Bridge main body structure health monitoring and abnormity identification method

Abstract

The invention belongs to the field of bridge main body structure monitoring, and particularly discloses a bridge main body structure health monitoring and abnormity identification method, which comprises the following steps: arranging a vibration sensor array in a bridge span structure load distribution area to carry out vibration data acquisition, and carrying out wind-induced component correction on a vibration signal in combination with real-time wind speed data; accurate acquisition of vibration data required by bridge structure health monitoring is realized, and at the same time, when bridge structure abnormity identification is carried out based on a vibration signal after wind-induced vibration correction, a vibration energy propagation path map is constructed by carrying out phase and amplitude conjoint analysis on vibration wave data. On the basis, anomaly recognition is performed in combination with the frequency concentration degree and the amplitude mutation degree of the nodes, and on one hand, non-structural vibration interference is effectively eliminated through propagation path modeling; and on the other hand, the energy state change of the node in the propagation path is utilized to realize accurate identification of the energy abnormal propagation position, and the accuracy of damage positioning is improved.

Description

Technical Field

[0001] The present invention belongs to the field of bridge main structure monitoring, and specifically discloses a method for bridge main structure health monitoring and anomaly identification. Background Art

[0002] As a key node of the transportation network, bridges play an important role in ensuring road traffic capacity and regional traffic connectivity. However, during long-term service, its structure is prone to performance degradation under the action of multiple factors such as vehicle dynamic loads and material aging, affecting its safety, durability and operation reliability. Therefore, it is necessary to conduct bridge structure health monitoring during bridge service.

[0003] During the service of a bridge, vehicle loads, as the main external excitation source, will cause periodic or transient vibration responses of the bridge span structure. This response not only reflects the dynamic characteristics of the structure, but may also exhibit characteristics such as deviation of the energy propagation path, dispersion of frequency components, and abnormal amplitude fluctuations due to local stiffness changes, connection looseness, or changes in mass distribution. Therefore, the monitoring method based on vibration signals has been widely used in the bridge structure health monitoring system due to its advantages such as high sensitivity and strong adaptability.

[0004] Currently, bridge structure health monitoring technologies based on vibration signals have been widely applied in engineering practice. For example, Chinese Patent Publication No. CN116842348A proposes an artificial intelligence-based bridge health monitoring system. This system periodically collects bridge vibration data caused during vehicle passage, identifies peak points of the vibration signal and fits the amplitude, extracts the initial anomaly degree index to evaluate the abnormal trend of the structural operation state. Further, this method decomposes the vibration data, combines the predicted vibration responses of each vehicle at different time points, dynamically corrects the initial anomaly degree, obtains a more representative target anomaly degree, thereby realizing the screening of abnormal data and the effective identification of the structural state, improving the reliability and accuracy of the monitoring results.

[0005] Although the above method has certain effectiveness in identifying vibration anomalies under vehicle load excitation, it does not fully consider the influence of wind load on the bridge vibration response during the modeling and data analysis process. For long-span bridges or bridges located in open areas, wind load is an important external excitation source that cannot be ignored, which may cause significant aerodynamic vibration, and then interfere with the vibration monitoring data. If this wind-induced vibration component is not identified and separated, it will lead to misjudgment of the vibration anomaly characteristics and affect the accuracy of structural damage identification. Summary of the Invention

[0006] To this end, an object of an embodiment of the present application is to provide a method for health monitoring and anomaly recognition of a bridge main structure. By arranging sensors on the bridge span structure to collect vibration signals and combining real-time wind speed data to correct the wind-induced vibration components of the vibration signals, accurate recognition of the abnormal structure area is realized based on the corrected vibration data, effectively solving the problems existing in the prior art.

[0007] The object of the present invention can be achieved through the following technical solutions: A method for health monitoring and anomaly recognition of a bridge main structure, comprising the following steps: (1) Divide the load distribution area on the bridge span structure for wind field simulation, analyze the wind pressure sensitivity accordingly, and set the layout spacing of vibration sensors based on the wind pressure sensitivity difference to form a vibration sensor array.

[0008] (2) Collect vibration wave data through the vibration sensor array, and at the same time collect the wind speed using an anemometer installed on the bridge deck, and add a unified time stamp to the vibration wave data and the wind speed data to generate a synchronous monitoring data set.

[0009] (3) Compare the wind speed in the synchronous monitoring data set with the safe wind speed. When the wind speed is less than the safe value, retain the original vibration wave data. When the wind speed reaches the safe value, perform wind-induced vibration correction on the vibration wave data.

[0010] (4) Analyze the phase and amplitude characteristics of the corrected vibration wave data, analyze the propagation path of the vibration energy in the bridge structure, and generate a vibration energy propagation path map.

[0011] (5) Calculate the frequency concentration degree and amplitude mutation degree of each node in the vibration energy propagation path map, and compare them with the historical health status database, and then identify the abnormal structure area.

