A method and system for evaluating the life of tension and compression bearings of cable-stayed bridges under random traffic loads

Abstract

The present invention discloses a method and system for evaluating the life of tension and compression bearings of cable-stayed bridges under random traffic loads. The method belongs to the field of bridge engineering and structural health monitoring and includes: statistical analysis of traffic load data, establishment of a random traffic load model and generation of a load spectrum; correction of cable forces and modal frequencies based on bridge monitoring data, and construction of a refined multi-scale finite element model; application of unit fatigue vehicle loads to the model, acquisition of stress influence lines, calculation of stress amplitude, number of cycles, and average stress using the rainflow counting method, conversion to daily average equivalent stress amplitude and equivalent daily cycle number using the fatigue damage equivalence principle, and evaluation of the fatigue life of tension and compression bearings using the Miner criterion. The present invention solves the problem that existing technologies cannot accurately reflect the effects of three-way alternating stress on bolt fatigue failure, improves the life prediction accuracy of tension and compression bearings of cable-stayed bridges, can provide early warning of tension and compression bearing damage, and provides data support for the safe operation and maintenance of cable-stayed bridges.

Description

Technical Field

[0001] The present invention belongs to the technical field of bridge engineering and structural health monitoring, and particularly relates to a fatigue life assessment method and system for tension and compression bearings of a cable-stayed bridge under locomotive flow loads. Background Art

[0002] Bridge tension and compression bearings are core components of cable-stayed bridges. Their main function is to stabilize the bridge and transfer loads to the piers. In cable-stayed bridges, tension and compression bearings must accurately transfer loads to avoid excessive stress and deformation of the structure. However, during operation, tension and compression bearings, especially top plate bolts, experience continuous changes in tension, compression, and shear stresses due to factors such as traffic loads, structural characteristics, material properties, and manufacturing processes. These stress changes may cause fatigue failure of the bolts, leading to failure of the tension and compression bearings, affecting the operational quality, safety, and durability of the bridge, and even causing economic losses and social impacts. Therefore, solving the problem of fatigue failure of tension and compression bearings in cable-stayed bridges is of great significance to ensuring the long-term performance of the bridge and public safety.

[0003] With the widespread use of structural health monitoring systems, fatigue characteristics of bridge tension and compression bearings can now be analyzed using real-time data. Statistical analysis of the monitoring data from these systems helps accurately predict the fatigue life of tension and compression bearings, providing critical information for assessing their safety and developing maintenance strategies and repair plans. This approach helps extend the service life of bridges and ensure public safety. However, existing technologies have the following shortcomings:

[0004] First, using traditional health monitoring systems to assess the fatigue state of bridge tension and compression bearings is costly, data-limited, and time-consuming.

[0005] Second, the existing fatigue design and assessment specifications cannot fully reflect the load-bearing history and fatigue failure process of tension and compression supports, and at the same time ignore the influence of random factors on fatigue effects, and lack universal applicability. Summary of the Invention

[0006] The purpose of the embodiments of the present invention is to provide a method and system for evaluating the life of the tension and compression bearings of a cable-stayed bridge under random traffic loads, which can solve the problems in the prior art that traditional health monitoring systems for evaluating the fatigue state of the tension and compression bearings of bridges are high cost, limited data, time-consuming and lack of universal applicability.

[0007] In order to solve the above-mentioned technical problems, the present invention is achieved as follows:

[0008] In a first aspect, an embodiment of the present invention provides a method for evaluating fatigue life of tension and compression bearings of a cable-stayed bridge under locomotive flow load, the method comprising:

[0009] Conduct statistical analysis on the probability distribution parameters of traffic load data to obtain a random traffic load model;

[0010] Obtain load spectra for fatigue assessment of tension and compression bearing details based on random traffic load models;

[0011] Modify the model parameters of the multi-scale finite element model of the bridge to obtain the modified multi-scale finite element model of the bridge. In the multi-scale finite element model of the bridge, the main beam and the bridge tower are simulated using beam elements, and the tension and compression supports are refinedly modeled using solid elements and connected through multi-point constraints.

[0012] Applying fatigue vehicle loads of preset units to the modified multi-scale finite element model of the bridge to obtain stress influence lines;

[0013] Obtain stress time history data of tension and compression supports based on load spectrum and stress influence line;

[0014] Calculate the stress amplitude and number of cyclic loading of stress time history data based on the rainflow counting method;

[0015] Based on the fatigue damage equivalence principle, the stress amplitude and the number of cyclic loading are equivalently treated to obtain the daily average equivalent stress amplitude and the equivalent daily cycle number. Combined with the Miner criterion, the fatigue life of the tension and compression bearing is obtained.

[0016] As an optional implementation scheme of the first aspect of the present application, the probability distribution parameters include: vehicle type, gross vehicle weight, axle weight, wheelbase, vehicle spacing, lane traffic volume ratio, vehicle type distribution ratio of each lane, and lateral distribution characteristics of vehicle centerline along the lane. The axle weight obeys the extreme value type I distribution, and the wheelbase obeys the normal distribution.

[0017] As an optional implementation method of the first aspect of the present application, the process of establishing a multi-scale finite element model of a bridge includes two processes: constructing the overall truss system model parameters and fine-tuning the tension and compression bearing modeling. When constructing the overall truss system model parameters, the model parameters include the main beam, bridge tower and inclined cable, wherein the main beam adopts a single-box three-chamber steel box beam, the bridge tower adopts a C50 concrete tower column, and the inclined cable adopts a truss unit simulation. When performing fine-tuning the tension and compression bearing modeling, it is necessary to divide the entity units, that is, the upper and lower pressure plates adopt C3D8R units, the anchor bolts adopt C3D10 units, and the rubber pads adopt Mooney-Rivlin parameters. The main beam and the tension and compression bearing top plate, and the bridge tower and the tension and compression bearing bottom plate are rigidly connected through MPC.

