Bridge stress monitoring detection system

Abstract

The application discloses a bridge stress monitoring and detecting system, and belongs to the technical field of bridge detection, comprising a monitoring arrangement module, a sensing module, a processing module and a platform detecting and analyzing module; the monitoring arrangement module is used for laying relevant monitoring equipment; the sensing module is used for acquiring response parameters; the processing module is used for pre-processing the acquired response parameters; the platform detecting and analyzing module is used for collecting and storing the response parameters, and simultaneously performing analysis according to the response parameters to judge whether the bridge stress is normal and to judge the potential stress abnormal risk of the bridge. The application can not only comprehensively analyze the acquired response parameters to find the stress abnormal conditions of the bridge, but also judge the potential stress abnormal risk of the bridge, so that the real-time stress change conditions and the potential abnormality of the bridge in the operation process can be captured in time, and thus response processing can be performed in time, and the occurrence of safety accidents can be reduced.

Description

Technical Field

[0001] This invention belongs to the field of bridge inspection technology, and specifically relates to a bridge stress monitoring and inspection system. Background Technology

[0002] As a key component of transportation infrastructure, bridges play a vital role in connecting regional transportation and promoting economic development. With the acceleration of urbanization and the continuous growth of traffic flow, bridges face increasingly severe operational pressures, and their structural safety is directly related to the safety of people's lives and property and social stability. During long-term use, bridges are affected by various factors such as vehicle loads, natural environment, and material aging, resulting in complex stress changes within the structure. If these stress changes are not monitored and analyzed in a timely and effective manner, they may lead to defects such as cracks and deformation, and in severe cases, even cause major safety accidents such as bridge collapse.

[0003] Existing bridge stress monitoring methods primarily rely on manual inspections and periodic testing. Manual inspections, typically conducted by professionals using visual inspections and simple measuring tools, have significant limitations. Firstly, manual inspections are inefficient and labor-intensive, making comprehensive and real-time monitoring of bridges difficult. Secondly, they are heavily influenced by subjective factors, making it difficult to accurately detect some hidden stress anomalies. While periodic testing can assess the structural condition of bridges to some extent, the testing cycle is long, failing to capture real-time stress changes during operation and thus failing to meet the demands of modern bridge safety operation. Summary of the Invention

[0004] The purpose of this invention is to provide a bridge stress monitoring and detection system to solve the problems encountered in the background art.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A bridge stress monitoring and detection system, the system comprising a monitoring deployment module, a sensing module, a processing module, and a platform detection and analysis module;

[0007] The monitoring deployment module is used to deploy relevant monitoring equipment in key areas of the bridge;

[0008] The sensing module is used to acquire response parameters collected by relevant monitoring equipment;

[0009] The processing module is used to preprocess the acquired response parameters and then send them to the platform detection and analysis module.

[0010] The platform's detection and analysis module is used to collect and store response parameters, and to analyze the acquired response parameters to determine whether the bridge stress is normal and to assess potential stress anomalies in the bridge.

[0011] Furthermore, the monitoring equipment includes a fiber optic strain sensor, an embedded concrete strain gauge, a GNSS displacement sensor, a humidity sensor, and a corrosion sensor. The response parameters include a first stress obtained by the fiber optic strain sensor, a second stress obtained by the embedded concrete strain gauge, a displacement obtained by the GNSS displacement sensor, humidity at the location obtained by the humidity sensor, and current density value obtained by the corrosion sensor.

[0012] Furthermore, the method by which the platform detection and analysis module determines whether the bridge stress is normal is as follows:

[0013] By installing monitoring equipment in the corresponding area of ​​the bridge, multiple response parameters are acquired and compared with their respective set abnormal threshold points. When the abnormal threshold points are exceeded, it is determined that the stress in that area is abnormal.

