Bridge structure monitoring method and device based on microwave deformation radar

Abstract

The application provides a kind of bridge structure monitoring method and device based on microwave deformation radar, it is related to bridge structure monitoring technical field, and the bridge structure multi-point three-dimensional displacement data is obtained by microwave deformation radar, and thermal expansion false displacement compensation and multi-point space smoothing are carried out in combination with temperature information, to obtain the displacement field after environmental correction;Secondly, the vertical component is extracted and separated into static deformation and dynamic vibration component by using variational mode decomposition;Further, through time window coherence coefficient analysis, the decoupling area is identified, the dynamic signal of the area is analyzed recursively, the bispectrum analysis and energy distribution entropy calculation are carried out, and a high-order damage sensitive feature set is constructed;Finally, the dynamic characteristics and static curvature change are fused to form a comprehensive degradation degree index, and the deviation degree is judged according to the working condition classification and Mahalanobis distance, to realize the multi-dimensional, cross-condition degradation identification and risk warning of bridge structure.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge structure monitoring, in particular to a bridge structure monitoring method and device based on microwave deformation radar. BACKGROUND

[0002] As an important infrastructure, the structural health of a bridge is directly related to public safety and traffic efficiency. However, many current bridge monitoring systems mainly rely on periodic physical inspections and simple strain measurements, which have a lag and inaccuracy in detecting potential structural damage. Traditional technologies often cannot accurately assess the health status of a bridge in real time, especially in dynamic characteristic changes that cannot be captured by simple measurement methods. For example, existing systems cannot effectively combine dynamic parameters such as frequency, amplitude, and phase for comprehensive evaluation, resulting in potential damage often being discovered only after it has developed to a serious stage. This technical limitation increases the cost and risk of bridge maintenance.

[0003] In existing bridge structure health monitoring technologies, a large number of point sensors such as strain gauges, accelerometers, or optical fiber sensors are mainly relied on to achieve the acquisition of local structural responses. However, such methods have low spatial resolution, complex installation, high operation and maintenance costs, and are difficult to achieve high-frequency, non-contact full-field monitoring of the entire bridge structure. In addition, traditional monitoring methods based on a single data source often cannot simultaneously capture static deformation and dynamic vibration characteristics, have limited compensation ability for environmental disturbances such as wind load and temperature effects, are prone to misjudgment or omission, and are difficult to achieve sensitive identification of early structural damage and local degradation.

[0004] Although certain radar or optical technologies have been tried in bridge monitoring in recent years, such as synthetic aperture radar (SAR) or laser scanning methods, these technologies mainly focus on single displacement monitoring and cannot simultaneously acquire multi-modal field information. They also lack dynamic decoupling of deformation fields, systematic extraction and fusion evaluation mechanisms for vibration damage characteristics, especially in complex working conditions. Existing systems cannot construct a structural response classification model combined with operating environment information, and lack risk quantification methods based on statistical distribution.

[0005] The above information disclosed in the background section is only intended to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The present application aims to provide a bridge structure monitoring method and device based on microwave deformation radar to solve the problems raised in the background.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0008] A bridge structure monitoring method based on microwave deformation radar, the specific steps comprising:

[0009] Step 1: high-frequency scanning of the bridge surface is performed using the microwave deformation radar, a full-field three-dimensional displacement and its change rate are generated by phase interference, and the structure surface thermal field distribution and bridge deck wind speed information are inversely obtained from the radar echo signal, the displacement field is subjected to thermal strain compensation and spatial continuity filtering, and the environment-corrected three-dimensional displacement field data, structure surface temperature field and wind speed field are output;

[0010] Step 2: the vertical component is extracted from the corrected three-dimensional displacement field, and variational modal decomposition is performed thereon to separate the static deformation field and the dynamic vibration field, the time-space coherence coefficient matrix is calculated based on the dynamic vibration field, the area where the coherence drops by more than 30% is defined as the decoupling area, the Hilbert instantaneous amplitude variance and instantaneous frequency variance, the bispectrum maximum peak value, the recursive quantization index and the vibration energy distribution entropy of the decoupling area are extracted, and the output is a damage sensitive feature set;

[0011] Step 3: each component of the damage sensitive feature set is normalized according to the historical baseline data of the bridge health state, the entropy weight method is used to determine the feature weight and fusion to generate a dynamic damage index, the curvature change of the whole bridge structure is calculated based on the static deformation field, and the dynamic damage index is fused to form the current structure comprehensive degradation degree;

[0012] Step 4: a historical working condition database is constructed within the bridge operation monitoring period, the database includes light load and heavy load working condition categories distinguished according to the vibration energy distribution entropy, and weak wind and strong wind working condition categories divided according to the wind speed; the 90% confidence interval of the structure comprehensive degradation degree is calculated for each working condition, and the corresponding working condition category is matched according to the current load and wind speed state, the Mahalanobis distance of the current comprehensive degradation degree relative to the historical distribution of the working condition is calculated, which is used to realize the structure risk identification.

[0013] Further, the logic for using the microwave deformation radar to perform high-frequency scanning of the bridge surface to generate a full-field three-dimensional displacement and its change rate is as follows:

[0014] The key monitoring areas of the bridge to be monitored are determined, which include piers, bridge deck centers, abutments, bridge connection areas and bearings, each key monitoring area is then evenly divided into a plurality of grid cells, the grid cells are indexed as i, and a key monitoring point is arranged at the center of each grid cell, and the key monitoring point at the center of the i-th grid is the i-th key monitoring point;

[0015] The microwave deformation radar performs high-frequency phase interference scanning of the key monitoring points on the bridge deck with a continuous wave signal with a frequency of , wherein the wavelength of the radar signal is , and is the speed of light. At the sampling time, the radar continuously scans the same point and records the phase difference between two adjacent echoes. Based on the principle of phase interference, the radial displacement of this point along the radar line of sight is obtained using the following formula:

[0016] ;

[0017] in, For the i-th key monitoring point at time... phase difference, For the i-th key monitoring point at time... radial displacement;

[0018] The elevation angle of the i-th key monitoring point relative to the radar is known. and azimuth , radial displacement Projecting this onto the local coordinate system of the bridge deck, we obtain the three-axis displacement components:

[0019] ;

[0020] in, , and To represent the instantaneous displacement components of the bridge in the east-west, north-south, and vertical directions respectively;

[0021] For each key monitoring point i, the triaxial displacement increment is divided by the time interval to obtain the instantaneous triaxial displacement rate; in the same radar echo, the reflected signal intensity is analyzed. and spectrum broadening The analysis was conducted to invert the surface temperature field of the structure. and bridge surface wind speed field The structural temperature Using pre-calibrated constants and The observed Mapped to corresponding temperature : The wind speed on the bridge deck Through formula Obtain;

[0022] Considering the pseudo-displacement caused by thermal expansion of bridge materials due to temperature changes, compensation is applied to each key monitoring point i as follows: [Setting...] Let be the coefficient of linear expansion of the bridge material corresponding to the i-th key monitoring point. Let be the equivalent observation length of the i-th critical monitoring point, where temperature-induced structural expansion and contraction are considered to unfold along this length. If the reference temperature is used, then at time [time]... The vertical thermal expansion pseudo displacement caused by temperature change is The value is deducted from the measured vertical displacement , that is, the thermal compensation vertical displacement, and the compensation processing method in the east-west and north-south directions is the same;

[0023] On the predefined grid on the bridge deck, for the grid unit where the ith key monitoring point is located, the set of its neighborhood units is denoted as The spatial distance between the ith key monitoring point and the jth key monitoring point in the set of neighborhood units is calculated , j is the index of the key monitoring point in the set of neighborhood units , and a Gaussian type weight is defined The thermal compensation displacement of each key monitoring point is smoothed in the three-axis direction in a weighted average manner, and the final output is the three-dimensional displacement field data of each key monitoring point i after environmental correction at time , and the structure temperature and wind speed obtained by inversion are also output.

