Bridge support column stress measurement method based on structural synergistic force analysis

Abstract

The present invention provides a bridge support column stress measurement method based on structural collaborative force analysis, which belongs to the field of stress measurement technology. It collects stress, strain, and displacement data of each bridge support column in real time; calculates the local stress index of each bridge support column based on the collected real-time data; establishes a structural collaborative force model based on the connection relationship between the overall structural parameters of the bridge and the bridge support columns; calculates the collaborative stress correction index of each bridge support column; compares the collaborative stress correction index with the dynamic stress assessment interval, combines historical data and environmental factors to perform trend analysis, determines whether there is a structural risk, and issues a warning signal when the threshold is exceeded. By introducing a structural collaborative force analysis mechanism, the present invention comprehensively considers the impact of the overall stress state of the bridge on a single bridge support column, can more accurately identify key structural risk points, and improve the reliability and early warning capabilities of bridge safety monitoring.

Description

Technical Field

[0001] The present invention belongs to the technical field of stress measurement, and in particular relates to a bridge support column stress measurement method based on structural collaborative force analysis. Background Art

[0002] Bridge support column stress measurement primarily utilizes strain monitoring technology, which assesses the stress state of a support column by measuring changes in strain under load. As mentioned in the prior art patent publication "CN119714652B," which describes a stress detection method and system for high-pier bridge support structures, the stress measurement of bridge support columns in this stress detection method and system for high-pier bridge support structures often utilizes an independent monitoring approach, collecting stress data for each support column individually and performing an assessment based on this data. This approach, in other words, focuses solely on the local stress state of a single support column, ignoring the interactions between the bridge support columns and the synergistic stress effects of the overall structure.

[0003] Due to their large height, long span, and complex loads, high-pier bridges often experience significant load transfer and stress redistribution between support columns. For example, when the bearing capacity of a bridge support column decreases due to foundation settlement or local damage, adjacent support columns will be subject to additional loads, potentially triggering cascading structural risks. Existing independent measurement methods struggle to promptly identify such structural risks, suffer from one-sided assessments, delayed early warnings, and carry static assumption errors. The correlation and timeliness between local stress indices and the overall stress state are poor, impacting the accuracy of bridge structural safety assessments. Summary of the Invention

[0004] In order to address the defects in the existing technology, the present invention provides a bridge support column stress measurement method based on structural collaborative force analysis, which overcomes the one-sided assessment problem caused by stress analysis based on independent stress data of a single bridge support column in the existing technology. By introducing a structural collaborative force analysis mechanism, the accuracy and timeliness of the overall structural safety assessment of the bridge are improved.

[0005] The present invention utilizes the following technical solutions.

[0006] A bridge support column stress measurement method based on structural synergistic force analysis includes:

[0007] Step 1: Perform multi-source data collection and filtering on the stress-bearing area of ​​each bridge support column;

[0008] Step 2: Dynamically calculate the local stress index based on the filtered data;

[0009] Step 3: Conduct collaborative force modeling of the entire bridge structure. This involves extracting structural parameters and geometric models from the bridge design drawings, generating an initial theoretical force model as the geometric model, and then modeling the stress transfer path to achieve dynamic collaborative influence coefficient updates.

[0010] Step 4: Perform a fusion analysis on the displacement and vibration frequency data of the bridge support column based on the updated synergistic influence coefficient to calculate the synergistic stress correction index;

[0011] Step 5: Conduct dynamic stress assessment and risk warning for bridge support columns;

[0012] In step 2, based on the filtered stress and strain data, the local stress index of each bridge support column is calculated in real time using a pre-set mechanical model. At the same time, temperature compensation and wind speed correction factors are introduced to eliminate the influence of environmental factors on the local stress index.

[0013] Step 2 specifically includes:

[0014] Step 2-1: Based on the filtered stress time series data and strain time series data, a dynamic background model is established using the set mechanical model;

[0015] Step 2-2: Calculate the temperature compensation factor based on the material thermal expansion coefficient and the filtered temperature value;

[0016] Step 2-3: Combine the temperature compensation factor with the wind speed correction factor to establish a multi-factor coupling correction model.

