Safety assessment method and system based on bridge digital twin model

Abstract

The invention relates to the technical field of bridge engineering and structure health monitoring, in particular to a safety assessment method and system based on a bridge digital twin model. The method comprises the following steps: performing three-dimensional reconstruction according to geometric information of a bridge to obtain a BIM model of the bridge; based on the BIM model, fusing multi-dimensional special parameters to construct a simulation digital twinborn model of the bridge; correcting the simulation digital twinborn model by using historical monitoring data; and in combination with real-time monitoring data and the corrected simulation digital twinborn model, carrying out safety assessment on the bridge. According to the method, the bridge digital twinborn model is constructed by fusing multi-dimensional special parameters, then the bridge digital twinborn model is dynamically corrected based on historical monitoring data, and finally, the actual state and performance change of the bridge are accurately mapped in real time through the corrected bridge digital twinborn model. The problem of comprehensive, accurate and real-time assessment of bridge structure safety is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge engineering and structural health monitoring, and in particular to a safety assessment method and system based on a bridge digital twin model. Background Art

[0002] As a vital component of transportation infrastructure, bridge safety is directly linked to the safety of people's lives and property, as well as the proper functioning of transportation networks. As bridges age, they are affected by environmental factors such as temperature fluctuations, corrosion, and earthquakes; traffic loads such as vehicle loads and fatigue; and structural aging. These factors can lead to various structural damages and defects, such as cracks, deformations, and material degradation, which can reduce their safety and reliability.

[0003] Ideally, a bridge's health status and abnormalities would be monitored promptly and comprehensively. While health monitoring systems offer real-time monitoring capabilities, they face challenges such as a limited number of monitoring points, insufficient system reliability, and a short lifespan. Therefore, assessing bridge health must rely on comprehensive and reliable inspections. Periodic inspections and daily patrol inspections are the two primary inspection methods for large bridges. Periodic inspections aim to cover the condition of major bridge components. However, due to the complexity of large bridge structures and their geometry, a single periodic inspection is prohibitively expensive in terms of time, labor, and traffic impact. Consequently, a typical large bridge requires several years between complete inspections. Therefore, daily patrol inspections are essential. These inspections, focusing on key, complexly loaded components, can, to a certain extent, reduce the likelihood of undetected and untreated structural damage. However, on large bridges, manual inspections cover a very limited area. For example, the surfaces of pylons, main cable sides, stay cables, and arch ribs are largely unreachable during daily inspections. Even during regular inspections, effective inspection equipment for these areas is lacking. Overall, the existing methods of health monitoring, regular inspections, and daily inspections cannot achieve a balance in timeliness, comprehensiveness, and reliability.

[0004] Digital twin technology uses digital means to create virtual models of physical entities, enabling real-time simulation, monitoring, analysis, and optimization of these entities. Applying digital twin technology to bridge engineering can construct digital twin models of bridges, mapping their actual state and performance changes in real time, providing more comprehensive, accurate, and real-time data support for bridge safety assessments. However, current safety assessment methods based on digital twin models of bridges are still imperfect, lacking effective data fusion, model updates, and safety assessment indicator systems, making it difficult to accurately assess and predict bridge structural safety. Summary of the Invention

[0005] In response to the shortcomings of existing methods and the needs of practical applications, in order to solve the problem of comprehensive, accurate and real-time assessment of bridge structural safety, the present invention provides a safety assessment method based on a bridge digital twin model, comprising the following steps: performing three-dimensional reconstruction based on the geometric information of the bridge to obtain a BIM model of the bridge; constructing a simulated digital twin model of the bridge based on the BIM model and incorporating multi-dimensional special parameters; correcting the simulated digital twin model using historical monitoring data; and performing a safety assessment of the bridge by combining the real-time monitoring data and the corrected simulated digital twin model.

