Optimized layout method for bridge monitoring nodes

Abstract

The invention provides a bridge monitoring node optimization layout method, and belongs to the technical field of bridge structure health monitoring. According to the method, key structure information and key monitoring areas of a bridge are determined through finite element analysis and field testing, and an initial layout scheme is generated; actual operation data of the bridge are collected in real time and processed by the data analysis module, the quality of the monitored data is evaluated, and an insufficient monitoring or data redundancy area is identified; based on an evaluation result, constructing a comprehensive optimization objective function, and dynamically adjusting an initial scheme by using an improved particle swarm optimization algorithm to obtain an optimized layout scheme; and finally verifying the effectiveness of the optimization method by comparing the monitoring data quality and the system operation efficiency before and after implementation. According to the bridge monitoring node optimization layout method, the monitoring node layout scheme can be dynamically adjusted according to the actual operation state of the bridge, the monitoring data quality is improved, and the performance of a monitoring system is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge structure health monitoring, and in particular to a bridge monitoring node optimization layout method. Background Art

[0002] In the field of bridge structural health monitoring, the existing monitoring node layout methods have obvious shortcomings. Traditional layout schemes rely on experience or simple structural analysis and lack dynamic adaptability to the actual operating status of the bridge. For example, many methods fail to fully consider the key structural information and key monitoring areas of the bridge during the initial layout, resulting in insufficient representativeness of the monitoring data and failure to accurately reflect the actual status of the bridge.

[0003] Existing technologies often only focus on a single goal when optimizing deployment, such as monitoring accuracy or cost control, while ignoring the balance between data transmission efficiency and overall system performance. This single-goal optimization method is difficult to meet the comprehensive improvement of multiple performance requirements in complex bridge environments.

[0004] In addition, some optimization algorithms have problems such as slow convergence and easy to fall into local optimality in practical applications, which affects the quality and reliability of the optimization results. For example, when dealing with high-dimensional complex problems, the traditional particle swarm optimization algorithm is prone to premature convergence and local optimal stagnation, which limits its application effect in the optimal layout of bridge monitoring nodes.

[0005] At the same time, the existing methods lack an effective dynamic adjustment mechanism for data redundancy or monitoring blind spots that occur during the monitoring process, and cannot timely optimize the deployment plan based on real-time feedback information. For example, during long-term monitoring, due to environmental changes or structural performance degradation, some monitoring areas may become unimportant, while new key areas may appear, but the existing deployment plan is difficult to flexibly adapt to such changes.

[0006] These problems limit the effectiveness of bridge monitoring systems. There is an urgent need for a monitoring node deployment method that can dynamically adapt to the operating status of the bridge, comprehensively consider multi-objective optimization, and have efficient algorithm support to overcome the shortcomings of existing technologies. Summary of the invention

[0007] The purpose of the present invention is to provide a method for optimizing the layout of bridge monitoring nodes, which can dynamically adjust the monitoring node layout plan according to the actual operating status of the bridge, improve the quality of monitoring data, optimize the performance of the monitoring system, and has significant innovation and practicality.

[0008] To achieve the above object, the present invention provides a method for optimizing the layout of bridge monitoring nodes, comprising the following steps: Finite element analysis and field testing were used to determine the key structural information and key monitoring areas of the bridge; According to the key structural information of the bridge and the location information of the key monitoring areas, determine the initial monitoring node layout plan, including the type and layout location of the monitoring nodes, to ensure that the main structural parts and key response points of the bridge are covered; Collect the actual operation data of the bridge in real time, including external excitation information and response data of the bridge structure; process the actual operation data through the data analysis module, evaluate the quality of the monitoring data under the current layout plan, obtain the evaluation results, and identify areas with insufficient monitoring or redundant data; An objective function with the comprehensive optimization of monitoring accuracy, data transmission efficiency and cost as the goal is to construct. Combined with the evaluation results, the improved particle swarm optimization algorithm is used to dynamically adjust the initial monitoring node layout plan to obtain the optimized layout plan.

[0009] Preferably, the monitoring node types include strain sensors, acceleration sensors, displacement sensors, inclinometers, and temperature and humidity sensors.

[0010] Preferably, the external excitation information includes traffic load, wind load, temperature change, humidity change; and the response data of the bridge structure includes strain, acceleration, displacement, and inclination.

