Bridge displacement early warning method based on monitoring point interconnection supervision

Abstract

The invention discloses a bridge displacement early warning method based on monitoring point interconnection supervision, and belongs to the field of bridge displacement early warning, and the method comprises the steps: determining a to-be-monitored bridge node set of a to-be-supervised bridge, and collecting a displacement working condition data set; constructing a simulation model of the to-be-supervised bridge, and performing precision adjustment of the simulation model based on the displacement working condition data set; obtaining bridge displacement data under each inelastic displacement working condition based on the simulation model after precision adjustment; determining a monitoring point and arranging a displacement monitoring sensor; fitting a displacement prediction model of each to-be-monitored bridge node based on displacement data of each monitoring point in the bridge displacement data under each working condition; and based on real-time data acquired by each displacement monitoring sensor, performing early warning judgment by utilizing the displacement prediction model of each to-be-monitored bridge node. According to the method, the problems that the integrity of a bridge structure is ignored and the change of bridge parameters is ignored when bridge simulation is carried out due to the fact that the displacement threshold value of a single monitoring point is mostly judged in an existing method are solved.

Description

Technical Field

[0001] This invention belongs to the field of bridge displacement early warning, and particularly relates to a bridge displacement early warning method based on interconnected monitoring points. Background Technology

[0002] As a key component of transportation infrastructure, the structural safety of bridges is directly related to the safety of people's lives and property and socio-economic development. During long-term operation, bridges are affected by various factors such as loads, environment, and material aging, resulting in displacement and deformation. Inelastic displacement is often a significant indicator of potential bridge safety hazards; if it is not monitored and warned of in a timely manner, it may lead to serious accidents such as bridge collapse.

[0003] Currently, bridge displacement monitoring mainly relies on deploying a large number of displacement sensors at key locations on the bridge to directly monitor the displacement of each node and determine the structural condition. However, this method has the following significant drawbacks: High cost: Large-scale deployment of sensors requires huge costs for equipment purchase, installation and maintenance, which is a huge economic burden for large bridges or bridge networks.

[0004] Monitoring blind spots: Due to the limited number and location of sensors, it is difficult to comprehensively monitor the displacement of all nodes of the bridge, which can easily lead to missed detection of key displacement changes.

[0005] Low data utilization: Traditional monitoring only focuses on the monitoring data of a single sensor, lacks the ability to explore the correlation between monitoring points, and cannot make full use of the mechanical correlation characteristics of the bridge structure to achieve indirect prediction and early warning of displacement.

[0006] Meanwhile, although bridge simulation modeling technology has been applied in engineering, most existing simulation models have not been fully optimized for accuracy by incorporating actual monitoring data. Since bridge parameters change over time, these changes can lead to discrepancies between simulation results and actual displacements, making them unsuitable for accurate displacement early warning. Furthermore, existing early warning methods often rely on displacement thresholds for single monitoring points, neglecting the overall structure of the bridge and failing to effectively utilize the interconnected monitoring relationships between monitoring points to improve the accuracy and reliability of early warnings.

[0007] Therefore, there is an urgent need for a low-cost, comprehensive, and reliable bridge displacement early warning method that can achieve interconnected monitoring of monitoring points and combine high-precision simulation models and displacement prediction models, in order to solve the above-mentioned problems in existing technologies. Summary of the Invention

[0008] To address the aforementioned shortcomings in existing technologies, this invention provides a bridge displacement early warning method based on interconnected monitoring points. This method solves the problems of existing methods that often judge the displacement threshold of a single monitoring point, neglecting the overall integrity of the bridge structure and ignoring changes in bridge parameters during bridge simulation.