[0024] Further, the vertical component is extracted from the corrected three-dimensional displacement field, and the logic of implementing variational mode decomposition on it to separate the static deformation field and the dynamic vibration field is as follows:

[0025] The vertical component of the ith key monitoring point is decomposed by variational mode decomposition, classified by the center frequency, and divided into low-frequency static deformation component and high-frequency dynamic vibration component , is the static deformation of the ith key monitoring point at time , and is the dynamic vibration of the ith key monitoring point at time ;

[0026] The time window is set, the vertical dynamic vibration component is extracted, and the signal sequences and in the time window are calculated , and the time window coherence coefficient matrix is obtained :

[0027] ;

[0028] wherein is the time variable from to , the average coherence coefficient of the ith key monitoring point is defined as , and when compared with the previous time , it decreases by more than 30%, that is When the i-th key monitoring point is located, the grid cell is identified as a decoupling region, and p is used to index the decoupling region;

[0029] For the marked decoupling region p, its dynamic vibration components To extract the instantaneous amplitude and frequency, a Hilbert transform is performed. The specific steps are as follows:

[0030] make Indicates to The Hilbert transform result is used to construct the analytic signal:

[0031] ;

[0032] in, The imaginary unit is the magnitude of the analytic signal. That is, the p-th decoupling region at time t. The instantaneous amplitude, its phase The derivative with respect to the phase is obtained That is, the instantaneous frequency of the decoupling region;

[0033] Calculate the variance of instantaneous amplitude and instantaneous frequency within the window. and ,when or If the value exceeds three standard deviations from the baseline value of the healthy state, the point is considered to have abnormal fluctuations, indicating signs of structural damage.

[0034] Furthermore, for the dynamic vibration signal of the decoupled region p Delayed embedding reconstruction is performed to construct its phase space trajectory, and the following two recursive quantitative analysis indicators are extracted:

[0035] Certainty Index and layering The determinism index represents the proportion of linear repeating segments in a phase space trajectory, while the stratification degree represents the proportion of time a phase space trajectory stays in the same state.

[0036] For the marked decoupling region p, smooth the vertical dynamic vibration signal. Second-order bispectral analysis is performed to detect higher-order nonlinear coupling characteristics. The specific formula used to define the second-order bispectral function is as follows:

[0037] ;

[0038] in, For signal Fourier transform, Indicates complex conjugation. This represents a statistical average over multiple time windows;

[0039] If there exists a frequency pair such that is significantly higher than the historical baseline value of the corresponding frequency pair in healthy state, it indicates that the signal has produced significant non-Gaussian coupling at these two frequencies, and the maximum peak value is recorded as:

[0040] ;

[0041] and taken as the high-order nonlinear damage indicator;

[0042] For each labeled decoupling zone p, take its vertical dynamic vibration signal Band-pass filter within 5-20Hz to get the band-pass signal, divide the band-pass signal into N segments in the time window, calculate the instantaneous total energy of the first segment, and then get the energy proportion, and then define the energy distribution entropy: where is the energy proportion of the first segment, measures the dispersion degree of the energy of the region in the selected frequency band on each sub-segment;

[0043] Finally, the above data extracted from the decoupling zone p form the damage sensitive feature set.

[0044] Further, the logic of normalizing each component of the damage sensitive feature set according to the historical baseline data of the bridge health state is as follows:

[0045] The damage sensitive feature set extracted from the decoupling zone p , the oth feature component in it is subtracted from its mean value in the healthy state of the bridge, and divided by the corresponding standard deviation to get the dimensionless standard component , o is the index of the feature component in the damage sensitive feature set, and m is the total number of feature components in the damage sensitive feature set;

[0046] By calculating the information entropy of each feature in the historical sample, and determining the weight of each feature according to the inverse proportion principle of information entropy , the greater the weight, the stronger the feature's ability to distinguish the state difference, and the dynamic damage index of the decoupling zone p is represented as the weighted sum of the standard features:

[0047] ;

[0048] where, is regarded as a comprehensive quantitative value reflecting the dynamic damage signal of the nonlinear characteristics, spectral abnormalities, and coupling structure variations of the current region;

[0049] The static part of the bridge deck deformation field , extract spatial second-order Laplacian , and calculate the curvature variation from the initial state :

[0050] ;

[0051] wherein, is the time when the bridge is built;

[0052] The dynamic damage index is normalized with the curvature variation , and a linear fusion model is used to obtain the deterioration index :

[0053] ;

[0054] wherein, is the normalized dynamic damage index and curvature variation, is a weight coefficient, which is set according to the structural characteristics of the bridge.

[0055] Further, a historical working condition database is constructed within the bridge operation monitoring period, which includes light load and heavy load working condition categories distinguished according to vibration energy distribution entropy, and weak wind and strong wind working condition categories divided according to wind speed:

[0056] Based on the external interference and load changes suffered by the bridge structure during operation, the working condition of each monitoring period is divided into the following four categories: if the vibration energy distribution entropy , it is determined to be a light load working condition, otherwise it is heavy; if the wind speed , it is a weak wind working condition, otherwise it is a strong wind, wherein , are both set threshold values; the combination of the two is divided into 4 typical working condition categories: light load weak wind, light load strong wind, heavy load weak wind, heavy load strong wind, and is used to index these categories;

[0057] For each working condition , the corresponding structural comprehensive deterioration degree in its historical period is recorded, and its 90% confidence interval , mean and covariance matrix are calculated;

[0058] For any sampling time t, if its corresponding current working condition category is , its comprehensive deterioration degree is recorded as , and its Mahalanobis distance relative to the historical distribution of the working condition is defined as:

[0059] ;

[0060] wherein, is the Mahalanobis distance, which represents the deviation of the current structural state from the same type of working condition, is the transpose of the matrix; if If it exceeds the 95% confidence upper limit of the historical maximum value, it is considered as a potential structural risk event, triggering an alarm.

[0061] The application further provides a bridge structure monitoring device based on microwave deformation radar, which is used to perform the above-mentioned bridge structure monitoring method based on microwave deformation radar, and comprises:

[0062] A data collection module is configured to perform high-frequency scanning on the bridge surface using the microwave deformation radar, generate a full-field three-dimensional displacement and its change rate through phase interference, and simultaneously obtain the structural surface thermal field distribution and the bridge surface wind speed information from the radar echo signal, so as to perform thermal strain compensation and spatial continuity filtering on the displacement field, and output the environment-corrected three-dimensional displacement field data, the structural surface temperature field and the wind speed field.

[0063] A data processing module is configured to extract the vertical component from the corrected three-dimensional displacement field, and perform variational modal decomposition on the vertical component to separate the static deformation field and the dynamic vibration field, calculate the space-time coherence coefficient matrix based on the dynamic vibration field, define the region with a coherence drop of more than 30% as a decoupling region, extract the Hilbert instantaneous amplitude variance and the instantaneous frequency variance, the bispectrum maximum peak value, the recursive quantization index and the vibration energy distribution entropy of the decoupling region, and output the damage sensitive feature set.

[0064] A comprehensive calculation module is configured to normalize each component of the damage sensitive feature set according to the historical baseline data of the bridge health state, determine the feature weight by using the entropy weight method and generate a dynamic damage index by fusion, calculate the curvature change of the whole bridge structure based on the static deformation field, and form the structural comprehensive degradation degree at the current time by fusing the dynamic damage index.

[0065] A risk identification module is configured to construct a historical working condition database within a bridge operation monitoring period, which includes the light load and heavy load working condition categories distinguished according to the vibration energy distribution entropy, and the weak wind and strong wind working condition categories divided according to the wind speed; the 90% confidence interval of the structural comprehensive degradation degree of each working condition is calculated, and the corresponding working condition category is matched according to the current load and wind speed state, the Mahalanobis distance of the current comprehensive degradation degree relative to the historical distribution of the working condition is calculated, and the structural risk identification is realized.

[0066] Compared with the prior art, the application has the following beneficial effects:

[0067] The present application significantly improves the performance of the prior art in the aspects of structure state perception dimension, abnormality identification sensitivity and working condition disturbance suppression capability by constructing a bridge structure health evaluation method combining microwave deformation radar monitoring, structure dynamic response feature extraction and typical working condition classification modeling; the full-field displacement field of the bridge structure (including the vertical deformation and lateral displacement of the bridge deck) is non-contact dynamically acquired by using a microwave phased array radar, and the static curvature evolution features are extracted by combining the spatial Laplace operator, so that the static deformation evolution trend caused by long-term hidden dangers such as creep, settlement and structure degradation is fully reflected.

