CN115937504A - Bridge structure damage identification method and system - Google Patents
Bridge structure damage identification method and system Download PDFInfo
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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
Technical Field
The invention belongs to the technical field of bridge structure damage identification, and particularly relates to a bridge structure damage identification method and system.
Background
The bridge is generally a structure which is erected on rivers, lakes and seas and allows vehicles, pedestrians and the like to smoothly pass through. In order to adapt to the modern high-speed developed traffic industry, bridges are also extended to be constructed to span mountain stream, unfavorable geology or meet other traffic needs, so that the buildings are convenient to pass. The bridge generally comprises an upper structure, a lower structure, a support and an auxiliary structure, wherein the upper structure is also called a bridge span structure and is a main structure for spanning obstacles; the lower structure comprises a bridge abutment, a bridge pier and a foundation; the support is a force transmission device arranged at the supporting positions of the bridge span structure and the bridge pier or the bridge abutment; the auxiliary structures refer to bridge head butt straps, conical revetments, bank protection, diversion projects and the like; however, the existing bridge structure damage identification system does not have a real-time calibration function, and when a structural position where a sensor is located has a large deviation, signal data is still based on an initial structural position, so that a monitoring result is inaccurate, and the monitoring state of a target bridge structure is difficult to objectively evaluate; meanwhile, the damage of the bridge cannot be accurately positioned.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) The existing bridge structure damage identification system does not have a real-time calibration function, and when a structural position where a sensor is located has large deviation, signal data still takes the initial structural position as a basis, so that the monitoring result is inaccurate, and the monitoring state of a target bridge structure is difficult to objectively evaluate.
(2) The damage of the bridge cannot be accurately positioned.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a system for identifying damage of a bridge structure.
The invention is realized in this way, a bridge structure damage identification system includes:
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 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.
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.
Further, the health monitoring module monitoring method comprises the following steps:
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 the target bridge structure according to the health monitoring data obtained through 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 the modal parameters obtained for modal recognition, [ 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.
Further, the monitoring method further comprises a model training step, wherein the model training step comprises the following steps:
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.
Further, 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 Markov chain-Monte Carlo method according to training data by adopting the following formula:
in the above formula, [ D ]]Containing the eigenfrequency omega i Harmonic vibration mode
P ({ E }) represents the prior distribution probability, 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.
Further, the monitoring method further comprises the following steps:
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.
Further, the monitoring method further comprises the following steps:
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.
Further, the method for positioning the damage positioning module comprises the following steps:
(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;
and the indication frequency space variation distribution function is a segmentation function covering the whole length of the target bridge structure.
Further, the spatial grid covers all critical sections and locations of the target bridge structure in both geometric profile and mechanical path.
Further, the frequency data are collected on line by using the acceleration sensor.
In combination with the above technical solutions and the technical problems to be solved, please analyze the advantages and positive effects of the technical solutions to be protected in the present invention from the following aspects:
first, aiming at the technical problems existing in the prior art and the difficulty in solving the problems, the technical problems to be solved by the technical scheme of the present invention are closely combined with results, data and the like in the research and development process, and some creative technical effects are brought after the problems are solved. The specific description is as follows:
according to the invention, the health monitoring module is used for acquiring the sensing data acquired based on the distributed computing method, and the health monitoring data of the target bridge structure can be acquired more objectively after the sensing data is compared with the reference model equation of the target bridge structure constructed based on the Bayesian principle, so that the accuracy is higher; meanwhile, the damage positioning module utilizes the traveling crane operated by the target bridge structure traffic as a power excitation source, artificial 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.
Secondly, considering the technical scheme as a whole or from the perspective of products, the technical effect and advantages of the technical scheme to be protected by the invention are specifically described as follows:
according to the invention, the health monitoring module is used for acquiring the sensing data acquired based on the distributed computing method, and the health monitoring data of the target bridge structure can be acquired more objectively after the sensing data is compared with the reference model equation of the target bridge structure constructed based on the Bayesian principle, so that the accuracy is higher; meanwhile, the damage positioning module utilizes the traveling crane operated by the target bridge structure traffic as a power excitation source, artificial 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.
Drawings
Fig. 1 is a flowchart of a method for identifying damage to a bridge structure according to an embodiment of the present invention.
Fig. 2 is a structural block diagram of a bridge structure damage identification system according to an embodiment of the present invention.
Fig. 3 is a flowchart of a health monitoring module monitoring method according to an embodiment of the present invention.
Fig. 4 is a flowchart of a method for positioning a damage positioning module according to an embodiment of the present invention.
In fig. 2: 1. a bridge structure image acquisition module; 2. a health monitoring module; 3. a central control module; 4. an image feature extraction module; 5. a damage identification module; 6. a damage positioning module; 7. a risk assessment module; 8. and a display module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
1. Illustrative embodiments are explained. This section is an illustrative example developed to explain the claims in order to enable those skilled in the art to fully understand how to implement the present invention.
As shown in fig. 1, the method for identifying damage to a bridge structure provided by the invention comprises the following steps:
s101, 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;
s102, the central control module extracts the characteristics of the bridge structure image by using a characteristic extraction program through an image characteristic extraction module;
s103, 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;
s104, 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.
