Risk assessment method and related device for sea-crossing bridge

Abstract

The embodiment of the present application provides a risk assessment method and related device for a cross-sea bridge, the method comprising: establishing a finite element model of a target bridge; performing a dynamic analysis on the finite element model to determine its accuracy; obtaining historical disaster data of the location of the target bridge; determining one or more risk events that affect the safety of the target bridge based on the historical disaster data; determining M key structures from N bridge parts of an accurate finite element model; dividing multiple damage indicators for each key structure in the M key components; calculating the failure probability of each key component in a corresponding damage state to obtain multiple first failure probabilities; calculating the failure probability of the target bridge based on each first failure probability in the multiple first failure probabilities to obtain multiple second failure probabilities; determining the risk level of the target bridge based on the multiple first failure probabilities and the multiple second failure probabilities. In this way, bridge risk assessment under the action of multiple risk factors is achieved.

Description

Technical Field

[0001] The present application belongs to the field of communication technology, and specifically relates to a risk assessment method and related devices for a cross-sea bridge. Background Art

[0002] At present, the structural system of cross-sea bridges is complex and there are many factors that affect the safety of bridge structures, so potential risk issues must also be taken seriously.

[0003] Most of the existing research on risk assessment of cross-sea bridges focuses on the risk assessment of a single structure under a single risk. In this way, if under the influence of relatively complex environmental factors, the accuracy of the risk assessment of the entire bridge cannot be ensured. Summary of the invention

[0004] The embodiments of the present application provide a cross-sea bridge risk assessment method and related devices, in order to achieve bridge risk assessment under the influence of multiple risk factors.

[0005] In a first aspect, an embodiment of the present application provides a risk assessment method for a cross-sea bridge, characterized in that it is applied to an electronic device and includes:

[0006] Establishing a finite element model of a target bridge, wherein the finite element model includes N bridge parts of the bridge, where N is a positive integer;

[0007] Performing dynamic analysis on the finite element model to verify the accuracy of the finite element model;

[0008] Acquire historical disaster data of the location of the target bridge;

[0009] Determine one or more risk events that affect the safety of the target bridge according to the historical disaster data;

[0010] Determine the key components of each bridge part from the N bridge parts of the finite element model that has been verified for accuracy, and obtain M key components, where M is a positive integer greater than N;

[0011] Assigning a plurality of damage indicators to each of the M key components, wherein the plurality of damage indicators correspond to a plurality of damage states one by one;

[0012] Calculating the failure probability of each key component in each of the corresponding multiple damage states to obtain multiple first failure probabilities;

[0013] Calculating the failure probability of the target bridge according to each first failure probability of the multiple first failure probabilities to obtain multiple second failure probabilities;

[0014] The risk level of the target bridge is determined according to the multiple first failure probabilities and the multiple second failure probabilities. In a second aspect, an embodiment of the present application provides a risk assessment device for a cross-sea bridge, characterized in that it includes:

[0015] A creation unit is used to establish a finite element model of a target bridge, wherein the finite element model includes N bridge parts of the bridge, where N is a positive integer;

[0016] An analysis unit, used for performing dynamic analysis on the finite element model to verify the accuracy of the finite element model;

[0017] A second determining unit is used to determine the key components of each bridge part from the N bridge parts of the finite element model that has passed the accuracy verification, and obtain M key components, where M is a positive integer greater than N;

[0018] An acquisition unit, used for acquiring historical disaster data of the location of the target bridge;

[0019] A first determining unit, configured to determine one or more risk events that affect the safety of the target bridge according to the historical disaster data;

[0020] A division unit, used for dividing a plurality of damage indicators for each key component of the M key components, wherein the plurality of damage indicators correspond to a plurality of damage states one by one;

[0021] A calculation unit is used to calculate the failure probability of each key component in each of the corresponding multiple damage states to obtain multiple first failure probabilities; and calculate the failure probability of the target bridge according to each of the multiple first failure probabilities to obtain multiple second failure probabilities;

[0022] The third determining unit is used to determine the risk level of the target bridge according to the multiple first failure probabilities and the multiple second failure probabilities.

[0023] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps of any one of the first and second aspects of the embodiments of the present application.

[0024] In a fourth aspect, an embodiment of the present application provides a computer storage medium, characterized in that it stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in any one of the first and second aspects of the present embodiment.

[0025] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in any one of the first and second aspects of the embodiment of the present application. The computer program product may be a software installation package.

[0026] It can be seen that in the embodiment of the present application, a finite element model of the target bridge is first established, the finite element model includes N bridge parts of the bridge, N is a positive integer; then the finite element model is subjected to dynamic analysis to verify the accuracy of the finite element model; then the historical disaster data of the location of the target bridge is obtained; then one or more risk events affecting the safety of the target bridge are determined according to the historical disaster data; the key components of each bridge part are determined from the N bridge parts of the finite element model that have been verified by accuracy, and M key structures are obtained, M is a positive integer greater than N; then multiple damage indicators are divided for each key structure in the M key components, and the multiple damage indicators correspond to multiple damage states one by one; the failure probability of each key component in each of the corresponding multiple damage states is calculated to obtain multiple first failure probabilities; the failure probability of the target bridge is calculated according to each first failure probability in the multiple first failure probabilities to obtain multiple second failure probabilities; finally, the risk level of the target bridge is determined according to the multiple first failure probabilities and the multiple second failure probabilities. In this way, bridge risk assessment under the action of multiple risk factors is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application;

[0029] Figure 2 It is a flow chart of a cross-sea bridge risk assessment method provided in an embodiment of the present application;

[0030] Figure 3 is a schematic diagram of an ALARP standard provided in an embodiment of the present application;

[0031] Figure 4 It is a fitting schematic diagram of a response surface equation provided in an embodiment of the present application;

[0032] Figure 5 is a schematic diagram of a Bayesian network model of a target bridge provided in an embodiment of the present application;

[0033] Figure 6 It is a structural schematic diagram of a cross-sea bridge risk assessment device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0035] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, systems, products or devices.

