Intelligent arch bridge structure health monitoring system in construction

Abstract

The invention relates to the technical field of bridge engineering, and particularly discloses an intelligent construction arch bridge structure health monitoring system which comprises a sensor arrangement module, a preprocessing module, a data analysis module, a health assessment module, an early warning module and a visualization module. According to the scheme, various sensor technologies, data processing algorithms and an intelligent analysis system are integrated, all-directional, high-precision and real-time monitoring of an arch bridge structure in the construction stage is achieved, potential safety problems are found and early warned in time, and the structural safety and reliability in the construction stage are improved; a health assessment model is constructed, the health state of the arch bridge structure is dynamically updated, a comprehensive assessment scheme is provided for arch bridge health at different stages, real-time early warning and decision support are provided, and optimal utilization of resources and efficient completion of tasks are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge engineering, and specifically refers to an intelligent health monitoring system for arch bridges during construction. Background Art

[0002] In the construction project of arch bridges, the health monitoring of arch bridge structures is particularly important. The use of intelligent monitoring systems helps to improve the safety, durability, and sustainability of bridge structures, timely grasp the health status of bridges, and give early warnings of potential structural risks so as to take repair or reinforcement measures in a timely manner. The existing health monitoring technologies for arch bridge structures have deficiencies in real-time monitoring and data analysis capabilities in complex construction environments, cannot quickly respond to sudden structural safety problems, and do not comprehensively cover the specific requirements during the construction stage; there are deficiencies in aspects such as dynamic force analysis, safety assessment, and real-time warning functions of arch bridge structures during general construction processes, which are difficult to effectively meet the high-precision, high-efficiency, and high-reliability requirements in modern construction processes, and it is difficult to provide a special solution for arch bridge structures. Summary of the Invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an intelligent health monitoring system for arch bridges during construction. Aiming at the deficiencies of the existing health monitoring technologies for arch bridge structures in real-time monitoring and data analysis capabilities in complex construction environments, inability to quickly respond to sudden structural safety problems, and incomplete coverage of specific requirements during the construction stage, this solution integrates a variety of sensor technologies, data processing algorithms, and intelligent analysis systems to achieve all-round, high-precision, and real-time monitoring of arch bridge structures during the construction stage, timely discover and warn of potential safety problems, and improve the structural safety and reliability during the construction stage; aiming at the deficiencies in aspects such as dynamic force analysis, safety assessment, and real-time warning functions of arch bridge structures during general construction processes, which are difficult to effectively meet the high-precision, high-efficiency, and high-reliability requirements in modern construction processes, and it is difficult to provide a special solution for arch bridge structures, this solution constructs a health assessment model, dynamically updates the health status of arch bridge structures, provides a comprehensive assessment plan for the health of arch bridges at different stages, and provides real-time warning and decision support to ensure the optimal utilization of resources and the efficient completion of tasks.

[0004] An intelligent health monitoring system for arch bridges during construction provided by the present invention includes a sensor arrangement module, a preprocessing module, a data analysis module, a health assessment module, a warning module, and a visualization module;

[0005] The sensor arrangement module arranges sensors at key parts of the arch bridge to comprehensively monitor the force conditions, environmental conditions, and deformation conditions of the arch bridge, obtain monitoring data, and send the monitoring data to the preprocessing module;

[0006] The preprocessing module cleans and preprocesses the monitoring data to obtain the processed data, and sends the processed data to the data analysis module;

[0007] The data analysis module performs multi-dimensional analysis on the processed data, including time-domain analysis, frequency-domain analysis, spatial analysis, and statistical analysis. By identifying and extracting key features, it generates a comprehensive analysis result and sends the comprehensive analysis result to the health assessment module;

[0008] The health assessment module establishes a health assessment model, comprehensively evaluates the health status of the arch bridge structure based on the comprehensive analysis result. The evaluation content includes the health index, damage location, and damage degree, generates an evaluation result, and sends the evaluation result to the warning module and the visualization module;

[0009] The warning module sets a warning threshold according to the evaluation result. When the evaluation result exceeds the warning threshold, it immediately issues a warning signal;

[0010] The visualization module intuitively displays the evaluation result in the form of a chart, providing decision-making support for construction personnel and management personnel.

