Traffic early warning and emergency method and system based on highway bridge disease detection

Abstract

The invention belongs to the field of highway disasters, and provides a traffic early warning and emergency method and system based on highway bridge disease detection, and the method comprises the steps: building a finite element model based on the structural characteristics of a highway bridge, and determining the stress-strain distribution of a key part of the bridge according to the finite element model; determining a detection point in the finite element model, and arranging a strain sensor at the corresponding detection point of the real bridge; comparing real-time data acquired by the strain sensor with the stress-strain distribution to determine sensor drift and / or sensitivity difference; correcting strain data output by the sensor according to sensor drift and / or sensitivity difference, generating corrected strain data, feeding back the corrected strain data to the finite element model, and dynamically updating the stress state of the bridge; based on the finite element model and the stress state, the health state of the bridge structure is judged, and if abnormity is found, a traffic early warning or emergency processing instruction is sent out.

Description

Technical Field

[0001] The present invention belongs to the field of highway disasters, and in particular relates to a traffic early warning and emergency response method and system based on highway bridge disease detection. Background Art

[0002] With the rapid development of modern transportation, highway bridges play a vital role in the transportation network. However, bridges are subject to the influence of vehicle loads, natural environment (such as wind, rain, temperature changes) and other external factors for a long time, which makes them prone to structural defects such as cracks, stress concentration, material aging, etc. If these defects are not discovered and treated in time, they may lead to a decrease in the bearing capacity of the bridge structure, and in severe cases even cause major safety accidents such as bridge collapse.

[0003] Traditional bridge monitoring methods usually rely on regular manual inspections and simple strain monitoring equipment, which have many limitations. Manual inspections are difficult to monitor the health of bridges in real time, and are limited by the experience and judgment of inspectors, and cannot accurately and comprehensively detect potential structural defects. At the same time, conventional strain monitoring equipment can often only provide local force data, lacking comprehensive stress-strain distribution analysis tools, making it difficult to make an accurate health assessment of the overall structure of the bridge. In addition, sensor drift and sensitivity differences will also affect the accuracy of monitoring results, increasing the risk of misjudgment. Summary of the invention

[0004] In order to solve the problems in the prior art, the present invention provides a traffic early warning and emergency method based on highway bridge disease detection, the method comprising the following steps: Establishing a finite element model based on the structural characteristics of the highway bridge, and determining the stress and strain distribution of key parts of the bridge according to the finite element model; Determine detection points in the finite element model, and arrange strain sensors at corresponding detection points of the real bridge, wherein the strain sensors are used to obtain strain data of the bridge under load; Comparing the real-time data acquired by the strain sensor with the stress-strain distribution to determine sensor drift and / or sensitivity difference; Corrects the strain data output by the sensor based on sensor drift and / or sensitivity differences to generate corrected strain data Feed the corrected strain data back to the finite element model to dynamically update the stress state of the bridge; Based on the finite element model and stress state, the health status of the bridge structure is judged. If any abnormality is found, a traffic warning or emergency response instruction is issued.

[0005] Furthermore, the key parts are: the mid-span of the main beam, the support, the top of the pier, and the end of the cantilever.

[0006] Furthermore, determining the detection point in the finite element model includes: obtaining a stress-strain distribution diagram through finite element calculation, obtaining a stress concentration area according to the stress-strain distribution diagram, and determining the stress concentration area as the detection point.

[0007] Furthermore, comparing the real-time data obtained by the strain sensor with the stress-strain distribution to determine the sensor drift and / or sensitivity difference includes: Get the strain distribution surface epsilon_fem(x,y) of a certain part, where x and y are the spatial coordinates of the bridge on the plane; Take the derivative of the surface and get the gradient at any point on the surface: gradient_fem=[d(epsilon_fem) / dx,d(epsilon_fem) / dy], in: d(epsilon_fem) / dx is the rate of change of strain in the x direction; d(epsilon_fem) / dy is the rate of change of strain in the y direction; The collected strain data forms a strain curve epsilon_sensor(x,y). By analyzing the measured strain curve, the rate of change of the sensor data is obtained: gradient_sensor=[d(epsilon_sensor) / dx,d(epsilon_sensor) / dy], in: d(epsilon_sensor) / dx is the rate of change of the measured strain in the x direction, d(epsilon_sensor) / dy is the rate of change of the measured strain in the y direction; Calculate the difference between the finite element model and the strain rate measured by the sensor: delta_gradient=gradient_sensor-gradient_fem, in: delta_gradient is the difference between the measured and theoretical strain change rates, gradient_sensor is the strain gradient measured by the sensor, gradient_fem is the strain gradient calculated by the finite element model; By analyzing this gradient difference, it is possible to identify whether there is an abnormality in the variation of strain data with space and determine the sensitivity difference; Correction formula for sensitivity difference: S=gradient_sensor / gradient_fem, epsilon_corrected=epsilon_sensor / S, in: S is the sensitivity factor, epsilon_corrected is the corrected strain value; If the difference in the gradient change rate in each direction does not show a constant ratio, it indicates that drift is the main cause of the error, and it can be corrected by the drift error correction formula: epsilon_corrected=epsilon_sensor-epsilon_drift, in: Epsilon_drift is the drift error inferred from gradient comparison.