[0036] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0037] The following is an introduction to the relevant terms involved in this application.

[0038] At present, most of the existing research on bridge risk assessment focuses on the risk assessment of a single structure under a single risk, and there is a lack of research on the risk assessment of the entire bridge under two or more risks. The risk assessment of the entire bridge during the operation stage is mostly based on qualitative methods, using expert scoring to grade the risk loss and probability of occurrence of the bridge. At present, there is no complete quantitative risk assessment method for the bridge system. At present, Bayesian networks are widely used in the field of risk assessment, but there is a lack of corresponding research applications in complex large structures such as bridges. Therefore, the technology of the present invention will consider establishing a multi-hazard risk assessment model for bridges based on Bayesian networks to form a unified, complete and universally applicable multi-hazard risk assessment framework for bridges.

[0039] To solve the above problems, the embodiment of the present application provides a cross-sea bridge risk assessment method, which can be applied to the scenario of risk assessment of bridges. The method first establishes a finite element model of the target bridge, and performs a dynamic analysis on the finite element model to verify the accuracy of the finite element model; then determines the key components of each bridge part from the N bridge parts of the finite element model that have been verified by accuracy, and obtains M key structures; then obtains the historical disaster data of the location of the target bridge, and determines one or more risk events that affect the safety of the target bridge according to the historical disaster data; divides multiple damage indicators for each key structure in the M key components, and the multiple damage indicators correspond to multiple damage states one by one; calculates the failure probability of each key component in each of the corresponding multiple damage states, and obtains multiple first failure probabilities; calculates the failure probability of the target bridge according to each first failure probability in the multiple first failure probabilities, and obtains multiple second failure probabilities; finally determines the risk level of the target bridge according to the multiple first failure probabilities and the multiple second failure probabilities. This solution can be applied to a variety of scenarios, including but not limited to the application scenarios mentioned above.

[0040] The following introduces the system architecture involved in the embodiments of the present application.

[0041] The present application also provides an electronic device 10, such as Figure 1 As shown, it includes at least one processor 11; a display screen 12; and a memory 13, and may also include a communications interface 15 and a bus 14. The processor 11, the display screen 12, the memory 13 and the communications interface 15 can communicate with each other through the bus 14. The display screen 12 is configured to display a preset user guide interface in the initial setting mode. The communications interface 15 can transmit information. The processor 11 can call the logic instructions in the memory 13 to execute the method in the above embodiment.

[0042] Optionally, the electronic device 10 may be a mobile electronic device, or an electronic device or other device, which is not limited here.

[0043] In addition, the logic instructions in the memory 13 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0044] The memory 13, as a computer-readable storage medium, can be configured to store software programs, computer executable programs, such as program instructions or modules corresponding to the methods in the embodiments of the present disclosure. The processor 11 executes functional applications and data processing by running the software programs, instructions or modules stored in the memory 13, that is, implementing the methods in the above embodiments.

[0045] The memory 13 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the electronic device 10, etc. In addition, the memory 13 may include a high-speed random access memory and may also include a non-volatile memory. For example, a variety of media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, may also be a transient storage medium.

[0046] The specific methods are introduced in detail below.

[0047] See also Figure 2 The present application also provides a risk assessment method for a cross-sea bridge, which can be applied to the electronic device described above. The risk assessment method for a cross-sea bridge includes:

[0048] Step 201: Establish a finite element model of a target bridge.

[0049] Wherein, the finite element model includes N bridge parts of the bridge, and N is a positive integer.

[0050] In a possible embodiment, establishing the finite element model of the target bridge includes: acquiring structural data of the target bridge; and establishing the finite element model according to the structural data.

[0051] For example, since a typical example of the cross-sea bridge is a suspension bridge, this embodiment takes the Lingdingyang Bridge as an example to illustrate the establishment of the finite element model, but is not limited to suspension bridges and can be applied to all bridges.

[0052] In the specific implementation, the Lingdingyang Bridge is a suspension bridge with a span of 2666m. The span of the main span is 1666m, and the spans of both side spans are 500m. The bridge tower is a portal structure with a height of 270m. The vertical span ratio of the main span main cable is 1:9.65. The main cable has a diameter of 1.1m and a spacing of 42.1m. The number of single cable strands in the side span is 279, and the number of single cable strands in the middle span is 271. The hangers of the Lingdingyang Bridge are ordinary hangers and reinforced hangers. The longitudinal spacing of the hangers along the bridge is 12.8m, and the transverse spacing along the bridge is 42.1m. The main beam adopts a steel box beam with a width of 49.7m and a height of 4m. According to the data of the Lingdingyang Bridge, the finite element model of the Lingdingyang Bridge was established in the ANSYS finite element software, and the bridge structure was discretized into five parts, namely the tower, pier, main cable, hanger and main beam. The towers and piers of the Lingdingyang Bridge are simulated using BEAM188 units, which can simulate their compression and bending stress forms very well. When establishing the main cable of the Lingdingyang Bridge, LINK10 units are used to simulate it, and the main cable is discretized according to the suspension points of the bridge hangers. In the finite element model of the Lingdingyang Bridge, the hangers are simulated using LINK10 units. The main beam of the Lingdingyang Bridge is a steel box beam, which is simulated using the spine beam mode. The main beam is simulated using BEAM4 units and discretized according to the suspension points of the hangers. The stiffness of the BEAM4 unit is the stiffness of the main beam, without considering the stiffness influence of the bridge deck system, and only its mass contribution as the second-phase dead load is included. The boundary and constraint conditions used in the modeling are shown in Table 1-1, where 1 represents constraint and 0 represents release of constraint.