[0017] Furthermore, in step 1, multiple types of sensors connected to the central data processing unit are arranged in the set stress-bearing area of ​​each bridge support column of the bridge. The multiple types of sensors include stress sensors, strain gauges, vibrometers and displacement sensors. The stress sensors, strain gauges, vibrometers, displacement sensors, temperature sensors and wind speed sensors are used to synchronously collect real-time stress, strain, vibration frequency, displacement, temperature and wind speed data of the bridge support columns and transmit them to the central data processing unit. Then, the central data processing unit uses a median filtering algorithm to filter the transmitted real-time stress, strain, vibration frequency, displacement, temperature and wind speed data of the bridge support columns.

[0018] Furthermore, in step 2-1, based on the filtered stress time series data and strain time series data, a dynamic background model as shown below is established using the set mechanical model:

[0019] ;

[0020] in Indicates the The moment of the bridge support column The initial local stress index, Indicates the The moment of the bridge support column The strain data after filtering, Indicates the The moment of the bridge support column The filtered stress data, Indicates the The elastic modulus of the material in the load-bearing area of ​​the root bridge support column, Indicates time No. Viscoelastic correction coefficient of the bridge support column.

[0021] Furthermore, in step 2-1, the time No. Viscoelastic correction coefficient of the bridge support column The calculation formula is:

[0022] ;

[0023] in Indicates the The viscoelastic modulus of the stress-bearing area of ​​the root bridge support column, 0 represents the time when the strain gauge starts collecting data, Indicates the time from the start of strain gauge acquisition to the time The time interval between represents a natural constant, Indicates a point in time.

[0024] Further, in step 2-2, based on Thermal expansion coefficient of the material in the load-bearing area of ​​the bridge support column With the The moment of the bridge support column The filtered temperature value , calculate the temperature compensation factor, the calculation formula of the temperature compensation factor is:

[0025] ;

[0026] in Indicates the The moment of the bridge support column The temperature compensation factor, Indicates the reference temperature;

[0027] The calculation formula for the wind speed correction factor is:

[0028] ;

[0029] in Indicates the The moment of the bridge support column The wind speed correction factor, Indicates the The moment of the bridge support column wind speed data.

[0030] Furthermore, in step 2-3, the temperature compensation factor is combined with the wind speed correction factor to establish a multi-factor coupling correction model as shown below:

[0031] ;

[0032] in Indicates the The moment of the bridge support column The local stress index, Indicates the The wind load shape coefficient of the load-bearing area of ​​the root bridge support column.

[0033] Furthermore, in step 3, an overall stress analysis model of the bridge is established based on the bridge design drawings and structural parameters.

[0034] Furthermore, step 3 specifically includes:

[0035] Step 3-1: Extract structural parameters and geometric models from bridge design drawings. This involves extracting key structural parameters, including the cross-sectional dimensions, material type, boundary conditions, and support layout of bridge components. These parameters are then fed into the BIM-based system to construct a 3D geometric model of the bridge. The 3D geometric model of the bridge is then input into the finite element analysis software through the IFC standard interface to generate an initial theoretical load model as the geometric model.

[0036] Step 3-2: Model the stress transfer path, that is, based on the initial theoretical stress model, identify the key stress transfer path, and use the graph neural network to construct the stress transfer relationship diagram between the bridge support column units, where the node represents the bridge support column unit, the edge connecting the two bridge support column units represents the stress transfer path, and the edge weight of the two bridge support column units represents the initial synergistic influence coefficient , Indicates the The bridge support column unit and the The initial synergistic influence coefficient between the bridge support column units.

[0037] Step 3-3: Perform collaborative force modeling to achieve dynamic collaborative influence coefficient update.

[0038] Furthermore, in step 3-3, a dynamic update model of the synergistic influence coefficient is established:

[0039] ;

[0040] in Indicates the The moment of the bridge support column The filtered stress data, Indicates the The moment of the bridge support column The filtered stress data, is the sampling period of the stress sensor, Indicates the The bridge support column unit and the The updated synergistic influence coefficient between the bridge support column units, represents the stress transfer gradient.

[0041] Furthermore, in step 4, the synergistic stress correction index of each support column is calculated based on the updated synergistic influence coefficient and the real-time monitored displacement and vibration frequency data of the bridge support column.