[0008] Furthermore, the dynamically updating the stress state of the bridge includes: Model parameter update: Dynamically update the key parameters of the finite element model by analyzing the difference between the measured strain data and the model strain distribution; Load update: If the actual use environment of the bridge changes, the load conditions in the finite element model are updated through the data obtained by the sensors; Iterative optimization: Iteratively compare the correction data fed back by the sensor with the model output, and gradually adjust the model so that the strain distribution of the model and the correction data gradually converge.

[0009] Another aspect of the present invention is to provide a traffic warning and emergency response system based on highway bridge disease detection, the system comprising the following modules: A modeling module, used to establish a finite element model based on the structural characteristics of the highway bridge, and determine the stress and strain distribution of key parts of the bridge according to the finite element model; A determination module, used to determine detection points in the finite element model and arrange strain sensors at corresponding detection points of the real bridge, wherein the strain sensors are used to obtain strain data of the bridge under load; A comparison module, used to compare the real-time data obtained by the strain sensor with the stress-strain distribution to determine sensor drift and / or sensitivity difference; A correction module, used for correcting the strain data output by the sensor according to the sensor drift and / or sensitivity difference to generate corrected strain data; An update module is used to feed back the corrected strain data to the finite element model and dynamically update the stress state of the bridge; The early warning module is used to judge the health status of the bridge structure based on the finite element model and stress state. If any abnormality is found, a traffic warning or emergency handling instruction will be issued.

[0010] Furthermore, the key parts are: the mid-span of the main beam, the support, the top of the pier, and the end of the cantilever. Furthermore, determining the detection point in the finite element model includes: obtaining a stress-strain distribution diagram through finite element calculation, obtaining a stress concentration area according to the stress-strain distribution diagram, and determining the stress concentration area as the detection point.

[0011] Furthermore, comparing the real-time data obtained by the strain sensor with the stress-strain distribution to determine the sensor drift and / or sensitivity difference includes: Get the strain distribution surface epsilon_fem(x,y) of a certain part, where x and y are the spatial coordinates of the bridge on the plane; Take the derivative of the surface and get the gradient at any point on the surface: gradient_fem=[d(epsilon_fem) / dx,d(epsilon_fem) / dy], in: d(epsilon_fem) / dx is the rate of change of strain in the x direction; d(epsilon_fem) / dy is the rate of change of strain in the y direction; The collected strain data forms a strain curve epsilon_sensor(x,y). By analyzing the measured strain curve, the rate of change of the sensor data is obtained: gradient_sensor=[d(epsilon_sensor) / dx,d(epsilon_sensor) / dy], in: d(epsilon_sensor) / dx is the rate of change of the measured strain in the x direction, d(epsilon_sensor) / dy is the rate of change of the measured strain in the y direction; Calculate the difference between the finite element model and the strain rate measured by the sensor: delta_gradient=gradient_sensor-gradient_fem, in: delta_gradient is the difference between the measured and theoretical strain change rates, gradient_sensor is the strain gradient measured by the sensor, gradient_fem is the strain gradient calculated by the finite element model; By analyzing this gradient difference, it is possible to identify whether there is an abnormality in the variation of strain data with space and determine the sensitivity difference; Correction formula for sensitivity difference: S=gradient_sensor / gradient_fem, epsilon_corrected=epsilon_sensor / S, in: S is the sensitivity factor, epsilon_corrected is the corrected strain value; If the difference in the gradient change rate in each direction does not show a constant ratio, it indicates that drift is the main cause of the error, and it can be corrected by the drift error correction formula: epsilon_corrected=epsilon_sensor-epsilon_drift, in: Epsilon_drift is the drift error calculated based on gradient comparison.

[0012] Furthermore, the dynamically updating the stress state of the bridge includes: Model parameter update: Dynamically update the key parameters of the finite element model by analyzing the difference between the measured strain data and the model strain distribution; Load update: If the actual use environment of the bridge changes, the load conditions in the finite element model are updated through the data obtained by the sensors; Iterative optimization: Iteratively compare the correction data fed back by the sensor with the model output, and gradually adjust the model so that the strain distribution of the model and the correction data gradually converge.

