Risk assessment method and device for influence of geological disasters on bridge cluster

Abstract

The invention provides a risk assessment method and device for a bridge cluster influenced by geological disasters. The method comprises the following steps: constructing a bridge three-dimensional live-action model in which a geological terrain and a bridge cluster are combined; determining a geological disaster susceptibility evaluation result based on the bridge three-dimensional live-action model; based on the geological disaster susceptibility evaluation result, geological parameters and collapse and slip disaster object parameters of the disaster high-risk area are determined, based on the geological parameters and the collapse and slip disaster object parameters, the motion trail of collapse and slip disaster objects is determined, and based on the motion trail, collided position data of the bridge is determined; based on the motion trail and the impacted position data, impact parameters of the collapse and slip disaster object at the impact moment are determined; and inputting the impact parameters into a trained residual bearing capacity analysis model to obtain the residual bearing capacity of the bridge, calculating a reliability index of the bridge based on the residual bearing capacity, and determining a safety level evaluation result of the bridge based on the reliability index. According to the invention, the accuracy of the risk assessment result of the bridge cluster affected by geological disasters is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge safety assessment, and in particular to a method and device for assessing the risk of a bridge cluster affected by geological disasters. Background Art

[0002] In recent years, bridge accidents caused by frequent geological disasters (land collapse, mudslides, etc.) have often occurred. Geological disasters are easily induced by external factors, are sudden, unstable and destructive, posing a great threat to the service safety of bridge structures.

[0003] At present, when conducting risk assessment on the impact of geological disasters on bridge clusters, geological disasters and bridge structural performance are often analyzed independently; since geological engineering and bridge engineering belong to two different disciplines, when judging whether bridge components are impacted by disaster objects, geological disasters are often simulated separately first, and then the geographical coordinates of the disaster objects in the geological disaster simulation results are overlapped with the geographical coordinates of the bridges. Although this method can complete the risk analysis of bridges impacted by disaster objects, it does not effectively integrate geological disasters with bridge structures, resulting in inaccurate risk assessment results of bridge clusters affected by geological disasters. Therefore, how to improve the accuracy of risk assessment results of bridge clusters affected by geological disasters is a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method and device for assessing the risk of a bridge cluster affected by geological disasters, so as to eliminate or improve one or more defects existing in the prior art.

[0005] One aspect of the present invention provides a risk assessment method for bridge clusters affected by geological disasters, the method comprising:

[0006] Construct a three-dimensional realistic model of bridges that combines geological terrain with bridge clusters;

[0007] Determining a geological disaster susceptibility assessment result based on the three-dimensional real-scene model of the bridge, wherein the geological disaster susceptibility assessment result includes disaster point data, disaster distribution data, and disaster risk level;

[0008] Determine geological parameters and collapse and landslide hazard parameters of high-risk areas based on the geological disaster susceptibility assessment results, determine movement trajectories of collapse and landslide hazard objects based on the geological parameters and collapse and landslide hazard parameters, and determine impact position data of the bridge based on the movement trajectory of the collapse and landslide hazard objects;

[0009] Determining the impact parameters of the landslide hazard object at the time of impact based on the movement trajectory of the landslide hazard object and the impacted position data of the bridge;

[0010] The impact parameters are input into a trained residual bearing capacity analysis model to obtain the residual bearing capacity of the bridge, the reliability index of the bridge is calculated based on the residual bearing capacity, and the safety level assessment result of the bridge is determined based on the reliability index of the bridge.

[0011] In some embodiments of the present invention, constructing a three-dimensional real-scene model of bridges combining geological terrain and bridge clusters includes:

[0012] Obtain Tiantu satellite imagery, DEM digital elevation data and bridge structure diagrams within the preset range;

[0013] A BIM+GIS bridge three-dimensional real-scene model is constructed based on the Tiandi Map satellite image, DEM digital elevation data and bridge structure diagram.

