Bridge structure monitoring method based on multi-source data and digital twinning and related device

Abstract

The invention discloses a bridge structure monitoring method based on multi-source data and digital twinning and a related device, and relates to the technical field of bridge engineering.The method comprises the steps that an initial finite element model of a target bridge is constructed based on a design drawing and structural parameters of the target bridge, and the initial finite element model of the target bridge is constructed based on the initial finite element model and preset load conditions; calculating to obtain simulation mechanical response data of each grid node of the target bridge; based on the simulation mechanical response data and the obtained actually measured mechanical response data of each grid node of the target bridge, obtaining a corrected finite element model; inputting the obtained traffic load data passing through the target bridge into a digital twinborn calculation model constructed based on the corrected influence line data for numerical calculation to obtain an overall structure response cloud picture and a structure response-time curve of the key monitoring point, and fusing the overall structure response cloud picture and the structure response-time curve with the obtained three-dimensional visualization model to obtain a three-dimensional visualization model; and obtaining the structural state of the target bridge. According to the invention, accurate monitoring and real-time state evaluation of the bridge structure are realized.

Description

Technical Field

[0001] This application relates to the field of bridge engineering technology, and in particular to a bridge structure monitoring method and related device based on multi-source data and digital twins. Background Technology

[0002] In modern transportation systems, highway bridges, as critical infrastructure, bear enormous traffic volumes and play an indispensable supporting role in regional economic development and social exchange. In recent years, with the booming economy, transportation demand has exploded, greatly accelerating the aging and damage of bridge structures, making bridge defects increasingly prominent. At the same time, the increasing service life of bridges has led to the gradual deterioration of material properties and structural stability in bridges built earlier, resulting in the continuous accumulation of safety hazards.

[0003] Traditional high-speed bridge operation and maintenance management models have revealed numerous shortcomings when dealing with these complex situations. On the one hand, information efficiency is low and real-time performance is poor. Relying on regular manual inspections is constrained by subjective factors of inspectors, inspection techniques, and inspection time intervals, making it difficult to detect subtle changes and early damage to the bridge structure in a timely manner. On the other hand, when abnormalities occur on the bridge, the process from problem discovery to developing a repair plan and implementing repairs is cumbersome, often leading to further deterioration of the damage and increasing maintenance costs and safety risks.

[0004] Furthermore, the supervision of high-speed bridges during their service life faces numerous challenges. Regarding vehicle management, traditional monitoring methods struggle to track the real-time driving status of key vehicle types such as large trucks and hazardous materials transport vehicles, failing to proactively eliminate potential risks. Accidents involving these vehicles can have extremely serious consequences. In terms of intervening in abnormal events, manual supervision is prone to errors and omissions when dealing with massive amounts of monitoring data, making it difficult to promptly complete processes such as event capture, reporting, and on-site execution, severely impacting response efficiency. Moreover, traditional monitoring methods suffer from limited monitoring areas, low levels of intelligence, and insufficient utilization of location and traceability information, making it difficult to comprehensively and accurately grasp the bridge's operational status. Various monitoring data are scattered and stored in independent systems, lacking efficient integration mechanisms, making it impossible to achieve correlation analysis of multi-dimensional data and accurate assessment of the overall bridge safety situation, let alone predict future bridge health based on current bridge health conditions. This makes it difficult to meet rapidly growing transportation demands and evolving needs, especially within the context of the national strategy of building a strong transportation nation based on smart highway construction. Summary of the Invention

[0005] The purpose of this application is to provide a bridge structure monitoring method and related device based on multi-source data and digital twins, which can realize accurate monitoring and real-time status assessment of bridge structures.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a bridge structure monitoring method based on multi-source data and digital twins, including: Based on the obtained design drawings and structural parameters of the target bridge, an initial finite element model of the target bridge is constructed, and based on the initial finite element model and the preset load conditions, the simulation mechanical response data of each mesh node of the target bridge is calculated. The measured mechanical response data of each grid node of the target bridge are obtained, and the parameters of the initial finite element model are corrected based on the simulated mechanical response data and the measured mechanical response data to obtain the corrected finite element model. The measured mechanical response data is the result of the on-site loading test of the target bridge using a test vehicle with a known axle load. The load conditions generated by the test vehicle are the same as the preset load conditions. Based on the modified influence line data in the modified finite element model, a digital twin calculation model is constructed, and the real-time traffic load data passing through the target bridge is input into the digital twin calculation model for numerical calculation to obtain the overall structural response cloud map and the structural response-time curves of key monitoring points; the traffic load data includes at least vehicle axle load information, vehicle position information and vehicle speed information. The overall structural response cloud map, the structural response-time curve, and the acquired 3D visualization model are fused to obtain the structural state of the target bridge; the structural state is used for real-time visualization and monitoring.