[0053] Table 1-1 Bridge restraint system table

[0054]

[0055] Step 202: Perform a dynamic analysis on the finite element model to verify the accuracy of the finite element model.

[0056] In a possible embodiment, the dynamic analysis of the finite element model to verify the accuracy of the finite element model includes: performing a dynamic characteristic analysis on the finite element model to obtain a first vibration mode and a first frequency of the finite element model; and comparing and analyzing the first vibration mode and the first frequency with the actual vibration mode and the actual frequency of the target bridge, respectively, to verify the accuracy of the bridge model established in the present application.

[0057] In the specific implementation, the established finite element model of the Lingdingyang Bridge was subjected to modal analysis to obtain the vibration mode and frequency of the finite element model, and a comparative analysis was performed with existing literature data to verify the accuracy of the bridge model established in this paper.

[0058] Specifically, the dynamic characteristics of the model (hereinafter referred to as Model O) were analyzed, and the first six natural frequencies were compared with the frequencies of the finite element model in the first document (hereinafter referred to as Model A) and the finite element model in the second document (hereinafter referred to as Model B), as shown in Table 1-2. The first document may be the design drawing of the Lingdingyang Bridge, and the second document may be a research report on the wind resistance of the Lingdingyang Bridge. It is understandable that the first document and the second document may also be other documents with corresponding authoritative dynamic characteristics analysis, and no uniqueness limitation is made here.

[0059] Table 1-2 Comparison of the first six natural frequencies of Lingdingyang Bridge

[0060]

[0061] As can be seen from Table 1-2, the maximum relative error between this model and model A is 5.063%, and the maximum relative error between this model and the first six natural frequencies of model B is 5.618%. The relative errors of the natural frequencies of this model and both models A and B are small and within a reasonable range, verifying the rationality of this model. It is understandable that if the error is large, for example, the error is greater than 10%, the finite element model is judged to be unreasonable.

[0062] Step 203: Acquire historical disaster data of the location of the target bridge.

[0063] For example, the historical disaster data may be public data corresponding to the target bridge, which may be obtained from the Internet or literature.

[0064] Step 204: Determine one or more risk events that affect the safety of the target bridge based on the historical disaster data.

[0065] In a possible embodiment, determining one or more risk events that affect the safety of the target bridge based on the historical disaster data includes: counting the number of occurrences of each disaster in the historical disaster data to obtain statistical results; and selecting one or more risk events whose occurrence frequency is greater than a first preset frequency from the statistical results.

[0066] In the specific implementation, statistics of bridge risk accidents occurring at home and abroad are collected, and these accidents are analyzed to obtain the main factors leading to bridge risk accidents. This paper analyzes the occurrence of wind accidents, earthquake accidents, fire accidents, water conservancy accidents, collision accidents, and fatigue accidents. The wind disaster with the highest frequency is determined. It is understandable that the disaster with the greatest impact on the bridge can also be selected as the risk event, for example, the earthquake disaster with the greatest impact on the safety of the target bridge.

[0067] It can be seen that in this embodiment, the determination of risk events is achieved.

[0068] Step 205: determine the key components of each bridge part from the N bridge parts of the finite element model that have passed the accuracy verification, and obtain M key components.

[0069] For example, M is a positive integer greater than N.

[0070] In a possible embodiment, determining the key components of each bridge part from N bridge parts of the finite element model that has been verified for accuracy to obtain M key constructions includes: simulating load data of the one or more risk events; inputting the load data into the finite element model to obtain response data of each component unit of each bridge part of the N bridge parts to the load data; determining the key components of each bridge part based on the response data to obtain M key constructions, wherein the M key components are bridge components whose responses are greater than a preset threshold.

[0071] In the specific implementation, in bridge risk assessment, it is difficult to calculate the failure probability of all components, and it is also unnecessary. In the current bridge risk monitoring, the response of the key positions of the bridge is also monitored. Therefore, only the failure probability calculation of the components with higher risks in each component can meet the risk assessment requirements. Therefore, in order to reduce the amount of calculation, only the key components on one side of the bridge are taken for failure probability calculation.

[0072] Specifically, in this embodiment, five main bridge parts are selected, namely, bridge towers, bridge piers, main beams, main cables and hangers. These parts are the main structures that affect bridge safety, and each bridge part also includes multiple components. After the finite element model passes the accuracy verification, wind loads and seismic wave loads are applied to multiple components of each bridge part, respectively, and the response data of each component to the wind load and seismic wave load are obtained, thereby determining M key components.

[0073] In the specific implementation, based on the wind measurement data of the Shenzhen-Zhongshan Channel wind tower and the Zhongshan National Meteorological Station, and referring to the wind conditions of the Hong Kong-Zhuhai-Macao Bridge, according to the meteorological observation and wind parameter research of the bridge site of the Shenzhen-Zhongshan cross-river channel project, it is found that the offshore wind speed of the Lingdingyang Bridge of the Shenzhen-Zhongshan Channel is 10% higher than the coastal wind speed, and the surface roughness coefficient α = 0.112; the wind profile coefficient of the Lingdingyang Bridge is 0.10 at the triangular island, and the average wind speed of three recurrence periods of 10 minutes at the bridge site of the Lingdingyang Bridge at an elevation of 10m is shown in Table 2-1. According to the "Highway Bridge Wind Resistance Design Code", under the bridge design level, the relationship between the frequency of natural wind and the recurrence period is:

[0074] F=1 / T

[0075] P=1-(1-F) N

[0076] Where T is the recurrence period (years); N is the designed life (years); F is the frequency of natural wind; and P is the probability of natural wind occurring within the design life.