[0012] Combining all the above technical solutions, the positive effects of the present invention are as follows: 1. The present invention realizes the accurate collection of vibration data required for bridge structure health monitoring by arranging a vibration sensor array on the load distribution area of the bridge span structure and combining real-time wind speed data to correct the wind-induced components of the vibration signals, ensuring data reliability, significantly reducing the misjudgment rate of abnormal characteristics, and improving the accuracy of damage recognition and the stability of the monitoring system.

[0013] The present invention analyzes the wind pressure sensitivity through wind field simulation of the load distribution area of the bridge span structure, and accordingly densely arranges sensors in the wind pressure sensitive area, while appropriately reducing the layout density in the area with less wind pressure influence, realizing the spatial differential optimization configuration of the sensor network, avoiding the problems of sensor redundancy or insufficient coverage caused by traditional uniform layout, improving the system deployment efficiency, providing a data basis with reasonable spatial distribution and strong time synchronization for subsequent vibration signal correction based on wind speed data, and improving the accuracy and stability of the structure health monitoring system.

[0014] When the present invention performs abnormal identification of the bridge structure based on the vibration signal after wind-induced vibration correction, by jointly analyzing the phase and amplitude of the vibration wave data, extracting the temporal correlation and energy transfer relationship between sensor nodes, and constructing a vibration energy propagation path map with physical significance. On this basis, combining the frequency concentration degree and amplitude mutation degree of each node for abnormal identification, on the one hand, effectively excluding non-structural vibration interference through propagation path modeling, focusing on the real structure response area, and reducing the misjudgment rate; on the other hand, using the change of the energy state of the node in the propagation path to accurately identify the position of abnormal energy propagation and improve the accuracy of damage location. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the following drawings.

[0016] Figure 1 It is a flowchart of the method implementation steps of the present invention.

[0017] Figure 2 It is a schematic diagram of the in-direction energy propagation and out-direction energy propagation corresponding to the nodes of the vibration energy propagation path map in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0019] See Figure 1 As shown, the present invention proposes a method for health monitoring and abnormal identification of the main body structure of a bridge, including the following steps: (1) Divide the load distribution area on the bridge span structure for wind field simulation, analyze the wind pressure sensitivity accordingly, and set the layout spacing of vibration sensors based on the wind pressure sensitivity difference to form a vibration sensor array.

[0020] It should be noted that the main body structure of the bridge involved in the present invention specifically refers to the bridge span structure, that is, the main load-bearing part of the bridge that spans obstacles, bears the direct action of vehicles, pedestrians and environmental loads, and effectively transfers these loads to the bridge piers and foundations. Typical bridge span structures include key components such as main girders, bridge decks, diaphragms, and bearings.

[0021] By arranging sensors on this structure to collect vibration signals, the dynamic response characteristics of the bridge under actual loads can be truly reflected. The obtained vibration data is characterized by strong representativeness and a high degree of correlation with the structural state, providing a high-quality data basis for subsequent anomaly identification and health assessment.

[0022] As a preferred implementation of the above steps, load distribution areas are divided on the bridge span structure for wind field simulation. Based on this, the wind pressure sensitivity is analyzed, including the establishment of a three-dimensional bridge model and wind field simulation. Among them, the implementation of the three-dimensional bridge structure model is as follows: According to the design load distribution diagram of the bridge, load distribution areas are divided on the bridge span structure.

[0023] It should be understood that although the bridge span structure is the main part of the bridge to bear external loads, the stress in each area is not uniform during the actual stress process, and not all parts directly participate in the main load transfer. Therefore, the present invention identifies and divides the load distribution areas in the bridge span structure based on the bridge design load distribution diagram, that is, the key areas in the structure where the bearing effect is significant and the stress and strain are concentrated.

[0024] By arranging sensors in this area and collecting vibration signals, the key dynamic response characteristics of the bridge under excitations such as vehicle passing can be captured more effectively. The obtained vibration data has stronger structural properties and representativeness, thereby improving the accuracy of anomaly identification and monitoring efficiency. This method avoids ineffective sensor placement in non-critical areas, realizes the optimal allocation of monitoring resources, and enhances the sensitivity of the system to structural state changes and the reliability of judgment.

[0025] Based on the bridge structure design drawings and geometric parameters, a high-precision three-dimensional bridge model is established, and mesh division is completed within the load distribution area.

[0026] Applied to the above scheme, the bridge geometric parameters include but are not limited to overall dimensions: total bridge length, main span length, deck width, cross-sectional shape and dimensions: main girder cross-sectional form, cross-sectional height, thickness of top plate, bottom plate, and web, material properties: elastic modulus, Poisson's ratio, density, detailed structure: diaphragm arrangement, support position and type, stiffener setting, connection details: welded or bolted connection joints, joint treatment methods.

[0027] The above establishment of a high-precision three-dimensional bridge model based on the bridge structure design drawings and geometric parameters aims to ensure that the model shape is consistent with the actual bridge structure.

[0028] Furthermore, completing mesh division within the load distribution area can achieve local mesh refinement in this area, thereby improving the spatial resolution of the numerical model. This helps to more accurately simulate the dynamic response behavior of the bridge structure under external excitations and enhances the ability to capture the vibration signal propagation path and local vibration characteristics.

