CN115937504A - A damage identification method and system for a bridge structure - Google Patents

Abstract

The invention belongs to the technical field of bridge structure damage identification, and discloses a method and a system for identifying bridge structure damage, wherein the method and the system for identifying the bridge structure damage comprise the following steps: the system comprises a bridge structure image acquisition module, a health monitoring module, a central control module, an image feature extraction module, a damage identification module, a damage positioning module, a risk assessment module and a display module. According to the invention, the health monitoring data of the target bridge structure can be more objectively obtained through the health monitoring module, and the accuracy is higher; meanwhile, the traveling crane operated by the target bridge structure traffic is used as a power excitation source through the damage positioning module, artificial manufacturing excitation is not needed, normal traffic operation is not interfered, and economic and time cost is saved. The hardware adopted by the invention can be replaced at any time, and the invention is suitable for bridges of different types, utilizes the acceleration response of the target bridge structure, has high data quality, stability and reliability and low test cost, and is suitable for large-area popularization and use.

Description

Method and system for identifying damage to bridge structures

technical field

The invention belongs to the technical field of bridge structure damage identification, and in particular relates to a bridge structure damage identification method and system.

Background technique

Bridges generally refer to structures erected on rivers, lakes and seas to allow vehicles and pedestrians to pass smoothly. In order to adapt to the modern high-speed development of the transportation industry, bridges are also extended to buildings that cross mountain streams, poor geology or meet other transportation needs to make traffic more convenient. A bridge is generally composed of a superstructure, a substructure, a support and ancillary structures. The superstructure is also called a bridge span structure, which is the main structure for crossing obstacles; the substructure includes abutments, piers and foundations; Or the force transmission device installed at the supporting place of the abutment; the auxiliary structure refers to the bridge head strap, conical slope protection, revetment, diversion works, etc.; however, the existing bridge structure damage identification system does not have real-time calibration function, when the sensor is located When there is a large deviation in the structural position of the target bridge, the signal data is still based on the initial structural position, resulting in inaccurate monitoring results, and it is difficult to objectively evaluate the monitoring status of the target bridge structure; at the same time, it is impossible to accurately locate the bridge damage.

Through the above analysis, the problems and defects in the prior art are:

(1) The existing bridge structure damage identification system does not have the real-time calibration function. When the structural position where the sensor is located has a large deviation, the signal data is still based on the initial structural position, resulting in inaccurate monitoring results, and it is difficult to objectively evaluate the target bridge. The monitoring status of the structure.

(2) The bridge damage cannot be accurately located.

Contents of the invention

Aiming at the problems existing in the prior art, the present invention provides a bridge structure damage identification method and system.

The present invention is achieved in that a bridge structure damage identification system includes:

Bridge structure image acquisition module, health monitoring module, central control module, image feature extraction module, damage identification module, damage location module, risk assessment module, display module;

The bridge structure image acquisition module is connected with the central control module, and is used to collect bridge structure images through the camera;

The health monitoring module is connected with the central control module to monitor the health status of the bridge structure;

The central control module is connected with the bridge structure image acquisition module, health monitoring module, image feature extraction module, damage identification module, damage location module, risk assessment module, and display module to control the normal operation of each module;

The image feature extraction module is connected with the central control module, and is used to extract bridge structure image features through an extraction program;

The damage identification module is connected with the central control module and is used to identify the damage of the bridge structure through the identification program;

The damage location module is connected with the central control module and is used to locate the damage of the bridge structure;

The risk assessment module is connected with the central control module and is used to assess the safety risk of the bridge structure;

The display module is connected with the central control module, and is used to display bridge structure images, image features, damage identification results, damage location information, and risk assessment results.

