CN116698318B - Bridge crack identification method based on acceleration monitoring data - Google Patents
Bridge crack identification method based on acceleration monitoring data Download PDFInfo
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention provides a bridge crack recognition device and method based on acceleration monitoring data, wherein the device comprises an acceleration sensor, a bottom plate, fixed steel plates and connecting rods, one end of each connecting rod is fixedly connected with the bottom plate, the other end of each connecting rod is fixedly connected with the bottom side of the fixed steel plate, four connecting rods are respectively arranged at four corners of the bottom plate, each connecting rod is correspondingly connected with one fixed steel plate, the top side of each fixed steel plate is fixedly connected with the bottom surface of a bridge structure, and the acceleration sensor is arranged on the bottom plate. According to the method, the kurtosis characteristic of the acceleration is obtained through the statistical analysis of the acceleration time course data, so that whether the bridge structure generates cracks is further judged according to the kurtosis characteristic of the acceleration, the formation of the cracks and the real-time capture of the development process are realized, the monitoring effect of the bridge cracks is good, and the monitoring range is wide.
Description
Technical Field
The invention relates to the technical field of bridge health monitoring, in particular to a device and a method for identifying bridge cracks based on bridge acceleration monitoring data.
Background
The crack is one of the most common diseases of the concrete bridge, can lead to the reduction of the rigidity of the bridge, accelerates the corrosion of the reinforcing steel bars, reduces the bearing capacity, and influences the service life and the operation safety of the bridge. It is counted that more than 90% of the damage to the concrete bridge structure is caused by cracks. Therefore, the concrete bridge crack needs to be identified in time, whether the crack is expanded or not is detected, whether the crack reaches the dangerous value of bridge damage or not is judged, and accordingly the occurrence of bridge collapse disasters is reduced.
Traditional detection methods for concrete bridge cracks comprise a manual test method, a crack width measuring detector method, an ultrasonic detection method, a photographic detection method and the like, and the method has high precision but can only detect cracks at known positions, and still belongs to a periodic detection method, so that the occurrence state of the cracks cannot be tracked in real time and early warned in time.
Along with the development of sensors and communication technologies, structural health monitoring systems are applied in bridge engineering, and crack sensors are also applied in real bridges. However, the layout of the crack sensor is effective only for the cracks that have already occurred, and the occurrence position of the crack needs to be known in advance, so that the newly occurring crack cannot be identified. In addition, the structural fracture may have a nonlinear "closed-open-closed" course under the load of the vehicle, and the above method cannot reflect this feature of the fracture.
However, many studies have been conducted at present to show that the acceleration amplitude will change significantly after the crack occurs, and the acceleration sensor in the health monitoring system can be used for modal recognition, and can also track the whole process of crack formation and development based on the acceleration monitoring data. However, a single acceleration sensor can be arranged on only one point of the structure, the monitoring range is limited, and the local damage detection method is difficult to accurately capture the form of the local damage of the bridge in real time due to the randomness of crack development, beam damage, coupling effect of the environment and the influence of measurement noise, so that the monitoring range needs to be enlarged to monitor the acceleration of one section.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides a bridge crack recognition device based on acceleration monitoring data, which comprises an acceleration sensor, a bottom plate, a fixed steel plate and connecting rods, wherein one end of each connecting rod is fixedly connected with the bottom plate, the other end of each connecting rod is fixedly connected with the bottom side of the fixed steel plate, four connecting rods are respectively arranged at four corners of the bottom plate, each connecting rod is correspondingly connected with one fixed steel plate, the top side of each fixed steel plate is fixedly connected with the bottom surface of a bridge structure, and the acceleration sensor is arranged on the bottom plate.
Preferably, the fixed steel plate is connected with the bridge structure through bolts, the connecting rod is connected with the fixed steel plate through welding, the acceleration sensor is bonded on the bottom plate, and the acceleration sensor is connected with a computer for analyzing and processing acceleration data.
Preferably, the length of the bottom plate is 1/20-1/10 of the length of the bridge structure, and the thickness of the bottom plate and the size of the connecting rod are determined on the principle that the prior ten-stage device does not generate larger vibration mode.