[0006] This invention builds a digital twin model of a bridge by incorporating multi-dimensional special parameters, and then dynamically modifies the digital twin model of the bridge based on historical monitoring data. It can accurately and in real time map the actual status and performance changes of the bridge, and solve the problem of comprehensive, accurate and real-time assessment of bridge structural safety.

[0007] Optionally, the multi-dimensional special parameters include concrete plastic damage parameters, steel bar corrosion degradation parameters, support nonlinear friction parameters, node bolt slip parameters, temperature gradient parameters, fluid-structure coupling parameters, crack propagation parameters, and support stiffness degradation parameters. By considering special parameters such as concrete plastic damage and support nonlinear friction, which are realistic stress characteristics, the present invention facilitates the accurate construction of a bridge digital twin model that conforms to actual conditions, further improving the accuracy of the present invention's safety assessment.

[0008] Optionally, the method of correcting the simulation digital twin model using historical monitoring data includes the following steps: A simulation effect evaluation function is constructed; an improved sparrow search algorithm is introduced, and based on the historical monitoring data, the simulation effect evaluation function and the improved sparrow search algorithm are combined to obtain optimal multi-dimensional special parameters; and the simulation digital twin model is corrected using the optimal multi-dimensional special parameters. The present invention, through the simulation effect evaluation function and the improved sparrow search algorithm, can quickly and accurately correct the simulation digital twin model, thereby improving the accuracy of the safety assessment of the present invention.

[0009] Optionally, constructing the simulation effect evaluation function includes the following steps: The present invention sets weight coefficients for key parts and constructs a simulation effect evaluation function based on the weight coefficients for key parts and the monitoring difference values. This method comprehensively considers the importance of each monitoring indicator to bridge performance evaluation through weighted methods, further facilitating the accurate evaluation of the digital twin model.

[0010] Optionally, the improved sparrow search algorithm includes the following steps: This paper introduces an initial sparrow population distribution strategy, based on which the initial state of the sparrow population is set; introduces an adaptive warning value to adjust the position update formula of the discoverer; and introduces a mutation strategy to adjust the discoverer and follower. By improving the sparrow algorithm, this paper increases the efficiency of searching for multi-dimensional special parameters, further facilitating the rapid and accurate correction of the bridge digital twin model.

[0011] Optionally, the introducing of the initial distribution strategy of the sparrow population and setting the initial state of the sparrow population based on the initial distribution strategy of the sparrow population include the following steps: A chaotic mapping model is constructed, and a mapping sequence is obtained based on the chaotic mapping model. The initial state of the sparrow population is set based on the mapping sequence and the value range of a multi-dimensional special parameter. By introducing the chaotic mapping model to construct the initial state of the sparrow population, the present invention avoids situations where a random setting results in an overly dispersed or dense population, effectively improving search and optimization efficiency.

[0012] Optionally, the adaptive warning value satisfies the following formula: in, Indicates the alarm value, represents half the number of discoverers, Indicates the ranking of simulation effect evaluation values from small to large The simulation effect evaluation value of the discoverer sparrow, Indicates a safe value. The present invention improves the alarm value and safety value of the finder, thereby avoiding the random search of the traditional sparrow algorithm, and further helps to improve the search and optimization efficiency.

[0013] Optionally, the position update formula of the adjustment discoverer satisfies the following formula: in, Indicates the The first iteration Only the location of the finder, Indicates the The first iteration Only the location of the finder, Indicates the current iteration number, represents the maximum number of iterations, Indicates the historical best sparrow position, Indicates the worst sparrow position in history, represents a random number that follows a normal distribution, Indicates a A matrix with all elements set to 1, The present invention further improves the search efficiency by improving the search capability of the finder in a safe area.

[0014] Optionally, the introducing of a mutation strategy and adjusting the discoverer and the follower using the mutation strategy comprises the following steps: The optimization effect is determined based on the simulation effect evaluation values of the finder and the follower; based on the optimization effect, the positions of the finder and the follower are perturbed. By perturbing and swapping the finder and the follower, the present invention avoids the situation where the optimization process is deadlocked, and further improves the search efficiency.