[0009] To achieve the aforementioned objectives, the technical solution adopted by this invention is: a bridge displacement early warning method based on interconnected monitoring points, comprising: The set of bridge nodes to be monitored for the bridge to be supervised is determined, and the elastic displacement conditions, the inelastic displacement conditions that have occurred, and the displacement data corresponding to each displacement condition of the bridge to be supervised are collected to obtain the displacement condition dataset. A simulation model of the bridge to be supervised is constructed, and the accuracy of the simulation model is adjusted based on the displacement condition dataset; Based on the simulation model with adjusted accuracy, simulations were performed under different inelastic displacement conditions to obtain bridge displacement data under each simulation condition. Based on the bridge displacement data under various simulation conditions, monitoring points are determined, and displacement monitoring sensors are deployed on the bridge to be monitored based on the determined monitoring points. Based on the displacement data of each monitoring point in the bridge displacement data under various simulation conditions, a displacement prediction model for each bridge node to be monitored is fitted. Based on the real-time data collected by each displacement monitoring sensor, the displacement prediction value of each bridge node to be monitored is obtained using the displacement prediction model of each bridge node to be monitored. Based on the displacement warning threshold of each bridge node to be monitored, it is determined whether there is a bridge node to be monitored whose displacement prediction value exceeds the displacement warning threshold. If so, a warning is issued; otherwise, no action is taken.

[0010] Furthermore, the process of adjusting the accuracy of the simulation model specifically involves: For each displacement condition in the displacement condition dataset, simulation models were used to simulate the conditions, and the following operations were performed to adjust the accuracy of the simulation models: Based on the simulation results, the displacement simulation results of each bridge node in the set of bridge nodes to be monitored are determined. Based on the displacement simulation results of each bridge node to be monitored and the displacement data corresponding to the current displacement condition, the simulation error of the current displacement condition is calculated. Adjust bridge sensitive parameters based on simulation errors under current displacement conditions.

[0011] Furthermore, the expression for the simulation error of the current displacement condition is:

[0012] in, This represents the simulation error for the current displacement condition. For displacement condition index; Index of bridge nodes to be monitored; This represents the total number of bridge nodes to be monitored. Based on and The difference obtained is the description of the first The value of the error level of each bridge node to be monitored; For the first The actual displacement data of each bridge node to be monitored under the current displacement conditions; For the first Simulated displacement data of a bridge node to be monitored under the current displacement condition; This is the displacement error amplification factor; For the first Displacement early warning threshold for each bridge node to be monitored; This is the adjustment coefficient for the sensitivity of displacement early warning.

[0013] Furthermore, the adjustment of bridge sensitive parameters specifically involves: When the simulation error of the current displacement condition exceeds the simulation error threshold, the following operations are performed: A1. Extract the current bridge parameters from the bridge parameters of the current simulation model; A2. Simulate the change process of the current bridge parameters: If the current bridge parameter is a positive bridge parameter, continuously increase the value of the current bridge parameter and obtain the simulation results during the growth process; if the current bridge parameter is a negative bridge parameter, continuously decrease the value of the current bridge parameter and obtain the simulation results during the growth process; the positive bridge parameter is the parameter whose overall bridge displacement decreases as the value increases; the negative bridge parameter is the parameter whose overall bridge displacement increases as the value increases; the overall bridge displacement is determined based on the number of monitored bridge nodes exceeding the displacement threshold, specifically, when the number of monitored bridge nodes exceeding the displacement threshold increases, the overall bridge displacement increases; when the number of monitored bridge nodes exceeding the displacement threshold decreases, the overall bridge displacement decreases. A3. Based on the simulation results of the current bridge parameter change process, calculate the number of bridge nodes to be monitored that exceed the displacement threshold under each numerical node of the current bridge parameter, and obtain the curve of current bridge parameter - number of bridge nodes to be monitored that exceed the limit. A4. Based on the curve of the current bridge parameters and the number of bridge nodes exceeding the limit to be monitored, confirm whether the type of the current bridge parameters is correct. If so, determine the updated value of the current bridge parameters based on the simulation results of the current bridge parameter change process. Otherwise, correct the parameter type of the current bridge parameters and return to A2 to re-simulate the change process of the current bridge parameters. A5. Update the simulation model based on the current updated values ​​of the bridge parameters, and return to update the next bridge parameter until all bridge parameters have been updated, thus obtaining the simulation model after adjusting the bridge sensitive parameters.

[0014] Furthermore, the determination of monitoring points based on bridge displacement data under various simulation conditions specifically involves: Based on the bridge displacement data under various simulation conditions, a line graph of simulation conditions and displacement data for each bridge node to be monitored is constructed. Based on the line graph of the simulation working condition-displacement data of each bridge node to be monitored, and based on the displacement warning threshold of each bridge node to be monitored, the data of each bridge node to be monitored that are greater than the displacement warning threshold are extracted to obtain the over-limit displacement data of each bridge node to be monitored. The feature vectors of each bridge node to be monitored are organized, and the bridge nodes to be monitored are clustered based on the feature vectors of each bridge node to be monitored, resulting in several bridge node clusters. For each bridge node cluster, based on the over-limit displacement data of each bridge node to be monitored, the bridge node with the highest sensitivity is selected as the monitoring point.