[0013] The present invention introduces a bridge disease detection method based on a finite element model and combines it with real-time monitoring data from strain sensors to achieve dynamic health monitoring of bridge structures and issue traffic warnings or emergency response instructions when structural abnormalities are found. Compared with traditional bridge monitoring methods, the present invention has the following beneficial effects: By collecting sensor data in real time and feeding the corrected data back to the finite element model, the stress state of the bridge can be dynamically updated. The system can not only reflect the health status of the bridge structure in real time, but also make judgments at the early stage of the occurrence of diseases, provide early warning information, and reduce the threat of potential structural problems to the safety of bridge operation.

[0014] By using the stress-strain distribution analysis of the finite element model, stress concentration and disease-prone areas in the bridge can be accurately identified. Combined with the real-time data of the sensor, the system can promptly detect abnormal changes in the local structure and judge the drift and sensitivity differences of the sensor by comparing the change rate, ensuring high accuracy of the monitoring results.

[0015] The present invention can ensure the long-term stable operation of the sensor and the accuracy of the data by correcting the drift and sensitivity difference of the sensor data. By correcting the strain data output by the sensor, the system can generate more accurate correction data and feed it back to the finite element model, thereby improving the accurate prediction of the stress state of the bridge structure.

[0016] By establishing a comprehensive health index, the system can comprehensively evaluate the overall health status of the bridge, taking into account multiple factors such as stress, strain, displacement, etc., and generate an overall health assessment result through weighted calculation, providing a decision-making basis for bridge maintenance and management.

[0017] In summary, the present invention effectively solves the problems of poor real-time performance, insufficient accuracy, low emergency handling efficiency, etc. existing in traditional bridge monitoring methods, improves the operating safety and service life of highway bridges, and is of great significance to traffic management and bridge maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0020] Below, the invention is preferably described in conjunction with the accompanying drawings and specific implementation methods.

[0021] This embodiment solves the above problem through the following steps: In one embodiment, reference Figure 1, the present invention proposes a traffic warning and emergency method based on highway bridge disease detection, which involves arranging strain sensors and other detection equipment on the highway bridge to obtain real-time strain data of the bridge under normal operating environment and load, and compare and analyze the data with the theoretical stress-strain distribution of the bridge structure to identify the disease or abnormality of the bridge structure. Combined with the finite element analysis model and the data correction algorithm, the present invention can accurately determine the sensor drift or sensitivity difference, and update the stress state of the bridge in real time after correcting the sensor data. Based on this state, when an abnormality or potential risk is detected in the bridge, the system can automatically issue a traffic warning signal to remind the vehicle driver to slow down or detour. If the bridge is in a dangerous state, the system will start the emergency handling procedure and take measures such as closing the bridge, evacuating traffic, and rapid maintenance to ensure the safe operation of the highway bridge and smooth traffic. It specifically includes the following steps: A finite element model is established based on the structural characteristics of the highway bridge, and the stress and strain distribution of key parts of the bridge is determined according to the finite element model.

[0022] In the present invention, firstly, an accurate finite element model is established according to the specific structural characteristics of the highway bridge. The finite element model includes not only the geometric shape, size, and structural type of the bridge (such as beam bridge, arch bridge, suspension bridge, etc.), but also the material properties of the bridge (such as elastic modulus and Poisson's ratio of concrete and steel), boundary conditions (such as fixed or hinged support mode of piers and abutments), and actual load conditions (such as vehicle load, wind load, temperature change, and other load effects under various working conditions).

[0023] The establishment of the finite element model is based on the meshing of the bridge structure, dividing it into several finite element units, each of which represents a small part of the bridge, and these units are interrelated. By calculating the mechanical behavior of these units, the stress and strain distribution of the bridge under different working conditions can be accurately simulated. Using this model, the overall stress and strain state of the bridge under load can be calculated, especially in the key parts of the bridge, such as the stress concentration areas at the mid-span of the main beam, the support, the top of the pier, the end of the cantilever, etc., and the corresponding stress and strain distribution data can be obtained.

[0024] Through this process, it is possible to determine which locations are the most critical parts of the bridge structure and assess whether there are excessive stresses or strains in these locations, thereby providing a scientific basis for the subsequent placement of sensors and disease detection. The simulation results of the finite element model can also provide a benchmark for the long-term health monitoring of the bridge. In actual testing, when the measured data obtained by the sensor deviates from the stress and strain data predicted by the finite element model, potential diseases or structural problems that may exist in the bridge can be identified. In addition, the finite element model can also provide data support for the repair and maintenance of the bridge, helping to formulate reasonable reinforcement or repair plans.

[0025] Detection points are determined in the finite element model, and strain sensors are arranged at corresponding detection points of the real bridge. The strain sensors are used to obtain strain data of the bridge under load.

