Concrete stress-strain real-time monitoring method and system
By deploying stress and strain sensors in the concrete structure of the arch bridge and analyzing stress and strain data in combination with machine learning models, the inefficiency and non-real-time problems of traditional monitoring methods are solved, and efficient safety monitoring of arch bridges is achieved.
Patent Information
- Application Number
- CN202510534908.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-27
- Publication Date
- 2025-08-08
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The traditional arch bridge concrete stress and strain monitoring method relies on manual measurement, has low data acquisition efficiency and poor real-time performance, poor adaptability to complex environments, affecting the stability and durability of the bridge structure.
Stress sensors and strain sensors are deployed at different locations in the concrete structure of the arch bridge, and stress and strain data are obtained through these sensors, stress and strain distribution maps are generated, and the data is analyzed using pre-trained machine learning models to identify abnormal situations.
It realizes efficient real-time monitoring of concrete stress and strain of arch bridges, and improves the safety, reliability and stability of the bridge.
Smart Images
Figure CN120452603A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of Internet of Things technology, and in particular, to a method and system for real-time monitoring of concrete stress and strain. Background Art
[0002] Arch bridges, as an important bridge structure, are widely used in transportation engineering. Their safety is directly related to the safety of people's lives and property, as well as social stability. The stress and strain of arch bridge concrete are key indicators for assessing the health of bridge structures.
[0003] However, traditional stress and strain monitoring methods have numerous shortcomings, such as reliance on manual measurement, inefficient data collection, poor real-time performance, and limited adaptability to complex environments. Furthermore, arch bridges are subject to numerous factors during construction and operation, such as temperature fluctuations, uneven sunlight, and loads. These factors can lead to dynamic changes in stress and strain within the concrete, thus impacting the stability and durability of the structure. Summary of the Invention
[0004] The purpose of the present invention is to provide a real-time monitoring method and system for the stress and strain of arch bridge concrete. The real-time monitoring method and system for the stress and strain of arch bridge concrete can realize real-time monitoring of the stress and strain of arch bridge concrete, thereby improving the safety, reliability and stability of the arch bridge.
[0005] In order to achieve the above-mentioned objectives, in a first aspect, the present disclosure provides a real-time monitoring method for stress and strain of arch bridge concrete, comprising: obtaining stress data to be monitored and strain data to be monitored, the stress data to be monitored including stress values respectively collected by multiple stress sensors, the strain data to be monitored including strain values respectively collected by multiple strain sensors, the multiple stress sensors are respectively arranged at different positions of the arch bridge concrete structure, and the multiple strain sensors are respectively arranged at different positions of the arch bridge concrete structure; generating a stress distribution map corresponding to the arch bridge concrete structure according to the stress data to be monitored; generating a strain distribution map corresponding to the arch bridge concrete structure according to the strain data to be monitored; determining arch bridge concrete monitoring information according to the stress distribution map and the strain distribution map through a pre-trained model, and the arch bridge concrete monitoring information is used to characterize the stress and strain abnormalities of the arch bridge concrete structure.
[0006] Optionally, generating a stress distribution map corresponding to the arch bridge concrete structure based on the stress data to be monitored includes: obtaining a first stress distribution map generated at a previous monitoring moment, the first stress distribution map including an arch bridge concrete structure pattern and a stress graphic identifier; determining, from the stress graphic identifiers, stress graphic identifiers corresponding to multiple stress values in the stress data to be monitored based on the respective arrangement positions of the multiple stress sensors; and updating, based on the multiple stress values, the stress graphic identifiers corresponding to the multiple stress values to obtain a second stress distribution map.
[0007] Optionally, determining, from the stress graphic identifiers, stress graphic identifiers corresponding to a plurality of stress values in the stress data to be monitored, respectively, based on the arrangement positions corresponding to the plurality of stress sensors, includes: for any stress value among the plurality of stress values, searching in the strain graphic identifier based on the arrangement position of the stress sensor corresponding to the stress value; if a stress graphic identifier whose distribution position in the arch bridge concrete structure pattern matches the arrangement position of the stress sensor corresponding to the stress value is found in the stress graphic identifier, determining the stress graphic identifier as the stress graphic identifier corresponding to the stress value; if a stress graphic identifier whose distribution position in the arch bridge concrete structure pattern matches the arrangement position of the stress sensor corresponding to the stress value is not found in the stress graphic identifier, determining the distribution position of the stress graphic identifier in the arch bridge concrete structure pattern based on the arrangement position of the stress sensor corresponding to the stress value; and determining the stress graphic identifier corresponding to the stress value based on stress graphic identifiers distributed near the distribution position of the stress graphic identifier.
[0008] Optionally, each stress graphic identifier in the first stress distribution map is associated with a stress value, and updating the stress graphic identifiers corresponding to the multiple stress values according to the multiple stress values to obtain the second stress distribution map includes: obtaining, for any stress value among the multiple stress values, a stress value associated with the stress graphic identifier corresponding to the stress value; updating the stress graphic identifier corresponding to the stress value according to the stress value and the stress value associated with the stress graphic identifier corresponding to the stress value, and associating each updated stress graphic identifier with the corresponding stress value to obtain the second stress distribution map.
[0009] Optionally, generating a strain distribution map corresponding to the arch bridge concrete structure based on the strain data to be monitored includes: obtaining a first strain distribution map generated at a previous monitoring moment, the first strain distribution map including a drawing of the arch bridge concrete structure and a strain graphic identifier; determining, from the strain graphic identifiers, strain graphic identifiers corresponding to multiple strain values in the strain data to be monitored, based on the respective arrangement positions of the multiple strain sensors; and updating, based on the multiple strain values, the strain graphic identifiers corresponding to the multiple strain values, to obtain a second strain distribution map.