[0068] The present application solves the problems of traditional single feature vibration index being easily disturbed by noise and having insufficient state discrimination by using multi-scale decomposition and entropy weight modeling of radar inversion displacement time series data, constructing a dynamic damage sensitive feature set, and using the information entropy inverse weight to realize the weighted fusion of damage indicators. In order to solve the problem that the structure response is greatly affected by external disturbances such as wind load and traffic load, and has high risk of misjudgment, the present application proposes a typical working condition recognition and Mahalanobis distance deviation analysis mechanism, which first divides the structure response working condition into four types of combinations of light load-heavy load and weak wind-strong wind based on vibration energy entropy and wind speed features, and then constructs a historical degradation degree distribution model according to the working condition.

[0069] The present application realizes consistent state evaluation across working conditions by calculating the Mahalanobis distance of the current structure state relative to the mean value within the working condition, which has the advantages of dimension independence and variance sensitivity, can effectively distinguish the response anomalies caused by environmental disturbance and structure intrinsic degradation, significantly reduces the misjudgment rate, and improves the reliability of early damage identification. The present application solves the core problems of single information dimension and lack of risk quantitative evaluation mechanism in the existing structure health monitoring means by constructing a multi-modal index system and a statistical driven risk criterion. BRIEF DESCRIPTION OF DRAWINGS

[0070] Figure 1 It is a whole method flowchart of the present application;

[0071] Figure 2 It is a whole device structure schematic diagram of the present application;

[0072] Figure 3 It is a raw vertical displacement-temperature compensation after vertical displacement comparison chart of the present application;

[0073] Figure 4 It is a structure degradation degree-dynamic loss index curve chart of the present application;

[0074] Figure 5 It is a static curvature change-structure degradation degree curve chart of the present application. DETAILED DESCRIPTION

[0075] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with specific examples.

[0076] It should be noted that, unless otherwise defined, technical terms or scientific terms used in the present application shall have the usual meaning understood by a person with ordinary skill in the art to which the present application belongs. The terms "first", "second" and similar words used in the present application do not represent any order, number or importance, but are only used to distinguish different components. The terms "include" or "contain" and similar words mean that the elements or objects before the words cover the elements or objects listed after the words and their equivalents, without excluding other elements or objects. The terms "connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right" and the like are only used to represent relative positional relationships, which can change accordingly when the absolute positions of the described objects change.

[0077] Embodiments:

[0078] Please refer to Figures 1-5 The present application provides a technical solution:

[0079] A bridge structure monitoring method based on microwave deformation radar, the specific steps comprising:

[0080] Step 1: Use the microwave deformation radar to perform high-frequency scanning on the bridge surface, generate a full-field three-dimensional displacement and its change rate through phase interference, and simultaneously obtain the structure surface thermal field distribution and bridge deck wind speed information from the radar echo signal, perform thermal strain compensation and spatial continuity filtering on the displacement field, and output the environment-corrected three-dimensional displacement field data, structure surface temperature field and wind speed field;

[0081] The key positions of the bridge surface to be monitored include the bridge pier, which bears and distributes the vertical and horizontal loads of the bridge. The displacement detection of the bridge pier can help identify the structural stress changes caused by foundation settlement, load changes or other factors;

[0082] The bridge deck center, which is the most concentrated place of bridge stress, especially for suspension bridges and cable-stayed bridges. Monitoring the displacement of the bridge deck center can reveal the influence of traffic load and environmental factors (such as wind load) on the bridge;

[0083] The abutment, which connects the bridge and the slope, bears the horizontal load of the bridge deck. The displacement change of the abutment can indicate the structural stress changes of the bridge caused by temperature changes or uneven foundation settlement;

[0084] The connecting points of the bridge, which allow the bridge to freely expand and contract with temperature changes, monitoring these points helps to identify structural deformation or wear of the connecting parts due to thermal expansion and contraction;

[0085] The support, which bears the weight and deformation of the bridge body, the displacement monitoring of the support can reveal the functional changes of the support caused by load changes or material fatigue;

[0086] The logic of using microwave deformation radar to scan the bridge surface at high frequency and generating full-field three-dimensional displacement and its change rate through phase interference is:

[0087] The key positions of the bridge surface to be monitored are divided into several key monitoring areas, and each key monitoring area is evenly divided into several grid cells, which are indexed by i, and a key monitoring point is arranged at the center of each grid cell. The key monitoring point at the center of the i-th grid is the i-th key monitoring point.

[0088] The microwave deformation radar scans the key monitoring points of the bridge deck at high frequency with a continuous wave signal with a frequency of , wherein the wavelength of the radar signal is , wherein is the speed of light, is the sampling time, and the radar continuously scans the same point and records the phase difference between the adjacent two echoes, according to the principle of phase interference, the radial displacement of the point along the radar line-of-sight direction is obtained, and the formula is:

[0089] ;

[0090] , wherein is the phase difference of the i-th key monitoring point at time , and is the radial displacement of the i-th key monitoring point at time ;

[0091] Reflects the distance change between the target and the radar, the greater the distance change, the more severe the structure vibration or deformation, Reflects the instantaneous displacement of the structure along the radar direction, the greater the displacement, the greater the structure deformation amplitude; the essence of the formula is phase-displacement conversion, and the coefficient is determined by the principle of electromagnetic wave interference, and for each increase in phase difference, the corresponding displacement change is ;

[0092] Given the pitch angle and the azimuth angle of the i-th key monitoring point relative to the radar, the radial displacement is projected into the local coordinate system of the bridge deck to obtain the three-axis displacement change components:

[0093] ;

[0094] in, , and To represent the instantaneous displacement components of the bridge in the east-west, north-south, and vertical directions respectively;

[0095] Pitch angle The azimuth angle is the angle between the radar line of sight and the horizontal plane. The angle between the radar line of sight projected onto the horizontal plane and true north. Reflecting the tilt of the radar line of sight, a larger value indicates a greater difference in elevation and determines the weight of the vertical component; azimuth angle. It reflects the horizontal direction of the radar line of sight; the larger the value, the greater the difference in the azimuth of the point, and it determines the direction of the horizontal component. It reflects the vertical deformation of the bridge; a larger value indicates a decrease in the bridge's vertical stiffness or an increase in load, and is proportional to... ; It reflects the east-west horizontal displacement of the bridge; the larger the value, the more likely the structure is laterally unstable or under eccentric load.

[0096] radial displacement Decomposed into the bridge's local coordinate system, pitch angle Control the proportion of vertical components ( (All displacements are horizontal) azimuth angle Distribute the horizontal displacement direction;

[0097] For each key monitoring point i, the triaxial displacement increment is divided by the time interval to obtain the instantaneous triaxial displacement rate; in the same radar echo, the reflected signal intensity is analyzed. and spectrum broadening The analysis was conducted to invert the surface temperature field of the structure. and bridge surface wind speed field The structural temperature Using pre-calibrated constants and The observed Mapped to corresponding temperature : The wind speed on the bridge deck Through formula Obtain;

[0098] It reflects the electromagnetic wave reflection capability of a material surface; a larger value indicates a change in the material's dielectric constant or surface condition. Reflects the real-time temperature of the structure surface, the greater the value indicates the ambient temperature rises or solar radiation enhances; the dielectric constant of the material changes with temperature, affecting the radar echo intensity, coefficient and Need to be calibrated for different materials (steel / concrete) respectively;

[0099] Reflects the degree of dispersion of the echo signal frequency, the greater the value indicates that the wind-induced turbulence intensifies or the wind speed increases, reflects the real-time wind speed of the bridge deck, the greater the value indicates that the wind load enhances; moving particles, such as wind-carrying water droplets, cause the echo frequency to spread, the spread amount is proportional to the wind speed , coefficient determined by the radar wavelength;

[0100] Consider the pseudo displacement of the thermal expansion of the bridge material caused by temperature changes, and compensate for each key monitoring point i as follows: set as the linear expansion coefficient of the bridge material corresponding to the ith key monitoring point, as the equivalent observation length of the ith key monitoring point, at which the temperature-induced structural expansion is considered to be developed along this length as a whole, as the reference temperature, then the point at time The vertical thermal expansion pseudo displacement caused by temperature changes is , deduct this value from the measured vertical displacement , that is, the vertical displacement after thermal compensation, the compensation processing method in the east-west and north-south directions is the same;

[0101] Reflects the temperature change amplitude, the greater the value indicates that the thermal expansion effect is enhanced, which is proportional to the pseudo displacement amount, Reflects the thermal deformation sensitivity, the greater the value indicates that the material thermal expansion or the structure size is larger, amplifying the temperature impact; when the temperature rises, the material expansion causes the vertical displacement measurement value to be positively offset (false uplift), the vertical thermal expansion pseudo displacement needs to be deducted to obtain the true deformation;

[0102] Where the parameters of the vertical displacement after temperature compensation are shown in Table 1.

