CN117592341B - Finite element prediction method and system for T-shaped bridge health state - Google Patents
Finite element prediction method and system for T-shaped bridge health state Download PDFInfo
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Abstract
The application provides a finite element prediction method and a finite element prediction system for the health state of a T-shaped bridge, wherein the method comprises the following steps: and (3) building a part model in the ABAQUS module according to unmanned inclined shooting technology and Revite later repair, and obtaining the natural frequency of the initial model through ABAQUS calculation. And the damaged bending stiffness is reversely deduced based on a residual bending stiffness ratio formula and the initial model natural frequency obtained by deduction, model data are updated, the accurate model construction of the damaged bridge is completed, and the damaged bridge natural frequency is obtained through an ABAQUS self-solver, so that the prediction of the bridge health state is completed. The method avoids errors between actual engineering and drawings, so that the construction errors of the finite element model structure and the actual engineering are close to zero, and the accuracy of model calculation results is important. Meanwhile, the damaged stiffness input model obtained through formula inversion enables model structure parameters to have practical significance, and model accuracy is further improved.
Description
Technical Field
The application relates to the technical field of T-shaped bridge health monitoring, in particular to a finite element prediction method and a finite element prediction system for the health state of a T-shaped bridge.
Background
In recent years, with the rapid development of economy in China, traffic volume is greatly improved, and as a traffic key point, bridges play an extremely important role. However, along with the increase of the number of bridges, the aging of the bridges is increased, and the influence of overload, natural climate environment and other factors is added, so that the bridge structure has different degrees of service performance reduction and structural damage, and the prediction of the real-time health state of the bridge has practical significance for bridge monitoring and maintenance work. The finite element model method is one of main methods for predicting the health state of the bridge, whether the model is accurate or not is an extremely important ring in the current field, is closely related to project implementation efficiency and quality, and is numerous based on the finite element method in the field, but the accuracy of simulation results still needs to be further explored in the field.
There are also many finite element methods in the field of bridge health monitoring. Finite element models of Svinesun have been developed to assess bridge health through static and dynamic measurements. Static responses of finite element models have been updated using three-dimensional terrestrial laser scanning measurements. It has been proposed to use a framework for fatigue damage identification using operational experimental measurements and high-fidelity finite element models to perform health monitoring of bridge health. The numerical finite element model of the truss and the framework structure is updated by the evolution optimization algorithm to ensure the precision of the finite element model and improve the prediction accuracy. In summary, the finite element method fundamentally improves the accuracy of the finite element model, so that the prediction accuracy of the finite element model is improved, and the high-accuracy prediction method is still a direction which is always explored in the field.
Disclosure of Invention
The invention aims to provide a correction method of a finite element model of a heavy haul railway bridge, which can effectively and accurately simulate a bridge structure, solve the problem of deviation between the existing method model structure and an actual bridge, and further improve the accuracy of bridge health problem prediction through a formula method.
Based on the above purpose, the application provides a finite element prediction method for the health state of a T-shaped bridge, which comprises the following steps:
s1, calculating the residual bending stiffness ratio representing the health state of the bridge;
s2, carrying out scanning modeling on the target T-shaped bridge through the unmanned aerial vehicle and carrying out post-treatment;
s3, importing finite element software to complete finite element simulation based on the entity model established in the S2, and establishing an initial finite element model;
s4, deriving fatigue bending stiffness EI based on natural frequency of the initial finite element model and a preset formula n ；
S5, updating the initial finite element model based on the fatigue bending rigidity, and predicting the current state of the bridge.
Based on the above purpose, the present application further provides a finite element prediction system for health status of a T-bridge, including:
the residual bending stiffness ratio module is used for calculating the residual bending stiffness ratio representing the health state of the bridge;
the unmanned aerial vehicle modeling module is used for carrying out scanning modeling on the target T-shaped bridge through the unmanned aerial vehicle and carrying out post-treatment;
the initial finite element module is used for importing finite element software to complete finite element simulation based on the entity model established in the step S2, and establishing an initial finite element model;
the fatigue bending stiffness module is used for deriving the fatigue bending stiffness EI based on the natural frequency of the initial finite element model and a preset formula n ；
And the state prediction module is used for updating the initial finite element model based on the fatigue bending rigidity and predicting the current state of the bridge.