[0026] Stress-strain analysis identifies potential damage areas The stress conditions of the bridge are analyzed in detail through the finite element model, and the stress and strain distribution of each part of the bridge is calculated under different working conditions (such as vehicle load, wind load, self-weight and other multiple load combinations). The following key parts of the bridge are analyzed in detail: Moment concentration area: The bending moment of the bridge under load will reach its maximum at the mid-span or near the middle of the span. Especially for simply supported beam bridges or continuous beam bridges, the mid-span part of the main beam usually bears a large bending moment. Therefore, this area is prone to large stress and strain, and is a high-risk area where diseases may occur.

[0027] Shear stress concentration area: At the connection between the pier and the main beam, or at the top of the pier, shear stress may be concentrated due to the combined effect of vertical and horizontal loads. These areas are often high-incidence points for bridge diseases (such as shear failure, cracks, etc.).

[0028] Support area: Due to the limitations of vertical pressure and horizontal displacement constraints, the bridge support connections are prone to large displacement differences and local stress concentration. Local crushing or cracks often occur at these locations, making them important inspection points.

[0029] Through finite element calculation, the stress or strain distribution diagram can be obtained, and the key parts can be determined accordingly.

[0030] Determine the specific location of the detection point Combined with the finite element model analysis results, the specific strain sensor layout location is determined. The following are several common bridge structure detection point determination methods: Beam bridge: For beam bridges, strain sensors should be arranged at the mid-span of the main beam to monitor the bending stress caused by vehicle load and deadweight. At the same time, shear strain sensors should be arranged near the support to monitor the change of shear stress and ensure timely detection of defects in the support area.

[0031] Suspension bridge or cable-stayed bridge: The connection between the main cable or cable and the bridge tower of a suspension bridge or cable-stayed bridge, as well as the connection between the cable and the bridge deck, is prone to stress concentration due to the complex combination of tension and compression. These parts need to be monitored. At the same time, the stress change of the cable is directly related to the structural safety of the bridge, and strain sensors should be arranged at the connection points of these cables.

[0032] Arch bridge: In an arch bridge structure, the arch top is the part that bears the greatest pressure, so the arch top area is an important detection point. At the same time, the arch foot of the arch bridge is subjected to complex forces, especially the uneven settlement of the foundation may cause large strain in this area, so strain sensors are also required.

[0033] When selecting inspection points at each key location, the geometric asymmetry of the bridge, the distribution of external loads, and the convenience of actual construction and maintenance should also be considered.

[0034] Optimization principles for sensor placement In the process of determining the detection points, in addition to the factors of stress and strain distribution, the optimization principle of sensor layout must also be considered to ensure that the number and layout of sensors can effectively cover the key stress areas while avoiding redundancy: Redundant design: In certain high-risk areas, multiple sensors can be deployed to increase the fault tolerance of the system. If a sensor fails, other sensors can provide supplementary data.

[0035] Combining local and global: When arranging sensors, we should not only pay attention to the local stress concentration areas, but also consider the overall stress conditions of the bridge. For example, in addition to the main beam and bearings, the stress state of the bridge deck may also indirectly reflect the health of the bridge.

[0036] Environmental factors: The placement of sensors also needs to take into account actual environmental conditions, such as temperature changes, humidity, wind speed, etc. These factors may affect the accuracy and stability of the sensors. When determining the detection points, priority should be given to locations that can ensure accurate data collection and are less susceptible to external interference.

[0037] Deployment of strain sensors in a real bridge Once the locations of the detection points are determined by the finite element model, the sensors can be arranged in the real bridge structure. The specific steps include: Strain sensors should be installed at key locations of the bridge structure. The sensors should fit tightly against the structural materials to ensure that the measured strain data accurately reflects the actual stress conditions.

[0038] The installation method of the sensor should be selected according to the bridge material and working environment. For example, in concrete structures, embedded sensors can be used to ensure the stability of long-term monitoring; in steel structures, sensors can be fixed by welding or bolts.

[0039] The arranged sensors need to be connected to the data acquisition system to transmit strain data in real time via wireless or wired means.

[0040] Sensors acquire strain data During the operation of the bridge, strain sensors arranged at key detection points will continuously monitor the strain changes of the bridge structure. The collected data include: Instantaneous strain under vehicle loads: Sensors can record the instantaneous strain changes in the bridge structure when vehicles pass by. These data can reflect the response of the bridge under different loads.

[0041] Long-term stress-strain data: The sensors will also record the chronic strain changes of the bridge under its own weight, which is critical for assessing the long-term structural health of the bridge.

[0042] The real-time data acquired by the strain sensor is compared with the stress-strain distribution to determine sensor drift and / or sensitivity difference.