A bridge crack identification method based on acceleration monitoring data comprises the following steps:
s1: designing a crack recognition device according to the size of the bridge structure to be monitored, ensuring that the length of a bottom plate in the crack recognition device is 1/20-1/10 of the length of the bridge structure, establishing a finite element model of the bridge structure and a crack recognition device model, calculating the dynamic characteristics of the bridge structure through modal analysis, and determining the thickness of the bottom plate and the size of a connecting rod on the principle that the prior ten-stage device does not generate larger vibration mode;
s2: the crack identification device is fixed to an area where cracks are easy to occur, such as a bridge structure, wherein the bent cracks of the simply supported beams are a midspan area, and the cut cracks are 1/4 midspan area, so that the crack identification device is located at the central position of the area, and acceleration data in the area are collected by the acceleration sensor and transmitted to the computer;
s3: preprocessing the acquired acceleration time course data by utilizing wavelet packet decomposition, removing interference factors in the data, obtaining vibration acceleration time course data of the bridge structure under the action of vehicle load, taking the acceleration time course obtained in a period of time after the crack identification device is installed as the initial state of the bridge, carrying out the same preprocessing on the acceleration time course data in the period of time every time the same time is later passed, and comparing the acceleration time course data obtained in the subsequent period of time with the acceleration time course data in the initial state to obtain an acceleration time course data comparison graph of the bridge structure under the action of vehicle load;
s4: and carrying out statistical analysis on the acceleration time course data in the time period by taking a period as a time window, namely calculating the acceleration kurtosis in the time period by utilizing a kurtosis calculation formula, wherein the acceleration time course data comprises the initial state of the bridge and the data of the bridge in a period after operation, calculating the change of the acceleration kurtosis in the time window, calculating the acceleration kurtosis in the time window after each movement through moving the time window, acquiring the time course change of the acceleration kurtosis, and obtaining an acceleration kurtosis time course data comparison graph by comparing the initial state of the bridge and the change of the acceleration kurtosis after operation, so as to judge whether the structure is cracked or not or comparing the change trend of the acceleration kurtosis in different time after operation, and judging whether the structure crack is further developed or not.
Preferably, the area where the bridge structure is easy to crack in the S2 is determined by the finite element analysis result of the bridge structure or the detection result of the similar bridge diseases.
Preferably, the disturbance factor in S3 includes acceleration response and noise data generated by the bridge structure under other effects, other effects being other than the effect of the vehicle load.
Preferably, the selection range of the period of time in the S3 and the S4 is 10min-1h, the specific selection duration is selected according to the actual traffic flow condition, the selection period of time is short when the traffic flow is large, and the selection period of time is long when the traffic flow is small.
The invention has the following beneficial effects:
1. the crack identification device is connected with the bridge structure in a bolt manner, and the relative angle between the crack identification device and the bridge structure is adjusted when the fixed steel plate is installed, so that the crack identification device can be used for monitoring transverse cracks and other cracks, and has high monitoring flexibility.
2. The crack recognition device ensures the fixing effect between the crack recognition device and the bridge structure through the cooperative work of the connecting rod, the bottom plate and other components, thereby ensuring the movement linkage of the acceleration sensor and the bridge and ensuring more reliable monitoring results.
3. According to the invention, the detection range of the acceleration sensor is extended, so that the acceleration monitoring in a certain section of the bridge is realized, and the problem that the measurement range of a single acceleration sensor is limited is solved.
4. According to the method, the kurtosis characteristic of the acceleration is obtained through statistical analysis of the acceleration time course data, so that whether the bridge structure generates cracks or not is further judged according to the kurtosis characteristic of the acceleration, and real-time capturing of crack formation and development processes is realized.
Drawings
FIG. 1 is a schematic diagram of a crack recognition device according to the present invention;
FIG. 2 is a schematic layout of the crack recognition device of the present invention on a bridge structure;
FIG. 3 is a graph comparing acceleration time course data according to the present invention;
FIG. 4 is a graph comparing the time course data of the acceleration kurtosis according to the present invention.
Reference numerals in the drawings: 1. an acceleration sensor; 2. fixing the steel plate; 3. a connecting rod; 4. a bottom plate; 5. bridge structure.
Detailed Description
In order that the invention may be more readily understood, a more particular description of the invention will be rendered by reference to specific embodiments that are illustrated in the appended drawings, it is to be understood that the appended embodiments are merely examples of implementations and are not necessarily representative of all embodiments.
Referring to fig. 1-4, a bridge crack recognition device based on acceleration monitoring data comprises an acceleration sensor 1, a bottom plate 4, a fixed steel plate 2 and a connecting rod 3, wherein one end of the connecting rod 3 is fixedly connected with the bottom plate 4, the other end of the connecting rod 3 is fixedly connected with the bottom side of the fixed steel plate 2, four connecting rods 3 are respectively arranged at four corners of the bottom plate 4, each connecting rod 3 is correspondingly connected with one fixed steel plate 2, the top side of the fixed steel plate 2 is fixedly connected with the bottom surface of a bridge structure 5, and the acceleration sensor 1 is arranged on the bottom plate 4.
Specifically, fixed steel sheet 2 passes through bolted connection with bridge structure 5, and connecting rod 3 passes through welding and is connected with fixed steel sheet 2, and acceleration sensor 1 bonds on bottom plate 4, and acceleration sensor 1 is connected with the computer that is used for analyzing and processing acceleration data.