[0042] Furthermore, step 4 specifically includes:

[0043] Step 4-1: Fusion analysis of the displacement and vibration frequency data of the bridge support column;

[0044] Step 4-2: Dynamically calculate the synergistic stress correction index.

[0045] Furthermore, in step 4-1, the corrected local stress index is obtained based on the following displacement-stress relationship equation:

[0046] ;

[0047] in Indicates the The moment of the bridge support column The displacement data after filtering, Indicates the The moment of the bridge support column The filtered vibration frequency data, Indicates the The moment of the bridge support column The modified local stress index.

[0048] Furthermore, in step 4-2, the corrected local stress index and the updated synergy influence coefficient are combined to dynamically calculate the synergy stress correction index using the following formula:

[0049] ;

[0050] in Indicates the The moment of the bridge support column The synergistic stress correction index.

[0051] Furthermore, in step 5, when the collaborative stress correction index of a bridge support column exceeds the set threshold, the central data processing unit automatically triggers the early warning mechanism, which is that the central data processing unit transmits the stress exceeding standard message of the bridge support column to the display screen connected to it.

[0052] The beneficial effects of the present invention are as follows:

[0053] The present invention deploys stress sensors, strain gauges, and displacement sensors on each bridge support column to collect stress, strain, and displacement data from each support column in real time. Based on the collected real-time data, the local stress index of each support column is calculated to reflect the current stress state of the support column. A structural synergistic force model is established based on the overall structural parameters of the bridge and the connection relationship between the support columns. The local stress index of each support column is input into the synergistic force model and, combined with the load transfer relationship between the support columns, a synergistic stress correction index is calculated for each support column to reflect the impact of the overall structural stress state on the individual support column. The synergistic stress correction index is compared with the dynamic stress assessment interval and trend analysis is performed based on historical data and environmental factors to determine whether structural risks exist. Warning signals are issued when thresholds are exceeded. By introducing a structural synergistic force analysis mechanism, the present invention comprehensively considers the impact of the overall bridge stress state on individual support columns, enabling more accurate identification of key structural risk points, improving the reliability and early warning capabilities of bridge safety monitoring, eliminating the static assumption errors of traditional methods, and improving the correlation and timeliness between the local stress index and the overall stress state. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of the bridge support column stress measurement method based on structural collaborative force analysis in the present invention. DETAILED DESCRIPTION

[0055] To make the objectives, technical solutions, and advantages of the present invention more clear, the following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely express the technical solutions of the present invention. The embodiments expressed in this application are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making creative work are all within the scope of protection of the present invention.

[0056] like Figure 1As shown, a bridge support column stress measurement method based on structural synergistic force analysis includes:

[0057] Step 1: Perform multi-source data collection and filtering on the stress-bearing area of ​​each bridge support column;

[0058] In a preferred but non-limiting embodiment of the present invention, in step 1, multiple sensors connected to a central data processing unit are arranged at designated key stress-bearing areas of each bridge support column. The multiple sensors include stress sensors, strain gauges, vibrometers, and displacement sensors. The stress sensors, strain gauges, vibrometers, displacement sensors, temperature sensors, and wind speed sensors are used to synchronously collect real-time stress, strain, vibration frequency, displacement, temperature, and wind speed data from the bridge support column and transmit them to the central data processing unit. The central data processing unit then uses a median filtering algorithm to filter the transmitted real-time stress, strain, vibration frequency, displacement, temperature, and wind speed data from the bridge support column, thereby eliminating interference from external noise and improving the accuracy and stability of data acquisition. The central data processing unit can be a PLC, industrial computer, or FPGA chip.

[0059] Step 2: Dynamically calculate the local stress index based on the filtered data;

[0060] The local stress index is an important indicator for measuring the stress state at key locations on bridge support columns. However, traditional stress assessment methods typically rely on static or periodically collected stress and strain data, failing to fully consider the viscoelastic behavior of the material, environmental factors (such as temperature and wind speed), and the structural synergistic stress characteristics. This can lead to deviations between the assessment results and the actual stress state. Therefore, it is necessary to provide a method for dynamically calculating the local stress index to more accurately reflect the actual stress state of the structure.