[0043] In the finite element model, the stress and strain distribution of the bridge is presented as a continuous surface in space. By performing load analysis on the entire bridge structure, the finite element model will output the stress and strain distribution of each detection point in space. The stress (sigma_fem) and strain (epsilon_fem) of each detection point can be used as a function to represent the law of how the stress and strain of the point change with the spatial position.

[0044] Assume that in the finite element model, the strain distribution of a certain part is a surface epsilon_fem(x,y), where x and y are the spatial coordinates of the bridge on the plane. The surface represents the distribution of strain at different positions.

[0045] Taking the derivative of the surface, we can get the rate of change (gradient) at any point on the surface. The gradient describes how the strain changes in space: gradient_fem=[d(epsilon_fem) / dx,d(epsilon_fem) / dy], in: d(epsilon_fem) / dx is the rate of change of strain in the x direction, d(epsilon_fem) / dy is the rate of change of strain in the y direction.

[0046] This gradient represents the rate at which strain in the bridge structure changes in different directions. It is a vector that can be used to evaluate the spatial variation of strain in a specific area of ​​the structure.

[0047] Similar to the strain surface of the theoretical model, the strain data measured by the sensor can also form one or more curves in space. For multiple strain sensors arranged at different positions, the collected strain data can form a strain curve epsilon_sensor(x,y). By analyzing the measured strain curve, the rate of change of the sensor data can also be obtained, which is calculated by numerical differentiation: gradient_sensor=[d(epsilon_sensor) / dx,d(epsilon_sensor) / dy], in: d(epsilon_sensor) / dx is the rate of change of the measured strain in the x direction, d(epsilon_sensor) / dy is the rate of change of the measured strain in the y direction.

[0048] In order to accurately identify sensor drift and sensitivity differences, we compare the strain rate predicted by the finite element model with the strain rate measured by the sensor. Specifically, the difference between the strain rate measured by the finite element model and the sensor can be calculated: delta_gradient=gradient_sensor-gradient_fem, in: delta_gradient is the difference between the measured and theoretical strain change rates (i.e., gradient difference), gradient_sensor is the strain gradient measured by the sensor, gradient_fem is the strain gradient calculated by the finite element model.

[0049] By analyzing this gradient difference, it is possible to identify whether there is an anomaly in the strain data as it changes over space. If the gradient difference is large and regular (such as constant proportional amplification or reduction), it indicates that there is a deviation in the sensitivity of the sensor; if the gradient difference changes irregularly over time or space, it may be an error caused by sensor drift.

[0050] The difference in sensor sensitivity is often manifested as a constant proportional deviation between the measured strain change rate and the theoretical change rate, that is, there is a constant amplification or reduction between the gradients. If delta_gradient is always constant in a certain area, it means that the sensor's measured value is amplified or reduced while changing, which can be judged as a sensitivity difference.

[0051] Correction formula for sensitivity difference: S=gradient_sensor / gradient_fem, epsilon_corrected=epsilon_sensor / S, in: S is the sensitivity factor, epsilon_corrected is the corrected strain value.

[0052] Drift error usually manifests itself as delta_gradient not changing with spatial regularity, but fluctuating randomly without regularity. If the difference in the gradient change rate in each direction does not show a constant ratio, it indicates that drift may be the main cause of the error. Drift error can be eliminated by zero-correcting the sensor signal.

[0053] Correction formula for drift error: epsilon_corrected=epsilon_sensor-epsilon_drift, in: Epsilon_drift is the drift error calculated based on gradient comparison.

[0054] For example: Assuming that we monitor the strain in the mid-span area of ​​the bridge girder, the strain distribution surface calculated by the finite element model is: epsilon_fem(x,y)=0.001*x^2+0.0005*y^2, Its gradient is: gradient_fem=[0.002*x,0.001*y], The strain curve measured by the sensor is: epsilon_sensor(x,y)=0.0012*x^2+0.0006*y^2, Its gradient is: gradient_sensor=[0.0024*x,0.0012*y], By comparing the gradients, the sensitivity difference can be calculated: delta_gradient=gradient_sensor-gradient_fem, In this case, we can see that the gradient difference is always a constant ratio, indicating that there is a sensitivity difference, and the sensitivity factor is: S=gradient_sensor / gradient_fem=1.2, Therefore, the strain measured by the sensor needs to be scaled down by a factor of 1.2 to correct for the difference in sensitivity.

[0055] By comparing the stress-strain distribution surface calculated by the finite element model with the rate of change of the strain curve measured by the sensor, the drift and sensitivity differences of the sensor can be judged more accurately. This gradient (rate of change)-based comparison method can effectively avoid the limitations brought by traditional numerical differences and is more suitable for complex stress-strain distributions in bridge structures. Ultimately, by analyzing the gradient differences, the sensor's measurement data can be accurately corrected to improve the accuracy and reliability of bridge disease detection.