[0018] As an optional implementation of the first aspect of the present application, the process of modifying the model parameters of the multi-scale finite element model of the bridge includes two processes: measured data acquisition and sensitivity analysis and iterative correction. In the process of measuring data acquisition, it includes: obtaining the vertical acceleration time history data of the main beam mid-span through the bridge health monitoring system, and extracting the first three natural frequencies; in the process of sensitivity analysis and iterative correction, it includes: determining the current parameter values ​​of the multi-scale finite element model, and the parameter values ​​include: the initial parameter values ​​of the model , initial damping factor , the maximum number of iterations MaxIter and the convergence threshold ; Calculate the error vector Error between the model output and the actual target value based on the current parameter value; Calculate the gradient vector g of the error vector with respect to the parameter by using the chain rule and back propagation algorithm; Construct the Jacobian matrix J, in which each element Indicates the Output pair Partial derivatives of parameters; update parameters of calculated parameters ; Select the correction parameters: main beam stiffness, cable tension, support horizontal stiffness, and perform multiple iterations to ensure that the corrected fundamental frequency error is within the preset range.

[0019] As an optional implementation scheme of the first aspect of the present application, the fatigue vehicle load of preset units is applied to meet the following requirements: the longitudinal movement step length of the vehicle is ≤0.5m; the lateral eccentric load coefficient is taken as 0.8~1.2 according to the actual distribution of the lane; the dynamic impact coefficient is increased by 5%~15% according to the smoothness grade of the bridge pavement, and the contact friction coefficient of the upper and lower pressure plates in the tension and compression bearings is set to 0.15~0.25; the preload force of the anchor bolt is applied as 40%~60% of the yield strength of the bolt material; the constitutive relationship of the rubber vibration isolation pad adopts the Mooney-Rivlin hyperelastic model.

[0020] As an optional implementation method of the first aspect of the present application, the stress amplitude and the number of cyclic loading are equivalently processed based on the fatigue damage equivalence principle to obtain the daily average equivalent stress amplitude and the equivalent daily cycle number, including two processes: moving influence line loading setting and stress output and processing. Among them, in the process of moving influence line loading setting, it includes: dividing the lane into multiple lanes, each lane has a different lateral load coefficient, the vehicle moves with a fixed distance step, and the impact coefficient is added according to the road surface flatness grade B; in the process of stress output and processing, it includes: extracting the three-dimensional stress time history of the dangerous point at the root of the support anchor bolt, and obtaining the maximum stress amplitude in the three directions.

[0021] As an optional implementation of the first aspect of the present application, the mathematical expressions of the daily average equivalent stress amplitude and the equivalent daily cycle number are:

[0022] The mathematical expressions of the daily average equivalent stress amplitude and the equivalent daily cycle number are:

[0023] ;

[0024] ;

[0025] The mathematical expression of fatigue life of tension and compression bearing is:

[0026] ;

[0027] in, represents the constant amplitude fatigue limit, Indicates greater than The stress amplitude, Indicates less than The stress amplitude, Indicates less than The number of cycles, Indicates greater than The number of cycles, and They represent the fatigue strength coefficients when the stress amplitude is greater than or less than the fatigue limit of the normal amplitude, represents the daily average equivalent stress amplitude, represents the equivalent daily cycle number, represents the annual traffic growth rate, Indicates the annual traffic growth rate The fatigue life of the tension and compression supports, Indicates the service life of the tension and compression bearing.

[0028] In a second aspect, an embodiment of the present invention provides a fatigue life assessment system for tension and compression bearings of a cable-stayed bridge under locomotive flow loads, the system comprising:

[0029] The vehicle load database construction module is used to obtain vehicle monitoring video data of the target bridge, extract vehicle axle weight, wheelbase, speed and travel time interval parameters, and obtain the probability distribution model of the parameters and the statistical characteristics of the traffic load;

[0030] A random traffic flow load model construction module is used to generate random traffic flow samples according to the probability distribution model and statistical characteristics, and to construct a random traffic flow load model based on the random traffic flow samples;

[0031] Load spectrum calculation module, used to obtain load spectra for fatigue assessment of tension and compression bearing details based on random traffic load models;

[0032] A multi-scale finite element model construction module for bridges is used to calculate cable forces and modal frequencies based on bridge monitoring data, and to perform static and dynamic corrections on these forces and modal frequencies to obtain a corrected multi-scale finite element model of the bridge. In this multi-scale finite element model, the main beams and towers are simulated using beam elements, while the tension and compression supports are refinedly modeled using solid elements and connected through multi-point constraints.

[0033] A stress time history data acquisition module is used to apply a fatigue vehicle load of preset units to the modified multi-scale finite element model of the bridge, obtain stress influence lines, and obtain stress time history data of the tension and compression bearings based on the load spectrum and stress influence lines;

[0034] The fatigue life assessment module is used to calculate the stress amplitude, number of loading cycles, and average stress of stress time history data based on the rain flow counting method. Based on the fatigue damage equivalence principle, the stress amplitude and number of loading cycles are equivalently processed to obtain the daily average equivalent stress amplitude and equivalent daily cycle number. Combined with the Miner criterion, the fatigue life of the tension and compression bearing is calculated.

[0035] The life warning module is used to trigger an alarm signal according to the relationship between the fatigue life of the tension and compression bearings and the service life threshold, and generate a priority list for replacing bolts in the tension and compression bearings based on the alarm signal.

[0036] In a third aspect, an embodiment of the present invention provides an electronic device comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method of the first aspect.

[0037] In a fourth aspect, an embodiment of the present invention provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method of the first aspect are implemented.

[0038] Compared with the prior art, the beneficial effect of the life assessment method of the tension and compression bearings of a cable-stayed bridge under random traffic load proposed in the present invention is that:

[0039] First, statistical analysis is performed on the probability distribution parameters of traffic load data to obtain a random traffic load model. Obtaining the probability distribution parameters in the random traffic load model through statistical analysis can more accurately predict future traffic flow and load distribution, improving prediction accuracy. Obtaining the load spectrum for fatigue assessment of tension and compression bearing details based on the random traffic load model can more accurately simulate the randomness and uncertainty of actual traffic loads, thereby improving the accuracy of fatigue performance assessment of tension and compression bearing details.

[0040] Second, cable forces and modal frequencies are calculated based on bridge monitoring data. Static and dynamic corrections are performed on the cable forces and modal frequencies, respectively, to obtain a corrected multi-scale finite element model of the bridge. This allows the multi-scale finite element model to more closely match the characteristics of the actual structure, thereby improving the reliability of the multi-scale finite element model when predicting responses.