[0061] In a preferred but non-limiting embodiment of the present invention, in step 2, a local stress index for each bridge support column is calculated in real time using a pre-defined mechanical model based on filtered stress and strain data. This local stress index not only reflects the current stress state but also integrates historical data to establish a time series analysis model, dynamically identifying stress trends within the bridge support column. Furthermore, temperature compensation and wind speed correction factors are introduced to mitigate the impact of environmental factors on the local stress index, improving assessment accuracy.

[0062] In a preferred but non-limiting embodiment of the present invention, step 2 specifically comprises:

[0063] Step 2-1: Based on the filtered stress time series data and strain time series data, a dynamic background model is established using the set mechanical model;

[0064] In a preferred but non-limiting embodiment of the present invention, in step 2-1, based on the filtered stress time series data and strain time series data, the stress time series data and strain time series data are time series data formed by arranging the filtered stress data and strain data according to the order of their sampling times, and a set mechanical model is used to establish a dynamic background model as shown below:

[0065] ;

[0066] in Indicates the The moment of the bridge support column The initial local stress index, Indicates the The moment of the bridge support column The strain data after filtering, Indicates the The moment of the bridge support column The filtered stress data, Indicates the The elastic modulus of the material in the load-bearing area of ​​the root bridge support column, Indicates time No. The dynamic background model can also update its parameters in real time through the finite element inversion algorithm to construct a dynamic baseline of the local stress state of the bridge support column.

[0067] Dynamically identify the viscoelastic behavior characteristics of the material, that is, the viscoelastic correction coefficient, and then calculate the viscoelastic correction coefficient applicable to the current working conditions to improve the accuracy and timeliness of the local stress index calculation.

[0068] In a preferred but non-limiting embodiment of the present invention, in step 2-1, the time No. Viscoelastic correction coefficient of the bridge support column The calculation formula is:

[0069] ;

[0070] in Indicates the The viscoelastic modulus of the stress-bearing area of ​​the root bridge support column, 0 represents the time when the strain gauge starts collecting data, Indicates the time from the start of strain gauge acquisition to the time The time interval between represents a natural constant, Indicates a point in time.

[0071] In this calculation formula, the viscoelastic correction coefficient is the ratio of the viscoelastic stress component (the numerator in the calculation formula) to the elastic stress component (the denominator in the calculation formula).

[0072] Step 2-2: Calculate the temperature compensation factor based on the material thermal expansion coefficient and the filtered temperature value;

[0073] In a preferred but non-limiting embodiment of the present invention, in step 2-2, a temperature compensation factor is introduced to dynamically correct the stress deviation caused by the thermal expansion effect. Thermal expansion coefficient of the material in the load-bearing area of ​​the bridge support column With the The moment of the bridge support column The filtered temperature value , calculate the temperature compensation factor, the calculation formula of the temperature compensation factor is:

[0074] ;

[0075] in Indicates the The moment of the bridge support column The temperature compensation factor, Indicates the reference temperature; the present invention calculates the temperature compensation factor in real time by fusing temperature data to correct stress fluctuations caused by temperature changes.

[0076] Combined with wind speed data, a wind speed correction factor is established. That is, the square relationship between wind pressure and wind speed is used to calculate the impact of wind load on the local stress of the bridge support column. The calculation formula for the wind speed correction factor is:

[0077] ;

[0078] in Indicates the The moment of the bridge support column The wind speed correction factor, Indicates the The moment of the bridge support column wind speed data.

[0079] Step 2-3: Combine the temperature compensation factor with the wind speed correction factor to establish a multi-factor coupling correction model.

[0080] In a preferred but non-limiting embodiment of the present invention, in step 2-3, the temperature compensation factor is combined with the wind speed correction factor to establish a multi-factor coupling correction model as shown below:

[0081] ;

[0082] in Indicates the The moment of the bridge support column The local stress index, Indicates the The wind load shape coefficient of the load-bearing area of ​​the root bridge support column. This paper adopts an online incremental learning framework and combines historical monitoring data to continuously optimize the temperature compensation factor and wind speed correction factor to improve the adaptability and accuracy of the assessment model.

