Stress performance analysis method of combined cable-stayed bridge and related equipment

Abstract

The invention provides a stress performance analysis method of a combined cable-stayed bridge and related equipment, and is applied to the technical field of data processing. The method comprises the steps of obtaining target bridge tower information, a target event set matched with the target bridge tower information, a training sample set and a preset cable-stayed bridge cable force prediction model; preprocessing the training sample set to generate a training sample set with target feature data; processing a preset cable-stayed bridge cable force prediction model based on the training sample set with the target feature data to generate a target cable-stayed bridge cable force prediction model; the target event set matched with the target tower information is processed, and tower cable force early warning information of the target tower bridge is generated; the target bridge tower information is processed based on the target cable-stayed bridge force prediction model, and attribute information of a target bridge tower is generated; and processing the attribute information of the target bridge tower and the bridge tower cable force early warning information of the target tower bridge based on the target cable-stayed bridge cable force prediction model, and generating target cable force early warning information of the target bridge tower information.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a force performance analysis method of a combined cable-stayed bridge and related equipment. Background Art

[0002] The cable is a key component for transmitting loads in a cable-stayed bridge, and its internal force changes have a significant impact on the stress condition of the entire bridge. After several years of service, the cable-stayed bridge will be affected by adverse long-term loads and external environmental effects, which will cause the cable to deteriorate and cause abnormal changes in cable force. There will inevitably be a certain deviation between the actual cable force and the cable force under normal operating conditions, which will seriously affect the stress condition and safe operation of the entire bridge.

[0003] For cable-stayed bridges, the stiffness and load-bearing capacity of the bridge deck structure are usually low, especially in the case of large spans. This design feature results in the main loads of the bridge being borne by multiple cables. The robustness of the cables, that is, their ability to maintain performance in the face of adverse conditions, becomes a key factor in ensuring the safety performance of the entire bridge structure. It is worth noting that the cables in a cable-stayed bridge do not work independently; the forces exerted on each cable are affected by the other cables in the bridge and also have an impact on the other cables. This interdependence means that if one or some cables fail, it may lead to a redistribution of the internal forces of the entire cable-stayed system, which may trigger an effect similar to "unbuttoning", which can lead to the failure of the cable-stayed system and even the entire cable-stayed bridge. Therefore, timely evaluation of the cable force state of the cables of a long-span cable-stayed bridge is of great significance for evaluating the overall stress state of the cable-stayed bridge.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the present application is to provide a stress performance analysis method for a combined cable-stayed bridge and related equipment and systems, which at least to a certain extent overcome the problems existing in the prior art. By conducting long-term stress performance analysis on a single-tower or multi-tower cable-stayed bridge with a combined bridge deck, automation can be achieved to the maximum extent possible, and long-term effects and geometric nonlinearity can be considered, so that cable-stayed bridge structure simulation can be achieved more realistically and efficiently, assisting structural design and operation and maintenance.

[0006] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present invention.

[0007] According to one aspect of the present application, a method for analyzing the stress performance of a combined cable-stayed bridge is provided, comprising: obtaining target bridge tower information, a target event set matching the target bridge tower information, and a training sample set, wherein the target bridge tower information comprises first beam unit information for characterizing a main beam steel structure, second beam unit information for characterizing a concrete bridge deck, and third beam unit information for characterizing a cable-stayed structure, and the training sample set is used to characterize other bridge tower information matching the target bridge tower information; preprocessing the training sample set to generate a training sample set with target feature data, wherein the target feature data is used to characterize risk factors affecting the bridge tower cable force; obtaining a preset cable-stayed bridge cable force prediction model, wherein the preset cable-stayed bridge cable force prediction model is based on the target bridge tower. The target event set matching the target bridge tower information is processed to generate the bridge tower cable force warning information of the target tower bridge; the target bridge tower information is processed based on the target cable-stayed bridge cable force prediction model to generate the attribute information of the target bridge tower, wherein the attribute information of the target bridge tower includes the initial cable force of the target bridge tower, the target temperature load value and the material shrinkage creep value of the target bridge tower; the attribute information of the target bridge tower and the bridge tower cable force warning information of the target tower bridge are processed based on the target cable-stayed bridge cable force prediction model to generate the target cable force warning information of the target bridge tower information.

[0008] Another aspect of the present application is a stress performance analysis device for a combined cable-stayed bridge, characterized in that it includes: an acquisition module for acquiring target bridge tower information, a target event set matching the target bridge tower information, and a training sample set, wherein the target bridge tower information includes first beam unit information for characterizing a main beam steel structure, second beam unit information for characterizing a concrete bridge deck, and third beam unit information for characterizing a cable-stayed structure, and the training sample set is used to characterize other bridge tower information matching the target bridge tower information; acquiring a preset cable-stayed bridge cable force prediction model, wherein the preset cable-stayed bridge cable force prediction model is generated based on historical structural information of the target bridge tower information; a processing module for preprocessing the training sample set to generate a training sample set with target feature data, wherein the The target characteristic data is used to characterize the risk factors that affect the cable force of the bridge tower; the preset cable-stayed bridge cable force prediction model is processed based on the training sample set with the target characteristic data to generate a target cable-stayed bridge cable force prediction model; the target event set matching the target bridge tower information is processed to generate the bridge tower cable force warning information of the target tower bridge; the target bridge tower information is processed based on the target cable-stayed bridge cable force prediction model to generate the attribute information of the target bridge tower, wherein the attribute information of the target bridge tower includes the initial cable force of the target bridge tower, the target temperature load value and the material shrinkage creep value of the target bridge tower; the attribute information of the target bridge tower and the bridge tower cable force warning information of the target tower bridge are processed based on the target cable-stayed bridge cable force prediction model to generate the target cable force warning information of the target bridge tower information.

[0009] According to another aspect of the present application, an electronic device is provided, characterized in that it comprises: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to implement the above-mentioned method for analyzing the stress performance of a combined cable-stayed bridge by executing the executable instructions.

[0010] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a second processor, the above-mentioned method for analyzing the stress performance of a combined cable-stayed bridge is implemented.

[0011] According to another aspect of the present application, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a third processor, the above-mentioned method for analyzing the stress performance of a combined cable-stayed bridge is implemented.

[0012] The present application provides a method and related equipment for analyzing the stress performance of a combined cable-stayed bridge, which obtains target bridge tower related information, a matching event set and a training sample set, wherein the target bridge tower information covers the beam unit information of the main beam steel structure, the concrete bridge deck and the cable-stayed structure. Then, the training sample set is preprocessed, and a training sample set with target feature data representing the risk factors affecting the bridge tower cable force is generated by obtaining the preset mapping table grouping, feature extraction, data set division, classifier prediction and algorithm training. Then, the preset cable-stayed bridge cable force prediction model is used to generate the target cable-stayed bridge cable force prediction model based on the processed training sample set. For the target event set, the type information, historical cable force value information, cable force influencing factor and weight information are obtained after processing, and then the bridge tower cable force warning information is generated. When generating the target bridge tower attribute information, the bending stiffness values ​​of the beam unit under different bearing weights are obtained to generate multiple groups of cable force values, and the target temperature load value is obtained by combining the incremental step and other information. At the same time, the effective creep strain, stress increment, true elastic strain and true creep strain are calculated according to the material information, so as to determine the material shrinkage creep value. Finally, based on the model, the target bridge tower attribute information and bridge tower cable force warning information are processed to calculate the target cable force value and cable force warning value, and then the target cable force warning information is generated, providing a comprehensive analysis and prediction for the target bridge tower cable force warning.

[0013] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart showing a method for analyzing the stress performance of a combined cable-stayed bridge provided in one embodiment of the present application;

[0015] Figure 2 A schematic structural diagram of a device for analyzing the force performance of a combined cable-stayed bridge provided in one embodiment of the present application is shown;

[0016] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present application is shown;

[0017] Figure 4 A schematic diagram of a storage medium provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0019] Combine the following Figure 1To describe the stress performance analysis method of the combined cable-stayed bridge according to the exemplary embodiment of the present application. It should be noted that the following application scenarios are only shown to facilitate understanding of the spirit and principle of the present application, and the embodiments of the present application are not limited in this regard. On the contrary, the embodiments of the present application can be applied to any applicable scenario.

[0020] In one implementation, the present application also proposes a method for analyzing the stress performance of a combined cable-stayed bridge and related equipment. Figure 1 The following schematically shows a flow chart of a method for analyzing the stress performance of a combined cable-stayed bridge according to an embodiment of the present application. Figure 1 As shown, the method is applied to a server, comprising:

[0021] S101, obtaining target bridge tower information, a target event set matching the target bridge tower information, and a training sample set.