[0007] Optionally, the structural parameters include the design span, cross-sectional properties, and material properties of the target bridge; based on the design drawings and structural parameters of the target bridge, an initial finite element model of the target bridge is constructed, specifically including: Based on the design drawings, design span, cross-sectional characteristics, and material properties of the target bridge, an initial finite element model of the target bridge is constructed using finite element software.

[0008] Optionally, based on the initial finite element model and preset load conditions, the simulated mechanical response data of each mesh node of the target bridge are calculated, specifically including: Based on the initial finite element model, the mechanical response of the key section of the target bridge under unit load is calculated to obtain the initial influence line data of the key section. Based on the initial influence line data and the preset load conditions, the total mechanical response of the target bridge is calculated using the principle of influence line superposition. The key sections of the target bridge are divided into longitudinal and transverse grids to obtain the grid nodes of the target bridge; The total mechanical response is discretized to obtain the discretized total mechanical response, and the discretized total mechanical response is mapped to each grid node to obtain the simulation mechanical response data of each grid node of the target bridge.

[0009] Optionally, based on the simulated mechanical response data and the measured mechanical response data, the initial finite element model is modified to obtain a modified finite element model, specifically including: Based on the simulated mechanical response data and the measured mechanical response data, the error result is calculated; Based on the error results, the parameters of the initial finite element model are corrected using a machine learning algorithm to obtain the corrected finite element model.

[0010] Optionally, real-time traffic load data passing over the target bridge is input into the digital twin computing model for numerical calculation to obtain an overall structural response cloud map and structural response-time curves for key monitoring points, specifically including: The real-time traffic load data of the target bridge is input into the digital twin computing model to obtain the real-time mechanical response data of each grid node of the target bridge, and the overall structural response cloud map is obtained based on the real-time mechanical response data of each grid node of the target bridge. Based on the traffic load data and the preset key monitoring points of the target bridge, the mechanical response values ​​of the key monitoring points are continuously calculated using the digital twin computing model and arranged in chronological order to obtain the structural response-time curves of the key monitoring points.

[0011] Optionally, the overall structural response cloud map, the structural response-time curve, and the acquired 3D visualization model are fused to obtain the structural state of the target bridge, specifically including: The overall structural response cloud map is mapped to the spatial location corresponding to the obtained 3D visualization model, and the structural response-time curve is processed using a coloring algorithm to obtain a color model. The color model is then associated with the corresponding key monitoring points in the 3D visualization model to obtain the structural state of the target bridge.

[0012] Optionally, the bridge structure monitoring method based on multi-source data and digital twins further includes: The structural condition of the target bridge is trend-predicted and degradation-analyzed to determine predictive maintenance recommendations.

[0013] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the bridge structure monitoring method based on multi-source data and digital twin as described above.

[0014] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the bridge structure monitoring method based on multi-source data and digital twin as described above.

[0015] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the bridge structure monitoring method based on multi-source data and digital twins described above.

