors, and child nodes composed of coupling types.

[0048] S62. Establish a dynamic Bayesian network for coupling analysis of daily risks in tunnels.

[0049] Based on the static Bayesian network for daily risk coupling analysis of tunnels established in S61, the transition probability matrix obtained in S4 is input to obtain the probability values ​​of each dynamic node, representing the change of probability information from time t to time t+1.

[0050] The effects of this invention are as follows:

[0051] (1) This invention proposes a daily risk coupling analysis method for shield tunnels by combining historical statistical data, which covers all subsystems in shield tunnels and reflects the coupling effect of risk factors when defects occur.

[0052] (2) This invention studies the risk factors of daily defects in shield tunnels, covering a wide range of factors and encompassing all influencing factors within the tunnel. It explores the intrinsic connections and coupling effects of daily risk factors in shield tunnels at their root, considers the coupling effects between different risk factors, comprehensively evaluates the daily service performance of the tunnel, improves operational efficiency, reduces operating costs, and promotes the sustainable development of the tunnel. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating a method for daily risk coupling analysis of shield tunnels according to the present invention;

[0054] Figure 2 This invention provides a diagram illustrating the risk factors for identifying daily operations of shield tunnels.

[0055] Figure 3 This is a diagram of the daily operation risk coupling model of the shield tunnel according to the present invention;

[0056] Figure 4 This is a schematic diagram of the static Bayesian network of the present invention;

[0057] Figure 5 This is a schematic diagram of the dynamic Bayesian network for the T10 time period of the present invention. Detailed Implementation

[0058] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0059] Reference Figures 1-5 As shown, a method for daily risk coupling analysis of shield tunnels includes the following steps:

[0060] S1. Identify risk factors based on historical statistical tunnel defect data. Specific steps are as follows:

[0061] S11. Based on historical statistical data on tunnel defects, risk factors are divided into four categories: human factors, tunnel factors, management factors, and environmental factors.

[0062] S12. Based on historical statistical data on tunnel defects, the basic risk factors for human factors, tunnel factors, management factors, and environmental factors are determined respectively. Among them, the basic risk factors for human factors include missed safety inspections, insufficient maintenance, traffic accidents, insufficient professional skills, unclear responsibilities, violations of regulations, and operational errors; the basic risk factors for tunnel factors include cracks, water leakage, poor road surface conditions, poor condition of maintenance access roads, poor operation of lighting facilities, poor operation of fire protection facilities, poor operation of ventilation facilities, poor operation of water supply and drainage facilities, poor operation of monitoring and communication facilities, poor operation of power supply and distribution facilities, and poor operation of other ancillary engineering facilities; the basic risk factors for management factors include lack of safety education and operating procedure training, inadequate implementation of regulations, unsound management systems, inadequate summarization of incident causes, and insufficient investment in safety resources; the environmental factors include adverse weather conditions, poor surrounding environment, external events, and adverse geological conditions.

[0063] S2. Establish a coupling model for daily operation risks of tunnels.

[0064] Based on the attributes and number of risk factors in S1, the risk coupling types in daily tunnel operation are classified as follows:

[0065] S21. Single-factor risk coupling refers to tunnel operation risk originating from a single risk factor, indicating that there are no multiple risk factors interacting simultaneously.

[0066] S22. Two-factor risk coupling refers to the interaction between two different factors that lead to tunnel operation risks, including human-tunnel coupling, human-management coupling, human-environment coupling, tunnel-management coupling, tunnel-environment coupling, and management-environment coupling.

[0067] S23. Multi-factor risk coupling refers to the interaction of three or more factors in operational risk, including human-tunnel-management coupling, human-tunnel-environment coupling, human-management-environment coupling, tunnel-management-environment coupling, and human-tunnel-management-environment coupling.

[0068] S3. Construct the NK model and calculate the risk coupling degree value.

[0069] The NK model is used to assign probabilities of risk factors, calculate the probability of risk coupling, and then calculate the degree of risk coupling. The specific steps are as follows:

[0070] S31. Statistical coupling frequency of different risk factors.