A bridge structure damage identification method includes the following steps:

Step 1, the bridge structure image acquisition module uses the camera to collect bridge structure images; the health monitoring module connects to the central control module to extract bridge structure image information, and monitors the health status of the bridge structure;

Step 2, the central control module utilizes the feature extraction program to perform feature extraction on the bridge structure image through the image feature extraction module;

Step 3: Use the identification program to identify the damage of the bridge structure through the damage identification module; accurately locate the damaged part of the bridge structure through the damage location module;

Step 4: Evaluate the safety risk of the bridge structure through the risk assessment module; display the bridge structure image, image features, damage identification results, damage location information and risk assessment results through the display module.

Further, the monitoring method of the health monitoring module is as follows:

1) Build a bridge database, store the acquired sensor data in the bridge database; acquire the sensing data of the main part of the target bridge structure based on the distributed computing method; perform modal identification based on the acquired sensing data, and obtain the corresponding Modal parameters; the modal parameters include eigenfrequency and the mode shape of the corresponding order;

2) Update the benchmark model equation of the target bridge structure based on the Bayesian principle according to the obtained modal parameters to obtain the error value of the updated model equation; analyze and judge the error value to obtain the health of the target bridge structure monitoring status;

3) According to the calculated health monitoring data, analyze and obtain the damage classification level corresponding to the target bridge structure, and then adaptively adjust the sampling frequency of the sensing data according to the damage classification level;

Among them, the benchmark model equation of the target bridge structure is:

表示第i阶振型，{ε}_i表示第i阶误差矢量，ω_i和

为模态识别所获得的模态参数，[M]、[C]和[K]为模型方程的模型参数，且均为模型方程的参数向量{E}的线性函数。In the above formula, [M] represents the mass matrix, [C] represents the damping matrix, [K] represents the stiffness matrix, ωi represents the i-th order eigenfrequency,

represents the i-th order mode shape, {ε} _i represents the i-th order error vector, ω _i and

are the modal parameters obtained by modal identification, [M], [C] and [K] are the model parameters of the model equation, and they are all linear functions of the parameter vector {E} of the model equation.

Further, the monitoring method also includes a model training step, and the model training step includes:

Collect multiple sets of acceleration data of the main part of the target bridge structure;

Using a modal recognition method to identify the modal parameters corresponding to the obtained acceleration data, and use the obtained multiple sets of modal parameters as training data;

After constructing the model equation of the target bridge structure, based on the Bayesian principle, the parameter vector of the model equation is calculated and updated according to the training data, and then the probability distribution of each model parameter of the model is updated;

After the model parameters are quantized according to the average value of the peak area of the probability distribution of each model parameter, the benchmark model equation after training is obtained.

Further, the step of calculating and updating the parameter vector of the model equation based on the Bayesian principle according to the training data, and then updating the probability distribution of each model parameter of the model, is specifically:

Based on the Bayesian principle, the following formula is used to update the parameter vector of the model equation based on the Markov chain-Monte Carlo method according to the training data:

P({E})表示先验分布概率，P({E}|[D])表示后验分布概率，P([D]|{E})表示似然函数；In the above formula, [D] includes the eigenfrequency ω _i and the mode shape

P({E}) represents the prior distribution probability, P({E}|[D]) represents the posterior distribution probability, and P([D]|{E}) represents the likelihood function;

Based on the updated parameter vector, update the probability distribution for each model parameter of the model equation.

Further, the monitoring method also includes the following steps:

After comparing the updated model equation with the preset model database, the damage location in the target bridge structure is obtained;

The preset model database is a database composed of all models obtained by updating the reference model equation of the target bridge structure after collecting and/or simulating the acceleration data when the target bridge structure is damaged at different positions.

Further, the monitoring method also includes the following steps:

Regularly collect multiple sets of acceleration data of the main part of the target bridge structure, and update the benchmark model equations for training.