[0014] In some embodiments of the present invention, determining the geological disaster susceptibility assessment result based on the three-dimensional real-life model of the bridge includes:

[0015] Acquire spatial distribution data of the three-dimensional real-scene model of the bridge, and obtain terrain factor data and distance factor data based on the spatial distribution data, wherein the spatial distribution data includes geological disaster point data, DEM digital elevation data, fault zone data, road data, and water system data;

[0016] The geological disaster susceptibility assessment result is determined based on the geological disaster point data, terrain factor data and distance factor data through the ALSA disaster susceptibility assessment tool.

[0017] In some embodiments of the present invention, determining the movement trajectory of the collapse and landslide hazard object based on the geological parameters and the collapse and landslide hazard object parameters, and determining the impact position data of the bridge based on the movement trajectory of the collapse and landslide hazard object includes:

[0018] Based on the geological parameters and the parameters of the landslide hazard object, the movement trajectory of the landslide hazard object is determined by a rockfall analysis tool;

[0019] The impact position data of the bridge is determined by the rockfall analysis tool based on the motion trajectory.

[0020] In some embodiments of the present invention, the method further comprises:

[0021] Constructing an initial bearing capacity analysis model and a sample data set, wherein the sample data in the sample data set includes impact parameter sample data and residual bearing capacity sample data;

[0022] The initial bearing capacity analysis model is pre-trained based on the sample data set to obtain a trained bearing capacity analysis model.

[0023] In some embodiments of the present invention, calculating the reliability index of the bridge based on the residual bearing capacity includes:

[0024] Based on the residual bearing capacity through the formula Calculate the damage index of the bridge;

[0025] Based on the calculated damage index, the failure probability of each level of damage of the bridge is calculated by the formula Pf=P(D>Di|A)=Nf / N;

[0026] Based on the calculated failure probability, the formula Calculate the reliability index of the bridge;

[0027] Among them, PR represents the residual bearing capacity, P represents the original bearing capacity, D represents the damage index of the bridge, Pf represents the failure probability of the i-level damage corresponding to the bridge, Di represents the i-level damage index, A represents the working condition event corresponding to the impact parameter combination, P(D>Di|A) represents the probability of the working condition event D>Di occurring under the condition of event A, Nf represents the number of sampled simulation samples whose damage degree exceeds the i-level damage index Di, N represents the number of Monte Carlo sampling, and β represents the reliability index.

[0028] In some embodiments of the present invention, determining the safety level assessment result of the bridge based on the reliability index of the bridge includes:

[0029] Determine the value range of reliability index corresponding to each safety level;

[0030] The actual safety level corresponding to the reliability index of the bridge is determined based on the reliability index value range corresponding to each safety level.

[0031] In some embodiments of the present invention, the method further comprises:

[0032] Based on the geological parameters, the parameters of the collapse and landslide hazard object, the movement trajectory of the collapse and landslide hazard object, the impact position data of the bridge and the impact parameters, the progressive collapse process of the bridge is simulated by discrete element particle flow software;

[0033] The safety level assessment result is verified based on the simulation result.

[0034] According to another aspect of the present invention, a risk assessment device for a bridge cluster affected by geological disasters is also disclosed. The device includes a processor, a memory, and a computer program stored in the memory. The processor is used to execute the computer program. When the computer program is executed, the device implements the steps of the method described in any of the above embodiments.

[0035] According to yet another aspect of the present invention, a computer-readable storage medium is disclosed, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0036] The risk assessment method and device for bridge clusters affected by geological disasters disclosed in the above-mentioned embodiments of the present invention conducts geological disaster susceptibility assessment through a three-dimensional real-life model of the bridge, and determines the movement trajectory of the collapse and landslide hazard object based on the geological disaster susceptibility assessment result, and further determines the impact position data of the bridge and the impact parameters of the collapse and landslide hazard object, and finally evaluates the safety level of the bridge based on the residual bearing capacity analysis model. This method analyzes the risk of bridge clusters being affected by geological disasters based on a three-dimensional real-life model of the bridge that combines geological terrain with bridge clusters, that is, it integrates the two disciplines of geological engineering and bridge engineering, integrates geological disasters with bridge structures for analysis, and accurately and quickly assesses the risk of bridges being affected by geological disasters; therefore, this application not only improves the accuracy of the risk assessment results of bridge clusters being affected by geological disasters, but also improves the disaster resistance and prevention capabilities of bridge structures.