[0029] As a further preferred implementation of the above steps, the wind field simulation is carried out as follows: Obtain long-term historical meteorological data, including wind speed, wind direction, and seasonal variation characteristics, from the meteorological station in the area where the bridge is located, and set the inlet wind speed boundary condition and the outlet pressure boundary condition in combination with the local topographic and geomorphic information, which conforms to the basic principles of fluid mechanics to ensure that the simulation environment reflects the actual working conditions as realistically as possible.

[0030] Arrange the historical wind speed data in ascending order, covering the whole process from conventional wind loads to extreme wind loads, which helps to identify the differences in structural responses at different wind speeds, and based on this sorting, simulate the wind loads under different wind speeds in turn in the established three-dimensional bridge model and its set boundary conditions to obtain the wind pressure distribution map of the load distribution area under different wind speed conditions.

[0031] It should be added that wind pressure refers to the pressure generated on the surface of the structure due to the action of wind. Commonly used wind field simulation methods include CFD simulation, which can describe in detail how the wind interacts with the bridge structure to generate a complex wind pressure distribution. By solving the Navier-Stokes equation or other turbulence models, the wind speed distribution at each point on the bridge surface can be obtained, and then the corresponding wind pressure distribution can be calculated according to Bernoulli's equation.

[0032] The above wind pressure distribution map connects the wind pressure values of adjacent measuring points into a continuous regional distribution through spatial interpolation methods, vividly expressing the spatial heterogeneity, gradient change, and local extreme value distribution of the wind pressure within the load distribution area.

[0033] Based on the wind pressure distribution map of the load distribution area under multi-wind speed simulation, the areas with an average wind pressure lower than the preset threshold are defined as wind pressure insensitive areas, and the remaining areas are classified as wind pressure sensitive areas.

[0034] It should be noted that the above preset threshold reflects the critical sensitivity level of the bridge structure's response to wind loads under multi-wind speed simulation conditions. Specifically, this threshold is used to distinguish the stress response intensity of different areas on the surface of the bridge span structure under the action of wind, and is the criterion for judging whether a certain area is significantly affected by wind-induced excitation.

[0035] The setting of the preset threshold can be based on the characteristics of the bridge structure and engineering experience. Specifically, typical wind environment conditions can be established using long-term meteorological data, and combined with CFD simulation to obtain the average wind pressure values of each area under various wind speeds, and the wind pressure distributions of all grid points are statistically analyzed, and the mean value or percentile (such as the 75th percentile) is selected as the threshold reference value.

[0036] In the above, the area where the average wind pressure is lower than the preset threshold is defined as the wind-pressure insensitive area, and the basis is as follows: Wind pressure is the main external excitation source causing wind-induced vibration. The greater the wind pressure, the stronger the wind-induced excitation on the structure, and the more significant the corresponding vibration response. When the average wind pressure in a certain area is lower than the set threshold, it indicates that the wind load received by this area in the typical wind environment is weak, and the probability of wind-induced vibration is low. Its vibration signal is mainly affected by the vehicle passing load and has low wind correlation. Therefore, this type of area can be reasonably classified as the wind-pressure insensitive area, so as to be treated differently in subsequent vibration data analysis and sensor layout, and improve the pertinence and effectiveness of the monitoring system.

[0037] For the wind-pressure sensitive area, extract the corresponding wind pressure values under different wind speed simulations, and draw the wind pressure response curve with the wind speed as the horizontal axis and the wind pressure value as the vertical axis.

[0038] Extract the overall change rate from the wind pressure response curve as the wind pressure growth amplitude, and extract the initial wind pressure value at the lowest wind speed as the basic wind pressure.

[0039] It should be noted that the overall change rate extracted from the wind pressure response curve is used to characterize the overall growth trend of the wind pressure with the increase of the wind speed, and reflects the dynamic evolution characteristics of the wind pressure borne by the bridge structure under different wind speed excitations.

[0040] Since the wind pressure response curve usually shows non-linear characteristics and its change trend is not a straight line with a constant slope. Exemplarily, to accurately obtain this change rate, the wind pressure response curve can be curve-fitted to establish the functional relationship between the wind pressure and the wind speed, and the average change rate is calculated based on the fitted function, that is, the ratio of the wind pressure change amount to the wind speed change amount within the wind speed interval is used as the quantification index of the overall growth trend.

[0041] Normalize the basic wind pressure and the wind pressure growth amplitude respectively, and then take the product of the normalized basic wind pressure and the wind pressure growth amplitude in the wind-pressure sensitive area as the wind pressure sensitivity.

[0042] In the present invention, the basic wind pressure reflects the initial stress level of the bridge structure under the lowest wind speed condition, while the wind pressure growth amplitude characterizes the overall change trend of the wind pressure with the increase of the wind speed. To eliminate the differences in dimension and numerical range between the two and improve the comparability between different areas, these two parameters are normalized so that their values are uniformly mapped to the interval [0,1].

