A real-time monitoring system for stress of large-span steel trestle

Abstract

The invention discloses a real-time stress monitoring system for a large-span steel trestle, and the invention relates to the technical field of bridge stress analysis. The real-time stress monitoring system for a large-span steel trestle comprises: a bridge body modeling unit, which is used to establish a three-dimensional model of the bridge body for the large-span steel trestle; a sensor monitoring unit, which is used to collect different parameters at different positions of the steel trestle for stress analysis; the real-time, accurate monitoring and instant analysis of the stress state of the large-span steel trestle are realized, and the early warning capability of the safety of the bridge structure is significantly improved; through the deep fusion of the three-dimensional modeling of the bridge body and the multi-source sensor data, not only the comprehensive perception of the stress state of each part of the bridge body is realized, but also the preset threshold can be automatically compared, and the abnormal stress state can be accurately identified and graded and marked; this efficient and intelligent monitoring method is helpful to timely discover potential safety hazards, provide a scientific basis for bridge maintenance decisions, effectively extend the service life of the bridge, and ensure the safety and smoothness of transportation.

Description

Technical Field

[0001] The invention relates to the technical field of bridge stress analysis, in particular to a real-time monitoring system for stress of a large-span steel trestle. Background Art

[0002] As an important transportation or construction facility, the structural safety of steel trestles is directly related to the safety of personnel and property. The main purpose of real-time stress monitoring is to timely detect and warn of stress anomalies in the structure to prevent structural damage accidents caused by stress concentration or exceeding the design limit. Through real-time monitoring, accurate data support can be provided to engineers so that repair or reinforcement measures can be taken in time to ensure the safe operation of steel trestles.

[0003] Publication No. CN113280953A discloses a bridge construction stress detection system based on blockchain, including a test module, a data processing module and an analysis module; the test module includes a data acquisition unit, a virtual construction unit and an information transmission unit; the present invention simultaneously detects the measurement of temperature and humidity and the change of stress, and then calculates the expansion and contraction efficiency caused by temperature and humidity on the concrete structure of the bridge, and then effectively removes the strain generated by the embedded steel string strain sensor due to the deformation of the bridge as an error, and then effectively detects the real change of the stress of the bridge body, which can effectively avoid the error caused by temperature and humidity in the measurement of bridge stress, and at the same time, the stress changes of each collection point of the bridge are detected in real time through the data processing module, which can effectively express the stress changes of the bridge intuitively, and effectively avoid excessive stress convergence and damage to the bridge body.

[0004] As shown in the above-mentioned prior art, the prior art does not fully consider the influencing factors of bridge stress monitoring, and there are still limitations in stress threshold setting, comprehensive consideration of environmental factors and abnormal warning mechanism. It is difficult to dynamically adapt to the complex and changeable operating environment of the bridge, and the warning capability for potential safety hazards is insufficient, which affects the accuracy of bridge structure safety assessment and maintenance efficiency. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a real-time monitoring system for stress of a large-span steel trestle, which solves the above-mentioned problems.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A real-time monitoring system for stress of a large-span steel trestle, comprising:

[0007] Bridge modeling unit, used to build a three-dimensional model of the bridge for a long-span steel trestle;

[0008] Sensor monitoring unit, used to collect different parameters at different positions of the steel trestle for stress analysis;

[0009] The sensor data analysis unit is used to process the data collected by the sensor monitoring unit, and to perform stress analysis and model display in combination with the three-dimensional bridge model of the bridge modeling unit. Before stress analysis, specific upper limits of threshold values ​​of various parameters are designed for each detection site, compared with the actual detection parameters, and abnormal values ​​are marked by different levels.

[0010] The sensor monitoring unit collects data through several groups of sensor groups. The sensor groups are arranged in several groups and installed at different positions of the steel trestle, and each group of sensors includes different types of sensors for detecting different parameters.

[0011] Preferably, the step of establishing the three-dimensional model of the bridge body by the bridge body modeling unit comprises:

[0012] S1.1 Software and interface: Utilize 3D design software and combine it with its API interface to develop a parametric modeling platform; build a bar cross-section graphic database of the steel trestle based on the parametric grid axis, and create a visual 3D model in combination with the axis line;

[0013] S1.2 introduces BIM technology to integrate information from each stage of design, construction, operation and maintenance into a three-dimensional model.