[0056] The strain data output by the sensor is corrected according to the sensor drift and / or sensitivity difference to generate corrected strain data, and the corrected strain data is fed back to the finite element model to dynamically update the stress state of the bridge.

[0057] The corrected strain data epsilon_corrected can reflect the stress state of the bridge under the actual load. Feeding this data back to the finite element model can realize the dynamic update of the bridge structure and continuously track the stress state and potential disease development of the bridge in the model. The process includes the following steps: Data Feedback Mechanism The corrected strain data is fed back to the finite element model through the sensor monitoring system. The specific operation steps include: Collect the corrected strain data epsilon_corrected of the sensor in real time.

[0058] The corrected strain data of each detection point is input into the finite element analysis software to update the stress-strain state corresponding to these detection points in the model.

[0059] The finite element model is adjusted through numerical optimization algorithms or iterative methods to make the calculation results of the model consistent with the measured data after sensor correction.

[0060] Dynamic update of models By comparing the corrected strain data with the theoretical calculation results of the finite element model, the parameters of the model can be adjusted to more accurately reflect the current stress state of the bridge. The update process includes: Model parameter update: By analyzing the difference between the measured strain data and the model strain distribution, the key parameters of the finite element model, such as material properties (elastic modulus, Poisson's ratio), boundary conditions (support constraints), etc., are dynamically updated to ensure that the model reflects the actual status of the current bridge.

[0061] For example, when the strain value epsilon_corrected measured by the sensor is significantly higher than the value calculated by the model, the elastic modulus E of the material may need to be adjusted to reflect the change in material properties.

[0062] The formula of the modified model is: epsilon_model_updated=sigma / E_updated, in: epsilon_model_updated is the updated strain value, sigma is the stress calculated by the model, E_updated is the updated material elastic modulus.

[0063] Load update: If the actual use environment of the bridge changes (such as increased traffic flow, changes in external environmental loads), the load conditions in the finite element model should be updated through the data obtained by the sensors. For example, if the strain of the bridge is monitored to increase over time, the traffic load model of the bridge can be updated.

[0064] Iterative optimization: Iteratively compare the correction data fed back by the sensor with the model output, and gradually adjust the model so that the strain distribution of the model and the correction data gradually converge. Through this iterative process, the prediction ability of the model is continuously enhanced, and it can better simulate the stress state of the bridge.

[0065] After feeding the corrected strain data back to the finite element model and dynamically updating it, the system can monitor the stress state of the bridge in real time, especially the stress concentration and strain changes in key parts. The finite element model will dynamically reflect the health status of the bridge structure based on these real-time data and detect potential diseases in a timely manner.

[0066] By dynamically updating the finite element model, the system can dynamically adjust the overall stress distribution of the bridge. The stress distribution of the model will be continuously updated as the load conditions and bridge status change, reflecting the current stress conditions of the bridge in real time, especially in high-risk areas (such as the mid-span of the bridge, bearings, pier tops, etc.).

[0067] Based on the finite element model and stress state, the health status of the bridge structure is judged. If any abnormality is found, a traffic warning or emergency response instruction is issued.

[0068] In order to judge the health status of the bridge, the system sets a series of criteria. These criteria are usually based on structural design specifications and historical monitoring data, covering the bridge's load-bearing capacity, deformation limit, and vibration tolerance. The health status criteria may include the following aspects: Stress determination: The normal working stress (sigma) of a bridge should be lower than the yield strength or ultimate strength of the material. If the stress at a certain part exceeds the allowable value in the design specification, the bridge is considered to be in a dangerous state.

[0069] Strain determination: The normal strain (epsilon) of a bridge should be within the elastic range of the material. If the measured strain exceeds the strain limit specified in the design specification, it indicates that the structure may have permanent deformation or damage.

[0070] Determination of displacement and deflection: The vertical deflection (vertical displacement) and horizontal displacement of the bridge should be within the design allowable range. If the displacement or deflection of a certain part exceeds the limit, it may indicate that the bridge has undergone excessive deformation or structural instability.

[0071] In addition to the determination of single indicators such as stress, strain, displacement, etc., the overall health status of the bridge can also be evaluated through the comprehensive health index. This index is based on the weighted comprehensive calculation of multiple factors and can provide a more comprehensive assessment of the health status of the bridge.

[0072] Comprehensive health index formula: H_index=w1*(sigma / sigma_limit)+w2*(epsilon / epsilon_limit)+w3*(displacement / displacement_limit), in: H_index is the comprehensive health index of the bridge, w1, w2, w3 are the weights of various indicators (set according to the design specifications and actual usage of the bridge). Sigma, epsilon, and displacement are the stress, strain, and displacement of the bridge. sigma_limit, epsilon_limit, displacement_limit are the respective design limits.

