An optimized layout method for bridge monitoring nodes

Abstract

The present invention provides a method for optimizing the layout of bridge monitoring nodes, belonging to the technical field of bridge structural health monitoring. This method determines the key structural information and key monitoring areas of the bridge through finite element analysis and on-site testing to generate an initial layout plan; it collects the actual operation data of the bridge in real time, processes it through a data analysis module, evaluates the quality of the monitoring data, and identifies areas with insufficient monitoring or data redundancy; based on the evaluation results, a comprehensive optimization objective function is constructed, and an improved particle swarm optimization algorithm is used to dynamically adjust the initial plan to obtain an optimized layout plan; finally, by comparing the quality of the monitoring data and the system operation efficiency before and after implementation, the effectiveness of the optimization method is verified. Through the above-mentioned method for optimizing the layout of bridge monitoring nodes, the present invention can dynamically adjust the layout plan of the monitoring nodes according to the actual operation state of the bridge, improve the quality of the monitoring data, and optimize the performance of the monitoring system.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge structure health monitoring, and particularly to an optimized layout method for bridge monitoring nodes. Background Art

[0002] In the field of bridge structure health monitoring, the existing methods for laying out monitoring nodes have obvious deficiencies. Traditional layout schemes mostly rely on experience or simple structural analysis, lacking dynamic adaptability to the actual operating state 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 inability to accurately reflect the true state of the bridge.

[0003] When optimizing the layout, the existing technologies often only focus on a single objective, such as only considering monitoring accuracy or cost control, while ignoring the balance between data transmission efficiency and the overall performance of the system. This single-objective optimization method is difficult to meet the comprehensive improvement of multi-faceted performance requirements in a complex bridge environment.

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

[0005] Meanwhile, for data redundancy or monitoring blind spots that occur during the monitoring process, the existing methods lack an effective dynamic adjustment mechanism and cannot optimize the layout scheme in a timely manner according to 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 layout schemes are difficult to flexibly adapt to such changes.

[0006] These problems limit the effectiveness of the bridge monitoring system, and there is an urgent need for a layout method for monitoring nodes that can dynamically adapt to the operating state of the bridge, comprehensively consider multi-objective optimization, and is supported by an efficient algorithm to overcome the deficiencies of the existing technologies. Summary of the Invention

[0007] The object of the present invention is to provide an optimized layout method for bridge monitoring nodes, which can dynamically adjust the layout scheme of monitoring nodes according to the actual operating state 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 an optimized layout method for bridge monitoring nodes, including the following steps:

[0009] Determine the key structural information and key monitoring areas of the bridge by using finite element analysis and on-site testing;

[0010] Determine the initial monitoring node layout plan according to the key structural information of the bridge and the location information of the key monitoring areas, including the types and layout positions of the monitoring nodes, to ensure that the main structural parts and key response points of the bridge are covered;

[0011] 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 the areas with insufficient monitoring or data redundancy;

[0012] Construct an objective function aiming at the comprehensive optimization of monitoring accuracy, data transmission efficiency and cost. Combine the evaluation results and use the improved particle swarm optimization algorithm to dynamically adjust the initial monitoring node layout plan to obtain the optimized layout plan.

[0013] Preferably, the types of monitoring nodes include strain sensors, acceleration sensors, displacement sensors, inclinometers, temperature and humidity sensors.

[0014] Preferably, the external excitation information includes traffic load, wind load, temperature change, humidity change; the response data of the bridge structure includes strain, acceleration, displacement, inclination.

[0015] Preferably, in the improved particle swarm optimization algorithm:

[0016] Each particle represents a possible monitoring node layout plan. The position of the particle represents the specific coordinates of the monitoring node on the bridge, and the velocity of the particle represents the amplitude and direction of the node position adjustment;

[0017] The update formulas for the particle position and velocity are:

[0018] ;

[0019] ;

[0020] Where, and respectively represent the velocity and position of the th particle at the th iteration; and respectively represent the velocity and position of the th particle at the th iteration; represents the inertia weight; , represent the learning factors; , represent random numbers; represents the The historical optimal position of a particle; Indicates the global optimal position;

[0021] ;

[0022] ;

[0023] Among them, 、 Respectively represent the maximum and minimum values of the inertia weight; Indicates the current iteration number; Indicates the maximum iteration number; Indicates the learning factor, 、 Respectively represent the maximum and minimum values of the learning factor; Indicates the index variable.

[0024] Preferably, the improved particle swarm optimization algorithm designs a mutation operator based on the node energy consumption to perturb the position of the particle:

[0025] ;

[0026] Among them, Indicates the new position of the th particle after perturbation; Indicates the mutation step size; Indicates a random number; Indicates the position of the node with the minimum energy consumption; Is the mutation operator.

