Dynamic stress monitoring method for incremental launching construction of steel box girder bridge, computer equipment and storage medium

Abstract

The invention discloses a dynamic stress monitoring method for incremental launching construction of a steel box girder bridge, computer equipment and a storage medium, and the method comprises the following steps: S1, initializing a model, and building a digital twin model and a static threshold library; s2, arranging a data acquisition module, and establishing a dynamic mechanical model; s3, constructing an environmental load dynamic prediction model for quantifying and predicting a dynamic superimposed effect of the environmental load on a structural mechanical state; s4, inputting originally monitored stress data, and outputting a dynamic threshold value based on the environment load dynamic prediction model; s5, performing multi-source fusion on the data, including performing real-time comparative analysis on the actually measured stress value and the dynamic threshold value of each monitoring point; s6, converting an analysis result in the step S5 into decision support capable of directly guiding construction; the problems that an existing stress analysis model is simple, an analysis result has deviation due to the fact that environmental loads are not considered, and practice is difficult to accurately guide are solved.

Description

Technical Field

[0001] This invention relates to the field of bridge stress monitoring technology, specifically to a method, computer equipment, and storage medium for dynamic stress monitoring during the jacking construction of a steel box girder bridge. Background Technology

[0002] Steel box girder bridges, due to their thin-walled closed-section main girder, possess advantages such as lightweight structure and high load-bearing capacity, and are widely used in long-span bridge projects. Their jacking construction requires assembling the girder segment by segment at the bridgehead, and then longitudinally jacking it with jacks to position it via temporary sliding supports on each pier. During construction, multiple longitudinally distributed jacking platforms are arranged along the main span. These platforms are equipped with sliding tracks and jacking equipment including jacking jacks, hydraulic pump stations, and control consoles. Synchronous cyclic operations are used to slide the steel box girder segment by segment to the design position.

[0003] However, traditional stress analysis in jacking construction is often based on simplified mechanical models, which do not adequately consider actual construction conditions. For example, it neglects the effects of temporary support system deformation, dynamic characteristics of the jacking equipment, and environmental loads such as wind load and temperature difference on the complex interactions between components. This leads to discrepancies between the analysis results and the actual stress conditions, making it difficult to accurately guide construction practice. Therefore, there is an urgent need for a method and system that can dynamically monitor the real-time stress state of each component during jacking construction to improve construction safety and structural stability. Summary of the Invention

[0004] This invention provides a method, computer equipment, and storage medium for dynamic stress monitoring during the launching construction of steel box girder bridges. It solves the problem that existing stress analysis models are simple, do not consider environmental loads, and therefore have biased analysis results, making it difficult to accurately guide practice.

[0005] A method for dynamic stress monitoring during the incremental launching construction of a steel box girder bridge includes the following steps:

[0006] S1. Model initialization: Establish the digital twin model and static threshold library;

[0007] S2. Deploy the data acquisition module and establish a dynamic mechanical model to demonstrate the load transfer path of the jacking system.

[0008] S3. Construct a dynamic prediction model for environmental loads to quantify and predict the dynamic superposition effect of environmental loads on the mechanical state of the structure.

[0009] S4. Input the original monitored stress data and output the dynamic threshold based on the environmental load dynamic prediction model;

[0010] S5. Perform multi-source fusion of data, including real-time comparison and analysis of the measured stress values ​​and dynamic thresholds of each monitoring point;

[0011] S6. Transform the analysis results of step S5 into a comprehensive report that can directly guide construction decisions, including data, charts, diagnostic conclusions, and treatment recommendations.

[0012] Furthermore, the digital twin model is a parametric finite element model established based on bridge design drawings, construction plans, and material parameters. This model includes: bridge piers, auxiliary supports for steel box girders, each section of the steel box girder bridge, steel guide beams, and support points of the jacking device, used to simulate the mass distribution, stiffness characteristics, and boundary conditions of the structure.

[0013] Furthermore, the static threshold library is simulated and analyzed based on a digital twin model to extract the stress conditions at each monitoring point under dead load and standard jacking load at each stage. Combined with design specifications and safety factors, an initial static threshold library is formed.

