Suspension bridge real-time state evaluation method and system based on BIM and FEM

Abstract

The invention discloses a suspension bridge real-time state evaluation method and system based on BIM and FEM, and the method comprises the steps: obtaining and storing the sensor monitoring data of a suspension bridge and the BIM information of the suspension bridge, and constructing a model database; constructing a suspension bridge finite element simulation model of a geometric dimension based on the BIM model information data in the model database; reducing the order of the finite element simulation model of the suspension bridge based on a POD (Particle Oriented Decomposition) method to obtain an order-reduced finite element simulation model; correcting the reduced-order finite element simulation model based on a response surface method to obtain a corrected finite element simulation model; and performing state monitoring and damage identification on the suspension bridge by using sensor monitoring data and the corrected finite element simulation model, and evaluating the operation state of the suspension bridge in real time. The method improves the efficiency and accuracy of the operation state evaluation and damage identification of the suspension bridge, and effectively supports the real-time evaluation of the operation state of the suspension bridge.

Description

Technical Field

[0001] The present invention relates to the technical field of BIM model digital application and bridge simulation numerical simulation, and in particular to a BIM and FEM-based suspension bridge real-time state evaluation method and system. Background Art

[0002] Road and bridge structures are key parts of transportation infrastructure and need to withstand multiple external forces such as wind loads and vehicle loads. As one of the main types of long-span bridges, the strength and reliability analysis of suspension bridges is crucial. In order to ensure the quality and safety of the entire construction process, real-time monitoring of the construction process is essential. During the operation of a suspension bridge, displacement, stress, acceleration and other data need to be monitored. At the same time, the cables, main beams, bridge towers and other parts need to work together to accurately reflect the changes in the structure. During the service of a suspension bridge, geological, hydrological, load and other conditions will continue to change over time, and the existing monitoring methods can only achieve separate monitoring of each part, and cannot reflect the mechanism of action between the various monitored quantities. On the other hand, traditional methods are based on empirical formulas and test data, and cannot accurately identify structural damage.

[0003] With the development of computer technology, numerical analysis methods have gradually become an important research direction. At present, BIM (Building Information Modeling) technology is widely used in the strength analysis of road and bridge structures. It effectively integrates various relevant information of the project through precise three-dimensional modeling, which is convenient for various analyses and optimizations. The finite element analysis (FEA) method uses the principle of mathematical approximation to simulate the real physical system. It approximates the real system of infinite unknown quantities through simple and interacting elements (units), and can accurately simulate the structural dynamic characteristics of the bridge. Combining BIM and FEA technology can realize the quality and safety monitoring of the whole process of bridge engineering operation. However, due to the information island characteristics of the BIM model, the traditional FEA technology has complex modeling and calculation call ports when facing complex and changeable on-site conditions, resulting in untimely update of analysis model information, and the traditional analysis process is complicated and time-consuming, which is not conducive to the rapid grasp of bridge structure damage information by engineering personnel. Summary of the invention

[0004] Based on the above background, the purpose of the present invention is to provide a real-time status assessment method and system for a suspension bridge based on BIM and FEM, which can quickly and accurately obtain the operating status of the bridge structure, perform real-time online monitoring of the operating status of the suspension bridge, and timely and accurately detect early damage to the bridge structure to avoid damage to the bridge structure, thereby solving the problems of low efficiency and low accuracy in status assessment and damage identification in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for real-time status assessment of a suspension bridge based on BIM and FEM, comprising: Obtain the sensor monitoring data of the suspension bridge and the BIM model information of the suspension bridge and store them to build a model database; Construct a finite element simulation model of a suspension bridge in geometric dimensions based on the BIM model information data in the model database; Based on the POD intrinsic orthogonal decomposition method, the finite element simulation model of the suspension bridge is reduced to obtain a reduced-order finite element simulation model; The reduced-order finite element simulation model is modified based on the response surface method to obtain a modified finite element simulation model; The sensor monitoring data and the modified finite element simulation model are used to monitor the condition and identify damage of the suspension bridge, and the operating status of the suspension bridge is evaluated in real time.