[0043] On this basis, multiply the normalized basic wind pressure by the wind pressure growth amplitude to construct a comprehensive index of wind pressure sensitivity. This index comprehensively considers the initial stress state of the structure at low wind speeds and the development trend of the wind pressure response at high wind speeds, and can scientifically and reasonably quantify the sensitivity of different areas to wind loads.

[0044] Its mathematical properties indicate that the wind pressure sensitivity will only increase significantly when both the basic wind pressure and the increase amplitude of the wind pressure are high; if either of these parameters is low, the comprehensive index value will also be suppressed, thus reflecting the synergistic constraint effect on the wind pressure response intensity. This construction mechanism conforms to the principle of superposition of structural responses and the characteristics of nonlinear excitation responses, and can more realistically reflect the mechanical behavior of bridge structures in complex wind environments.

[0045] Furthermore, based on the differential setting of the wind pressure sensitivity, the layout spacing of vibration sensors is formed to form a vibration sensor array, which includes the following content: vibration sensors are arranged at a set spacing in the wind pressure insensitive area to meet the needs of monitoring the basic vibration state of this area.

[0046] For the wind pressure sensitive area, the wind pressure sensitivity is introduced as a layout density adjustment factor, and then the product of the set spacing and the layout density adjustment factor is used to obtain the layout spacing of the corresponding area.

[0047] Among them, the layout density adjustment factor is defined as the value of 1 minus the wind pressure sensitivity value.

[0048] It should be explained that arranging vibration sensors at a set spacing in the wind pressure insensitive area and introducing the wind pressure sensitivity for densification in the wind pressure sensitive area is because the vibration energy caused by the wind in the wind pressure sensitive area is stronger and the frequency is higher. Therefore, a higher spatial resolution is required to capture vibration signals. This can improve the monitoring resolution of the area with strong wind-induced vibration responses on the premise of ensuring the overall monitoring efficiency, optimize resource allocation, and reduce system costs.

[0049] In the present invention, the wind pressure sensitivity is analyzed by simulating the wind field in the load distribution area of the bridge span structure, and accordingly, sensors are densely arranged in the wind pressure sensitive area, while the layout density is appropriately reduced in the area with less wind pressure influence, realizing the spatial differential optimization configuration of the sensor network, avoiding problems such as sensor redundancy or insufficient coverage caused by traditional uniform layout, improving the system deployment efficiency, providing a data basis with reasonable spatial distribution and strong time synchronization for subsequent vibration signal correction based on wind speed data, and improving the accuracy and stability of the structural health monitoring system.

[0050] (2) Vibration wave data is collected through the vibration sensor array, and at the same time, the wind speed is collected by an anemometer installed on the bridge deck. A unified time stamp is added to the vibration wave data and the wind speed data to generate a synchronous monitoring data set.

[0051] Applied to the above steps, the synchronous monitoring data set is specifically formed as follows: ultrasonic anemometers are symmetrically installed on both sides of the bridge deck to measure the wind speed in real time. The measured wind speed and the vibration sensor data are time synchronized through a unified clock source to generate a synchronous monitoring data set with time stamps.

[0052] Compare the wind speed in the synchronous monitoring dataset with the safe wind speed. When the wind speed is less than the safe value, retain the original vibration wave data. When the wind speed reaches the safe value, perform wind-induced vibration correction on the vibration wave data.

[0053] It should be understood that when the wind speed is lower than the safe value, it indicates that the excitation generated by the current wind load on the bridge structure is relatively small, and the wind-induced vibration response is negligible or of secondary importance. At this time, the vibration of the bridge is mainly caused by structural excitations such as vehicle loads and environmental perturbations, which has good physical representativeness and stability. Therefore, wind-induced vibration correction is not required, and the original vibration wave data can be directly used to meet the requirements of structural health monitoring for signal authenticity.

[0054] When the wind speed reaches the safe value, the wind load becomes an important external excitation source that cannot be ignored, which may trigger significant wind-induced vibrations. Such vibration components mixed in the original vibration signal may mask the true structural response characteristics, leading to misjudgment of abnormal identification or distortion of modal parameter estimation. Therefore, it is necessary to separate and correct the wind-induced vibration components in the original vibration signal to extract the effective vibration information reflecting the state of the structure itself.

[0055] Specifically, the safe wind speed can be set according to the critical wind speed parameters determined in the structural design and wind resistance performance evaluation of the bridge, and this parameter usually comes from the wind resistance design standard of the bridge.

[0056] In an optional implementation of the above steps, the wind-induced vibration correction refers to the following process: perform frequency-domain decomposition on the vibration wave data collected by each vibration sensor in the load distribution area to separate the low-frequency structural vibration component and the high-frequency wind-induced vibration component.

[0057] It should be noted that decomposing the vibration wave data into low-frequency structural vibration components and high-frequency wind-induced vibration components is based on the difference characteristics of the natural frequency of the bridge structure and the wind vibration excitation frequency band. Among them, the low-frequency structural vibration component mainly reflects the intrinsic response of the structure caused by traffic loads, foundation excitations, etc., and the high-frequency wind-induced vibration component mainly comes from the aerodynamic excitation under the action of the wind load, showing broadband fluctuations.