[0071] In the NK model calculation, the coupling frequencies are statistically analyzed based on 15 coupling scenarios involving four risk factors (human, tunnel, management, and environment) in S2. In the probability symbols, 1 represents the occurrence of the risk, and 0 represents its non-occurrence, as detailed below:

[0072] The probability of single-factor risk coupling occurring is expressed as: "P" 1000 P 0100 P 0010 P 0001 ”, where “P” 1000 "P" indicates the probability that routine tunnel defects are caused by human-related risk factors. 0100 "P" indicates the probability that routine tunnel defects are caused by risk factors within the tunnel. 0010 "P" indicates the probability that routine tunnel defects are caused by management-related risk factors. 0001 This indicates the probability that routine tunnel defects are caused by environmental factors related to management.

[0073] The probability of two-factor risk coupling occurring is expressed as: "P" 1100 P 1010 P 1001 P 0110 P 0101 P 0011 ”, where “P” 1100 "P" indicates the probability that routine tunnel defects are caused by the combined effects of human risk factors and tunnel risk factors. 1010 "P" indicates the probability that routine tunnel defects are caused by the combined effects of human and management risk factors. 1001 "P" indicates the probability that routine tunnel defects are caused by the combined effects of human and environmental risk factors. 0110 "P" indicates the probability that routine tunnel defects are caused by the combined effects of tunnel risk factors and management risk factors. 0101 "P" indicates the probability that routine tunnel defects are caused by the combined effects of tunnel risk factors and environmental risk factors. 0011 This indicates the probability that routine tunnel defects are caused by the combined effects of management risk factors and environmental risk factors.

[0074] The probability of multi-factor risk coupling occurring is expressed as: "P" 1110 P 1101 P 1011 P 0111 P 1111 ”, where “P” 1110 "P" indicates the probability that routine tunnel defects are caused by the combined effects of personnel risk factors, tunnel risk factors, and management risk factors. 1101 "P" indicates the probability that routine tunnel defects are caused by the combined effects of human risk factors, tunnel risk factors, and environmental risk factors. 1011"P" indicates the probability that routine tunnel defects are caused by the combined effects of human, management, and environmental risk factors. 0111 "P" indicates the probability that routine tunnel defects are caused by the combined effects of tunnel risk factors, management risk factors, and environmental risk factors. 1111 "This indicates that routine tunnel defects are caused by the combined effects of personnel risk factors, tunnel risk factors, management risk factors, and environmental risk factors; the probability of an accident occurring in any of the four types of risk factors that are not involved in the risk effect is 0, i.e., P." 0000 =0.

[0075] S32. Calculate the probabilities of different forms of risk coupling.

[0076] Based on the statistical coupling frequency of S31, calculate the probabilities of different forms of single-factor risk coupling, different forms of two-factor risk coupling, and different forms of multi-factor risk coupling, specifically including:

[0077] S321. Single-factor risk coupled with different forms of probability.

[0078] Based on the coupling frequency statistically derived in S31, P is calculated under different single-factor risk coupling scenarios. h P s P m P e The value of P, where P h The probability that a person's risk factors are involved in risk coupling, P s P represents the probability that risk factors in the tunnel participate in risk coupling. m The probability that the managed risk factors participate in risk coupling, P e Let P be the probability that environmental risk factors participate in risk coupling. Specifically, when routine tunnel risks occur, the probability that human risk factors participate in risk coupling is: P h =P 1000 +P 1100 +P 1010 +P 1001 +P 1110 +P 1101 +P 1011 +P 1111 Similarly, other single-factor P-values ​​can be calculated.

[0079] S322. Two-factor risk coupling of different forms of probability.

[0080] Based on the coupling frequency statistically derived in S31, P is calculated under different conditions of two-factor risk coupling. h,s P h,m P h,e P s,m Ps,e P m,e The value of P, where P h,s The probability of daily tunnel risks occurring when human risk factors and tunnel risk factors are involved in risk coupling, P h,m The probability of daily tunnel risks occurring when human and managerial risk factors are involved in risk coupling, P h,e The probability of daily tunnel risks occurring when human and environmental risk factors are involved in risk coupling, P s,m P represents the probability of daily tunnel risks occurring when the tunnel's risk factors and management risk factors are involved in risk coupling. s,e P represents the probability of daily tunnel risks occurring when tunnel risk factors and environmental risk factors are involved in risk coupling. m,e The probability of daily tunnel risks occurring when managed risk factors and environmental risk factors are involved in risk coupling. Specifically, the probability of daily tunnel risks occurring when human risk factors and tunnel risk factors are involved in risk coupling is: P h,s =P 1100 +P 1101 +P 1110 +P 1111 Similarly, the P-value can be calculated for other two-factor risk coupling scenarios.