[0014] Simultaneously, when determining that the response parameter of a single detection item does not exceed the abnormal threshold, based on the obtained response parameters of each item... And based on the degree of influence of each test item, a weight percentage is assigned to each test item. Through formula Determine the stress state value Then compare it with the set stress condition threshold. Compare the stress state values. Exceeding the stress condition threshold If n is the total number of detection items, then it is determined that the stress in that area is abnormal.

[0015] Furthermore, the method by which the platform detection and analysis module judges the potential stress anomaly risk of the bridge is as follows:

[0016] When it is determined that no abnormalities have occurred in the bridge stress, stress values ​​at various locations within the corresponding area of ​​the bridge are collected in time series under both loaded and unloaded conditions. , And based on the stress condition value , The curve function of the proposed stress state value as a function of the number of data acquisitions x , ;

[0017] Through formula Derive the concealment coefficient ;

[0018] when If so, it is determined that there is a potential risk of abnormal stress in the area corresponding to the bridge;

[0019] in, This is the environmental impact value. This is the first collection. This is the last collection. The set stress condition threshold is a function that varies with the number of data acquisitions. as well as These are the weighting coefficients. Threshold for determining hidden coefficients.

[0020] Furthermore, the environmental impact value The method of obtaining this information is as follows: obtain the number of extreme weather events, including the number of days with high temperatures, from the local meteorological bureau within the monitoring and collection period. Number of days with low temperatures Number of acid rain days Number of days with strong winds Blizzard days Thus through the formula Determine the environmental impact value ,in This is to detect the total number of days within the data collection period.

[0021] Furthermore, the method by which the platform detection and analysis module judges the potential stress anomaly risk of the bridge also includes:

[0022] Set detection cycle During the inspection period, stress values ​​at multiple locations within the same critical area of ​​the bridge are obtained. ,

[0023] Through formula Outliers were found;

[0024] when If so, it is determined that there is a potential risk of abnormal stress in the area corresponding to the bridge;

[0025] in, For the detection cycle Start time, For the detection cycle End time, Let be the stress value at the j-th location at time t. The total number of location points, and , The threshold for anomaly detection This is the fluctuation coefficient.

[0026] Furthermore, the method for obtaining the fluctuation coefficient is as follows:

[0027] First, through the formula The conclusion is Fluctuation value within a time period ;

[0028] Thus through the formula The fluctuation coefficient within the detection period is obtained. .

[0029] The beneficial effects of this invention are:

[0030] This invention involves installing various monitoring devices on bridges and analyzing the parameters acquired by these devices to monitor bridge stress in real time. This eliminates the need for manual inspections and offers high accuracy. Furthermore, the platform's detection and analysis module not only comprehensively analyzes the acquired response parameters to detect existing stress anomalies but also assesses potential stress anomaly risks. This allows for timely detection of real-time stress changes and potential anomalies during bridge operation, enabling prompt response and ensuring bridge safety while reducing the occurrence of accidents.

[0031] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a system module block diagram of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] In one embodiment, a bridge stress monitoring and detection system is disclosed, such as Figure 1 As shown, the detection system mainly includes a monitoring deployment module, a sensing module, a processing module, and a platform detection and analysis module.

[0036] The system comprises several modules: a monitoring deployment module for deploying monitoring equipment in key areas of the bridge, including fiber optic strain sensors, embedded concrete strain gauges, GNSS displacement sensors, humidity sensors, and corrosion sensors; a sensing module for acquiring response parameters from these monitoring devices, including the first stress from the fiber optic strain sensor, the second stress from the embedded concrete strain gauge, the displacement from the GNSS displacement sensor, the humidity of the area from the humidity sensor, and the current density from the corrosion sensor; a processing module for preprocessing the acquired response parameters before sending them to the platform detection and analysis module, including parameter cleaning, time synchronization, and parameter standardization to facilitate subsequent calculations; and a platform detection and analysis module for collecting and storing response parameters, and analyzing these parameters to determine whether the bridge stress is normal and to assess potential stress anomalies. The platform acquires response parameters from various monitoring devices to obtain stress status values. Based on these values, it performs real-time detection of bridge stress anomalies. Furthermore, it analyzes the cumulative changes in stress status values ​​and various influencing factors to derive hidden coefficients and outliers, enabling the assessment of potential stress anomaly risks. This allows for real-time detection and assessment of whether bridge stress is normal and of potential stress anomaly risks. In this way, the platform acquires and analyzes response parameters through detection equipment, enabling real-time monitoring of bridge stress without manual inspection. It boasts high accuracy. The platform's detection and analysis module not only comprehensively analyzes the acquired response parameters to detect existing stress anomalies but also assesses potential stress anomaly risks. This allows for timely capture of real-time stress changes and potential anomalies during bridge operation, facilitating timely response and ensuring bridge safety while reducing the occurrence of accidents.