[0015] In the second aspect, in order to be able to efficiently execute the safety assessment method based on the digital twin model of a bridge provided by the present invention, the present invention also provides a safety assessment system based on the digital twin model of a bridge, including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the safety assessment method based on the digital twin model of a bridge as described in the first aspect of the present invention. The safety assessment system based on the digital twin model of a bridge of the present invention has a compact structure and stable performance, and can stably execute the safety assessment method based on the digital twin model of a bridge provided by the present invention, further improving the overall applicability and practical application capabilities of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a safety assessment method based on a bridge digital twin model provided by an embodiment of the present invention; Figure 2 A framework diagram of a safety assessment system based on a bridge digital twin model provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the present invention. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details are not necessarily required to practice the present invention. In other instances, well-known circuits, software, or methods are not specifically described to avoid obscuring the present invention.

[0018] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples. Furthermore, those of ordinary skill in the art will appreciate that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0019] See also Figure 1 ,In order to solve the problem of comprehensive, accurate and real-time evaluation of bridge structure safety, the present invention provides a safety assessment method based on the bridge digital twin model, such as Figure 1 As shown, in one embodiment, the method includes the following steps: S1. Perform three-dimensional reconstruction based on geometric information of the bridge to obtain a BIM model of the bridge.

[0020] In the embodiment, laser 3D scanning technology (such as the Trimble SX10 scanner) is used to obtain point cloud data of the bridge surface, and drone oblique photogrammetry (such as the DJI Phantom 4 RTK) is combined to generate an overall 3D texture model of the bridge. Point cloud stitching and coordinate calibration are performed using Pix4Dmapper software to construct a spatial geometric framework of the bridge with millimeter-level accuracy.

[0021] Furthermore, close-up high-definition photography of key bridge components (such as supports, expansion joints, and prestressed pipes) is performed to reconstruct the structural details in three dimensions, and obtain micro-geometric parameters such as component geometric dimensions and connection methods.

[0022] Next, the laser scanning point cloud data was imported into Autodesk Recap software for denoising, thinning, and coordinate conversion (unified to the bridge engineering coordinate system) to generate an overall point cloud model of the bridge, which serves as the spatial positioning benchmark for BIM modeling.

[0023] Create a dedicated bridge family library in the Revit platform, including beam families (classified by cross-section into box beams, T-beams, etc.), pier families (differentiating between gravity piers and thin-walled piers), and bearing families (plate rubber bearings, pot bearings, etc.). Each family file integrates information such as geometric dimensions (length / width / height), material properties (concrete strength grade, steel grade), and engineering parameters (reinforcement ratio, cover thickness). Based on the spatial positioning of the point cloud model, component by component is modeled using the "reference plane + parametric drive" method to ensure that the geometric size error between the BIM model and the actual bridge is ≤5mm.

[0024] S2. Based on the BIM model, multi-dimensional special parameters are incorporated to construct a simulated digital twin model of the bridge.

[0025] In yet another embodiment, the BIM model is divided into multi-scale grids based on computing power, and multi-dimensional special parameter integration is performed based on the division results.

[0026] Furthermore, the multidimensional special parameters include concrete plastic damage parameters, steel bar corrosion degradation parameters, support nonlinear friction parameters, node bolt slip parameters, temperature gradient parameters, fluid-structure coupling parameters, crack propagation parameters and support stiffness degradation parameters.

[0027] Specifically, the plastic damage parameters of concrete include key indicators such as compressive damage factor, tensile fracture energy and stiffness recovery coefficient. The compressive damage factor reflects the stiffness degradation of concrete when it is under compression and is associated with plastic strain; the tensile fracture energy controls the resistance to crack propagation and its value is related to the aggregate particle size; the stiffness recovery coefficient refers to the stiffness recovery ability under cyclic loading.

[0028] Steel bar corrosion degradation parameters, including corrosion rate and equivalent diameter of corroded steel bars.