[0041] Third, the stress time history data of the tension and compression bearings is obtained based on the load spectrum and stress influence lines. By combining the load spectrum and stress influence lines, the stress changes of the tension and compression bearings under actual traffic loads can be more accurately simulated and predicted. The load spectrum provides detailed traffic load information, including the magnitude, frequency, and distribution of the load, while the stress influence lines describe the load transmission path and impact in the structure. This combination enables more accurate calculation of the stress time history data of the tension and compression bearings, thereby improving the accuracy of fatigue assessment.

[0042] Fourth, the stress amplitude and number of cyclic loading of stress time history data are calculated based on the rain flow counting method. The rain flow counting method can accurately identify and calculate the number of cyclic loading and stress amplitude, thereby improving the assessment accuracy of the fatigue life of tension and compression bearings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is an overall flow chart of a method for evaluating the life of tension and compression bearings of a cable-stayed bridge under random traffic loads provided by the first embodiment of the present invention;

[0044] Figure 2 This is a panoramic view of the finite element spatial analysis model of the cable-stayed bridge provided by the first embodiment of the present invention;

[0045] Figure 3 is a partial enlarged view of a finite element model of an auxiliary pier of a cable-stayed bridge provided by the first embodiment of the present invention;

[0046] Figure 4 This is a diagram of a "beam-solid" finite element model of an auxiliary pier tension and compression support based on multi-scale modeling provided by the first embodiment of the present invention;

[0047] Figure 5 This is a finite element model diagram of a tension and compression support and bolt based on multi-scale modeling provided by the first embodiment of the present invention;

[0048] Figure 6 This is a schematic diagram of a fatigue life assessment process for tension and compression bearings of a cable-stayed bridge under random traffic loads provided by the first embodiment of the present invention;

[0049] Figure 7 This is a load spectrum diagram for fatigue assessment of a tension and compression bearing based on random traffic flow simulation provided by the first embodiment of the present invention;

[0050] Figure 8This is a characteristic diagram of the frequency statistics and probability distribution of the lane vehicle spacing provided by the first embodiment of the present invention;

[0051] Figure 9 is a vehicle gross weight probability distribution characteristic diagram based on vehicle monitoring data provided by the first embodiment of the present invention;

[0052] Figure 10 is a proportional distribution diagram of various vehicle types provided by the first embodiment of the present invention;

[0053] Figure 11 This is a characteristic diagram of vehicle type distribution in each lane provided by the first embodiment of the present invention;

[0054] Figure 12 This is a stress time history diagram of a tension-compression bearing under dynamic loading of random vehicle loads using the moving influence line method provided by the first embodiment of the present invention;

[0055] Figure 13 is a three-dimensional histogram of average stress, stress amplitude, and cycle number obtained by the rainflow counting method provided in the first embodiment of the present invention;

[0056] Figure 14 This is a prediction diagram for evaluating the life of tension and compression bearings of a cable-stayed bridge under various annual traffic growth rates, provided by the first embodiment of the present invention;

[0057] Figure 15 This is a structural diagram of a system for evaluating the life of tension and compression bearings of a cable-stayed bridge under random traffic loads provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0059] The terms "first," "second," and the like in the specification and claims of the present invention are used to distinguish similar objects and are not intended to describe a particular order or precedence. It should be understood that such terms are interchangeable where appropriate, so that embodiments of the present invention can be implemented in sequences other than those illustrated or described herein. Furthermore, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the connected objects are in an "or" relationship.

[0060] In the following, in conjunction with the accompanying drawings, a method and system for evaluating the life of tension and compression bearings of a cable-stayed bridge under random traffic loads provided by an embodiment of the present invention will be described in detail through specific embodiments and application scenarios.

[0061] Example 1

[0062] See also Figure 1 , shown is an overall flow chart of a method for evaluating the life of tension and compression bearings of a cable-stayed bridge under random traffic loads provided by the present invention, the method comprising steps S1 to S7.

[0063] S1: Perform statistical analysis on the probability distribution parameters of traffic load data to obtain a random traffic load model.

[0064] Specifically, the process of obtaining a random traffic load model involves two steps: data collection and processing, and generating random vehicle queues. The data collection and processing process involves first installing dynamic weighing sensors, such as piezoelectric film sensors or bridge sensors, at appropriate locations on the road. The sensors must be buried under the road surface, ensuring they are flush with the surface, to accurately sense pressure changes as vehicles pass. A data acquisition system, wireless communication equipment, and power supply system are then installed. The data acquisition system connects to the sensors to collect and initially process data; the wireless communication equipment transmits the data to the monitoring center; and the power supply system provides stable power to the entire system. When a vehicle enters the detection area, a vehicle separator or similar device detects its arrival, triggering the dynamic weighing sensors to activate. Using dynamic weighing (WIM) technology within a structural health monitoring (SHM) system installed on the bridge, the system operates 24 / 7, continuously monitoring passing vehicles and ensuring timely traffic load data. Secondly, 30 consecutive days of surveillance video of the target bridge were obtained. Dynamic images of vehicles were captured using high-definition cameras. The images were grayscaled and denoised to improve the accuracy of feature extraction. Then, the image recognition HOG algorithm was used to extract the vehicle's feature vector. The feature vector was classified using an SVM support vector machine to identify the vehicle's axle. Finally, a statistical analysis was performed on the probability distribution parameters of the traffic load data, namely, vehicle type (small passenger car, truck, overweight vehicle), axle weight (range 5-55t), wheelbase (1.5-14m), speed (20-80km / h), and travel time interval (an average of 2s / vehicle during peak hours and 8s / vehicle during non-peak hours). The axle weight was fitted with an extreme value I-type distribution (shape parameter =0.12, position parameter =28.5), so that the wheelbase follows the normal distribution N( =4.2m =0.63m). Figure 9 Represents the probability distribution characteristic graph of the gross vehicle weight based on vehicle monitoring data.