[0081] S323. Multi-factor risk coupling of different forms of probability.

[0082] Based on the coupling frequency statistically derived in S31, P is calculated under different scenarios of multi-factor risk coupling. h,s,m P h,s,e P h,m,e P s,m,e P h,s,m,e The value of P, where P h,s,m The probability, P, of daily tunnel risks occurring due to the combined risk coupling of human risk factors, tunnel risk factors, and management risk factors. h,s,e The probability, P, of daily tunnel risks occurring due to the combined risk coupling of human risk factors, tunnel risk factors, and environmental risk factors. h,m,e The probability, P, of daily tunnel risks occurring due to the combined risk coupling of human, managerial, and environmental risk factors is considered. s,m,e P represents the probability of daily tunnel risks occurring due to the combined risk coupling of tunnel risk factors, management risk factors, and environmental risk factors. h,s,m,e The probability of daily tunnel risks occurring due to the combined risk coupling of personnel risk factors, tunnel risk factors, management risk factors, and environmental risk factors is given by: Ph,s,m =P 1110 +P 1111 Similarly, the factor coupling probability can be calculated under other multi-factor risk coupling conditions.

[0083] S33. Calculate the risk coupling degree value.

[0084] The interaction information T between the factors involved in the coupling is calculated using the NK model. The larger the coupling degree T of a certain risk coupling, the greater the risk value of that coupling form, and the greater the probability that the coupling form will cause an accident. The formula for calculating the coupling degree of a two-factor risk is as follows:

[0085]

[0086] The formula for calculating the coupling degree of multi-factor risks is as follows:

[0087]

[0088] S4. Calculate the transition probability matrix.

[0089] Dynamic factors are identified from four major risk categories: personnel, tunnel, management, and environment. These dynamic factors are then evaluated for their dynamic performance, and the evaluated dynamic factors are used to construct dynamic nodes. A state transition probability matrix is ​​then calculated. Specifically, human factors, tunnel factors, and management factors are identified as dynamic factors. The transition probability matrix of the root node is explored from three perspectives: human factors, tunnel factors, and management factors. This transition probability matrix quantitatively describes the probability distribution of event state transitions. Details are as follows:

[0090] Regarding S41 human factors: During operation and maintenance, it is assumed that the probability of human error follows an exponential distribution. The calculation formula is shown in Table 1, where λ1 is the constant failure rate of the exponential distribution, and Δt is the change over time.

[0091] Regarding factors affecting the S42 tunnel: As the tunnel's operating time increases, equipment and facilities will experience a certain degree of aging, and the operator will regularly maintain all equipment and facilities. It is assumed that the failure probability and maintenance probability of the equipment and facilities follow an exponential distribution. The calculation formulas are shown in Table 1. In the formulas, λ² is the constant failure rate of the exponential distribution, μ represents that maintenance of equipment and facilities can reduce the failure rate by 10%, and Δt is the change over time.

[0092] Regarding S43 management factors: During operation and maintenance, as the organization's management time increases, the accumulated management experience will gradually increase. Assume the management experience enhancement coefficient is v, with a value of 0.1. The calculation formula is shown in Table 1.

[0093]

[0094]

[0095] Table 1 State transition probability matrix

[0096] S5. Determine the BN parameters and construct the BN model based on the BN parameters.

[0097] The BN parameters include the prior probability of the BN node and the node state value of the BN node, as follows:

[0098] S51. Determine the prior probability of a BN node.

[0099] Basic risk factors are used as BN nodes, and the prior probability of the corresponding BN node is determined based on historical statistical data of the basic risk factors and the ratio of annual maintenance volume to workload. For example, the probability of severe weather conditions is determined based on the proportion of extreme weather days in the local area.