Further, the positioning method of the damage localization module is as follows:

(1) data collection is carried out at each spatial position test point of the target bridge structure, and the test point forms a spatial grid;

(2) Collect the vibration frequency data of each spatial location test point of the target bridge structure; according to the change rule of the vibration frequency of the different spatial location test points in the time domain, the damage location of the target bridge structure is carried out;

The damage location of the target bridge structure according to the change law of the vibration frequency of the test points at different spatial positions in the time domain specifically includes

Based on the vibration frequency data collected at each test point at the initial moment of the target bridge structure test, the distribution shape function of the indicative frequency spatial variation of each test point at the specified time is constructed; and, for different types of target bridge structures, using finite element parameter analysis and /or obtain the distribution shape function of the spatial variation of the index frequency by means of fitting the structural experiment results;

Obtain the distribution shape function of the spatial variation of the index frequency;

Using the interpolation method to find the maximum point of the distribution shape function of the spatial variation of the index frequency, the coordinate position corresponding to the maximum point is the damage position of the target bridge structure;

Wherein, the distribution shape function of the spatial variation of the index frequency is a piecewise function covering the entire length of the target bridge structure.

Further, the spatial grid covers all key sections and parts of the target bridge structure on the geometric outline and the mechanical path.

Further, the online collection of frequency data is performed by using an acceleration sensor.

Combining the above-mentioned technical solutions and technical problems to be solved, please analyze the advantages and positive effects of the technical solutions to be protected by the present invention from the following aspects:

First, in view of the technical problems existing in the above-mentioned prior art and the difficulty of solving the problems, closely combine the technical solution to be protected in the present invention and the results and data in the research and development process, etc., to analyze in detail and profoundly how to solve the technical solution of the present invention Technical problems, some creative technical effects brought about after solving the problems. The specific description is as follows:

The invention obtains the sensing data based on the distributed computing method through the health monitoring module, and compares the sensing data with the benchmark model equation of the target bridge structure based on the Bayesian principle to obtain the target bridge more objectively The health monitoring data of the structure has high accuracy; at the same time, the traffic operation of the target bridge structure is used as the power incentive source through the damage location module, which does not need artificial incentives, does not interfere with normal traffic operations, and saves economic and time costs. The hardware adopted in the present invention can be replaced at any time, and is suitable for various types of bridges, utilizes the acceleration response of the target bridge structure, has high data quality, is stable and reliable, and has low test cost, and is suitable for popularization and use in a large area.

Second, regarding the technical solution as a whole or from the perspective of a product, the technical effects and advantages of the technical solution to be protected by the present invention are specifically described as follows:

The invention obtains the sensing data based on the distributed computing method through the health monitoring module, and compares the sensing data with the benchmark model equation of the target bridge structure based on the Bayesian principle to obtain the target bridge more objectively The health monitoring data of the structure has high accuracy; at the same time, the traffic operation of the target bridge structure is used as the power incentive source through the damage location module, which does not need artificial incentives, does not interfere with normal traffic operations, and saves economic and time costs. The hardware adopted in the present invention can be replaced at any time, and is suitable for various types of bridges, utilizes the acceleration response of the target bridge structure, has high data quality, is stable and reliable, and has low test cost, and is suitable for popularization and use in a large area.

Description of drawings

Fig. 1 is a flowchart of a bridge structure damage identification method provided by an embodiment of the present invention.

Fig. 2 is a structural block diagram of a bridge structure damage identification system provided by an embodiment of the present invention.

Fig. 3 is a flowchart of a monitoring method of a health monitoring module provided by an embodiment of the present invention.

Fig. 4 is a flow chart of a damage location module location method provided by an embodiment of the present invention.

In Figure 2: 1. Bridge structure image acquisition module; 2. Health monitoring module; 3. Central control module; 4. Image feature extraction module; 5. Damage identification module; 6. Damage location module; 7. Risk assessment module; 8 , display module.

Detailed ways

In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

1. Explain the embodiment. In order to make those skilled in the art fully understand how to implement the present invention, this part is an explanatory embodiment for explaining the technical solution of the claims.