[0103] Table 1

[0104]

[0105] The original vertical displacement gradually increased from 10 at sample No. 1 to 17 at sample No. 15, indicating that the deformation characteristics of the material at different temperatures also changed as the sample advanced. Although the change in the linear expansion coefficient of the material was not significant, it slightly increased to 0.000011 in samples No. 6 to 10, which may reflect the influence of changes in material properties on thermal expansion;

[0106] When analyzing the relationship between the current temperature and the vertical displacement after temperature compensation, the current temperature showed an increasing trend as the sample number increased, gradually increasing from 20°C to 37°C. The corresponding vertical displacement after temperature compensation significantly increased to 12.47 at sample No. 6 and reached 14.35 at sample No. 10, indicating that the vertical displacement after temperature compensation increased as the current temperature increased, reflecting the influence of temperature on material deformation;

[0107] The reference temperature remained unchanged at 20°C, further emphasizing that changes in the current temperature are a key factor affecting the vertical displacement after temperature compensation. From sample No. 9 to No. 15, although the original vertical displacement continued to increase, the increase in the vertical displacement after temperature compensation significantly decreased, which may be because at higher temperatures, the thermal expansion effect of the material began to stabilize. The relationship between the vertical displacement after temperature compensation and the original vertical displacement, the linear expansion coefficient of the material, the current temperature, and the reference temperature indicates that the thermal expansion effect plays an important role in the deformation behavior of the material. As the sample number increases, the increase in the current temperature directly leads to an increase in the vertical displacement after temperature compensation, emphasizing the influence of temperature on structural health assessment. In practical applications, these data can provide important basis for material selection and engineering design to optimize the stability and safety of the structure.

[0108] Equivalent observation length is the length of the structure along which the thermal expansion-induced displacement is considered as a whole change by radar. Its physical meaning and acquisition method usually have two types: structural drawing or design parameter method and experimental calibration method.

[0109] For the structural drawing or design parameter method, if the i-th key monitoring point is directly opposite to a beam segment, plate thickness, or expansion joint segment, the actual length of this segment is usually taken as the reference length of thermal expansion, that is, the nominal scale of the overall stretching or shrinking of the material when the temperature changes. For example, if the monitoring point is located in the center of the bridge deck, the thickness or a small length of the midspan of the plate, such as 1 m, 2 m, etc., can be taken according to the monitoring requirements. If it is located on the surface of the vertical steel beam, the length of the corresponding segment on the beam span can be taken. This has the advantage of directly obtaining a clear and engineering-understandable expansion length from the structural drawing of the bridge or on-site measurement;

[0110] ​For the experimental calibration method, a known temperature difference calibration test is done on a monitoring point under field or laboratory conditions: artificially change the temperature, measure the displacement corresponding to the phase change of the radar, and then obtain The obtained by this calibration means is equivalent to concentrating the entire expansion behavior on an equivalent length, so that the theoretical compensation formula is consistent with the measured value;

[0111] On the predefined grid of the bridge deck, for the grid unit where the ith key monitoring point is located, the set of its neighborhood units is denoted as , the spatial distance between the ith key monitoring point and the jth key monitoring point in the set of neighborhood units is calculated , j is the index of the key monitoring point in the set of neighborhood units , and a Gaussian type weight is defined In a weighted average manner, the thermal compensation displacement of each key monitoring point is smoothed in the three-axis direction, and the final output is the three-dimensional displacement field data of the environmental corrected key monitoring point i at time , and the structure temperature and wind speed obtained by inversion are also output;

[0112] Reflects the proximity of spatial position, and the larger the value, the weaker the neighborhood correlation, Reflects the contribution weight of neighborhood points, and the larger the value, the greater the influence of nearby points;

[0113] Filtering is to eliminate sudden changes caused by radar measurement noise or local interference and retain the true structure deformation trend. The Gaussian weight ensures that nearby points have a greater impact, in line with the spatial continuity principle of engineering structure deformation;

[0114] First, the bridge deck is pre-divided into regular grids, such as rectangular grids, triangular grids, etc. For regular rectangular grids, units that share a side or a vertex with the ith key monitoring point are all considered as neighborhood, i.e. eight-connected or four-connected. Four-connected shares four directions (up, down, left, right), and eight-connected includes four units in diagonal directions in addition to four-connected;

[0115] First, calculate the coordinates of the center point of each unit , set a spatial radius threshold R, and classify all key monitoring points j that satisfy into the neighborhood unit set to complete the preliminary screening. Then, in the screening results, sort the distances from small to large, and select the top K nearest units to form, ensuring that the neighborhood is neither too large due to sparse areas nor too many due to dense areas;

[0116] According to the bridge structure characteristics, such as beam span, support position or stress concentration area, the neighborhood range of each unit is adaptively adjusted, for example, the units close to the support are spatially dense, but their structural responses are relatively independent, and R or K can be appropriately reduced, and the neighborhood can be increased in the middle of the span to capture the overall synergistic effect, and according to the density of the monitoring grid, the bridge span, the monitoring target and the calculation resources, a suitable neighborhood determination strategy is selected, so that the accuracy and efficiency of the coherence analysis and the smoothing filter can be ensured.

[0117] Step 2: Extract the vertical component from the corrected three-dimensional displacement field, and perform variational modal decomposition on it to separate the static deformation field and the dynamic vibration field, calculate the space-time coherence coefficient matrix based on the dynamic vibration field, define the area with a coherence drop of more than 30% as the decoupling area, extract the Hilbert instantaneous amplitude variance and instantaneous frequency variance, the maximum peak value of the bispectrum, the recursive quantization index and the vibration energy distribution entropy of the decoupling area, and output as the damage sensitive feature set;

[0118] The logic of extracting the vertical component from the corrected three-dimensional displacement field and performing variational modal decomposition to separate the static deformation field and the dynamic vibration field is as follows:

[0119] The vertical component of the i-th key monitoring point is decomposed by variational modal decomposition, and is classified according to the size of the center frequency, and is divided into low-frequency static deformation component and high-frequency dynamic vibration component , is the static deformation of the i-th key monitoring point at time , is the dynamic vibration of the i-th key monitoring point at time ;

[0120] Reflects the slow deformation of the bridge, such as creep and foundation settlement, and the greater the value, the more severe the long-term deformation accumulation, and is positively correlated with the load history and environmental temperature; Reflects the instantaneous vibration of the bridge, such as wind vibration and vehicle vibration, and the greater the value, the greater the dynamic response, and is positively correlated with the structure stiffness decay or load excitation; variational modal decomposition separates signals according to center frequency, and static component center frequency is close to 0Hz, and dynamic component is concentrated near the structure fundamental frequency, such as 0.5-10Hz;

[0121] Set the time window , extract the vertical dynamic vibration component to calculate the signal sequence and in the time window , and calculate the coherence coefficient matrix to obtain the time window coherence coefficient matrix :

[0122] ;

[0123] in, yes arrive The time variable is defined as the average coherence coefficient of the i-th key monitoring point. ,when Compared to the previous moment A decrease of more than 30%, that is When the i-th key monitoring point is located, the grid cell is identified as a decoupling area, and p is used to index the decoupling area; the processing method is the same for the east-west and north-south directions.