[0037] Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention may be achieved and obtained by the structures specifically indicated in the specification and the accompanying drawings.

[0038] Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of this application, and do not constitute a limitation of the present invention. The components in the drawings are not drawn to scale, but are only for illustrating the principles of the present invention. In order to facilitate the illustration and description of some parts of the present invention, the corresponding parts in the drawings may be enlarged, that is, they may become larger relative to other components in the exemplary device actually manufactured according to the present invention. In the drawings:

[0040] Figure 1 A schematic flow chart of a method for risk assessment of a bridge cluster affected by geological disasters according to an embodiment of the present application.

[0041] Figure 2 This is a flow chart of a method for risk assessment of a bridge cluster affected by geological disasters according to another embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0043] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0044] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0045] It should also be noted here that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection with an intermediary, not only to a wired connection, but also to a wireless connection, and the specific connection can be changed based on the actual application scenario.

[0046] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0047] Figure 1 FIG. 1 is a flow chart of a method for risk assessment of a bridge cluster affected by geological disasters according to an embodiment of the present application. Figure 1 As shown, the risk assessment method for bridge clusters affected by geological disasters at least includes steps S10 to S50.

[0048] Step S10: constructing a three-dimensional real-scene model of bridges that combines geological terrain with bridge clusters.

[0049] In this step, a three-dimensional real-scene model of the bridge is constructed, and the three-dimensional real-scene model of the bridge is used to evaluate the susceptibility of geological disasters and to analyze the movement trajectory of landslide hazards.

[0050] Exemplarily, a three-dimensional real-life model of a bridge can be constructed based on the fusion technology of BIM and GIS. The three-dimensional real-life model of the bridge in this embodiment is a three-dimensional real-life model of a BIM+GIS bridge. Specifically, the construction of a three-dimensional real-life model of a bridge combining geological terrain with a bridge cluster may include the following steps: obtaining a satellite image of a world map, DEM digital elevation data, and a bridge structure diagram within a preset range; constructing a three-dimensional real-life model of a BIM+GIS bridge combining geological terrain with a bridge cluster based on the satellite image of the world map, the DEM digital elevation data, and the bridge structure diagram. That is, in this embodiment, a GIS three-dimensional real-life model is established based on the satellite image of the world map and the DEM digital elevation data; a refined three-dimensional BIM model of a bridge cluster is established based on the CAD drawing of the bridge; the BIM model is imported into the GIS three-dimensional real-life model to obtain a refined three-dimensional real-life digital model of a bridge of BIM+GIS. It can be understood that the generation of a three-dimensional real-life model of a bridge based on the BIM+GIS technology exemplified in this embodiment is only an example. In addition, other technologies can also be used to generate a three-dimensional real-life model of a bridge.

[0051] Step S20: Determine a geological disaster susceptibility assessment result based on the three-dimensional real-scene model of the bridge, wherein the geological disaster susceptibility assessment result includes disaster point data, disaster distribution data, and disaster risk level.

[0052] This step conducts a rapid assessment of disaster susceptibility based on the three-dimensional real-life model of the bridge to determine the location of disasters, disaster distribution, and disaster risk level.