[0022] In one embodiment, the target bridge tower information includes first beam unit information for characterizing the main beam steel structure, second beam unit information for characterizing the concrete bridge deck, and third beam unit information for characterizing the cable-stayed structure, and the training sample set is used to characterize other bridge tower information that matches the target bridge tower information. Specifically, the first beam unit information (main beam steel structure): Assuming that the main beam steel structure is composed of multiple steel beams, the first beam unit information may include the cross-sectional dimensions of each steel beam (such as height, width, thickness, etc.), the material properties of the steel beam (such as elastic modulus, yield strength, etc.), the connection method between the steel beams (welding, bolt connection, etc.), the position coordinates of the steel beam in the bridge tower (such as coordinates along the length of the bridge and in the vertical direction), etc. For example, the cross-sectional height of a steel beam is 1.5 meters, the width is 0.5 meters, the thickness is 0.05 meters, the elastic modulus is 200 GPa, the yield strength is 345 MPa, and it is connected to the adjacent steel beam by welding. The coordinates in the bridge tower are (10 meters, 5 meters). Second beam unit information (concrete bridge deck): For concrete bridge deck, the second beam unit information can include the strength grade of concrete (such as C40, C50, etc.), the thickness of the bridge deck, the mix ratio of concrete (the ratio of cement, aggregate, water, etc.), the reinforcement conditions in the bridge deck (steel bar diameter, spacing, layout method, etc.), and the connection method between the bridge deck and the main beam steel structure (such as connection through shear nails). For example, the thickness of the concrete bridge deck is 0.3 meters, the strength grade is C50, the mix ratio is cement: aggregate: water = 1:2:0.4, the reinforcement uses 16mm diameter steel bars, spacing 0.2 meters, double-layer bidirectional arrangement, and connected to the main beam through 22mm diameter shear nails. Third beam unit information (cable-stayed structure): The third beam unit information of the cable-stayed structure includes the material of the cable (such as high-strength steel wire, steel strand, etc.), the diameter of the cable, the length of the cable, the initial tension of the cable, the anchoring method of the cable to the main beam and the bridge tower, etc. For example, the cable-stayed cable is made of high-strength steel wire with a diameter of 7mm, a cable length of 100 meters, an initial tension of 500kN, and is anchored at the main beam and bridge tower with hot-cast anchors.

[0023] The training sample set contains similar information of multiple other bridge towers that match the target bridge tower information. For example, there are three other bridge towers. For the first other bridge tower, the cross-sectional dimensions of the steel beam of its main beam steel structure are different from those of the target bridge tower. The height is 1.2 meters, the width is 0.6 meters, the thickness is 0.04 meters, the material elastic modulus is 180GPa, the yield strength is 300MPa, and the connection method is bolt connection; the concrete bridge deck is 0.25 meters thick, the strength grade is C45, the mix ratio is slightly different, the reinforcement diameter is 14mm, and the spacing is 0.25 meters; the cable is made of the same material but has a diameter of 6.5mm, a cable length of 95 meters, and an initial tension of 450kN. The second and third other bridge towers also have their own different parameter information. The information of these different bridge towers is combined to form a training sample set for subsequent model training and analysis to help predict the performance of the target bridge tower such as cable force under different conditions.

[0024] The target event set that matches the target bridge tower information is as follows: Earthquake event: A magnitude 5.5 earthquake occurred locally, with the epicenter 20 kilometers away from the target bridge tower. Parameters such as ground acceleration and vibration duration generated when the earthquake occurred constitute the specific information of the earthquake event. For example, the horizontal ground acceleration peak reached 0.2g, the vertical ground acceleration peak was 0.15g, and the vibration duration was about 30 seconds. This earthquake event will have different degrees of impact on the structure of the target bridge tower, such as causing the displacement of the bridge tower foundation and the intensification of the vibration of the cable, thereby affecting the cable force. Strong wind event: The area where the target bridge tower is located encountered a strong wind weather lasting 12 hours, with a maximum wind speed of 40 meters per second (equivalent to a force 13 wind), and the wind direction was mainly northwest. Strong winds will generate large wind loads on the bridge tower and main beam, generate lateral forces and torques on the bridge tower structure, and cause the cable to bear additional tension or pressure changes. For example, strong winds cause the main beam to have a certain lateral displacement, with the maximum displacement reaching 0.5 meters, which will affect the cable force distribution of the inclined cable through structural transmission.

[0025] Overweight vehicle crossing bridge event: An overweight truck with a total weight exceeding the design weight limit of the bridge by 50% (for example, the design weight limit of the bridge is 50 tons, and the actual total weight of the truck is 75 tons) passes through the bridge where the target bridge tower is located. The vehicle's axle weight distribution, driving speed, driving trajectory on the bridge and other information constitute the characteristics of this event. Overweight vehicles will cause the bridge structure to bear a greater load, especially the stress on the main beam and the inclined cable. For example, when the vehicle passed, the main beam part near the inclined cable on one side produced a large bending deformation, causing the cable force on that side to increase by 20% instantly. Traffic congestion event: During a peak period, the bridge where the target bridge tower is located experienced severe traffic congestion, and the vehicle density on the bridge reached 3 times that of normal traffic conditions, and the vehicles were moving slowly or even stagnant. Long-term traffic congestion will cause the bridge structure to bear continuous dynamic loads, which is different from the stress conditions under normal traffic flow. In this case, the impact force generated by the frequent starting and braking of the vehicle will accumulate, resulting in aggravated fatigue damage to the bridge tower and the inclined cable, affecting the long-term stability of the cable force. For example, monitoring has found that during traffic congestion, the frequency of cable force fluctuations in the inclined cables increases, and the fluctuation amplitude is 10% larger than normal.

[0026] Nearby foundation pit excavation event: A large foundation pit excavation project was carried out 50 meters around the target bridge tower, with an excavation depth of 10 meters and an excavation range of 100 meters long and 80 meters wide. The foundation pit excavation process will cause stress release and displacement of the surrounding soil, which will affect the foundation of the bridge tower. For example, the foundation pit excavation caused a 5 cm horizontal displacement of the soil near the bridge tower foundation, which will cause the bridge tower to tilt to a certain extent, thereby changing the stress state of the inclined cable, and the cable force will change unevenly, and some cable forces will increase or decrease. Adjacent building foundation construction event: A high-rise building is undergoing foundation piling construction 80 meters away from the target bridge tower. A hammer pile driver is used. The vibration and soil squeezing effect generated during the piling process will be transmitted to the bridge tower structure. The frequency of the piling vibration is 15 Hz, the amplitude is 2 mm, and the soil squeezing pressure is estimated to be 100 kPa. These factors will cause the vibration response of the bridge tower and affect the cable force stability of the inclined cable. For example, monitoring found that during the piling period, the cable force of some inclined cables fluctuated periodically, with the maximum fluctuation amplitude reaching 15 kN.

[0027] S102, preprocessing the training sample set to generate a training sample set with target feature data.

[0028] In one embodiment, a preset mapping table is obtained, wherein the preset mapping table is used to characterize the historical structural information of the target bridge tower information and the corresponding information of the historical event set of the target bridge tower information. Assume that the target bridge tower is a cable-stayed bridge tower located on a city main road. The preset mapping table records the correspondence between the historical structural information and the historical event set of the bridge tower over the past ten years. For example, in terms of historical structural information, the maintenance and reinforcement of the steel structure of the main beam of the bridge tower in different years (such as the replacement of part of the steel beam of the main beam in 2015), the repair record of the concrete bridge deck (such as grouting treatment of local cracks in the bridge deck in 2018), and the replacement or adjustment record of the cable (such as the replacement of two cables in 2020), etc. are recorded. At the same time, corresponding to the changes in these structural information, historical events that occurred during the same period were recorded, such as a magnitude 4.5 earthquake in the vicinity in 2015 (although it did not cause serious damage to the bridge tower, it affected the structural state), the bridge experienced a flood in 2018 (the flood water level reached a certain height near the bridge tower foundation), and the increase in bridge overload in 2020 due to increased traffic flow. This information constitutes a preset mapping table for subsequent analysis of the relationship between different structural states and events.

[0029] The training sample set is grouped based on the preset mapping table to generate a grouped training sample set, wherein the grouped training sample set includes monitoring points to be predicted with different monitoring lengths. The training sample set contains information about other similar bridge towers. According to the preset mapping table, these bridge towers are grouped according to different monitoring lengths. For example, the monitoring length is set to three categories: 1 year, 3 years, and 5 years. For the group with a monitoring length of 1 year, those bridge towers with similar structural maintenance records (such as local reinforcement of steel beams) and similar changes in the surrounding environment (such as small construction activities nearby) in the past year are selected as monitoring points to be predicted; for the group with a monitoring length of 3 years, the bridge towers that have undergone major structural adjustments (such as re-paving of bridge decks) within three years and have experienced certain climate disasters (such as multiple rainstorms) are selected; for the group with a monitoring length of 5 years, the bridge towers that have undergone cable adjustments within five years and have significant changes in traffic flow in the area are focused on. In this way, a grouped training sample set is generated, and the bridge towers in each group have similar monitoring cycles and characteristics.