[0011] Preferably, in the improved particle swarm optimization algorithm: Each particle represents a possible monitoring node deployment scheme, the particle position indicates the specific coordinates of the monitoring node on the bridge, and the particle speed indicates the amplitude and direction of the node position adjustment; The update formula for particle position and velocity is: ; ; in, and Respectively represent The particle in The speed and position at the iteration; and Respectively represent The particle in The speed and position at the iteration; represents the inertia weight; , represents the learning factor; , Represents a random number; Indicates The historical optimal position of a particle; represents the global optimal position; ; ; in, , Respectively represent the maximum and minimum values ​​of the inertia weight; Indicates the current iteration number; Indicates the maximum number of iterations; represents the learning factor, , Respectively represent the maximum and minimum values ​​of the learning factor; Represents an index variable.

[0012] Preferably, the improved particle swarm optimization algorithm designs a mutation operator based on node energy consumption to perturb the position of particles: ; in, Indicates The new position of the particle after the disturbance; Indicates variable asynchronous length; Represents a random number; Indicates the node location with the minimum energy consumption; is the mutation operator.

[0013] Preferably, the objective function is as follows: ; in, represents the objective function; , , Indicates the dynamic adjustment of weight coefficients to balance the relative importance of different objectives; Indicates the number of key monitoring areas; Indicates The weight of each key area; Indicates Quality of monitoring data in key areas; Indicates the total number of monitoring nodes; Indicates The data transmission rate of each node; Indicates Energy consumption of each node; Indicates The deployment cost of each node.

[0014] Preferably, , , The dynamic adjustment method is as follows: According to the specific bridge monitoring requirements and priorities, the initial weight values ​​are determined through expert experience or historical data statistics; The quality of current monitoring data , data transmission efficiency and cost As state input, represented as a state vector ; Define a set of actions , , , represents the adjustment operation of the weight coefficient; Designing the reward function To evaluate the adjustment effect, the reward function is expressed as: ; in, , , represents the reward coefficient; , , They represent the changes in monitoring data quality, data transmission efficiency and cost respectively; Use reinforcement learning algorithm to update weight coefficients based on the current state and action rewards , adjust the weight coefficient to maximize the long-term cumulative reward, and the update formula is: ; ; ; in, , , Represents the updated weight coefficient; Represents the learning rate, which controls the step size of weight coefficient adjustment.

[0015] Preferably, the constraints of the objective function are as follows: Communication distance constraints between nodes: ; in, Indicates Nodes and The communication distance between nodes; Indicates the maximum communication distance; Coverage constraints: ; in, Indicates the coverage of the monitoring node; Indicates the minimum coverage required; Energy consumption constraints: ; in, Represents the maximum allowed energy consumption of the node.

[0016] Therefore, the present invention adopts the above-mentioned bridge monitoring node optimization layout method, and the beneficial technical effects are as follows: (1) Strong dynamic adaptability: The present invention can collect the actual operation data of the bridge in real time by introducing a dynamic feedback mechanism, and dynamically adjust the monitoring node layout plan according to the data evaluation results. This dynamic adjustment capability enables the monitoring system to adapt to the monitoring needs of the bridge under different operating conditions, overcoming the shortcomings of the existing technology that the layout plan is static and fixed and cannot adapt to changes in the actual operating state of the bridge.

[0017] (2) Multi-objective comprehensive optimization: The present invention constructs an objective function with the comprehensive optimization of monitoring accuracy, data transmission efficiency and cost as the goal, and uses an improved particle swarm optimization algorithm for dynamic adjustment. Compared with the single-objective optimization method in the prior art, the present invention can consider multiple key factors at the same time, achieve the improvement of monitoring accuracy, the optimization of data transmission efficiency and the effective control of cost, and avoid the problem of unbalanced system performance caused by single-objective optimization.

[0018] (3) Efficient and reliable optimization algorithm: The improved particle swarm optimization algorithm introduces an adaptive parameter adjustment mechanism and a mutation operator based on node energy consumption, which improves the algorithm's search efficiency and global convergence ability, and avoids the problems of existing optimization algorithms that are prone to falling into local optimality and slow convergence. In the optimization layout of bridge monitoring nodes, this means that a better node layout scheme can be found more quickly, improving the response speed and adaptability of the monitoring system. By adaptively adjusting the inertia weight and learning factor, the algorithm can better balance the global search and local search capabilities, thereby obtaining better quality and more reliable optimization results, ensuring that the layout of monitoring nodes can accurately meet the needs of bridge structure health monitoring.