Overall, the advantages of the present application and the experience brought to the user are: according to the method, errors between actual engineering and drawings are avoided through unmanned aerial vehicle shooting modeling and post-processing repair, so that the construction errors of the finite element model structure and the actual engineering are close to zero, and the accuracy of model calculation results is important. Meanwhile, the damaged rigidity input model is obtained through formula inversion, so that the model structure parameter has more practical significance, and the model precision is further improved. The invention provides a certain reference meaning for the prediction of the health state of the T-shaped bridge and promotes the further development of the field.
Drawings
In the drawings, the same reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily drawn to scale. It is appreciated that these drawings depict only some embodiments according to the disclosure and are not therefore to be considered limiting of its scope.
Fig. 1 shows a flow chart of a finite element prediction method for the health status of a T-bridge according to an embodiment of the present application.
Fig. 2 shows a unmanned aerial vehicle modeling flowchart according to an embodiment of the present application.
FIG. 3 illustrates an initial finite element model building flow diagram according to an embodiment of the present application.
FIG. 4 illustrates a flow chart for solving for damaged stiffness based on a modified finite element model according to an embodiment of the application.
Fig. 5 shows a configuration diagram of a finite element prediction system for T-bridge health according to an embodiment of the present application.
Fig. 6 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 7 shows a schematic diagram of a storage medium according to an embodiment of the present application.
Fig. 8 shows an engineering physical elevation according to an embodiment of the present application.
Fig. 9 shows an engineering practice wide middle section view, a fulcrum section view according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
According to the method, the model construction of the part in the ABAQUS module is completed by combining the unmanned inclined shooting technology with the Revite later repair, and the natural frequency of the initial model is obtained through ABAQUS calculation. And the damaged bending stiffness is reversely deduced based on a residual bending stiffness ratio formula and the initial model natural frequency obtained by deduction, model data are updated, the accurate model construction of the damaged bridge is completed, and the damaged bridge natural frequency is obtained through an ABAQUS self-solver, so that the prediction of the bridge health state is completed.
Fig. 1 shows a flow chart of a finite element prediction method for the health status of a T-bridge according to an embodiment of the present application. As shown in fig. 1, the finite element prediction method for the health state of the T-bridge includes:
s1, calculating the residual bending stiffness ratio representing the health state of the bridge;
s2, carrying out scanning modeling on the target T-shaped bridge through the unmanned aerial vehicle and carrying out post-treatment;
s3, importing finite element software to complete finite element simulation based on the entity model established in the S2, and establishing an initial finite element model;
s4, deriving fatigue bending stiffness EI based on natural frequency of the initial finite element model and a preset formula n ；
S5, updating the initial finite element model based on the fatigue bending rigidity, and predicting the current state of the bridge.
Further, in step S1, the derivation of the residual bending stiffness ratio:
s11, establishing a free vibration differential equation of the Euler beam based on a structural dynamics theory, wherein the free vibration differential equation is as follows:
equation 1
Where EI (x) is expressed as bending stiffness, ρA (x) is mass per unit length, v (x, t) is lateral vibration displacement,
e is expressed as modulus of elasticity and ρ is expressed as density.
S12, defining a residual bending stiffness ratio formula:
equation 2
Wherein eta n For defining the residual bending stiffness ratio, EI n 、EI o The flexural rigidity and the damage rigidity of the repeated alternating load for n times are respectively.
S13, setting a free vibration differential equation of the Euler beam according to a separation variable method:
equation 3
Where Φ (x) represents the bridge vibration mode, ω represents the angular velocity at the time of vibration, and θ represents the initial phase angle.
S14, solving a free vibration differential equation general solution of the Euler beam by adopting the solution in S13:
equation 4
Wherein alpha is represented as，C 1 、C 2 、C 3 、C 4 Determined according to boundary conditions.