[0006] In a second aspect, the present invention provides a real-time state assessment system for a suspension bridge based on BIM and FEM, comprising a data acquisition and transmission module, a modeling simulation module and a visual state assessment module, wherein: The data acquisition and transmission module includes a sensor module, a suspension bridge BIM model module and a data transmission interface, which is used to obtain sensor monitoring data and suspension bridge BIM model data of the suspension bridge, and transmit the obtained data to the modeling and simulation module; The modeling and simulation module includes a model database, a finite element geometry modeling module and a finite element model reduction module, which are used to receive sensor monitoring data and suspension bridge BIM model data and construct a suspension bridge finite element simulation model, and at the same time, perform model reduction on the constructed suspension bridge finite element simulation model to obtain a reduced-order finite element simulation model; The visual state assessment module includes a finite element model correction module, a state monitoring module and an auxiliary maintenance module, which are used to correct the reduced-order finite element simulation model, obtain the corrected finite element simulation model, and perform visual display and state assessment by calling the simulation results of the modeling simulation module.

[0007] The beneficial effects of the present invention are as follows: The present invention utilizes a model reduction method based on POD and a model correction algorithm based on response surface, which simplifies the calculation model while retaining the main features of the structure. The degree of freedom and the amount of calculation of the model can be greatly reduced during calculation, and the solution speed is greatly improved. The calculation result has high accuracy, which improves the efficiency and accuracy of suspension bridge operation status evaluation and damage identification, effectively supports the real-time evaluation of the suspension bridge operation status, and can realize online real-time evaluation of the bridge status. It has little impact on traffic and high test efficiency, and provides a basis and reference for daily maintenance and management of suspension bridges.

[0008] The API data transmission interface developed in the present invention can extract the BIM model information in the corresponding JSON file through the defined interface function, and can also call the sensor monitoring data subscribed to the MQTT server in real time based on the MQTT publish-subscribe mechanism, thereby realizing the integrated access of sensor monitoring data and BIM model information, and quickly building a mapping relationship between the finite element simulation model and the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0010] Figure 1 It is a flow chart of the real-time state assessment method of a suspension bridge based on BIM and FEM of the present invention; Figure 2 This is a schematic diagram of the data call information flow with the model database as the core of the present invention; Figure 3 It is a schematic diagram of the dynamic response effect of the suspension bridge before the model order reduction in the example described in the present invention; Figure 4 It is a schematic diagram of the dynamic response effect of the suspension bridge after the model is reduced in the example described in the present invention; Figure 5 It is a structural schematic diagram of the real-time status assessment system of a suspension bridge based on BIM and FEM of the present invention. DETAILED DESCRIPTION

[0011] In order to further understand the present invention, preferred embodiments of the present invention are described below in conjunction with examples. However, it should be understood that these descriptions are only for further illustrating the features and advantages of the present invention, rather than limiting the claims of the present invention.

[0012] Terminology explanation: FEM: Finite Element Method (FEM) is a numerical technique for finding approximate solutions to boundary value problems of partial differential equations. When solving, the entire problem area is decomposed, and each sub-area becomes a simple part, which is called a finite element.

[0013] POD: Proper Orthogonal Decomposition (POD) is a statistical method used to extract the most important characteristic patterns from a set of data. When dealing with finite element simulation models, POD can be used to reduce the order of the model, that is, to extract key dynamic features from a detailed high-fidelity model in order to reduce the computational cost while maintaining the main characteristics of the physical phenomenon.

[0014] Embodiment 1:

[0015] See also Figure 1 ,This embodiment provides a real-time state assessment method for a suspension bridge based on BIM and FEM, including: S1: Obtain sensor monitoring data of the suspension bridge and BIM model information of the suspension bridge and store them to build a model database; S2: Construct a finite element simulation model of a suspension bridge in geometric dimensions based on the BIM model information data in the model database; S3: Based on the POD intrinsic orthogonal decomposition method, the finite element simulation model of the suspension bridge is reduced to obtain a reduced-order finite element simulation model; S4: Modify the reduced-order finite element simulation model based on the response surface method to obtain a modified finite element simulation model; S5: Use sensor monitoring data and modified finite element simulation models to monitor the condition and identify damage of the suspension bridge, and evaluate the operating status of the suspension bridge in real time.