[0077] The average annual maximum wind speed of Lingdingyang Bridge is shown in Table 2-1.

[0078] Table 2-1 Average annual maximum wind speed at 10m above Lingdingyang Bridge for 10 minutes

[0079]

[0080] According to the "Highway Bridge Wind Resistance Design Code", the average wind profile of the sea surface wind field can be described by an exponential law:

[0081]

[0082] Where U is the wind speed at the bridge height d; α is the surface roughness coefficient at the bridge site; f is the wind risk coefficient, which is taken as 1.05.

[0083] The linear filtering method of the autoregressive model is used to simulate the wind speed time history of the pulsating wind. The wind speed spectrum adopts the Davenport spectrum, because the Davenport spectrum is the response spectrum adopted by my country's standards and is more in line with my country's wind environment.

[0084]

[0085]

[0086] Where f is the frequency and K is the von Kaman constant, which is usually 0.4.

[0087] Specifically, the fluctuating wind speed at the main beam of the Lingdingyang Bridge is simulated based on the Davenport spectrum model to obtain the corresponding power spectrum and wind speed time history diagram.

[0088] In earthquake engineering, the frequent earthquake intensity refers to the earthquake intensity value that may be encountered within a 50-year period with a probability of exceeding 63%, and the rare earthquake intensity refers to the earthquake intensity value that may be encountered within a 50-year period with a probability of exceeding 2% to 3%

[76] .

[0089] The relationship between earthquake exceedance probability and return period is:

[0090] P n =1-(1-p) n

[0091] Where Pn is the exceedance probability; p is the recurrence period; and n is the design life.

[0092] The 50-year exceedance probability of 2% to 3% is equivalent to an earthquake that occurs once every 1600 to 2500 years. For bridges, due to their importance, they need to ensure that they are safe enough, so the seismic performance requirements under rare earthquakes are considered during design, and the design life of bridges is generally 100 years. Based on this, this paper selects an earthquake action with a 100-year exceedance probability of 5%, that is, a recurrence period of 1950, to conduct risk assessment on bridges, and evaluates whether the bridge is still in a safe state when a rare earthquake occurs. At the same time, considering the need to evaluate the impact of small earthquakes with a high probability of occurring during the operation period of the bridge on the safety risk of the bridge, this paper selects an earthquake action with a exceedance probability of 63%, that is, a once-in-a-century earthquake action, to assess the safety risk of the bridge. At the same time, in order to provide an important reference and basis for the risk assessment of bridges under earthquake actions that exceed their defense requirements, it is necessary to assess the risk under earthquake actions that are greater than the defense requirements of the bridge. Therefore, this paper selects earthquake actions with a exceedance probability of greater than 5%, that is, earthquake actions with a exceedance probability of 2%, to conduct risk assessment on bridges.

[0093] Specifically, the earthquake model is constructed by the trigonometric series method to simulate the corresponding earthquake waves. Usually, the fitted earthquake waves need to be baseline adjusted. The baseline adjustment can solve the problem of baseline drift when the displacement curve is obtained by integrating the acceleration time history curve. The baseline adjustment method commonly used at present is the least squares method. This paper also considers using the least squares method to adjust the baseline of earthquake acceleration. Taking the earthquake condition with a return period of 2% in a hundred years as an example, the time history comparison after baseline adjustment is obtained.

[0094] Furthermore, wind loads and seismic waves are simulated in the above manner and applied to each component of the bridge, and finally key components that meet the requirements are screened out based on the response results.

[0095] It can be seen that in this embodiment, the determination of the key construction is achieved.

[0096] Step 206: assign multiple damage indicators to each key component in the M key components.

[0097] The multiple damage indicators correspond one-to-one to the multiple damage states.

[0098] In specific implementation, establishing risk assessment indicators for wind and earthquake disasters for bridges is actually to determine reasonable and acceptable risk limits. At present, there are many risk acceptance criteria, such as the ALARP criteria, risk matrix, etc. However, different risk acceptance criteria have their scope of use and characteristics. For earthquake and wind disasters in bridge engineering, there is currently no scientific and reasonable risk acceptance criterion. Based on the ALARP principle, this paper adopts a method that combines quantitative risk probability and qualitative risk assessment to express the evaluation indicators of bridge earthquake and wind damage. The ALARP standard is as follows: Figure 3 shown.

[0099] In the ALARP risk acceptance criteria, ALARP represents the maximum acceptable risk, that is, if the risk level exceeds this area, it is an unacceptable risk level. Different risk levels have different risk probability limits, but at present, there is no recognized acceptable risk limit for bridge earthquake disaster risk assessment. This article refers to the commonly used risk probability allocation standards and research results in the references, and divides the probability of safety risk assessment based on highway bridge and tunnel engineering design, as shown in Table 3-1.

[0100] Table 3-1 Risk probability limit range

[0101]

[0102] The risk level of bridge projects can be divided into four levels, and the risk level is divided according to the risk acceptance level of cross-sea bridges, as shown in Table 3-2.

[0103] Table 3-2 Risk level acceptance criteria

[0104]

[0105] The greater the possibility of the structural integrity level, the smaller the possibility of other structural damage levels, and the safer the structure. The bridge damage level is divided into four levels, and its risk level is also four levels. According to the risk acceptability criteria, the risk assessment matrix of the bridge is determined, as shown in Table 3-3.

[0106] Table 3-3 Risk Assessment Matrix

[0107]

[0108] Step 207: Calculate the failure probability of each key component in each of the corresponding multiple damage states to obtain multiple first failure probabilities.