[0029] Nonlinear friction parameters of the support, including Coulomb friction coefficient and initial static friction.

[0030] Node bolt slip parameters, including critical slip stress and post-slip stiffness.

[0031] Temperature gradient parameters include the gradient mode, i.e., the temperature gradient of the box girder top plate, and the loading method, i.e., conversion into node body load, with a positive temperature difference applied to the top plate unit and the web / bottom plate distributed according to the gradient.

[0032] Fluid-structure coupling parameters, including hydrodynamic parameters (such as flow velocity, water depth, fluid density, drag coefficient, inertia coefficient) and unit length impact force (coupling the flow field and structural field through the CEL method).

[0033] Crack growth parameters, including stress intensity factor amplitude and crack growth rate.

[0034] Support stiffness degradation parameters, including stiffness degradation rate and support equivalent stiffness.

[0035] It's understandable that incorporating multi-dimensional, specialized parameters to construct a simulated digital twin model of the bridge is essentially a mechanical twinning step. Mechanical twinning ensures that the mechanical properties and state of the digital model are consistent with those of the physical object. This enables the digital model (referred to as the mechanical twin model) to express, analyze, and predict the stress state of the physical object. When the complete parameters required for structural stress analysis are input, mechanical analysis models such as finite element methods are capable of accurately analyzing and predicting the structural stress state. Furthermore, during the bridge design phase, the mechanical twin model can be used to simulate performance under varying loads and environmental conditions, optimizing structural design and improving safety.

[0036] S3. Use historical monitoring data to correct the simulation digital twin model.

[0037] In an optional embodiment, the use of historical monitoring data to correct the simulation digital twin model includes the following steps: S31. Construct a simulation effect evaluation function.

[0038] In the embodiment, the objective function, i.e., the simulation effect evaluation function, is centered on minimizing the relative root mean square error between the monitoring data and the model prediction value, and comprehensively considers the importance of each monitoring indicator to the bridge performance evaluation through weighted summation.

[0039] Specifically, the construction of the simulation effect evaluation function includes the following steps: S311. Set the weight coefficient of key parts.

[0040] When constructing the objective function, the analytic hierarchy process (AHP) was used to determine the weights of each monitoring indicator. Through expert scoring and pairwise comparison, a judgment matrix was constructed and consistency tested to quantify the importance of different monitoring data to bridge performance evaluation, thereby making the objective function weight allocation more scientific and reasonable.

[0041] S312: Construct a simulation effect evaluation function based on the key part weight coefficients and the monitoring difference values.

[0042] Specifically, the simulation effect evaluation function satisfies the following formula: in, represents the simulation effect evaluation value, Indicates the number of stress-bearing parts of the bridge structure, Indicates the The weight of each stress-bearing part of the bridge structure, Indicates the The predicted value of the stress position of the bridge structure, Indicates the The actual measured values of the stress-bearing parts of the bridge structure are used. It should be understood that the weight of each monitoring indicator is determined according to the stress-bearing parts of the bridge structure. For example, the monitoring data of key parts such as the strain at the mid-span of the main span and the displacement of the support nodes are given higher weights to ensure that the model accurately reflects the core performance of the bridge. At the same time, the physical meaning of parameters such as material properties and component mechanical properties is taken into consideration, and their value ranges are strictly set. For example, the elastic modulus of concrete must be within a reasonable material property range.

[0043] The monitoring data include structural response data such as stress / strain values of main beams, piers, towers, and cables, structural dynamic characteristic parameters such as main beam deflection, horizontal displacement of pier tops, tower displacement, natural frequency, modal vibration shape, damping ratio, as well as vibration acceleration, amplitude, cable force, expansion joint displacement, pier settlement, inclination rate, foundation soil pressure, pore water pressure, main beam line shape (such as box beam web verticality, arch rib axis deviation), and overall structural shape changes (such as arch axis deformation of arch bridges) that reflect the safety of bridge structures.