[0058] Extract the amplitude envelope of the high-frequency wind-induced vibration component, where the amplitude envelope reflects the variation law of the wind-induced vibration intensity with time, and perform time series alignment based on the real-time wind speed data recorded by the anemometer and the amplitude envelope of the high-frequency wind-induced vibration in the corresponding time period.

[0059] Establish a quantitative mapping relationship between the wind speed and the amplitude of the wind-induced vibration based on historical data.

[0060] In the above - mentioned implementation example, the quantitative mapping between the wind speed and the amplitude of wind - induced vibration can be constructed based on historical monitoring data to form a comparison data set of the wind speed and the amplitude of wind - induced vibration, and a regression analysis method can be used to fit the functional relationship between the two, so as to obtain a prediction model that can be used for real - time wind - induced vibration correction.

[0061] Use the above - mentioned mapping relationship to predict the amplitude of wind - induced vibration under the current wind speed condition, and subtract it from the original vibration signal as a background interference component.

[0062] In the example application of the above operation, the specific implementation method of subtracting from the original vibration signal is: subtract the estimated wind - induced vibration component from the original signal in the time domain.

[0063] The wind - induced vibration correction of the vibration wave signal collected by the vibration sensor as described above aims to eliminate the interference caused by wind loads to the vibration monitoring data of the bridge structure and improve the recognition accuracy of the structural self - response component in the vibration signal.

[0064] (4) Conduct phase and amplitude characteristic analysis on the corrected vibration wave data, analyze the propagation path of vibration energy in the bridge structure, and generate a vibration energy propagation path map.

[0065] The specific implementation process of the above steps is as follows: (41) Define the set of directly adjacent sensors for each sensor node according to the actual layout position of each vibration sensor to form the adjacent area of the node.

[0066] (42) Calculate the phase difference and amplitude difference for each sensor node between the vibration wave signals corrected by wind - induced vibration of it and each sensor in its adjacent area at the same time stamp. If a certain sensor node and a certain sensor in its adjacent area meet the following conditions: a) The phase difference is less than the preset phase threshold, indicating an obvious sequence of vibration between the two. b) The amplitude difference is within the set attenuation range, indicating that the energy is transmitted from one end to the other end rather than randomly fluctuating.

[0067] Then it is determined that there is a vibration energy propagation relationship between the two, and the physical position connection line between the sensor node and its adjacent sensor is marked as a vibration energy propagation segment.

[0068] It should be explained that the propagation of vibration waves in the bridge structure follows the classical wave equation, and the vibration energy generally propagates along the direction with the maximum structural stiffness and the most continuous mass distribution. By constructing an adjacent area for local vibration propagation analysis, compared with directly identifying the global propagation path in the load - distribution area, the calculation amount is large and it is vulnerable to noise interference. By defining the adjacent area and using the local comparison method, the search space can be effectively reduced, the recognition efficiency and robustness can be improved, and it also more conforms to the local propagation characteristics of actual vibration waves.

[0069] In addition, in the adjacent area, the vibration energy propagation path is analyzed based on the phase difference and amplitude difference of the vibration waves collected by two adjacent vibration sensors. The reason is that the phase difference reflects the time delay characteristic of the vibration energy propagating from one point to another, and can be used to judge the propagation direction of the vibration energy; the amplitude difference reflects the energy attenuation caused by material damping, interface reflection, local defects, etc. during the propagation of the vibration wave in the structure, and is used to verify whether the vibration energy has actually been transmitted. The combination of the two can significantly improve the reliability of the propagation path identification. In this process, by setting the phase threshold and the amplitude difference attenuation range, false propagation paths can be effectively filtered out to avoid misjudgment. Specifically, the phase threshold and the amplitude difference attenuation range can simulate the vibration propagation process under typical excitations using a finite element model, and extract the theoretical phase difference and amplitude difference between different sensors as a reference.

[0070] Taking the newly identified nodes on the vibration energy propagation segment as the starting point, continue to use its own adjacent area as the analysis object and repeat (42), gradually expanding the propagation path outward until the entire sensor network is covered or the set maximum tracking depth is reached or no new effective propagation segments are found.

[0071] Integrate all the identified propagation segments into a complete vibration energy propagation path map, and mark the propagation direction on the propagation path map.

[0072] The above-mentioned continuous extension outward based on the identified propagation segments to form a complete propagation path reflects the spatial continuity of the structural response.

[0073] (5) Calculate the frequency concentration degree and amplitude mutation degree of each node in the vibration energy propagation path map, and compare them with the historical health status database to further identify the structural abnormal area.

[0074] In an optional implementation of the above steps, the specific implementation of calculating the frequency concentration degree and amplitude mutation degree of each node in the vibration energy propagation path map is as follows: Mark the nodes of the vibration energy propagation path map, and perform a frequency domain transformation on the vibration wave data at each node to convert the time domain signal into a frequency domain representation form to obtain the spectral amplitude distribution.