[0016] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a bridge structure monitoring method and related device based on multi-source data and digital twins. The method constructs an initial finite element model of the target bridge and calculates simulated mechanical response data based on design drawings, structural parameters, and preset load conditions. Subsequently, measured mechanical response data is obtained through on-site loading tests, and the parameters of the initial finite element model are corrected based on the simulated and measured mechanical response data to obtain a corrected finite element model. Real-time traffic load data (including vehicle axle load, position, speed, etc.) is acquired and input into a digital twin calculation model constructed based on the corrected influence line data for numerical calculation, generating an overall structural response cloud map and response-time curves for key monitoring points, comprehensively reflecting the actual operating status of the bridge. Finally, the structural response data is integrated with a three-dimensional visualization model to display and monitor the bridge structural status in real time. This application, by combining multi-source data and digital twin technology, achieves accurate monitoring and real-time status assessment of bridge structures, significantly reducing the resources required for finite element calculations and improving the accuracy and efficiency of bridge maintenance and safety monitoring. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an application environment diagram of a bridge structure monitoring method based on multi-source data and digital twins in one embodiment of this application; Figure 2A flowchart illustrating a bridge structure monitoring method based on multi-source data and digital twin, provided as an embodiment of this application; Figure 3 A schematic diagram of a numerical simulation model of the target bridge provided in an embodiment of this application; Figure 4 This is a schematic diagram illustrating traffic load data provided in one embodiment of this application; Figure 5 This is a schematic diagram of the structural response-time curves of key monitoring points provided in an embodiment of this application; Figure 6 This is a schematic diagram illustrating the structural state of the target bridge provided in one embodiment of this application; Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] In the field of bridge engineering, accurate monitoring and timely maintenance of bridge structural health are crucial for ensuring their long-term safety and stable operation. Throughout a bridge's lifespan, the combined effects of various external environmental factors and continuous traffic loads gradually lead to material damage and changes in internal force states in its main components. These changes can cause varying degrees of reduction in the structure's load-bearing capacity, and if not identified and addressed in a timely manner, they may further evolve into major safety hazards or even trigger catastrophic structural accidents. Therefore, establishing an efficient and reliable bridge health monitoring system is of great significance.

[0021] Currently, traditional bridge health monitoring systems primarily rely on Internet of Things (IoT) technology. This involves deploying various types of sensors at key locations on the bridge structure to collect structural performance parameters, including stress, deflection, and vibration. Subsequent data analysis is then used to assess the overall health of the bridge. However, this approach still has limitations. Firstly, the various monitoring subsystems are typically independent, lacking a unified data integration and interaction mechanism, which can easily create information silos and hinder the overall effectiveness of the system. Secondly, traditional monitoring methods largely depend on periodic report analysis, making it difficult to provide real-time feedback on bridge health status and potentially leading to delays in response to emergencies.

[0022] To overcome the shortcomings of traditional methods, digital twin technology offers a new path for revolutionizing bridge monitoring. This technology constructs a digital virtual model corresponding to the physical structure, integrating historical data, real-time monitoring information, and specialized algorithms to simulate, verify, predict, and regulate the entire lifecycle of the physical structure. In bridge health monitoring scenarios, the digital twin model can integrate real bridge operational safety data and map the mechanical behavior of key components in virtual space, thereby supporting real-time monitoring and comprehensive analysis of the bridge's structural condition.

[0023] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] The bridge structure monitoring method based on multi-source data and digital twins provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send the design drawings, structural parameters, measured mechanical response data of each grid node, and traffic load data of the target bridge to server 104. Server 104 constructs an initial finite element model of the target bridge based on the design drawings and structural parameters, and calculates the simulated mechanical response data of each grid node of the target bridge based on the initial finite element model and preset load conditions. Based on the simulated mechanical response data and the measured mechanical response data of each grid node of the target bridge, a corrected finite element model is obtained. Real-time traffic load data is input into a digital twin calculation model constructed based on the corrected influence line data for numerical calculation, obtaining an overall structural response cloud map and structural response-time curves of key monitoring points, which are then fused with a three-dimensional visualization model to obtain the structural state of the target bridge.

[0025] The terminal 102 can be, but is not limited to, various desktop computers, laptops, and IoT devices. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0026] In one exemplary embodiment, such as Figure 2 As shown, a bridge structure monitoring method based on multi-source data and digital twins is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 204. Wherein: Step 201: Based on the obtained design drawings and structural parameters of the target bridge, construct an initial finite element model of the target bridge, and calculate the simulation mechanical response data of each mesh node of the target bridge based on the initial finite element model and the preset load conditions.

[0027] Step 202: Obtain the measured mechanical response data of each grid node of the target bridge, and based on the simulated mechanical response data and the measured mechanical response data, correct the parameters of the initial finite element model to obtain the corrected finite element model; the measured mechanical response data is the result obtained by conducting on-site loading tests on the target bridge using a test vehicle with a known axle load, and the load conditions generated by the test vehicle are the same as the preset load conditions.