[0053] Exemplarily, determining the geological disaster susceptibility assessment result based on the three-dimensional real-scene model of the bridge may specifically include: acquiring spatial distribution data of the three-dimensional real-scene model of the bridge, and obtaining terrain factor data and distance factor data based on the spatial distribution data, wherein the spatial distribution data includes geological disaster point data, DEM digital elevation data, fault zone data, road data and water system data; and determining the geological disaster susceptibility assessment result based on the geological disaster point data, terrain factor data and distance factor data using the ALSA disaster susceptibility assessment tool. In this embodiment, the ALSA disaster susceptibility assessment tool developed based on the GIS environment can be used to visualize the disaster area and disaster risk type. Specifically, first add data information such as geological disaster point data, DEM digital elevation data, fault zone data, road data and water system data, and perform 3D analysis and calculation based on the acquired DEM digital elevation data information through GIS software to obtain raster layer data of four "terrain factors", which are terrain slope, terrain curvature, terrain elevation and terrain slope; in addition, 3D analysis and calculation are performed based on the acquired fault zone data, road data and water system data through GIS software to obtain raster layer data of three "distance factors", which are distance from the fault, distance from the road and distance from the river. It can be understood that the GIS mentioned in this application represents geographic information system, and BIM represents building information model.

[0054] After determining the four terrain factor data and the three distance factor data, the seven continuous value factor grid layers are further divided into five levels through the data classification algorithm, and the data classification algorithm can be the Natural Breaks (Jenks) classification algorithm; further, click the "Start" button in the ALSA disaster susceptibility assessment tool, input the geological hazard point data, the seven continuous value factor grid layers after classification and the four coordinate values ​​of the processing range, generate only the frequency ratio grid of each factor and superimpose the frequency ratio of each factor to form a susceptibility grid; finally, the geological hazard susceptibility assessment results can be displayed in the BIM+GIS bridge three-dimensional real-life model.

[0055] Step S30: Determine the geological parameters and landslide hazard parameters of the high-risk disaster area based on the geological parameters and landslide hazard parameters, determine the movement trajectory of the landslide hazard based on the geological parameters and landslide hazard parameters, and determine the impact position data of the bridge based on the movement trajectory of the landslide hazard.

[0056] In this step, geological information and collapse and landslide hazard information of high-risk areas are obtained, geological disaster simulation is performed, and then disaster intrusion detection is performed to determine whether the bridge cluster is impacted by the hazard. Among them, geological parameters may include surface rock type, normal restitution coefficient Rn, tangential restitution coefficient Rt, friction angle ψ, etc., and collapse and landslide hazard parameters may include the number and mass of falling rocks, initial horizontal velocity Vxy, initial vertical velocity Vz, starting direction and angle, translation velocity, translation span, translation angle, and trajectory sampling distance, etc. In one embodiment, the geological parameters and collapse and landslide hazard parameters of the high-risk area mountain in the disaster susceptibility assessment results can be obtained through multi-source non-contact measurement technology.

[0057] Exemplarily, the movement trajectory of the landslide hazard object is determined based on the geological parameters and the landslide hazard object parameters, and the impact position data of the bridge is determined based on the movement trajectory of the landslide hazard object. Specifically, the following steps may be included: based on the geological parameters and the landslide hazard object parameters, the movement trajectory of the landslide hazard object is determined by a rockfall analysis tool; based on the movement trajectory, the impact position data of the bridge is determined by the rockfall analysis tool. Rockfall analysis tools, such as the RA (Rockfall Analyst) model, can obtain key disaster-causing information such as the movement path, frequency, speed, energy, and jump height of landslides in complex mountainous terrain areas, and realize regional landslide disaster risk assessment.

[0058] In some embodiments, a RA plug-in based on a GIS platform is constructed, and the movement path, speed, height, energy and other elements of the disaster object are quickly simulated based on the DEM digital elevation data, so as to obtain the possible movement trajectories of all landslide disaster objects in the high-risk area, and the velocity-distance profile (Velocity-distance profile) and elevation-distance profile (Elevation-distance profile) of each trajectory in the real scene can be obtained through the profile function (Profile function) of the RA plug-in. Finally, the frequency, speed, height, energy and risk assessment of landslide disaster objects are analyzed by using the rockfall frequency, rockfall speed, rockfall height, rockfall energy and rockfall risk assessment functions in the RA plug-in, and the movement trajectory with the frequency, speed, height, energy and risk assessment attributes of the landslide disaster object is output, and different movement paths can be visualized in different colors in the BIM+GIS bridge three-dimensional real scene model.