S15, obtaining a standard angular frequency formula of the damaged T-shaped beam based on a concrete beam step stiffness damage model and a Rayleigh method:
equation 5
Wherein the method comprises the steps ofFor the total length of the concrete x 1 For the left end to the damaged distance x of the middle of the concrete 2 The damaged distance from the left end to the middle of the concrete;
s16, according to the first order vibration mode functionObtaining standard angular frequency of a concrete beam to be damaged:
equation 6
Wherein the method comprises the steps of。
S17, based onAnd the first-order natural frequency of the undamaged concrete simply supported beam is further simplified after the uniform section is obtained:
equation 7
S18, obtaining the natural frequency of the damage of the constant-section simply supported beam according to the mass of the unit length of m=ρA:
equation 8
S19, obtaining a residual bending stiffness ratio based on S17 and S18:
equation 9
Where f is the natural frequency of the bridge.
Further, as shown in fig. 2, in step S2, the unmanned aerial vehicle aerial modeling and post-processing process is as follows:
s21, presetting a scheme of a rectangular unmanned aerial vehicle aerial flight route based on a bridge plan view and an elevation view;
s22, acquiring data according to a flight scheme according to the aerial unmanned aerial vehicle and sending the data to a three-dimensional modeling module;
s23, a three-dimensional modeling module establishes a bridge three-dimensional model according to the acquired modeling data;
s24, repairing the edges and corners of the model in the step S23 by combining the Revite in the BIM software with an engineering actual drawing, and deleting unnecessary construction.
Further, as shown in fig. 3, the mechanical model is established in step S3:
s31, importing ABAQUS software according to a Revite software model to complete component module establishment;
s32, finishing setting of materials, analysis steps, grids and constraints based on the basic data;
s33, dividing the ABAQUS grids by combining the finite element method basic knowledge.
Further, in step S4, as shown in fig. 4, the method includes:
s41, modifying elastic modulus parameter to be E based on ABAQUS material module n ；
S42, completing the same constraint and load setting by combining the initial finite element model;
s43, calculating the natural frequency f after damage by adopting an ABAQUS software self-contained solver 0 According to natural frequency f 0 The return equation 9 gives the fatigue stiffness EI n 。
Further, in step S5, the finite element model updating and bridge health prediction process:
s51, obtaining E based on the fact that the material does not deform greatly and the inertia distance of I is unchanged n (modulus of elasticity under n repeated alternating loads);
s52, according to E n Updating an original elastic model at an ABAQUS material interface and performing operation to obtain the current natural frequency f 1 ；
S53 based on f 1 And the current health condition of the bridge can be known by comparing the bridge design specification with the bridge design specification.
Fig. 8 shows an engineering physical elevation according to an embodiment of the present application. Fig. 9 shows an engineering practice wide middle section view, a fulcrum section view according to an embodiment of the present application. According to the method, errors between actual engineering and drawings are avoided through unmanned aerial vehicle shooting modeling and post-processing repair, so that the construction errors of the finite element model structure and the actual engineering are close to zero, and the accuracy of model calculation results is important. Meanwhile, the damaged rigidity input model is obtained through formula inversion, so that the model structure parameter has more practical significance, and the model precision is further improved. The invention provides a certain reference meaning for the prediction of the health state of the T-shaped bridge and promotes the further development of the field.
An embodiment of the present application provides a finite element prediction system for a T-bridge health, where the system is configured to perform the finite element prediction method for a T-bridge health according to the foregoing embodiment, as shown in fig. 5, and the system includes:
a residual bending stiffness ratio module 501 for calculating a residual bending stiffness ratio indicative of a bridge health;
the unmanned aerial vehicle modeling module 502 is used for carrying out scanning modeling on the target T-shaped bridge through the unmanned aerial vehicle and carrying out post-processing;
an initial finite element module 503, configured to introduce finite element software to complete finite element simulation based on the entity model established in S2, and establish an initial finite element model;
a fatigue bending stiffness module 504 for deriving a fatigue bending stiffness EI based on the natural frequency of the initial finite element model and a preset formula n ；
The state prediction module 505 is configured to update the initial finite element model based on the fatigue bending stiffness, and predict a current state of the bridge.
The finite element prediction system for the health state of the T-shaped bridge provided by the embodiment of the application and the finite element prediction method for the health state of the T-shaped bridge provided by the embodiment of the application are the same in invention conception, and have the same beneficial effects as the method adopted, operated or realized by the stored application program.