[0109] In a possible embodiment, the calculation of the failure probability of each key component in each of the corresponding multiple damage states to obtain multiple first failure probabilities includes: determining the first state function of the multiple key components; calculating the limit state equation based on the first state function; obtaining the structural material parameters of the target bridge; randomly sampling multiple groups of parameter fitting response surfaces from the structural material parameters to obtain a response surface equation; normalizing the random variables of the response surface equation based on the JC method to obtain a second state function; for each key component, performing the following processing to obtain multiple first failure probabilities: calculating the reliability of the corresponding key component based on the second state function; and obtaining the first failure probability of the key component based on the reliability.

[0110] In specific implementation, reliability can be used to evaluate the safety performance of a structure in a certain state and to conduct a safety evaluation of the state of the structure. Structural reliability is generally affected by the structure itself or external factors, such as the random uncertainties of the structure's own material strength, geometric dimensions, and external loads. These factors can be regarded as random variables when calculating structural reliability. The formula is:

[0111] Z = g(x1, x2, ..., x n ) = g(R, S) = RS

[0112] Where S is the structural response, such as stress, displacement, etc.; R is the structural resistance, such as section strength, stiffness, etc.; x i is a random parameter, where i=1,2,…,n.

[0113] When Z = 0, it is the structural limit state equation, as shown below:

[0114] Z = g(x1, x2, ..., x n )=0

[0115] Taking the Lingdingyang Bridge as an example, the limit state equation for the reliability analysis of the Lingdingyang Bridge is defined as:

[0116] Z(X)=R i -S imax

[0117] Where Z(X) represents the limit state function of the i-th unit; X represents the random vector; R i represents the resistance of the ith unit, which is the stress of the unit cross section for the main beam, hanger and main cable, and the curvature of the cross section for the bridge tower and pier; S imax It represents the maximum structural response of the i-th unit. For main beams, hangers and main cables, it is the maximum stress of the unit section under wind load and seismic load. For bridge towers and piers, it is the maximum section curvature under wind load and seismic load.

[0118] The Lingdingyang Bridge was completed in ANSYS using the response surface method to fit the functional function, and was implemented using the reliability analysis module in ANSYS. The uncertainty of the structural parameters was taken into account. The variability of the elastic modulus, the uncertainty of the cross-sectional area, the uncertainty of the material density, and the uncertainty of the bending moment of inertia were considered in the analysis of the Lingdingyang Bridge. By defining the output variables and the output variables, the functional function fitted by the sampled points was used to solve the reliability of the components using the compiled JC method solution program. The calculation process is shown in the above limit state equation.

[0119] The random variable parameters of the structural materials of the Lingdingyang Bridge are referenced from relevant literature and taken based on actual experience, as shown in Table 4-1.

[0120] Table 4-1 Random parameters of materials for Lingdingyang Bridge

[0121]

[0122] Specifically, this paper uses ANSYS finite element software to calculate the failure probability of the key components determined in the previous chapter. Taking the No. 40 hanger as an example, the maximum stress response surface of the No. 40 hanger fitted by the response surface method is as follows: Figure 4 As shown in Figure 1, it represents the three-dimensional relationship between the maximum stress of the hanger and the elastic modulus and material density. According to the response surface fitted by ANSYS, the response surface equation can be obtained. According to the response surface equation, the failure probability of the structure under different given damage states can be obtained using the JC method.

[0123] Due to the strong nonlinear characteristics of large structures, the structural function function cannot be simply expressed directly. It is usually an implicit function and difficult to express directly. The response surface method can be used to approximate the function function of complex structures with a simple and explicit formula. The selection of the function form is crucial when using the response surface method. The function form should meet the following two points: first, the function form should be as simple as possible and be able to express the true function; second, the number of undetermined coefficients in the selected function should not be too large to avoid a large workload during analysis and calculation. The calculation process of the response surface method is as follows:

[0124] P f =P(Z(x)≤0)

[0125] Where x = (x1, x2, ..., x n ) T is a basic random variable; P(·) represents the probability.

[0126] Z is the response of the structure under inputs such as load or material uncertainty, which is called the response surface. Z can be represented by a quadratic perfect polynomial, as shown in the following formula:

[0127]

[0128] Where, X i = (i = 1, 2, ..., n) is the basic random design variable, a, bi, c i is the coefficient to be determined.

[0129] The specific steps of fitting the limit state equation by response surface method are as follows:

[0130] (1) Assume that the initial Generally take the mean point.

[0131] (2) Calculation function and 2n+1 response values ​​are obtained.

[0132] (3) Using 2n+1 response values, we can solve the unknown parameters a and b of the response surface. i , c ij , the approximate performance function expressed as a quadratic polynomial is obtained, thereby determining the limit state equation.

[0133] In actual engineering, random variables of structural components do not all obey normal distribution. The JC method can be used to solve this problem. The calculation process is as follows:

[0134]

[0135]

[0136] In the formula, is the standard normal distribution function, is the inverse function of the standard normal distribution function, u Yi and σ Yi is the mean and standard deviation of the normally distributed random scalar iY, is the distribution function value of the non-normal variable, is the probability density function value of the non-normal variable.

[0137] After normalization, the functional formula is transformed into:

[0138] Z=g Y (Y1, Y2, ..., Y n )

[0139] According to the functional function, the following three formulas can be obtained:

[0140]

[0141]

[0142]

[0143] The reliability index β of the key component can be calculated through the above three formulas, and then the corresponding failure probability can be obtained through the reliability index, wherein the failure probability = 1-reliability index.