[0014] Preferably, the sensor monitoring unit further includes:

[0015] The sensor installation position design module is used to design the installation position and installation method of the sensor; the design of the installation position is based on the stress analysis and structural characteristics of the bridge, and the optimization algorithm is used to optimize the sensor position to ensure full coverage of key positions while reducing unnecessary sensor redundancy; the installation method is selected according to the structural characteristics of the bridge to ensure the accuracy of the monitoring data and the safety of the bridge structure;

[0016] The sensor editing control module is used to classify and sort sensors according to type and quantity, and to create a collection of sensor numbers. , where x represents the sensor type and y represents the serial number of the same type of sensor; and the operation of the sensor is controlled, and the data collection frequency of each sensor is set according to the bridge usage and monitoring requirements, and data synchronization technology is introduced to synchronize the time through a unified clock signal or time server to keep the data collection frequency consistent;

[0017] The sensor data input module is used to transmit the data detected by the sensor to the sensor data analysis unit.

[0018] Preferably, the sensor data analysis unit comprises:

[0019] The data preprocessing module is used to classify the data input by the sensor data input module according to the sensor type, and to establish a curve graph for the values ​​collected by each sensor. Based on the curve graph, the data anomaly detection algorithm is used to mark the abnormal values ​​in the curve graph, and the corresponding sensor is re-detected to obtain the value;

[0020] The abnormal analysis module includes force analysis and threshold setting for different detection sites, and compares the actual detection data with the threshold to analyze whether the detection data exceeds the threshold. If it exceeds the threshold, it is judged as abnormal data;

[0021] The abnormal warning module marks and warns abnormal data that exceeds the threshold. At the same time, the warning level is subdivided considering the superposition effect of different stress influencing factors;

[0022] Mechanical three-dimensional model, substitute the sensor detection value into the space coordinates of the corresponding sensor, load it into the three-dimensional coordinate model of the bridge body, and establish the mechanical three-dimensional model;

[0023] The abnormal point coloring module marks the sensors corresponding to the abnormal values ​​in red and sets different depths of red according to the degree of abnormality P.

[0024] Preferably, when the abnormal sensor re-detects and obtains a value, the re-detected value is inconsistent with the detection values ​​of other sensors in time sequence, so data is collected from the abnormal sensor multiple times in succession, and the average value is calculated as the value obtained by re-detection.

[0025] Preferably, the stress analysis and threshold setting steps of the abnormality analysis module specifically include:

[0026] S2.1 Stress analysis: Use finite element analysis software to perform stress analysis on the bridge and determine the theoretical stress conditions at each location;

[0027] S2.2 Threshold setting: According to the analysis results, based on the principles of structural mechanics and material mechanics, combined with sensor types and monitoring objects, set the upper threshold limit B of different types of sensor detection data. MAX .

[0028] Preferably, in S2.1 stress analysis, the basic structure of the bridge and various stress influencing factors are considered to construct a stress analysis model, and the specific parameter settings and analysis steps are as follows:

[0029] S2.1.1 Design structural parameters, including:

[0030] Relative deflection Δr between the bridge deck and the abutment: The allowable deflection limit of the bridge deck relative to the abutment is set according to the design specifications. In the model, this limit is simulated by adjusting the bearing constraints to ensure that the deflection change is within the allowable range;

[0031] S2.1.2 Design raw material performance parameters, including:

[0032] Elastic modulus E: In the model, specify the correct elastic modulus value for each material;

[0033] Density ρ: Set the corresponding density value according to the material type;

[0034] Prestressed in-situ stress σ0: Consider the prestressed in-situ stress generated during the construction process and load it into the model as the initial stress field;

[0035] S2.1.3 Design load and external force parameters, including:

[0036] Construction load F construction : Simulate various loads during the construction process according to construction plans and specifications;

[0037] Traffic load F traffic :According to the bridge design standards, simulate the dynamic load effect generated by vehicles passing by; use standard vehicle models or load according to actual traffic flow;