[0016] Specifically: In step S1, the sensor monitoring data of the suspension bridge and the BIM model information of the suspension bridge are obtained and stored to build a model database, including: S101, select sensors and arrange the sensors on the suspension bridge.

[0017] When selecting a sensor, not only the technical characteristics of the sensor itself (resolution, sensitivity, anti-interference ability, etc.) should be considered, but also factors such as the sensor's compatibility and price. Sensors commonly used for bridge health monitoring include displacement sensors, stress or strain sensors, and acceleration sensors, which are the basic components of bridge health monitoring.

[0018] When arranging sensors, it is impossible to monitor all sections in actual projects because the stress and deformation of each section of the bridge structure are different. Taking into account the accuracy and monitoring cost of bridge health monitoring, sensors are arranged in key sections of the bridge, such as the mid-span section, piers, side spans, etc., and the sensors are arranged symmetrically along the longitudinal axis of the bridge to facilitate mutual verification of monitoring data.

[0019] S102, transmitting the monitoring data of various types of sensors through the MQTT protocol.

[0020] like Figure 2 As shown, various types of sensors upload monitoring data to the server (MQTTBroker) through the MQTT communication protocol, and the server aggregates and transmits the data.

[0021] S103, construct a suspension bridge BIM model based on Revit software and obtain relevant information of the model and store it in JSON file format.

[0022] The model structured data of the suspension bridge BIM model includes geometric information, material information and attribute information. The API (Application Programming Interface) is used to define the globally unique identifier Uniqueld. The information sharing of the BIM model, sensor subscription database and simulation model of the same simulation object is associated through the globally unique identifier Uniqueld. The API defines the GetBimElement interface function to obtain the geometric information (get_Geometry), material information (materialElement) and attribute information (Parameter) of the Revit software BIM model, and convert the geometric information in stp format into an OBJ format file and store it in the model area of ​​the JSON file, that is, the collection physical data area; the material information and attribute information in dat format are stored in the information area of ​​the JSON file, that is, the real scene information data area.

[0023] S104, storing the JSON file of the sensor monitoring data and the BIM model information, and constructing a model database (Database), that is, the model database contains the real-time monitoring data and historical monitoring data of the sensor, and related information of the BIM model of the suspension bridge, including geometric information, material information and attribute information.

[0024] In step S2, constructing a finite element simulation model of geometric dimensions based on the model database includes extracting structured data of the suspension bridge BIM model in the model database and performing finite element geometric three-dimensional modeling using OpenDeformer software. Including, S201: Extract structured data from the model database through the developed API data transmission interface, specifically: The API defines the objView interface function to extract the geometric information of the model area of ​​the JSON file, and uses the objLoader loader to access the 3D real-scene modeling data to view the geometric information; the API defines the GetFemData interface function to extract the geometric information, material information, and attribute information of the JSON file, thereby implementing the call of model information for subsequent calculations.

[0025] At the same time, the API interface can also call the sensor monitoring data subscribed to the MQTT server in real time based on the MQTT publish-subscribe mechanism, for subsequent calls to the sensor monitoring data.

[0026] S202: The process of finite element geometric 3D modeling includes: Define the unit types and their related parameters that will be used in the analysis process. In this embodiment, the Beam unit is used to simplify the suspension bridge frame. The Beam unit, also known as the beam unit, is a commonly used element type in structural engineering and finite element analysis, used to simulate the beam structure of buildings and other slender structures. The "tension-only truss unit" is used to simplify the suspension bridge, simulating the axial tension of the suspension cable, thereby obtaining a suspension bridge finite element simulation model of the geometric dimensions of the suspension bridge.

[0027] It should be noted that those skilled in the art know that the finite element geometric three-dimensional modeling process is a prior art, and the model can be constructed according to actual needs, all of which fall within the protection scope of the present invention and will not be described in detail here.