[0144] For bridge risk identification and risk probability estimation, this paper adopts a quantitative calculation method and solves the risk probability based on the finite element response surface method. First, a bridge model is established, and the bridge is divided into five parts: bridge tower, pier, main cable, main beam and hanger. The key components of each part of the bridge are obtained based on the earthquake and wind disaster response analysis of the bridge. Then, based on the ANSYS reliability analysis module, the material uncertainty of each bridge structure is considered, and the response surface method is used to fit the state function function of the key components of the bridge. Then, the JC method is used to calculate the first failure probability of the key components.

[0145] It can be seen that in this embodiment, the calculation of the first failure probability of the key components is achieved.

[0146] Step 208: Calculate the failure probability of the target bridge according to each first failure probability of the multiple first failure probabilities to obtain multiple second failure probabilities.

[0147] This section applies the Bayesian network method to a long-span bridge structure system that is subjected to multiple loads, namely, seismic loads and wind loads. Three cases are taken for wind loads and seismic loads respectively. The wind load size is selected according to the recurrence period of 10 years, 50 years, and 100 years mentioned above, and the seismic load is selected according to the exceedance probability of 2%, 5%, and 63% mentioned above. The structural components are divided into bridge towers, piers, main beams, hangers, and main cables. According to the quantitative analysis of the damage indicators of the bridge structure, the bridge tower has four damage states, the main beam has three damage states, the hanger has two damage states, and the main cable has four damage states. At the same time, since the bridge is a structural system, this paper considers the relationship between its key structures as a series relationship, that is, a certain damage state of a key structure represents the occurrence of this damage state of the entire bridge. The corresponding relationship between the damage states of the entire bridge components is shown in Table 5-1.

[0148] Table 5-1 Damage index of bridge components

[0149]

[0150] It can be seen that in this embodiment, the calculation of the first failure probability of the key components is achieved.

[0151] Step 209: Determine the risk level of the target bridge according to the multiple first failure probabilities and the multiple second failure probabilities.

[0152] In the specific implementation, the established Bayesian network model is as follows Figure 5As shown, F represents wind load, E represents earthquake load; H represents hanger, with three key components, namely H1, H2, H3, each key component has two damage states, namely Hi1, Hi2, where i = 1, 2, 3; C represents main cable, with two key components, namely C1, C2, each key component has four damage states, namely Ci1, Ci2, Ci3, Ci4, where i = 1, 2; G represents main beam, with two key components, namely G1, G2, each key component has three damage states, namely Gi1, Gi2, Gi3, where i = 1, 2; T represents bridge tower, with three key components, namely T1, T2, T3, each key component has four damage states, namely Ti1, Ti2, Ti3, Ti4, where i = 1, 2, 3; P represents bridge pier, with two key components, namely P1, P2, each key component has four damage states, namely Pi1, Pi2, Pi3, Pi4, where i = 1, 2.

[0153] Specifically, the failure probability of the entire bridge under various load conditions is calculated based on the Bayesian network as shown in Table 6-1. F0 and E0 indicate that there is no wind load and earthquake action.

[0154] Table 6-1 Full bridge failure probability

[0155]

[0156] As can be seen from Table 6-1, the failure probability of the whole bridge calculated by the Bayesian network is greater than the failure probability of a single component, so the risk of the whole bridge under the disaster must be greater than the risk of only considering part of the bridge structure, so it is necessary to consider the risk of the whole bridge under the disaster risk. At the same time, because the components are connected in series, the components with greater damage probability have a greater impact on the overall damage of the bridge. Under the action of wind load, the damage of the main beam has the greatest impact on the overall damage of the bridge, and under the action of earthquake, the damage of the bridge tower and pier has the greatest impact on the overall damage of the bridge.

[0157] Based on the risk assessment matrix and the calculated probability of failure of the entire bridge, the risk level of the Lingdingyang Bridge under different disasters can be obtained, as shown in Table 6-2.

[0158] Table 6-2 Results of risk assessment of the entire bridge

[0159]

[0160] It can be seen from the risk assessment results in Table 6-2 that the maximum risk of the Lingdingyang Bridge under wind load is level one risk. Under the maximum wind load selected in this article, that is, the wind load with a recurrence period of 100 years, the Lingdingyang Bridge is in a safe state.

[0161] The earthquake actions selected in this paper are those with exceedance probabilities of 63%, 5% and 2%. It can be seen from Table 5-4 that the risk level of the Lingdingyang Bridge is level 1 under the earthquake action with a exceedance probability of 63%, and the bridge is in a safe state. When the exceedance probability is 5%, the probability of slight damage to the bridge structure is high, and the maximum risk level of the Lingdingyang Bridge is level 3 risk. When the exceedance probability is 2%, the probability of slight damage is very high, but the maximum risk level of the bridge is also at the level of level 3 risk. At the same time, when wind and earthquake loads act simultaneously, the maximum risk level is also level 3 risk.

[0162] According to the overall design report of the Lingdingyang Bridge, the design life of the Lingdingyang Bridge is 100 years, and its seismic fortification standard is two levels. Level 1 is a 100-year exceedance probability of 10%. Under the level 1 earthquake, the bridge towers, piers, main beams, main cables and suspenders are all in an undamaged state, and vehicles operate normally; Level 2 is a 100-year exceedance probability of 4%. Under the level 2 earthquake, the bridge towers, piers, main beams and main cables are slightly damaged, and the suspenders are in an undamaged state. The bridge can continue to be used after simple repairs. According to the risk assessment criteria, the risk is determined by the risk consequences and the probability of risk occurrence. Although the damage to the bridge is minor, due to its high probability of occurrence, its risk may also be high. When the structure is slightly damaged but the probability of occurrence exceeds 0.03, its risk level is level 3 risk. Therefore, when an earthquake with a exceedance probability of 4% occurs, the Lingdingyang Bridge is in a level 3 risk state.