[0030] The feature extraction of the grouped training sample set is performed to determine the original feature library. For the grouped training sample set, feature extraction is started. Taking the group with a monitoring length of 1 year as an example, the extracted features include: the maximum horizontal displacement of the bridge tower within this year (obtained through sensor monitoring), the maximum stress change value of the main beam steel structure (calculated based on the strain gauge measurement data), the crack development rate of the concrete bridge deck (regular inspection records), the cable force change amplitude of the inclined cable (cable force monitoring system data), the ambient temperature change range (weather station data), the daily average and peak changes in traffic flow (traffic monitoring equipment data), etc. These features are summarized to form the original feature library for this group. For groups with other monitoring lengths, similar methods are also used to extract corresponding features. For example, the group with a monitoring length of 3 years can also add features such as structural material performance change indicators (such as concrete strength attenuation rate), and the group with a monitoring length of 5 years can consider features such as long-term cable fatigue damage factors.

[0031] Divide each feature data set according to the original feature library to generate a training set and a validation set. Assume that there are 100 feature data in the original feature library (consider all groups comprehensively). Divide these data into training sets and validation sets according to a certain ratio (such as 70% and 30%). For example, 70 feature data are randomly selected as the training set to train the prediction model; the remaining 30 feature data are used as the validation set to test the accuracy of the model. In the division process, it is necessary to ensure that the data of the training set and the validation set are representative and can cover different types of bridge tower structural states and environmental conditions. For example, the training set should have both bridge tower feature data that have experienced minor natural disasters and bridge tower data that have been subjected to large changes in traffic loads; the same is true for the validation set, and the data distribution should be similar to that of the training set to avoid data bias that causes model overfitting or underfitting.

[0032] Based on the classifier, each validation set divided by the original feature library is predicted and processed to generate prediction results. The support vector machine (SVM) classifier is used to predict the validation set. The SVM classifier classifies and predicts the bridge tower feature data in the validation set based on the feature patterns learned from the training set. For example, it predicts whether the bridge tower will have abnormal cable tension in the future (divided into normal and abnormal categories). For each bridge tower feature data in the validation set, the SVM classifier calculates the probability of it belonging to a normal or abnormal cable tension state. For example, for a certain bridge tower, after its feature data is input into the SVM classifier, the probability of belonging to a normal cable tension state is 0.7, and the probability of belonging to an abnormal state is 0.3. These predicted probabilities are used as prediction results for subsequent comparison with actual conditions to evaluate the performance of the classifier.

[0033] Based on the preset algorithm, each training set divided in the original feature library is trained and processed to generate the validation set class prediction results. The training set is trained and processed using a neural network algorithm. The neural network consists of an input layer (receiving bridge tower feature data), a hidden layer (converting and learning data features), and an output layer (outputting prediction results). For example, the input layer has 10 nodes, corresponding to 10 key bridge tower features (such as main beam stress, cable force changes, etc.); the hidden layer is set to 3 layers, with 20, 15 and 10 nodes in each layer; the output layer has 2 nodes, representing the normal and abnormal cable force states. Through multiple iterative training, the weights and thresholds of the neural network are adjusted so that the model can accurately predict the cable force state based on the input training set feature data. Then the trained model is applied to the validation set to obtain the validation set class prediction results, that is, the prediction of the cable force state (normal or abnormal) of each bridge tower in the validation set.

[0034] The prediction results and the validation set prediction results are processed to generate target feature data, which are used to characterize the risk factors affecting the cable force of bridge towers. The prediction results obtained by the SVM classifier (such as the predicted probability of the cable force state of each bridge tower) and the validation set prediction results obtained by the neural network (such as the specific normal or abnormal classification of cable force) are processed comprehensively. For example, the consistency ratio of the two is calculated. If the SVM predicts that the probability of normal cable force of a bridge tower is 0.8, and the neural network also predicts that the cable force of the bridge tower is normal, then the consistency ratio of the bridge tower is high. These consistency ratios, the average value of the prediction probability and other information are used as target feature data. These target feature data can characterize the risk factors affecting the cable force of bridge towers. For example, a high consistency ratio indicates that the model's prediction of the cable force state of the bridge tower is relatively reliable, which can be used as an important basis for risk assessment; if the average value of the prediction probability is close to 0.5, it means that the cable force state of the bridge tower is on the edge of instability and there is a high risk.

[0035] S103, obtaining a preset cable-stayed bridge cable force prediction model.

[0036] In one embodiment, the preset cable-stayed bridge cable force prediction model is composed of the following models: Structural mechanics analysis module: Based on the historical structural information of the target bridge tower information, this module contains mechanical analysis models for the bridge tower, main beam steel structure, concrete bridge deck and cable structure. For example, for the main beam steel structure, the beam unit model is used to calculate the internal force (bending moment, shear force, axial force) and deformation (deflection, rotation angle) of the steel beam under different loads. The model considers the influence of the cross-sectional characteristics of the steel beam (such as moment of inertia, cross-sectional area), the elastic modulus of the material and the node connection method on the mechanical properties. For the concrete bridge deck, the plate-shell unit model is used to analyze its stress distribution and deformation under factors such as vehicle load and temperature change, while considering the nonlinear characteristics of concrete (such as cracking and creep). The cable unit model is used for the cable to simulate its axial force characteristics under tension, considering factors such as the sag effect of the cable and the change in elastic modulus. By combining these mechanical models of different structures, the mechanical response of the entire cable-stayed bridge structure under various working conditions can be accurately simulated, providing a basis for cable force prediction.

[0037] Data-driven prediction module: This module uses a large amount of monitoring data (such as cable force values, temperature changes, traffic flow, structural deformation, etc. in different periods) in the historical structural information of the target bridge tower information to train the model. For example, a neural network algorithm is used, and the input layer nodes of the neural network correspond to different types of monitoring data (such as cable force, temperature, traffic flow, etc.), and the output layer nodes are the predicted cable force values. The hidden layer establishes a complex nonlinear relationship between the input data and the output cable force by learning from historical data. During the training process, the model continuously adjusts the connection weights between neurons to minimize the error between the predicted cable force and the actual monitored cable force. At the same time, time series analysis methods, such as the ARIMA (autoregressive moving average) model, can also be used to predict future cable force values ​​based on the historical change law of cable force. This module can fully mine the information in historical data and improve the accuracy of cable force prediction.

[0038] Environmental impact assessment module: Considering the impact of environmental factors on cable force, this module is modeled based on the historical environmental data of the target bridge tower (such as temperature change range, humidity change, wind load history records, etc.). For example, a temperature-cable force relationship model is established to analyze the impact of thermal expansion and contraction of materials caused by temperature changes on cable force, and the periodic law of temperature changes in different seasons and day and night is considered. For wind loads, a relationship model between wind load and cable force dynamic response is established through wind tunnel test data or on-site measured wind data, and the impact of different wind speeds and wind directions on the vibration characteristics of the cable and cable force changes are analyzed. Humidity changes affect the material properties of concrete bridge decks and cables, and then affect the cable force. This module will also conduct a quantitative assessment of this, and incorporate environmental factors into the cable force prediction model to make the prediction results more in line with actual conditions.

[0039] Uncertainty analysis module: Due to the complexity of bridge structures, the uncertainty of material properties, and the randomness of external loads, this module is used to analyze the uncertainty in cable force prediction. For example, considering the uncertainty of the elastic modulus of steel, its value range is described by probability distribution (such as normal distribution), and the impact of this uncertainty on the cable force prediction results is analyzed. For traffic loads, considering the randomness of vehicle type, weight, and driving speed, the Monte Carlo simulation method is used to generate a large number of random traffic load conditions, calculate the range of cable force changes under different conditions, and give the confidence interval of cable force prediction. At the same time, the uncertainty of structural parameters (such as dimensional errors of main beams and bridge towers, discreteness of concrete strength, etc.) is also quantitatively analyzed to provide a more comprehensive risk assessment for cable force prediction.

[0040] The historical structural information based on the target bridge tower information includes the following: Structural geometry change history: records the structural geometry information of the target bridge tower at different times, such as the length, width, and height changes of the main beam (caused by maintenance, reinforcement, or long-term deformation), the verticality changes of the bridge tower, and the length adjustment of the inclined cable. This information is used in the structural mechanics analysis module in the model to accurately update the geometric model of the structure and make the mechanical calculation more consistent with the actual structural state. For example, if the length of the main beam increases by 0.5 meters during a maintenance, the model will adjust the length parameters of the beam unit accordingly, thereby recalculating the internal forces and deformations of the structure under various loads, which in turn affects the cable force prediction results.

[0041] Material performance evolution history: Contains data on the changes in the material performance of concrete bridge decks and main beam steel structures over time, such as the growth and decay of concrete strength, the accumulation of fatigue damage to steel, and changes in the material elastic modulus. In the mechanical analysis and prediction modules of the model, these data are used to update material parameters and more accurately simulate the mechanical behavior of the structure at different service stages. For example, as the strength of concrete decreases over time, the model will correspondingly reduce the bearing capacity calculation parameters of the concrete bridge deck, resulting in a redistribution of the internal forces of the structure, affecting the stress on the cable, and thus correcting the cable force prediction.