[0065] Furthermore, the process of generating random vehicle queues involves first collecting measured data on vehicle weight, axle load, and headway. This data is then cleaned to remove outliers and statistically analyzed to determine its distribution type. Then, based on the measured data, statistical methods are used to estimate the probability distribution parameters of vehicle weight, axle load distribution, and headway. Finally, based on the probability distribution parameters and statistical characteristics of random traffic loads, a mathematical model for simulating multi-lane mixed-vehicle random traffic flow is established. A random traffic load simulation program is developed, and Monte Carlo sampling is used to simulate the random traffic loads. This simulation generates a 24-hour dynamic vehicle load sequence consisting of 10,000 random vehicles, each loaded according to the actual travel timestamp. Figure 10 is the characteristic map of vehicle type distribution in each lane, Figure 11 It is the characteristic map of vehicle type distribution in each lane.

[0066] The present invention performs statistical analysis on the probability distribution parameters of traffic load data to obtain a random traffic load model. The beneficial effect is that by statistically analyzing the probability distribution parameters in the random traffic load model, future traffic flow and load distribution can be predicted more accurately, thereby improving the accuracy of the prediction.

[0067] S2: Obtaining load spectra for fatigue assessment of tension and compression bearing details based on a random traffic load model.

[0068] Specifically, the process of obtaining the load spectrum for fatigue assessment of tension and compression bearing details includes: in the finite element simulation software MATLAB, for the established random traffic load model, constructing a random traffic load simulation program, using the Monte Carlo sampling method to simulate the random traffic load, obtaining random traffic sample data, and thus establishing the load spectrum for fatigue assessment of tension and compression bearing structural details. Figure 7 Represents the load spectrum for fatigue assessment of tension and compression supports based on random traffic flow simulation.

[0069] Specifically, the process of constructing a random traffic load simulation program includes: in the finite element simulation software MATLAB, using the state update formula of the cellular automaton to simulate the randomness and uncertainty of vehicle loads in actual traffic flow to construct a random traffic load model program. The state update formula of the cellular automaton can be expressed as:

[0070] ;

[0071] in, It is a cell At time step status, It is a cell At time step status, It is a cell At time step The state set of neighbor cells of is the state update rule function.

[0072] Furthermore, the Monte Carlo sampling method uses a random number generator to generate a large number of random numbers according to a certain probability distribution. These random numbers represent the values ​​of different vehicle parameters. The generated random numbers are mapped to the corresponding vehicle parameters, and specific parameter values ​​are assigned to each vehicle, such as vehicle model, weight, speed, etc., to form multiple specific vehicle samples. According to certain traffic flow rules, such as vehicle driving direction, lane selection, vehicle spacing, etc., the vehicle samples are combined into traffic flow to simulate the vehicle driving process on the road. The simulated traffic flow data is sorted and analyzed to obtain random traffic flow sample data. Figure 8 A characteristic graph representing the frequency statistics and probability distribution of lane spacing.

[0073] Furthermore, the random traffic sample data obtained by simulation are used to calculate the stress, strain and other responses of the tension and compression support components under the action of vehicle loads to obtain the load effect time history.

[0074] Furthermore, the rain flow counting method is used to perform statistical analysis on the load effect time history, converting the complex load effect history into a series of combinations of stress amplitudes and cycle numbers to form a stress cycle statistical matrix. Based on the stress cycle statistical matrix, the load spectrum for fatigue assessment of tension and compression support details is obtained.

[0075] The beneficial effect of the present invention in obtaining the load spectrum for fatigue assessment of tension and compression bearing details based on the random traffic load model is that the random traffic load model of the present invention can more accurately simulate the randomness and uncertainty of actual traffic loads, thereby improving the assessment accuracy of the fatigue life of tension and compression bearing details.

[0076] S3: Modify the model parameters of the multi-scale finite element model of the bridge to obtain the modified multi-scale finite element model of the bridge. The main beams and bridge towers in the multi-scale finite element model of the bridge are simulated using beam elements, and the tension and compression supports are refinedly modeled using solid elements and connected through multi-point constraints.

[0077] Specifically, the process of establishing a multi-scale finite element model of the bridge includes two steps: constructing the overall member model parameters and refining the tension and compression support modeling. When constructing the overall member model parameters, the model parameters include the main beam, bridge tower and inclined cable. Among them, the main beam adopts a single-box three-chamber steel box beam, the beam unit division length is 2m, and it is given an elastic modulus of 206,000MPa and a density of 7,850kg / m³. The bridge tower adopts C50 concrete tower column, simulated by beam unit, with an elastic modulus of 34,500MPa and a Poisson's ratio of 0.2. The inclined cable adopts truss unit, and the initial tension is applied according to the design value, with an elastic modulus of 195,000MPa. When performing refined modeling of the tension and compression supports, it is necessary to divide the entity elements, that is, the upper and lower pressure plates use C3D8R elements with a grid size of 5mm; the anchor bolts use C3D10 elements, the preload is applied as 50% of the bolt yield strength (640MPa), the friction coefficient between the pressure plates is 0.18, the rubber pads use Mooney-Rivlin parameters C10=0.8MPa, C01=0.2MPa, and the main beam and the tension and compression support top plate, as well as the bridge tower and the tension and compression support bottom plate are rigidly connected through MPC. Figure 4 The “beam-solid” finite element model diagram of the auxiliary pier tension and compression support based on multi-scale modeling, where 2 represents the main beam, 3 represents the tension and compression support, and 4 represents the auxiliary pier beam. Figure 5 Diagram showing the finite element model of tension and compression supports and bolts based on multi-scale modeling.

[0078] After completing the refined modeling of the tension and compression supports, the model parameters of the multi-scale finite element model of the bridge need to be corrected. The parameter correction process specifically includes two steps: field data collection, sensitivity analysis, and iterative correction. During the field data collection process, the vertical acceleration time history data of the main beam at the mid-span (sampling frequency 100Hz) is obtained through the bridge health monitoring system, and the first three natural frequencies are extracted: =0.48Hz (theoretical value is 0.50Hz), =1.12Hz (theoretical value is 1.20Hz); in the process of sensitivity analysis and iterative correction, including: determining the current parameter values ​​of the multi-scale finite element model, the parameter values ​​include: the initial parameter values ​​of the model , initial damping factor , the maximum number of iterations MaxIter and the convergence threshold ; Calculate the error vector Error between the model output and the actual target value based on the current parameter value; Calculate the gradient vector g of the error vector with respect to the parameter through the chain rule and back propagation algorithm; Construct the Jacobian matrix , in the Jacobian matrix J, each element Indicates the Output pair Partial derivatives of parameters; update parameters of calculated parameters The correction parameters were selected as follows: main beam stiffness (+5%), cable tension (-3%), and support horizontal stiffness (+8%). The Levenberg-Marquardt algorithm was used for 20 iterations, which reduced the fundamental frequency error to 0.49 Hz after correction. The error was 2.08%, meeting the requirement of error ≤ 3%.