[0014] Furthermore, the digital twin model also includes a sensor deployment generation unit, which is used to simulate the entire jacking construction process and extract stress cloud diagrams, displacement cloud diagrams, reaction force values, and instability modes during the jacking process. Based on the above data, a jacking construction monitoring sensor deployment information table is generated, which serves as the basis for the deployment of the data acquisition module.

[0015] Furthermore, the dynamic mechanics model is a real-time data analysis algorithm used to receive data from the data acquisition module, calculate the load transfer path of the entire jacking system, and intuitively demonstrate how the jacking force is distributed to each temporary support through the beam in each jacking cycle, as well as the internal force distribution of the beam itself.

[0016] Furthermore, the dynamic prediction model for environmental loads includes a wind load disturbance model and a temperature load disturbance model.

[0017] Furthermore, the specific implementation steps of step S4 include:

[0018] S4-1: Receive raw data streams from the S2 data acquisition module, including: structural mechanics data, jacking equipment data, and environmental data.

[0019] S4-2. Call the dynamic prediction model of environmental load, input and calculate the predicted value of the environmental load stress increment for each monitoring point;

[0020] S4-3. Based on the current jacking stage identifier and monitoring point, query the static threshold of the point under this stage from the static threshold library established in step S1, and then adjust the safety threshold based on the predicted value of environmental load stress increment to obtain the dynamic threshold.

[0021] S4-4 Finally, output the dynamic threshold and send it to the visualization interface of the monitoring terminal.

[0022] Furthermore, the specific implementation steps of step S5 include:

[0023] S5-1. Continuously compare the measured data of each monitoring point after S4 preprocessing with the output dynamic threshold. If the dynamic threshold is exceeded, an early warning will be issued.

[0024] S5-2. When one of the monitoring points triggers an early warning, take the time of the event as the center, extract all relevant, time-synchronized multi-source data within a short time window, and perform correlation analysis.

[0025] S5-3. Perform automatic reasoning on the above-mentioned related data to determine the most probable cause of the anomaly.

[0026] S5-4. Based on the reasoning results, generate a structured diagnostic report.

[0027] In a second aspect, embodiments of the present invention provide a computer device for dynamic stress monitoring during the launching construction of a steel box girder bridge, comprising at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, the instructions being executed by the at least one control processor to enable the at least one control processor to perform the method as described in any one of claims 1 to 8.

[0028] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method as described in any one of claims 1 to 8.

[0029] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0030] This invention establishes a digital twin model and static threshold library for a steel box girder bridge, combined with simulation analysis of the entire construction process, to generate a sensor deployment scheme and achieve precise installation. Secondly, it constructs a dynamic mechanical model and a dynamic environmental load prediction model to calculate stress increments caused by environmental factors such as wind load and temperature in real time, dynamically adjusting safety thresholds. Finally, by fusing and comparing measured stress with dynamic thresholds through multi-source data fusion, it triggers an early warning and correlates environmental, equipment, and structural data for root cause diagnosis and generates a decision report. This invention achieves refined simulation of the stress and displacement fields of the entire bridge through a digital twin model, overcoming the limitations of traditional simplified models; the dynamic threshold mechanism combined with environmental load prediction achieves a safety upgrade from "real-time alarm" to "predictive early warning"; multi-source data fusion and root cause diagnosis technology can accurately locate the causes of anomalies (such as increased wind load, asynchronous jacking, or structural damage) and provide targeted treatment suggestions, significantly improving construction safety and structural stability, and effectively solving the analytical bias problem caused by neglecting dynamic characteristics and environmental factors in traditional methods.

[0031] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0032] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0033] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0034] Figure 1 This is a flowchart of a method for monitoring the dynamic stress during the jacking construction of a steel box girder bridge, as disclosed in an embodiment of the present invention.