[0059] In the above embodiment, DEM digital elevation data is added to the BIM+GIS three-dimensional real-life model of the bridge, and rockfall simulation is performed based on the DEM digital elevation data. The speed-distance profile and elevation-distance profile of the movement trajectory of the landslide hazard object are obtained based on the profile function of the RA plug-in. When the landslide hazard object collides with the bridge in the three-dimensional real-life model of the bridge, the trajectory of the landslide hazard object suddenly changes, and the speed-distance curve and the elevation-distance curve also suddenly change, so that the impact position of the bridge can be accurately determined.

[0060] Step S40: Determine the impact parameters of the landslide hazard object at the impact moment based on the movement trajectory of the landslide hazard object and the impacted position data of the bridge.

[0061] When a component of a bridge in a bridge cluster is impacted by a landslide hazard, the motion parameters of the landslide hazard at the time of impact are obtained. Based on the RA plug-in mentioned in the above step S30, the motion trajectory of the landslide hazard (not limited to one) and the precise spatial position of the impacted part of the bridge component can be determined. Similarly, the impact parameters of this step can also be obtained based on the RA plug-in, that is, after simulating the movement of the landslide hazard based on the RA plug-in, the impact parameters such as the horizontal coordinates, height coordinates, speed, angle and rotation speed of the landslide hazard that hits the bridge can be directly output.

[0062] Step S50: input the impact parameters into the trained residual bearing capacity analysis model to obtain the residual bearing capacity of the bridge, calculate the reliability index of the bridge based on the residual bearing capacity, and determine the safety level assessment result of the bridge based on the reliability index of the bridge.

[0063] In this step, the trained residual bearing capacity analysis model is first determined, the input of the residual bearing capacity analysis model is the impact parameter, and the output is the residual bearing capacity. After the residual bearing capacity of the bridge is obtained based on the residual bearing capacity analysis model, the reliability index of the bridge is further calculated based on the residual bearing capacity.

[0064] Exemplarily, calculating the reliability index of the bridge based on the residual bearing capacity may specifically include: calculating the reliability index of the bridge based on the residual bearing capacity by the formula Calculate the damage index of the bridge; calculate the failure probability of each level of damage of the bridge based on the calculated damage index by the formula Pf=P(D>Di|A)=Nf / N; calculate the failure probability based on the calculated failure probability by the formula Calculate the reliability index of the bridge; where PR represents the residual bearing capacity, P represents the original bearing capacity, D represents the damage index of the bridge, Pf represents the failure probability of the i-level damage corresponding to the bridge, Di represents the i-level damage index, A represents the working condition event corresponding to the impact parameter combination, P(D>Di|A) represents the probability of the working condition event D>Di occurring under the condition of event A, Nf represents the number of all sampled simulation samples whose damage degree exceeds the i-level damage index Di, N represents the number of Monte Carlo sampling, and β represents the reliability index.

[0065] Furthermore, determining a safety level assessment result of the bridge based on the reliability index of the bridge includes: determining a reliability index value range corresponding to each safety level; and determining an actual safety level corresponding to the reliability index of the bridge based on the reliability index value range corresponding to each safety level.