[0037] The platform's detection and analysis module determines whether the bridge stress is normal by: acquiring multiple response parameters through monitoring equipment installed in the corresponding area of ​​the bridge, and comparing them with their respective set abnormal threshold points. When the abnormal threshold points are exceeded, it is determined that the stress in that area is abnormal.

[0038] Simultaneously, when determining that the response parameter of a single detection item does not exceed the abnormal threshold, based on the obtained response parameters of each item... And based on the degree of influence of each test item, a weight percentage is assigned to each test item. Through formula Determine the stress state value Then compare it with the set stress condition threshold. Compare the stress state values. Exceeding the stress condition threshold If n is the total number of detection items, then it is determined that the stress in that area is abnormal.

[0039] The above scheme provides a specific method for real-time detection of bridge stress anomalies. First, monitoring equipment installed at corresponding locations on the bridge acquires multiple response parameters, which are then compared to their respective set anomaly thresholds. If a parameter exceeds an anomaly threshold, it is determined that the stress in that area is abnormal. Individual detection items are compared to their respective anomaly thresholds; if they exceed, it indicates a stress anomaly. For example, if the stress value at a certain location exceeds the normal stress (anomaly threshold), it indicates a stress anomaly at that location. Simultaneously, if the response parameter of an individual detection item does not exceed the anomaly threshold, the acquired response parameters are used to... And based on the degree of influence of each test item, a weight percentage is assigned to each test item. Through formula Determine the stress state value Then compare it with the set stress condition threshold. Compare the stress state values. Exceeding the stress condition threshold If an anomaly is detected in a certain area, it is determined that the stress in that area is abnormal. By comprehensively analyzing all the detection items, it is determined whether the stress in that area is normal. For example, although all the response parameters detected in a certain area do not exceed the abnormal threshold, they are all near the abnormal threshold. Since there may be some influence between the parameters, by assigning certain weights to all detection items and conducting comprehensive analysis, the stress of the bridge can be more accurately detected in real time, thus ensuring the safety of the bridge.

[0040] The platform's detection and analysis module uses the following method to assess the potential stress anomaly risk of bridges:

[0041] When it is determined that no abnormalities have occurred in the bridge stress, stress values ​​at various locations within the corresponding area of ​​the bridge are collected in time series under both loaded and unloaded conditions. , And based on the stress condition value , The curve function of the proposed stress state value as a function of the number of data acquisitions x , ;

[0042] Through formula Derive the concealment coefficient ;

[0043] when If so, it is determined that there is a potential risk of abnormal stress in the area corresponding to the bridge;

[0044] in, This is the environmental impact value. This is the first collection. This is the last collection. The set stress condition threshold is a function that varies with the number of data acquisitions. as well as These are the weighting coefficients. The threshold for determining the hidden coefficient is the environmental impact value. The method of obtaining this information is as follows: obtain the number of extreme weather events, including the number of days with high temperatures, from the local meteorological bureau within the monitoring and collection period. Number of days with low temperatures Number of acid rain days Number of days with strong winds Blizzard days Thus through the formula Determine the environmental impact value ,in This is to detect the total number of days within the data collection period.