[0075] Based on the obtained spectral amplitude distribution, identify the main frequency components with significant energy contributions, and count the proportion of the sum of the energies corresponding to all the main frequency points in the total energy of the entire spectrum as the frequency concentration degree of the node.

[0076] In the example operation of the above implementation, the main frequency components can be extracted from the spectral amplitude distribution in the following ways: Find the frequency corresponding to the peak in the spectrum or screen out the significant frequency components according to a pre-set frequency range, such as the natural frequency interval of the bridge structure.

[0077] It should be noted that the frequency concentration degree refers to the proportion of the energy of the main frequency components in the vibration signals collected at specific nodes. A high frequency concentration degree means that the vibration response at that node is more stable and controlled by the structural dominant mode, while a low frequency concentration degree indicates that the vibration spectrum is relatively dispersed. Under healthy conditions, the main vibration frequencies of the bridge structure are usually stable and concentrated on a few natural frequencies, so the frequency concentration degree is relatively high. When local damage or stiffness change occurs in the structure, new frequency components may be introduced or the energy distribution of the original frequency components may be changed, resulting in a decrease in the frequency concentration degree.

[0078] Perform time series analysis on the vibration wave data at each node to extract its instantaneous amplitude sequence, and then calculate the change amount of the amplitude between adjacent time points to form an amplitude difference sequence.

[0079] Based on the obtained amplitude difference sequence, further calculate its mean absolute difference value as the amplitude mutation degree of the node.

[0080] It should be noted that the amplitude mutation degree is used to measure the severity of the change of the vibration signal amplitude over time. It reflects whether there is a sudden energy release or absorption phenomenon in the node area. Under normal circumstances, the change of the vibration amplitude of the bridge structure should be relatively stable and the amplitude mutation degree is relatively low. If problems such as crack propagation or support failure occur in a certain area, it may lead to significant fluctuations in the local vibration amplitude, manifested as an increase in the amplitude mutation degree.

[0081] By combining the above two indicators, the state of the bridge structure can be evaluated more comprehensively and potential problem areas can be detected in a timely manner.

[0082] In a further optional implementation of the above steps, compare it with the historical health status database, and then identify the structural abnormal areas including the following process: Refer to Figure 2 As shown, for each node in the vibration energy propagation path map, construct a node weight factor by combining the number of energy propagation segments pointing to the node and the number of propagation segments starting from the node, and using a normalization process and a weighted fusion strategy.

[0083] It should be added that in the above Figure 2 The black dots represent nodes.

[0084] Specifically, the number of energy propagation segments pointing to the node reflects the receiving ability of the node in the structural dynamic response, and the number of propagation segments starting from the node reflects the driving effect of the node on the surrounding area.

[0085] When constructing the node weight factor using the quantities of the two, to eliminate the influence of the atlas scale and node distribution density, the above two parameters are respectively divided by the corresponding maximum number of in - propagation segments and maximum number of out - propagation segments in the entire atlas to unify the numerical range, and an adjustable parameter is further introduced to linearly weight the normalized in - propagation and out - propagation segments, obtaining the node weight factor.

[0086] In an example expression, , where represents the node number, , , respectively represent the number of energy propagation segments pointing to the node, the number of propagation segments starting from the node, , respectively represent the corresponding maximum number of in - propagation segments and maximum number of out - propagation segments in the entire atlas, represents the adjustable parameter, whose value is in , and the adjustable parameter can be flexibly set according to the actual monitoring target and analysis requirements to adjust the relative influence weights of the in - propagation and out - propagation segments in the node weight factor. When the focus is on the energy convergence characteristic, a higher weight can be given to the in - propagation segment, that is, a larger value of the adjustable parameter is selected; conversely, if the focus is on the energy divergence ability, the value of the adjustable parameter can be reduced to highlight the influence of the out - propagation segment.

[0087] The node weight factor reflects the importance of the node as an energy convergence point, and the higher the value, the more critical the position of the node in the vibration energy propagation path.

[0088] The frequency concentration degree and amplitude mutation degree of each node in the current monitoring state are respectively compared with the corresponding reference frequency concentration degree and reference amplitude mutation degree in the historical health state database to calculate the frequency deviation degree and amplitude deviation degree.

[0089] The above - mentioned historical health state database stores the reference frequency concentration degree and reference amplitude mutation degree of each node of the bridge in the known health state. The specific construction method is as follows: After the bridge is built and undergoes preliminary commissioning, a comprehensive vibration test and environmental monitoring are carried out, the vibration signals of each sensor node under various typical working conditions are collected, and the corresponding frequency concentration degree and amplitude mutation degree are calculated and stored in the database as the initial baseline data. The bridge is regularly inspected for health according to the preset time interval, and the latest normal state data is supplemented to the database to ensure that the baseline remains up - to - date over time.

[0090] Specifically, the frequency deviation degree can be obtained by subtracting the reference frequency concentration degree of the node from the frequency concentration degree and then dividing by the reference frequency concentration degree, which reflects the deviation degree of the node frequency concentration relative to the reference. The amplitude deviation degree can be obtained by subtracting the amplitude mutation degree of the node from the reference amplitude mutation degree and then dividing the result by the reference amplitude mutation degree, which reflects the deviation degree of the node amplitude mutation relative to the reference.