[0028] Step 203: Based on the modified influence line data in the modified finite element model, construct a digital twin calculation model, and input the real-time traffic load data of the target bridge into the digital twin calculation model for numerical calculation to obtain the overall structural response cloud map and the structural response-time curve of key monitoring points; the traffic load data includes at least vehicle axle load information, vehicle position information and vehicle speed information.

[0029] Step 204: The overall structural response cloud map, the structural response-time curve, and the acquired three-dimensional visualization model are fused to obtain the structural state of the target bridge; the structural state is used for real-time visualization and monitoring.

[0030] By implementing steps 201 to 204 above, this application constructs an initial finite element model of the target bridge and calculates simulated mechanical response data based on design drawings, structural parameters, and preset load conditions. Subsequently, measured mechanical response data is obtained through on-site loading tests, and the parameters of the initial finite element model are corrected based on the simulated and measured mechanical response data to obtain a corrected finite element model. Real-time traffic load data (including vehicle axle load, position, speed, etc.) is acquired and input into a digital twin calculation model constructed based on the corrected influence line data for numerical calculation, generating an overall structural response cloud map and response-time curves for key monitoring points, which can comprehensively reflect the actual operating status of the bridge. Finally, the structural response data is integrated with a three-dimensional visualization model to display and monitor the bridge structural status in real time. By combining multi-source data and digital twin technology, this application achieves accurate monitoring and real-time status assessment of bridge structures, greatly reducing the resources required for finite element calculations and improving the accuracy and efficiency of bridge maintenance and safety monitoring.

[0031] Furthermore, in step 201, the structural parameters include the design span, cross-sectional properties, and material properties of the target bridge; based on the design drawings and structural parameters of the target bridge, an initial finite element model of the target bridge is constructed, specifically including: Based on the design drawings, design span, cross-sectional properties, and material properties of the target bridge, an initial finite element model of the target bridge is constructed using finite element software. Numerical simulation can be performed using finite element software such as Ansys and Abaqus. The numerical simulation model of the target bridge is shown below. Figure 3 As shown.

[0032] Furthermore, in step 201, based on the initial finite element model and preset load conditions, the simulated mechanical response data of each mesh node of the target bridge are calculated, specifically including: Step a1: Based on the initial finite element model, calculate the mechanical response of the key section of the target bridge under unit load to obtain the initial influence line data of the key section.

[0033] Step a2: Based on the initial influence line data and the preset load conditions, the total mechanical response of the target bridge is calculated using the principle of influence line superposition.

[0034] Step a3: Divide the key sections of the target bridge into longitudinal and transverse grids to obtain the grid nodes of the target bridge.

[0035] Step a4: Discretize the total mechanical response to obtain the discretized total mechanical response, and map the discretized total mechanical response to each grid node to obtain the simulated mechanical response data of each grid node of the target bridge; thus, the grid division matrix along the longitudinal and transverse directions of the bridge and the mechanical response value of each node or unit under the monitored vehicle load corresponding to this grid division are calculated.

[0036] Influence lines are a core concept in structural mechanics, used to describe the variation of a specific mechanical response of a structure with respect to the load position under a unit moving load. Essentially, they are a diagram showing the correspondence between load position and structural response. Digital twin models, by directly applying influence line data obtained from numerical simulations, can significantly shorten the digital twin simulation time and ensure real-time display of the 3D model. The calculation approach for the influence line superposition principle is as follows: 1. Coupling calculation of axle load and influence line reading: For each axle load , (k=1,2,...,n), calculate the mechanical response produced by the structure; where k is the number of axes and n is the total number of axes. Let k be the longitudinal projection coordinates of the k-th axle on the bridge plane. The k-th axle is positioned laterally (in the direction of lane width) on the bridge plane. Let be the axle load of the k-th axle; Mechanical response of a single axle load: ;in, The overall meaning is: to make the size of The axial load applied to the finite element model is at coordinates ( , At the position of ) The total mechanical response is obtained using the principle of influence line superposition: .

[0037] 2. Mesh node response calculation: Bridge structure according to (Vertical) and (Horizontal) Divided into grid nodes (i=1...N) x j=1...N y ); For each grid node, its total response under all axial loads is calculated using the principle of influence line superposition. This forms a gridded result matrix.