[0045] The above scheme provides a specific method for assessing the potential stress anomaly risk of bridges. First, when it is determined that no stress anomalies have occurred in the bridge, stress values ​​at various locations within the corresponding area of ​​the bridge are collected in time series under both loaded and unloaded conditions. , And based on the stress condition value , The curve function of the proposed stress state value as a function of the number of data acquisitions x , Then obtain the number of extreme weather events during the monitoring and collection period from the local meteorological bureau, including the number of days with high temperatures. Number of days with low temperatures Number of acid rain days Number of days with strong winds Blizzard days Thus through the formula Determine the environmental impact value Then through the formula Derive the concealment coefficient Finally, it is compared with the hidden coefficient to determine the threshold. When a comparison is performed, This indicates that there is a potential risk of abnormal stress in the corresponding area of ​​the bridge; formula This represents the cumulative difference between the change in stress condition value of a bridge under load and the change in a set stress condition threshold value, and the formula... This represents the cumulative difference between the change in the bridge's stress condition value under unloaded conditions and the change in the set stress condition threshold. Generally, the smaller both values ​​are, the closer the real-time stress condition is to the alarm threshold, and the greater the potential for abnormality. The higher the value, the greater the potential anomaly risk to the bridge. Similarly, extreme weather events can also affect bridge stress anomalies. For example, extreme high temperatures, low temperatures, acid rain, strong winds, and blizzards can all affect bridge stress. Therefore, it is important to obtain the number of extreme weather events, including the number of high-temperature days, from the local meteorological bureau during the monitoring period. Number of days with low temperatures Number of acid rain days Number of days with strong winds Blizzard days Thus through the formula Determine the environmental impact value It can be seen that when the environmental impact value The larger the hidden coefficient, the greater the potential abnormal risks to the bridge. The larger the value, the greater the potential risk of abnormal stress on the bridge. This method allows for a comprehensive analysis combining extreme weather factors and stress changes in the bridge under load and without load, to assess the potential risk of abnormal stress at monitoring points in the area, enabling early warning and mitigation to reduce the occurrence of safety accidents. (In the formula...) The set stress condition threshold is a function that varies with the number of data acquisitions. The threshold for determining the hidden coefficient can be determined based on a combination of historical and empirical data. as well as The weighting coefficients can be determined based on their respective influence percentages, but the sum of the two is always 1.

[0046] The platform's detection and analysis module also includes methods for assessing potential stress anomaly risks in bridges, such as:

[0047] Set detection cycle During the inspection period, stress values ​​at multiple locations within the same critical area of ​​the bridge are obtained. ,

[0048] Through formula Outliers were found;

[0049] when If so, it is determined that there is a potential risk of abnormal stress in the area corresponding to the bridge;

[0050] in, For the detection cycle Start time, For the detection cycle End time, Let be the stress value at the j-th location at time t. The total number of location points, and , The threshold for anomaly detection The volatility coefficient is obtained by first using the formula... The conclusion is Fluctuation value within a time period ;

[0051] Thus through the formula The fluctuation coefficient within the detection period is obtained. .

[0052] The above scheme provides another method for assessing the potential stress anomaly risk of bridges, first by setting a detection cycle. During the inspection period, stress values ​​at multiple locations within the same critical area of ​​the bridge are obtained. Then through the formula The conclusion is Fluctuation value within a time period Then through the formula The fluctuation coefficient within the detection period is obtained. Finally, through the formula Outliers and compare it with the anomaly detection threshold. When a comparison is performed, If so, it is determined that there is a potential risk of abnormal stress in the corresponding area of ​​the bridge; formula This indicates the difference between the stress condition value at a single location point and the overall stress condition at all locations in the entire area within the detection period. A higher value indicates a greater risk of potential stress anomalies. The formula... Then it means The fluctuation of all detection locations at any given time is then expressed by the formula. The fluctuation coefficient within the detection period is obtained. Obviously, the larger the value, the greater the fluctuation. Since the stress conditions at points within the same critical area are generally similar, a large fluctuation indicates a greater potential risk of stress anomalies in the bridge. Therefore, a large abnormal value indicates a higher potential risk of stress anomalies in the area corresponding to the bridge. This method allows for a comprehensive analysis of the changes in each detection point relative to the overall area and the overall fluctuation, enabling the assessment of potential stress anomalies in the area corresponding to the bridge. This allows for early warning and reduces the occurrence of safety accidents. The threshold for anomaly detection can be determined based on historical data and empirical data.