[0100] S52. Determine the node state value of the BN node.

[0101] The risk coupling degree value calculated by S3 is input into the BN model to obtain the node state value of the BN node.

[0102] S6. Construct a coupling analysis model for daily risks in tunnels.

[0103] The tunnel daily risk coupling analysis model includes a static Bayesian network and a dynamic Bayesian network for tunnel daily risk coupling analysis. The specific steps are as follows:

[0104] S61. Establish a static Bayesian network for coupling analysis of daily risks in tunnels.

[0105] The basic risk factors, four major categories of risk factors, and coupling types are mapped to parameter nodes in a Bayesian network. A static Bayesian network for daily risk coupling analysis of tunnels is established based on the causal relationships among these factors (e.g., ...). Figure 4 (As shown). The parameter nodes include parent nodes composed of basic risk factors, intermediate nodes composed of four major categories of risk factors, and child nodes composed of coupling types.

[0106] The parent nodes include cracks, water leakage, poor road surface condition, poor maintenance access condition, poor lighting, poor fire protection, poor ventilation, poor water supply and drainage, poor monitoring and communication, poor power supply and distribution, poor operation of other ancillary facilities, adverse weather conditions, poor surrounding environment, external events, adverse geological conditions, missed safety inspections, insufficient maintenance, traffic accidents, insufficient professional skills, unclear responsibilities, violations, operational errors, lack of safety education and operating procedure training, inadequate implementation of systems, unsound management systems, inadequate summary of incident causes, and insufficient investment in safety resources. The intermediate nodes include tunnel factors, environmental factors, personnel factors, and management factors. The child nodes include couplings of personnel and tunnel factors, personnel and management factors, personnel and environmental factors, tunnel and management factors, tunnel and environmental factors, management and environmental factors, personnel and tunnel and management factors, personnel and management factors, tunnel and management factors, and personnel and tunnel and management factors.

[0107] S62. Establish a dynamic Bayesian network for coupling analysis of daily risks in tunnels.

[0108] Based on the static Bayesian network for daily risk coupling analysis of tunnels established in S61, the transition probability matrix obtained in S4 is input to obtain the probability values ​​of each dynamic node, representing the change in probability information from time t to time t+1. Figure 5 (For a dynamic Bayesian network with a time interval of t10).

[0109] The daily risk coupling analysis model for tunnels models the risk coupling analysis of daily defects in shield tunnels. Based on historical defect statistics, it analyzes the coupling effect of risk factors that lead to daily risks, systematically adopts risk prevention and emergency response measures, and constructs a comprehensive operation and management strategy library to promote safe tunnel operation. This model is of great significance for reducing the probability of daily risk events in tunnels, mitigating the impact of defects, and improving safe tunnel operation.

[0110] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for daily risk coupling analysis of shield tunnels, characterized by the following steps: as follows: S1. Identify risk factors based on historical statistical tunnel defect data; S2. Based on the attributes and number of risk factors in S1, classify the risk coupling types in daily tunnel operation and establish a risk coupling model for daily tunnel operation; S3. Construct the NK model, allocate the probability of risk factors through the NK model, calculate the probability of risk coupling, and then calculate the risk coupling degree value; S4. Calculate the transition probability matrix, determine the dynamic factors, then perform dynamic performance evaluation on the selected dynamic factors, construct the evaluated dynamic factors as dynamic nodes, and calculate the state transition probability matrix. S5. Determine the BN parameters, which include the prior probability of the BN node and the node state value of the BN node; S6. Construct a daily risk coupling analysis model for tunnels, which includes a static Bayesian network and a dynamic Bayesian network for daily risk coupling analysis of tunnels.

2. The method for daily risk coupling analysis of shield tunnels according to claim 1, characterized in that: S1. The specific steps for identifying risk factors include: S11. Based on historical statistical data on tunnel defects, risk factors are divided into four categories: human factors, tunnel factors, management factors, and environmental factors. S12. Identify the basic risk factors for human factors, tunnel factors, management factors, and environmental factors respectively.