[0010] Optionally, determining, from the strain graphic identifier, strain graphic identifiers corresponding to the plurality of strain values in the strain data to be monitored, respectively, based on the arrangement positions corresponding to the plurality of strain sensors, includes: searching, for any strain value among the plurality of strain values, in the strain graphic identifier according to the arrangement position of the strain sensor corresponding to the strain value; if a strain graphic identifier whose distribution position in the arch bridge concrete structure pattern matches the arrangement position of the strain sensor corresponding to the strain value is found in the strain graphic identifier, determining the strain graphic identifier as the strain graphic identifier corresponding to the strain value; if a strain graphic identifier whose distribution position in the arch bridge concrete structure pattern matches the arrangement position of the strain sensor corresponding to the strain value is not found in the strain graphic identifier, determining the distribution position of the strain graphic identifier in the arch bridge concrete structure pattern according to the arrangement position of the strain sensor corresponding to the strain value; and determining the strain graphic identifier corresponding to the strain value based on strain graphic identifiers distributed near the distribution position of the strain graphic identifier.
[0011] Optionally, each strain graphic identifier in the first strain distribution map is associated with a strain value, and updating the strain graphic identifiers corresponding to the multiple strain values respectively according to the multiple strain values to obtain the second strain distribution map includes: obtaining, for any strain value among the multiple strain values, a strain value associated with the strain graphic identifier corresponding to the strain value; updating the strain graphic identifier corresponding to the strain value according to the strain value and the strain value associated with the strain graphic identifier corresponding to the strain value, and associating each updated strain graphic identifier with the corresponding strain value to obtain the second strain distribution map.
[0012] Optionally, the pre-trained model determines the arch bridge concrete monitoring information according to the stress distribution map and the strain distribution map, including: obtaining a monitoring image collected by an image sensor for the arch bridge concrete structure; obtaining environmental data collected by an environmental sensor for the environment where the arch bridge concrete structure is located; determining a first model weight according to the monitoring image; determining a second model weight according to the environmental data; and determining the arch bridge concrete monitoring information according to the stress distribution map, the strain distribution map, the first model weight and the second model weight through the pre-trained model.
[0013] Optionally, the pre-trained model includes a feature extraction unit and a prediction unit, and the pre-trained model determines the arch bridge concrete monitoring information based on the stress distribution map, the strain distribution map, the first model weight and the second model weight, including: performing feature extraction by the feature extraction unit based on the stress distribution map, the first model weight and the second model weight to determine the stress distribution characteristics; performing feature extraction by the feature extraction unit based on the strain distribution map, the first model weight and the second model weight to determine the strain distribution characteristics; and determining the arch bridge concrete monitoring information by the prediction unit based on the stress distribution characteristics and the strain distribution characteristics, wherein the arch bridge concrete monitoring information includes the stress abnormality probability and the strain abnormality probability.
[0014] In a second aspect, the present disclosure provides a real-time monitoring system for stress and strain of arch bridge concrete, which is characterized by comprising: a plurality of stress sensors respectively arranged at different positions of the arch bridge concrete structure; a plurality of strain sensors respectively arranged at different positions of the arch bridge concrete structure; a monitoring device, which is communicatively connected to the plurality of stress sensors and the plurality of strain sensors respectively, and the monitoring device is used to execute the real-time monitoring method for stress and strain of arch bridge concrete as described in the first aspect of the present disclosure.
[0015] Through the above technical solution, stress sensors and strain sensors are deployed at different locations on the arch bridge concrete structure. The stress sensors obtain the stress data to be monitored, and the strain sensors obtain the strain data to be monitored. Based on the stress data to be monitored, a stress distribution map corresponding to the arch bridge concrete structure is generated, and based on the strain data to be monitored, a strain distribution map corresponding to the arch bridge concrete structure is generated. Furthermore, a pre-trained model is used to determine arch bridge concrete monitoring information characterizing stress and strain anomalies in the arch bridge concrete structure based on the stress and strain distribution maps. This technical solution combines sensor placement, distribution map generation, and machine learning models based on the arch bridge concrete structure to achieve efficient and real-time monitoring of arch bridge concrete stress and strain, thereby improving the safety, reliability, and stability of the arch bridge.
[0016] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following detailed description, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:
[0018] Figure 1 The figure is a structural block diagram of a real-time monitoring system for stress and strain of arch bridge concrete according to an exemplary embodiment.
[0019] Figure 2 The present invention is a flowchart showing a method for real-time monitoring of stress and strain of arch bridge concrete according to an exemplary embodiment.
[0020] Figure 3 is a schematic diagram showing a stress distribution diagram according to an exemplary embodiment.
[0021] Figure 4 is a block diagram of yet another monitoring system according to an exemplary embodiment.
[0022] Figure 5 It is a structural block diagram of a pre-trained model according to an exemplary embodiment.
[0023] Figure 6 The figure is a block diagram of a device for real-time monitoring of stress and strain in arch bridge concrete according to an exemplary embodiment.
[0024] Figure 7 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION
[0025] The following describes the specific embodiments of the present disclosure in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure and are not intended to limit the present disclosure.