[0011] Further, the data analysis module performs multi-dimensional analysis on the processed data, including the following steps:

[0012] Step S1: Time-domain analysis. Select time-domain data from the processed data, detect the mean value, standard deviation, and peak value in the time-domain data, identify abnormal fluctuations, and judge whether there is sudden damage to the arch bridge structure;

[0013] Step S2: Frequency-domain analysis. Perform frequency-domain transformation on the time-domain data, identify the frequency characteristics in the data, and judge whether there is a resonance phenomenon in the arch bridge;

[0014] Step S3: Spatial analysis. Obtain strain data at different positions from the processed data, identify the force distribution and deformation of the arch bridge structure, and judge the damage location;

[0015] Step S4: Statistical analysis. Obtain the dependent variables related to the damage of the arch bridge structure from the processed data, analyze the historical monitoring values of the dependent variables, use the regression analysis method to predict the future damage trend, and output the damage degree prediction value. The formula used is as follows:

[0016] D(t) = β0 + β1t;

[0017] In the formula, β0 and β1 represent regression coefficients, t represents time, and D(t) represents the damage degree prediction value;

[0018] Step S5: Comprehensive analysis. Perform weighted summation on the results of time-domain analysis, frequency-domain analysis, spatial analysis, and statistical analysis to generate a comprehensive analysis result and provide it to the health assessment module.

[0019] Furthermore, the health assessment module establishes a health assessment model to comprehensively evaluate the health status of the arch bridge structure based on the analysis results, including the following steps:

[0020] Step A1: Initialize the health assessment model, including the damage identification model, damage location model, and health index model;

[0021] Step A2: Construct a damage identification model based on the decision tree, analyze the comprehensive analysis results, and output the damage identification result. The formula used is as follows:

[0022] Damage=f(T,F,S,R);

[0023] In the formula, Damage represents the output of the damage identification model, T represents the time-domain analysis result, F represents the frequency-domain analysis result, S represents the spatial analysis result, R represents the statistical analysis result, and f represents the parameters of the damage identification model;

[0024] Step A3: Construct a damage location model, and based on the spatial analysis and the installation location of the sensors, locate the specific location of the damage and output the damage location result. The formula used is as follows:

[0025] Location=g(S,Pos);

[0026] In the formula, Location represents the damage location coordinates, Pos represents the sensor installation location, and g represents the parameters of the damage location model;

[0027] Step A4: Construct a health index model, combine the damage identification result and the damage location result, and calculate the health index of the arch bridge structure. The formula used is as follows:

[0028] Health=h(Damage,Location,D(t));

[0029] In the formula, Health represents the health index, and h represents the parameters of the health index model;

[0030] Step A5: Result verification, compare the health index with the historical data, optimize the parameters of the health assessment model, and output the final assessment result.

[0031] The beneficial effects achieved by the present invention using the above solution are as follows:

[0032] (1)In view of the deficiencies of the existing arch bridge structure health monitoring technology in real-time monitoring and data analysis capabilities in complex construction environments, its inability to quickly respond to sudden structural safety problems, and its incomplete coverage of specific requirements during the construction stage, this solution integrates multiple sensor technologies, data processing algorithms, and intelligent analysis systems to achieve all-round, high-precision, and real-time monitoring of the arch bridge structure during the construction stage, promptly detect and warn of potential safety problems, and improve the structural safety and reliability during the construction stage.

[0033] (2)Regarding the deficiencies in aspects such as dynamic force analysis, safety assessment, and real-time warning functions of arch bridge structures during general construction processes, which are difficult to effectively meet the requirements of high precision, high efficiency, and high reliability in modern construction processes, and it is difficult to provide a special solution for arch bridge structures, this solution constructs a health assessment model to dynamically update the health status of the arch bridge structure, provides a comprehensive assessment plan for the health of arch bridges at different stages, and provides real-time warning and decision-making support to ensure the optimal utilization of resources and the efficient completion of tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of an intelligent health monitoring system for arch bridge structures during construction proposed by the present invention;

[0035] Figure 2 It is a flowchart of multi-dimensional analysis in the data analysis module.