[0027] Preferably, the objective function is as follows:

[0028] ;

[0029] Among them, Indicates the objective function; 、 、 Indicate the dynamically adjusted weight coefficients, which are used to balance the relative importance between different objectives; Indicates the number of key monitoring areas; Indicates the th weight of the key area; Indicates the th monitoring data quality of the key area; Indicates the total number of monitoring nodes; Indicates the th data transmission rate of the node; Indicates the th energy consumption of the node; Represents the layout cost of the th node.

[0030] Preferably, , , The dynamic adjustment method is as follows:

[0031] According to the specific bridge monitoring requirements and priorities, determine the initial weight value through expert experience or historical data statistics;

[0032] Take the current monitoring data quality , data transmission efficiency and cost as the state input, expressed as the state vector ;

[0033] Define a set of actions , , , representing the adjustment operation of the weight coefficient;

[0034] Design a reward function to evaluate the adjustment effect, and the reward function is expressed as:

[0035] ;

[0036] Among them, , , represent the reward coefficients; , , respectively represent the change amounts of the monitoring data quality, data transmission efficiency, and cost;

[0037] Use the reinforcement learning algorithm to update the weight coefficient. According to the rewards of the current state and action , adjust the weight coefficient to maximize the long-term cumulative reward, and the update formula is:

[0038] ;

[0039] ;

[0040] ;

[0041] Among them, , , represent the updated weight coefficients; represents the learning rate, which controls the step size of the weight coefficient adjustment.

[0042] Preferably, the constraint conditions of the objective function are as follows:

[0043] Communication distance constraint between nodes:

[0044] ;

[0045] Among them, represents the communication distance between the th node and the th node; represents the maximum communication distance;

[0046] Coverage constraint:

[0047] ;

[0048] Among them, represents the coverage range of the monitoring node; represents the required minimum coverage range;

[0049] Energy consumption constraint:

[0050] ;

[0051] Among them, represents the maximum allowable energy consumption of the node.

[0052] Therefore, the present invention adopts the above-mentioned method for optimizing the layout of bridge monitoring nodes, and the beneficial technical effects are as follows:

[0053] (1) Strong dynamic adaptability: By introducing a dynamic feedback mechanism, the present invention can collect the actual operation data of the bridge in real time and dynamically adjust the layout scheme of the monitoring nodes according to the data evaluation results. This dynamic adjustment ability enables the monitoring system to adapt to the monitoring requirements of the bridge in different operating states, overcoming the deficiencies of the existing technology in which the layout scheme is statically fixed and cannot adapt to the changes in the actual operating state of the bridge.

[0054] (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 existing technology, 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.

[0055] (3) The optimization algorithm is efficient and reliable: The improved particle swarm optimization algorithm introduces an adaptive parameter adjustment mechanism and a mutation operator based on node energy consumption, which improves the search efficiency and global convergence ability of the algorithm, and avoids problems such as the existing optimization algorithms being prone to falling into local optima and having a slow convergence speed. In the optimal layout of bridge monitoring nodes, this means that a better node layout scheme can be found faster, 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 and more reliable optimization results, ensuring that the layout of the monitoring nodes can accurately meet the requirements of bridge structural health monitoring.

[0056] (4) Real-time feedback and continuous optimization: The present invention continuously collects and analyzes data during the monitoring process, can timely detect areas with insufficient monitoring or data redundancy, and adjusts the layout scheme accordingly. This solves the problem of the lack of an effective dynamic adjustment mechanism in the prior art, enabling the monitoring system to continuously optimize the layout scheme according to real-time feedback information and always maintain good monitoring performance. Brief Description of the Drawings

[0057] Figure 1 is a flowchart of a method for optimizing the layout of bridge monitoring nodes according to the present invention;

[0058] Figure 2 is a schematic diagram of the system architecture for optimizing the layout of bridge monitoring nodes. Detailed Embodiments

[0059] The technical solutions of the present invention will be further described below with reference to the drawings and embodiments.

[0060] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs.

[0061] Embodiment 1

[0062] As Figure 1 shown, the present invention provides a method for optimizing the layout of bridge monitoring nodes, including the following steps:

[0063] Step 1: Use the finite element analysis software ANSYS to model the bridge. During the modeling process, consider factors such as the actual geometric dimensions, material properties, and load conditions of the bridge to ensure the accuracy of the model. Obtain the key structural information of the bridge through on-site testing, including stress concentration areas, areas with large deformations, vibration mode characteristics, etc. A variety of means are used for on-site testing, such as using strain gauges to measure stress and acceleration sensors to measure vibration responses. Determine the structural weak points and key monitoring areas of the bridge.