[0015] Furthermore, the expression for the feature vector of the bridge node to be tested is:

[0016] in, For the first The feature vector of each bridge node to be tested; For the first One simulated working condition; For the first The bridge node to be tested is at the first Simulated displacement data under simulated working conditions; This represents the total number of simulation conditions.

[0017] Furthermore, the expression for the sensitivity is:

[0018] in, For the first The sensitivity of each bridge node to be tested; An index for simulation conditions of over-limit displacement data; This represents the total number of simulation conditions with out-of-range displacement data. For the first The bridge node to be tested is at the first Simulated displacement data under simulated working conditions; For the first Displacement warning threshold for each bridge node to be monitored.

[0019] The beneficial effects of this invention are as follows: By collecting displacement data under real-world working conditions, this invention adjusts the accuracy of the bridge simulation model to offset the simulation model errors caused by time variations in bridge parameters, thereby improving the accuracy of subsequent working condition simulations. Simultaneously, by using clustering processing, highly correlated bridge nodes are grouped into the same cluster. This reduces the number of monitoring points while ensuring the representativeness of nodes in each cluster. Furthermore, it guarantees the authenticity of displacement data for at least one highly correlated bridge node within the same cluster, thus ensuring the accuracy of predictions for the remaining bridge nodes in that cluster. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0021] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0022] like Figure 1 As shown, in one embodiment of the present invention, a bridge displacement early warning method based on interconnected monitoring points includes: The set of bridge nodes to be monitored for the bridge to be supervised is determined, and the elastic displacement conditions, the inelastic displacement conditions that have occurred, and the displacement data corresponding to each displacement condition of the bridge to be supervised are collected to obtain the displacement condition dataset. A simulation model of the bridge to be supervised is constructed, and the accuracy of the simulation model is adjusted based on the displacement condition dataset; Based on the simulation model with adjusted accuracy, simulations were performed under different inelastic displacement conditions to obtain bridge displacement data under each simulation condition. Based on the bridge displacement data under various simulation conditions, monitoring points are determined, and displacement monitoring sensors are deployed on the bridge to be monitored based on the determined monitoring points. Based on the displacement data of each monitoring point in the bridge displacement data under various simulation conditions, a displacement prediction model for each bridge node to be monitored is fitted. Based on the real-time data collected by each displacement monitoring sensor, the displacement prediction value of each bridge node to be monitored is obtained using the displacement prediction model of each bridge node to be monitored. Based on the displacement warning threshold of each bridge node to be monitored, it is determined whether there is a bridge node to be monitored whose displacement prediction value exceeds the displacement warning threshold. If so, a warning is issued; otherwise, no action is taken.

[0023] In this embodiment, the acquisition of data under the elastic displacement condition ensures that the displacement condition dataset is not empty; and the elastic displacement condition requires condition data from adjacent time periods; while the non-elastic displacement conditions that have already occurred, since their number is smaller than that of the elastic displacement conditions, will not have a greater impact on the adjustment of the simulation model than the elastic displacement conditions, so time restrictions can be waived or relaxed.

[0024] In this embodiment, based on the data obtained from simulation, the fitting formulas for the displacement data of the remaining bridge nodes to be monitored and each monitoring point can be obtained respectively. Based on this, the displacement of the remaining bridge nodes to be monitored can be predicted based on the actual data of each monitoring point under actual working conditions, thereby providing an early warning.

[0025] The process of adjusting the accuracy of the simulation model specifically involves: For each displacement condition in the displacement condition dataset, simulation models were used to simulate the conditions, and the following operations were performed to adjust the accuracy of the simulation models: Based on the simulation results, the displacement simulation results of each bridge node in the set of bridge nodes to be monitored are determined. Based on the displacement simulation results of each bridge node to be monitored and the displacement data corresponding to the current displacement condition, the simulation error of the current displacement condition is calculated. Adjust bridge sensitive parameters based on simulation errors under current displacement conditions.