[0026] Arch bridges, as an important bridge structure, are widely used in transportation engineering. Their safety is directly related to the safety of people's lives and property, as well as social stability. The stress and strain of arch bridge concrete are key indicators for assessing the health of bridge structures.
[0027] However, traditional stress and strain monitoring methods have numerous shortcomings, such as reliance on manual measurement, inefficient data collection, poor real-time performance, and limited adaptability to complex environments. Furthermore, arch bridges are subject to numerous factors during construction and operation, such as temperature fluctuations, uneven sunlight, and loads. These factors can lead to dynamic changes in stress and strain within the concrete, thus impacting the stability and durability of the structure.
[0028] With the rapid development of sensor technology, data acquisition techniques, and machine learning algorithms, real-time monitoring of the stress and strain of arch bridge concrete has become possible. By deploying high-precision sensors at key locations on arch bridges and combining them with advanced data processing and analysis methods, real-time monitoring and early warning of concrete stress and strain can be achieved. For example, a monitoring system based on fiber Bragg grating sensors can collect stress and strain data in real time and transmit the data to a monitoring center via wireless transmission technology. In addition, machine learning algorithms (such as long short-term memory networks, convolutional neural networks, and radial basis function neural networks) have been successfully applied to the analysis and prediction of time series data. They can effectively identify abnormal patterns in stress and strain data, thus providing a scientific basis for bridge safety assessment.
[0029] Based on this, the disclosed embodiments provide a technical solution that deploys stress sensors and strain sensors at different locations within the concrete structure of an arch bridge. The stress sensors obtain stress data to be monitored, and the strain sensors obtain strain data to be monitored. Based on the stress data to be monitored, a stress distribution map corresponding to the concrete structure of the arch bridge is generated, and based on the strain data to be monitored, a strain distribution map corresponding to the concrete structure of the arch bridge is generated.
[0030] Furthermore, the pre-trained model determines arch bridge concrete monitoring information, characterizing stress and strain anomalies within the arch bridge concrete structure, based on the stress and strain distribution maps. This technical solution, combining sensor placement, distribution map generation, and machine learning models based on the arch bridge concrete structure, enables efficient and real-time monitoring of arch bridge concrete stress and strain, thereby improving the safety, reliability, and stability of arch bridges.
[0031] Therefore, it can not only effectively solve the deficiencies in existing technologies, but also provide reliable technical support for the safe operation of arch bridges, which has important practical significance and broad application prospects.
[0032] Figure 1 FIG. 1 is a structural block diagram of a real-time monitoring system for stress and strain of arch bridge concrete according to an exemplary embodiment. Figure 1 As shown, the monitoring system includes: a stress sensor, a strain sensor and a monitoring device.
[0033] There are multiple stress sensors and strain sensors, which are respectively deployed at different locations of the arch bridge concrete structure. In addition, the stress sensors and strain sensors can be deployed at the same location of the arch bridge concrete structure.
[0034] Regarding the monitoring equipment, it is communicatively connected with multiple stress sensors and multiple strain sensors respectively, wherein the communication connection method can be implemented based on the Internet of Things deployment.
[0035] In some embodiments, strain sensors are commonly used tools for measuring strain, and their principle is based on resistance changes. When a material is deformed by force, the resistance value of the strain gauge changes, and the strain value can be calculated by measuring the resistance change. Common strain sensors include: Metal foil strain gauges: Made of metal foil, attached to the surface of the object being measured, its resistance change is proportional to the strain. Fiber Bragg grating (FBG) sensor: uses the wavelength change of the fiber grating to measure strain, and has the advantages of anti-electromagnetic interference, high precision and long-distance transmission. Semiconductor strain gauges: Higher sensitivity, suitable for high-precision measurement.
[0036] In some embodiments, stress sensors indirectly acquire stress information by measuring physical quantities related to stress, such as displacement and resistance changes. Common stress sensors include: Resistance strain gauge sensors: A strain gauge is attached to the surface of an elastic element and stress is calculated by measuring resistance changes. Piezoelectric sensors: Utilize the piezoelectric effect to convert stress into an electrical signal. Capacitive sensors: Measure stress by measuring changes in capacitance.
[0037] Therefore, different types of stress sensors or strain sensors can be selected according to different usage scenarios.
[0038] In some embodiments, stress and strain may have a corresponding relationship. For example, in the elastic stage: within the elastic range of the material, stress and strain are proportional, and the relationship is described by Hooke's law. In the plastic stage: when the stress exceeds the yield strength of the material, the material enters the plastic deformation stage, and the relationship between stress and strain becomes complicated. It is usually necessary to obtain a stress-strain curve through experiments for analysis. In the fracture stage: when the stress reaches the ultimate strength of the material, the material will fracture or break.
[0039] In some embodiments, the measurement principle of the stress sensor may also be: measuring strain and then calculating stress based on the strain.
[0040] For example, the strain is expressed as: , where ε is the normal strain, ΔL is the deformation, and L0 is the original length. Stress can then be expressed using Hooke's law as: σ = E·ε, where E is the elastic modulus of the material, a measure of its stiffness, and σ represents stress.
[0041] For stress and strain measurement of arch bridge concrete, the sensor placement can be optimized based on the arch bridge's structural characteristics, stress characteristics, and monitoring objectives. The following are some optional placement options:
[0042] Prioritize placement at key locations: The crown and foot of an arch bridge are the most critical locations subject to stress, with significant changes in stress and strain. Placing sensors at these locations effectively monitors the stress state of the structure. Midspan and quarter-span locations: These locations are areas of concentrated stress and significant deformation within an arch bridge structure. Placing sensors at these locations can capture changes in stress and strain within the structure.