[0044] S32. Introducing an improved sparrow search algorithm, based on the historical monitoring data, combining the simulation effect evaluation function and the improved sparrow search algorithm, to obtain optimal multi-dimensional special parameters.

[0045] By simulating the foraging and anti-predator behaviors of a sparrow colony, global optimization within the solution space is achieved. The sparrow algorithm simulates the two roles of a sparrow colony during foraging: finders and followers, as well as the colony's vigilance mechanism. The finder, typically the fittest individual in the colony, is responsible for exploring the global optimal region in the search space. Followers are divided into two categories based on their fitness: high-fitness followers move toward the finder, while low-fitness individuals search randomly.

[0046] The traditional sparrow algorithm steps include: Initialize the population: randomly generate the location of the sparrow population and calculate the fitness value; The discoverer updates the position according to the formula and explores the global optimal area; The follower updates its position according to the formula, moves towards the discoverer or searches randomly; The alerter updates the position according to the formula, adjusting the position to reduce danger; Boundary processing: ensure that the updated position is within the search space; Termination condition: Stop when the maximum number of iterations is reached or the accuracy requirement is met.

[0047] In an embodiment, the improved sparrow search algorithm comprises the following steps: S321 . Introduce an initial distribution strategy for the sparrow population, and set an initial state of the sparrow population based on the initial distribution strategy.

[0048] Specifically, the introducing of the initial distribution strategy of the sparrow population and setting the initial state of the sparrow population based on the initial distribution strategy of the sparrow population include the following steps: S3211. Construct a chaotic mapping model, and obtain a mapping sequence according to the chaotic mapping model.

[0049] Chaotic mapping models are a type of mathematical model based on nonlinear dynamical systems. Their core characteristics are extreme sensitivity to initial conditions, unpredictable long-term behavior, and chaotic properties within a specific parameter range. In one embodiment, the chaotic mapping model includes one or more combinations of logistic mapping, tent mapping, circle mapping, Chebyshev mapping, and sin mapping. A mapping sequence is obtained by iteratively generating a sequence using the chaotic mapping model.

[0050] S3212. Setting the initial state of the sparrow population according to the mapping sequence and the value range of the multi-dimensional special parameter.

[0051] First, the problem dimension and parameter value range are determined based on the multi-dimensional special parameters. Then, the chaotic sequence is mapped to the parameter range of the problem to obtain the position of each sparrow individual, which corresponds to the multi-dimensional special parameter solution.

[0052] S322. Introduce an adaptive warning value and adjust the discoverer's position update formula.

[0053] Specifically, the adaptive warning value satisfies the following formula: in, Indicates the alarm value, represents half the number of discoverers, Indicates the ranking of simulation effect evaluation values from small to large The simulation effect evaluation value of the discoverer sparrow, Indicates a safe value. It should be understood that the smaller the simulation effect evaluation value, the higher the quality of the solution represented by the sparrow.

[0054] Furthermore, the position update formula of the adjustment discoverer satisfies the following formula: in, Indicates the The first iteration Only the location of the finder, Indicates the The first iteration Only the location of the finder, Indicates the current iteration number, represents the maximum number of iterations, Indicates the historical best sparrow position, Indicates the worst sparrow position in history, represents a random number that follows a normal distribution, Indicates a A matrix with all elements set to 1, Represents the dimension of the sparrow position space.

[0055] S323: Introduce a mutation strategy, and use the mutation strategy to adjust the discoverer and the follower.

[0056] In the embodiment, the introduction of the mutation strategy and the use of the mutation strategy to adjust the finder and the follower include the following steps: S3231. Determine the optimization effect based on the simulation effect evaluation values of the finder and the follower.

[0057] It should be understood that bridge structures are complex and coupled with various parameters, making them prone to falling into local optimal solutions. Therefore, judging the optimization effect based on the simulation evaluation values of the finder and follower is particularly important for accurately obtaining the global optimal solution.