[0042] Load history records: Detailed records of various load histories borne by the target bridge tower, including traffic loads (flow of different types of vehicles, changes in weight distribution over time), wind loads (wind speed and direction records in different seasons and weather conditions), temperature loads (daily and annual temperature change curves), etc. These load history data are important inputs to the environmental impact assessment module and data-driven prediction module in the model. For example, by analyzing many years of traffic load data, the model can predict the trend of cable force changes under future traffic flow growth; based on the temperature load history, it can accurately calculate the seasonal fluctuation law of cable force caused by temperature changes, thereby improving the accuracy of cable force prediction.

[0043] Structural maintenance and reinforcement records: Records all the structural maintenance and reinforcement work that has been done on the target bridge tower in the past, such as the replacement time of the cable and the cable force adjustment value after replacement, the weld repair of the main beam steel structure, and the crack repair measures of the concrete bridge deck. These records are used to update and calibrate the model to ensure that the model can reflect the changes in the actual state of the structure. For example, in the cable force prediction model, when there is a record of cable replacement, the model will recalculate the mechanical properties of the entire structure based on the cable parameters after replacement (such as diameter, elastic modulus, initial tension, etc.), so that the subsequent cable force prediction is based on the latest structural state, improving the reliability of the prediction results.

[0044] Structural health monitoring data history: including structural response data collected over a long period of time through various sensors (such as cable force sensors, strain gauges, displacement sensors, etc.), such as historical values ​​of cable forces at different locations, strain changes at key parts of the main beam and bridge tower, and displacement monitoring data of the entire structure. These rich monitoring data are the basis for training models in the data-driven prediction module. By learning from a large amount of historical monitoring data, the model can discover the intrinsic relationship between cable forces and other structural responses, thereby accurately predicting future cable force changes. At the same time, the monitoring data history is also used to verify the accuracy of the model. By comparing the prediction results with the actual monitoring data, the model parameters and algorithms are continuously optimized to improve the performance of the cable force prediction model.

[0045] S104: Processing the preset cable-stayed bridge cable force prediction model based on the training sample set with target feature data to generate a target cable-stayed bridge cable force prediction model.

[0046] In one embodiment, it is assumed that the preset cable-stayed bridge cable force prediction model is constructed using a neural network algorithm, and the neural network structure includes an input layer, a hidden layer, and an output layer. The input layer nodes correspond to the target feature data in the training sample set, such as the bridge tower foundation settlement, the main beam temperature change, the traffic flow level, the ambient humidity, the current cable force value of the cable, whether there is an earthquake or strong wind event in the near future (expressed as 0 / 1), etc. The hidden layer is set to several layers (such as 2-3 layers), and the number of nodes in each layer is determined according to the actual situation (such as 10-20 nodes), which is used to perform complex nonlinear transformations on the input data. The output layer nodes are the predicted cable force values.

[0047] First, the training sample set with target feature data is divided into a training set and a validation set, for example, in a ratio of 70%:30%. Then, the training set data is input into a preset neural network model for training. During the training process, the weight and threshold of the neural network are adjusted using the back propagation algorithm according to the difference (error) between the output predicted cable force value and the actual cable force value in the training sample set. For example, the mean square error (MSE) is used as the loss function. This application does not limit the specific loss function, and the applicant can choose it according to actual needs. Through continuous iterative training, the loss function value is gradually reduced until the set convergence condition is met (such as the loss function value is less than a certain threshold or the number of iterations reaches the upper limit). After the training is completed, the validation set is used to evaluate the trained model. Calculate the prediction accuracy of the model on the validation set, root mean square error (RMSE) and other evaluation indicators. For example, the prediction accuracy can be obtained by calculating the ratio of the number of correctly predicted cable force values ​​to the total number of samples in the validation set. If the evaluation index does not meet the requirements, the model is optimized. Optimization methods can include adjusting the neural network structure (such as increasing or decreasing the number of hidden layers or nodes), adjusting training parameters (such as learning rate, number of iterations), and using regularization techniques to prevent overfitting (such as L1 or L2 regularization).

[0048] For example, in one training, the RMSE of the initial model on the validation set was 50kN. After analysis, it was found that the excessive number of hidden layer nodes caused overfitting. Therefore, the number of hidden layer nodes was reduced from 20 to 15, and the model was retrained. The RMSE of the new model on the validation set was reduced to 30kN, and the prediction accuracy was increased from 70% to 80%, meeting the set performance requirements. At this point, the model is the target cable-stayed bridge cable force prediction model after training and optimization, which can be used to predict the cable force of the target bridge tower.

[0049] When the real-time monitoring data of the target bridge tower is obtained (including target feature data of the same type as the training sample set), these data are input into the target cable-stayed bridge cable force prediction model, and the model can output the predicted cable force value. For example, real-time monitoring shows that the current main beam temperature of the target bridge tower has increased by 10°C, the traffic flow is at a medium level, the ambient humidity is 60%, the current cable force of the cable is 1000kN, and there are no earthquakes or strong wind events in the near future. These data are sorted into the same format as the training sample set and input into the model. After calculation, the model outputs the predicted cable force value for the next period of time as 1020kN. By continuously inputting real-time data, the model can continuously predict the trend of cable force changes, providing an important reference for the operation and maintenance of the bridge.

[0050] S105, processing the target event set matching the target bridge tower information to generate bridge tower cable force warning information of the target tower bridge.

[0051] In one implementation, the target event set matching the target bridge tower information is processed to generate the type information of the target event, wherein the type information of the target event is used to characterize the risk warning level of the target event to the target bridge tower. Assume that the target event set matching the target bridge tower information includes the following events: Earthquake event: A magnitude 5.0 earthquake occurred in the area where the target bridge tower is located, and the epicenter was about 30 kilometers away from the bridge tower. According to factors such as the magnitude of the earthquake, the distance from the epicenter, and the local geological conditions, the earthquake event is classified as a medium-risk event, and the corresponding risk warning level is 3 (the risk warning level range is set to 1-5, 1 is low risk, and 5 is high risk). Strong wind event: In a certain season, the area encountered strong winds lasting 8 hours, with a maximum wind speed of 35 meters per second. Considering the relative relationship between the wind speed, duration, and wind direction and the bridge tower structure, this strong wind event is evaluated as a medium-low risk event, and the risk warning level is 2. Overweight vehicle crossing the bridge event: A large truck that is 30% overweight (relative to the design weight limit of the bridge) passes through the bridge. Based on the vehicle's overweight, driving speed and the sensitivity of the bridge structure to overloads, the incident was judged to be a high-risk event with a risk warning level of 4.

[0052] The type information of the target event is processed to generate the historical cable force value information of the target bridge tower. According to the type information of the target event, the cable force monitoring data of the target bridge tower in a period of time before and after the corresponding event is queried to obtain the historical cable force value information. For example, for the above earthquake event, the cable force monitoring records from 1 hour before the earthquake to 2 hours after the earthquake are checked. Assume that before the earthquake, the average cable force of the inclined cable is 800kN. When the earthquake occurs, the cable force fluctuates instantly, reaching a maximum of 950kN. After the earthquake, the cable force gradually stabilizes at around 820kN for a period of time. For the strong wind event, the cable force value during the strong wind and half an hour before and after is checked, and it is found that the cable force fluctuates between 780kN and 830kN under the action of strong wind. In the event of an overweight vehicle crossing the bridge, the cable force briefly rises from 800kN to 880kN when the vehicle passes, and then returns to a value close to the initial value after the vehicle passes. These historical cable force value information can reflect the immediate and subsequent impact of the target event on the cable force, and provide data support for analyzing the law of cable force change.

[0053] The historical cable force value information of the target bridge tower is processed to generate the cable force influence factor of the target bridge tower and the weight information corresponding to the cable force influence factor of the target bridge tower. Based on the historical cable force value information, the cable force influence factor is analyzed and determined. For earthquake events, the influencing factors include the peak value of seismic acceleration (such as 0.15g), the duration of the earthquake (30 seconds), the natural vibration period of the bridge tower structure (2 seconds), etc. The influencing factors of strong wind events include wind speed (35 meters / second), the angle between wind direction and the bridge tower (45 degrees), and the pulsation characteristics of wind (such as turbulence intensity 0.2). The influencing factors of overweight vehicle crossing the bridge event are the vehicle overweight ratio (30%), vehicle speed (40 kilometers / hour), vehicle wheelbase (5 meters), etc.

[0054] The weight information corresponding to each cable force influencing factor is determined by statistical analysis or numerical simulation methods. For example, after studying the relationship between multiple earthquake events and cable force changes, it was found that the peak value of seismic acceleration has a greater impact on cable force, and the weight is set to 0.4; the weight of earthquake duration is 0.3; the weight of the natural vibration period of the bridge tower is 0.3. For strong wind events, the wind speed weight is 0.5, the angle between wind direction and bridge tower is 0.3, and the weight of wind pulsation characteristics is 0.2. In the event of overweight vehicles crossing the bridge, the weight of the vehicle overweight ratio is 0.6, the weight of the driving speed is 0.2, and the weight of the wheelbase is 0.2. These weight information reflects the relative importance of each influencing factor in affecting the change of cable force.