[0036] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] Example 1, referring to Figure 1 , an intelligent health monitoring system for arch bridge structures during construction provided by the present invention includes a sensor arrangement module, a preprocessing module, a data analysis module, a health assessment module, a warning module, and a visualization module;

[0039] The sensor arrangement module arranges sensors at key parts of the arch bridge, including strain sensors, vibration sensors, temperature sensors, humidity sensors, and displacement sensors, to comprehensively monitor the stress conditions, environmental conditions, and deformation conditions of the arch bridge, obtain monitoring data, and send the monitoring data to the preprocessing module;

[0040] The preprocessing module cleans and preprocesses the monitoring data to obtain processed data, and sends the processed data to the data analysis module;

[0041] The data analysis module conducts multi-dimensional analysis on the processed data, including time-domain analysis, frequency-domain analysis, spatial analysis, and statistical analysis. By identifying and extracting key features, it generates a comprehensive analysis result and sends the comprehensive analysis result to the health assessment module;

[0042] The health assessment module establishes a health assessment model, comprehensively assesses the health status of the arch bridge structure based on the analysis results. The assessment content includes health index, damage location, and damage degree, generates an assessment result, and sends the assessment result to the warning module and the visualization module;

[0043] The warning module sets a warning threshold according to the assessment result. When the assessment result exceeds the warning threshold, it immediately issues a warning signal;

[0044] The visualization module visually displays the assessment result in the form of a chart, providing decision-making support for construction personnel and management personnel.

[0045] Embodiment 2, refer to Figure 1 , this embodiment is based on the above embodiment. The sensor arrangement module installs sensors according to the specific structure and construction requirements of the arch bridge. Strain sensors are installed at the key stress points of the main arch ring, piers, and beam body of the arch bridge to monitor the stress and strain conditions of the structure; vibration sensors are installed on the bridge deck, bearings, and main arch ring of the arch bridge to monitor the vibration conditions of the structure; temperature sensors and humidity sensors are installed on the surface and inside of the arch bridge to monitor the environmental conditions; displacement sensors are installed on the bearings, piers, and main arch ring of the arch bridge to monitor the displacement and deformation conditions of the structure.

[0046] Embodiment 3, refer to Figure 1 , this embodiment is based on the above embodiment. The preprocessing module cleans and preprocesses the monitoring data, including a data acquisition unit and a data preprocessing unit: The data acquisition unit is connected to the sensors through wireless communication to obtain monitoring data in real time; the data preprocessing unit cleans, filters, interpolates, and standardizes the monitoring data.

[0047] Embodiment 4, refer to Figure 1 and Figure 2, this embodiment is based on the above embodiment, and the data analysis module performs multi-dimensional analysis on the processed data, including the following steps:

[0048] Step S1: Time-domain analysis. Select time-domain data from the processed data, detect the mean value, standard deviation, and peak value in the time-domain data, identify abnormal fluctuations, and determine whether there is sudden damage to the arch bridge structure, including the following steps:

[0049] Step S11: Mean value and standard deviation detection. Set a threshold T. If the monitoring value of the time-domain data at a certain moment deviates from the mean value by more than T×σ, it is considered that there is an abnormal fluctuation. The formula used is as follows:

[0050]

[0051] In the formula, μ represents the mean value of the time-domain data, σ represents the standard deviation, N represents the number of points of the time-domain data, i represents the time point of the time-domain data, and x i represents the monitoring value of the time-domain data at each moment;

[0052] Step S12: Peak value detection. Calculate the peak value of the time-domain data. If the instantaneous amplitude Max(x(t)) at time t exceeds the historical normal amplitude range of the time-domain data, it is considered that sudden damage may have occurred;

[0053] Step S2: Frequency-domain analysis. Perform discrete Fourier transform on the time-domain data, identify the frequency characteristics in the data, and determine whether there is a resonance phenomenon in the arch bridge, including the following steps:

[0054] Step S21: Perform discrete Fourier transform on the time-domain data to obtain frequency-domain data, and display the response of the arch bridge at different frequencies. The formula used is as follows:

[0055]

[0056] In the formula, X(f) represents the frequency-domain representation of the time-domain data, f represents the frequency, represents the influence of the complex sine wave of frequency f on each time point i;

[0057] Step S22: Resonance frequency detection. Obtain the design parameters of the arch bridge, analyze the natural frequency of the arch bridge structure, and determine whether the peak frequency is close to the resonance frequency from the frequency-domain data. If it is close, it is considered that resonance may occur;

[0058] Step S3: Spatial analysis. Obtain strain data at different positions from the processed data, identify the force distribution and deformation of the arch bridge structure, and determine the damage location, including the following steps:

[0059] Step S31: Spatial interpolation. According to the strain data at different positions, the inverse distance weighted method is used to estimate the strain condition of the interpolation points. The formula used is as follows:

[0060]

[0061] In the formula, p represents the position of the interpolation point, represents the estimated value of the interpolation point p, s represents the index of the strain data, x s represents the monitored value of the s-th strain data, k represents the total number of strain data samples, w s (p) represents the weight function between the position of the strain data and the interpolation point;

[0062] Step S32: Force distribution analysis. Call the finite element analysis model to calculate the stress and strain distributions of different parts of the arch bridge, and output the prediction results of the damage positions of the arch bridge;

[0063] Step S4: Statistical analysis. Obtain the dependent variables related to the structural damage of the arch bridge from the processed data, analyze the historical monitored values of the dependent variables, and use the regression analysis method to predict the future damage trend and the predicted value of the damage degree. The formula used is as follows:

[0064] D(t) = β0 + β1t;

[0065] In the formula, β0 and β1 represent the regression coefficients, t represents time, and D(t) represents the predicted value of the damage degree;

[0066] Step S5: Comprehensive analysis. Perform weighted summation on the results of time-domain analysis, frequency-domain analysis, spatial analysis, and statistical analysis to generate a comprehensive analysis result and provide it to the health assessment module.

[0067] By performing the above operations, aiming at the problems that the existing arch bridge structural health monitoring technology has insufficient real-time monitoring and data analysis capabilities in complex construction environments, cannot quickly respond to sudden structural safety problems, and does not comprehensively cover the specific requirements during the construction stage, this solution integrates a variety of sensor technologies, data processing algorithms, and intelligent analysis systems to achieve all-round, high-precision, and real-time monitoring of the arch bridge structure during the construction stage, timely discover and warn potential safety problems, and improve the structural safety and reliability during the construction stage.

[0068] Example 5, refer to Figure 1 , based on the above example, the health assessment module establishes a health assessment model and comprehensively evaluates the health status of the arch bridge structure according to the analysis results, including the following steps:

[0069] Step A1: Initialize the health assessment model, including a damage identification model, a damage location model, and a health index model;

[0070] Step A2: Build a damage identification model based on a decision tree, analyze the comprehensive analysis results, and output the damage identification results. The formula used is as follows:

[0071] Damage = f(T, F, S, R);

[0072] In the formula, Damage represents the output of the damage identification model, T represents the time-domain analysis result, F represents the frequency-domain analysis result, S represents the spatial analysis result, R represents the statistical analysis result, and f represents the parameter of the damage identification model;

[0073] Step A3: Build a damage location model. Based on the spatial analysis and the installation positions of the sensors, locate the specific location of the damage and output the damage location result. The formula used is as follows:

[0074] Location = g(S, Pos);

[0075] In the formula, Location represents the damage location coordinates, Pos represents the sensor installation position, and g represents the parameter of the damage location model;

[0076] Step A4: Build a health index model. Combine the damage identification result and the damage location result to calculate the health index of the arch bridge structure. The formula used is as follows:

[0077] Health = h(Damage, Location, D(t));

[0078] In the formula, Health represents the health index, and h represents the parameter of the health index model;

[0079] Step A5: Result verification. Compare the health index with the historical data, optimize the parameters of the health assessment model, and output the final assessment result.

[0080] By performing the above operations, for the deficiencies in aspects such as the dynamic stress analysis, safety assessment, and real-time warning function of the arch bridge structure during the general construction process, it is difficult to effectively meet the high-precision, high-efficiency, and high-reliability requirements in the modern construction process, and it is difficult to provide a special solution for the arch bridge structure. This solution constructs a health assessment model, dynamically updates the health status of the arch bridge structure, provides a comprehensive assessment solution for the health of the arch bridge at different stages, and provides real-time warning and decision support to ensure the optimal utilization of resources and the efficient completion of tasks.

[0081] Example 6, refer to Figure 1 , based on the above example, the warning module sets the warning threshold to 0.7 according to the evaluation result. When the health index is lower than 0.7, the system immediately issues a warning signal, displays the damage location and the damage degree, and automatically generates inspection and maintenance suggestions.

[0082] Example 7, refer to Figure 1 , based on the above embodiments, the visualization module visually displays the evaluation results in the form of charts. The display content includes a health index trend chart, a damage location distribution chart, and a damage degree grading chart. The display methods include a Web interface and a mobile application, supporting real-time update and historical data query.