[0026] In this embodiment, adjusting the simulation model based on the collected real displacement data can, to some extent, offset the time-consuming bridge parameters.

[0027] The expression for the simulation error of the current displacement condition is:

[0028] in, This represents the simulation error for the current displacement condition. For displacement condition index; Index of bridge nodes to be monitored; This represents the total number of bridge nodes to be monitored. Based on and The difference obtained is the description of the first The value of the error level of each bridge node to be monitored; For the first The actual displacement data of each bridge node to be monitored under the current displacement conditions; For the first Simulated displacement data of a bridge node to be monitored under the current displacement condition; This is the displacement error amplification factor; For the first Displacement early warning threshold for each bridge node to be monitored; This is the adjustment coefficient for the sensitivity of displacement early warning.

[0029] In this embodiment, the displacement warning threshold is used as the dividing point. The degree of influence of the error varies under different circumstances. When the predicted value is less than the warning threshold and the actual value is greater than the warning threshold, the error has a significant impact. For the sake of bridge safety, the displacement prediction value of any node should be larger rather than smaller. Therefore, an error ratio and amplification coefficient are set to amplify the impact of the error. Similarly, when the predicted value is greater than the warning threshold and the actual value is less than the warning threshold, this usually leads to warning sensitivity. Therefore, the displacement warning sensitivity adjustment coefficient is used to amplify the error value of the value to a certain extent to reduce the warning sensitivity.

[0030] The adjustment of bridge sensitive parameters specifically involves: When the simulation error of the current displacement condition exceeds the simulation error threshold, the following operations are performed: A1. Extract the current bridge parameters from the bridge parameters of the current simulation model; A2. Simulate the change process of the current bridge parameters: If the current bridge parameter is a positive bridge parameter, continuously increase the value of the current bridge parameter and obtain the simulation results during the growth process; if the current bridge parameter is a negative bridge parameter, continuously decrease the value of the current bridge parameter and obtain the simulation results during the growth process; the positive bridge parameter is the parameter whose overall bridge displacement decreases as the value increases; the negative bridge parameter is the parameter whose overall bridge displacement increases as the value increases; the overall bridge displacement is determined based on the number of monitored bridge nodes exceeding the displacement threshold, specifically, when the number of monitored bridge nodes exceeding the displacement threshold increases, the overall bridge displacement increases; when the number of monitored bridge nodes exceeding the displacement threshold decreases, the overall bridge displacement decreases. A3. Based on the simulation results of the current bridge parameter change process, calculate the number of bridge nodes to be monitored that exceed the displacement threshold under each numerical node of the current bridge parameter, and obtain the curve of current bridge parameter - number of bridge nodes to be monitored that exceed the limit. A4. Based on the curve of the current bridge parameters and the number of bridge nodes exceeding the limit to be monitored, confirm whether the type of the current bridge parameters is correct. If so, determine the updated value of the current bridge parameters based on the simulation results of the current bridge parameter change process. Otherwise, correct the parameter type of the current bridge parameters and return to A2 to re-simulate the change process of the current bridge parameters. A5. Update the simulation model based on the current updated values ​​of the bridge parameters, and return to update the next bridge parameter until all bridge parameters have been updated, thus obtaining the simulation model after adjusting the bridge sensitive parameters.

[0031] In this embodiment, when the simulation error of the current displacement condition is greater than the simulation error threshold, it indicates that the simulation error of the current simulation model is large. Therefore, it is necessary to adjust some bridge parameters.

[0032] In practical applications, bridge parameters that need to be adjusted can be eliminated based on actual working conditions. For example, bridge parameters that can be directly measured and those that are certain will not change based on time or displacement conditions do not need to be adjusted.

[0033] The updated value of the current bridge parameters is the minimum value of the current bridge parameters in the curve of the number of monitored bridge nodes exceeding the displacement threshold, which minimizes the number of monitored bridge nodes exceeding the displacement threshold.