As shown in fig. 2, a bridge structure damage identification system provided in an embodiment of the present invention includes:
the system comprises a bridge structure image acquisition module 1, a health monitoring module 2, a central control module 3, an image feature extraction module 4, a damage identification module 5, a damage positioning module 6, a risk assessment module 7 and a display module 8.
The bridge structure image acquisition module 1 is connected with the central control module 3 and is used for acquiring a bridge structure image through a camera;
the health monitoring module 2 is connected with the central control module 3 and is used for monitoring the health state 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 positioning module 6, the risk assessment module 7 and the display module 8 and is used for controlling the modules to normally work;
the image feature extraction module 4 is connected with the central control module 3 and is used for extracting the bridge structure image features through an extraction program;
the damage identification module 5 is connected with the central control module 3 and used for identifying the damage of the bridge structure through an identification program;
the damage positioning module 6 is connected with the central control module 3 and used for positioning the damage of the bridge structure;
the risk evaluation module 7 is connected with the central control module 3 and used for evaluating the safety risk of the bridge structure;
and the display module 8 is connected with the central control module 3 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.
As shown in fig. 3, the health monitoring module 2 provided by the present invention has the following monitoring method:
s201, constructing a bridge database, and storing 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 mode shapes of corresponding orders;
s202, 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;
s203, analyzing and obtaining the damage classification grade corresponding to the target bridge structure according to the health monitoring data obtained through 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 &>
Modality parameters obtained for modality identification, [ M ]]、[C]And [ K ]]Are model parameters of the model equation and are linear functions of a parameter vector { E } of the model equation.
The monitoring method provided by the invention further comprises a model training step, wherein the model training step comprises the following steps:
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.
The invention provides a Bayesian principle-based method for calculating and updating parameter vectors of a model equation according to training data so as to update probability distribution of each model parameter of a model, which comprises the following steps:
based on Bayes principle, the parameter vector of the model equation is calculated and updated based on Markov chain-Monte Carlo method according to training data by adopting the following formula:
in the above formula, [ D ]]Containing the eigenfrequency omega i Harmonic vibration mode
P ({ E }) represents the prior distribution probability, 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.
The monitoring method provided by the invention further comprises the following steps:
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.
The monitoring method provided by the invention also comprises the following steps:
and 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.
As shown in fig. 4, the method for positioning the damage positioning module 6 provided by the present invention is as follows:
s301, carrying out data acquisition on test points at each spatial position of a target bridge structure, wherein the test points form a spatial grid;
s302, 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.
The space grid provided by the invention covers all key sections and parts of the target bridge structure on the geometric outline and the mechanical path.
The invention provides an on-line acquisition method for frequency data by using an acceleration sensor.
2. Application examples. In order to prove the creativity and the technical value of the technical scheme of the invention, the part is the application example of the technical scheme of the claims on specific products or related technologies.
According to the invention, the health monitoring module is used for acquiring the sensing data acquired based on the distributed computing method, and the health monitoring data of the target bridge structure can be acquired more objectively after the sensing data is compared with the reference model equation of the target bridge structure constructed based on the Bayesian principle, so that 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.
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 portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. It will be appreciated by those skilled in the art that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, for example such code provided on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware) or a data carrier such as an optical or electronic signal carrier. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
3. Evidence of the relevant effects of the examples. The embodiment of the invention achieves some positive effects in the process of research and development or use, and has great advantages compared with the prior art, and the following contents are described by combining data, diagrams and the like in the test process.
According to the invention, the health monitoring module is used for acquiring the sensing data acquired based on the distributed computing method, and the health monitoring data of the target bridge structure can be acquired more objectively after the sensing data is compared with the reference model equation of the target bridge structure constructed based on the Bayesian principle, so that 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.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
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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| CN116738152A (en) * | 2023-08-14 | 2023-09-12 | 南京邮电大学 | Underground fluid pipeline leakage damage distributed vibration monitoring and evaluating system and method |
| CN117392515A (en) * | 2023-09-15 | 2024-01-12 | 中亿丰(苏州)城市建设发展股份有限公司 | Bridge structure measurement detecting system based on vision sensing |
| CN117688480A (en) * | 2024-02-04 | 2024-03-12 | 四川华腾公路试验检测有限责任公司 | Bridge damage identification method based on damage frequency panorama and random forest |
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| CN117392515B (en) * | 2023-09-15 | 2024-04-30 | 中亿丰(苏州)城市建设发展股份有限公司 | Bridge structure measurement detecting system based on vision sensing |
| CN117688480A (en) * | 2024-02-04 | 2024-03-12 | 四川华腾公路试验检测有限责任公司 | Bridge damage identification method based on damage frequency panorama and random forest |
| CN117688480B (en) * | 2024-02-04 | 2024-05-14 | 四川华腾公路试验检测有限责任公司 | Bridge damage identification method based on damage frequency panorama and random forest |
| CN118153963A (en) * | 2024-04-18 | 2024-06-07 | 天创信用服务有限公司 | Asset security risk management system based on big data |
| CN118758975A (en) * | 2024-09-06 | 2024-10-11 | 中公智联（北京）科技有限公司 | EMD-based radar target detection method, device, equipment and medium |
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