[0053] This invention involves installing various monitoring devices on bridges and analyzing the parameters acquired by these devices to monitor bridge stress in real time. This eliminates the need for manual inspections and offers high accuracy. Furthermore, the platform's detection and analysis module not only comprehensively analyzes the acquired response parameters to detect existing stress anomalies but also assesses potential stress anomaly risks. This allows for timely detection of real-time stress changes and potential anomalies during bridge operation, enabling prompt response and ensuring bridge safety while reducing the occurrence of accidents.

[0054] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.

Claims

1. A bridge stress monitoring and detection system, characterized in that, The bridge stress monitoring and detection system includes a monitoring deployment module, a sensing module, a processing module, and a platform detection and analysis module. The monitoring deployment module is used to deploy relevant monitoring equipment in key areas of the bridge; The sensing module is used to acquire response parameters collected by relevant monitoring equipment; The processing module is used to preprocess the acquired response parameters and then send them to the platform detection and analysis module; The platform detection and analysis module is used to collect and store response parameters, and analyze the acquired response parameters to determine whether the bridge stress is normal and to assess the potential stress anomaly risk of the bridge. The monitoring equipment includes a fiber optic strain sensor, an embedded concrete strain gauge, a GNSS displacement sensor, a humidity sensor, and a corrosion sensor. The response parameters include a first stress obtained by the fiber optic strain sensor, a second stress obtained by the embedded concrete strain gauge, a displacement obtained by the GNSS displacement sensor, a humidity obtained by the humidity sensor, and a current density value obtained by the corrosion sensor. The platform detection and analysis module determines whether the bridge stress is normal by: acquiring the response parameters of multiple detection items through monitoring equipment installed at key areas of the bridge, and comparing them with the abnormal threshold points set for each detection item. When the abnormal threshold points are exceeded, it is determined that the stress at the key area is abnormal. Simultaneously, when determining that the response parameter of a single detection item does not exceed the abnormal threshold, the response parameters of multiple detection items are used as a basis. Based on the degree of influence of each test item, a weight percentage is assigned to each test item. Through formula Determine the stress state value Then compare it with the set stress condition threshold. Compare the stress state values. Exceeding the stress condition threshold If n is the total number of detection items, then it is determined that the stress in the critical area is abnormal. The platform's detection and analysis module assesses potential stress anomaly risks in bridges by: when no stress anomalies are detected, collecting stress values ​​at various locations within key areas of the bridge under both loaded and unloaded conditions using time series data. , And based on the stress condition value , The curve function of the proposed stress state value as a function of the number of data acquisitions x , ; Through formula Derive the concealment coefficient ; when When this is the case, the potential stress anomaly risk in the critical areas of the bridge should be assessed. in, This is the environmental impact value. This is the first collection. This is the last collection. The set stress condition threshold is a function that varies with the number of data acquisitions. as well as These are the weighting coefficients. Threshold for determining hidden coefficients.

2. The bridge stress monitoring and detection system according to claim 1, characterized in that, The environmental impact value The method of obtaining this information is as follows: obtain the number of extreme weather events, including the number of days with high temperatures, from the local meteorological bureau within the monitoring period. Number of days with low temperatures Number of acid rain days Number of days with strong winds Blizzard days Thus through the formula Determine the environmental impact value ,in This represents the total number of days within the detection period.

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