[0083] Step 3: Conduct collaborative force modeling of the entire bridge structure. This involves extracting structural parameters and geometric models from the bridge design drawings, generating an initial theoretical force model as the geometric model, and then modeling the stress transfer path to achieve dynamic collaborative influence coefficient updates.

[0084] In a preferred but non-limiting embodiment of the present invention, in step 3, a bridge overall stress analysis model is established based on the bridge design drawings and structural parameters. This overall stress analysis model, based on structural mechanics and finite element theory, simulates the load transfer paths and stress redistribution patterns between each bridge support column, establishing a stress coupling relationship between the columns. This ensures that the stress state of each bridge support column is not only determined by its own load but also by the influence of adjacent bridge support columns.

[0085] In a preferred but non-limiting embodiment of the present invention, step 3 specifically comprises:

[0086] Step 3-1: Extract structural parameters and geometric models from bridge design drawings. This involves extracting key structural parameters, including the cross-sectional dimensions, material type, boundary conditions, and support layout of bridge components. These parameters are then fed into a BIM (Building Information Model)-based system to construct a 3D geometric model of the bridge. This 3D geometric model is then input into finite element analysis software (such as ANSYS or ABAQUS) via the IFC standard interface to generate an initial theoretical load model for the geometric model.

[0087] Step 3-2: Model the stress transfer path, that is, based on the initial theoretical stress model, identify the key stress transfer path, and use the graph neural network (GNN) to construct the stress transfer relationship diagram between the bridge support column units, where the node represents the bridge support column unit, the edge connecting the two bridge support column units represents the stress transfer path, and the edge weight of the two bridge support column units represents the initial synergistic influence coefficient , Indicates the The bridge support column unit and the The initial synergistic influence coefficient between the bridge support column units.

[0088] Step 3-3: Perform collaborative force modeling to achieve dynamic collaborative influence coefficient update.

[0089] In a preferred but non-limiting embodiment of the present invention, in step 3-3, a dynamic update model of the synergistic influence coefficient (that is, a bridge overall stress analysis model) is established based on mechanical theory and dynamic load transfer theory:

[0090] ;

[0091] in Indicates the The moment of the bridge support column The filtered stress data, Indicates the The moment of the bridge support column The filtered stress data, is the sampling period of the stress sensor, Indicates the The bridge support column unit and the The updated synergistic influence coefficient between the bridge support column units, represents the stress transfer gradient.

[0092] In step 3, by combining bridge design drawings, structural parameters and real-time monitoring data, a dynamic update model of the synergistic influence coefficient is established to eliminate the static assumption error of the traditional method and improve the correlation and timeliness between the local stress index and the overall stress state.

[0093] Step 4: Perform a fusion analysis on the displacement and vibration frequency data of the bridge support column based on the updated synergistic influence coefficient to calculate the synergistic stress correction index;

[0094] In a preferred but non-limiting embodiment of the present invention, in step 4, a synergistic stress correction index is calculated for each support column based on the updated synergistic influence coefficient and real-time monitored displacement and vibration frequency data of the bridge support columns. This synergistic stress correction index comprehensively considers the effects of the overall stress state of the bridge, load variations on adjacent support columns, and structural stiffness variations. It more accurately reflects the true stress level of each support column in a complex stress system and avoids misjudgments due to local data deviations.

[0095] In a preferred but non-limiting embodiment of the present invention, step 4 specifically comprises:

[0096] Step 4-1: Fusion analysis of the displacement and vibration frequency data of the bridge support column;

[0097] In a preferred but non-limiting embodiment of the present invention, in step 4-1, the modified local stress index is obtained based on the following displacement-stress relationship equation:

[0098] ;

[0099] in Indicates the The moment of the bridge support column The displacement data after filtering, Indicates the The moment of the bridge support column The filtered vibration frequency data, Indicates the The moment of the bridge support column The modified local stress index.

[0100] Step 4-2: Dynamically calculate the synergistic stress correction index.

[0101] In a preferred but non-limiting embodiment of the present invention, in step 4-2, the corrected local stress index and the updated synergy influence coefficient are combined to dynamically calculate the synergy stress correction index using the following formula:

[0102] ;

[0103] in Indicates the The moment of the bridge support column The synergistic stress correction index.