[0035] Figure 2 This is a flowchart of the method for dynamic prediction of environmental loads disclosed in an embodiment of the present invention;

[0036] Figure 3 This is a flowchart of a method for performing multi-source fusion analysis on data disclosed in an embodiment of the present invention. Detailed Implementation

[0037] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0038] Example 1

[0039] like Figure 1-3 As shown in the figure, this invention provides a method for dynamic stress monitoring during the jacking construction of a steel box girder bridge, comprising the following steps:

[0040] S1. Model initialization: Establish the digital twin model and static threshold library;

[0041] The digital twin model is based on the final design drawings of the steel box girder bridge (including the detailed dimensions of the piers, each segment of the steel box girder bridge, the steel guide beam, plate thickness, stiffening rib arrangement, etc.), the design drawings of the temporary structure (the precise geometry and construction of the auxiliary support of the steel box girder), and the installation drawings of the jacking equipment (such as the support position of the jacks). A three-dimensional geometric model of the entire bridge is established using finite element analysis software. Subsequently, a fine finite element mesh is generated, especially in stress concentration areas (such as near welds, support contact points, the connection between the steel guide beam and the box girder, and the connection between the support and the foundation) to ensure the accuracy of the calculation results. The model accurately simulates the mass distribution, stiffness characteristics, and boundary conditions of the structure.

[0042] Its function is to monitor the reference value for the system to judge "normal" and "abnormal". All subsequent dynamic analysis begins with the comparison with this ideal benchmark. Sensors can only monitor data at discrete points, while digital twin models can calculate the stress field and displacement field of the entire bridge through finite point interpolation, revealing the stress state of the "blind zone".

[0043] Furthermore, a simulation analysis of the entire construction process was conducted, simulating every key working condition from the first jacking cycle until the beam was in place.

[0044] For example, simulating typical construction stages such as the maximum cantilever state of the steel guide beam, the first arrival of the guide beam at the pier in front, and the maximum cantilever length of the box girder.

[0045] Through simulation calculations, the theoretical stress, displacement, and support reaction values ​​of each monitoring point (support root, key beam section) are extracted at each stage under the action of only dead load and standard jacking load. Based on this, and combined with design specifications and safety factors, an initial static threshold library is formed, through which the safe range of each parameter under ideal working conditions is defined.

[0046] In this embodiment, the digital twin model also includes a sensor deployment generation unit, which is used to simulate the entire jacking construction process and extract stress cloud diagrams, displacement cloud diagrams, reaction force values, and instability modes during the jacking process. Based on the above data, a jacking construction monitoring sensor deployment information table is generated, which serves as the basis for the deployment of the data acquisition module. The specific steps are as follows:

[0047] Run the digital twin model to simulate the entire construction process;

[0048] Extracting the entire pushing process:

[0049] Stress cloud map: Identifies the points of maximum Mises stress, maximum principal compressive stress, and principal tensile stress. These points are the key locations where strain sensors (such as fiber Bragg gratings (FBGs) or resistance strain gauges) need to be placed;

[0050] Displacement cloud map: Identify the point of maximum vertical displacement (downward deflection) and points where lateral displacement may occur. Displacement sensors (such as wire displacement gauges) or high-precision GNSS positioning modules need to be deployed at these points.

[0051] Reaction force value: Read the time history curve of the support reaction force at the bottom of each steel box girder auxiliary support to determine the support with the largest reaction force and the support most prone to sudden change. Pressure sensors (to measure reaction force) and tilt sensors (to measure stability) need to be installed at the root of these supports.

[0052] Instability Modes: Eigenvalue buckling analysis is performed to obtain the buckling modes in which the structure is most prone to instability. Triaxial tilt sensors (IMUs) are deployed in the regions with the largest modal displacements to monitor the overall attitude.

[0053] Based on the above information, a sensor deployment information table for the jacking construction monitoring is generated. This table includes: sensor ID, monitoring point location, measured physical quantity (e.g., micro-strain, triaxial angle, pressure value), early warning threshold (set as the initial threshold, directly from the static threshold library), and installation requirements remarks.

[0054] S2. Based on the above-mentioned sensor deployment information table for jacking construction monitoring, deploy the data acquisition module. Precisely install sensors at designated locations on the steel box girder bridge, steel guide beam, and auxiliary support. The entire data acquisition network adopts a unified time synchronization protocol to ensure that all monitoring data have a unified high-precision timestamp.