[0079] Specifically, the model parameters that need to be corrected include cable force, model frequency, main beam stiffness, cable tension, and support horizontal stiffness. The cable force is measured using the vibration frequency method. This method uses a highly sensitive sensor fastened to the cable to pick up the cable's vibration signal under environmental vibration excitation. After filtering, amplification, and spectral analysis, the natural frequency of the cable is obtained. Based on the relationship between the natural frequency and cable force, the cable force is quickly determined. The cable is regarded as a string vibration. When a unit body is arbitrarily cut from the cable, its basic equilibrium equation is:

[0080]

[0081] in, is the bending stiffness of the cable, It is the tension of the cable. is the mass per unit length of the cable, is the amplitude of the cable, is the coordinate along the direction of the cable, It's time, represents the phase angle along the cable direction, Represents an imaginary number.

[0082] Among them, the modal frequency is obtained through finite element analysis software. This method first establishes the mass matrix and main beam stiffness based on the geometric parameters and material properties of the structure. The value of the natural circular frequency can be obtained by solving the equation. The solution equation is:

[0083] ;

[0084] ;

[0085] in, represents the natural circular frequency, represents the mode shape, represents the main beam stiffness, represents the mass matrix, is the modal frequency.

[0086] When correcting the main beam stiffness, cable tension and support horizontal stiffness, the Levenberg-Marquardt algorithm was used for 20 iterations to ensure that the fundamental frequency error after correction was ≤3%.

[0087] S4: Apply fatigue vehicle loads with preset units to the modified multi-scale finite element model of the bridge to obtain stress influence lines.

[0088] Specifically, the application of preset unit fatigue vehicle loads must meet the following conditions: the vehicle's longitudinal movement step length is ≤0.5m; the lateral eccentric load coefficient is taken as 0.8~1.2 according to the actual lane distribution; the dynamic impact coefficient is increased by 5%~15% based on the bridge pavement smoothness grade; the contact friction coefficient of the upper and lower bearing plates in the tension and compression bearings is set to 0.15~0.25; the anchor bolt preload is applied at 40%~60% of the bolt material yield strength; and the constitutive relationship of the rubber vibration isolation pad adopts the Mooney-Rivlin hyperelastic model.

[0089] Specifically, the process of obtaining stress influence lines involves identifying and determining the critical vulnerable details requiring fatigue assessment and their associated tension and compression supports through static and dynamic analysis based on the multi-scale finite element model of the bridge. In finite element analysis software, a fatigue vehicle load of a preset unit, i.e., 1 kN, is applied to the multi-scale finite element model of the bridge. Post-processing analysis is then performed using the finite element analysis software to extract stress influence lines at the vulnerable details of the tension and compression supports.

[0090] S5: Obtain stress time history data of tension and compression supports based on load spectrum and stress influence line.

[0091] Specifically, the stress influence lines at the vulnerable details of the tension and compression supports are extracted through finite element analysis software. In the finite element simulation software MATLAB, the load spectrum obtained in step S2 and the stress influence lines obtained in step S5 are spline interpolated using the cubic spline interpolation method. The interpolation results are stored in two cell array matrices of equal length, and the two cell arrays are multiplied to obtain a cell array. The values ​​stored in the final cell array are the stress time history data of the tension and compression supports.

[0092] The present invention's method of obtaining stress time-history data for tension and compression bearings based on load spectra and stress influence lines offers the advantage of more accurately simulating and predicting stress changes in tension and compression bearings under actual traffic loads. The load spectrum provides detailed traffic load information, including load magnitude, frequency, and distribution, while the stress influence lines describe the load transfer path and impact within the structure. This combination enables more precise calculation of stress time-history data for tension and compression bearings, thereby improving the accuracy of fatigue assessments.

[0093] S6: Calculate the stress amplitude and number of loading cycles of stress time history data based on the rainflow counting method.

[0094] Specifically, before calculating the stress amplitude and the number of cyclic loading cycles, a three-dimensional stress time history analysis is required, which includes two processes: moving influence line loading setting and stress output and processing. Among them, the moving influence line loading setting process includes: dividing the lane into 3 lanes according to the actual design plan, the lateral eccentric load coefficient of the middle lane is 1.0, the lateral eccentric load coefficient of the left lane is 0.9, and the lateral eccentric load coefficient of the right lane is 1.1. The vehicle moves with a step length of 0.5m, and the impact coefficient is increased by 10% according to the road surface flatness level B. Figure 12 Stress time history diagram of a tension-compression support under dynamic loading with random vehicle loads using the moving influence line method.

[0095] See also Figure 3 , 1 represents the root of the support anchor bolt. The stress output and processing process includes: extracting the three-dimensional stress time history of the dangerous point at the root of the support anchor bolt and obtaining the maximum stress amplitude in the three directions. Among them, the maximum stress amplitude in the vertical direction is expressed as: =85MPa (fluctuation range is ±12MPa); the maximum stress amplitude in the horizontal direction is expressed as: =32MPa (fluctuation range is ±8MPa); the maximum stress amplitude in the transverse direction is expressed as: =18MPa (fluctuation range is ±5MPa), and finally the stress time history curve is constructed based on the maximum stress amplitude in three directions.