[0066] In the above embodiment, the residual bearing capacity of the bridge is obtained based on the trained bearing capacity analysis model. In addition to the above, the impact parameters can also be analyzed by LS-DYNA finite element analysis software to evaluate the residual performance of the bridge after impact. Specifically, the material, mass, speed and other parameters of the collapse and landslide disaster are used as input parameters of the LS-DYNA finite element analysis software to accurately simulate the damage cloud map of the bridge structure after impact, and obtain the bridge damage distribution, impact force and displacement time history curves. The restart function in the LS-DYNA finite element analysis software is used to obtain the structural load-displacement curve of the damaged bridge, and the P R , P U , K R and K U The value of R Represents the residual value of the vertical ultimate bearing capacity of the bridge structure after damage, P U Represents the vertical ultimate bearing capacity of the bridge structure when it is not damaged, K R Represents the residual value of deformation stiffness of the bridge structure after damage, K U Represents the deformation stiffness value of the bridge structure when it is not damaged; further, the residual capacity index (RRI) and residual stiffness index (RSI) can be directly used to evaluate the residual performance of the bridge after impact, RRI = P R / P U , RSI = K R / K U Although this method can also evaluate the residual performance of bridges, it has the disadvantages of long evaluation time and low evaluation efficiency compared with the method based on bearing capacity analysis model.

[0067] In a specific embodiment, in order to obtain a trained bearing capacity analysis model, an initial bearing capacity analysis model and a sample data set are also constructed, and the sample data in the sample data set include impact parameter sample data and residual bearing capacity sample data; based on the sample data set, the initial bearing capacity analysis model is pre-trained to obtain a trained bearing capacity analysis model.

[0068] Based on the above content, it can be found that the residual performance of the bridge can also be evaluated through the LS-DYNA finite element analysis software. Therefore, when determining the sample data set, N groups of impact parameters (Vx, Vy, w, m) of collapse hazards can be randomly extracted from the data set to generate an impact parameter sample data set (V, W, H, α, m). Then, the impact parameter sample data is used as the input information of the LS-DYNA finite element analysis software. The output information of the residual bearing capacity obtained by the LS-DYNA finite element analysis software is used as the residual bearing capacity sample data. When training the initial bearing capacity analysis model (also called the ML model), the input information of the LS-DYNA finite element analysis software (Xi = {Vi, Hi, αi, mi, Wi}) is used as the impact parameter sample data of the bearing capacity analysis model, and the output information of the LS-DYNA finite element analysis software is used as the residual bearing capacity sample data of the bearing capacity analysis model to update the model parameters of the bearing capacity analysis model, thereby realizing the pre-training of the bearing capacity analysis model. Among them, Vx represents the initial horizontal velocity, Vy represents the initial vertical velocity, w represents the initial rotation speed, V represents the impact velocity, W represents the rotation speed of the disaster object, H represents the impact height, and α represents the impact angle.

[0069] Further, after the trained bearing capacity analysis model is obtained, the impact parameter generated in step S40 is used as the input information of the trained bearing capacity analysis model, and the residual bearing capacity of the bridge can be quickly output based on the bearing capacity analysis model. After the residual bearing capacity of the bridge is obtained, the damage index of the bridge, the failure probability of each level of damage corresponding to the bridge, and the reliability index of the bridge can be calculated in sequence.

[0070] In addition, the corresponding relationship between the damage index value range and the damage level is shown in the following table:

[0071] Serial number Upper limit of damage index Di The lower limit of the damage index Di Damage level 1 0 0.1 Basically intact 2 0.1 0.3 Minor injuries 3 0.3 0.6 Moderate damage 4 0.6 0.85 Serious injuries 5 0.85 1.0 Total failure (collapse)

[0072] According to the Unified Standard for Reliability Design of Highway Engineering Structures (GB / T 50283-1999), when ductile failure occurs, the corresponding relationship between the safety level of highway bridge structure and the target reliability index is: first-level safety (β≥3.7), second-level safety (3.2≤β<3.7), and third-level safety (2.7≤β<3.2).