[0055] Furthermore, a dynamic mechanical model was established to demonstrate the load transfer path of the jacking system.

[0056] It should be noted that this model is not a newly established finite element model, but a lightweight mechanical analysis algorithm and visualization engine based on real-time data.

[0057] Its core algorithms include:

[0058] Load balance verification: Calculate the difference between the sum of all jacking forces and the sum of all support reaction forces in real time. Under ideal conditions (unaffected by environmental loads), the two should be balanced (small deviations are allowed considering factors such as friction). If the difference is too large, an alarm will be triggered immediately, indicating that there is a measurement error or unknown load.

[0059] Bending moment and shear force calculation: Using the strain value of the key section, the real-time bending moment and shear force of the section are calculated according to the material mechanics formula and section properties;

[0060] Load transfer path visualization: The jacking force and support reaction force are dynamically displayed on the simplified bridge structure diagram in the form of vector arrows in a graphical way. The thickness of the arrow represents the magnitude of the force. The internal force diagram (bending moment diagram, shear force diagram) of the structure is updated and drawn in real time, which makes it easy to see how the jacking force is transferred from the point of application to each temporary support through the beam, forming a clear and visualized load transfer path.

[0061] Its function is to promptly determine whether the jacking system is in overall force balance, effectively preventing the risk of overall structural imbalance or overturning caused by the loss of control of the jacking force of individual jacks.

[0062] S3. Construct a dynamic prediction model for environmental loads, including a wind load disturbance model and a temperature load disturbance model. This model is used to quantify and predict the dynamic superposition effect of environmental loads on the structural mechanical state. Specifically, it includes:

[0063] Construction of the coupling mechanism model between environment and structure:

[0064] Wind load disturbance model

[0065] Wind tunnel simulation: Based on the finite element model, the computational fluid dynamics software is imported, and different incoming wind direction angles (0° to 360°, with intervals of 15° or 30°) and wind speed gradients (such as 5m / s, 10m / s, 15m / s...) are set to simulate and calculate the wind pressure distribution cloud map of the structural surface (especially the steel guide beam and cantilever box girder) under each working condition;

[0066] Wind load database generation: The calculated wind pressure data is mapped back to the finite element model nodes in the digital twin model. Static analysis is run to calculate the additional stress increment and lateral displacement increment at each key monitoring point (consistent with the S2 deployment location) under unit wind speed (1 m / s) for each wind direction angle, and finally form a "wind-induced response database".

[0067] Temperature load disturbance model

[0068] Thermal-structural sequential coupling analysis: In the finite element model, the thermal expansion coefficient of steel is defined to simulate the nonlinear temperature field formed inside the structure under typical solar radiation conditions (such as the top plate being heated by sunlight and the bottom plate being cooler in the shade).

[0069] Thermal stress calculation: The calculated temperature field is used as a load condition to perform static analysis and calculate the resulting secondary temperature stress, thereby establishing a quantitative relationship model between the temperature gradient and the thermal stress at the monitoring point.

[0070] Real-time data-driven dynamic prediction coefficient calculation:

[0071] Model Input

[0072] Real-time environmental data: Real-time acquisition of current wind speed, wind direction, and structural surface temperature from environmental sensors (temperature sensors, anemometers);

[0073] Real-time structural status data: The current cantilever length of the steel box girder is obtained from the construction control system. This parameter directly determines the moment and area of ​​wind load.

[0074] Predictive calculation

[0075] Based on the wind direction, the stress influence coefficient per unit wind speed at each monitoring point under the current wind direction is retrieved from the "Wind-induced Response Database".

[0076] Calculate the dynamic prediction value of wind-induced stress: Dynamic prediction value of wind-induced stress = Stress influence coefficient per unit wind speed * Current wind speed * f(L_now), where f(L_now) is an amplification function of the cantilever length, used to quantify the leverage effect brought about by the change in the lever arm (e.g., stress is proportional to the square of the cantilever length).