[0019] (4) Real-time feedback and continuous optimization: The present invention continuously collects and analyzes data during the monitoring process, and can promptly identify areas with insufficient monitoring or redundant data, and adjust the deployment plan accordingly. This solves the problem of the lack of an effective dynamic adjustment mechanism in the prior art, allowing the monitoring system to continuously optimize the deployment plan based on real-time feedback information and always maintain good monitoring performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flow chart of a bridge monitoring node optimization layout method of the present invention; Figure 2 Schematic diagram of the system architecture for optimizing the layout of bridge monitoring nodes. DETAILED DESCRIPTION

[0021] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0022] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0023] Embodiment 1 like Figure 1 As shown, the present invention provides a bridge monitoring node optimization layout method, comprising the following steps: Step 1: Use finite element analysis software ANSYS to model the bridge. In the modeling process, the actual geometric dimensions, material properties, load conditions and other factors of the bridge are considered to ensure the accuracy of the model. Through field testing, key structural information of the bridge is obtained, including stress concentration areas, large deformation areas, vibration mode characteristics, etc. Field testing uses a variety of methods, such as strain gauges to measure stress and acceleration sensors to measure vibration response. Determine the structural weaknesses and key monitoring areas of the bridge.

[0024] Step 2: Preliminarily determine the type and approximate location of monitoring nodes based on the structural characteristics of the bridge and conventional monitoring requirements. Monitoring node types include strain sensors, acceleration sensors, displacement sensors, inclinometers, temperature and humidity sensors, etc. The locations of different types of sensors are arranged in a targeted manner according to the characteristics of their monitoring objects. For example, strain sensors are mainly deployed in stress concentration areas, and acceleration sensors are deployed at key vibration nodes of the bridge.

[0025] In the initial deployment plan, a total of 120 monitoring nodes were deployed on the bridge, including 40 strain sensors, 30 acceleration sensors, 20 displacement sensors, 20 inclinometers, and 10 temperature and humidity sensors. These sensors cover the main structural parts of the bridge, but the initial deployment plan may have monitoring blind spots or data redundancy problems.

[0026] Step 3: Set up a feedback link in the monitoring system to collect the actual operation data of the bridge in real time, including external excitation information such as traffic load, wind load, temperature change, humidity change, and response data of the bridge structure, such as strain, acceleration, displacement, inclination, etc. The data are processed through the data analysis module to evaluate the quality and representativeness of the monitoring data under the current layout plan and identify areas with insufficient monitoring or redundant data.

[0027] The specific process of data analysis module processing data includes: Filter, denoise, and normalize the collected monitoring data to improve the quality and comparability of the data; Check whether the monitoring data is missing, abnormal or inconsistent to ensure the integrity of the data; Evaluate the accuracy and reliability of data by comparing monitoring data from different sensors; Analyze the collection and transmission time of monitoring data to ensure the real-time nature of the data; Combined with the evaluation results of data completeness, accuracy and real-time performance, a comprehensive quality score for monitoring data is given; Identify areas with insufficient monitoring data based on data quality scores and monitoring needs; By analyzing the correlation of data from different sensors, areas of data redundancy are identified.

[0028] In actual operation, the system collects data every 30 minutes and generates about 96 sets of data every day. Through data analysis, it was found that the monitoring data in some areas of the initial layout plan was obviously insufficient. For example, in some parts of the bottom of the bridge tower, the data of the strain sensor fluctuated greatly and could not accurately reflect the actual stress state of the structure. At the same time, there was data redundancy in some areas. For example, the acceleration sensor data in some main beam areas was highly correlated and had a lot of repeated information.

[0029] Step 4: construct an objective function with the comprehensive optimization of monitoring accuracy, data transmission efficiency and cost as the goal, and use the improved particle swarm optimization algorithm to dynamically adjust the initial monitoring node layout plan in combination with the evaluation results to obtain an optimized layout plan; during the adjustment process, comprehensively consider the communication distance, coverage, energy consumption and other constraints between nodes to ensure that the optimized layout plan has good feasibility and economy while meeting the monitoring requirements; In the improved particle swarm optimization algorithm: Each particle represents a possible monitoring node deployment scheme, the particle position indicates the specific coordinates of the monitoring node on the bridge, and the particle speed indicates the amplitude and direction of the node position adjustment; The update formula for particle position and velocity is: ; ; in, and Respectively represent The particle in The velocity and position at the iteration, and Respectively represent The particle in The velocity and position at the iteration, represents the inertia weight, , represents the learning factor, , Represents a random number, Indicates The historical optimal position of a particle, represents the global optimal position; ; ; in, , Respectively represent the maximum and minimum values ​​of the inertia weight; Indicates the current iteration number; Indicates the maximum number of iterations; represents the learning factor, , Respectively represent the maximum and minimum values ​​of the learning factor; Represents an index variable.