[0034] The determination of monitoring points based on bridge displacement data under various simulation conditions is specifically as follows: Based on the bridge displacement data under various simulation conditions, a line graph of simulation conditions and displacement data for each bridge node to be monitored is constructed. Based on the line graph of the simulation working condition-displacement data of each bridge node to be monitored, and based on the displacement warning threshold of each bridge node to be monitored, the data of each bridge node to be monitored that are greater than the displacement warning threshold are extracted to obtain the over-limit displacement data of each bridge node to be monitored. The feature vectors of each bridge node to be monitored are organized, and the bridge nodes to be monitored are clustered based on the feature vectors of each bridge node to be monitored, resulting in several bridge node clusters. For each bridge node cluster, based on the over-limit displacement data of each bridge node to be monitored, the bridge node with the highest sensitivity is selected as the monitoring point.

[0035] In this embodiment, clustering is used to group the bridge nodes under test with high correlation into the same cluster. This reduces the number of monitoring points while ensuring the representativeness of the nodes in each cluster. It also ensures the authenticity of the displacement data of at least one node among the bridge nodes with high correlation in the same cluster, thereby ensuring the accuracy of the prediction of the remaining bridge nodes in this cluster.

[0036] The expression for the feature vector of the bridge node to be tested is:

[0037] in, For the first The feature vector of each bridge node to be tested; For the first One simulated working condition; For the first The bridge node to be tested is at the first Simulated displacement data under simulated working conditions; This represents the total number of simulation conditions.

[0038] The expression for the sensitivity is:

[0039] in, For the first The sensitivity of each bridge node to be tested; An index for simulation conditions of over-limit displacement data; This represents the total number of simulation conditions with out-of-range displacement data. For the first The bridge node to be tested is at the first Simulated displacement data under simulated working conditions; For the first Displacement warning threshold for each bridge node to be monitored.

[0040] In this embodiment, the magnitude of the displacement prediction value can directly reflect the degree of response of the node to the working condition. Since the scheme is to provide early warning of displacement, the sensitivity calculation is only based on the out-of-limit data. At the same time, based on the displacement early warning threshold of each node, the out-of-limit displacement data is converted into the degree of out-of-limit displacement, which is measured and unified for easy comparison.

Claims

1. A bridge displacement early warning method based on interconnected monitoring points, characterized in that, include: The set of bridge nodes to be monitored for the bridge to be supervised is determined, and the elastic displacement condition, the inelastic displacement condition that has occurred, and the displacement data corresponding to each displacement condition of the bridge to be supervised are collected to obtain the displacement condition dataset. A simulation model of the bridge to be supervised is constructed, and the accuracy of the simulation model is adjusted based on the displacement condition dataset; Based on the simulation model with adjusted accuracy, simulations were performed under different inelastic displacement conditions to obtain bridge displacement data under each simulation condition. Based on the bridge displacement data under various simulation conditions, monitoring points are determined, and displacement monitoring sensors are deployed on the bridge to be monitored based on the determined monitoring points. Based on the displacement data of each monitoring point in the bridge displacement data under various simulation conditions, a displacement prediction model for each bridge node to be monitored is fitted. Based on the real-time data collected by each displacement monitoring sensor, the displacement prediction value of each bridge node to be monitored is obtained using the displacement prediction model of each bridge node to be monitored. Based on the displacement warning threshold of each bridge node to be monitored, it is determined whether there is a bridge node to be monitored whose displacement prediction value exceeds the displacement warning threshold. If so, a warning is issued; otherwise, no action is taken.

2. The bridge displacement early warning method based on interconnected monitoring points according to claim 1, characterized in that, The process of adjusting the accuracy of the simulation model specifically involves: For each displacement condition in the displacement condition dataset, simulation models were used to simulate the conditions, and the following operations were performed to adjust the accuracy of the simulation models: Based on the simulation results, the displacement simulation results of each bridge node in the set of bridge nodes to be monitored are determined. Based on the displacement simulation results of each bridge node to be monitored and the displacement data corresponding to the current displacement condition, the simulation error of the current displacement condition is calculated. Adjust bridge sensitive parameters based on simulation errors under current displacement conditions.

3. The bridge displacement early warning method based on interconnected monitoring points according to claim 2, characterized in that, The expression for the simulation error of the current displacement condition is: in, This represents the simulation error for the current displacement condition. For displacement condition index; Index of bridge nodes to be monitored; This represents the total number of bridge nodes to be monitored. For based on and The difference obtained is the description of the first The value of the error level of each bridge node to be monitored; For the first The actual displacement data of each bridge node to be monitored under the current displacement condition; For the first Simulated displacement data of a bridge node to be monitored under the current displacement condition; This is the displacement error amplification factor; For the first Displacement early warning threshold for each bridge node to be monitored; This is the adjustment coefficient for the sensitivity of displacement early warning.