Specifically, the length of the bottom plate 4 is 1/20-1/10 of the length of the bridge structure 5, and the thickness of the bottom plate 4 and the size of the connecting rod 3 are determined by taking the principle that the prior ten-stage device does not generate larger vibration mode.
A bridge crack identification method based on acceleration monitoring data comprises the following steps:
s1: designing a crack recognition device according to the size of the bridge structure to be monitored, ensuring that the length of a bottom plate 4 in the crack recognition device is 1/20-1/10 of the length of the bridge structure 5, establishing a finite element model of the bridge structure 5 and a crack recognition device model, calculating the dynamic characteristics of the bridge structure 5 through modal analysis, and determining the thickness of the bottom plate 4 and the size of a connecting rod 3 on the basis that the prior ten-stage device does not generate larger vibration mode;
s2: the crack recognition device is fixed to the area where the bridge structure 5 is easy to crack through bolts, so that the crack recognition device is positioned at the center of the area, and the acceleration sensor 1 collects acceleration data in the area and transmits the acceleration data to the computer;
s3: preprocessing the acquired acceleration time course data by utilizing wavelet packet decomposition, removing interference factors in the data, and only reserving acceleration response generated when a vehicle passes through a bridge so as to further calculate the dynamic characteristics of the bridge structure; the interference factors are specifically that the acceleration response and noise data generated by other action effects of the bridge structure 5 are removed, the other actions are actions except for the action of the vehicle load, only the acceleration response generated when the vehicle passes through the bridge is reserved, vibration acceleration time course data of the bridge under the action of the vehicle load are obtained, the acceleration time course obtained in a period of time after the crack identification device is installed is used as the initial state of the bridge, the acceleration time course data in the period of time are subjected to the same preprocessing every time the same time is later experienced, and the acceleration time course data obtained in the subsequent period of time and the acceleration time course data in the initial state are compared to obtain an acceleration time course data comparison graph of the bridge under the action of the vehicle load;
it can be seen that it is difficult to directly determine whether the structure is cracked from the acceleration time-course data, so that statistical analysis is required according to the acceleration time-course data, as described in step S4;
s4: and carrying out statistical analysis on the acceleration time course data in the time period by taking a period as a time window, namely calculating the acceleration kurtosis in the time period by utilizing a kurtosis calculation formula, wherein the acceleration time course data comprises the initial state of the bridge and the data of the bridge in two conditions after the operation for a period, calculating the change of the acceleration kurtosis in the time window, calculating the acceleration kurtosis in the time window after each movement through the moving time window, obtaining the time course change of the acceleration kurtosis, and judging whether the structural crack is further developed or not by comparing the change trend of the acceleration kurtosis in different time after the operation.
Specifically, the selection range of the period of time in S3 and S4 is 10min-1h, the specific selection duration is selected according to the actual traffic flow condition, the selection period of time is short when the traffic flow is high, the selection period of time is long when the traffic flow is low, and then the specific period of time is 1h when the traffic flow is less than 100 vehicles/h of each lane, the selection period of time is 10min when the traffic flow is greater than 500 vehicles/h of each lane, and the linear interpolation selection period of time can be adopted when the traffic flow is between 100 vehicles/h of each lane and 500 vehicles/h of each lane;
more specifically, the vehicle vibration caused when the vehicle passes through the bridge is collected in a period of time, and the vehicle response in each period of time has statistics, so that a longer period of time is selected as much as possible when the vehicle flow is small, and the vehicle vibration response with more number in the period of time is contained as much as possible, so that the stability of statistics is ensured; meanwhile, on the premise of meeting statistical stability, the analysis time is reduced as much as possible to improve the analysis efficiency, so that a shorter time period can be selected when more vehicles exist.
Specifically, the time course change of the acceleration kurtosis is calculated through a moving time window, the acceleration kurtosis of a bridge before the vehicle passes through the cracking is taken as a reference, the acceleration kurtosis change in different time windows is compared, and the reduction of the kurtosis is taken as a characteristic index of the cracking of the structure, so that whether the structure is cracked or not is judged.
Specifically, the calculation formula of the acceleration kurtosis is as follows:
where kur represents kurtosis, μ represents the data mean, and σ represents the sample standard deviation.
Specifically, the area where the bridge structure 5 is prone to crack in S2 is determined by the result of finite element analysis of the bridge structure 5, such as the bending moment and the section with larger shearing force of the bridge structure 5 calculated by finite element analysis, or by the detection result of similar bridge diseases.