[0077] Calculate the dynamic predicted value of temperature-induced stress: k * temperature gradient (where k is the thermal stress coefficient);

[0078] Total environmental load prediction = Dynamic prediction of wind-induced stress + Dynamic prediction of temperature-induced stress;

[0079] This value is the core output of the model, predicting how much the structural stress will change due to environmental factors in the near future.

[0080] As a preferred embodiment, machine learning algorithms are added to fine-tune and correct the coefficients (such as the parameters of the amplification function) in the prediction model, so that the predicted values ​​continuously approach the measured values, making the model increasingly "smart" and accurate as construction progresses.

[0081] This model can predictively determine that "with the current wind speed and direction, after the beam is pushed forward another 5 meters, the tensile stress at the root of the guide beam will increase by 50 MPa due to wind load." This allows the system to issue an early warning before the stress exceeds the limit, giving operators valuable reaction time and transforming it from "real-time alarm" to "predictive early warning," achieving a fundamental upgrade in safety.

[0082] S4. Input the original monitored stress data, and output the dynamic threshold based on the environmental load dynamic prediction model. The specific implementation steps include:

[0083] S4-1. Receiving and preprocessing multi-source raw data: The system continuously receives raw data streams from the data acquisition module, including:

[0084] Structural mechanics data: raw strain readings at each monitoring point, voltage signals from pressure sensors, and accelerometer and gyroscope signals from the IMU;

[0085] Data from the jacking equipment: hydraulic pressure sensor signal of the jack, and jacking stroke encoder signal;

[0086] Environmental data: analog / digital signals from anemometers and wind vanes, and resistance / voltage signals from temperature sensors;

[0087] Furthermore, the above raw data undergoes data cleaning (removing outliers), unit conversion (e.g., converting voltage values ​​to engineering units such as microstrain, MPa, KN, °C, m / s), and time alignment (ensuring strict synchronization of all data timestamps). Then, necessary mechanical transformations are performed, such as converting strain values ​​to stress values ​​using the material's elastic modulus, and converting hydraulic pressure values ​​to thrust using the piston area.

[0088] S4-2, Call the environmental model and calculate the predicted stress increment.

[0089] The preprocessed real-time environmental data and structural status data are sent to the dynamic prediction model of environmental loads;

[0090] Based on the current input, the model quickly calculates and returns the predicted value of the environmental load stress increment for each key monitoring point. This value represents the amount of additional stress change that the structure may bear in the short term (such as the next jacking step) under the current environment.

[0091] S4-3, Querying Static Benchmarks and Generating Dynamic Thresholds

[0092] The system also retrieves the static early warning threshold and static alarm threshold for the current jacking stage from the static threshold database based on the current jacking stage identifier (such as the jacking length or number of cycles) and the monitoring point ID.

[0093] Threshold calculation: Generating dynamic thresholds:

[0094] Dynamic warning threshold = Static warning threshold - Predicted value of environmental load stress increment

[0095] Dynamic alarm threshold = Static alarm threshold - Predicted value of environmental load stress increment

[0096] (It should be noted that the minus sign here applies when the predicted value of the environmental load stress increment is the tensile stress increment. If the environmental load produces a compressive stress increment, then it should be a plus sign. The logic is that the environmental load adversely consumes the structure's safety reserve, so the upper limit of the safety threshold must be lowered.)

[0097] S4-4 Dynamic Threshold Output and Visualization

[0098] The calculated dynamic threshold is output in real time and sent to the database for storage along with the preprocessed measured stress value, and then sent to the visualization interface of the monitoring terminal.

[0099] S5. Perform multi-source data fusion, including real-time comparison and analysis of measured stress values ​​and dynamic thresholds at each monitoring point. Specific implementation steps include:

[0100] S5-1, Real-time Comparison and Primary Early Warning Triggering

[0101] The system continuously and rapidly compares the measured stress values ​​of each monitoring point after S4 preprocessing with the dynamic thresholds calculated in real time. This process is a parallel process, and the comparison of all monitoring points is carried out synchronously.