[0030] The improved particle swarm optimization algorithm designs a mutation operator based on node energy consumption to perturb the position of particles: ; in, Indicates The new position of the particle after the disturbance; Indicates variable asynchronous length; Represents a random number; Indicates the node location with the minimum energy consumption; is the mutation operator.

[0031] The objective function is as follows: ; in, represents the objective function; , , Indicates the dynamic adjustment of weight coefficients to balance the relative importance of different objectives; Indicates the number of key monitoring areas; Indicates The weight of each key area; Indicates Quality of monitoring data in key areas; Indicates the total number of monitoring nodes; Indicates The data transmission rate of each node; Indicates Energy consumption of each node; Indicates The deployment cost of each node.

[0032] During the optimization process, the parameters of the improved particle swarm optimization algorithm were set as follows: the population size was 50, the maximum number of iterations was 200, the inertia weight decreased linearly from 0.9 to 0.4, and the learning factor , The initial value is 2.0, and it decreases linearly to 0.5. Through multiple iterations of optimization, the algorithm gradually adjusts the location and number of monitoring nodes to achieve the optimal layout plan.

[0033] The constraints of the objective function include communication distance constraints, coverage constraints and energy consumption constraints between nodes.

[0034] Communication distance constraints between nodes: ; in, Indicates Nodes and The communication distance between nodes; Indicates the maximum communication distance; Coverage constraints: ; in, Indicates the coverage of the monitoring node; Indicates the minimum coverage required; Energy consumption constraints: ; in, Represents the maximum allowed energy consumption of the node.

[0035] , , The dynamic adjustment method is as follows: According to the specific bridge monitoring requirements and priorities, the initial weight values ​​are determined through expert experience or historical data statistics; The quality of current monitoring data , data transmission efficiency and cost As state input, represented as a state vector ; Define a set of actions , , , represents the adjustment operation of the weight coefficient; Designing the reward function To evaluate the adjustment effect, the reward function is expressed as: ; in, , , represents the reward coefficient; , , They represent the changes in monitoring data quality, transmission efficiency and cost respectively; Use reinforcement learning algorithm to update weight coefficients based on the current state and action rewards , adjust the weight coefficient to maximize the long-term cumulative reward, and the update formula is: ; ; ; in, , , Represents the updated weight coefficient; Represents the learning rate, which controls the step size of weight coefficient adjustment.

[0036] Step 5: Apply the optimized layout plan to actual bridge monitoring, and verify the effectiveness of the optimized layout method by comparing the monitoring data quality and system operation efficiency before and after implementation.

[0037] As shown in Table 1, in the optimized layout scheme, the total number of monitoring nodes is reduced to 100, including 30 strain sensors, 25 acceleration sensors, 20 displacement sensors, 15 inclinometers, and 10 temperature and humidity sensors. Through practical application, it is found that the optimized scheme significantly improves the monitoring data quality of key areas, with data integrity increased from 85% to 95%, and data accuracy increased from 90% to 97%. At the same time, data transmission efficiency is increased by 30%, system energy consumption is reduced by 25%, and cost is reduced by 20%.

[0038] Table 1 Comparison before and after optimization ;

[0039] like Figure 2 As shown in the figure, the bridge monitoring node optimization layout system includes: Finite element analysis module, used to model and analyze bridges and determine key structural information of bridges; Field test module, used to obtain the actual structural information of the bridge, including stress concentration areas, large deformation areas, vibration mode characteristics, etc. The initial layout plan generation module preliminarily determines the type and approximate layout location of monitoring nodes based on the structural characteristics of the bridge and conventional monitoring requirements; The monitoring node layout module is responsible for arranging various types of monitoring nodes on the bridge according to the initial layout plan, including strain sensors, acceleration sensors, displacement sensors, inclinometers, and temperature and humidity sensors; Data acquisition module, which collects data from monitoring nodes in real time, including external excitation information and response data of bridge structures; The data analysis module processes the collected data and evaluates the quality and representativeness of the monitoring data; The data quality assessment module evaluates the quality of monitoring data under the current deployment plan based on the completeness, accuracy and real-time nature of the data; The optimization decision module determines whether the deployment plan needs to be optimized and adjusted based on the data quality assessment results; Improve the particle swarm optimization algorithm module, use the improved particle swarm optimization algorithm, combined with the dynamically adjusted weight coefficient, to optimize and adjust the initial monitoring node layout plan to improve monitoring accuracy, data transmission efficiency and control costs; The deployment plan adjustment module dynamically adjusts the deployment plan of the monitoring nodes according to the instructions of the optimization decision module and the calculation results of the improved particle swarm optimization algorithm module; The comparison and verification module verifies the effectiveness of the optimized deployment method by comparing the monitoring data quality and system operation efficiency before and after implementation.