[0028] In step S3, the model is reduced in order to reduce the computational complexity and computational time. By simplifying the details of the model and retaining the main physical characteristics, the degree of freedom of the model is reduced, that is, the main features of the structure are retained while the amount of computation is greatly reduced. For the reduction of the order of the finite element simulation model, the POD intrinsic orthogonal decomposition method provides an effective means to significantly reduce the complexity of the model while maintaining the main features of the physical phenomenon, thereby saving computing resources during the design and analysis process. In this embodiment, the specific process of selecting the POD intrinsic orthogonal decomposition method to reduce the model order of the finite element simulation model is as follows: S301, calculate steady-state dynamic response using all measured or simulated data : , In the formula, It is represented as the eigenvector (N×1) of α, i.e., the vibration mode vector; H 0α represents the amplitude of the complex transfer function of the system when the mode is α; f 0α represents the amplitude of the projected load vector; Ω is the external excitation frequency; t represents time; Φ α It represents the phase of the system response when the mode is α; Then use the instantaneous dynamic response matrix composed of different observation points Subtract steady-state dynamic response , get the pulsation response matrix at each moment : .

[0029] S302, in the sample space by Latin hypercube sampling Get the data of n sampling points and assemble them into a snapshot matrix : .

[0030] S303, snapshot matrix Perform singular value decomposition and get: , In the formula, B and V They are m × m and n × n The standard orthogonal matrix of S yes m × n A diagonal matrix with the elements on the main diagonal (i=1,2,3,... m ) is called the matrix B The singular values ​​of .

[0031] S304, solve the time coefficient: α = B·S .

[0032] S305, calculate eigenvalues λ i , that is, the energy of each mode , select the characteristic mode according to the threshold of the reduced-order model accuracy p, l The reduced-order model accuracy p of a mode is defined as: , In the formula l Indicates the number of modes selected by the enumeration; m It should be noted that those skilled in the art know that the threshold of the reduced-order model accuracy p is selected and determined according to actual needs, all of which fall within the protection scope of the present invention and will not be described in detail here.

[0033] S306: Reconstruct the structural dynamic response at any time according to the selected characteristic modes , , in, represents the time coefficient of the i-th eigenmode , represents the standard orthogonal matrix under the i-th eigenmode V .

[0034] In this example, the actual measurement data of the displacement sensor is used as the sample set, and the POD method is used to analyze the dynamic response of the suspension bridge finite element model under wind load. The pulsating wind load converted from the field data collected for 10 seconds is applied to obtain the dynamic response data of the suspension bridge to form a snapshot matrix. Then, the Latin hypercube sampling method is used to extract sample points in the input parameter range, and singular value decomposition is performed to obtain the characteristic mode. The accuracy of the reduced-order model is selected as 95%, and the corresponding full-order model solution time is 22 minutes. The actual solution time of the reduced-order model is 13 seconds. The dynamic response of the suspension bridge before and after the model reduction is obtained as follows: Figure 3 and Figure 4 As shown in the figure, different color values ​​represent the degree of stress and deformation of the suspension bridge. The redder the color, the greater the stress and deformation, and the bluer the color, the smaller the stress and deformation. It can be seen that the stress and deformation distribution of the suspension bridge before and after the model reduction is basically the same. The reduced-order model greatly reduces the solution time while ensuring the accuracy of the model solution.

[0035] In step S4, the finite element simulation model is modified based on the response surface method, wherein the response surface method (RSM) constructs an approximate function expression between the objective function and the variables, namely the response surface model, through a reasonable experimental design method, and makes the originally implicit objective function explicit, and then optimizes the objective function, thereby reducing the repeated modeling and simulation process, thereby improving the efficiency of model modification and optimization of the finite element simulation model.

[0036] In this example, the quadratic polynomial response surface model is selected for modeling. The quadratic polynomial can ensure that the model is as simple as possible while describing the real function, which can reduce the workload of experiments or numerical analysis. Its expression is: (1) In the formula, y is the objective function, β 0 , β j , β jj and β ij are the polynomial coefficients, x j is the experimental variable, n is the number of experimental variables, formula (1) can be expressed in the following matrix form: (2) or (3) ε for yThe error is calculated by using the least squares principle, and the polynomial coefficients are obtained according to formula (3) by minimizing the sum of squares of the errors: (4) Substituting the coefficients into formula (1) we can get the expression of the response surface function.