[0163] It is understandable that the Bayesian network program proposed above has been implemented using the Bayesian network toolbox. The network structures presented by the Bayesian network model can be generated using simple algorithms, which can use the basic network structure to automatically generate the Bayesian network of more complex systems.

[0164] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that in order to realize the above functions, the mobile electronic device includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiment provided in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0165] The embodiment of the present application can divide the electronic device into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0166] See also Figure 6 The present application also provides a cross-sea bridge risk assessment device 60, comprising:

[0167] A creation unit 601 is used to establish a finite element model of a target bridge, wherein the finite element model includes N bridge parts of the bridge, where N is a positive integer;

[0168] An analysis unit 602 is used to perform a dynamic analysis on the finite element model to verify the accuracy of the finite element model;

[0169] An acquisition unit 603 is used to acquire historical disaster data of the location of the target bridge;

[0170] A first determining unit 604 is used to determine one or more risk events that affect the safety of the target bridge according to the historical disaster data;

[0171] A second determining unit 605 is used to determine the key components of each bridge part from the N bridge parts of the finite element model that have passed the accuracy verification, and obtain M key components, where M is a positive integer greater than N;

[0172] A division unit 606 is used to divide each key component of the M key components into multiple damage indicators, wherein the multiple damage indicators correspond to multiple damage states one by one;

[0173] The calculation unit 607 is used to calculate the failure probability of each key component in each of the corresponding multiple damage states to obtain multiple first failure probabilities; and calculate the failure probability of the target bridge according to each of the multiple first failure probabilities to obtain multiple second failure probabilities;

[0174] The third determining unit 608 is configured to determine the risk level of the target bridge according to the multiple first failure probabilities and the multiple second failure probabilities.

[0175] It can be seen that in the embodiment of the present application, a finite element model of the target bridge is first established, the finite element model includes N bridge parts of the bridge, N is a positive integer; then the finite element model is subjected to dynamic analysis to verify the accuracy of the finite element model; then the historical disaster data of the location of the target bridge is obtained; then one or more risk events affecting the safety of the target bridge are determined according to the historical disaster data; the key components of each bridge part are determined from the N bridge parts of the finite element model that have been verified by accuracy, and M key structures are obtained, M is a positive integer greater than N; then multiple damage indicators are divided for each key structure in the M key components, and the multiple damage indicators correspond to multiple damage states one by one; the failure probability of each key component in each of the corresponding multiple damage states is calculated to obtain multiple first failure probabilities; the failure probability of the target bridge is calculated according to each first failure probability in the multiple first failure probabilities to obtain multiple second failure probabilities; finally, the risk level of the target bridge is determined according to the multiple first failure probabilities and the multiple second failure probabilities. In this way, bridge risk assessment under the action of multiple risk factors is realized.

[0176] In a possible embodiment, in the aspect of establishing the finite element model of the target bridge, the creation unit 601 is specifically used to: obtain structural data of the target bridge; and establish the finite element model according to the structural data.

[0177] In a possible embodiment, in the aspect of performing dynamic analysis on the finite element model to verify the accuracy of the finite element model, the analysis unit 602 is specifically used to: perform dynamic characteristic analysis on the finite element model to obtain a first vibration shape and a first frequency of the finite element model; and compare and analyze the first vibration shape and the first frequency with the actual vibration shape and the actual frequency of the target bridge, respectively, to verify the accuracy of the bridge model constructed in this article.

[0178] In a possible embodiment, in terms of determining the key components of each bridge part from the N bridge parts of the finite element model that has been verified for accuracy to obtain M key constructions, the second determination unit 605 is specifically used to: simulate the load data of the one or more risk events; input the load data into the finite element model to obtain response data of each component unit of each bridge part of the N bridge parts to the load data; determine the key components of each bridge part based on the response data to obtain M key constructions, wherein the M key components are bridge components whose responses are greater than a preset threshold.

[0179] In a possible embodiment, the aspects of one or more risk events that affect the safety of the target bridge are determined based on the historical disaster data, and the first determination unit 604 is specifically used to: count the number of occurrences of each disaster in the historical disaster data to obtain statistical results; and select one or more risk events whose occurrence frequency is greater than a first preset frequency from the statistical results.

[0180] In a possible embodiment, the failure probability of each key component in each of the corresponding multiple damage states is calculated to obtain multiple first failure probabilities, and the calculation unit 607 is specifically used to: determine the first state function function of the multiple key components; calculate the limit state equation according to the first state function function; obtain the structural material parameters of the target bridge; randomly sample multiple groups of parameter fitting response surfaces from the structural material parameters to obtain a response surface equation; normalize the random variables of the response surface equation based on the JC method to obtain a second state function function; for each key component, perform the following processing to obtain multiple first failure probabilities: calculate the reliability of the corresponding key component according to the second state function function; and obtain the first failure probability of the key component according to the reliability.

[0181] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0182] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0183] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0184] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0185] In the several embodiments provided in the present application, it should be understood that the disclosed methods, devices and systems can be implemented in other ways. For example, the device embodiments described above are merely schematic; for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0186] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0187] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may be physically included separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0188] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a volatile memory or a non-volatile memory. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM) or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM) and direct RAM bus RAM (DR RAM). Various media that can store program code are available.

[0189] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions without departing from the spirit and scope of the present invention, and can make various changes and modifications, including the combination of the above-mentioned different functions and implementation steps, including software and hardware implementation methods, all of which are within the scope of protection of the present invention.