[0058] Specifically, the optimization effect is judged according to the simulation effect evaluation values of the discoverer and the follower, satisfying the following formula: in, represents the optimization effect judgment factor, It means falling into a local optimal solution. It means that it has not fallen into the local optimal solution. represents the evaluation value of the best follower, represents the evaluation value of the best finder, Represents the comparison coefficient, which is set according to actual experience. , represents the evaluation value of the worst follower, Indicates the evaluation value of the worst finder. In each iteration, judging the optimization effect is helpful to improve the optimization efficiency and accurately and quickly obtain the optimal multi-dimensional special parameters.

[0059] S3232: Based on the optimization result, the positions of the finder and the follower are disrupted.

[0060] In the embodiment, when the iterative process does not fall into a local optimal solution, the original iterative method is used; when the iterative process falls into a local optimal solution, the positions of the discoverer and the follower are disturbed.

[0061] Specifically, the positions of the discoverer and the follower are disturbed to satisfy the following formula: in, Indicates the position of the sparrow after the position is disturbed, Indicates the position of the sparrow before the position disturbance, Indicates the current iteration number, represents the maximum number of iterations, represents a random number that satisfies the Cauchy distribution, Represents random numbers that follow a Gaussian distribution.

[0062] S33. Use the optimal multi-dimensional special parameters to correct the simulation digital twin model.

[0063] In the embodiment, the optimal multi-dimensional special parameters are synchronized to the simulation digital twin model to generate a new version of the model, which is the revised simulation digital twin model, and the revision period is the real-time data collection interval.

[0064] S4. Combine real-time monitoring data and the revised simulation digital twin model to conduct a safety assessment of the bridge.

[0065] The real-time monitored load, environment, and structural response data are input into the revised simulation digital twin model to carry out nonlinear mechanical analysis and safety index verification, thereby achieving an accurate assessment of the structural safety status of the bridge under actual working conditions.

[0066] Specifically, through data fusion technology, the real-time collected traffic load spectrum (including dynamic axle load distribution), multi-physical field environmental parameters (temperature and humidity / wind field / corrosive medium concentration) and structural response data (strain / displacement / vibration mode) are synchronously injected into the digital twin after multiple rounds of corrections. The Newton-Raphson iteration method is used to perform material nonlinearity and geometric nonlinearity coupling analysis, and the component-level and system-level safety indicators are calculated based on reliability theory. Finally, the structural performance degradation curve and remaining life prediction matrix considering time-varying effects are output, realizing the service status assessment of the bridge with millimeter-level accuracy throughout its entire life cycle.

[0067] See also Figure 2In an embodiment, to efficiently implement the bridge digital twin model-based safety assessment method provided by the present invention, the present invention further provides a bridge digital twin model-based safety assessment system, comprising: an input device, an output device, a processor, and a memory, wherein the input device, output device, processor, and memory are interconnected, and the memory contains program instructions for performing the steps of the bridge digital twin model-based safety assessment method. The bridge digital twin model-based safety assessment system of the present invention has a compact structure and stable performance, and is capable of stably implementing the bridge digital twin model-based safety assessment method of the present invention, further enhancing the overall applicability and practical application capabilities of the present invention.

[0068] In an embodiment, the processor may be a central processing unit (CPU), which may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The input device may be used to obtain data information. The output device may be used to output the results obtained by storing the program instructions contained in the computer program in the memory provided by the present invention. The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory.

[0069] In one possible implementation, the memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function, etc.; the data storage area may store data created during use. In addition, the memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0070] An embodiment also provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned safety assessment method based on the bridge digital twin model are implemented.

[0071] The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes.

[0072] In summary, the present invention constructs a digital twin model of a bridge by incorporating multi-dimensional special parameters, and then dynamically modifies the digital twin model of the bridge based on historical monitoring data. This can accurately and in real time map the actual status and performance changes of the bridge, solving the problem of comprehensive, accurate, and real-time assessment of bridge structural safety.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope described in the present invention.