[0055] Based on the preset bridge tower cable force influence feature set, the cable force influence factor of the target bridge tower and the weight information corresponding to the cable force influence factor of the target bridge tower are processed to generate the target cable force warning information of the target bridge tower. It is assumed that the preset bridge tower cable force influence feature set contains the following features: Cable force change amplitude feature: records the maximum change value, average change value and standard deviation of the cable force of the target bridge tower in different time intervals. For example, in the past week, the maximum change amplitude of the cable force of the cable-stayed cable was 50kN (relative to the initial cable force), the average change amplitude was 20kN, and the standard deviation was 10kN. These data can reflect the short-term fluctuation of the cable force. The larger change amplitude implies that the structure is abnormally stressed or the external interference factor is strong. Cable force change rate feature: calculates the change amount of cable force per unit time, such as the change rate of cable force per hour. If the cable force change rate suddenly increases within a certain period of time, for example, from a normal change of 1kN per hour to 5kN per hour, this indicates that the structure is undergoing rapid stress adjustment due to sudden events (such as strong winds, earthquakes, etc.) or the development of structural damage. Environmental factor correlation characteristics: Consider the impact of environmental factors on cable force, such as the correlation between temperature changes and cable force changes. If the cable force increases by 20kN on average for every 10°C increase in temperature, and this correlation has a high consistency in historical data, then temperature change is an important cable force influencing factor, and its corresponding weight will be higher. In addition, it also includes the relationship characteristics between environmental factors such as humidity and wind speed and cable force changes. Structural status characteristics: Indicators involving the health status of the bridge tower structure itself, such as the degree of corrosion of the main beam steel structure (quantified by the percentage of corrosion area or remaining wall thickness), the development of cracks in the concrete bridge deck (crack length, width, number, etc.), and the degree of fatigue damage of the cable (by monitoring the number of stress cycles, wire breakage rate, etc.). These structural status characteristics interact with the cable force. Structural damage leads to abnormal cable force distribution, and changes in cable force also accelerate the development of structural damage.

[0056] For the cable force influencing factors and weight information of the target bridge tower, it is assumed that the following main cable force influencing factors and their weights are determined through previous analysis: Cable force variation caused by strong wind (weight 0.3): In a strong wind event, the cable force variation reached 40kN. Cable force variation caused by temperature change (weight 0.25): The temperature has risen by 15℃ recently. According to the temperature-cable force relationship model, the cable force is expected to increase by 30kN (there is a certain deviation in actual monitoring). Main beam steel structure corrosion degree (weight 0.2): After testing, the corrosion area of ​​the main beam steel structure accounts for 5%. According to the empirical formula, this leads to a gradual increase in cable force in the long term. It is assumed that the current cable force increases by 15kN (considering the impact of corrosion on structural stiffness and internal force distribution). Cable force fluctuation caused by traffic flow change (weight 0.15): Due to the construction of nearby roads, the traffic flow temporarily increased by 30%, resulting in an increase in the cable force fluctuation amplitude, with an average fluctuation value of 10kN. Fatigue damage of the cable (weight 0.1): Through cable force monitoring and fatigue analysis, it is found that the fatigue damage of the cable accumulates, and the impact on the cable force is equivalent to a 5kN reduction in the current cable force (because fatigue damage reduces the stiffness of the cable and redistributes the cable force).

[0057] According to the above influencing factors and weights, the target cable force warning value is calculated. The calculation formula can be set as: target cable force warning value = (strong wind cable force change amplitude × weight + temperature cable force change × weight + steel structure corrosion cable force change × weight + traffic flow cable force fluctuation × weight + cable fatigue cable force change × weight) × correction coefficient. Assume that the correction factor is 1.2 (to consider other potential factors or uncertainties that are not fully covered in the above influencing factors). Substitute the data for calculation: target cable force warning value = (40kN × 0.3 + 30kN × 0.25 + 15kN × 0.2 + 10kN × 0.15-5kN × 0.1) × 1.2 = (12kN + 7.5kN + 3kN + 1.5kN-0.5kN) × 1.2 = 23.5kN × 1.2 = 28.2kN.

[0058] According to the calculated target cable force warning value, set the warning level. For example, the warning level is set as follows: When the target cable force warning value is less than 10kN, it is a green warning, indicating that the cable force is within the normal fluctuation range and the structural safety is in good condition. When the target cable force warning value is between 10kN-20kN, it is a yellow warning, indicating that the cable force has changed to a certain extent and needs to be monitored more closely, but there is no obvious safety risk to the structure for the time being. When the target cable force warning value is greater than 20kN, it is a red warning, indicating that the cable force has changed significantly and there are structural safety hazards. Further inspections and response measures need to be taken immediately, such as detailed inspections of the structure, traffic flow restrictions, and assessments of whether emergency repairs are needed.

[0059] In this example, the target cable force warning value is 28.2kN, so a red warning message is generated to remind relevant personnel to pay attention to the cable force status of the target bridge tower and take timely measures to ensure the safe operation of the bridge. At the same time, the warning information can also include detailed analysis results of influencing factors, such as the contribution of strong winds, temperature changes and other factors to the cable force, so that technicians can more accurately determine the cause of the structural problem and develop targeted solutions.

[0060] S106: Process the target bridge tower information based on the target cable-stayed bridge cable force prediction model to generate attribute information of the target bridge tower.

[0061] In one implementation, the bending stiffness values ​​of the first beam unit and the second beam unit under different bearing weights are obtained. It is assumed that the main beam steel structure (first beam unit) of the target bridge tower and the concrete bridge deck (second beam unit) jointly bear the bridge deck load. The bending stiffness value is obtained by performing a loading test on a scaled model with the same material and size ratio as the actual structure in the laboratory. For example, for the first beam unit (steel beam), different bearing weights are set, starting from a lighter 100kN and gradually increasing to 500kN, with an increase of 100kN each time. Under each bearing weight, the deflection of the steel beam is measured by the displacement measurement method, and according to the material mechanics formula (where K is the bending stiffness, P is the applied load, and δ is the deflection generated) to calculate the bending stiffness value. Similar tests are also conducted for the second beam unit (concrete bridge deck). Due to the nonlinear characteristics of concrete, more complex test methods are required, such as graded loading and continuous monitoring of strain changes, and the use of structural analysis software to assist in the calculation of bending stiffness. Assuming that the bending stiffness of the first beam unit is calculated to be 1×10 6 kN.m 2 , the second beam element is 0.5×10 6 kN.m 2 ; Under the load of 200 kN, the bending stiffness of the first beam unit becomes 0.9×10 6 kN.m 2 (Since the steel beam enters the elastic-plastic stage, the stiffness is slightly reduced). The second beam element is 0.48×10 6 kN.m 2 (The development of micro cracks inside the concrete leads to a decrease in stiffness), etc., and so on, the bending stiffness values ​​under different bearing weights are obtained.

[0062] The bending stiffness values ​​of the first beam unit and the second beam unit under different bearing weights are processed respectively to generate multiple sets of cable force values. According to the principles of structural mechanics, the bridge tower, the main beam and the inclined cable constitute an interactive system. After the bending stiffness values ​​of the first beam unit and the second beam unit under different bearing weights are known, the cable force values ​​are calculated by establishing the mechanical equilibrium equation of the structure. For example, the overall model of the bridge tower-main beam-inclined cable is established using finite element analysis software, and the bending stiffness values ​​under different bearing weights are used as input parameters to simulate the internal force distribution of the structure under various load conditions, thereby obtaining the corresponding cable force values ​​of the inclined cable. Assuming that under a certain bearing weight, a set of cable force values ​​calculated are: the cable force of cable 1 is 300kN, the cable force of cable 2 is 320kN, the cable force of cable 3 is 280kN, etc., and multiple sets of different cable force values ​​will be obtained under different bearing weights. These cable force values ​​reflect the stress state of the inclined cable under different load conditions, providing a data basis for the subsequent determination of reasonable preset cable force values.

[0063] Based on the target cable-stayed bridge cable force prediction model, multiple sets of cable force values ​​are processed to generate preset cable force values. Multiple sets of cable force values ​​are input into the target cable-stayed bridge cable force prediction model, which is a neural network model or empirical formula model established based on a large amount of actual bridge monitoring data and theoretical analysis. For example, the neural network model processes the input multiple sets of cable force values ​​by learning the complex nonlinear relationship between the cable force value and other structural parameters (such as the bending stiffness of the beam unit, the deformation of the bridge tower, etc.). After the calculation and optimization algorithm inside the model, a preset cable force value is output after comprehensive consideration of various factors. Assuming that after model processing, the preset cable force value obtained is 350kN (this value is an example, and it is actually determined according to the model calculation), this preset cable force value will be used as the basic parameter for subsequent calculations of the target temperature load value, etc., and also provides a reference for cable force adjustment during bridge construction or operation.