As shown in Figure 1, the bridge structure damage identification method provided by the present invention includes the following steps:

S101, using the bridge structure image acquisition module to collect the bridge structure image with the camera; the health monitoring module is connected to the central control module to extract the bridge structure image information, and monitor the health status of the bridge structure;

S102, the central control module uses the feature extraction program to extract features of the bridge structure image through the image feature extraction module;

S103, using the identification program to identify the damage of the bridge structure through the damage identification module; accurately locating the damaged part of the bridge structure through the damage location module;

S104, assessing the safety risk of the bridge structure through the risk assessment module; displaying the bridge structure image, image features, damage identification results, damage location information and risk assessment results through the display module.

As shown in Figure 2, the bridge structure damage identification system provided by the embodiment of the present invention includes:

Bridge structure image acquisition module 1, health monitoring module 2, central control module 3, image feature extraction module 4, damage identification module 5, damage location module 6, risk assessment module 7, and display module 8.

The bridge structure image acquisition module 1 is connected with the central control module 3, and is used to collect bridge structure images through the camera;

The health monitoring module 2 is connected with the central control module 3 for monitoring the health status of the bridge structure;

The central control module 3 is connected with the bridge structure image acquisition module 1, the health monitoring module 2, the image feature extraction module 4, the damage identification module 5, the damage location module 6, the risk assessment module 7, and the display module 8, and is used to control the normal operation of each module Work;

Image feature extraction module 4, is connected with central control module 3, is used for extracting bridge structure image feature by extraction program;

The damage identification module 5 is connected with the central control module 3, and is used to identify the damage of the bridge structure through the identification program;

The damage location module 6 is connected with the central control module 3, and is used for locating the damage of the bridge structure;

The risk assessment module 7 is connected with the central control module 3, and is used to assess the safety risk of the bridge structure;

The display module 8 is connected with the central control module 3 and is used to display bridge structure images, image features, damage identification results, damage location information, and risk assessment results.

As shown in Figure 3, the health monitoring module 2 monitoring method provided by the present invention is as follows:

S201, build a bridge database, and store the acquired sensor data in the bridge database; acquire the sensing data of the main part of the target bridge structure based on the distributed computing method; perform modal identification based on the acquired sensing data, and obtain the corresponding Modal parameters; the modal parameters include eigenfrequency and the mode shape of the corresponding order;

S202, update the benchmark model equation of the target bridge structure based on the Bayesian principle according to the obtained modal parameters, and obtain the error value of the updated model equation; analyze and judge the error value, and obtain the health of the target bridge structure monitoring status;

S203, analyzing and obtaining the damage classification level corresponding to the target bridge structure according to the calculated health monitoring data, and then adaptively adjusting the sampling frequency of the sensing data according to the damage classification level;

Among them, the benchmark model equation of the target bridge structure is:

表示第i阶振型，{ε}_i表示第i阶误差矢量，ω_i和

为模态识别所获得的模态参数，[M]、[C]和[K]为模型方程的模型参数，且均为模型方程的参数向量{E}的线性函数。In the above formula, [M] represents the mass matrix, [C] represents the damping matrix, [K] represents the stiffness matrix, ωi represents the i-th order eigenfrequency,

represents the i-th order mode shape, {ε} _i represents the i-th order error vector, ω _i and

are the modal parameters obtained by modal identification, [M], [C] and [K] are the model parameters of the model equation, and they are all linear functions of the parameter vector {E} of the model equation.

The monitoring method provided by the present invention also includes a model training step, and the model training step includes:

Collect multiple sets of acceleration data of the main part of the target bridge structure;

Using a modal recognition method to identify the modal parameters corresponding to the obtained acceleration data, and use the obtained multiple sets of modal parameters as training data;

After constructing the model equation of the target bridge structure, based on the Bayesian principle, the parameter vector of the model equation is calculated and updated according to the training data, and then the probability distribution of each model parameter of the model is updated;

After the model parameters are quantized according to the average value of the peak area of the probability distribution of each model parameter, the benchmark model equation after training is obtained.