[0096] Furthermore, the specific steps for obtaining the stress amplitude, number of cyclic loading cycles and average stress are as follows: in the stress-time history curve, for each valley value, compare the maximum values ​​on the left and right. Only when the maximum values ​​on the left and right are both higher or lower than the current valley value can a complete cycle be formed. The part between the maximum and minimum values ​​that meet the conditions is recorded as a load cycle. The rain flow starts to flow from the inside of the peak position of the load time history, stops flowing when encountering a peak larger than the starting peak, and stops flowing when encountering rain flow flowing from above. All complete cycles are recorded, and the amplitude of each cycle is counted. For cycles that are not completely closed, they are treated as half cycles, retaining the maximum and minimum values ​​in the stress history, and removing the intermediate points. In the stress-time history curve, the hysteresis loop formed by the peak and valley values ​​is identified. Each hysteresis loop represents a complete loading and unloading cycle. The stress amplitude is half of the difference between the peak and valley values ​​in the hysteresis loop. The average stress is the average of the peak and valley values ​​in the hysteresis loop. The number of cycles is counted for each hysteresis loop, and its stress amplitude and average stress are recorded. In a complex load history, the superposition method is used to identify and count overlapping cycles, and finally the stress amplitude, number of cyclic loading and average stress are obtained. Figure 13 A three-dimensional histogram showing the mean stress, stress amplitude, and number of cycles obtained by the rainflow counting method.

[0097] Furthermore, the present invention corrects the equivalent stress amplitude, and the corrected mathematical expression is:

[0098] ;

[0099] in, represents the daily average equivalent stress amplitude, represents the equivalent stress amplitude before correction, The average stress is 50 MPa. The cumulative damage degree D is calculated to be 0.77 (when the service life is 3 years). The remaining life is: (Year). Figure 2 Panoramic view of the finite element spatial analysis model of a cable-stayed bridge. Figure 6 Schematic diagram showing the fatigue life assessment process of tension and compression bearings of cable-stayed bridges under random traffic loads.

[0100] Furthermore, through early warning and decision support, the 3# and 5# support bolts will be replaced within 3 years (damage degree D>0.7), and the remaining bolts will be inspected within 5 years. If the bolts need to be replaced, an early warning signal will be generated and pushed to the maintenance department terminal through the Internet of Things platform.

[0101] The present invention calculates the stress amplitude, number of cyclic loading and average stress of stress time history data through the rain flow counting method. The rain flow counting method can accurately identify and calculate the number of cyclic loading and stress amplitude, thereby improving the assessment accuracy of fatigue life.

[0102] S7: Based on the fatigue damage equivalence principle, the stress amplitude and the number of cyclic loading are equivalently processed to obtain the daily average equivalent stress amplitude and the equivalent daily number of cycles. Combined with the Miner criterion, the fatigue life of the tension and compression bearing is obtained.

[0103] Specifically, obtain the daily average equivalent stress amplitude and equivalent daily cycles The process is:

[0104] ;

[0105] ;

[0106] in, represents the constant amplitude fatigue limit, Indicates less than The stress amplitude, Indicates less than The number of cycles, and They represent the fatigue strength coefficients when the stress amplitude is greater than or less than the fatigue limit of the normal amplitude, represents the daily average equivalent stress amplitude, represents the equivalent daily cycle number, Indicates greater than The stress amplitude, Indicates greater than The number of cycles, Indicates that the structural detail stress amplitude is greater than The fatigue stress amplitude, Indicates less than The stress amplitude, Indicated as structural detail stress amplitude less than fatigue stress amplitude.

[0107] Furthermore, the daily average equivalent stress amplitude is converted into the fatigue stress amplitude for fatigue life assessment, where the mathematical expression of the fatigue stress amplitude is:

[0108] ;

[0109] ;

[0110] in, express SN The fatigue stress amplitude of the curve, represents the number of stress cycles, Indicates the fatigue strength coefficient when the stress amplitude is greater than the fatigue limit of the normal amplitude, The fatigue strength coefficient indicating that the stress amplitude is less than the fatigue limit of the normal amplitude, represents the power of fatigue stress amplitude, represents the constant amplitude fatigue limit, Indicates the corresponding structural detail number.

[0111] Furthermore, after obtaining the daily average equivalent stress amplitude and equivalent daily cycle number, the fatigue life of the tension and compression bearing is obtained by combining the Miner criterion as follows:

[0112] ;

[0113] ;

[0114] Furthermore, the process of obtaining the fatigue life of the tension and compression supports includes:

[0115] ;

[0116] in, Indicates greater than The stress amplitude, Indicates less than The stress amplitude, Indicates less than The number of cycles, Indicates greater than The number of cycles, represents the daily average equivalent stress amplitude, represents the equivalent daily cycle number, represents the annual traffic growth rate, Indicates the annual traffic growth rate The fatigue life of the tension and compression supports, Indicates the service life of the tension and compression bearing. Figure 14 The figure shows the life assessment prediction diagram of the tension and compression bearings of the cable-stayed bridge under the traffic growth rate in each year.

[0117] The beneficial effects of the fatigue life assessment method of the tension and compression bearings of a cable-stayed bridge under locomotive flow load provided by the present invention are as follows:

[0118] First, the present invention uses dynamic weighing technology in a structural health monitoring system installed on a bridge to conduct long-term real-time monitoring of traffic loads, collect measured vehicle load data and probability distribution parameters, and construct a fatigue vehicle load model that can accurately describe the characteristics of actual traffic loads. Probability distribution models are also established for key probability distribution parameters (such as vehicle type and weight). The fatigue vehicle load model and probability distribution model will be used in subsequent traffic flow simulations.

[0119] Second, the present invention combines the measured vibration data of the bridge health monitoring system to correct the multi-scale finite element model (main beam stiffness, cable tension and support horizontal stiffness) so that the fundamental frequency error is ≤3%. By correcting the parameters of the multi-scale finite element model, the multi-scale finite element model can be made closer to the characteristics of the actual structure, thereby improving the reliability of the multi-scale finite element model when performing response prediction.

[0120] Third, the present invention applies a unit 1 kN fatigue vehicle load to the modified multi-scale finite element model to obtain stress influence lines at vulnerable details, which will provide necessary data for subsequent fatigue analysis.

[0121] Fourth, the present invention calculates the stress amplitude, number of cyclic loading and average stress of stress time history data through the rain flow counting method. The rain flow counting method can accurately identify and calculate the number of stress cycles and amplitude, thereby improving the assessment accuracy of the fatigue life of the structure.