[0038] Furthermore, in step 202, based on the simulated mechanical response data and the measured mechanical response data, the parameters of the initial finite element model are corrected to obtain the corrected finite element model of the target bridge, specifically including: Step b1: Based on the simulated mechanical response data and the measured mechanical response data, the error result is calculated.

[0039] Step b2: Based on the error results, the parameters of the initial finite element model are corrected using a machine learning algorithm until the error results meet the preset standard threshold, thus obtaining the corrected finite element model of the target bridge.

[0040] Furthermore, in step 203, traffic load data is acquired in real time through the BeiDou radar and checkpoints of the intelligent highway platform, such as... Figure 4 As shown, traffic load data is input into a digital twin computing model for numerical calculation, resulting in an overall structural response cloud map and structural response-time curves for key monitoring points, specifically including: Step c1 involves inputting the real-time traffic load data of the target bridge into the digital twin computing model to obtain the real-time mechanical response data of each grid node of the target bridge, and based on the real-time mechanical response data of each grid node of the target bridge, obtaining the overall structural response cloud map.

[0041] Step c2: Based on traffic load data and preset key monitoring points of the target bridge, the mechanical response values ​​of the key monitoring points are continuously calculated using a digital twin computing model and arranged in chronological order to obtain the structural response-time curves of the key monitoring points, as shown below. Figure 5 As shown.

[0042] Furthermore, in step 204, the overall structural response cloud map, structural response-time curve, and the acquired 3D visualization model (i.e., Building Information Model, BIM model) are fused to obtain the structural state of the target bridge, specifically including: The overall structural response cloud map is mapped to the corresponding spatial location of the acquired 3D visualization model. A coloring algorithm is then used to process the structural response-time curve to obtain a color model. This color model is then associated with the corresponding key monitoring points in the 3D visualization model to obtain the structural state of the target bridge. This structural state is used for real-time visualization and monitoring, such as... Figure 6 As shown.

[0043] Furthermore, bridge structure monitoring methods based on multi-source data and digital twins also include: The structural state of the target bridge is trend-predicted and degradation-analyzed to determine predictive maintenance recommendations. Specifically, by comparing the structural state of the target bridge at different time points, the changing trend of the structural response parameters of the target bridge (i.e., trend prediction results) can be clearly obtained. The trend prediction results are then input into the material degradation model for calculation, and the trend prediction results are compared with preset safety thresholds. Based on the degree of degradation and the comparison results, predictive maintenance recommendations are generated.

[0044] This application constructs a numerical simulation model that dynamically matches physical entities, integrates multi-source monitoring data to achieve real-time accurate mapping and visualization, breaks through the time and space limitations of traditional inspections, and enhances the ability to perceive conditions; it uses historical data, real-time feedback and simulation to achieve predictive maintenance, accurately predict facility degradation trends, optimize maintenance plans and reduce sudden failures, thereby reducing the total life cycle cost; its core lies in solving the pain points of traditional monitoring with a virtual-real integrated dynamic management model, providing key technical support for the construction of intelligent transportation systems.

[0045] This application also provides an application scenario in which the aforementioned bridge structure monitoring method based on multi-source data and digital twins is applied. Specifically, the bridge structure monitoring method based on multi-source data and digital twins provided in this embodiment can be applied to a bridge structure monitoring scenario. The bridge structure monitoring scenario includes a digital twin computational model construction stage and a target bridge structural status display stage. The digital twin computational model construction stage is used to construct a digital twin computational model based on the design drawings and structural parameters of the target bridge. The target bridge structural status display stage is used to visualize and monitor the structural status of the target bridge in real time. The bridge structure monitoring method based on multi-source data and digital twins provided in this embodiment belongs to the bridge structure monitoring scenario including the digital twin computational model construction stage and the target bridge structural status display stage.

[0046] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 7 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores and processes data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a bridge structure monitoring method based on multi-source data and digital twins.