[0043] Consider structural symmetry: For symmetrical arch bridge structures, sensors can be arranged symmetrically to reduce the number of sensors and improve monitoring efficiency.
[0044] Combined with structural modal analysis: The vibration mode and modal frequency of the structure are determined through modal analysis. Sensors should be placed at the nodes and anti-nodes of the modal vibration mode to better capture the dynamic response of the structure.
[0045] Combination of multiple types of sensors: Combining different types of sensors (such as strain gauges, fiber Bragg grating sensors, piezoelectric sensors, etc.) to improve monitoring accuracy and reliability.
[0046] Optimal placement methods: Optimization algorithms (such as the effective independence method and the improved modal strain energy method) can be used to determine the optimal placement of sensors. For example, the energy coefficient-effective independence method has been shown to be effective in bridge structures.
[0047] Consider environmental and construction factors: Sensor layout should avoid construction interference and environmental impact to ensure the accuracy and stability of monitoring data.
[0048] Dynamic adjustment and redundancy design: During the monitoring process, the sensor placement is dynamically adjusted based on the actual stress conditions of the structure and the feedback from monitoring data. At the same time, redundant sensors are appropriately added to improve system reliability.
[0049] Therefore, through some optional arrangements, multiple deployments of stress sensors and strain sensors can be achieved at different locations of the arch bridge concrete structure.
[0050] Figure 2 FIG. 1 is a flow chart showing a method for real-time monitoring of stress and strain in arch bridge concrete according to an exemplary embodiment. Figure 2 As shown, the method includes the following steps:
[0051] Step S21, obtaining stress data to be monitored and strain data to be monitored, wherein the stress data to be monitored includes stress values respectively collected by multiple stress sensors, and the strain data to be monitored includes strain values respectively collected by multiple strain sensors, wherein the multiple stress sensors are respectively arranged at different positions of the arch bridge concrete structure, and the multiple strain sensors are respectively arranged at different positions of the arch bridge concrete structure.
[0052] Step S22: generating a stress distribution diagram corresponding to the arch bridge concrete structure according to the stress data to be monitored.
[0053] Step S23: generating a strain distribution diagram corresponding to the arch bridge concrete structure according to the strain data to be monitored.
[0054] Step S24: determining the arch bridge concrete monitoring information based on the stress distribution diagram and the strain distribution diagram using the pre-trained model. The arch bridge concrete monitoring information is used to characterize the stress and strain abnormalities of the arch bridge concrete structure.
[0055] In step S21 , regarding the arrangement of the stress sensors and the strain sensors, refer to the description of the above embodiments and will not be repeated here.
[0056] In step S22, a stress distribution diagram corresponding to the arch bridge concrete structure may be generated in combination with the stress data to be monitored.
[0057] In the embodiment of the present disclosure, since a real-time monitoring method is adopted, the stress distribution diagrams corresponding to different moments may be different, that is, as time changes, the stress distribution diagram may also change accordingly.
[0058] Therefore, as an optional implementation, step S21 includes: obtaining a first stress distribution map generated at a previous monitoring moment, the first stress distribution map including an arch bridge concrete structure pattern and a stress graphic identifier; determining, from the stress graphic identifier, stress graphic identifiers corresponding to multiple stress values in the stress data to be monitored according to the respective arrangement positions of multiple stress sensors; and updating, according to the multiple stress values, the stress graphic identifiers corresponding to the multiple stress values to obtain a second stress distribution map.
[0059] In some embodiments, for any monitoring moment, the corresponding stress distribution diagram may include an arch bridge concrete structure pattern and a stress graphic identifier.
[0060] Regarding the stress distribution map, it can be a 2D distribution map, a 3D distribution map, or a bird's-eye view distribution map.
[0061] Correspondingly, the arch bridge concrete structure pattern in the stress distribution diagram may be a 2D pattern, a 3D pattern or a bird's-eye view pattern, and the pattern may describe the arch bridge concrete structure.
[0062] Based on the arch bridge concrete structure drawing, a stress graphic symbol can be generated to represent stress. This stress graphic symbol can take various forms, such as arrows, line segments, and vectors. Accordingly, different graphic symbol forms can represent different stress values. For example, longer arrows indicate higher stress values; longer line segments indicate higher stress values; and longer vectors indicate higher stress values.
[0063] Alternatively, the stress graphic mark can also be in the form of a circle, rectangle, or other graphic form. Different graphic mark shapes can represent different stress values. For example, these graphic marks can be colored, and different color depths represent different stress values.
[0064] Figure 3 is a schematic diagram showing a stress distribution diagram according to an exemplary embodiment, such as Figure 3 As shown, the stress graphic marks are represented by arrows, and different arrow lengths represent different stress values. In addition, these stress graphic marks are distributed at different structural positions to represent the stress conditions at different structural positions.
[0065] In some embodiments, at the initial monitoring moment, a corresponding initial stress graphic marker can be generated based on the initial stress value of the arch bridge concrete structure (which can be determined through simulation or offline testing). Then, during subsequent monitoring, the stress graphic marker is continuously updated as the monitoring moment changes, thereby updating the stress distribution map.
[0066] In some embodiments, stress graphic identifiers corresponding to a plurality of stress values in the stress data to be monitored may be determined from the stress graphic identifiers according to the respective arrangement positions of the plurality of stress sensors.