[0038] Wind load F wind : According to the local wind conditions, the wind pressure is calculated and converted into wind load, which is then loaded onto the model. The wind load is expressed as:

[0039] ;

[0040] Among them, ρ air is the air density, C d is the drag coefficient, A is the frontal area, and V is the wind speed;

[0041] S2.1.4 Design temperature and environmental factor parameters, including:

[0042] Temperature change ΔT: Set different temperature scenarios and calculate the structural deformation and stress changes caused by temperature changes through the thermal expansion coefficient α. The thermal expansion deformation ΔL is expressed as:

[0043] ;

[0044] Where L is the original length;

[0045] Humidity and corrosion: It is impossible to accurately simulate directly through the finite element model, so the humidity and corrosion effects are indirectly reflected by reducing the material performance parameters;

[0046] S2.1.5 Other factors to consider include:

[0047] Initial geometric defect δ: Introducing geometric deviation into the model to simulate the initial defects in the actual structure;

[0048] Component residual stress σresidual : According to the manufacturing process and heat treatment process, the residual stress field inside the component is set and loaded into the model as the initial stress.

[0049] Preferably, according to the expanded stress analysis results, taking into account the superposition effect of different stress influencing factors, the threshold is set by the following method:

[0050] Basic threshold B0: According to the bridge design specifications and material properties, the basic threshold of each type of sensor is set;

[0051] Superposition effect adjustment DJ: According to the force analysis results, the basic threshold is adjusted by considering the superposition effect of different stress influencing factors;

[0052] Position-specific K position :According to the importance of the monitoring location and the degree of stress concentration, the location coefficient K is introduced position Different upper threshold limits are set for different types of sensors and different monitoring locations. The location coefficient K position It reflects the sensitivity of the monitoring location to stress changes;

[0053] Safety margin MoS: To ensure the safety of the bridge, a safety margin MoS is added to the set threshold; the size of the safety margin depends on the importance of the bridge, its age and maintenance plan factors;

[0054] In summary, the upper threshold B MAX It is expressed as:

[0055] .

[0056] Preferably, the calculation formula of the abnormality degree P is:

[0057] ;

[0058] Among them, B 实 Indicates the actual detection value, B MAX Indicates the upper normal threshold.

[0059] Preferably, at least three P value gears and red depth gears are set according to actual needs:

[0060] P>100% is light red;

[0061] P>150% is medium red;

[0062] P>200% is dark red.

[0063] The present invention provides a real-time stress monitoring system for a large-span steel trestle. Compared with the prior art, the present invention has the following beneficial effects:

[0064] 1. The real-time stress monitoring system for large-span steel trestles realizes real-time, accurate monitoring and instant analysis of the stress state of large-span steel trestles, significantly improving the early warning capability of bridge structure safety. Through the deep integration of bridge body three-dimensional modeling and multi-source sensor data, it not only realizes the comprehensive perception of the stress state of various parts of the bridge body, but also can automatically compare the preset thresholds, accurately identify and grade abnormal stress conditions. This efficient and intelligent monitoring method helps to timely discover potential safety hazards, provides a scientific basis for bridge maintenance decisions, effectively extends the service life of the bridge, and ensures the safety and smoothness of transportation.

[0065] 2. The real-time stress monitoring system for large-span steel trestles realizes comprehensive, real-time and accurate monitoring of the stress state of steel trestles through sophisticated three-dimensional modeling of the bridge body, optimized sensor layout and intelligent data analysis. It not only improves the automation and intelligence level of bridge structure safety monitoring, but also ensures the accuracy of monitoring data through data preprocessing and anomaly detection algorithms, and timely warns of potential structural problems. At the same time, the construction of mechanical three-dimensional models and the visual marking of abnormal points provide engineers with intuitive and clear monitoring results, greatly improving the efficiency and accuracy of bridge maintenance.