As another embodiment, when the recognition device is not put into use or the recognition device fails, data acquisition can be performed through finite element simulation, acceleration data obtained when a vehicle passes through the bridge under the conditions of intact structure and cracking are simulated through finite elements, the proposed crack recognition method is described, specifically, a bridge and vehicle model is established, a displacement coupling method is adopted to perform transient analysis to obtain structural dynamic acceleration data when the vehicle passes through the bridge in a coupling mode, and the acceleration data under the two conditions are compared.
The above embodiments merely illustrate the basic principles and features of the present invention, but are not limited by the above embodiments, it should be understood that various changes and modifications can be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. A bridge crack identification method based on acceleration monitoring data is characterized by comprising the following steps of: the device used for the bridge crack identification method based on the acceleration monitoring data comprises an acceleration sensor (1), a bottom plate (4), fixed steel plates (2) and connecting rods (3), wherein one end of each connecting rod (3) is fixedly connected with the bottom plate (4), the other end of each connecting rod (3) is fixedly connected with the bottom side of each fixed steel plate (2), four connecting rods (3) are respectively arranged at four corners of the bottom plate (4), each connecting rod (3) is correspondingly connected with one fixed steel plate (2), the top side of each fixed steel plate (2) is fixedly connected with the bottom surface of a bridge structure (5), and the acceleration sensor (1) is arranged on the bottom plate (4);
the fixed steel plate (2) is connected with the bridge structure (5) through bolts, the connecting rod (3) is connected with the fixed steel plate (2) through welding, the acceleration sensor (1) is adhered to the bottom plate (4), and the acceleration sensor (1) is connected with a computer for analyzing and processing acceleration data;
the length of the bottom plate (4) is 1/20-1/10 of the length of the bridge structure (5), and the thickness of the bottom plate (4) and the size of the connecting rod (3) are determined by taking the principle that the prior ten-stage device does not generate larger vibration mode;
the bridge crack identification method based on the acceleration monitoring data comprises the following steps:
s1: designing a crack recognition device according to the size of the bridge structure to be monitored, ensuring that the length of a bottom plate (4) in the crack recognition device is 1/20-1/10 of the length of the bridge structure (5), establishing a finite element model of the bridge structure (5) and a crack recognition device model, calculating the dynamic characteristic of the bridge structure (5) through modal analysis, and determining the thickness of the bottom plate (4) and the size of a connecting rod (3) on the basis that the previous tenth-order device does not generate larger vibration mode;
s2: the crack identification device is fixed to the area where the bridge structure (5) is easy to crack through bolts, so that the crack identification device is positioned at the center of the area, and the acceleration sensor (1) collects acceleration data in the area and transmits the acceleration data to the computer;
s3: preprocessing the acquired acceleration time course data by utilizing wavelet packet decomposition, removing interference factors in the data, obtaining vibration acceleration time course data of the bridge under the action of vehicle load, taking the acceleration time course obtained in a period of time after the crack identification device is installed as the initial state of the bridge, carrying out the same preprocessing on the acceleration time course data in the period of time every time the same time is later passed, and comparing the acceleration time course data obtained in the subsequent period of time with the acceleration time course data in the initial state to obtain an acceleration time course data comparison graph of the bridge under the action of vehicle load;
s4: and carrying out statistical analysis on the acceleration time course data in the time period by taking a period as a time window, namely calculating the acceleration kurtosis in the time period by utilizing a kurtosis calculation formula, wherein the acceleration time course data comprises the initial state of the bridge and the data of the bridge in a period after operation, calculating the change of the acceleration kurtosis in the time window, calculating the acceleration kurtosis in the time window after each movement through moving the time window, acquiring the time course change of the acceleration kurtosis, and acquiring an acceleration kurtosis time course data comparison graph by comparing the initial state of the bridge and the change of the acceleration kurtosis after operation, so as to judge whether the structure is cracked or not or compare the change trend of the acceleration kurtosis in different time after operation, and judge whether the structural crack is further developed or not.
2. The bridge crack identification method based on acceleration monitoring data according to claim 1, wherein the method comprises the following steps: and S2, determining the area where the bridge structure (5) is easy to crack through the finite element analysis result of the bridge structure (5) or the detection result of the similar bridge diseases.
3. The bridge crack identification method based on acceleration monitoring data according to claim 1, wherein the method comprises the following steps: the disturbance factors in S3 include acceleration response and noise data generated by the bridge structure (5) under other effects, other effects being other than the effect of the vehicle load.
4. The bridge crack identification method based on acceleration monitoring data according to claim 1, wherein the method comprises the following steps: and S3 and S4, wherein the selection range of the period of time is 10min-1h, the specific selection duration is selected according to the actual traffic flow condition, the selection period of time is short when the traffic flow is large, and the selection period of time is long when the traffic flow is small.
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