3. The method for daily risk coupling analysis of shield tunnels according to claim 1, Its characteristics are as follows: The specific classification of risk coupling types in the daily operation of tunnel S2 is as follows: S21. Single-factor risk coupling refers to tunnel operation risks originating from a single risk factor; S22. Two-factor risk coupling refers to the interaction between two different factors that lead to tunnel operation risks, including human-tunnel coupling, human-management coupling, human-environment coupling, tunnel-management coupling, tunnel-environment coupling, and management-environment coupling. S23. Multi-factor risk coupling refers to the interaction of three or more factors in operational risk, including human-tunnel-management coupling, human-tunnel-environment coupling, human-management-environment coupling, tunnel-management-environment coupling, and human-tunnel-management-environment coupling.

4. The method for daily risk coupling analysis of shield tunnels according to claim 1, characterized in that: S3. The specific steps for calculating the risk coupling degree are as follows: S31. Statistical coupling frequency of different risk factors In the NK model calculation, the coupling frequency is statistically analyzed based on the 15 coupling scenarios of four risk factors in S2: people, tunnels, management, and environment. S32. Calculate the probabilities of different forms of risk coupling. Based on the statistical coupling frequency of S31, calculate the probabilities of different forms of single-factor risk coupling, different forms of two-factor risk coupling, and different forms of multi-factor risk coupling. S33. Calculate the risk coupling degree value. The interaction information T between the factors involved in the coupling is calculated using the NK model. The larger the coupling degree T of a certain risk coupling, the greater the risk value of that coupling form, and the greater the probability that the coupling form will cause an accident. The formula for calculating the coupling degree of a two-factor risk is as follows: The formula for calculating the coupling degree of multi-factor risks is as follows:

5. The method for daily risk coupling analysis of shield tunnels according to claim 1, characterized in that: In S4, human factors, tunnel factors, and management factors are identified as dynamic factors. The transition probability matrix of the node is calculated from the three perspectives of human factors, tunnel factors, and management factors. The transition probability matrix is ​​used to quantitatively describe the probability distribution of the state transition of the event.

6. The method for daily risk coupling analysis of shield tunnels according to claim 4, characterized in that: In S31, a 1 in the probability symbol represents the occurrence of the risk, and 0 represents its non-occurrence. The probability of occurrence of a single-factor risk coupling is represented as: "P 1000 P 0100 P 0010 P 0001 ”, where “P” 1000 "P" indicates the probability that routine tunnel defects are caused by human-related risk factors. 0100 "P" indicates the probability that routine tunnel defects are caused by risk factors within the tunnel. 0010 "P" indicates the probability that routine tunnel defects are caused by management-related risk factors. 0001 This indicates the probability that routine tunnel defects are caused by environmental factors related to management. The probability of two-factor risk coupling occurring is expressed as: "P 1100 P 1010 P 1001 P 0110 P 0101 , P 0011 ”, where “P” 1100 "P" indicates the probability that routine tunnel defects are caused by the combined effects of human risk factors and tunnel risk factors. 1010 "P" indicates the probability that routine tunnel defects are caused by the combined effects of personnel and management risk factors. 1001 "P" indicates the probability that routine tunnel defects are caused by the combined effects of human and environmental risk factors. 0110 "P" represents the probability that routine tunnel defects occur due to the combined effects of tunnel risk factors and management risk factors. 0101 "P" represents the probability that routine tunnel defects are caused by the combined effects of tunnel risk factors and environmental risk factors. 0011 This indicates the probability that routine tunnel defects are caused by the combined effects of management risk factors and environmental risk factors. The probability of multi-factor risk coupling occurring is expressed as: "P 1110 P 1101 P 1011 P 0111 P 1111 ”, where “P” 1110 "P" indicates that routine tunnel defects are caused by the combined effects of personnel risk factors, tunnel risk factors, and management risk factors, resulting in a probability of occurrence. 1101 "P" indicates the probability that routine tunnel defects are caused by the combined effects of human risk factors, tunnel risk factors, and environmental risk factors. 1011 "P" indicates that routine tunnel defects are caused by the combined effects of human, management, and environmental risk factors, resulting in a probability of occurrence. 0111 "P" indicates the probability that routine tunnel defects are caused by the combined effects of tunnel risk factors, management risk factors, and environmental risk factors. 1111 This indicates that the probability of routine tunnel defects occurring is caused by the combined effects of personnel risk factors, tunnel risk factors, management risk factors, and environmental risk factors. If the number of accidents occurring in any of the four risk factors that do not participate in the risk effect is 0, then P 0000 =0.