[0066] 3. The real-time stress monitoring system for the large-span steel trestle bridge has achieved a comprehensive simulation and accurate analysis of the stress conditions of the bridge structure through sophisticated finite element analysis and comprehensive consideration of multiple factors. It can accurately set the upper threshold limits of various types of sensors to ensure the scientificity and effectiveness of the monitoring data. At the same time, it comprehensively considers various environmental factors such as temperature, humidity, corrosion, and load effects such as construction and traffic, improves the accuracy and reliability of the safety assessment of the bridge structure, and provides solid data support for subsequent maintenance and management.

[0067] 4. The real-time stress monitoring system for large-span steel trestle bridges realizes the refined setting of bridge monitoring thresholds by comprehensively considering basic thresholds, superposition effects, location specificity and safety margins. It not only improves the accuracy and pertinence of stress monitoring, but also ensures the safety of bridges under different environmental conditions and stress states. By dynamically adjusting thresholds and introducing location coefficients, the system can more accurately capture potential safety hazards, provide strong support for bridge maintenance and management, and significantly improve the safety and reliability of bridge operations.

[0068] 5. The real-time stress monitoring system for large-span steel trestles achieves accurate assessment and rapid response to abnormal bridge stress conditions by introducing the calculation of the abnormality degree P and a graded early warning mechanism. By setting different red depth levels, the severity of the abnormality is intuitively displayed, making it easier for engineers to quickly locate the problem and take corresponding measures. This graded early warning not only improves monitoring efficiency, but also enhances the initiative and foresight of bridge maintenance, which is of great significance for ensuring the safe operation of bridges. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a system principle block diagram of the present invention;

[0070] Figure 2 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] See also Figure 1-Figure 2 , the present invention provides the following five technical solutions:

[0073] The first implementation method: a real-time monitoring system for stress of a large-span steel trestle, comprising:

[0074] Bridge modeling unit, used to build a three-dimensional model of the bridge for a long-span steel trestle;

[0075] Sensor monitoring unit, used to collect different parameters at different positions of the steel trestle for stress analysis;

[0076] The sensor data analysis unit is used to process the data collected by the sensor monitoring unit, and to perform stress analysis and model display in combination with the three-dimensional bridge model of the bridge modeling unit. Before stress analysis, specific upper limits of threshold values ​​of various parameters are designed for each detection site, compared with the actual detection parameters, and abnormal values ​​are marked by different levels.

[0077] The sensor monitoring unit collects data through several groups of sensor groups. Several sensor groups are installed at different positions of the steel trestle, and each group of sensors includes different types of sensors for detecting different parameters.

[0078] The system realizes real-time, accurate monitoring and instant analysis of the stress state of large-span steel trestles, significantly improving the early warning capability of bridge structure safety. Through the deep integration of bridge 3D modeling and multi-source sensor data, it not only realizes the comprehensive perception of the stress state of various parts of the bridge, but also can automatically compare the preset thresholds, accurately identify and grade abnormal stress conditions. This efficient and intelligent monitoring method helps to timely discover potential safety hazards, provides a scientific basis for bridge maintenance decisions, effectively extends the service life of the bridge, and ensures the safety and smoothness of transportation.

[0079] The second embodiment is mainly different from the first embodiment in that the steps of establishing the three-dimensional model of the bridge body by the bridge body modeling unit include:

[0080] S1.1 Software and interface: Use 3D design software such as Autodesk Inventor or SolidWorks, and develop a parametric modeling platform in combination with its API interface; build a bar cross-section graphic database of the steel trestle based on the parametric grid axis, and create a visual 3D model in combination with the axis line;

[0081] S1.2 introduces BIM (Building Information Modeling) technology to integrate information from various stages such as design, construction, operation and maintenance into a three-dimensional model to improve information sharing and collaboration efficiency.

[0082] The sensor monitoring unit also includes:

[0083] The sensor installation position design module is used to design the installation position and installation method of the sensor. The design of the installation position is based on the stress analysis and structural characteristics of the bridge. The optimization algorithm (such as genetic algorithm, simulated annealing algorithm, optimization algorithm is a mathematical method or technology to find the optimal solution under given conditions) is used to optimize the sensor position to ensure full coverage of key positions (such as the main beam mid-span, support, node connection, etc.) while reducing unnecessary sensor redundancy. The installation method is based on the characteristics of the bridge structure, and the appropriate installation method (suspension, bonding, embedded) is selected to ensure the accuracy of the monitoring data and the safety of the bridge structure.