[0067] As an optional implementation manner, stress graphic identifiers corresponding to multiple stress values in the stress data to be monitored are determined from the stress graphic identifier based on the layout positions corresponding to the multiple stress sensors, including: for any stress value among the multiple stress values, searching the strain graphic identifier based on the layout position of the stress sensor corresponding to the stress value; if a stress graphic identifier whose distribution position in the arch bridge concrete structure pattern matches the layout position of the stress sensor corresponding to the stress value is found in the stress graphic identifier, the stress graphic identifier is determined as the stress graphic identifier corresponding to the stress value; if a stress graphic identifier whose distribution position in the arch bridge concrete structure pattern matches the layout position of the stress sensor corresponding to the stress value is not found in the stress graphic identifier, the distribution position of the stress graphic identifier in the arch bridge concrete structure pattern is determined based on the layout position of the stress sensor corresponding to the stress value; and the stress graphic identifier corresponding to the stress value is determined based on stress graphic identifiers distributed near the distribution position of the stress graphic identifier.
[0068] In this embodiment, the corresponding stress graphic identifier is searched based on each stress value, and the search method for each stress value is the same.
[0069] In some embodiments, the stress graphic identifier may have graphic coordinates compared to the arch bridge concrete structure pattern, and the stress value corresponds to the stress sensor layout position coordinates. The layout position coordinates are the coordinates in the real world, and the graphic coordinates are the coordinates in the graphic coordinate system. Through the coordinate conversion relationship, the matching coordinates can be determined, that is, the coordinates that are consistent with the position of the arch bridge concrete structure.
[0070] Correspondingly, if there is no match, it means that the previous monitoring moment does not involve monitoring data of the corresponding position; if there is a match, it means that the previous monitoring moment involves monitoring data of the corresponding position.
[0071] Furthermore, in the case of a match, the corresponding stress graph coordinates can be directly determined.
[0072] In the case of mismatch, you can look for nearby stress graphic identifiers as the corresponding stress graphic identifiers.
[0073] In some embodiments, the vicinity of the stress pattern marker distribution location may be defined based on pixels. For example, the pattern locations separated by one pixel unit or two pixel units are referred to as nearby locations.
[0074] In some embodiments, each stress graphic marker in the first stress distribution map is associated with a stress value, and the associated stress value may be a stress value collected by the sensor at a previous monitoring moment.
[0075] Furthermore, updating stress graphic identifiers corresponding to the multiple stress values respectively according to the multiple stress values to obtain the second stress distribution map may include: obtaining, for any stress value among the multiple stress values, a stress value associated with the stress graphic identifier corresponding to the stress value; updating the stress graphic identifier corresponding to the stress value according to the stress value and the stress value associated with the stress graphic identifier corresponding to the stress value, and associating each updated stress graphic identifier with the corresponding stress value to obtain the second stress distribution map.
[0076] In this embodiment, the corresponding updating method can be determined by comparing the stress values.
[0077] For example, if the stress value and the stress value associated with the stress graphic identifier corresponding to the stress value are substantially consistent, there is no need to update the stress graphic identifier.
[0078] For example, if the stress value is higher than the stress value associated with the stress graphic identifier corresponding to the stress value, the stress graphic identifier needs to be transformed. For example, if the longer the arrow length, the higher the stress value represented, the arrow length needs to be increased.
[0079] In some embodiments, a graphic mark transformation rule corresponding to a unit stress value transformation may be pre-configured, for example, a stress value of 10 corresponds to a length increase of 1 mm. Based on this rule, the graphic mark may be updated accordingly by comparing the stress values.
[0080] Through the above implementation, the monitoring status of the stress value can be fed back to the stress distribution map in a relatively simple manner, and can also be continuously updated as the time changes.
[0081] In step S23, a strain distribution diagram corresponding to the arch bridge concrete structure is generated according to the strain data to be monitored.
[0082] In some embodiments, a strain distribution map corresponding to the concrete structure of the arch bridge is generated based on the strain data to be monitored, including: obtaining a first strain distribution map generated at a previous monitoring moment, the first strain distribution map including a drawing of the concrete structure of the arch bridge and a strain graphic identifier; determining, from the strain graphic identifier, strain graphic identifiers corresponding to multiple strain values in the strain data to be monitored based on the respective arrangement positions of multiple strain sensors; and updating the strain graphic identifiers corresponding to the multiple strain values based on the multiple strain values to obtain a second strain distribution map.
[0083] In some embodiments, strain graphic identifiers corresponding to multiple strain values in the strain data to be monitored are determined from the strain graphic identifier based on the arrangement positions corresponding to the multiple strain sensors, including: for any strain value among the multiple strain values, searching the strain graphic identifier based on the arrangement position of the strain sensor corresponding to the strain value; if a strain graphic identifier whose distribution position in the arch bridge concrete structure drawing is the same as the arrangement position of the strain sensor corresponding to the strain value is found in the strain graphic identifier, determining the strain graphic identifier as the strain graphic identifier corresponding to the strain value; if a strain graphic identifier whose distribution position in the arch bridge concrete structure drawing is the same as the arrangement position of the strain sensor corresponding to the strain value is not found in the strain graphic identifier, determining the distribution position of the strain graphic identifier in the arch bridge concrete structure drawing based on the arrangement position of the strain sensor corresponding to the strain value; and determining the strain graphic identifier corresponding to the strain value based on strain graphic identifiers distributed near the distribution position of the strain graphic identifier.