[0073] In some embodiments of the present invention, the risk assessment method for bridge clusters affected by geological disasters also includes the following steps: simulating the progressive collapse process of the bridge through discrete element particle flow software based on the geological parameters, collapse and landslide hazard parameters, the movement trajectory of collapse and landslide hazard objects, the impact position data of the bridge, and the impact parameters; verifying the safety level assessment result based on the simulation result. In this embodiment, the progressive destruction process of the bridge cluster under the impact of the hazard object is simulated to verify the accuracy of the assessment result of the risk assessment method; specifically, the progressive collapse process of the bridge is simulated through discrete element particle flow software based on the discrete element method. In addition, the accuracy of the risk assessment method disclosed in this application can also be verified based on actual geological disaster-induced bridge accidents. It is found that the fusion analysis method combining geological disasters and bridge structures in this application can ensure the accuracy of the risk assessment results of bridge clusters affected by geological disasters.

[0074] Figure 2 This is a flow chart of a method for risk assessment of a bridge cluster affected by geological disasters according to another embodiment of the present application. Figure 2 As shown in FIG. 1 , the method specifically includes the following steps: step S01, constructing a BIM+GIS bridge three-dimensional real-life digital model to display the real scene of the geological terrain and the bridge cluster; step S02, according to the BIM+GIS model, conducting a rapid evaluation of the susceptibility of disasters, determining the disaster points, disaster distribution and disaster risk level; step S03, obtaining geological information of high-risk areas, conducting geological disaster simulation, and then conducting disaster intrusion detection to determine whether the bridge cluster is impacted by the disaster object; step S04, when a certain component of a certain bridge in the bridge cluster is impacted by the disaster object, obtaining the motion parameters of the disaster object at the time of impact; step S05, inputting the parameters into the finite element model, obtaining the damage cloud map of the bridge structure and evaluating its residual performance, inputting the parameters into the intelligent evaluation agent model, and conducting a post-disaster bridge failure risk analysis; step S06, simulating the progressive destruction process of the bridge cluster under the action of the disaster object. Based on the risk assessment method, risk assessment of the cluster can not only improve the accuracy of the risk assessment results, but also improve the assessment speed, and realize the visualization of the risk analysis process.

[0075] Correspondingly, the present invention also provides a risk assessment device for a bridge cluster affected by geological disasters, the device comprising a processor, a memory and a computer program stored in the memory, the processor is used to execute the computer program, and when the computer program is executed, the device implements the steps of the method described in any of the above embodiments.

[0076] Through the above embodiments, it can be found that the risk assessment method and device for bridge clusters affected by geological disasters of the present application realizes the analysis of the risk of bridge clusters affected by geological disasters based on the three-dimensional real-life model of bridges that combines geological terrain with bridge clusters, that is, it integrates the two disciplines of geological engineering and bridge engineering, and integrates the geological disasters and bridge structures for analysis, so as to accurately and quickly assess the risk of bridges affected by geological disasters; therefore, the present application not only improves the accuracy of the risk assessment results of bridge clusters affected by geological disasters, but also improves the disaster resistance and prevention capabilities of bridge structures.

[0077] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in any of the above embodiments are implemented. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0078] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software 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 the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0079] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0080] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A risk assessment method for bridge clusters affected by geological disasters, characterized in that: The method comprises: Construct a three-dimensional realistic model of bridges that combines geological terrain with bridge clusters; Determining a geological disaster susceptibility assessment result based on the three-dimensional real-scene model of the bridge, wherein the geological disaster susceptibility assessment result includes disaster point data, disaster distribution data, and disaster risk level; Determine geological parameters and collapse and landslide hazard parameters of high-risk areas based on the geological disaster susceptibility assessment results, determine movement trajectories of collapse and landslide hazard objects based on the geological parameters and collapse and landslide hazard parameters, and determine impact position data of the bridge based on the movement trajectory of the collapse and landslide hazard objects; Determining the impact parameters of the landslide hazard object at the time of impact based on the movement trajectory of the landslide hazard object and the impacted position data of the bridge; The impact parameters are input into a trained residual bearing capacity analysis model to obtain the residual bearing capacity of the bridge, the reliability index of the bridge is calculated based on the residual bearing capacity, and the safety level assessment result of the bridge is determined based on the reliability index of the bridge.