[0073] When the health index exceeds a certain set threshold, the system will consider the bridge's structural condition to be poor and require further assessment or emergency treatment measures.

[0074] When the system determines that a bridge has structural anomalies (such as stress or strain exceeding the standard, or displacement exceeding the safety limit) through finite element models and real-time monitoring data, the system will immediately issue a traffic warning signal. This mechanism is designed to inform drivers and traffic management departments in advance and reduce the potential threat of bridge defects to traffic safety.

[0075] On the other hand, the present invention also provides a traffic warning and emergency response system based on highway bridge disease detection, comprising: A modeling module, used to establish a finite element model based on the structural characteristics of the highway bridge, and determine the stress and strain distribution of key parts of the bridge according to the finite element model; A determination module, used to determine detection points in the finite element model and arrange strain sensors at corresponding detection points of the real bridge, wherein the strain sensors are used to obtain strain data of the bridge under load; A comparison module, used to compare the real-time data obtained by the strain sensor with the stress-strain distribution to determine sensor drift and / or sensitivity difference; A correction module, used for correcting the strain data output by the sensor according to the sensor drift and / or sensitivity difference to generate corrected strain data; An update module is used to feed back the corrected strain data to the finite element model and dynamically update the stress state of the bridge; The early warning module is used to judge the health status of the bridge structure based on the finite element model and stress state. If any abnormality is found, a traffic warning or emergency handling instruction will be issued.

[0076] The prior art mentioned in the above background technology section and specific embodiment section of the present invention can be used as part of the present invention to understand the meaning of some technical features or parameters.

Claims

1. A traffic warning and emergency response method based on highway bridge disease detection, characterized in that: The following steps are involved: Establishing a finite element model based on the structural characteristics of the highway bridge, and determining the stress and strain distribution of key parts of the bridge according to the finite element model; Determine detection points in the finite element model, and arrange strain sensors at corresponding detection points of the real bridge, wherein the strain sensors are used to obtain strain data of the bridge under load; Comparing the real-time data acquired by the strain sensor with the stress-strain distribution to determine sensor drift and / or sensitivity difference; Corrects the strain data output by the sensor based on sensor drift and / or sensitivity differences to generate corrected strain data Feed the corrected strain data back to the finite element model to dynamically update the stress state of the bridge; Based on the finite element model and stress state, the health status of the bridge structure is judged. If any abnormality is found, a traffic warning or emergency response instruction is issued.

2. The traffic warning and emergency response method based on highway bridge disease detection according to claim 1 is characterized in that: The key parts are: the mid-span of the main beam, the support, the top of the pier, and the end of the cantilever.

3. The traffic early warning and emergency response method based on highway bridge disease detection according to claim 1 is characterized in that: Determining the detection point in the finite element model includes: obtaining a stress-strain distribution diagram through finite element calculation, obtaining a stress concentration area according to the stress-strain distribution diagram, and determining the stress concentration area as the detection point.

4. The traffic warning and emergency response method based on highway bridge disease detection according to claim 1 is characterized in that: Comparing the real-time data obtained by the strain sensor with the stress-strain distribution to determine the sensor drift and / or sensitivity difference includes: Get the strain distribution surface epsilon_fem(x,y) of a certain part, where x and y are the spatial coordinates of the bridge on the plane; Take the derivative of the surface and get the gradient at any point on the surface: gradient_fem=[d(epsilon_fem) / dx,d(epsilon_fem) / dy], in: d(epsilon_fem) / dx is the rate of change of strain in the x direction; d(epsilon_fem) / dy is the rate of change of strain in the y direction; The collected strain data forms a strain curve epsilon_sensor(x,y). By analyzing the measured strain curve, the rate of change of the sensor data is obtained: gradient_sensor=[d(epsilon_sensor) / dx,d(epsilon_sensor) / dy], in: d(epsilon_sensor) / dx is the rate of change of the measured strain in the x direction, d(epsilon_sensor) / dy is the rate of change of the measured strain in the y direction; Calculate the difference between the finite element model and the strain rate measured by the sensor: delta_gradient=gradient_sensor-gradient_fem, in: delta_gradient is the difference between the measured and theoretical strain change rates, gradient_sensor is the strain gradient measured by the sensor, gradient_fem is the strain gradient calculated by the finite element model; By analyzing this gradient difference, it is possible to identify whether there is an abnormality in the variation of strain data with space and determine the sensitivity difference; Correction formula for sensitivity difference: S=gradient_sensor / gradient_fem, epsilon_corrected=epsilon_sensor / S, in: S is the sensitivity factor, epsilon_corrected is the corrected strain value; If the difference in the gradient change rate in each direction does not show a constant ratio, it indicates that drift is the main cause of the error, and it can be corrected by the drift error correction formula: epsilon_corrected=epsilon_sensor-epsilon_drift, in: Epsilon_drift is the drift error inferred from gradient comparison.