[0064] Step 2: According to the structural characteristics of the bridge and the conventional monitoring requirements, preliminarily determine the types and approximate layout positions of the monitoring nodes. The types of monitoring nodes include strain sensors, acceleration sensors, displacement sensors, inclinometers, temperature and humidity sensors, etc. The layout positions of different types of sensors are arranged specifically according to the characteristics of their monitoring objects. For example, strain sensors are mainly arranged in stress concentration areas, and acceleration sensors are arranged at the key vibration mode nodes of the bridge.

[0065] In the initial layout plan, a total of 120 monitoring nodes were arranged 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 there may be problems of monitoring blind spots or data redundancy in the initial layout plan.

[0066] 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, etc., and response data of the bridge structure, such as strain, acceleration, displacement, inclination, etc. Process these data 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 data redundancy.

[0067] The specific process of the data analysis module processing the data includes:

[0068] Process the collected monitoring data through filtering, denoising, normalization, etc. to improve the quality and comparability of the data;

[0069] Check whether there are missing, abnormal or inconsistent situations in the monitoring data to ensure the integrity of the data;

[0070] Evaluate the accuracy and reliability of the data by comparing the monitoring data of different sensors;

[0071] Analyze the acquisition and transmission time of the monitoring data to ensure the real-time nature of the data;

[0072] Combined with the evaluation results of data integrity, accuracy and real-time nature, give a comprehensive quality score for the monitoring data;

[0073] According to the data quality score and monitoring requirements, identify the areas with insufficient monitoring data;

[0074] Identify the areas with data redundancy by analyzing the correlation of the data of different sensors.

[0075] In actual operation, the system collects data every 30 minutes and generates approximately 96 groups of data per day. Through data analysis, it is found that there are obvious deficiencies in the monitoring data of some areas in the initial layout plan. For example, in some parts at the bottom of the bridge tower, the data of the strain sensors fluctuates greatly and cannot accurately reflect the actual stress state of the structure. At the same time, there is data redundancy in some areas. For example, the data of the acceleration sensors in some main girder areas has a high correlation and a large amount of repetitive information.

[0076] Step 4: Construct an objective function aiming at the comprehensive optimization of monitoring accuracy, data transmission efficiency, and cost. Combining the evaluation results, use the improved particle swarm optimization algorithm to dynamically adjust the initial monitoring node layout plan to obtain an optimized layout plan; during the adjustment process, comprehensively consider the constraint conditions such as the communication distance, coverage range, and energy consumption between nodes to ensure that the optimized layout plan has good feasibility and economy while meeting the monitoring requirements;

[0077] In the improved particle swarm optimization algorithm:

[0078] Each particle represents a possible monitoring node layout plan. The position of the particle represents the specific coordinates of the monitoring node on the bridge, and the velocity of the particle represents the amplitude and direction of the node position adjustment;

[0079] The update formulas for the particle position and velocity are:

[0080] ;

[0081] ;

[0082] Among them, and respectively represent the velocity and position of the th particle at the th iteration, and respectively represent the velocity and position of the th particle at the th iteration, represents the inertia weight, , represent the learning factors, , represent random numbers, represents the historical optimal position of the th particle, represents the global optimal position;

[0083] ;

[0084] ;

[0085] Among them, and respectively represent the maximum and minimum values of the inertia weight; represents the current iteration number; represents the maximum number of iterations; represents the learning factor, and respectively represent the maximum and minimum values of the learning factor; represents the index variable.

[0086] The improved particle swarm optimization algorithm designs a mutation operator based on the node energy consumption to perturb the positions of the particles:

[0087] ;

[0088] Among them, represents the new position of the th particle after perturbation; represents the mutation step size; represents a random number; represents the position of the node with the minimum energy consumption; is the mutation operator.

[0089] The objective function is as follows:

[0090] ;

[0091] Among them, represents the objective function; and and represent the dynamically adjusted weight coefficients, which are used to balance the relative importance between different objectives; represents the number of key monitoring areas; represents the weight of the th key area; represents the monitoring data quality of the th key area; represents the total number of monitoring nodes; represents the data transmission rate of the th node; represents the energy consumption of the th node; represents the deployment cost of the th node.

[0092] During the optimization process, the parameter settings of the improved particle swarm optimization algorithm are as follows: the population size is 50, the maximum number of iterations is 200, the inertia weight linearly decreases from 0.9 to 0.4, and the learning factors and The initial value is 2.0 and linearly decreases to 0.5 respectively. Through multiple iterations of optimization, the algorithm gradually adjusts the positions and quantities of the monitoring nodes to achieve the optimal layout plan.