[0102] When the system detects that the measured value of any monitoring point meets the condition that the measured stress value is greater than the dynamic threshold, a primary warning is triggered. This warning will mark the ID of the abnormal point, the abnormal value, the excess amplitude, and the timestamp, and serve as the starting point for subsequent in-depth diagnosis.

[0103] S5-1. Synchronous Extraction and Correlation Analysis of Multi-Source Data

[0104] When a monitoring point triggers an alert, the system automatically extracts all relevant, time-synchronized multi-source data within a short time window (e.g., 30 seconds before and 10 seconds after the alert) centered on the event's time point. This data includes:

[0105] Environmental data: historical curves of wind speed, wind direction, and temperature within this time window;

[0106] Data for the jacking equipment: hydraulic pressure and stroke data for all jacks;

[0107] Other relevant monitoring point data: stress, displacement, and tilt data of other points that are structurally adjacent to or mechanically related to the monitoring point;

[0108] The system plots synchronous time-series graphs of these multi-source data within the event time window and performs correlation analysis. For example, it overlays and compares the stress change curves of the monitoring points with wind speed curves and thrust curves.

[0109] S5-1, Rule-based Root Cause Diagnosis Reasoning

[0110] The system invokes a pre-defined rule base to automatically infer the most likely cause of the anomaly from the associated data. The rule base includes the following judgment logic, for example:

[0111] Environmental factor diagnosis: If the stress change trend at the monitoring point is highly consistent with the wind speed change trend (the calculated correlation coefficient is >0.8), and the data of the jacking equipment is stable, the diagnosis conclusion is "the stress exceedance is mainly caused by the increase of wind load", with a confidence level of 90%.

[0112] Diagnosis of asynchronous jacking: If the stress at the monitoring point changes abruptly, and the oil pressure of a certain jack deviates significantly from that of other jacks at the same time, and the support reaction data verifies the uneven load distribution, then the diagnosis is "asynchronous jacking leads to abnormal load distribution", with a confidence level of 85%.

[0113] Diagnosis of suspected structural damage: If the stress at the monitoring point increases abnormally, but the environmental load and jacking equipment data do not change significantly, and the stress at adjacent points also shows an abnormal distribution pattern, the diagnosis conclusion is "suspected local structural damage (such as support buckling, weld cracking)", with a confidence level of 75%, and it is recommended to conduct an immediate manual inspection.

[0114] Sensor fault diagnosis: If only a single point of data is abnormal, and there are no related changes in the data of all its surrounding neighboring points, the diagnosis conclusion is "suspected sensor fault or data transmission interference", with a confidence level of 70%.

[0115] S5-1. Generate diagnostic reports and preliminary treatment recommendations.

[0116] Based on the root cause diagnosis results, a structured diagnostic report is automatically generated, which includes:

[0117] Description of the abnormal event: time, location, and extent of exceeding the limit.

[0118] Primary root cause diagnosis: Clearly identify the most likely cause (e.g., increased wind load from the southeast).

[0119] Supporting evidence: List the results of multi-source data correlation analysis that support the diagnosis (e.g., stress-wind speed correlation coefficient of 0.9).

[0120] Confidence assessment: Indicates the reliability of this diagnosis.

[0121] Based on the diagnosis, the corresponding preliminary suggestions are output (e.g., "caused by wind load, it is recommended to closely monitor wind speed changes, and there is no need to adjust the jacking parameters for the time being" or "jacking is not synchronized, it is recommended to calibrate the hydraulic pressure of jack No. 3").

[0122] S6. Transform the analysis results of step S5 into a comprehensive report that can directly guide construction decisions, including data, charts, diagnostic conclusions, and treatment recommendations.

[0123] Example 2

[0124] A computer device for dynamic stress monitoring during the launching construction of a steel box girder bridge includes at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to perform the above-described method.

[0125] Example 3

[0126] A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the above-described method.

[0127] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0128] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0129] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0130] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0131] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0132] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as interpreted when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."