The embodiment of the application also provides electronic equipment corresponding to the finite element prediction method of the health state of the T-shaped bridge provided by the embodiment, so as to execute the finite element prediction method of the health state of the T-shaped bridge. The embodiments of the present application are not limited.
Referring to fig. 6, a schematic diagram of an electronic device according to some embodiments of the present application is shown. As shown in fig. 6, the electronic device 20 includes: a processor 200, a memory 201, a bus 202 and a communication interface 203, the processor 200, the communication interface 203 and the memory 201 being connected by the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the method for predicting the health status of the T-bridge according to any one of the foregoing embodiments of the present application when executing the computer program.
The memory 201 may include a high-speed random access memory (RAM: random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory. The communication connection between the system network element and at least one other network element is implemented via at least one communication interface 203 (which may be wired or wireless), the internet, a wide area network, a local network, a metropolitan area network, etc. may be used.
Bus 202 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. The memory 201 is configured to store a program, and the processor 200 executes the program after receiving an execution instruction, and the method for predicting the health status of the T-bridge disclosed in any embodiment of the present application may be applied to the processor 200 or implemented by the processor 200.
The processor 200 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 200 or by instructions in the form of software. The processor 200 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201, and in combination with its hardware, performs the steps of the above method.
The electronic equipment provided by the embodiment of the application and the finite element prediction method for the health state of the T-shaped bridge provided by the embodiment of the application have the same beneficial effects as the method adopted, operated or realized by the electronic equipment based on the same inventive concept.
The present embodiment also provides a computer readable storage medium corresponding to the method for predicting the health status of the T-bridge provided in the foregoing embodiment, please refer to fig. 7, which illustrates the computer readable storage medium as an optical disc 30, on which a computer program (i.e. a program product) is stored, wherein the computer program, when executed by a processor, performs the method for predicting the health status of the T-bridge provided in any of the foregoing embodiments.
It should be noted that examples of the computer readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical or magnetic storage medium, which will not be described in detail herein.
The computer readable storage medium provided by the above embodiment of the present application and the method for predicting the health status of the T-bridge provided by the embodiment of the present application are the same inventive concept, and have the same beneficial effects as the method adopted, operated or implemented by the application program stored therein.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, the present application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and the above description of specific languages is provided for disclosure of preferred embodiments of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the present application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed application requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the present application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functions of some or all of the components in a virtual machine creation system according to embodiments of the present application may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present application may also be embodied as a device or system program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present application may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of various changes or substitutions within the technical scope of the present application, and these should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. The finite element prediction method for the health state of the T-shaped bridge is characterized by comprising the following steps of:
s1, calculating the residual bending stiffness ratio representing the health state of the bridge;
s2, carrying out scanning modeling on the target T-shaped bridge through the unmanned aerial vehicle and carrying out post-treatment;
s3, importing finite element software to complete finite element simulation based on the entity model established in the S2, and establishing an initial finite element model;
s4, deriving fatigue bending stiffness EI based on natural frequency of the initial finite element model and a preset formula n ；
S5, updating the initial finite element model based on the fatigue bending rigidity, and predicting the current state of the bridge;
in step S1, the remaining bending stiffness ratio is calculated as follows:
s11, establishing a free vibration differential equation of the Euler beam based on a structural dynamics theory, wherein the free vibration differential equation is as follows:
equation 1;
wherein the method comprises the steps ofExpressed as flexural rigidity>Mass per unit length>For transverse vibrational displacement, E is expressed as modulus of elasticity and ρ is expressed as density;
s12, defining a residual bending stiffness ratio formula:
equation 2;
wherein the method comprises the steps ofFor a defined residual flexural rigidity ratio, +.>、/>Bending rigidity of n repeated alternating loads and undamaged bending rigidity are respectively obtained;
s13, setting a free vibration differential equation solution of the Euler beam according to a separation variable method:
equation 3;
wherein the method comprises the steps ofRepresenting a bridge vibration mode, ω representing an angular velocity at the time of vibration, θ representing an initial phase angle;
s14, solving a free vibration differential equation general solution of the Euler beam by adopting the solution in S13:
equation 4;
wherein the method comprises the steps ofDenoted as->，C 1 、C 2 、C 3 、C 4 Determining according to boundary conditions;
s15, obtaining a standard angular frequency formula of the damaged T-shaped beam based on a concrete beam step stiffness damage model and a Rayleigh method:
equation 5;
wherein the method comprises the steps ofFor the total length of the concrete x 1 For the left end to the damaged distance x of the middle of the concrete 2 The damaged distance from the left end to the middle of the concrete;
s16, according to the first order vibration mode functionObtaining standard angular frequency of a concrete beam to be damaged:
equation 6;
wherein the method comprises the steps of；
S17, based onAnd the first-order natural frequency of the undamaged concrete simply supported beam is further simplified after the uniform section is obtained:
equation 7;
s18, according to the mass per unit lengthObtaining the natural frequency of damage of the constant-section simply supported beam:
equation 8;
s19, obtaining a residual bending stiffness ratio based on S17 and S18:
equation 9;
where f is the natural frequency of the bridge.