[0064] The target number of incremental step information, target cable force value, iteration acceleration factor, and each incremental step information respectively include the cable force value corresponding to the current incremental step information and the temperature load corresponding to the incremental step information, wherein the target number of incremental step information is used to characterize several sub-step information for obtaining the target temperature load value, and the preset cable force value, the target number of incremental step information, the iteration acceleration factor, and each incremental step information respectively include the cable force value corresponding to the incremental step information and the temperature load corresponding to the incremental step information are processed based on the target cable-stayed bridge cable force prediction model to generate the target temperature load value. The target number of incremental step information is set, for example, set to 10 incremental steps to gradually approach the actual temperature load process. In each incremental step, the cable force value and temperature load corresponding to the current incremental step information are measured or calculated. Assuming that the initial temperature is 20°C, in the first incremental step, the temperature rises to 21°C, and the cable force value at this time is measured to be 360kN; in the second incremental step, the temperature rises to 22°C, and the cable force value becomes 370kN, etc. At the same time, the iteration acceleration factor is obtained, which is assumed to be 0.8 (this value is determined according to the model convergence characteristics and calculation efficiency requirements). The preset cable force value (350kN), the 10 incremental step information (including the cable force value and temperature load of each incremental step) and the iteration acceleration factor are input into the target cable-stayed bridge cable force prediction model. According to the built-in temperature-cable force relationship algorithm (based on the principle of thermal expansion and contraction and structural mechanics analysis), the model gradually corrects the influence of temperature load on cable force through iterative calculation, and finally obtains the target temperature load value. For example, after a series of calculations, such as through the calculation formula below, the target temperature load value is obtained to be 25°C (that is, when the temperature reaches 25°C, considering other structural factors, the cable force reaches a stable state near the preset cable force value).

[0065] Obtain the material information of the target bridge tower, where the material information of the target bridge tower includes creep coefficient, strain component, strain change information caused by external load, elastic modulus and reference elastic modulus. The creep coefficient and strain component are processed to generate effective creep strain. Obtain the material information of the target bridge tower, such as the creep coefficient of concrete. Assume that the creep coefficient of concrete under specific environmental and loading conditions is known over time. At the current time point, the creep coefficient is 0.3. At the same time, the strain component is known (obtained by strain gauge measurement), for example, the strain component in a certain direction is 5×10 -5 According to the effective creep strain calculation formula (Assume that there is only one time period in this simple example, that is, m = 0, β0 = 0.3, γ0 (0) =5×10 -5 ), the effective creep strain Δε' is calculated to be (1-0.3)×5×10 -5 .

[0066] The effective creep strain, the strain change information caused by external loads and the elastic modulus are processed to generate stress increments. The strain change information caused by external loads is known (for example, the strain change of a certain part under the vehicle load is 2×10 -4 ), elastic modulus (assuming the elastic modulus of concrete is 3×10 4 MPa). According to the stress increment calculation formula (Here we assume That is, the equivalent elastic modulus is equal to the elastic modulus), and the stress increment Δσ is calculated as 3×10 4 Mpa×(2×10 -4 -3.5×10 -5 )=4.95MPa.

[0067] The stress increment and reference elastic modulus are processed to generate true elastic strain and true creep strain. Assume that the reference elastic modulus is 3.2×10 4 MPa, according to the true elastic strain calculation formula ρ = E (Δε-Δε') / E0, it is calculated that ρ = 3 × 10 4 Mpa×(2×10 -4 -3.5×10 -5 ) / (3.2×10 4 MPa)=1.54×10 -4 . Then the true creep strain Δε-ρ=2×10 -4 -1.54×10 -4 =4.6×10 -5 .

[0068] The effective creep strain, stress increment, true elastic strain and true creep strain are processed to generate the material shrinkage creep value of the target bridge tower. -5 ), stress increment (4.95MPa), true elastic strain (1.54×10 -4 ) and true creep strain (4.6×10 -5 ) for comprehensive processing, such as weighted summation or other empirical formulas (assuming that the weights are 0.3, 0.4, 0.2, and 0.1, respectively), to calculate the material shrinkage creep value of the target bridge tower. Material shrinkage creep value = 0.3 × 3.5 × 10 -5 +0.4×4.95MPa+0.2×1.54×10 -4 +0.1×4.6×10 -5 , and finally a value that comprehensively reflects the shrinkage and creep characteristics of the material is obtained, which is used to evaluate the impact of the material on the structural performance of the bridge tower during long-term use.

[0069] In another embodiment, the target cable-stayed bridge cable force prediction model includes a calculation formula for obtaining a target temperature load value, and the calculation formula is:

[0070] Among them, N p is the preset cable force value, N j-2 is the cable force calculated in the j-2th increment, T j-2 Temperature load calculated for the j-2th increment, N j-1 is the cable force calculated in the j-1th increment, T j-1 is the temperature load calculated in the j-1th incremental step, and r2 is the iteration acceleration factor. We will not repeat this discussion here.

[0071] The target cable-stayed bridge cable force prediction model includes a calculation formula for obtaining the effective creep strain, which is:

[0072] Where m represents the number of items considered when calculating the effective creep strain, β i represents the creep coefficient, γ i (n) It is assumed that the creep coefficient of the concrete material of the target bridge tower changes with time as follows (here simplified into three time stages): Time stage (i) is 0-1 years, and its creep coefficient (β i ) is 0.2; the time period (i) is 1-5 years, and its creep coefficient (β i ) is 0.3; the time period (i) is 5-10 years, and its creep coefficient (β i ) is 0.4.

[0073] Strain component γ i (n) The strain gauges were placed at the key positions of the bridge towers. Assuming that in the first year, γ0 (1) =1×10 -4 ; In the third year (1-5 years), γ1 (3) =0.8×10 -4 ; In the eighth year (5-10 years), γ2 (8) =0.6×10 -4 .

[0074] According to the effective creep strain calculation formula For the first year: Δε'1=(1-0.2)×1×10 -4 =0.8×10 -4 ;

[0075] For the third year: Δε'3=(1-0.3)×0.8×10 -4 =0.56×10 -4 ;

[0076] For the eighth year: Δε'8 = (1-0.4) × 0.6 × 10 -4 =0.36×10 -4 ;

[0077] Assuming that we only consider the contribution of these three time points to the effective creep strain (i.e., m = 2), the total effective creep strain Δε' = Δε'1 + Δε'3 + Δε'8 = 1.72 × 10 -4 .

[0078] The target cable-stayed bridge cable force prediction model includes a calculation formula for obtaining stress increment, which is: in, represents the equivalent elastic modulus, Δε represents the total strain change, and Δε' represents the effective creep strain. It is known that the strain change information Δε caused by external loads (such as vehicle loads, wind loads, etc.) is measured by the structural monitoring system. Assume that at the current calculation time, Δε=2.5×10 -4 The elastic modulus of the concrete material of the target bridge tower is determined by material testing, assuming that E = 3 × 10 4 MPa. According to the stress increment calculation formula , here we assume Δσ=3×10 4 MPa×(2.5×10 -4 -1.72×10 -4 )=2.34MPa.

[0079] The target cable-stayed bridge cable force prediction model includes a calculation formula for obtaining the true elastic strain, which is: in, represents the equivalent elastic modulus, Δε represents the total strain change, Δε' represents the effective creep strain, and E0 represents the reference elastic modulus.

[0080] The target cable-stayed bridge cable force prediction model includes a calculation formula for obtaining the true creep strain, which is: Among them, Δε represents the total strain change, ρ represents the true elastic strain, represents the equivalent elastic modulus, Δε' represents the effective creep strain, and E0 represents the reference elastic modulus.

[0081] Assume that the reference elastic modulus E0 = 3.2 × 10 4 MPa.

[0082] According to the true elastic strain calculation formula Then: ρ = 3 × 10 4 MPa×(2.5×10 -4 -1.72×10 4MPa)=0.73×10 -4 .

[0083] True creep strain Δε-ρ=2.5×10 -4 -0.73×10 -4 =1.77×10 -4 .

[0084] Calculate the shrinkage creep value of the material (using the weighted summation method), and set the weights of the effective creep strain, stress increment (converted to strain equivalent value, assuming the stress increment is Δσ, then the equivalent strain increment is Δσ / E), true elastic strain and true creep strain as w1=0.2, w2=0.3, w3=0.25, and w4=0.25, respectively.

[0085] The stress increment is converted into the equivalent strain value: Δσ / E=2.34MPa / (3×10 4 MPa)=0.78×10 -4 .

[0086] Material shrinkage creep value = w1×Δε'+w2×(Δσ / E)+w3×ρ+w4×(Δε-ρ)=0.2×10 -4 +0.3×0.78×10 -4 +0.25×0.73×10 -4 +0.25×1.77×10 -4 =1.203×10 -4 .

[0087] The calculated material shrinkage creep value comprehensively considers the effects of effective creep strain, stress increment, true elastic strain and true creep strain, and can reflect to a certain extent the impact of the shrinkage creep characteristics of the target bridge tower material on the structural performance during long-term use, and can be used for subsequent structural analysis and evaluation, such as predicting structural deformation, cable force changes, etc.