Based on the Bayesian principle provided by the present invention, the parameter vector of the model equation is calculated and updated according to the training data, and then the steps of updating the probability distribution of each model parameter of the model are specifically as follows:

Based on the Bayesian principle, the following formula is used to update the parameter vector of the model equation based on the Markov chain-Monte Carlo method according to the training data:

P({E})表示先验分布概率，P({E}|[D])表示后验分布概率，P([D]|{E})表示似然函数；In the above formula, [D] includes the eigenfrequency ω _i and the mode shape

P({E}) represents the prior distribution probability, P({E}|[D]) represents the posterior distribution probability, and P([D]|{E}) represents the likelihood function;

Based on the updated parameter vector, update the probability distribution for each model parameter of the model equation.

The monitoring method provided by the invention also includes the following steps:

After comparing the updated model equation with the preset model database, the damage location in the target bridge structure is obtained;

The preset model database is a database composed of all models obtained by updating the reference model equation of the target bridge structure after collecting and/or simulating the acceleration data when the target bridge structure is damaged at different positions.

The monitoring method provided by the invention also includes the following steps:

Regularly collect multiple sets of acceleration data of the main part of the target bridge structure, and update the benchmark model equations for training.

As shown in Figure 4, the positioning method of the damage localization module 6 provided by the present invention is as follows:

S301. Collect data at test points at each spatial position of the target bridge structure, where the test points form a spatial grid;

S302, collecting the vibration frequency data of test points at various spatial positions of the target bridge structure; and locating the damage of the target bridge structure according to the change law of the vibration frequency of the test points at different spatial positions in the time domain;

The damage location of the target bridge structure according to the change law of the vibration frequency of the test points at different spatial positions in the time domain specifically includes

Based on the vibration frequency data collected at each test point at the initial moment of the target bridge structure test, the distribution shape function of the indicative frequency spatial variation of each test point at the specified time is constructed; and, for different types of target bridge structures, using finite element parameter analysis and /or obtain the distribution shape function of the spatial variation of the index frequency by means of fitting the structural experiment results;

Obtain the distribution shape function of the spatial variation of the index frequency;

Using the interpolation method to find the maximum point of the distribution shape function of the spatial variation of the index frequency, the coordinate position corresponding to the maximum point is the damage position of the target bridge structure;

Wherein, the distribution shape function of the spatial variation of the index frequency is a piecewise function covering the entire length of the target bridge structure.

The space grid provided by the invention covers all key sections and parts of the target bridge structure on the geometric outline and mechanical path.

The invention provides the online collection of frequency data by using an acceleration sensor.

2. Application examples. In order to prove the creativity and technical value of the technical solution of the present invention, this part is the application example of the claimed technical solution on specific products or related technologies.

The invention obtains the sensing data based on the distributed computing method through the health monitoring module, and compares the sensing data with the benchmark model equation of the target bridge structure based on the Bayesian principle to obtain the target bridge more objectively The health monitoring data of the structure has high accuracy; at the same time, the traffic operation of the target bridge structure is used as the power incentive source through the damage location module, which does not need artificial incentives, does not interfere with normal traffic operations, and saves economic and time costs. The hardware adopted in the present invention can be replaced at any time, and is suitable for various types of bridges, utilizes the acceleration response of the target bridge structure, has high data quality, is stable and reliable, and has low test cost, and is suitable for popularization and use in a large area.

It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in memory and executed by a suitable instruction execution system such as a microprocessor or specially designed hardware. Those of ordinary skill in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and/or contained in processor control code, for example, on a carrier medium such as a magnetic disk, CD or DVD-ROM, such as a read-only memory Such code is provided on a programmable memory (firmware) or on a data carrier such as an optical or electronic signal carrier. The device and its modules of the present invention may be implemented by hardware circuits such as VLSI or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., It can also be realized by software executed by various types of processors, or by a combination of the above-mentioned hardware circuits and software such as firmware.