2. The method for predicting the health of a T-bridge according to claim 1, wherein,
in step S2, performing scan modeling and post-processing on the target T-bridge by using the unmanned aerial vehicle, including: the method comprises the steps of a rectangular route planning scheme of the unmanned aerial vehicle, unmanned aerial vehicle parameter setting, aerial photographing data and model examples.
3. The method for predicting the health status of a T-bridge according to claim 2, wherein in step S2, the unmanned aerial vehicle aerial modeling and post-processing process is as follows:
s21, presetting a flight scheme of the aerial unmanned aerial vehicle based on a bridge plan view and an elevation view;
s22, the unmanned aerial vehicle collects data according to a flight scheme and sends the data to the three-dimensional modeling module;
s23, a three-dimensional modeling module establishes a bridge three-dimensional model according to the acquired modeling data;
s24, repairing the edges and corners of the bridge three-dimensional model in the step S23 through Revite in BIM software.
4. The method for predicting the health status of a T-bridge according to claim 1, wherein in step S3, finite element simulation is completed by importing finite element software based on the entity model established in step S2, and data used by establishing an initial finite element model includes an elastic modulus E, a density ρ and a mechanical model.
5. The method for predicting the health of a T-bridge according to claim 4, wherein in step S3, a mechanical model is established:
s31, importing ABAQUS software according to a Revite software model to complete component module establishment;
s32, setting sections, analysis steps, grids and constraints based on the basic data;
s33, dividing the ABAQUS grids by combining the finite element method basic knowledge.
6. The method for predicting the health of a T-bridge as set forth in claim 1, wherein, in step S4,
s41, modifying elastic modulus parameter to be E based on ABAQUS material module n ；
S42, completing the same constraint and load setting by combining the initial finite element model;
s43, calculating the natural frequency f after damage by adopting an ABAQUS software self-contained solver 0 According to natural frequency f 0 The return equation 9 gives the fatigue stiffness EI n 。
7. The method for predicting the health of a T-bridge according to claim 1, wherein in step S5, the finite element model updating and bridge health predicting process is as follows:
s51, obtaining the elastic modulus E under n times of repeated alternating load based on the fact that the material does not deform greatly and the inertia distance I is unchanged n ；
S52, according to E n Updating an original elastic model at an ABAQUS material interface and performing operation to obtain the current natural frequency f 1 ；
S53 based on f 1 Comparing with bridge design specification to obtainBridge current health.
8. A T-bridge health finite element prediction system using the method of any one of claims 1-7, comprising:
the residual bending stiffness ratio module is used for calculating the residual bending stiffness ratio representing the health state of the bridge;
the unmanned aerial vehicle modeling module is used for carrying out scanning modeling on the target T-shaped bridge through the unmanned aerial vehicle and carrying out post-treatment;
the initial finite element module is used for importing finite element software to complete finite element simulation based on the established entity model, and establishing an initial finite element model;
the fatigue bending stiffness module is used for deriving the fatigue bending stiffness EI based on the natural frequency of the initial finite element model and a preset formula n ；
And the state prediction module is used for updating the initial finite element model based on the fatigue bending rigidity and predicting the current state of the bridge.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor runs the computer program to implement the method of any one of claims 1-7.
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