[0124] It reflects the intensity of the local dynamic response of the structure; the larger the value, the greater the vibration energy. It reflects the similarity of the vibration waveforms at two points. The larger the value, the stronger the synchronicity of the structural dynamic response. It is proportional to the integral value of the signal product. It reflects the overall synergy between the monitoring point and its surrounding area; a larger value indicates enhanced local dynamic coupling of the structure. This reflects a sharp drop in coherence; a larger value indicates a sudden change in the local stiffness of the structure. It reflects the loss of absolute synergy; the larger the value, the more complete the structural decoupling.

[0125] For the marked decoupling region p, its dynamic vibration components To extract the instantaneous amplitude and frequency, a Hilbert transform is performed. The specific steps are as follows:

[0126] make Indicates to The Hilbert transform result is used to construct the analytic signal:

[0127] ;

[0128] in, The imaginary unit, The magnitude of the analyzed signal That is, the p-th decoupling region at time t. The instantaneous amplitude, its phase The derivative with respect to the phase is obtained That is, the instantaneous frequency of the decoupling region;

[0129] The Hilbert transform converts a real signal into an imaginary part with a 90° phase shift, forming an analytic signal in the complex plane. This is used to avoid negative frequency interference and accurately extract instantaneous features. It reflects the instantaneous intensity of vibration energy; a larger value indicates the occurrence of impact load or resonance. It reflects the instantaneous value of the dominant vibration frequency; the larger the value, the greater the change in the effective stiffness of the structure.

[0130] Calculate the variance of instantaneous amplitude and instantaneous frequency within the window. and ,when or If the value exceeds three standard deviations from the baseline value of the health status, the point is considered to have abnormal fluctuations, indicating signs of structural damage.

[0131] ;

[0132] Damage conditions: or ;

[0133] It reflects the severity of amplitude fluctuations; a larger value indicates enhanced nonlinear vibration. It reflects the degree of frequency fluctuation; the larger the value, the more significant the time-varying nature of stiffness. , The mean and standard deviation of historical health status data.

[0134] For the dynamic vibration signal of the decoupling region p Delayed embedding reconstruction is performed to construct its phase space trajectory, and the following two recursive quantitative analysis indicators are extracted:

[0135] Certainty Index and layering The determinism index represents the proportion of linear repeating segments in a phase space trajectory, while the stratification degree represents the proportion of time a phase space trajectory stays in the same state.

[0136] Reconstruction aims to convert one-dimensional vibration signals into high-dimensional phase space trajectories, revealing hidden dynamic system characteristics. Healthy structures exhibit regular and orderly trajectories, while damaged structures exhibit divergent and chaotic trajectories.

[0137] Determinism measures the proportion of line segments on the diagonal of the phase space trajectory (that recover to the same or similar state), which can also be understood as the regularity of signal repetition.

[0138] ;

[0139] in, It is a length of The number of repeating line segments in the phase space trajectory. It is the minimum line segment length that is counted. The maximum length of the line segment included; the sum of the total lengths of all consecutive repeating line segments in the numerator, reflecting the cumulative time of the system in the diagonal repeating state in phase space; the sum of the lengths of all repeating points (turning trajectories) in the denominator, including isolated points, representing the time of all repeating behavior.

[0140] The higher the value, the more and longer the segments of the signal repeat along the diagonal in phase space, making the system trajectory more predictable and linear. In the early stages of damage initiation or crack propagation, this enhanced nonlinearity leads to… It will decrease significantly;

[0141] If the trajectory contains more and longer diagonal structures, that is, if it maintains a similar state for a long time, For length A significant increase, Increase; when the system becomes chaotic or cracks trigger abrupt changes, the phase space trajectory tends to spread out, with shorter segments. Rising, with fewer long segments. reduce;

[0142] Layering degree measures the proportion of point pairs in a phase space trajectory that remain in the same state (same row / column), also known as the percentage of vertical lines:

[0143] ;

[0144] in, The length of the vertical line is The count is calculated as follows: the numerator is all vertical line segments, which reflects the total time the system stays in one state, and the denominator is the sum of the durations of all repeated points appearing in the recursion graph.

[0145] The higher the value, the longer the system lingers or remains between certain states, and the more viscous or hysteretic the dynamic response; when cracks initiate or local relaxation occurs, vertical vibrations may linger in certain modes, increasing the vertical segment size, leading to... Ascending, when the trajectory repeats in the same phase space region, and the vertical line segments are numerous and long, then... Increase, As the system grows larger, undergoes rapid changes, or experiences increased chaos, vertical aggregation decreases. reduce;

[0146] For the marked decoupling region p, smooth the vertical dynamic vibration signal. Second-order bispectral analysis is performed to detect higher-order nonlinear coupling characteristics. The specific formula used to define the second-order bispectral function is as follows:

[0147] ;

[0148] in, For signal Fourier transform, Indicates complex conjugation. This represents a statistical average over multiple time windows;

[0149] Measuring frequency pairs Rather than frequency The third-order phase coupling between them, if the signal is a purely linear Gaussian process, then Theoretically, it is zero. The larger the peak value at the frequency pair, the more significant the non-Gaussian and nonlinear interaction exists between the three components of the system vibration.

[0150] If frequency pairs exist Make Significantly higher than the frequency pair corresponding to the region in a healthy state. The historical baseline value indicates that the signal has significant non-Gaussian coupling at these two frequencies. This maximum peak value is denoted as:

[0151] ;

[0152] And it is used as a high-order nonlinear damage index;

[0153] The higher the frequency, the stronger the nonlinear coupling generated by the initial cracking or relaxation, which is a sensitive characteristic of high-order damage. When nonlinear coupling intensifies, the frequency of a certain frequency pair or a set of frequency pairs... Significantly increased When the peak value increases and the structure is healthy, each frequency component is not specifically phase locked, and the bispectral peak value approaches zero.

[0154] For each marked decoupling region p, its vertical dynamic vibration signal is taken. Bandpass filtering is performed within the 5-20Hz range to obtain a bandpass signal. This bandpass signal is then divided into N equal segments within a time window, and the _th_ segment is calculated. The total energy at any given instant is used to obtain the energy percentage, and then the energy distribution entropy is defined: ,in It is the first Segment energy percentage It measures the degree of energy dispersion in each sub-band within the selected frequency band of the region;

[0155] ;

[0156] This measures the dispersion / uniformity of energy across N sub-segments. If energy is concentrated in a few segments, such as when a crack resonates, [the energy is considered concentrated]. The distribution is skewed. Smaller; The higher the temperature, the more dispersed the vibrational energy becomes, and the system may enter a state of multimodal coupling or broad-spectrum excitation. Significant changes often correspond to energy redistribution or changes in crack aperture before damage;

[0157] When certain segments of energy A surge or a sharp drop makes becomes non-uniform, decreases; energy is evenly distributed in more sub-sections, increases;

[0158] Finally, the above data extracted from the decoupling area p forms the damage sensitive feature set.