[0037] The response surface method uses the dynamic response of the system as the objective function and the finite element model parameters as the test variables. The response surface model is fitted by the response surface and replaces the original finite element model to obtain the optimal estimate. The dynamic response of the system includes displacement, velocity, acceleration and / or vibration mode, and the finite element model parameters include the density and elastic modulus of the main beam, the density and elastic modulus of the bridge tower, the elastic modulus of the cable and the applied load.

[0038] In step S5, the sensor data and the modified finite element simulation model are used to monitor the condition and identify damage of the suspension bridge, evaluate the operating status of the suspension bridge in real time, and use machine learning algorithms for auxiliary maintenance.

[0039] The monitoring data is used based on WebGL and other rendering and real-scene display plug-ins, and the monitoring data and finite element simulation calculation results are combined to display the reduced-order model of the suspension bridge in three dimensions. When the monitoring data exceeds the preset abnormal value, an alarm is issued, and the corrected model is used for damage identification, providing important information for structural health monitoring and safety assessment. The damage identification methods include area reduction method, elastic modulus reduction method, reverse load method and entity simulation method.

[0040] It should be noted that those skilled in the art know that the preset abnormal value can be adjusted according to actual needs, all of which fall within the protection scope of the present invention and will not be described in detail here.

[0041] The auxiliary maintenance is based on machine learning and artificial intelligence algorithms. The machine learning algorithm can automatically learn and extract features from a large amount of data, identify the key factors affecting the state of the suspension bridge and their changing patterns, and the artificial intelligence algorithm further uses these learning results to simulate the decision-making process of human experts, and conduct intelligent analysis and judgment on the bridge state, which helps the staff to formulate more reasonable and efficient maintenance strategies, reduce unnecessary maintenance costs, and ensure the safe operation of the bridge. Including, S501: Collect and integrate historical data and real-time operation data of suspension bridges. Historical data includes bridge design information, construction records, and previous maintenance reports, which provide rich background information for the algorithm; real-time operation data covers real-time monitoring data such as stress, vibration, and temperature of bridges in daily use, as well as environmental factors such as wind speed, temperature, and humidity, which reflect the current real-time status of the bridge.

[0042] S502: Based on the above data, the potential defects of the bridge are identified and their development trends are predicted through advanced algorithm models, such as time series analysis and deep learning networks. This prediction not only focuses on currently known problems, but also discovers potential and unrevealed defect risks through data analysis.

[0043] S503: Comprehensively evaluate the full life cycle status of the suspension bridge, considering the entire process from design, construction to operation, evaluate its overall performance, safety level and remaining service life, and provide scientific and accurate maintenance suggestions for staff. And generate a detailed maintenance plan based on the development trend of defects and the full life cycle status evaluation results, including recommended maintenance time, location, method and required resources.

[0044] Embodiment 2: See also Figure 5 This embodiment provides a real-time status assessment system for a suspension bridge based on BIM and FEM, including a data acquisition and transmission module, a modeling and simulation module, and a visual status assessment module; wherein, The data acquisition and transmission module is used to obtain the sensor monitoring data of the suspension bridge and the structural data of the finite element simulation model, and transmit the data to the modeling simulation module; it includes a sensor module, a suspension bridge BIM model and a data transmission interface; the sensor module includes a displacement sensor, a stress or strain sensor and / or an acceleration sensor, etc. The sensor layout points are selected from key sections such as the mid-span section, the pier, and the side span, and the sensors are symmetrically arranged along the longitudinal axis of the bridge to facilitate mutual verification of the monitoring data. The data transmission interface is: each type of sensor uploads the monitoring data to the server (MQTT Broker) through the MQTT communication protocol, and the server summarizes the data and sends it to the system model database; the BIM model separates the geometric information, material information, and attribute information of the suspension bridge model through the API, and converts the geometric information into an OBJ format file, stores it in the model area of ​​the JSON file, stores the material information and attribute information in the information area of ​​the JSON file, and writes the JSON files of the above-mentioned geometric information, material information, and attribute information into the system model database to realize the separation and call of the model information.