[0122] Fifth, by combining load spectra and stress influence lines, the present invention can more accurately simulate and predict stress changes in tension and compression bearings under actual traffic loads. This allows for more precise calculation of stress time history data for tension and compression bearings, thereby improving the accuracy of fatigue assessments.

[0123] Example 2

[0124] See also Figure 15 The second embodiment of the present invention further provides a system for evaluating the life of tension and compression bearings of a cable-stayed bridge under random traffic loads, the system comprising:

[0125] The vehicle load database construction module 100 is used to obtain vehicle monitoring video data of the target bridge, extract vehicle axle weight, wheelbase, speed and travel time interval parameters, and obtain the probability distribution model of the parameters and the statistical characteristics of the traffic load;

[0126] A random traffic flow load model construction module 200 is used to generate random traffic flow samples according to a probability distribution model and statistical characteristics, and to construct a random traffic flow load model based on the random traffic flow samples;

[0127] A load spectrum calculation module 300 is used to obtain a load spectrum for fatigue assessment of tension and compression bearing details based on a random traffic load model;

[0128] A multi-scale finite element model construction module 400 for calculating cable forces and modal frequencies based on bridge monitoring data, and performing static and dynamic corrections on the cable forces and modal frequencies to obtain a corrected multi-scale finite element model of the bridge. In the multi-scale finite element model of the bridge, the main beams and pylons are simulated using beam elements, and the tension and compression supports are finely modeled using solid elements and connected through multi-point constraints.

[0129] The stress time history data acquisition module 500 is used to apply a fatigue vehicle load of preset units to the modified multi-scale finite element model of the bridge to obtain stress influence lines, and obtain stress time history data of the tension and compression bearings based on the load spectrum and stress influence lines;

[0130] The fatigue life assessment module 600 is used to calculate the stress amplitude, number of loading cycles, and average stress of the stress time history data based on the rain flow counting method. Based on the fatigue damage equivalence principle, the stress amplitude and number of loading cycles are equivalently processed to obtain the daily average equivalent stress amplitude and equivalent daily cycle number. In combination with the Miner criterion, the fatigue life of the tension and compression bearing is calculated.

[0131] The life warning module 700 is used to trigger an alarm signal according to the relationship between the fatigue life of the tension and compression bearing and the service life threshold, and generate a priority list for replacing bolts in the tension and compression bearing based on the alarm signal.

[0132] In summary, the beneficial effects of the life assessment system for tension and compression bearings of cable-stayed bridges under random traffic loads provided by the present invention are:

[0133] First, the vehicle load database construction module 100 is used to extract vehicle axle weight, wheelbase, speed and travel time interval parameters, and obtain the probability distribution model of the parameters and the statistical characteristics of the traffic load, so that the generated random traffic load model can be more accurate when constructing the random traffic load model.

[0134] Second, the bridge multi-scale finite element model construction module 400 is used to perform static and dynamic corrections on the cable forces and modal frequencies to obtain a corrected bridge multi-scale finite element model. This can make the model closer to the characteristics of the actual structure, thereby improving the reliability of the response prediction of the structure under different working conditions.

[0135] Third, the stress-history data acquisition module 500 is used to apply a fatigue vehicle load of preset units to the modified multi-scale finite element model of the bridge to obtain the stress influence line. The stress-history data of the tension and compression supports are obtained based on the load spectrum and the stress influence line. This combination can more accurately calculate the stress-history data of the tension and compression supports, thereby improving the accuracy of fatigue assessment.

[0136] Fourth, the fatigue life assessment module 600 calculates the stress amplitude, number of cyclic loading cycles and average stress obtained from the stress time history data using the rain flow counting method, and can accurately identify and calculate the number of stress cycles and amplitude, thereby improving the assessment accuracy of the structural fatigue life.

[0137] In an embodiment of the present invention, a system for assessing the life of tension and compression bearings of a cable-stayed bridge under random traffic loads can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, a mobile electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), while a non-mobile electronic device can be a server, network attached storage (NAS), personal computer (PC), television, ATM, or self-service machine, etc., without any specific limitation in the embodiment of the present invention.

[0138] In an embodiment of the present invention, a system for assessing the life of tension and compression bearings of a cable-stayed bridge under random traffic loads may represent a device having an operating system. The operating system may be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in this embodiment of the present invention.

[0139] The embodiment of the present invention provides a system for evaluating the life of tension and compression bearings of cable-stayed bridges under random traffic loads. Figures 1 to 14 In order to avoid repetition, the various processes of implementing a method for evaluating the life of tension and compression bearings of a cable-stayed bridge under random traffic loads in the method embodiment will not be repeated here.

[0140] Optionally, an embodiment of the present invention further provides an electronic device, including a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, each process of the above-mentioned embodiment of the method for evaluating the life of the tension and compression bearings of a cable-stayed bridge under random traffic loads is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0141] An embodiment of the present invention also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned embodiment of the method for evaluating the life of the tension and compression bearings of a cable-stayed bridge under random traffic loads is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0142] The processor refers to the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk.

[0143] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present invention is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0144] Through the above description of the embodiments, those skilled in the art will clearly understand that the methods of the above embodiments can be implemented using software plus the necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is the more preferred implementation method. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, or optical disk) and includes a number of instructions for enabling a terminal (such as a mobile phone, computer, server, air conditioner, or network device) to execute the methods described in the various embodiments of the present invention.

[0145] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. A method for evaluating the life of tension and compression bearings of cable-stayed bridges under random traffic loads, characterized in that: include: Conduct statistical analysis on the probability distribution parameters of traffic load data to obtain a random traffic load model; Obtaining a load spectrum for fatigue assessment of tension and compression bearing details based on the random traffic load model; Modifying model parameters of a multi-scale finite element model of the bridge to obtain a modified multi-scale finite element model of the bridge, wherein the main beam and the bridge tower are simulated using beam elements, and the tension and compression supports are finely modeled using solid elements and connected through multi-point constraints; Applying a fatigue vehicle load of a preset unit to the modified multi-scale finite element model of the bridge to obtain a stress influence line; Obtaining stress time history data of the tension and compression support based on the load spectrum and the stress influence line; Calculating the stress amplitude and the number of cyclic loading of the stress time history data based on the rain flow counting method; Based on the fatigue damage equivalence principle, the stress amplitude and the number of cyclic loading are equivalently processed to obtain the daily average equivalent stress amplitude and the equivalent daily cycle number. The fatigue life of the tension and compression bearing is obtained by combining the Miner criterion.