2. The risk assessment method for bridge clusters affected by geological disasters according to claim 1 is characterized in that: Construct a three-dimensional realistic model of bridges that combines geological terrain with bridge clusters, including: Obtain Tiantu satellite imagery, DEM digital elevation data and bridge structure diagrams within the preset range; Based on the Tiandi map satellite image, DEM digital elevation data and bridge structure diagram, a BIM+GIS bridge three-dimensional real-life model combining geological topography and bridge clusters is constructed.

3. The risk assessment method for bridge clusters affected by geological disasters according to claim 2 is characterized in that: The geological disaster susceptibility assessment result is determined based on the three-dimensional real-scene model of the bridge, including: Acquire spatial distribution data of the three-dimensional real-scene model of the bridge, and obtain terrain factor data and distance factor data based on the spatial distribution data, wherein the spatial distribution data includes geological disaster point data, DEM digital elevation data, fault zone data, road data, and water system data; The geological disaster susceptibility assessment result is determined based on the geological disaster point data, terrain factor data and distance factor data through the ALSA disaster susceptibility assessment tool.

4. The risk assessment method for bridge clusters affected by geological disasters according to claim 2 is characterized in that: Determining the movement trajectory of the collapse and landslide hazard object based on the geological parameters and the collapse and landslide hazard object parameters, and determining the impacted position data of the bridge based on the movement trajectory of the collapse and landslide hazard object, including: Based on the geological parameters and the parameters of the landslide hazard object, the movement trajectory of the landslide hazard object is determined by a rockfall analysis tool; The impact position data of the bridge is determined by the rockfall analysis tool based on the motion trajectory.

5. The risk assessment method for bridge clusters affected by geological disasters according to claim 1 is characterized in that: The method further comprises: Constructing an initial bearing capacity analysis model and a sample data set, wherein the sample data in the sample data set includes impact parameter sample data and residual bearing capacity sample data; The initial bearing capacity analysis model is pre-trained based on the sample data set to obtain a trained bearing capacity analysis model.

6. The risk assessment method for bridge clusters affected by geological disasters according to claim 5 is characterized in that: Calculating the reliability index of the bridge based on the residual bearing capacity includes: Based on the residual bearing capacity through the formula Calculate the damage index of the bridge; Based on the calculated damage index, the failure probability of each level of damage of the bridge is calculated by the formula Pf=P(D>Di|A)=Nf / N; Based on the calculated failure probability, the formula Calculate the reliability index of the bridge; Among them, PR represents the residual bearing capacity, P represents the original bearing capacity, D represents the damage index of the bridge, Pf represents the failure probability of the i-level damage corresponding to the bridge, Di represents the i-level damage index, A represents the working condition event corresponding to the impact parameter combination, P(D>Di|A) represents the probability of the working condition event D>Di occurring under the condition of event A, Nf represents the number of sampled simulation samples whose damage degree exceeds the i-level damage index Di, N represents the number of Monte Carlo sampling, and β represents the reliability index.

7. The risk assessment method for bridge clusters affected by geological disasters according to claim 6 is characterized in that: Determining a safety level assessment result of the bridge based on the reliability index of the bridge includes: Determine the value range of reliability index corresponding to each safety level; The actual safety level corresponding to the reliability index of the bridge is determined based on the reliability index value range corresponding to each safety level.

8. The risk assessment method for bridge clusters affected by geological disasters according to claim 1 is characterized in that: The method further comprises: Based on the geological parameters, the parameters of the collapse and landslide hazard object, the movement trajectory of the collapse and landslide hazard object, the impact position data of the bridge and the impact parameters, the progressive collapse process of the bridge is simulated by discrete element particle flow software; The safety level assessment result is verified based on the simulation result.

9. A risk assessment device for bridge clusters affected by geological disasters, the device comprising a processor, a memory, and a computer program stored in the memory, characterized in that: The processor is used to execute the computer program. When the computer program is executed, the device implements the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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