Claims

1. A risk assessment method for a cross-sea bridge, characterized in that: Used in electronic equipment, including: Establishing a finite element model of a target bridge, wherein the finite element model includes N bridge parts of the bridge, where N is a positive integer; Performing dynamic analysis on the finite element model to verify the accuracy of the finite element model; Acquire historical disaster data of the location of the target bridge; Determine one or more risk events that affect the safety of the target bridge according to the historical disaster data; Determine the key components of each bridge part from the N bridge parts of the finite element model that has been verified for accuracy, and obtain M key components, where M is a positive integer greater than N; Assigning a plurality of damage indicators to each of the M key components, wherein the plurality of damage indicators correspond one-to-one to a plurality of damage states; Calculating the failure probability of each key component in each of the corresponding multiple damage states to obtain multiple first failure probabilities includes: Determining a first state function of the M key components; Calculate the limit state equation based on the first state function; Acquiring structural material parameters of the target bridge; Randomly sampling multiple groups of parameters from the structural material parameters to fit the response surface and obtain the response surface equation, including: determining the response surface formula Where, is a basic random variable; P(·) represents the probability, Z is the response of the structure under load or material uncertainty input, and Z is represented by a quadratic perfect polynomial, and is obtained , where are basic random design variables, a, b i , c ij is the coefficient to be determined; The specific steps of fitting the response surface equation according to the response surface formula are as follows: Assume that the initial , take the mean point; calculate the function and , and get 2n+1 response values; use 2n+1 response values ​​to solve the unknown parameters a and b of the response surface i , c ij , the approximate functional function expressed by a quadratic polynomial is obtained, thereby determining the response surface equation; Normalizing the random variables of the response surface equation based on the JC method to obtain a second state function function; performing the following processing for each key component to obtain a plurality of first failure probabilities: calculating the reliability of the corresponding key component according to the second state function function; obtaining the first failure probability of the key component currently being processed according to the reliability; Calculating the failure probability of the target bridge according to each first failure probability of the multiple first failure probabilities to obtain multiple second failure probabilities; A risk level of the target bridge is determined according to the plurality of first failure probabilities and the plurality of second failure probabilities.

2. The method according to claim 1, characterized in that The finite element model of the target bridge is established, including: Acquiring structural data of the target bridge; The finite element model is established according to the structural data.

3. The method according to claim 1, characterized in that The performing of dynamic analysis on the finite element model to verify the accuracy of the finite element model includes: Performing dynamic characteristic analysis on the finite element model to obtain a first vibration shape and a first frequency of the finite element model; The first vibration mode and the first frequency are compared and analyzed with the actual vibration mode and the actual frequency of the target bridge respectively to verify the accuracy of the finite element model.

4. The method according to claim 1, characterized in that: The key components of each bridge part are determined from the N bridge parts of the finite element model that has been verified for accuracy, and M key components are obtained, including: Simulating load data for the one or more risk events; Inputting the load data into the finite element model to obtain response data of each component unit of each bridge position in the N bridge positions to the load data; Based on the response data, key components of each bridge part are determined to obtain M key components, wherein the M key components are bridge components whose responses are greater than a preset threshold.

5. The method according to claim 1, characterized in that The step of determining one or more risk events that affect the safety of the target bridge according to the historical disaster data includes: Counting the number of occurrences of each disaster in the historical disaster data to obtain statistical results; One or more risk events having an occurrence frequency greater than a first preset frequency are selected from the statistical results.

6. A risk assessment device for a cross-sea bridge, characterized in that: include: A creation unit is used to establish a finite element model of a target bridge, wherein the finite element model includes N bridge parts of the bridge, where N is a positive integer; An analysis unit, used for performing dynamic analysis on the finite element model to verify the accuracy of the finite element model; An acquisition unit, used for acquiring historical disaster data of the location of the target bridge; A first determining unit, configured to determine one or more risk events that affect the safety of the target bridge according to the historical disaster data; A second determining unit is used to determine the key components of each bridge part from the N bridge parts of the finite element model that has passed the accuracy verification, to obtain M key components, where M is a positive integer greater than N; A division unit, used for dividing each of the M key components into a plurality of damage indicators, wherein the plurality of damage indicators correspond to a plurality of damage states one by one; A calculation unit is used to calculate the failure probability of each key component in each of the corresponding multiple damage states to obtain multiple first failure probabilities, including: determining the first state function function of the M key components; calculating the limit state equation according to the first state function function; obtaining the structural material parameters of the target bridge; randomly sampling multiple groups of parameters from the structural material parameters to fit the response surface to obtain the response surface equation, including: determining the response surface formula Where, is a basic random variable; P(·) represents the probability, Z is the response of the structure under load or material uncertainty input, and Z is represented by a quadratic perfect polynomial, and is obtained , where are basic random design variables, a, b i , c ij is the coefficient to be determined; Fitting the response surface equation according to the response surface formula includes: assuming that the initial , take the mean point; calculate the function and , and get 2n+1 response values; use 2n+1 response values ​​to solve the unknown parameters a and b of the response surface i , c ij , the approximate functional function expressed by a quadratic polynomial is obtained, thereby determining the response surface equation; Based on the JC method, the random variables of the response surface equation are normalized to obtain a second state function function; for each key component, the following processing is performed to obtain a plurality of first failure probabilities: the reliability of the corresponding key component is calculated according to the second state function function; the first failure probability of the key component is obtained according to the reliability; and the failure probability of the target bridge is calculated according to each first failure probability of the plurality of first failure probabilities to obtain a plurality of second failure probabilities; The third determining unit is used to determine the risk level of the target bridge according to the multiple first failure probabilities and the multiple second failure probabilities.

7. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute instructions of the steps in the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that A non-transitory computer-readable storage medium storing a computer program, wherein the computer program can be operated to cause a computer to execute instructions of the steps in any one of the methods of claims 1-5.

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