[0091] A preset global warning deviation degree threshold is introduced and dynamically adjusted according to the weight factor of each node to obtain the warning deviation degree threshold of each node.

[0092] As the above specific implementation example, the calculation expression of the warning deviation degree threshold of each node is , where represents the global warning deviation degree threshold, represents the warning deviation degree threshold of the

[0093] It should be noted that the introduced global warning deviation degree threshold refers to the unified reference deviation degree upper limit for determining whether there is abnormal behavior in the structural node.

[0094] An exemplary acquisition method is to set a reasonable deviation degree tolerance value with reference to the relevant industry standards for bridge structure health monitoring.

[0095] It can be seen from the above expression that when the weight factor of the node is larger, is smaller, making the warning deviation degree threshold of the node lower than the global warning deviation degree threshold. This means that for the nodes with higher importance in the vibration energy propagation path, the allowable deviation upper limit is smaller and the abnormal identification standard is more stringent.

[0096] For each node, compare the calculated frequency deviation degree and amplitude deviation degree with the corresponding warning deviation degree threshold respectively. If any index exceeds the corresponding threshold, it is determined that there is a structural abnormality in the area corresponding to the node, and the abnormal area is marked.

[0097] The above node abnormal identification mechanism fully considers the topological role differences of different sensor nodes in the vibration energy propagation path. By introducing the node weight factor to dynamically adjust the warning deviation degree threshold, the abnormal criterion not only depends on the deviation degree of local vibration characteristics, but also integrates the influence weight of the node in the entire structural response network. This method improves the accuracy of abnormal identification while enhancing the physical consistency and engineering interpretability of the determination results, and helps to more effectively identify the key abnormal areas with structural influence.

[0098] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0099] Those of ordinary skill in the art can realize that the algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0100] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

[0101] Finally, the above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should all be included in the protection scope of the present invention.

Claims

1. A method for health monitoring and abnormal identification of a bridge main structure, characterized in that It includes the following steps: (1) Divide the load distribution area on the bridge span structure for wind field simulation, analyze the wind pressure sensitivity accordingly, and set the layout spacing of vibration sensors based on the difference in wind pressure sensitivity to form a vibration sensor array; (2) Collect vibration wave data through the vibration sensor array, and at the same time collect the wind speed using an anemometer installed on the bridge deck, and add a unified time stamp to the vibration wave data and wind speed data to generate a synchronous monitoring data set; (3) Compare the wind speed in the synchronous monitoring data set with the safe wind speed. When the wind speed is less than the safe value, retain the original vibration wave data. When the wind speed reaches the safe value, correct the vibration wave data for wind-induced vibration; (4) Analyze the phase and amplitude characteristics of the corrected vibration wave data, analyze the propagation path of vibration energy in the bridge structure, and generate a vibration energy propagation path map; (5) Calculate the frequency concentration degree and amplitude mutation degree of each node in the vibration energy propagation path map, and compare them with the historical health status database to further identify the structural abnormal area.

2. The method for health monitoring and anomaly identification of a bridge main structure according to claim 1, wherein: The division of the load distribution area on the bridge span structure for wind field simulation and the analysis of wind pressure sensitivity accordingly include the establishment of a bridge three-dimensional model and wind field simulation. The establishment of the bridge structure three-dimensional model is as follows: Divide the load distribution area on the bridge span structure according to the design load distribution diagram of the bridge; Based on the bridge structure design drawings and geometric parameters, establish a high-precision bridge three-dimensional model, and complete the mesh division within the load distribution area.

3. The method for health monitoring and anomaly identification of a bridge main structure according to claim 2, characterized in that: The wind field simulation is implemented as follows: Obtain long-term historical meteorological data from the meteorological station in the area where the bridge is located, including wind speed, wind direction, and seasonal change characteristics, and set the inlet wind speed boundary condition and outlet pressure boundary condition in combination with the local terrain and landform information; Arrange the historical wind speed data in ascending order, and based on this sorting, simulate the wind loads under different wind speeds in the established bridge three-dimensional model and its set boundary conditions in turn to obtain the wind pressure distribution diagrams of the load distribution area under different wind speed conditions; Select the areas with an average wind pressure lower than the preset threshold from the wind pressure distribution diagrams of each load distribution area under multi-wind speed simulation and define them as wind pressure non-sensitive areas, and the remaining areas are classified as wind pressure sensitive areas; Extract the corresponding wind pressure values of the wind pressure sensitive areas under different wind speed simulations, and draw a wind pressure response curve with the wind speed as the horizontal axis and the wind pressure value as the vertical axis; Extract the overall change rate from the wind pressure response curve as the wind pressure growth amplitude, and extract the initial wind pressure value at the lowest wind speed as the basic wind pressure; Normalize the basic wind pressure and the wind pressure growth amplitude respectively, and then use the product of the normalized basic wind pressure and the wind pressure growth amplitude in the wind pressure sensitive area as the wind pressure sensitivity.