Claims

1. A steel box girder bridge pushing construction dynamic stress monitoring method, characterized in that, Comprise the following steps: S1, model initialization, establish digital twin model and static threshold library; S2, data acquisition module is laid out, and a dynamic mechanical model is established, which is used to show the load transfer path of the incremental launching system; S3, the environmental load dynamic prediction model is constructed, which is used to quantify and predict the dynamic superposition effect of environmental load on the mechanical state of the structure; S4, input the original monitoring stress data, and output the dynamic threshold based on the environmental load dynamic prediction model; S5, multi-source fusion is carried out on the data, including real-time comparison and analysis of the measured stress values of each monitoring point and the dynamic threshold; S6, the analysis results of step S5 are converted into decision support which can directly guide the construction, including comprehensive report of data, charts, diagnostic conclusions and treatment suggestions.

2. The method for monitoring dynamic stress of steel box girder bridge during incremental launching construction according to claim 1, characterized in that, The digital twin model is a parameterized finite element model established based on bridge design drawings, construction scheme and material parameters. The model includes: pier, steel box girder auxiliary support, each section of steel box girder bridge, steel guide beam, and support point of incremental launching device, which is used to simulate the mass distribution, stiffness characteristics and boundary conditions of the structure.

3. The method for monitoring the dynamic stress of a steel box girder bridge during incremental launching construction according to claim 2, characterized in that, The static threshold library is simulated and analyzed based on the digital twin model. The stress of each monitoring point under the action of dead load and standard incremental launching load at each stage is extracted, and combined with design specifications and safety factors to form an initial static threshold library.

4. The method for monitoring dynamic stress of steel box girder bridge during incremental launching construction according to claim 2, characterized in that, The digital twin model also includes a sensor layout generation unit, which is used to simulate the whole incremental launching construction process, extract stress nephogram, displacement nephogram, reaction force value and instability mode in the incremental launching process, and generate incremental launching construction monitoring sensor layout information table based on the above data, which is used as the basis for data acquisition module layout.

5. The method for monitoring the dynamic stress of steel box girder bridge during incremental launching construction according to claim 4, characterized in that, The dynamic mechanical model is a real-time data analysis algorithm, which is used to receive data from the data acquisition module, calculate the load transfer path of the whole incremental launching system, and intuitively show how the incremental launching force is distributed to each temporary support through the beam body in each incremental launching cycle, as well as the internal force distribution of the beam body itself.

6. The method for monitoring the dynamic stress of steel box girder bridge incremental launching construction according to claim 1, characterized in that, The environmental load dynamic prediction model includes wind load interference model and temperature load interference model.

7. The method for monitoring the dynamic stress of steel box girder bridge incremental launching construction according to claim 1, characterized in that, The specific implementation steps of step S4 include: S4-1, receiving the original data stream from S2 data acquisition module, including: structural mechanics data, incremental launching equipment data and environmental data, S4-2, calling the environmental load dynamic prediction model, inputting and calculating the environmental load stress increment prediction value of each monitoring point; S4-3, according to the current incremental launching stage mark and monitoring point, querying the static threshold of the point in the stage from the static threshold library established in step S1, and then down-regulating the safety threshold based on the environmental load stress increment prediction value, that is, obtaining the dynamic threshold; S4-4, finally output the dynamic threshold and send it to the visualization interface of the monitoring terminal.

8. The method for monitoring the dynamic stress of steel box girder bridge incremental launching construction according to claim 1, characterized in that, The specific implementation steps of step S5 include: S5-1, continuously compare the measured data of each monitoring point after S4 pretreatment with the output dynamic threshold, and give a warning when the dynamic threshold is exceeded; S5-2, when one of the monitoring points triggers the warning, extract all related time-synchronized multi-source data within a short time window centered on the event time point for correlation analysis; S5-3, automatically reasoning the above-mentioned correlation data to determine the most probable abnormal reason; S5-4, generating a structured diagnosis report according to the reasoning result.

9. A computer device for monitoring dynamic stress of steel box girder bridge incremental launching construction, characterized in that, The computer readable storage medium stores computer executable instructions for causing a computer to perform the method of any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer executable instructions for causing a computer to perform the method of any one of claims 1-8.

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