[0088] S107, processing the attribute information of the target bridge tower and the bridge tower cable force warning information of the target tower bridge based on the target cable-stayed bridge cable force prediction model to generate target cable force warning information of the target bridge tower information.

[0089] In one implementation, the preset cable force value, the target temperature load value, and the material shrinkage creep value of the target bridge tower are processed based on the target cable-stayed bridge cable force prediction model to generate the target cable force value of the target bridge tower. Assume that the target cable-stayed bridge cable force prediction model uses the following formula to calculate the target cable force value: N t =N p +k1T j +k2Δε sc; Among them, the target cable force value Nt, the preset cable force value Np, the target temperature load value Tj, the material shrinkage creep value Δε sc , k1 represents the influence coefficient of the target temperature load value on the target cable force value, and k2 represents the influence coefficient of the material shrinkage creep value of the target bridge tower on the target cable force value.

[0090] For example, the preset cable force value Np = 500 kN, the target temperature load value Tj = 10 °C (here it is assumed that the temperature load value is in degrees Celsius and has a linear relationship with the cable force change), and the material shrinkage creep value Δε of the target bridge tower sc =5×10 -5 (Assume that the shrinkage creep value is expressed in the form of strain and has been calculated according to the method described above). Through model analysis and calibration, it is determined that K1 = 20kN / ℃ (indicates that the cable force changes by 20kN for every 1℃ change in temperature), K2 = 10000kN (indicates the change in cable force caused by unit shrinkage creep strain). Substituting these values ​​into the formula, we get: N t =500kN+20kN / ℃×10℃+10000kN×5×10 -5 ; Therefore, the target cable force value of the target bridge tower is 700.5kN.

[0091] The cable force warning information of the target tower bridge is processed based on the target cable-stayed bridge cable force prediction model to generate the cable force warning value of the target tower bridge. Assume that the cable force prediction model of the target cable-stayed bridge calculates the cable force warning value based on the cable force warning information of the tower bridge as follows: Among them, R is the risk warning level of the target event (the value range is assumed to be 1-5, the larger the number, the higher the risk), N h is the historical cable force information of the target bridge tower (e.g., the average cable force value in the past period of time), F i is the i-th cable force influencing factor (such as the quantitative value of the influence of environmental factors, traffic flow, etc. on the cable force), w i is the weight information corresponding to the cable tension influencing factor, and a, b, and c are the coefficients determined by the model based on experience and data analysis.

[0092] For example, the risk warning level of the target event is R = 3, and the historical cable force value information of the target bridge tower is N h =400kN, there are two cable tension influencing factors, the ambient temperature change influencing factor F1 = 5 (assuming that the temperature change is large and has a significant impact on the cable tension), its weight w1 = 0.4; the traffic flow change influencing factor F2 = 8 (assuming that the traffic flow suddenly increases a lot), its weight w2 = 0.6. Through model calibration, it is determined that a = 50kN, b = 0.8, c = 10kN. Substituting these values ​​into the formula, we get: W = 50kN × 3 + 0.8 × 400kN +

[0093] 10kN×(5×0.4+8×0.6)=538kN. Therefore, the cable force warning value of the target bridge tower is 538kN.

[0094] The target cable force value of the target bridge tower is processed based on the cable force warning value of the target bridge tower to generate the target cable force warning information of the target bridge tower. Assume that the warning rule is set as follows: When the target cable force value N t When it is greater than 1.2 times the cable force warning value W, a red warning is issued, indicating that the cable force is too high and the structure is at great risk of safety. Immediate measures need to be taken, such as restricting traffic and inspecting the structure. t When the target cable force value N is between 1.0 and 1.2 times the cable force warning value, a yellow warning is issued, indicating that the cable force is too high and that monitoring needs to be strengthened and changes in the structural state need to be paid attention to. t When it is less than 1.0 times the cable tension warning value, a green warning is issued, indicating that the cable tension is within the normal range and the structural safety condition is good.

[0095] In the above example, the target cable force value N t =700.5kN, cable force warning value W = 538kN, W×1.2 = 645.6kN. Because 700.5kN>645.6kN, a red warning message is issued, indicating that the cable force of the target bridge tower is too high. Relevant personnel need to conduct a comprehensive inspection and assessment of the bridge structure in a timely manner and take corresponding measures to ensure the safe operation of the bridge.

[0096] This application obtains target bridge tower related information, matching event sets and training sample sets from the server, where the target bridge tower information covers the beam unit information of the main beam steel structure, concrete bridge deck and cable-stayed structure. Then, the training sample set is preprocessed, and a training sample set with target feature data that characterizes the risk factors affecting the bridge tower cable force is generated by obtaining preset mapping table grouping, feature extraction, data set division, classifier prediction and algorithm training. Then, the preset cable-stayed bridge cable force prediction model is used to generate the target cable-stayed bridge cable force prediction model based on the processed training sample set. For the target event set, type information, historical cable force value information, cable force influencing factors and weight information are obtained after processing, and then bridge tower cable force warning information is generated. When generating the target bridge tower attribute information, the bending stiffness values ​​of the beam units under different bearing weights are obtained to generate multiple groups of cable force values, and the target temperature load value is obtained by combining the incremental step and other information. At the same time, the effective creep strain, stress increment, true elastic strain and true creep strain are calculated according to the material information, so as to determine the material shrinkage creep value. Finally, based on the model, the target bridge tower attribute information and bridge tower cable force warning information are processed to calculate the target cable force value and cable force warning value, and then the target cable force warning information is generated, providing a comprehensive analysis and prediction for the target bridge tower cable force warning.

[0097] In one embodiment, if Figure 2As shown, the present application also provides a stress performance analysis device for a combined cable-stayed bridge, comprising:

[0098] The acquisition module 201 is used to acquire target bridge tower information, a target event set and a training sample set that match the target bridge tower information, wherein the target bridge tower information includes first beam unit information for characterizing a main beam steel structure, second beam unit information for characterizing a concrete bridge deck and third beam unit information for characterizing a cable-stayed structure, and the training sample set is used to characterize other bridge tower information that matches the target bridge tower information; acquire a preset cable-stayed bridge cable force prediction model, wherein the preset cable-stayed bridge cable force prediction model is generated based on historical structural information of the target bridge tower information;

[0099] The processing module 202 is used to pre-process the training sample set to generate a training sample set with target feature data, wherein the target feature data is used to characterize the risk factors affecting the bridge tower cable force; based on the training sample set with target feature data, the preset cable-stayed bridge cable force prediction model is processed to generate a target cable-stayed bridge cable force prediction model; the target event set matching the target bridge tower information is processed to generate bridge tower cable force warning information of the target tower bridge; based on the target cable-stayed bridge cable force prediction model, the target bridge tower information is processed to generate attribute information of the target bridge tower, wherein the attribute information of the target bridge tower includes the initial cable force of the target bridge tower, the target temperature load value and the material shrinkage creep value of the target bridge tower; based on the target cable-stayed bridge cable force prediction model, the attribute information of the target bridge tower and the bridge tower cable force warning information of the target tower bridge are processed to generate target cable force warning information of the target bridge tower information.

[0100] The present application embodiment provides an electronic device, such as Figure 3 As shown, the electronic device 3 includes a first processor 300, a memory 301, a bus 302 and a communication interface 303. The first processor 300, the communication interface 303 and the memory 301 are connected via the bus 302; the memory 301 stores a computer program that can be run on the first processor 300, and when the first processor 300 runs the computer program, the force performance analysis method of the combined cable-stayed bridge provided in any of the aforementioned embodiments of the present application is executed.

[0101] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store a program, and the first processor 300 executes the program after receiving an execution instruction. The force performance analysis method of the combined cable-stayed bridge disclosed in any implementation of the embodiment of the present application may be applied to the first processor 300, or implemented by the first processor 300.

[0102] The first processor 300 is an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the first processor 300 or the instruction in the form of software. The above-mentioned first processor 300 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 301, and the first processor 300 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.

[0103] The present application embodiment provides a computer-readable storage medium, such as Figure 4 As shown, the computer-readable storage medium stores 401 a computer program, and when the computer program is read and executed by the second processor 402, the stress performance analysis method of the combined cable-stayed bridge as described above is implemented.

[0104] The computer program product provided by the above-mentioned embodiments of the present application and the method for analyzing the stress performance of a combined cable-stayed bridge provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

Claims

1. A method for analyzing the mechanical properties of a combined cable-stayed bridge, characterized in that: include: Acquire target bridge tower information, a target event set matching the target bridge tower information, and a training sample set, wherein the target bridge tower information includes first beam unit information for characterizing a main beam steel structure, second beam unit information for characterizing a concrete bridge deck, and third beam unit information for characterizing a cable-stayed structure, and the training sample set is used to characterize other bridge tower information matching the target bridge tower information; Preprocessing the training sample set to generate a training sample set with target feature data, wherein the target feature data is used to characterize risk factors affecting the cable force of the bridge tower; Acquire a preset cable-stayed bridge cable force prediction model, wherein the preset cable-stayed bridge cable force prediction model is generated based on historical structural information of target bridge tower information; Processing the preset cable-stayed bridge cable force prediction model based on the training sample set with target feature data to generate a target cable-stayed bridge cable force prediction model; Processing the target event set that matches the target bridge tower information to generate bridge tower cable force warning information of the target tower bridge; Processing the target bridge tower information based on the target cable-stayed bridge cable force prediction model to generate attribute information of the target bridge tower, wherein the attribute information of the target bridge tower includes an initial cable force of the target bridge tower, a target temperature load value, and a material shrinkage creep value of the target bridge tower; The target cable-stayed bridge cable force prediction model is used to process the target bridge tower attribute information and the target tower cable force warning information of the target tower bridge to generate target cable force warning information of the target bridge tower information.