[0084] In some embodiments, each strain graphic identifier in the first strain distribution map is associated with a strain value, and the strain graphic identifiers corresponding to the multiple strain values are updated according to the multiple strain values to obtain the second strain distribution map, including: obtaining, for any strain value among the multiple strain values, the strain value associated with the strain graphic identifier corresponding to the strain value; updating the strain graphic identifier corresponding to the strain value according to the strain value associated with the strain graphic identifier corresponding to the strain value, and associating each updated strain graphic identifier with the corresponding strain value to obtain the second strain distribution map.
[0085] It can be understood that the implementation principles of the stress distribution map and the strain distribution map are the same, but the corresponding specific contents are different. Therefore, the relevant implementation methods of the strain distribution map can refer to the implementation methods of the stress distribution map mentioned above, and will not be repeated here.
[0086] In some embodiments, the strain graphic identifier and the stress graphic identifier may be different, but their corresponding updating principles may be the same.
[0087] In step S24, the arch bridge concrete monitoring information is determined based on the stress distribution map and the strain distribution map using the pre-trained model.
[0088] In some embodiments, the pre-trained model can be an image processing model or a large language model, etc., which is not limited here.
[0089] In some embodiments, the stress distribution map and the strain distribution map can be directly input into the pre-trained model, so that the pre-trained model outputs the arch bridge concrete monitoring information.
[0090] In some embodiments, in order to improve the accuracy of the arch bridge concrete monitoring information, more information can be combined for processing.
[0091] Therefore, as an optional implementation, step S24 includes: obtaining a monitoring image collected by an image sensor for the arch bridge concrete structure; obtaining environmental data collected by an environmental sensor for the environment in which the arch bridge concrete structure is located; determining a first model weight based on the monitoring image; determining a second model weight based on the environmental data; and determining the arch bridge concrete monitoring information through a pre-trained model based on the stress distribution map, the strain distribution map, the first model weight, and the second model weight.
[0092] In conjunction with this implementation, Figure 4 is a block diagram of another monitoring system according to an exemplary embodiment. Figure 4 As shown, the monitoring system further includes: an image sensor and an environmental sensor, and these two sensors are respectively connected to the monitoring equipment for communication.
[0093] Among them, the image sensor is used to collect monitoring images of the arch bridge concrete structure. The image sensor can be set at a fixed position or a key position. It can collect panoramic images or local images of the structure, which is not limited here.
[0094] In addition, environmental sensors are used to collect environmental data for the environment in which the arch bridge concrete structure is located. The sensors can be set at fixed positions or key positions. They can collect panoramic environmental data of the structure or local environmental data, which is not limited here.
[0095] The environmental data may include temperature, humidity, weather, etc., which are not limited here.
[0096] Furthermore, a first model weight is determined based on the monitoring image, and a second model weight is determined based on the environmental data.
[0097] It's understandable that current image-based monitoring technologies, such as those implemented using machine learning models and rule-based implementations, are relatively mature. Therefore, the probability of anomalies can be determined based on the monitored images, and the first model weight can be determined based on this probability. For example, the higher the probability of anomalies, the greater the first model weight. Alternatively, the probability of anomalies can be directly used as the first model weight.
[0098] Furthermore, current technologies for monitoring based on environmental data are relatively mature, such as machine learning model implementations and rule-based implementations. Therefore, the probability of anomalies can be determined based on the environmental data, and the weight of the second model can be determined based on this probability. For example, the higher the probability of anomalies, the greater the weight of the second model. Alternatively, the probability of anomalies can be directly used as the weight of the second model.
[0099] Furthermore, the arch bridge concrete monitoring information is determined by the pre-trained model according to the stress distribution map, the strain distribution map, the first model weight and the second model weight.
[0100] Figure 5 is a structural block diagram of a pre-trained model according to an exemplary embodiment. Figure 5 As shown, the pre-trained model includes a feature extraction unit and a prediction unit.
[0101] Combine Figure 5, determining the arch bridge concrete monitoring information according to the stress distribution map, the strain distribution map, the first model weight and the second model weight through the pre-trained model, can include: performing feature extraction according to the stress distribution map, the first model weight and the second model weight through the feature extraction unit to determine the stress distribution characteristics; performing feature extraction according to the strain distribution map, the first model weight and the second model weight through the feature extraction unit to determine the strain distribution characteristics; determining the arch bridge concrete monitoring information according to the stress distribution characteristics and the strain distribution characteristics through the prediction unit, the arch bridge concrete monitoring information including the stress abnormality probability and the strain abnormality probability.
[0102] In this embodiment, the feature extraction unit may combine the first model weight and the second model weight to perform feature extraction on the stress distribution map to obtain the stress distribution feature, and may combine the first model weight and the second model weight to perform feature extraction on the strain distribution map to obtain the strain distribution feature.
[0103] In some embodiments, the stress distribution feature and the strain distribution feature can be understood as a type of graphic feature, and the first model weight and the second model weight can affect the feature extraction result.
[0104] In some embodiments, the prediction unit may determine the stress anomaly probability and the strain anomaly probability based on the stress distribution characteristics and the strain distribution characteristics, and then output the arch bridge concrete monitoring information.
[0105] Based on the application mode of the above model, the training of the model may involve separate training of the feature extraction unit and the prediction unit.
[0106] The feature extraction unit can be trained based on a sample distribution map and sample weights. The sample distribution map can include sample distribution maps corresponding to stress and strain. Furthermore, during training, the two distribution maps can be trained separately first, and then integrated training can be performed by combining the two distribution maps.