[0159] Step 3: Normalize each component of the damage sensitive feature set according to the historical baseline data of the bridge health state, determine the feature weight by entropy weight method and generate the dynamic damage index by fusion, calculate the curvature change of the whole bridge structure based on the static deformation field, and form the current structure comprehensive degradation degree by fusing the dynamic damage index;

[0160] The logic of normalizing each component of the damage sensitive feature set according to the historical baseline data of the bridge health state is as follows:

[0161] Damage sensitive feature set extracted from the decoupling area p , the oth feature component in it is subtracted from its mean value under the bridge health state and divided by the corresponding standard deviation to obtain the dimensionless standard component , o is the index of the feature component in the damage sensitive feature set, and m is the total number of feature components in the damage sensitive feature set;

[0162] reflects the original intensity of the damage feature, and the greater the value, the more severe the damage, reflects the standard deviation multiple of the deviation from the health baseline, and the greater the value, the more significantly higher the current value of the feature is than the health mean, the more serious the deviation, which implies that the damage signal corresponding to the feature is more significant, which represents the deviation of the feature at this moment relative to the health state (historical baseline), and the unit becomes several standard deviations; when the original feature increases, the normalized value increases linearly;

[0163] By calculating the information entropy of each feature in the historical sample, the weight of each feature is determined according to the inverse proportion principle of information entropy , the greater the weight, the stronger the feature's ability to distinguish the state difference, and the final dynamic damage index of the decoupling area p is represented as the weighted sum of each standard feature:

[0164] ;

[0165] wherein, is regarded as a comprehensive quantitative value reflecting the dynamic damage signals such as nonlinear features, spectral abnormalities, and coupling structure variations in the current area; it comprehensively reflects the overall intensity of various dynamic damage signals including nonlinear coupling, high-order spectral features, and energy distribution abnormalities;

[0166] Reflects the intensity of regional comprehensive damage, the greater the value, the more serious the dynamic damage, the more likely the structure is unhealthy, such as crack development, relaxation instability; When there is an anomaly, the greater the value, the more severe the damage, When the state is better than the historical baseline; The greater or its corresponding weight Rises, all of which will cause To rise;

[0167] Information entropy: , Where is the number of historical samples of the health state, is the proportion of feature o in the kth sample, is the information entropy of feature o, which measures the degree of dispersion, and is the weight of feature o; Reflects the degree of dispersion of feature data, the greater the value, the more uniform and disordered the data, Reflects the importance of the feature, the greater the value, the more discriminative it is in the historical samples, and the greater the contribution to the comprehensive index; the smaller the fluctuation of the feature in the healthy samples, i.e. , the more significant the change when damaged, i.e. the greater the weight ;

[0168] The greater the information entropy, the greater the fluctuation of the feature itself in the healthy state, and the weaker the ability to distinguish between health and damage, which should be given a lower weight; the smaller the information entropy, the stronger the difference, and a higher weight is given;

[0169] The static part of the bridge deck deformation field , extracts the spatial second-order Laplacian , and calculates the curvature change from the initial state :

[0170] ;

[0171] Where, is the time when the bridge deck is built;

[0172] Reflects the local curvature of the displacement field, Reflects the change in relative initial curvature; the current static deformation Increases, Increases positively; if the deformation recovery approaches the initial state, then Approaches 0;

[0173] Curvature Describes the bending or concave-convex degree of the deformation field, and the difference from the initial state Then reflects the persistent deformation evolution due to creep, settlement or material degradation, the greater the value, the more the deformation curvature rises in the region, and the structure may appear uneven sagging, local settlement or large creep;

[0174] The dynamic damage index is normalized and the curvature change After normalization, the deterioration degree index is obtained by using a linear fusion model :

[0175] ;

[0176] wherein, is the normalized dynamic damage index and the curvature change, is a weight coefficient, which is set according to the structural characteristics of the bridge; the formula simultaneously quantifies the dual deterioration effects of dynamic abnormalities and static deformation of the structure;

[0177] reflects the regional deterioration degree, and the greater the value, the lower the structural safety, and the worse the overall health of the structure under the current working condition, which not only has dynamic vibration abnormalities, but also has static deformation accumulation, reflects the dynamic damage contribution weight, which needs to be increased for dynamic sensitive structures ; the dynamic index captures short-term damage such as impact and crack propagation, and the static curvature reflects cumulative damage such as creep and corrosion; the two are complementary to avoid missed detection;

[0178] When dynamic abnormalities dominate, is larger, the increase of which will make obviously rise, when static deformation dominates, is smaller, the increase of which will more significantly promote rise.

[0179] Wherein, the parameters for calculating the structural deterioration degree are shown in Table 2.

[0180] Table 2

[0181]

[0182] Through analysis of the data in the first 15 rows of the table, it can be observed that there is a certain correlation between the dynamic damage index and the static curvature change, for example, the dynamic damage index and the static curvature change increase with the increase of the sample number. Specifically, as the sample number increases from 1 to 15, the dynamic damage index increases from 0.1 to 0.8, and the static curvature change increases from 0.2 to 0.9. This indicates that the degree of damage gradually increases under the influence of continuous dynamic load, accompanied by changes in static curvature;

[0183] When analyzing the influence of fusion weight on structural deterioration degree, the numerical value of fusion weight changes little with the increase of sample number, from 0.1 to 0.55. Although the change of weight is relatively stable, it significantly affects the calculation of the final structural deterioration degree. For example, the fusion weight of sample number 4 is 0.15, and the corresponding structural deterioration degree is 0.26, while the fusion weight of sample number 15 is 0.55, and the structural deterioration degree increases to 0.73. This shows that in the comprehensive evaluation of dynamic damage index and static curvature change, the setting of fusion weight plays a key role in the final result.

[0184] The weight combination between dynamic damage index and static curvature change affects the judgment of the health status of the structure. Taking sample number 10 as an example, the dynamic damage index is 0.55, the static curvature change is 0.65, and the fusion weight is 0.35. The calculated deterioration degree is 0.53. This combination shows that in the condition of higher dynamic damage, the contribution of static curvature change cannot be ignored, and its result is crucial to the evaluation of the health status of the structure. In summary, the relationship between dynamic damage index, static curvature change and fusion weight reflects the complexity of structural health evaluation.

[0185] Step 4: Construct a historical working condition database within the bridge operation monitoring period, which includes light load and heavy load working condition categories distinguished by vibration energy distribution entropy, and weak wind and strong wind working condition categories divided by wind speed; for each category, statistics its 90% confidence interval of structural comprehensive deterioration degree, and combine the current load and wind speed state to match the corresponding working condition category, calculate the Mahalanobis distance of the current comprehensive deterioration degree relative to the historical distribution of the working condition, for realizing the risk identification of the structure;

[0186] A historical working condition database is constructed within the bridge operation monitoring period, which includes light load and heavy load working condition categories distinguished by vibration energy distribution entropy, and weak wind and strong wind working condition categories divided by wind speed:

[0187] Based on the external interference and load changes of the bridge structure during operation, each monitoring period is divided into the following four categories: if the vibration energy distribution entropy is less than the threshold value, it is determined as a light load working condition, otherwise as a heavy load; if the wind speed is less than the threshold value, it is a weak wind working condition, otherwise it is a strong wind, where , are the set threshold values; the two are combined to divide into 4 typical working condition categories: light load weak wind, light load strong wind, heavy load weak wind, heavy load strong wind, and is used to index these categories;

[0188] Reflects the uniformity of vibration energy time distribution, the larger the value, the more dispersed and random the energy is, reflecting the critical value of load intensity, usually taking the median of history ; reflecting the intensity of environmental wind load, the greater the value, the stronger the wind-induced vibration, reflecting the critical value of wind vibration influence, usually taking the upper limit of 6-level wind; from below to exceed the threshold, the classification cuts to strong wind, below the threshold, keep weak wind;

[0189] vibration energy distribution entropy The lower the value, the more concentrated the energy in a few frequency bands, which often corresponds to local modal response under small load, and the higher the entropy, the more dispersed the energy, indicating the superposition effect of multi-modal vibration caused by live load such as vehicles; when from below the threshold increase and cross the threshold, the classification switches from light load to heavy load; as long as below , all belong to light load;

[0190] For each type of working condition , record the corresponding structure comprehensive degradation degree in the history period, calculate its 90% confidence interval and mean and covariance matrix ; The upper and lower 5% samples outside the interval are considered as extreme values, corresponding to the rare or known relatively serious structure state in history; represent the normal degradation level under the typical working condition, then describes the fluctuation range and mutual correlation of the index under the working condition;

[0191] For any sampling time t, if its corresponding current working condition category is , its comprehensive degradation degree is recorded as , and its Mahalanobis distance relative to the historical distribution of the working condition is defined as:

[0192] ;

[0193] where, is the Mahalanobis distance, which represents the deviation of the current structure state relative to the same working condition, with the advantages of dimensionless and variance sensitivity, is the transpose of the matrix; if exceeds the 95% upper limit of the maximum value in history, it is considered as a potential structure risk event, triggering an alarm;

[0194] reflecting the absolute deviation from the benchmark, the greater the value, the higher the damage possibility, reflecting the historical volatility correction, the greater the value, the more suppression of false positives in high fluctuation working conditions, It is a column vector representing the current deviation from the historical mean of this category. Transpose this column vector into a row vector;

[0195] The current overall degradation level Distribution center with similar working conditions in history The standardized Euclidean distance between them The larger the value, the further the current structural state deviates from the typical healthy center in the multidimensional indicator space, indicating a more severe deviation; when any component... Comparison Increase or decrease, exceeding its... The standard deviation in the middle will drive Increase; if Exactly equal to ,but This indicates consistency with typical historical conditions; when Exceeding the upper limit of the historical 95% range indicates that the current state has exceeded the historical normal fluctuation limit of 95%, which may indicate structural risks, such as crack propagation and changes in support constraints.