[0104] Step 4 establishes a dynamic collaborative stress correction model by integrating the analysis results of local stress index, displacement data and vibration frequency data, eliminating the static assumption error of the traditional method and improving the correlation and timeliness between the local stress index and the overall stress state.

[0105] Step 5: Conduct dynamic stress assessment and risk warning for bridge support columns.

[0106] In a preferred but non-limiting embodiment of the present invention, in step 5, when the coordinated stress correction index of a particular bridge support column exceeds a set threshold, the central data processing unit automatically triggers an early warning mechanism. This early warning mechanism involves the central data processing unit transmitting a message to a connected display screen indicating that the stress of the bridge support column has exceeded the standard, thereby alerting operators to perform maintenance on the bridge support column. The set threshold is the maximum stress value that the bridge support column can reasonably withstand.

[0107] The beneficial effects of the present invention are as follows:

[0108] The present invention deploys stress sensors, strain gauges, and displacement sensors on each bridge support column to collect stress, strain, and displacement data from each support column in real time. Based on the collected real-time data, the local stress index of each support column is calculated to reflect the current stress state of the support column. A structural synergistic force model is established based on the overall structural parameters of the bridge and the connection relationship between the support columns. The local stress index of each support column is input into the synergistic force model and, combined with the load transfer relationship between the support columns, a synergistic stress correction index is calculated for each support column to reflect the impact of the overall structural stress state on the individual support column. The synergistic stress correction index is compared with the dynamic stress assessment interval and trend analysis is performed based on historical data and environmental factors to determine whether structural risks exist. Warning signals are issued when thresholds are exceeded. By introducing a structural synergistic force analysis mechanism, the present invention comprehensively considers the impact of the overall bridge stress state on individual support columns, enabling more accurate identification of key structural risk points, improving the reliability and early warning capabilities of bridge safety monitoring, eliminating the static assumption errors of traditional methods, and improving the correlation and timeliness between the local stress index and the overall stress state.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not deviate from the spirit and scope of the present invention should be covered within the protection space of the claims of the present invention.

Claims

1. A bridge support column stress measurement method based on structural synergistic force analysis, characterized in that: include: Step 1: Perform multi-source data collection and filtering on the stress-bearing area of ​​each bridge support column; Step 2: Dynamically calculate the local stress index based on the filtered data; Step 3: Conduct collaborative force modeling of the entire bridge structure. This involves extracting structural parameters and geometric models from the bridge design drawings, generating an initial theoretical force model as the geometric model, and then modeling the stress transfer path to achieve dynamic collaborative influence coefficient updates. Step 4: Perform a fusion analysis on the displacement and vibration frequency data of the bridge support column based on the updated synergistic influence coefficient to calculate the synergistic stress correction index; Step 5: Conduct dynamic stress assessment and risk warning for bridge support columns; In step 2, based on the filtered stress and strain data, the local stress index of each bridge support column is calculated in real time using a pre-set mechanical model. At the same time, temperature compensation and wind speed correction factors are introduced to eliminate the influence of environmental factors on the local stress index. Step 2 specifically includes: Step 2-1: Based on the filtered stress time series data and strain time series data, a dynamic background model is established using the set mechanical model; Step 2-2: Calculate the temperature compensation factor based on the material thermal expansion coefficient and the filtered temperature value; Step 2-3: Combine the temperature compensation factor with the wind speed correction factor to establish a multi-factor coupling correction model; Step 3 specifically includes: Step 3-1: Extract structural parameters and geometric models from bridge design drawings. This involves extracting key structural parameters, including the cross-sectional dimensions, material type, boundary conditions, and support layout of bridge components. These parameters are then fed into the BIM-based system to construct a 3D geometric model of the bridge. The 3D geometric model of the bridge is then input into the finite element analysis software through the IFC standard interface to generate an initial theoretical load model as the geometric model. Step 3-2: Model the stress transfer path, that is, based on the initial theoretical stress model, identify the key stress transfer path, and use the graph neural network to construct the stress transfer relationship diagram between the bridge support column units, where the node represents the bridge support column unit, the edge connecting the two bridge support column units represents the stress transfer path, and the edge weight of the two bridge support column units represents the initial synergistic influence coefficient , Indicates the The bridge support column unit and the The initial synergistic influence coefficient between the bridge support column units; Step 3-3: Perform collaborative force modeling to update the dynamic collaborative influence coefficient; In step 3-3, a dynamic update model of the synergistic influence coefficient is established: ； in Indicates the The moment of the bridge support column The filtered stress data, Indicates the The moment of the bridge support column The filtered stress data, is the sampling period of the stress sensor, Indicates the The bridge support column unit and the The updated synergistic influence coefficient between the bridge support column units, represents the stress transfer gradient; In step 4, the synergistic stress correction index of each support column is calculated based on the updated synergistic influence coefficient and the real-time monitored displacement and vibration frequency data of the bridge support column; In step 4-1, the modified local stress index is obtained based on the following displacement-stress relationship equation: ； in Indicates the The moment of the bridge support column The displacement data after filtering, Indicates the The moment of the bridge support column The filtered vibration frequency data, Indicates the The moment of the bridge support column The modified local stress index; In step 4-2, the corrected local stress index and the updated synergy influence coefficient are combined to dynamically calculate the synergy stress correction index using the following formula: ； in Indicates the The moment of the bridge support column The synergistic stress correction index.