[0107] The training method for feature extraction can be supervised training, that is, corresponding labels are set; or it can be unsupervised training, which is not limited here.
[0108] Regarding the prediction unit, it can be trained based on the sample distribution features and the prediction labels.
[0109] During the training process of the feature extraction unit and the prediction unit, they may also be trained together, for example, using the features extracted by the feature extraction unit to test the prediction unit.
[0110] Furthermore, the finally obtained monitoring information of the arch bridge concrete includes the probability of stress anomaly and the probability of strain anomaly.
[0111] Furthermore, by combining the stress anomaly probability and the strain anomaly probability, anomaly processing can be performed.
[0112] For example, when the probability of stress anomaly and / or the probability of strain anomaly are both high, an abnormality warning is issued.
[0113] For example, when both the stress abnormality probability and the strain abnormality probability are low, monitoring is continued at the next moment.
[0114] Figure 6 FIG. 6 is a block diagram of a real-time monitoring device 600 for stress and strain of arch bridge concrete according to an exemplary embodiment. Figure 6 As shown, the device includes:
[0115] The acquisition module 601 is configured to acquire stress data to be monitored and strain data to be monitored, wherein the stress data to be monitored includes stress values respectively collected by multiple stress sensors, and the strain data to be monitored includes strain values respectively collected by multiple strain sensors, wherein the multiple stress sensors are respectively arranged at different positions of the arch bridge concrete structure, and the multiple strain sensors are respectively arranged at different positions of the arch bridge concrete structure.
[0116] The generating module 602 is configured to generate a stress distribution diagram corresponding to the concrete structure of the arch bridge according to the stress data to be monitored; and generate a strain distribution diagram corresponding to the concrete structure of the arch bridge according to the strain data to be monitored.
[0117] The determination module 603 is configured to determine the arch bridge concrete monitoring information according to the stress distribution map and the strain distribution map through a pre-trained model, where the arch bridge concrete monitoring information is used to characterize the stress and strain abnormalities of the arch bridge concrete structure.
[0118] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.
[0119] Figure 7 FIG. 7 is a block diagram of an electronic device 700 according to an exemplary embodiment. Figure 7 As shown, the electronic device 700 may include: a processor 701 , a memory 702 , and may further include one or more of a multimedia component 703 , an input / output (I / O) interface 704 , and a communication component 705 .
[0120] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned method for real-time monitoring of stress and strain in arch bridge concrete. The memory 702 is used to store various types of data to support the operation of the electronic device 700. Such data may include, for example, instructions for any application or method operating on the electronic device 700, as well as application-related data, such as contact information, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules. The above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more thereof, so the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module.
[0121] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the above-mentioned arch bridge concrete stress and strain real-time monitoring method.
[0122] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When executed by a processor, the program instructions implement the steps of the above-described method for real-time monitoring of stress and strain in arch bridge concrete. For example, the computer-readable storage medium may be the aforementioned memory 702 including the program instructions. The program instructions may be executed by the processor 701 of the electronic device 700 to implement the above-described method for real-time monitoring of stress and strain in arch bridge concrete.
[0123] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program that can be executed by a processor. When the computer program is executed by the processor, the steps of the above-mentioned method for real-time monitoring of stress and strain of arch bridge concrete are implemented.
[0124] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details of the above embodiments. Within the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the scope of protection of the present disclosure.
[0125] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.
[0126] In addition, the various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.
Claims
1. A method for real-time monitoring of concrete stress and strain, characterized in that: include: Acquiring stress data to be monitored and strain data to be monitored, wherein the stress data to be monitored includes stress values respectively collected by a plurality of stress sensors, and the strain data to be monitored includes strain values respectively collected by a plurality of strain sensors, wherein the plurality of stress sensors are respectively arranged at different positions of the arch bridge concrete structure, and the plurality of strain sensors are respectively arranged at different positions of the arch bridge concrete structure; generating a stress distribution diagram corresponding to the arch bridge concrete structure according to the stress data to be monitored; generating a strain distribution diagram corresponding to the arch bridge concrete structure according to the strain data to be monitored; The arch bridge concrete monitoring information is determined based on the stress distribution map and the strain distribution map through a pre-trained model, and the arch bridge concrete monitoring information is used to characterize the stress and strain abnormalities of the arch bridge concrete structure.
2. The method for real-time monitoring of concrete stress and strain according to claim 1, characterized in that: Generating a stress distribution diagram corresponding to the arch bridge concrete structure according to the stress data to be monitored includes: Obtaining a first stress distribution map generated at a previous monitoring moment, wherein the first stress distribution map includes a concrete structure pattern of the arch bridge and a stress graphic identifier; determining, from the stress graphic identifiers, stress graphic identifiers corresponding to the plurality of stress values in the stress data to be monitored, respectively, according to the respective arrangement positions of the plurality of stress sensors; According to the multiple stress values, the stress graphic identifiers respectively corresponding to the multiple stress values are updated to obtain a second stress distribution diagram.