[0196] The present invention also provides a bridge structure monitoring device based on micro-waveform variable radar, the device being used to execute the above-described bridge structure monitoring method based on micro-waveform variable radar, comprising:

[0197] The data collection module is used to perform high-frequency scanning of the bridge surface using a micro-waveform variable radar, generate full-field three-dimensional displacement and its rate of change through phase interference, and simultaneously retrieve the thermal field distribution and bridge surface wind speed information from the radar echo signal. It performs thermal strain compensation and spatial continuity filtering on the displacement field, and outputs environmentally corrected three-dimensional displacement field data, structural surface temperature field and wind speed field.

[0198] The data processing module is used to extract the vertical component from the corrected three-dimensional displacement field and perform variational mode decomposition on it to separate the static deformation field and the dynamic vibration field. Based on the dynamic vibration field, the spatiotemporal coherence coefficient matrix is ​​calculated, and the region where the coherence drops sharply by more than 30% is defined as the decoupling region. Hilbert instantaneous amplitude variance and instantaneous frequency variance, bispectral maximum peak value, recursive quantization index and vibration energy distribution entropy are extracted from the decoupling region, and the output is a damage-sensitive feature set.

[0199] The comprehensive calculation module is used to normalize each component of the damage-sensitive feature set based on the historical baseline data of the bridge's health status, determine the feature weights using the entropy weight method, and fuse them to generate a dynamic damage index. Based on the static deformation field, it calculates the curvature change of the entire bridge structure and fuses it with the dynamic damage index to form the current comprehensive structural deterioration degree.

[0200] The risk identification module is configured to construct a historical working condition database in a bridge operation monitoring period, the database including light load and heavy load working condition categories distinguished according to vibration energy distribution entropy, and weak wind and strong wind working condition categories divided according to wind speed; 90% confidence intervals of structure comprehensive deterioration degrees of each working condition category are counted, and a current load and wind speed state are matched with a corresponding working condition category to calculate Mahalanobis distance of a current comprehensive deterioration degree relative to a historical distribution of the working condition, so as to realize structure risk identification.

[0201] The above formulas are dimensionless values calculated, the formulas are obtained by collecting a large amount of data to simulate a formula of the nearest real situation, and preset parameters in the formulas are set by a person skilled in the art according to actual conditions.

[0202] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product. Those skilled in the art can realize that units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software methods depends on specific application and design constraints of the technical solutions.

[0203] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, which can be located in one place or distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0204] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for monitoring a bridge structure based on microwave deformation radar, characterized by, The specific steps include: Step 1: High-frequency scanning of the bridge surface using microwave deformation radar, generating full-field three-dimensional displacement and its rate of change through phase interference, simultaneously inverting the structural surface thermal field distribution and bridge deck wind speed information from the radar echo signal, compensating the displacement field for thermal strain and spatial continuity filtering, and outputting the environment-corrected three-dimensional displacement field data, structural surface temperature field and wind speed field; Step 2: Extracting the vertical component from the corrected three-dimensional displacement field and performing variational modal decomposition to separate the static deformation field and dynamic vibration field, calculating the space-time coherence coefficient matrix based on the dynamic vibration field, defining the area with a coherence drop of more than 30% as the decoupling area, extracting the Hilbert instantaneous amplitude variance and instantaneous frequency variance, bispectrum maximum peak, recursive quantization index and vibration energy distribution entropy from the decoupling area, and outputting as the damage sensitive feature set; Step 3: Normalizing each component of the damage sensitive feature set according to the historical baseline data of the bridge health state, determining the feature weight using the entropy weight method and fusing to generate the dynamic damage index, calculating the curvature change of the whole bridge structure based on the static deformation field, and fusing with the dynamic damage index to form the current structural comprehensive degradation degree; Step 4: Building a historical working condition database within the bridge operation monitoring period, which includes light and heavy load working condition categories distinguished according to vibration energy distribution entropy, and weak and strong wind working condition categories divided by wind speed; for each working condition, statistics its 90% confidence interval of the structural comprehensive degradation degree, and matches the corresponding working condition category according to the current load and wind speed state, calculates the Mahalanobis distance of the current comprehensive degradation degree relative to the historical distribution of the working condition, which is used to realize the structure risk identification; The logic for using microwave deformation radar to perform high-frequency scanning of the bridge surface and generating full-field three-dimensional displacement and its rate of change through phase interference is as follows: Determine the key monitoring area of the bridge to be monitored, which includes piers, bridge deck center, abutments, bridge connection areas and bearings, then divide each key monitoring area into several grid cells, index these grid cells with i, and place key monitoring points at the center of each grid cell, then the key monitoring point at the center of the i-th grid is the i-th key monitoring point; The microwave deformation radar scans the key monitoring points on the bridge deck with high frequency phase interference by using continuous wave signals with frequency f0, wherein the radar signal wavelength λ=c / f0, t is the sampling time, the radar continuously scans the same point, and the phase difference ΔΦ between the adjacent two echoes is recorded i (t) = Φ i (t) - Φ i (t-1), according to the phase interference principle, the radial displacement of the point along the radar line-of-sight direction is obtained, and the formula is: where ΔΦ i (t) is the phase difference of the ith key monitoring point at time t, u i (t) is the radial displacement of the ith key monitoring point at time t; The i-th key monitoring point is known relative to the radar's pitch angle θ i and azimuth angle The radial displacement u i (t) is projected into the bridge deck local coordinate system, resulting in three-axis displacement variation components: wherein X i (t), Y i (t), and Z i (t) are respectively the instantaneous displacement variation components of the bridge in the east-west, north-south, and vertical directions. For each key monitoring point i, the three-axis displacement increment is divided by the time interval to obtain the three-axis instantaneous displacement change rate; in the same radar echo, the reflection signal intensity I i (t) and the spectrum broadening Δf i (t) are analyzed, and the structure surface temperature field T i (t) and the bridge deck wind speed field W i (t) are respectively inverted i (t) and the bridge deck wind speed field W i (t) are respectively inverted i (t) are respectively inverted i (t) are respectively inverted i (t) are respectively inverted i (t) are respectively inverted i (t) are respectively inverted i (t) are respectively inverted i (t) are respectively inverted i (t) are respectively inverted i (t) are respectively inverted i (t) are respectively inverted Considering the pseudo-displacement caused by thermal expansion of bridge materials due to temperature changes, compensation is applied to each key monitoring point i as follows: α is set... i Let L be the coefficient of linear expansion of the bridge material corresponding to the i-th key monitoring point. i Let T be the equivalent observation length of the i-th critical monitoring point, where temperature-induced structural expansion and contraction are considered to unfold along this length. i,0 If the reference temperature is given, then the pseudo-displacement of vertical thermal expansion at that point at time t due to temperature change is α. i L i [T i (t)-T i，0 This value is derived from the measured vertical displacement Z. i Subtracting from (t) yields the vertical displacement after thermal compensation. The compensation methods for the east-west and north-south directions are the same. On the predefined grid of the bridge deck, for the grid cell where the ith key monitoring point is located, the set of its neighborhood cells is denoted as N i , the spatial distance d i between the ith key monitoring point and the jth key monitoring point in the set of its neighborhood cells N ij is calculated, j is the index of the key monitoring point in the set of neighborhood cells N i , and a Gaussian-type weight w ij is defined. In a weighted average manner, the thermal-compensated displacement of each key monitoring point is smoothed in the three-axis direction respectively, and the final output is the three-dimensional displacement field data of the environmental-corrected key monitoring point i at time t, while the structure temperature and wind speed obtained by inversion are also output.