[0084] The sensor editing control module is used to classify and sort sensors according to type and quantity, and to create a collection of sensor numbers. , where x represents the sensor type (such as displacement sensor D, stress sensor S, acceleration sensor A, etc.), and y represents the serial number of the same type of sensor; and the operation of the sensor is controlled, and the data collection frequency of each sensor is set according to the use of the bridge and the monitoring requirements, and the data synchronization technology is introduced to synchronize the time through a unified clock signal or time server to keep the data collection frequency consistent;

[0085] Data synchronization technology is an important part of modern information system construction. It ensures the consistency and accuracy of data between different systems and devices. Through data synchronization, real-time data update, backup, recovery and sharing can be achieved, improving the reliability and security of the system. Common data synchronization methods include: Direct connection synchronization: directly connecting to the business library through a defined standard interface API and a dynamic link library-based method. This method is simple to configure and easy to implement, but it may have a significant impact on the performance of the source system.

[0086] Data file synchronization: Export the data from the data source as a file, then transfer the file to the target system and import it. This method is suitable for scenarios with large data volumes and low real-time requirements.

[0087] Database log parsing synchronization: Obtain data change information by parsing database log files (such as MySQL binlog) and synchronize it to the target system. This method can achieve real-time data synchronization, but requires a deep understanding and processing ability of database logs.

[0088] The sensor data input module is used to transmit the data detected by the sensor to the sensor data analysis unit.

[0089] The sensor data analysis unit includes:

[0090] The data preprocessing module is used to classify the data input by the sensor data input module according to the sensor type, and to create a curve graph for the values ​​collected by each sensor. Based on the curve graph, a data anomaly detection algorithm (such as single variable pattern anomaly detection based on KNN distance) is used to mark the abnormal values ​​in the curve graph, and the corresponding sensor is re-detected to obtain the value;

[0091] The abnormal analysis module includes force analysis and threshold setting for different detection sites, and compares the actual detection data with the threshold to analyze whether the detection data exceeds the threshold. If it exceeds the threshold, it is judged as abnormal data;

[0092] The abnormal warning module marks and warns abnormal data that exceeds the threshold. At the same time, the warning level is subdivided considering the superposition effect of different stress influencing factors;

[0093] Mechanical three-dimensional model, substitute the sensor detection value into the space coordinates of the corresponding sensor, load it into the three-dimensional coordinate model of the bridge body, and establish the mechanical three-dimensional model;

[0094] The abnormal point coloring module marks the sensors corresponding to the abnormal values ​​in red and sets different depths of red according to the degree of abnormality P.

[0095] When the abnormal sensor re-detects and obtains the value, the re-detected value is considered to be inconsistent with the detection values ​​of other sensors in time sequence. Therefore, the abnormal sensor is continuously collected for multiple times, and the average value is calculated as the re-detected value.

[0096] Through sophisticated three-dimensional modeling of the bridge, optimized sensor layout and intelligent data analysis, comprehensive, real-time and accurate monitoring of the stress state of the steel trestle is achieved; it not only improves the automation and intelligence level of bridge structure safety monitoring, but also ensures the accuracy of monitoring data through data preprocessing and anomaly detection algorithms, and timely warns of potential structural problems; at the same time, the construction of the mechanical three-dimensional model and the visual marking of abnormal points provide engineers with intuitive and clear monitoring results, greatly improving the efficiency and accuracy of bridge maintenance.

[0097] The third implementation mode mainly differs from the second implementation mode in that the stress analysis and threshold setting steps of the abnormality analysis module specifically include:

[0098] S2.1 Stress analysis: Use finite element analysis software (such as ANSYS, Abaqus) to perform stress analysis on the bridge and determine the theoretical stress conditions at each location;

[0099] S2.2 Threshold setting: According to the analysis results, based on the principles of structural mechanics and material mechanics, combined with sensor types and monitoring objects, set the upper threshold limit B of different types of sensor detection data. MAX .