[0083] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0084] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

[0085] The above describes the present invention and its implementation manners. Such description is not restrictive. What is shown in the drawings is only one of the implementation manners of the present invention, and the actual structure is not limited thereto. Generally speaking, if those of ordinary skill in the art are inspired by it and design similar structural manners and embodiments without creative efforts without departing from the purpose of the present invention, they shall fall within the protection scope of the present invention.

Claims

1. An intelligent structural health monitoring system for an arch bridge under construction, characterized by: It includes sensor arrangement module, preprocessing module, data analysis module, health assessment module, early warning module and visualization module; The sensor arrangement module arranges sensors at key positions of the arch bridge to comprehensively monitor the stress, environmental conditions and deformation of the arch bridge, obtains monitoring data, and sends the monitoring data to the preprocessing module; The preprocessing module cleans and preprocesses the monitoring data to obtain processed data, and sends the processed data to the data analysis module; The data analysis module performs multi-dimensional analysis on the processed data, including time domain analysis, frequency domain analysis, spatial analysis and statistical analysis, generates comprehensive analysis results by identifying and extracting key features, and sends the comprehensive analysis results to the health assessment module; The health assessment module establishes a health assessment model, conducts a comprehensive assessment of the health status of the arch bridge structure according to the comprehensive analysis results, the assessment content includes the health index, damage location and damage degree, generates an assessment result, and sends the assessment result to the early warning module and the visualization module; The warning module sets a warning threshold according to the evaluation result, and immediately issues a warning signal when the evaluation result exceeds the warning threshold; The visualization module intuitively displays the evaluation results in the form of charts, providing decision support for construction personnel and management personnel.

2. The intelligent structural health monitoring system for an arch bridge under construction according to claim 1 is characterized in that: The data analysis module performs multi-dimensional analysis on the processed data, including the following steps: Step S1: time domain analysis, selecting time domain data from the processed data, detecting the mean, standard deviation and peak value in the time domain data, identifying abnormal fluctuations, and determining whether there is sudden damage to the arch bridge structure; Step S2: frequency domain analysis, performing frequency domain transformation on the time domain data, identifying the frequency characteristics in the data, and determining whether there is resonance in the arch bridge; Step S3: spatial analysis, obtaining strain data at different positions from the processed data, identifying the stress distribution and deformation of the arch bridge structure, and determining the damage location; Step S4: Statistical analysis: obtain the dependent variables related to the arch bridge structure damage from the processed data, analyze the historical monitoring values ​​of the dependent variables, use the regression analysis method to predict the future damage trend, and output the damage degree prediction value. The formula used is as follows: D(t) = β0 + β1t; In the formula, β0 and β1 represent regression coefficients, t represents time, and D(t) represents the predicted value of damage degree; Step S5: Comprehensive analysis: weighted sum of the results of time domain analysis, frequency domain analysis, spatial analysis and statistical analysis to generate a comprehensive analysis result, which is provided to the health assessment module.

3. The intelligent structural health monitoring system for an arch bridge under construction according to claim 2 is characterized in that: The health assessment module establishes a health assessment model and performs a comprehensive assessment of the health status of the arch bridge structure according to the analysis results, including the following steps: Step A1: Initialize the health assessment model, including the damage identification model, the damage location model and the health index model; Step A2: Build a damage identification model based on the decision tree, analyze the comprehensive analysis results, and output the damage identification results. The formula used is as follows: Damage = f(T, F, S, R); In the formula, Damage represents the output of the damage identification model, T represents the time domain analysis result, F represents the frequency domain analysis result, S represents the spatial analysis result, R represents the statistical analysis result, and f represents the parameter of the damage identification model; Step A3: Construct a damage location model, locate the specific location of the damage based on spatial analysis and the installation location of the sensor, and output the damage location result. The formula used is as follows: Location = g(S,Pos); Where Location represents the coordinates of the damage location, Pos represents the sensor installation position, and g represents the parameters of the damage location model; Step A4: Construct a health index model, combine the damage identification results and damage location results, and calculate the health index of the arch bridge structure. The formula used is as follows: Health=h(Damage,Location,D(t)); In the formula, Health represents the health index, and h represents the parameter of the health index model; Step A5: Result verification, compare the health index with historical data, optimize the parameters of the health assessment model, and output the final assessment results.

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