3. The method for real-time monitoring of concrete stress and strain according to claim 2, characterized in that: The step of determining, from the stress graphic identifiers, stress graphic identifiers corresponding to the plurality of stress values in the stress data to be monitored based on the arrangement positions corresponding to the plurality of stress sensors, includes: For any stress value among the plurality of stress values, searching in the strain graphic identifier according to the arrangement position of the stress sensor corresponding to the stress value; If a stress graphic identifier is found in the stress graphic identifiers whose distribution position in the arch bridge concrete structure drawing matches the arrangement position of the stress sensor corresponding to the stress value, the stress graphic identifier is determined as the stress graphic identifier corresponding to the stress value; If no stress graphic identifier is found in the stress graphic identifier whose distribution position in the arch bridge concrete structure drawing matches the stress sensor arrangement position corresponding to the stress value, the stress graphic identifier distribution position in the arch bridge concrete structure drawing is determined based on the stress sensor arrangement position corresponding to the stress value; and the stress graphic identifier corresponding to the stress value is determined based on the stress graphic identifiers distributed near the stress graphic identifier distribution position.
4. The method for real-time monitoring of concrete stress and strain according to claim 2 or 3, characterized in that: Each stress graphic identifier in the first stress distribution map is associated with a stress value, and updating the stress graphic identifiers corresponding to the multiple stress values respectively according to the multiple stress values to obtain a second stress distribution map includes: For any stress value among the multiple stress values, obtaining a stress value associated with a stress graphic identifier corresponding to the stress value; According to the stress value and the stress value associated with the stress graphic identifier corresponding to the stress value, the stress graphic identifier corresponding to the stress value is updated, and each updated stress graphic identifier is associated with the corresponding stress value to obtain the second stress distribution map.
5. The method for real-time monitoring of concrete stress and strain according to claim 1, characterized in that: Generating a strain distribution diagram corresponding to the arch bridge concrete structure according to the strain data to be monitored includes: Acquire a first strain distribution diagram generated at a previous monitoring moment, wherein the first strain distribution diagram includes a concrete structure pattern of the arch bridge and a strain graphic identifier; determining, from the strain graphic identifiers, strain graphic identifiers corresponding to the plurality of strain values in the strain data to be monitored, respectively, according to the arrangement positions corresponding to the plurality of strain sensors; According to the multiple strain values, the strain graphic identifiers respectively corresponding to the multiple strain values are updated to obtain a second strain distribution diagram.
6. The method for real-time monitoring of concrete stress and strain according to claim 5, characterized in that: The step of determining, from the strain graphic identifiers, strain graphic identifiers corresponding to the plurality of strain values in the strain data to be monitored based on the arrangement positions corresponding to the plurality of strain sensors, includes: For any strain value among the plurality of strain values, searching in the strain graphic identifier according to the arrangement position of the strain sensor corresponding to the strain value; If a strain graphic identifier is found in the strain graphic identifiers whose distribution position in the arch bridge concrete structure drawing matches the arrangement position of the strain sensor corresponding to the strain value, the strain graphic identifier is determined as the strain graphic identifier corresponding to the strain value; If a strain graphic identifier whose distribution position in the arch bridge concrete structure drawing matches the strain sensor layout position corresponding to the strain value is not found in the strain graphic identifier, the strain graphic identifier distribution position in the arch bridge concrete structure drawing is determined based on the strain sensor layout position corresponding to the strain value; and the strain graphic identifier corresponding to the strain value is determined based on the strain graphic identifiers distributed near the strain graphic identifier distribution position.
7. The method for real-time monitoring of concrete stress and strain according to claim 5 or 6, characterized in that: Each strain graphic identifier in the first strain distribution map is associated with a strain value, and updating the strain graphic identifiers corresponding to the multiple strain values according to the multiple strain values to obtain a second strain distribution map includes: For any strain value among the multiple strain values, obtaining a strain value associated with a strain graphic identifier corresponding to the strain value; The strain graphic identifier corresponding to the strain value is updated according to the strain value and the strain value associated with the strain graphic identifier corresponding to the strain value, and each updated strain graphic identifier is associated with the corresponding strain value to obtain the second strain distribution diagram.
8. The method for real-time monitoring of concrete stress and strain according to claim 1, wherein determining the arch bridge concrete monitoring information based on the stress distribution map and the strain distribution map using a pre-trained model comprises: Acquiring a monitoring image captured by an image sensor on the concrete structure of the arch bridge; Acquire environmental data collected by environmental sensors for the environment where the arch bridge concrete structure is located; Determining a first model weight according to the monitoring image; determining a second model weight based on the environmental data; Arch bridge concrete monitoring information is determined by a pre-trained model based on the stress distribution map, the strain distribution map, the first model weight, and the second model weight.
9. The method for real-time monitoring of concrete stress and strain according to claim 8, wherein the pre-trained model comprises a feature extraction unit and a prediction unit, and wherein the pre-trained model determines the arch bridge concrete monitoring information based on the stress distribution map, the strain distribution map, the first model weight, and the second model weight, comprising: performing feature extraction by the feature extraction unit based on the stress distribution map, the first model weight, and the second model weight to determine stress distribution features; performing feature extraction by the feature extraction unit based on the strain distribution map, the first model weight, and the second model weight to determine a strain distribution feature; The prediction unit determines arch bridge concrete monitoring information according to the stress distribution characteristics and the strain distribution characteristics, where the arch bridge concrete monitoring information includes stress abnormality probability and strain abnormality probability.
10. A real-time monitoring system for concrete stress and strain, characterized in that: include: A plurality of stress sensors are respectively arranged at different locations of the arch bridge concrete structure; multiple strain sensors arranged at different locations of the arch bridge concrete structure; A monitoring device is communicatively connected to the multiple stress sensors and the multiple strain sensors respectively, and the monitoring device is used to execute the real-time monitoring method for stress and strain of arch bridge concrete according to any one of claims 1 to 9.