[0100] In S2.1 stress analysis, a stress analysis model is constructed by considering the basic structure of the bridge and various stress influencing factors. The specific parameter settings and analysis steps are as follows:

[0101] S2.1.1 Design structural parameters, including:

[0102] Relative deflection Δr between the bridge deck and the abutment: The allowable deflection limit of the bridge deck relative to the abutment is set according to the design specifications. In the model, this limit is simulated by adjusting the bearing constraints to ensure that the deflection change is within the allowable range;

[0103] S2.1.2 Design raw material performance parameters, including:

[0104] Modulus of elasticity E: In the model, specify the correct value of the modulus of elasticity for each material (e.g., steel, concrete);

[0105] Density ρ: Set the corresponding density value according to the material type; density affects the deadweight of the structure, and thus affects the stress distribution;

[0106] Prestressed in-situ stress σ0: Consider the prestressed in-situ stress generated during the construction process and load it into the model as the initial stress field;

[0107] S2.1.3 Design load and external force parameters, including:

[0108] Construction load F construction:According to the construction plan and specifications, simulate various loads during the construction process, such as hoisting loads, temporary support loads, etc.;

[0109] Traffic load F traffic :According to the bridge design standards, simulate the dynamic load effect generated by vehicles passing by; use standard vehicle models or load according to actual traffic flow;

[0110] Wind load F wind : According to the local wind conditions, the wind pressure is calculated and converted into wind load, which is then loaded onto the model. The wind load is expressed as:

[0111] ;

[0112] Among them, ρ air is the air density, C d is the drag coefficient, A is the frontal area, and V is the wind speed;

[0113] S2.1.4 Design temperature and environmental factor parameters, including:

[0114] Temperature change ΔT: Set different temperature scenarios and calculate the structural deformation and stress changes caused by temperature changes through the thermal expansion coefficient α. The thermal expansion deformation ΔL is expressed as:

[0115] ;

[0116] Where L is the original length;

[0117] Humidity and corrosion: It is impossible to accurately simulate directly through the finite element model, so the humidity and corrosion effects are indirectly reflected by reducing the material performance parameters (such as elastic modulus and yield strength);

[0118] S2.1.5 Other factors to consider include:

[0119] Initial geometric defects δ: Introducing geometric deviations into the model, such as welding deformation and cross-section unevenness, to simulate initial defects in the actual structure;

[0120] Component residual stress σ residual : According to the manufacturing process and heat treatment process, the residual stress field inside the component is set and loaded into the model as the initial stress.

[0121] Through sophisticated finite element analysis and comprehensive consideration of multiple factors, a comprehensive simulation and precise analysis of the stress conditions of the bridge structure is achieved. The upper threshold limits of various types of sensors can be accurately set to ensure the scientificity and effectiveness of the monitoring data. At the same time, a variety of environmental factors such as temperature, humidity, corrosion, and load effects such as construction and traffic are comprehensively considered to improve the accuracy and reliability of bridge structure safety assessments, providing solid data support for subsequent maintenance and management.

[0122] The fourth implementation mode is mainly different from the third implementation mode in that: according to the expanded stress analysis results, taking into account the superposition effect of different stress influencing factors, the threshold is set by the following method:

[0123] Basic threshold B0: According to the bridge design specifications and material properties, the basic thresholds of various types of sensors (such as stress, displacement, acceleration, etc.) are set;

[0124] Superposition effect adjustment DJ: According to the results of stress analysis, the basic threshold is adjusted by considering the superposition effect of different stress influencing factors; for example, for areas significantly affected by wind loads, the stress threshold can be appropriately lowered; for areas with large temperature changes, a dynamic threshold related to temperature can be set;

[0125] Position-specific K position :According to the importance of the monitoring location and the degree of stress concentration, the location coefficient K is introduced position Different upper threshold limits are set for different types of sensors and different monitoring locations. The location coefficient K position It reflects the sensitivity of the monitoring location to stress changes;

[0126] Safety margin MoS: To ensure the safety of the bridge, a safety margin MoS (Margin of Safety) is added to the set threshold; the size of the safety margin depends on factors such as the importance of the bridge, its service life and maintenance plan;

[0127] In summary, the upper threshold B MAX It is expressed as:

[0128] .