3. Evidence of the relevant effects of the embodiment. The embodiment of the present invention has achieved some positive effects in the process of research and development or use, and indeed has great advantages compared with the prior art. The following content is described in conjunction with the data and charts of the test process.

The invention obtains the sensing data based on the distributed computing method through the health monitoring module, and compares the sensing data with the benchmark model equation of the target bridge structure based on the Bayesian principle to obtain the target bridge more objectively The health monitoring data of the structure has high accuracy; at the same time, the traffic operation of the target bridge structure is used as the power incentive source through the damage location module, which does not need artificial incentives, does not interfere with normal traffic operations, and saves economic and time costs. The hardware adopted in the present invention can be replaced at any time, and is suitable for various types of bridges, utilizes the acceleration response of the target bridge structure, has high data quality, is stable and reliable, and has low test cost, and is suitable for popularization and use in a large area.

The above is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Anyone familiar with the technical field within the technical scope disclosed in the present invention, whoever is within the spirit and principles of the present invention Any modifications, equivalent replacements and improvements made within shall fall within the protection scope of the present invention.

Claims (10)

1. A bridge structure damage identification system is characterized in that the bridge structure damage identification method and the system comprise:

the system comprises a bridge structure image acquisition module, a health monitoring module, a central control module, an image feature extraction module, a damage identification module, a damage positioning module, a risk assessment module and a display module;

the bridge structure image acquisition module is connected with the central control module and is used for acquiring a bridge structure image through the camera;

the health monitoring module is connected with the central control module and is used for monitoring the health state of the bridge structure;

the central control module is connected with the bridge structure image acquisition module, the health monitoring module, the image feature extraction module, the damage identification module, the damage positioning module, the risk assessment module and the display module and is used for controlling each module to work normally;

the image feature extraction module is connected with the central control module and used for extracting the bridge structure image features through an extraction program;

the damage identification module is connected with the central control module and is used for identifying the damage of the bridge structure through an identification program;

the damage positioning module is connected with the central control module and is used for positioning the damage of the bridge structure;

the risk evaluation module is connected with the central control module and is used for evaluating the safety risk of the bridge structure;

and the display module is connected with the central control module and is used for displaying the bridge structure image, the image characteristics, the damage identification result, the damage positioning information and the risk assessment result.

2. The bridge structure damage identification method according to claim 1, wherein the bridge structure damage identification method comprises the following steps:

acquiring a bridge structure image by using a camera through a bridge structure image acquisition module; the health monitoring module is connected with the central control module to extract the image information of the bridge structure and monitor the health state of the bridge structure;

secondly, the central control module extracts the characteristics of the bridge structure image by using a characteristic extraction program through an image characteristic extraction module;

identifying the damage of the bridge structure by using an identification program through a damage identification module; accurately positioning the damaged part of the bridge structure through a damage positioning module;

evaluating the safety risk of the bridge structure through a risk evaluation module; and displaying the bridge structure image, the image characteristics, the damage identification result, the damage positioning information and the risk assessment result through a display module.

3. The bridge structure damage identification method system of claim 1, wherein the health monitoring module monitoring method is as follows:

1) Constructing a bridge database, and storing the acquired sensor data into the bridge database; acquiring sensing data of a main body part of a target bridge structure, which is acquired based on a distributed computing method; after modal identification is carried out on the basis of the acquired sensing data, corresponding modal parameters are acquired; the modal parameters comprise eigenfrequency and a mode shape of a corresponding order;

2) Updating a reference model equation of the target bridge structure constructed based on the Bayesian principle according to the obtained modal parameters to obtain an error value of the updated model equation; analyzing and judging the error value to obtain the health monitoring state of the target bridge structure;

3) Analyzing and obtaining a damage classification grade corresponding to a target bridge structure according to the health monitoring data obtained by calculation, and further adaptively adjusting the sampling frequency of the sensing data according to the damage classification grade;

the reference model equation of the target bridge structure is as follows:

in the above formula, [ M ]]Represents a quality matrix, [ C ]]Represents a damping matrix, [ K ]]Representing the stiffness matrix, ω i representing the ith order eigenfrequency,

denotes the ith order mode, { ε } _i Representing the error vector of order i, ω _i And &>

For modal recognitionThe obtained modal parameter, [ M ]]、[C]And [ K ]]Are the model parameters of the model equation and are linear functions of the parameter vector { E } of the model equation.