4. The method for health monitoring and abnormal identification of a bridge main structure according to claim 3, characterized in that: The differential setting of the vibration sensor layout spacing based on the wind pressure sensitivity to form a vibration sensor array includes the following: Arrange vibration sensors in the wind pressure non-sensitive area according to the set spacing; Introduce the wind pressure sensitivity as a layout density adjustment factor for the wind pressure sensitive area, and then obtain the layout spacing of the corresponding area by multiplying the set spacing by the layout density adjustment factor; Among them, the layout density adjustment factor is defined as the value 1 minus the wind pressure sensitivity value.

5. The method for health monitoring and abnormal identification of a bridge main body structure according to claim 1, wherein: The synchronous monitoring data set is specifically formed as follows: Install ultrasonic anemometers symmetrically on both sides of the bridge deck to measure the wind speed in real time; Synchronize the measured wind speed and the data of the vibration sensors through a unified clock source to generate a synchronized monitoring data set with time stamps.

6. The health monitoring and abnormal identification method for a bridge main structure according to claim 3, characterized in that: The wind-induced vibration correction is as follows: Perform frequency-domain decomposition on the vibration wave data collected by each vibration sensor in the load distribution area to separate the low-frequency structural vibration component and the high-frequency wind-induced vibration component; Extract the amplitude envelope of the high-frequency wind-induced vibration component, and align the time series of the real-time wind speed data recorded by the anemometer and the amplitude envelope of the high-frequency wind-induced vibration in the corresponding time period; Establish a quantitative mapping relationship between the wind speed and the amplitude of the wind-induced vibration based on historical data; Predict the amplitude of the wind-induced vibration under the current wind speed condition using the above mapping relationship, and subtract it from the original vibration signal as the background interference component.

7. The method for health monitoring and abnormal identification of a bridge main body structure according to claim 1, characterized in that: The specific implementation process of step (4) is as follows: (41) Define the set of directly adjacent sensors for each sensor node according to the actual layout position of each vibration sensor to form the adjacent area of the node; (42) Calculate the phase difference and amplitude difference for each sensor node between the vibration wave signals corrected by wind-induced vibration at the same time stamp with each sensor in its adjacent area. If a certain sensor node and a certain sensor in its adjacent area meet the following conditions: a) The phase difference is less than the preset phase threshold; b) The amplitude difference is within the set attenuation range; Then it is determined that there is a vibration energy propagation relationship between the two, and mark the physical position connection line between the sensor node and its adjacent sensor as a vibration energy propagation segment; Starting from the new node identified on the vibration energy propagation segment, continue to repeat (42) with its own adjacent area as the analysis object, gradually expanding the propagation path outward until the entire sensor network is covered or the set maximum tracking depth is reached or no new effective propagation segments are found; Integrate all the identified propagation segments into a complete vibration energy propagation path map, and mark the propagation direction in the propagation path map.

8. The method for health monitoring and abnormal identification of a bridge main structure according to claim 1, characterized in that: The specific implementation of calculating the frequency concentration degree of each node in the vibration energy propagation path map is as follows: Mark the nodes of the vibration energy propagation path map, and perform frequency-domain transformation on the vibration wave data at each node to convert the time-domain signal into a frequency-domain representation to obtain the spectral amplitude distribution; Identify the main frequency components with significant energy contributions based on the obtained spectral amplitude distribution, and calculate the proportion of the sum of the energies corresponding to all main frequency points in the total energy of the entire spectrum as the frequency concentration degree of the node.

9. The method for health monitoring and anomaly identification of a bridge main structure according to claim 1, characterized in that: The calculation of the amplitude mutation degree is as follows: Perform time series analysis on the vibration wave data at each node to extract its instantaneous amplitude sequence, and then calculate the change amount of the amplitude between adjacent time points to form an amplitude difference sequence; Further calculate the average absolute difference value of the amplitude difference sequence as the amplitude mutation degree of the node on the basis of obtaining the amplitude difference sequence.

10. A method for health monitoring and abnormal identification of a bridge main structure according to claim 1, characterized in that: Comparing it with the historical health status database to further identify the structural abnormal area includes the following process: Construct a node weight factor for each node in the vibration energy propagation path map based on the number of energy propagation segments pointing to the node and the number of propagation segments starting from the node, combined with a normalization process and a weighted fusion strategy; Compare and calculate the frequency deviation and amplitude deviation by comparing the frequency concentration and amplitude mutation degree of each node in the current monitoring state with the corresponding reference frequency concentration and reference amplitude mutation degree in the historical health state database; Introduce a preset global warning deviation threshold and dynamically adjust it according to the weight factor of each node to obtain the warning deviation threshold of each node; For each node, compare the calculated frequency deviation and amplitude deviation with the corresponding warning deviation threshold respectively. If any index exceeds the corresponding threshold, it is determined that there is a structural anomaly in the area corresponding to the node, and the abnormal area is marked.

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