Claims

1. A safety assessment method based on a bridge digital twin model, characterized in that: The following steps are involved: Performing three-dimensional reconstruction based on geometric information of the bridge to obtain a BIM model of the bridge; Based on the BIM model, a simulation digital twin model of the bridge is constructed by incorporating multi-dimensional special parameters; Correcting the simulation digital twin model using historical monitoring data; The safety assessment of the bridge is carried out by combining real-time monitoring data and the revised simulation digital twin model.

2. The safety assessment method based on the bridge digital twin model according to claim 1 is characterized in that: The multi-dimensional special parameters include concrete plastic damage parameters, steel bar corrosion degradation parameters, support nonlinear friction parameters, node bolt slip parameters, temperature gradient parameters, fluid-structure coupling parameters, crack extension parameters and support stiffness degradation parameters.

3. The safety assessment method based on the bridge digital twin model according to claim 1 is characterized in that: The method of correcting the simulation digital twin model using historical monitoring data includes the following steps: Construct simulation effect evaluation function; Introducing an improved sparrow search algorithm, based on the historical monitoring data, combining the simulation effect evaluation function and the improved sparrow search algorithm, to obtain optimal multi-dimensional special parameters; The simulation digital twin model is corrected using the optimal multi-dimensional special parameters.

4. The safety assessment method based on the bridge digital twin model according to claim 3 is characterized in that: The construction of the simulation effect evaluation function comprises the following steps: Set the weight coefficient of key parts; Combining the key parts weight coefficients and monitoring difference values, a simulation effect evaluation function is constructed.

5. The safety assessment method based on the bridge digital twin model according to claim 3 is characterized in that: The improved sparrow search algorithm comprises the following steps: Introducing an initial distribution strategy for a sparrow population, and setting an initial state of the sparrow population based on the initial distribution strategy for the sparrow population; Introducing adaptive warning values and adjusting the discoverer's position update formula; A mutation strategy is introduced, and the discoverer and the follower are adjusted using the mutation strategy.

6. The safety assessment method based on the bridge digital twin model according to claim 5 is characterized in that: The method of introducing the initial distribution strategy of the sparrow population and setting the initial state of the sparrow population based on the initial distribution strategy of the sparrow population comprises the following steps: constructing a chaotic mapping model, and obtaining a mapping sequence according to the chaotic mapping model; The initial state of the sparrow population is set according to the mapping sequence and the value range of the multi-dimensional special parameter.

7. The safety assessment method based on the bridge digital twin model according to claim 5 is characterized in that: The adaptive warning value satisfies the following formula: in, Indicates the alarm value, represents half the number of discoverers, Indicates the ranking of simulation effect evaluation values from small to large The simulation effect evaluation value of the discoverer sparrow, Indicates a safe value. Indicates the simulation effect evaluation value of the discoverer sparrow with the highest simulation effect evaluation value.

8. The safety assessment method based on the bridge digital twin model according to claim 5 is characterized in that: The position update formula of the adjustment discoverer satisfies the following formula: in, Indicates the The first iteration Only the location of the finder, Indicates the The first iteration Only the location of the finder, Indicates the current iteration number, represents the maximum number of iterations, Indicates the historical best sparrow position, Indicates the worst sparrow position in history, represents a random number that follows a normal distribution, Indicates a A matrix with all elements set to 1, Represents the dimension of the sparrow position space.

9. The safety assessment method based on the bridge digital twin model according to claim 5 is characterized in that: The introduction of the mutation strategy and the use of the mutation strategy to adjust the discoverer and the follower include the following steps: The optimization effect is judged based on the simulation effect evaluation values of the discoverer and the follower; Based on the optimization result, positions of the finder and the follower are disturbed.

10. A safety assessment system based on a bridge digital twin model, characterized in that: The safety assessment system based on the bridge digital twin model includes: an input device, an output device, a processor, and a memory. The input device, output device, processor, and memory are interconnected. The memory includes program instructions, and the program instructions are used to execute the safety assessment method based on the bridge digital twin model according to any one of claims 1 to 9.

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