[0129] By comprehensively considering the basic threshold, superposition effect, location specificity and safety margin, the bridge monitoring threshold is finely set. It not only improves the accuracy and pertinence of stress monitoring, but also ensures the safety of the bridge under different environmental conditions and stress states. By dynamically adjusting the threshold and introducing the location coefficient, the system can more accurately capture potential safety hazards, provide strong support for the maintenance and management of the bridge, and significantly improve the safety and reliability of bridge operation.

[0130] The fifth implementation is mainly different from the fourth implementation in that the calculation formula of the abnormality level P is:

[0131] ;

[0132] Among them, B 实 Indicates the actual detected value.

[0133] Set at least three P value levels and red depth level according to actual needs:

[0134] P>100% is light red;

[0135] P>150% is medium red;

[0136] P>200% is dark red.

[0137] By introducing the calculation of the abnormality degree P and the graded warning mechanism, accurate assessment and rapid response to abnormal bridge stress conditions are achieved; by setting different red depth levels, the severity of the abnormality is intuitively displayed, making it easier for engineers to quickly locate the problem and take corresponding measures; this graded warning not only improves monitoring efficiency, but also enhances the initiative and foresight of bridge maintenance, which is of great significance for ensuring the safe operation of bridges.

[0138] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0139] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0140] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time monitoring system for stress of a long-span steel trestle, characterized in that: include: Bridge modeling unit, used to build a three-dimensional model of the bridge for a long-span steel trestle; Sensor monitoring unit, used to collect different parameters at different positions of the steel trestle for stress analysis; The sensor data analysis unit is used to process the data collected by the sensor monitoring unit, and to perform stress analysis and model display in combination with the three-dimensional bridge model of the bridge modeling unit. Before stress analysis, specific upper limits of threshold values ​​of various parameters are designed for each detection site, compared with the actual detection parameters, and abnormal values ​​are marked by different levels. The sensor monitoring unit collects data through several groups of sensor groups, wherein the sensor groups are arranged in several groups and installed at different positions of the steel trestle, and each group of sensors includes different types of sensors for detecting different parameters, and the sensor monitoring unit also includes: Sensor installation location design module, used to design the installation location and installation method of the sensor; The sensor editing control module is used to classify and sort sensors according to type and quantity, and to create a collection of sensor numbers. , where x represents the sensor type and y represents the serial number of the same type of sensor; and the operation of the sensor is controlled, and the data collection frequency of each sensor is set according to the bridge usage and monitoring requirements, and data synchronization technology is introduced to synchronize the time through a unified clock signal or time server to keep the data collection frequency consistent; A sensor data input module, used to transmit the data detected by the sensor to the sensor data analysis unit; The sensor data analysis unit comprises: The data preprocessing module is used to classify the data input by the sensor data input module according to the sensor type, and to establish a curve graph for the values ​​collected by each sensor. Based on the curve graph, the data anomaly detection algorithm is used to mark the abnormal values ​​in the curve graph, and the corresponding sensor is re-detected to obtain the value; The abnormal analysis module includes force analysis and threshold setting for different detection sites, and compares the actual detection data with the threshold to analyze whether the detection data exceeds the threshold. If it exceeds the threshold, it is judged as abnormal data; The abnormal warning module marks and warns abnormal data that exceeds the threshold. At the same time, the warning level is subdivided considering the superposition effect of different stress influencing factors; Mechanical three-dimensional model, substitute the sensor detection value into the space coordinates of the corresponding sensor, load it into the three-dimensional coordinate model of the bridge body, and establish the mechanical three-dimensional model; The abnormal point coloring module marks the sensors corresponding to the abnormal values ​​in red and sets different depths of red according to the abnormality degree P; The stress analysis and threshold setting steps of the abnormal analysis module specifically include: S2.1 Stress analysis: Use finite element analysis software to perform stress analysis on the bridge and determine the theoretical stress conditions at each location; S2.2 Threshold setting: According to the analysis results, based on the principles of structural mechanics and material mechanics, combined with sensor types and monitoring objects, set the upper threshold limit