2. The bridge support column stress measurement method based on structural collaborative force analysis according to claim 1 is characterized in that: In step 1, multiple types of sensors connected to the central data processing unit are arranged in the set stress-bearing area of ​​each bridge support column of the bridge. The multiple types of sensors include stress sensors, strain gauges, vibrometers and displacement sensors. The stress sensors, strain gauges, vibrometers, displacement sensors, temperature sensors and wind speed sensors are used to synchronously collect real-time stress, strain, vibration frequency, displacement, temperature and wind speed data of the bridge support columns and transmit them to the central data processing unit. Then, the central data processing unit uses a median filtering algorithm to filter the transmitted real-time stress, strain, vibration frequency, displacement, temperature and wind speed data of the bridge support columns.

3. The bridge support column stress measurement method based on structural collaborative force analysis according to claim 2 is characterized in that: In step 2-1, based on the filtered stress time series data and strain time series data, the set mechanical model is used to establish the dynamic background model shown below: ； in Indicates the The moment of the bridge support column The initial local stress index, Indicates the The moment of the bridge support column The strain data after filtering, Indicates the The moment of the bridge support column The filtered stress data, Indicates the The elastic modulus of the material in the load-bearing area of ​​the root bridge support column, Indicates time No. viscoelastic correction factor of the root bridge support column; In step 2-1, the time No. Viscoelastic correction coefficient of the bridge support column The calculation formula is: ； in Indicates the The viscoelastic modulus of the stress-bearing area of ​​the root bridge support column, 0 represents the time when the strain gauge starts collecting data, Indicates the time from the start of strain gauge acquisition to the time The time interval between represents a natural constant, Indicates a point in time; In step 2-2, based on Thermal expansion coefficient of the material in the load-bearing area of ​​the bridge support column With the The moment of the bridge support column The filtered temperature value , calculate the temperature compensation factor, the calculation formula of the temperature compensation factor is: ； in Indicates the The moment of the bridge support column The temperature compensation factor, Indicates the reference temperature; The calculation formula for the wind speed correction factor is: ； in Indicates the The moment of the bridge support column The wind speed correction factor, Indicates the The moment of the bridge support column Wind speed data; In steps 2-3, the temperature compensation factor is combined with the wind speed correction factor to establish a multi-factor coupling correction model as shown below: ； in Indicates the The moment of the bridge support column The local stress index, Indicates the Wind load shape coefficient of the load-bearing area of ​​the root bridge support column; In step 3, an overall stress analysis model of the bridge is established based on the bridge design drawings and structural parameters.

4. The bridge support column stress measurement method based on structural collaborative force analysis according to claim 3 is characterized in that: In step 5, when the coordinated stress correction index of a bridge support column exceeds the set threshold, the central data processing unit automatically triggers the early warning mechanism, which is that the central data processing unit transmits the stress exceeding standard message of the bridge support column to the display screen connected to it.

Images