7. The method for daily risk coupling analysis of shield tunnels according to claim 4, characterized in that: S32 calculates the probabilities of different forms of risk coupling, specifically including: S321. Single-factor risk coupled with different forms of probability Based on the coupling frequency statistically derived in S31, P is calculated under different single-factor risk coupling scenarios. h P s P m P e The value of P, where P h The probability that a person's risk factors are involved in risk coupling, P s P represents the probability that risk factors in the tunnel participate in risk coupling. m The probability that the managed risk factors participate in risk coupling, P e The probability of environmental risk factors participating in risk coupling; S322. Two-factor risk coupling of different forms of probability Based on the coupling frequency statistically derived in S31, P is calculated under different conditions of two-factor risk coupling. h,s P h,m P h,e P s,m P s,e P m,e The value of P, where P h,s The probability of daily tunnel risks occurring when human risk factors and tunnel risk factors are involved in risk coupling, P h,m The probability of daily tunnel risks occurring when human and managerial risk factors are involved in risk coupling, P h,e The probability of daily tunnel risks occurring when human and environmental risk factors are involved in risk coupling, P s,m P represents the probability of daily tunnel risks occurring when the tunnel's risk factors and management risk factors are involved in risk coupling. s,e P represents the probability of daily tunnel risks occurring when tunnel risk factors and environmental risk factors are involved in risk coupling. m,e The probability of daily tunnel risks occurring when managed risk factors and environmental risk factors are involved in risk coupling; S323. Multi-factor risk coupling of different forms of probability Based on the coupling frequency statistically derived in S31, P is calculated under different scenarios of multi-factor risk coupling. h,s,m P h,s,e P h,m,e P s,m,e P h,s,m,e The value of P, where P h,s,m The probability, P, of daily tunnel risks occurring due to the combined risk coupling of human risk factors, tunnel risk factors, and management risk factors. h,s,e The probability, P, of daily tunnel risks occurring due to the combined risk coupling of human risk factors, tunnel risk factors, and environmental risk factors. h,m,e The probability, P, of daily tunnel risks occurring due to the combined risk coupling of human, managerial, and environmental risk factors is considered. s,m,e P represents the probability of daily tunnel risks occurring due to the combined risk coupling of tunnel risk factors, management risk factors, and environmental risk factors. h,s,m,e The probability of daily tunnel risks occurring is caused by the combined risk coupling of personnel risk factors, tunnel risk factors, management risk factors, and environmental risk factors.

8. The method for daily risk coupling analysis of shield tunnels according to claim 1, characterized in that: S5. The specific steps for determining the BN parameter are as follows: S51. Determine the prior probability of a BN node. Basic risk factors are used as BN nodes, and the prior probability of the corresponding BN node is determined based on the historical statistical data of the basic risk factors and the ratio of annual maintenance volume to workload. S52. Determine the node state value of the BN node. The risk coupling degree value calculated by S3 is input into the BN model to obtain the node state value of the BN node.

9. The method for daily risk coupling analysis of shield tunnels according to claim 8, characterized in that: The specific steps for constructing the daily risk coupling analysis model for the tunnel in S6 are as follows: S61. Establish a static Bayesian network for coupling analysis of daily risks in tunnels. The basic risk factors, four major categories of risk factors, and coupling types are mapped to parameter nodes in a Bayesian network. The prior probabilities of the BN nodes determined by S51 are input. A static Bayesian network for daily risk coupling analysis of tunnels is established based on the causal relationship between the basic risk factors, four major categories of risk factors, and coupling types. The parameter nodes include parent nodes composed of basic risk factors, intermediate nodes composed of four major categories of risk factors, and child nodes composed of coupling types. S62. Establish a dynamic Bayesian network for coupling analysis of daily risks in tunnels. Based on the static Bayesian network for daily risk coupling analysis of tunnels established in S61, the transition probability matrix obtained in S4 is input to obtain the probability values ​​of each dynamic node, representing the change of probability information from time t to time t+1.

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