4. The bridge displacement early warning method based on interconnected monitoring points according to claim 2, characterized in that, The adjustment of bridge sensitive parameters specifically involves: When the simulation error of the current displacement condition exceeds the simulation error threshold, the following operations are performed: A1. Extract the current bridge parameters from the bridge parameters of the current simulation model; A2. Simulate the change process of the current bridge parameters: If the current bridge parameters are positive bridge parameters, continuously increase the value of the current bridge parameters and obtain the simulation results during the growth process of the current bridge parameters. If the current bridge parameter is a negative bridge parameter, the value of the current bridge parameter is continuously decreased, and the simulation results during the growth process of the current bridge parameter are obtained; the positive bridge parameter is the parameter whose overall bridge displacement decreases as the value increases; the negative bridge parameter is the parameter whose overall bridge displacement increases as the value increases; the overall bridge displacement is determined based on the number of monitored bridge nodes exceeding the displacement threshold, specifically, when the number of monitored bridge nodes exceeding the displacement threshold increases, the overall bridge displacement increases; when the number of monitored bridge nodes exceeding the displacement threshold decreases, the overall bridge displacement decreases. A3. Based on the simulation results of the current bridge parameter change process, calculate the number of bridge nodes to be monitored that exceed the displacement threshold under each numerical node of the current bridge parameter, and obtain the current bridge parameter - number of bridge nodes to be monitored that exceed the limit curve. A4. Based on the curve of the current bridge parameters and the number of bridge nodes exceeding the limit to be monitored, confirm whether the type of the current bridge parameters is correct. If so, determine the updated value of the current bridge parameters based on the simulation results of the current bridge parameter change process. Otherwise, correct the parameter type of the current bridge parameters and return to A2 to re-simulate the change process of the current bridge parameters. A5. Update the simulation model based on the current updated values ​​of the bridge parameters, and return to update the next bridge parameter until all bridge parameters have been updated, thus obtaining the simulation model after adjusting the bridge sensitive parameters.

5. The bridge displacement early warning method based on interconnected monitoring points according to claim 4, characterized in that, The updated value of the current bridge parameters is the minimum value of the current bridge parameters in the curve of the number of monitored bridge nodes exceeding the displacement threshold, which minimizes the number of monitored bridge nodes exceeding the displacement threshold.

6. The bridge displacement early warning method based on interconnected monitoring points according to claim 1, characterized in that, The determination of monitoring points based on bridge displacement data under various simulation conditions is specifically as follows: Based on the bridge displacement data under various simulation conditions, a line graph of simulation conditions and displacement data for each bridge node to be monitored is constructed. Based on the line graph of the simulation working condition-displacement data of each bridge node to be monitored, and based on the displacement warning threshold of each bridge node to be monitored, the data of each bridge node to be monitored that are greater than the displacement warning threshold are extracted to obtain the over-limit displacement data of each bridge node to be monitored. The feature vectors of each bridge node to be monitored are organized, and the bridge nodes to be monitored are clustered based on the feature vectors of each bridge node to be monitored, resulting in several bridge node clusters. For each bridge node cluster, based on the over-limit displacement data of each bridge node to be monitored, the bridge node with the highest sensitivity is selected as the monitoring point.

7. The bridge displacement early warning method based on interconnected monitoring points according to claim 6, characterized in that, The expression for the feature vector of the bridge node to be tested is: in, For the first The feature vector of each bridge node to be tested; For the first One simulated working condition; For the first The bridge node to be tested is at the first Simulated displacement data under simulated working conditions; This represents the total number of simulation conditions.

8. The bridge displacement early warning method based on interconnected monitoring points according to claim 6, characterized in that, The expression for the sensitivity is: in, For the first The sensitivity of each bridge node to be tested; An index for simulation conditions of out-of-range displacement data; This represents the total number of simulation conditions with out-of-range displacement data. For the first The bridge node to be tested is at the first Simulated displacement data under simulated working conditions; For the first Displacement warning threshold for each bridge node to be monitored.

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