[0047] Those skilled in the art will understand that Figure 7 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0048] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0049] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0050] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0051] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0052] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0053] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0054] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A bridge structure monitoring method based on multi-source data and digital twin, characterized in that, include: Based on the obtained design drawings and structural parameters of the target bridge, an initial finite element model of the target bridge is constructed, and based on the initial finite element model and the preset load conditions, the simulation mechanical response data of each mesh node of the target bridge is calculated. The measured mechanical response data of each grid node of the target bridge are obtained, and the parameters of the initial finite element model are corrected based on the simulated mechanical response data and the measured mechanical response data to obtain the corrected finite element model. The measured mechanical response data is the result of the on-site loading test of the target bridge using a test vehicle with a known axle load. The load conditions generated by the test vehicle are the same as the preset load conditions. Based on the corrected influence line data in the corrected finite element model, a digital twin calculation model is constructed, and the real-time traffic load data of the target bridge is input into the digital twin calculation model for numerical calculation to obtain the overall structural response cloud map and the structural response-time curve of key monitoring points. The traffic load data includes at least vehicle axle load information, vehicle location information, and vehicle speed information; The overall structural response cloud map, the structural response-time curve, and the acquired 3D visualization model are fused to obtain the structural state of the target bridge; the structural state is used for real-time visualization and monitoring.

2. The bridge structure monitoring method based on multi-source data and digital twin as described in claim 1, characterized in that, The structural parameters include the design span, cross-sectional properties, and material properties of the target bridge; Based on the design drawings and structural parameters of the target bridge, an initial finite element model of the target bridge is constructed, specifically including: Based on the design drawings, design span, cross-sectional characteristics, and material properties of the target bridge, an initial finite element model of the target bridge is constructed using finite element software.

3. The bridge structure monitoring method based on multi-source data and digital twin as described in claim 1, characterized in that, Based on the initial finite element model and the preset load conditions, the simulated mechanical response data of each mesh node of the target bridge are calculated, specifically including: Based on the initial finite element model, the mechanical response of the key section of the target bridge under unit load is calculated to obtain the initial influence line data of the key section. Based on the initial influence line data and the preset load conditions, the total mechanical response of the target bridge is calculated using the principle of influence line superposition. The key sections of the target bridge are divided into longitudinal and transverse grids to obtain the grid nodes of the target bridge; The total mechanical response is discretized to obtain the discretized total mechanical response, and the discretized total mechanical response is mapped to each grid node to obtain the simulation mechanical response data of each grid node of the target bridge.

4. The bridge structure monitoring method based on multi-source data and digital twin as described in claim 3, characterized in that, Based on the simulated mechanical response data and the measured mechanical response data, the parameters of the initial finite element model are corrected to obtain the corrected finite element model, specifically including: Based on the simulated mechanical response data and the measured mechanical response data, the error result is calculated; Based on the error results, the parameters of the initial finite element model are corrected using a machine learning algorithm to obtain the corrected finite element model.

5. The bridge structure monitoring method based on multi-source data and digital twin as described in claim 1, characterized in that, The real-time traffic load data passing over the target bridge is input into the digital twin computing model for numerical calculation, resulting in an overall structural response cloud map and structural response-time curves for key monitoring points, specifically including: The real-time traffic load data of the target bridge is input into the digital twin computing model to obtain the real-time mechanical response data of each grid node of the target bridge, and the overall structural response cloud map is obtained based on the real-time mechanical response data of each grid node of the target bridge. Based on the traffic load data and the preset key monitoring points of the target bridge, the mechanical response values ​​of the key monitoring points are continuously calculated using the digital twin computing model and arranged in chronological order to obtain the structural response-time curves of the key monitoring points.

6. The bridge structure monitoring method based on multi-source data and digital twin as described in claim 1, characterized in that, The overall structural response cloud map, the structural response-time curve, and the acquired 3D visualization model are fused to obtain the structural state of the target bridge, specifically including: The overall structural response cloud map is mapped to the spatial location corresponding to the obtained 3D visualization model, and the structural response-time curve is processed using a coloring algorithm to obtain a color model. The color model is then associated with the corresponding key monitoring points in the 3D visualization model to obtain the structural state of the target bridge.

7. The bridge structure monitoring method based on multi-source data and digital twin as described in claim 1, characterized in that, The bridge structure monitoring method based on multi-source data and digital twins also includes: The structural condition of the target bridge is trend-predicted and degradation-analyzed to determine predictive maintenance recommendations.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the bridge structure monitoring method based on multi-source data and digital twins as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the bridge structure monitoring method based on multi-source data and digital twin as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the bridge structure monitoring method based on multi-source data and digital twin as described in any one of claims 1-7.

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