5. The traffic warning and emergency response method based on highway bridge disease detection according to claim 1 is characterized in that: The dynamically updating stress state of the bridge includes: Model parameter update: Dynamically update the key parameters of the finite element model by analyzing the difference between the measured strain data and the model strain distribution; Load update: If the actual use environment of the bridge changes, the load conditions in the finite element model are updated through the data obtained by the sensors; Iterative optimization: Iteratively compare the correction data fed back by the sensor with the model output, and gradually adjust the model so that the strain distribution of the model and the correction data gradually converge.

6. A traffic warning and emergency response system based on highway bridge disease detection, characterized in that: The system includes the following modules: A modeling module, used to establish a finite element model based on the structural characteristics of the highway bridge, and determine the stress and strain distribution of key parts of the bridge according to the finite element model; A determination module, used to determine detection points in the finite element model and arrange strain sensors at corresponding detection points of the real bridge, wherein the strain sensors are used to obtain strain data of the bridge under load; A comparison module, used to compare the real-time data obtained by the strain sensor with the stress-strain distribution to determine sensor drift and / or sensitivity difference; A correction module, used for correcting the strain data output by the sensor according to the sensor drift and / or sensitivity difference to generate corrected strain data; An update module is used to feed back the corrected strain data to the finite element model and dynamically update the stress state of the bridge; The early warning module is used to judge the health status of the bridge structure based on the finite element model and stress state. If any abnormality is found, a traffic warning or emergency handling instruction will be issued.

7. The traffic warning and emergency response system based on highway bridge disease detection according to claim 6 is characterized in that: The key parts are: the mid-span of the main beam, the support, the top of the pier, and the end of the cantilever.

8. The traffic warning and emergency response system based on highway bridge disease detection according to claim 6 is characterized in that: Determining the detection point in the finite element model includes: obtaining a stress-strain distribution diagram through finite element calculation, obtaining a stress concentration area according to the stress-strain distribution diagram, and determining the stress concentration area as the detection point.

9. The traffic warning and emergency response system based on highway bridge disease detection according to claim 6 is characterized in that: Comparing the real-time data obtained by the strain sensor with the stress-strain distribution to determine the sensor drift and / or sensitivity difference includes: Get the strain distribution surface epsilon_fem(x,y) of a certain part, where x and y are the spatial coordinates of the bridge on the plane; Take the derivative of the surface and get the gradient at any point on the surface: gradient_fem=[d(epsilon_fem) / dx,d(epsilon_fem) / dy], in: d(epsilon_fem) / dx is the rate of change of strain in the x direction; d(epsilon_fem) / dy is the rate of change of strain in the y direction; The collected strain data forms a strain curve epsilon_sensor(x,y). By analyzing the measured strain curve, the rate of change of the sensor data is obtained: gradient_sensor=[d(epsilon_sensor) / dx,d(epsilon_sensor) / dy], in: d(epsilon_sensor) / dx is the rate of change of the measured strain in the x direction, d(epsilon_sensor) / dy is the rate of change of the measured strain in the y direction; Calculate the difference between the finite element model and the strain rate measured by the sensor: delta_gradient=gradient_sensor-gradient_fem, in: delta_gradient is the difference between the measured and theoretical strain change rates, gradient_sensor is the strain gradient measured by the sensor, gradient_fem is the strain gradient calculated by the finite element model; By analyzing this gradient difference, it is possible to identify whether there is an abnormality in the variation of strain data with space and determine the sensitivity difference; Correction formula for sensitivity difference: S=gradient_sensor / gradient_fem, epsilon_corrected=epsilon_sensor / S, in: S is the sensitivity factor, epsilon_corrected is the corrected strain value; If the difference in the gradient change rate in each direction does not show a constant ratio, it indicates that drift is the main cause of the error, and it can be corrected by the drift error correction formula: epsilon_corrected=epsilon_sensor-epsilon_drift, in: Epsilon_drift is the drift error inferred from gradient comparison.

10. The traffic warning and emergency response system based on highway bridge disease detection according to claim 6 is characterized in that: The dynamically updating stress state of the bridge includes: Model parameter update: Dynamically update the key parameters of the finite element model by analyzing the difference between the measured strain data and the model strain distribution; Load update: If the actual use environment of the bridge changes, the load conditions in the finite element model are updated through the data obtained by the sensors; Iterative optimization: Iteratively compare the correction data fed back by the sensor with the model output, and gradually adjust the model so that the strain distribution of the model and the correction data gradually converge.

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