[0045] The modeling and simulation module is used to receive sensor monitoring data and structural data of the finite element simulation model and construct a finite element simulation model of a suspension bridge, and reduce the model order of the finite element simulation model; it includes a model database, a finite element geometric modeling module and a finite element model reduction module. The finite element model reduction module uses an order reduction algorithm to retain the main features of the structure while greatly reducing the amount of calculation. The model database includes real-time monitoring data and historical monitoring data of sensors, as well as BIM model information data; the finite element geometric modeling module performs three-dimensional modeling by extracting BIM model information data from the model database, and the suspension bridge frame is simplified using Beam units. The finite element model reduction module uses proper orthogonal decomposition (POD) to reduce the order of the finite element simulation model of the suspension bridge.

[0046] The visualization state assessment module is used to call the simulation results of the modeling and simulation module and perform visualization display and state assessment. It includes a finite element model correction module, a state monitoring module and an auxiliary maintenance module. The finite element model correction module adopts the response surface method, takes the dynamic response of the system as the objective function, takes the finite element model parameters as the test variables, and fits the response surface model through the response surface and replaces the original finite element model to obtain the optimal estimate; the response surface method described in this embodiment uses quadratic polynomials for modeling; the state monitoring module is based on WebGL and other rendering and real-scene display plug-ins, combined with monitoring data to perform a three-dimensional real-scene display of the suspension bridge, when the monitoring data exceeds the abnormal value, an alarm is issued, and the corrected model is used for damage identification; the damage identification method includes area reduction method, elastic modulus reduction method, reverse load method and entity simulation method. The auxiliary maintenance module is based on machine learning and artificial intelligence algorithms, combined with historical and operating data to give a defect development trend analysis to assist staff in making maintenance decisions.

[0047] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0048] The above embodiments are only used to help understand the method and core idea of ​​the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0050] It should be noted that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the steps as sequential processes, many of the steps can be implemented in parallel, concurrently or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

Claims

1. A real-time status assessment method for a suspension bridge based on BIM and FEM, characterized in that: include, Obtain the sensor monitoring data of the suspension bridge and the BIM model information of the suspension bridge and store them to build a model database; Construct a finite element simulation model of a suspension bridge in geometric dimensions based on the BIM model information data in the model database; Based on the POD intrinsic orthogonal decomposition method, the finite element simulation model of the suspension bridge is reduced to obtain a reduced-order finite element simulation model; The reduced-order finite element simulation model is modified based on the response surface method to obtain a modified finite element simulation model; The sensor monitoring data and the modified finite element simulation model are used to monitor the condition and identify damage of the suspension bridge, and the operating status of the suspension bridge is evaluated in real time.

2. The real-time status assessment method of a suspension bridge based on BIM and FEM according to claim 1 is characterized in that: The obtaining of sensor monitoring data of the suspension bridge comprises: The sensors are symmetrically arranged along the longitudinal axis of the bridge, wherein the sensors include displacement sensors, stress or strain sensors and / or acceleration sensors; The sensor monitoring data is uploaded to the server through the MQTT communication protocol, and the server aggregates and transmits the sensor monitoring data.

3. The real-time status assessment method for suspension bridge based on BIM and FEM according to claim 2 is characterized in that: The obtaining of suspension bridge BIM model information includes: The BIM model of the suspension bridge is constructed based on Revit software. The BIM model information includes geometric information, material information and attribute information. The application programming interface API defines the GetBimElement interface function for obtaining the geometric information, material information and attribute information of the suspension bridge BIM model; and storing the geometric information in the model area of ​​the JSON file; and storing the material information and attribute information in the information area of ​​the JSON file; The sensor monitoring data and BIM model information are stored in JSON files to build a model database.

4. The method for real-time status assessment of a suspension bridge based on BIM and FEM according to claim 1, characterized in that: The geometric finite element simulation model of the suspension bridge constructed based on the BIM model information data in the model database includes: Extract structured data from the model database through the API application programming interface; The beam unit is used to simplify the simulation of the suspension bridge frame; The suspension bridge is simplified by using "tension-only truss unit" to simulate the axial tension of the cable, thereby obtaining a finite element simulation model of the suspension bridge with geometric dimensions.