4. The bridge structure damage identification method system of claim 3, wherein the monitoring method further comprises a model training step, the model training step comprising:

collecting multiple groups of acceleration data of a main body part of a target bridge structure;

identifying modal parameters corresponding to the obtained acceleration data by adopting a modal identification method, and taking a plurality of groups of obtained modal parameters as training data;

after a model equation of the target bridge structure is constructed, calculating and updating parameter vectors of the model equation according to training data based on the Bayesian principle, and further updating the probability distribution of each model parameter of the model;

and quantizing the model parameters according to the average value of the peak value area of the probability distribution of each model parameter to obtain a trained reference model equation.

5. The bridge structure damage identification method system according to claim 4, wherein the step of calculating and updating the parameter vector of the model equation according to the training data based on the Bayesian principle to further update the probability distribution of each model parameter of the model specifically comprises:

based on Bayes principle, the parameter vector of the model equation is calculated and updated based on the Markov chain-Monte Carlo method according to the training data by adopting the following formula:

in the above formula, [ D ]]Containing the eigenfrequency omega _i Harmonic vibration mode

P ({ E }) denotes an a priori scoreProbability of distribution, P ({ E } | [ D ]]) Represents the posterior distribution probability, P ([ D ]]{ E }) represents a likelihood function;

based on the updated parameter vector, the probability distribution of each model parameter of the model equation is updated.

6. The bridge structure damage identification method system of claim 3, wherein the monitoring method further comprises the steps of:

comparing the updated model equation with a preset model database to obtain the position of the damage in the target bridge structure;

the preset model database is a database consisting of all models obtained by updating a reference model equation of the target bridge structure after acquiring and/or simulating acceleration data of the target bridge structure when the target bridge structure is damaged at different positions.

7. The bridge structure damage identification method system of claim 3, wherein the monitoring method further comprises the steps of:

and (3) regularly acquiring multiple groups of acceleration data of the main body part of the target bridge structure, and updating and training the reference model equation.

8. The bridge structure damage identification system of claim 1, wherein the damage positioning module positioning method is as follows:

(1) Carrying out data acquisition on test points at each spatial position of a target bridge structure, wherein the test points form a spatial grid;

(2) Collecting vibration frequency data of each space position test point of a target bridge structure; according to the change rule of the vibration frequencies of the test points at different spatial positions in the time domain, carrying out damage positioning on the target bridge structure;

the step of carrying out damage positioning on the target bridge structure according to the change rule of the vibration frequencies of the test points at different spatial positions in the time domain specifically comprises the step of

Constructing a variation distribution shape function of the index frequency space of each test point at the appointed moment by taking the vibration frequency data collected by each test point at the initial moment of the target bridge structure test as a reference; aiming at different types of target bridge structures, acquiring indication frequency space change distribution shape functions by means of finite element parameter analysis and/or structural experiment result fitting;

acquiring a distribution shape function of the indication frequency spatial variation;

searching a maximum value point indicating a frequency space variation distribution function by using an interpolation method, wherein a coordinate position corresponding to the maximum value point is a target bridge structure damage position;

wherein the indication frequency space variation distribution shape function is a segment function covering the whole length of the target bridge structure.

9. The bridge structure damage identification system of claim 8, wherein the spatial grid covers all critical sections and locations of the target bridge structure on geometric contours and mechanical paths.

10. The bridge structure damage identification system of claim 8, wherein the frequency data is collected online using an acceleration sensor.

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