[0093] The constraint conditions of the objective function include the communication distance constraint, coverage range constraint, and energy consumption constraint between nodes.

[0094] Communication distance constraint between nodes:

[0095] ;

[0096] Among them, represents the communication distance between the th node and the th node; represents the maximum communication distance;

[0097] Coverage range constraint:

[0098] ;

[0099] Among them, represents the coverage range of the monitoring node; represents the required minimum coverage range;

[0100] Energy consumption constraint:

[0101] ;

[0102] Among them, represents the maximum allowable energy consumption of the node.

[0103] , , The dynamic adjustment methods of

[0104] are as follows: Determine the initial weight value according to the specific bridge monitoring requirements and priorities through expert experience or historical data statistics;

[0105] Take the current monitoring data quality , data transmission efficiency and cost as the state input, expressed as the state vector ;

[0106] Define a set of actions , , , representing the adjustment operations on the weight coefficients;

[0107] Design a reward function to evaluate the adjustment effect. The reward function is expressed as:

[0108] ;

[0109] Among them, , , represent the reward coefficients; , , respectively represent the change amounts of the monitoring data quality, transmission efficiency, and cost;

[0110] Use the reinforcement learning algorithm to update the weight coefficients, and adjust the weight coefficients according to the rewards of the current state and action to maximize the long-term cumulative reward. The update formula is:

[0111] ;

[0112] ;

[0113] ;

[0114] Among them, , , represent the updated weight coefficients; represents the learning rate, which controls the step size of the weight coefficient adjustment.

[0115] Step 5: Apply the optimized layout plan to the 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.

[0116] As shown in Table 1, in the optimized layout plan, 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 applications, it is found that the optimized plan significantly improves the monitoring data quality in key areas. The data integrity is increased from 85% to 95%, and the data accuracy is increased from 90% to 97%. At the same time, the data transmission efficiency is increased by 30%, the system energy consumption is reduced by 25%, and the cost is reduced by 20%.

[0117] Table 1 Comparison before and after optimization

[0118] ;

[0119] As Figure 2 shown, the optimized layout system for bridge monitoring nodes includes:

[0120] A finite element analysis module for modeling and analyzing the bridge to determine the key structural information of the bridge;

[0121] On-site testing module, used to obtain the actual structural information of the bridge, including stress concentration areas, areas with large deformations, vibration mode characteristics, etc.;

[0122] Initial layout plan generation module, which preliminarily determines the types and approximate layout positions of monitoring nodes according to the structural characteristics of the bridge and conventional monitoring requirements;

[0123] Monitoring node layout module, 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;

[0124] Data acquisition module, which collects data from monitoring nodes in real time, including external excitation information and response data of the bridge structure;

[0125] Data analysis module, which processes the collected data and evaluates the quality and representativeness of the monitoring data;

[0126] Data quality assessment module, which evaluates the quality of monitoring data under the current layout plan according to the integrity, accuracy, and real-time nature of the data;

[0127] Optimization decision-making module, which decides whether to optimize and adjust the layout plan according to the data quality assessment results;

[0128] Improved particle swarm optimization algorithm module, which uses the improved particle swarm optimization algorithm and combines dynamically adjusted weight coefficients to optimize and adjust the initial monitoring node layout plan to improve monitoring accuracy, data transmission efficiency, and control costs;

[0129] Layout plan adjustment module, which dynamically adjusts the layout plan of monitoring nodes according to the instructions of the optimization decision-making module and the calculation results of the improved particle swarm optimization algorithm module;

[0130] Comparison and verification module, which verifies the effectiveness of the optimized layout method by comparing the quality of monitoring data and the system operation efficiency before and after implementation.

[0131] It should be noted that the content not elaborated in detail in the present invention is all prior art and is well-known to those skilled in the art.

[0132] Therefore, by adopting the above-mentioned method for optimizing the layout of bridge monitoring nodes, the present invention can dynamically adjust the layout plan of monitoring nodes according to the actual operating state of the bridge, improve the quality of monitoring data, and optimize the performance of the monitoring system.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions of the present invention or make equivalent replacements, and these modifications or equivalent replacements do not enable the modified technical solutions to deviate from the spirit and scope of the technical solutions 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 constructed. 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. 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; Indicates The particle in The position at the iteration; 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.

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. The bridge monitoring node optimization layout method according to claim 1 is 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.

6. A bridge monitoring node optimization layout method according to claim 5, 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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