5. The method for real-time status assessment of a suspension bridge based on BIM and FEM according to claim 1, characterized in that: The method of reducing the order of the suspension bridge finite element simulation model based on the POD intrinsic orthogonal decomposition method to obtain the reduced-order finite element simulation model includes calculating the steady-state dynamic response using all the measured data. , using the instantaneous dynamic response matrix composed of different observation points Subtract steady-state dynamic response , get the pulsation response matrix at each moment ; In the sample space by Latin hypercube sampling Get the data of n sampling points and assemble them into a snapshot matrix ; For the snapshot matrix Perform singular value decomposition and get: ,in B and V They are m × m and n × n The orthonormal matrix of S yes m × n A diagonal matrix with the elements on the main diagonal (i=1,2,3,... m ) is called the matrix B The singular values ​​of Solve for the time coefficient: α = B·S ; Calculate eigenvalues λ i , that is, the energy of each mode According to the threshold of the reduced-order model accuracy p, the characteristic modes are selected. l The reduced-order model accuracy p of a mode is defined as: ， in, l Indicates the number of modes selected by the enumeration; m represents the number of all modes; Reconstruct the instantaneous dynamic response at any time based on the selected eigenmodes , ， in, represents the time coefficient of the i-th eigenmode , represents the standard orthogonal matrix under the i-th eigenmode V .

6. The method for real-time status assessment of a suspension bridge based on BIM and FEM according to claim 1, characterized in that: The response surface method uses the dynamic response of the system as the objective function and the finite element model parameters as the test variables, and obtains the optimal estimate by fitting the response surface model through the response surface and replacing the original finite element model; the dynamic response of the system includes displacement, velocity, acceleration and / or vibration mode, and the finite element model parameters include the density and elastic modulus of the main beam, the density and elastic modulus of the bridge tower, the elastic modulus of the cable and / or the applied load.

7. The method for real-time status assessment of a suspension bridge based on BIM and FEM according to claim 6 is characterized in that: The modified finite element simulation model is modified based on the response surface method to obtain the modified finite element simulation model, comprising: The quadratic polynomial response surface model is selected for modeling, and its expression is: （1） In the formula, y is the objective function, β 0 , β j , β jj and β ij are the polynomial coefficients, x j is the experimental variable, n is the number of experimental variables, formula (1) can be expressed in the following matrix form: （2） or （3） ε for y The error is calculated by using the least squares principle, and the polynomial coefficients are obtained according to formula (3) by minimizing the sum of squares of the errors. （4） Substituting the coefficients into equation (1) we can get the expression of the response surface function.

8. The method for real-time status assessment of a suspension bridge based on BIM and FEM according to claim 1, characterized in that: The use of sensor monitoring data and a modified finite element simulation model to monitor the condition and identify damage of the suspension bridge and to evaluate the operating condition of the suspension bridge in real time includes: Based on the rendering and real-scene display plug-in, the reduced-order model of the suspension bridge is displayed in three dimensions through sensor monitoring data and finite element simulation calculation results. When the monitoring data exceeds the abnormal value, an alarm is issued, and the corrected model is used for damage identification, providing information for health monitoring and safety assessment of the suspension bridge.

9. The method for real-time status assessment of a suspension bridge based on BIM and FEM according to claim 8, characterized in that: The damage identification method includes an area reduction method, an elastic modulus reduction method, a reverse load method and / or a solid simulation method.

10. A real-time status assessment system for suspension bridges based on BIM and FEM, characterized in that: It includes data acquisition and transmission module, modeling simulation module and visual status assessment module, among which: The data acquisition and transmission module includes a sensor module, a suspension bridge BIM model module and a data transmission interface, which is used to obtain sensor monitoring data and suspension bridge BIM model data of the suspension bridge, and transmit the obtained data to the modeling and simulation module; The modeling and simulation module includes a model database, a finite element geometry modeling module and a finite element model reduction module, which are used to receive sensor monitoring data and suspension bridge BIM model data and construct a suspension bridge finite element simulation model, and at the same time, perform model reduction on the constructed suspension bridge finite element simulation model to obtain a reduced-order finite element simulation model; The visual state assessment module includes a finite element model correction module, a state monitoring module and an auxiliary maintenance module, which are used to correct the reduced-order finite element simulation model, obtain the corrected finite element simulation model, and perform visual display and state assessment by calling the simulation results of the modeling simulation module.

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