2. The method according to claim 1, characterized in that Preprocessing the training sample set to generate a training sample set with target feature data includes: Acquire a preset mapping table, wherein the preset mapping table is used to represent the historical structure information of the target bridge tower information and the corresponding information of the historical event set of the target bridge tower information; The training sample set is grouped based on the preset mapping table to generate a grouped training sample set, wherein the grouped training sample set includes monitoring points to be predicted with different monitoring lengths; Extract features from the grouped training sample set to determine the original feature library; Divide each feature data set according to the original feature library to generate a training set and a validation set; Based on the classifier, each verification set divided by the original feature library is predicted and processed to generate a prediction result; Based on the preset algorithm, each training set divided in the original feature library is trained and processed to generate the prediction results of the validation set; The prediction results and the validation set prediction results are processed to generate target feature data, where the target feature data is used to characterize risk factors that affect the bridge tower cable force.

3. The method according to claim 1, characterized in that The target event set matching the target bridge tower information is processed to generate the bridge tower cable force warning information of the target tower bridge, including: Processing the target event set matching the target bridge tower information to generate type information of the target event, wherein the type information of the target event is used to characterize the risk warning level of the target event to the target bridge tower; Processing the type information of the target event to generate historical cable force value information of the target bridge tower; Processing the historical cable force value information of the target bridge tower to generate a cable force influence factor of the target bridge tower and weight information corresponding to the cable force influence factor of the target bridge tower; The cable force influence factor of the target bridge tower and the weight information corresponding to the cable force influence factor of the target bridge tower are processed based on a preset bridge tower cable force influence feature set to generate target cable force warning information of the target bridge tower.

4. The method according to claim 3, characterized in that The target bridge tower information is processed based on the target cable-stayed bridge cable force prediction model to generate attribute information of the target bridge tower, including: Obtaining the bending stiffness values ​​of the first beam element and the second beam element under different bearing weights; The bending stiffness values ​​of the first beam unit and the second beam unit under different bearing weights are processed respectively to generate multiple sets of cable force values; Processing multiple groups of cable force values ​​based on the target cable-stayed bridge cable force prediction model to generate preset cable force values; Obtaining target number of incremental step information, target cable force value, iteration acceleration factor, and each incremental step information includes the cable force value corresponding to the current incremental step information and the temperature load corresponding to the incremental step information, wherein the target number of incremental step information is used to represent several sub-step information for obtaining the target temperature load value; Based on the target cable-stayed bridge cable force prediction model, a preset cable force value, target number of incremental step information, an iteration acceleration factor, and each incremental step information including a cable force value corresponding to the incremental step information and a temperature load corresponding to the incremental step information are processed to generate a target temperature load value; Acquiring material information of a target bridge tower, wherein the material information of the target bridge tower includes creep coefficient, strain component, strain change information caused by external load, elastic modulus and reference elastic modulus; Processing the creep coefficient and the strain component to generate an effective creep strain; Processing the effective creep strain, the strain change information caused by the external load and the elastic modulus to generate a stress increment; Processing the stress increment and the reference elastic modulus to generate true elastic strain and true creep strain; The effective creep strain, the stress increment, the true elastic strain and the true creep strain are processed to generate a material shrinkage creep value of a target bridge tower.

5. The method according to claim 4, characterized in that The target bridge tower information is processed based on the target cable-stayed bridge cable force prediction model to generate attribute information of the target bridge tower, and further includes: The target cable-stayed bridge cable force prediction model includes a calculation formula for obtaining a target temperature load value, and the calculation formula is: Among them, N p is the preset cable force value, N j-2 is the cable force calculated in the j-2th increment, T j-2 Temperature load calculated for the j-2th increment, N j-1 is the cable force calculated in the j-1th increment, T j-1 is the temperature load calculated in the j-1th incremental step, and r2 is the iteration acceleration factor; The target cable-stayed bridge cable force prediction model includes a calculation formula for obtaining effective creep strain, which is: Where m represents the number of items considered when calculating the effective creep strain, β i represents the creep coefficient, γ i (n) represents the strain component; The target cable-stayed bridge cable force prediction model includes a calculation formula for obtaining stress increment, and the calculation formula is: in, represents the equivalent elastic modulus, Δε represents the total strain change, and Δε' represents the effective creep strain; The target cable-stayed bridge cable force prediction model includes a calculation formula for obtaining the true elastic strain, and the calculation formula is: in, represents the equivalent elastic modulus, Δε represents the total strain change, Δε' represents the effective creep strain, and E0 represents the reference elastic modulus; The target cable-stayed bridge cable force prediction model includes a calculation formula for obtaining the true creep strain, which is: Among them, Δε represents the total strain change, ρ represents the true elastic strain, represents the equivalent elastic modulus, Δε' represents the effective creep strain, and E0 represents the reference elastic modulus.

6. The method according to claim 5, characterized in that The target cable-stayed bridge cable force prediction model is used to process the target bridge tower attribute information and the bridge tower cable force warning information of the target bridge tower to generate target cable force warning information of the target bridge tower information, including: The preset cable force value, the target temperature load value and the material shrinkage creep value of the target bridge tower are processed based on the target cable-stayed bridge cable force prediction model to generate a target cable force value of the target bridge tower; Processing the bridge tower cable force warning information of the target tower bridge based on the target cable-stayed bridge cable force prediction model to generate a cable force warning value of the target bridge tower; The target cable force value of the target bridge tower is processed based on the cable force warning value of the target bridge tower to generate target cable force warning information of the target bridge tower.

7. The method according to claim 6, characterized in that The target cable-stayed bridge cable force prediction model is used to process the target bridge tower attribute information and the bridge tower cable force warning information of the target bridge tower to generate target bridge tower information target cable force warning information, further comprising: The target cable-stayed bridge cable force prediction model also includes a calculation formula for obtaining a target cable force value of a target bridge tower, and the calculation formula is: OF t =N p +k1T j +k2Δε sc ; Among them, the target cable force value Nt, the preset cable force value Np, the target temperature load value Tj, the material shrinkage creep value Δε sc , k1 represents the influence coefficient of the target temperature load value on the target cable force value, k2 represents the influence coefficient of the material shrinkage creep value of the target bridge tower on the target cable force value; The target cable-stayed bridge cable force prediction model also includes a calculation formula for obtaining a cable force warning value of a target bridge tower, and the calculation formula is: Among them, R is the risk warning level of the target event, N h is the historical cable force information of the target bridge tower, F i is the i-th cable force influencing factor, w i is the weight information corresponding to the cable tension influencing factor, and a, b, and c are coefficients.

8. A device for analyzing the mechanical properties of a combined cable-stayed bridge, characterized in that: The device comprises: An acquisition module is used to acquire target bridge tower information, a target event set and a training sample set that match the target bridge tower information, wherein the target bridge tower information includes first beam unit information for characterizing a main beam steel structure, second beam unit information for characterizing a concrete bridge deck and third beam unit information for characterizing a cable-stayed structure, and the training sample set is used to characterize other bridge tower information that matches the target bridge tower information; acquire a preset cable-stayed bridge cable force prediction model, wherein the preset cable-stayed bridge cable force prediction model is generated based on historical structural information of the target bridge tower information; A processing module is used to preprocess the training sample set to generate a training sample set with target feature data, wherein the target feature data is used to characterize the risk factors affecting the bridge tower cable force; based on the training sample set with target feature data, the preset cable-stayed bridge cable force prediction model is processed to generate a target cable-stayed bridge cable force prediction model; a target event set matching the target bridge tower information is processed to generate bridge tower cable force warning information of a target tower bridge; based on the target cable-stayed bridge cable force prediction model, the target bridge tower information is processed to generate attribute information of the target bridge tower, wherein the attribute information of the target bridge tower includes the initial cable force of the target bridge tower, the target temperature load value and the material shrinkage creep value of the target bridge tower; based on the target cable-stayed bridge cable force prediction model, the attribute information of the target bridge tower and the bridge tower cable force warning information of the target tower bridge are processed to generate target cable force warning information of the target bridge tower information.

9. An electronic device, characterized in that: include: a first processor; and a memory for storing executable instructions of the first processor; Wherein, the first processor is configured to execute the stress performance analysis method of a combined cable-stayed bridge according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the second processor, the method for analyzing the stress performance of a combined cable-stayed bridge according to any one of claims 1 to 7 is implemented.

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