2. The microwave deformation radar-based bridge structure monitoring method according to claim 1, characterized in that: The logic for extracting the vertical component from the corrected three-dimensional displacement field and performing variational modal decomposition to separate the static deformation field and dynamic vibration field is as follows: Variational mode decomposition was used to decompose the vertical component of the i-th key monitoring point, and it was classified according to the magnitude of the center frequency, into low-frequency static deformation components. and high-frequency dynamic vibration components Let be the static deformation of the i-th key monitoring point at time t. The dynamic vibration of the i-th key monitoring point at time t; Set time window ΔT = 10 / f0, extract its vertical dynamic vibration component to calculate the signal sequence in time window ΔT and Calculate the coherence coefficient matrix to obtain its time window coherence coefficient matrix C ij (t): wherein τ is a time variable from t-ΔT to t, defining the average coherence factor of the ith key monitoring point When Compared with the last time Fall by more than 30%, that is And C ij When (t) < 0.3, the grid cell where the ith key monitoring point is located is judged as a decoupling area, and the decoupling area is indexed with p. For the decoupling region p marked, the dynamic vibration component The Hilbert transform is performed to extract the instantaneous amplitude and instantaneous frequency, and the specific steps are as follows: Let denote the Hilbert transform of and construct the analytic signal as wherein, is the imaginary unit, the modulus of the analytic signal is the instantaneous amplitude of the pth decoupling zone at time t, whose phase is the derivative of the phase is the instantaneous frequency of the decoupling zone; Instantaneous amplitude of variance within a computation window and variance of instantaneous frequency and When or If it exceeds the baseline value of health status 3 times the standard deviation, the point is considered to have abnormal fluctuations, indicating the presence of structural damage signs.

3. The microwave deformation radar-based bridge structure monitoring method according to claim 2, characterized in that: Dynamic vibration signals for decoupling region p The delay-embedding reconstruction is implemented, the phase space trajectory is constructed, and the following two recursive quantification analysis indexes are extracted: deterministic index DET p and laminarity LAM p The deterministic index indicates the proportion of linearly repeating segments in the phase space trajectory, and the laminarity indicates the proportion of time length that the phase space trajectory stays in the same state. For the marked decoupling region p, its smooth vertical dynamic vibration signal Second-order bispectrum analysis is performed to detect high-order nonlinear coupling characteristics. The formula for defining the second-order bispectrum function is: wherein is the Fourier transform of the signal denotes the complex conjugate, denotes a statistical average over a plurality of time windows; If there exists a frequency pair (f1, f2) such that |B p (f1,f2)| is significantly higher than the historical baseline value of the decoupling zone for the frequency pair (f1, f2) in the healthy state, it means that the signal produces significant non-Gaussian coupling at the two frequencies. The maximum peak is recorded as: And take it as a high-order nonlinear damage index; For each marked decoupling area p, take its vertical dynamic vibration signal Band-pass filtering within 5-20Hz to obtain a band-pass signal, dividing the band-pass signal into N segments in a time window, calculating the instantaneous total energy of the lth segment, and then obtaining the energy proportion, and then defining the energy distribution entropy: Where P p,l is the energy proportion of the lth segment, H p measures the dispersion degree of the energy of the area in the selected frequency band on each sub-segment; Finally, the above data extracted from the decoupling area p form the damage sensitive feature set.

4. The microwave deformation radar-based bridge structure monitoring method according to claim 3, characterized in that: The logic for normalizing each component of the damage sensitive feature set according to the historical baseline data of the bridge health state is as follows: A set of damage-sensitive features extracted for decoupling zone p wherein the oth feature component F po (t) is subtracted from its mean value at the bridge health state and divided by the corresponding standard deviation, resulting in a dimensionless standard component o is the index of the feature component in the set of damage-sensitive features, and m is the total number of feature components in the set of damage-sensitive features. The information entropy of each feature is calculated by counting the discrete degree of each feature in the historical samples, and the weight w of each feature is determined according to the inverse proportion principle of information entropy o The greater the weight is, the stronger the distinguishing ability of the feature to the state difference is. Finally, the dynamic damage index of the decoupling area p is expressed as the weighted sum of each standard feature. wherein E p (t) is regarded as a comprehensive quantitative value reflecting the dynamic damage signals of the nonlinear characteristics, spectral anomalies, coupling structure variations, etc. of the current region; from the static part of the bridge deck deformation field extracting spatial second order laplacian and calculating the curvature change amount ΔK from the initial state p (t): Where t0 is the time when the bridge deck is built; The dynamic damage index E p (t) is normalized with the curvature change amount ΔK p (t), and a linear fusion model is used to obtain the deterioration degree index S p (t): wherein, is the normalized dynamic damage index and the curvature variation, and a e [0, 1] is a weight coefficient, which is set according to the structural characteristics of the bridge.

5. The microwave deformation radar-based bridge structure monitoring method according to claim 3, characterized in that: Build a historical working condition database within the bridge operation monitoring period, which includes light and heavy load working condition categories distinguished according to vibration energy distribution entropy, and weak and strong wind working condition categories divided by wind speed: Based on the external interference and load changes of the bridge structure in the operation process, the working conditions of each monitoring time period are divided into the following four categories: if the vibration energy distribution entropy H p (t)<θ H , it is determined as light load working condition, otherwise as heavy load; if the wind speed W p (t)<θ W , it is weak wind working condition, otherwise as strong wind, wherein θ H , θ W are both set threshold values; the two are combined to divide into four typical working condition categories: light load weak wind, light load strong wind, heavy load weak wind, heavy load strong wind, and the categories are indexed by c; For each type of working condition c, record the corresponding structure comprehensive deterioration degree in its history period, and calculate its 90% confidence interval and the mean μ c and the covariance matrix Σ c ; For any sampling time t, if its corresponding current working condition category is c, then its comprehensive degradation degree is denoted as S(t), and its Mahalanobis distance relative to the historical distribution of the working condition is defined as: Wherein, M(t) is the Mahalanobis distance, which represents the deviation of the current structure state from the same working condition, T is the transpose of the matrix; if M(t) exceeds the 95% upper limit of the historical maximum value, it is considered as a potential structural risk event, triggering an alarm.

6. A microwave deformation radar-based bridge structure monitoring apparatus, characterized by: The device is used to execute the microwave deformation radar-based bridge structure monitoring method of any one of claims 1-5, comprising: A data collection module is configured to use the microwave deformation radar to perform high-frequency scanning on the bridge surface, generate a full-field three-dimensional displacement and its change rate through phase interference, and simultaneously obtain the structure surface thermal field distribution and bridge surface wind speed information from the radar echo signal, perform thermal strain compensation and spatial continuity filtering on the displacement field, and output the environment-corrected three-dimensional displacement field data, structure surface temperature field, and wind speed field. A data processing module is configured to extract the vertical component from the corrected three-dimensional displacement field, and perform variational mode decomposition to separate the static deformation field and the dynamic vibration field, calculate the space-time coherence coefficient matrix based on the dynamic vibration field, define the region with a coherence drop of more than 30% as a decoupling region, extract the Hilbert instantaneous amplitude variance and instantaneous frequency variance, the bispectrum maximum peak value, the recursive quantization index, and the vibration energy distribution entropy of the decoupling region, and output the damage-sensitive feature set. A comprehensive calculation module is configured to normalize each component of the damage-sensitive feature set according to the historical baseline data of the bridge health state, determine the feature weight using the entropy weight method, and fuse to generate a dynamic damage index, calculate the curvature change of the whole bridge structure based on the static deformation field, and fuse the dynamic damage index to form the current structure comprehensive degradation degree. A risk identification module is configured to construct a historical working condition database within the bridge operation monitoring period, which includes light load and heavy load working condition categories distinguished according to the vibration energy distribution entropy, and weak wind and strong wind working condition categories divided according to the wind speed; for each working condition, the 90% confidence interval of the structure comprehensive degradation degree is calculated, and the corresponding working condition category is matched according to the current load and wind speed state, the Mahalanobis distance of the current comprehensive degradation degree relative to the historical distribution of the working condition is calculated, which is used to realize the structure risk identification.

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