B of different types of sensor detection data. MAX ; In S2.1 stress analysis, the basic structure of the bridge and stress influencing factors are considered to construct a stress analysis model. The specific parameter settings and analysis steps are as follows: S2.1.1 Design structural parameters, including: Relative deflection Δr between the bridge deck and the abutment: The allowable deflection limit of the bridge deck relative to the abutment is set according to the design specifications. In the model, this limit is simulated by adjusting the bearing constraints to ensure that the deflection change is within the allowable range; S2.1.2 Design raw material performance parameters, including: Elastic modulus E: In the model, specify the correct elastic modulus value for each material; Density ρ: Set the corresponding density value according to the material type; Prestressed in-situ stress σ0: Consider the prestressed in-situ stress generated during the construction process and load it into the model as the initial stress field; S2.1.3 Design load and external force parameters, including: Construction load F construction : Simulate various loads during the construction process according to construction plans and specifications; Traffic load F traffic :According to the bridge design standards, simulate the dynamic load effect generated by vehicles passing by; use standard vehicle models or load according to actual traffic flow; Wind load F wind : According to the local wind conditions, the wind pressure is calculated and converted into wind load, which is then loaded onto the model. The wind load is expressed as: ； Among them, ρ air is the air density, C d is the drag coefficient, A is the frontal area, and V is the wind speed; S2.1.4 Design temperature and environmental factor parameters, including: Temperature change ΔT: Set different temperature scenarios and calculate the structural deformation and stress changes caused by temperature changes through the thermal expansion coefficient α. The thermal expansion deformation ΔL is expressed as: ； Where L is the original length; Humidity and corrosion: It is impossible to accurately simulate directly through the finite element model, so the humidity and corrosion effects are indirectly reflected by reducing the material performance parameters; S2.1.5 Consider the following factors, including: Initial geometric defect δ: Introducing geometric deviation into the model to simulate the initial defects in the actual structure; Component residual stress σ residual :According to the manufacturing process and heat treatment process, the residual stress field inside the component is set and loaded into the model as the initial stress; According to the results of stress analysis and considering the superposition effect of different stress influencing factors, the following method is used to set the threshold: Basic threshold B0: According to the bridge design specifications and material properties, the basic threshold of each type of sensor is set; Superposition effect adjustment DJ: According to the force analysis results, the basic threshold is adjusted by considering the superposition effect of different stress influencing factors; Position-specific K position :According to the importance of the monitoring location and the degree of stress concentration, the location coefficient K is introduced position Different upper threshold limits are set for different types of sensors and different monitoring locations. The location coefficient K position It reflects the sensitivity of the monitoring location to stress changes; Safety margin MoS: To ensure the safety of the bridge, a safety margin MoS is added to the set threshold; the size of the safety margin depends on the importance of the bridge, its age and maintenance plan factors; In summary, the upper threshold B MAX It is expressed as: 。 2. The long-span steel trestle stress real-time monitoring system according to claim 1 is characterized in that: The steps of establishing a three-dimensional bridge model by the bridge modeling unit include: S1.1 Software and interface: Utilize 3D design software and combine it with its API interface to develop a parametric modeling platform; build a bar cross-section graphic database of the steel trestle based on the parametric grid axis, and create a visual 3D model in combination with the axis line; S1.2 introduces BIM technology to integrate information from each stage of design, construction, operation and maintenance into a three-dimensional model.

3. The long-span steel trestle stress real-time monitoring system according to claim 1 is characterized in that: When the abnormal sensor re-detects and obtains the value, the re-detected value is considered to be inconsistent with the detection values ​​of other sensors in time sequence. Therefore, the abnormal sensor is continuously collected for multiple times, and the average value is calculated as the re-detected value.

4. The long-span steel trestle stress real-time monitoring system according to claim 1 is characterized in that: The calculation formula of the abnormality degree P is: ； Among them, B 实 Indicates the actual detection value, B MAX Indicates the upper normal threshold.

5. The long-span steel trestle real-time stress monitoring system according to claim 4 is characterized in that: Set at least three P value levels and red depth level according to actual needs: P>100% is light red; P>150% is medium red; P>200% is dark red.

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