[0040] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0041] Therefore, the present invention adopts the above-mentioned bridge monitoring node optimization layout method, which can dynamically adjust the monitoring node layout plan according to the actual operating status of the bridge, improve the monitoring data quality, and optimize the monitoring system performance.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A bridge monitoring node optimization layout method, characterized in that: The following steps are involved: Finite element analysis and field testing were used to determine the key structural information and key monitoring areas of the bridge; According to the key structural information of the bridge and the location information of the key monitoring areas, determine the initial monitoring node layout plan, including the type and layout location of the monitoring nodes; Collect the actual operation data of the bridge in real time, including external excitation information and response data of the bridge structure; process the actual operation data through the data analysis module, evaluate the quality of the monitoring data under the current layout plan, and obtain the evaluation results; An objective function with the comprehensive optimization of monitoring accuracy, data transmission efficiency and cost as the goal is to construct. Combined with the evaluation results, the improved particle swarm optimization algorithm is used to dynamically adjust the initial monitoring node layout plan to obtain the optimized layout plan.

2. A bridge monitoring node optimization layout method according to claim 1, characterized in that: Monitoring node types include strain sensors, accelerometers, displacement sensors, inclinometers, and temperature and humidity sensors.

3. The bridge monitoring node optimization layout method according to claim 1 is characterized in that: External excitation information includes traffic loads, wind loads, temperature changes, and humidity changes; the response data of the bridge structure includes strain, acceleration, displacement, and inclination.

4. The bridge monitoring node optimization layout method according to claim 1 is characterized in that: In the improved particle swarm optimization algorithm: Each particle represents a possible monitoring node deployment scheme, the particle position indicates the specific coordinates of the monitoring node on the bridge, and the particle speed indicates the amplitude and direction of the node position adjustment; The update formula for particle position and velocity is: ； ； in, and Respectively represent The particle in The speed and position at the iteration; and Respectively represent The particle in The speed and position at the iteration; represents the inertia weight; , represents the learning factor; , Represents a random number; Indicates The historical optimal position of a particle; represents the global optimal position; ； ； in, , Respectively represent the maximum and minimum values ​​of the inertia weight; Indicates the current iteration number; Indicates the maximum number of iterations; represents the learning factor, , Respectively represent the maximum and minimum values ​​of the learning factor; Represents an index variable.

5. A bridge monitoring node optimization layout method according to claim 4, characterized in that: The improved particle swarm optimization algorithm designs a mutation operator based on node energy consumption to perturb the position of particles: ； in, Indicates The new position of the particle after the disturbance; Indicates variable asynchronous length; Represents a random number; Indicates the node location with the minimum energy consumption; is the mutation operator.

6. A bridge monitoring node optimization layout method according to claim 5, characterized in that: The objective function is as follows: ； in, represents the objective function; , , Indicates the dynamic adjustment of weight coefficients to balance the relative importance of different objectives; Indicates the number of key monitoring areas; Indicates The weight of each key area; Indicates Quality of monitoring data in key areas; Indicates the total number of monitoring nodes; Indicates The data transmission rate of each node; Indicates Energy consumption of each node; Indicates The deployment cost of each node.

7. A bridge monitoring node optimization layout method according to claim 6, characterized in that: , , The dynamic adjustment method is as follows: According to the specific bridge monitoring requirements and priorities, the initial weight values ​​are determined through expert experience or historical data statistics; The quality of current monitoring data , data transmission efficiency and cost As state input, represented as a state vector ; Define a set of actions , , , represents the adjustment operation of the weight coefficient; Designing the reward function To evaluate the adjustment effect, the reward function is expressed as: ； in, , , represents the reward coefficient; , , They represent the changes in monitoring data quality, data transmission efficiency and cost respectively; Use reinforcement learning algorithm to update weight coefficients based on the current state and action rewards , adjust the weight coefficient to maximize the long-term cumulative reward, and the update formula is: ； ； ； in, , , Represents the updated weight coefficient; Represents the learning rate, which controls the step size of weight coefficient adjustment.

8. A bridge monitoring node optimization layout method according to claim 7, characterized in that: The constraints of the objective function are as follows: Communication distance constraints between nodes: ； in, Indicates Nodes and The communication distance between nodes; Indicates the maximum communication distance; Coverage constraints: ； in, Indicates the coverage of the monitoring node; Indicates the minimum coverage required; Energy consumption constraints: ； in, Represents the maximum allowed energy consumption of the node.

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