2. The method for evaluating the life of tension and compression bearings of a cable-stayed bridge under random traffic loads according to claim 1 is characterized in that: The probability distribution parameters include: vehicle type, gross vehicle weight, axle weight, wheelbase, vehicle spacing, lane traffic volume ratio, vehicle type distribution ratio in each lane, and lateral distribution characteristics of vehicle centerline along the lane. The axle weight obeys extreme value type I distribution, and the wheelbase obeys normal distribution.

3. The method for evaluating the life of tension and compression bearings of a cable-stayed bridge under random traffic loads according to claim 1 is characterized in that: The process of establishing the multi-scale finite element model of the bridge includes two processes: constructing the overall truss model parameters and fine-tuning the tension and compression bearing modeling. When constructing the overall truss model parameters, the model parameters include the main beam, bridge tower and inclined cable. Among them, the main beam adopts a single-box three-chamber steel box beam, the bridge tower adopts a C50 concrete tower column, and is simulated by beam units. The inclined cable adopts a truss unit. When performing fine-tuning the tension and compression bearing modeling, it is necessary to divide the entity units, that is, the upper and lower pressure plates adopt C3D8R units, the anchor bolts adopt C3D10 units, and the rubber pads adopt Mooney-Rivlin parameters. The main beam and the top plate of the tension and compression bearing, and the bridge tower and the bottom plate of the tension and compression bearing are rigidly connected through MPC.

4. The method for evaluating the life of tension and compression bearings of a cable-stayed bridge under random traffic loads according to claim 1 is characterized in that: The process of modifying the model parameters of the multi-scale finite element model of the bridge includes two processes: measuring data acquisition and sensitivity analysis and iterative correction. In the process of measuring data acquisition, the process includes: obtaining the vertical acceleration time history data of the main beam mid-span through the bridge health monitoring system and extracting the first three natural frequencies; in the process of sensitivity analysis and iterative correction, the process includes: determining the current parameter values ​​of the multi-scale finite element model, and the parameter values ​​include: the initial parameter values ​​of the model , initial damping factor , the maximum number of iterations MaxIter and the convergence threshold ; Calculate the error vector Error between the model output and the actual target value based on the current parameter value; Calculate the gradient vector g of the error vector with respect to the parameter through the chain rule and back propagation algorithm; Construct the Jacobian matrix , the Jacobian matrix Within each element Indicates the Output pair Partial derivatives of parameters; update parameters of calculated parameters ; Select the correction parameters: main beam stiffness, cable tension, support horizontal stiffness, and perform multiple iterations to ensure that the corrected fundamental frequency error is within the preset range.

5. The method for evaluating the life of tension and compression bearings of a cable-stayed bridge under random traffic loads according to claim 1 is characterized in that: The fatigue vehicle load of the preset unit is applied to meet the following requirements: the longitudinal movement step length of the vehicle is ≤ 0.5m; the lateral load coefficient is 0.8-1.2 according to the actual lane distribution; The dynamic impact coefficient is increased by 5% to 15% according to the smoothness grade of the bridge pavement. The contact friction coefficient of the upper and lower pressure plates in the tension and compression bearings is set to 0.15 to 0.

25. The preload force of the anchor bolts is applied at 40% to 60% of the yield strength of the bolt material. The constitutive relationship of the rubber vibration isolation pad adopts the Mooney-Rivlin hyperelastic model.

6. The method for evaluating the life of tension and compression bearings of a cable-stayed bridge under random traffic loads according to claim 1 is characterized in that: The process of performing equivalent processing on the stress amplitude and the number of cyclic loading based on the fatigue damage equivalence principle to obtain the daily average equivalent stress amplitude and the equivalent daily cycle number includes two processes: moving influence line loading setting and stress output and processing. The moving influence line loading setting process includes: dividing the lane into multiple lanes, each lane has a different lateral load coefficient, the vehicle moves in steps of a fixed distance, and the impact coefficient is added according to the road surface flatness grade B; the stress output and processing process includes: extracting the three-dimensional stress time history of the dangerous point at the root of the support anchor bolt to obtain the maximum stress amplitude in the three directions.

7. The method for evaluating the life of tension and compression bearings of a cable-stayed bridge under random traffic loads according to claim 1 is characterized in that: The mathematical expressions of the daily average equivalent stress amplitude and the equivalent daily cycle number are: ； ； The mathematical expression of the fatigue life of the tension and compression support is: ； in, represents the constant amplitude fatigue limit, Indicates greater than The stress amplitude, Indicates less than The stress amplitude, Indicates less than The number of cycles, Indicates greater than The number of cycles, and They represent the fatigue strength coefficients when the stress amplitude is greater than or less than the fatigue limit of the normal amplitude, represents the daily average equivalent stress amplitude, represents the equivalent daily cycle number, represents the annual traffic growth rate, Indicates the annual traffic growth rate The fatigue life of the tension and compression supports, Indicates the service life of the tension and compression bearing.

8. A system for evaluating the life of tension and compression bearings of cable-stayed bridges under random traffic loads, characterized by: include: A vehicle load database construction module is used to obtain vehicle monitoring video data of the target bridge, extract vehicle axle weight, wheelbase, speed and travel time interval parameters, and obtain the probability distribution model of the parameters and the statistical characteristics of the traffic load; A random traffic flow load model construction module is used to generate random traffic flow samples according to the probability distribution model and statistical characteristics, and to construct a random traffic flow load model based on the random traffic flow samples; A load spectrum calculation module, used for obtaining a load spectrum for fatigue assessment of tension and compression bearing details according to the random traffic load model; A multi-scale finite element model construction module for bridges, which is used to calculate cable forces and modal frequencies based on bridge monitoring data, and perform static and dynamic corrections on the cable forces and modal frequencies to obtain a corrected multi-scale finite element model of the bridge. In the multi-scale finite element model of the bridge, the main beams and pylons are simulated using beam elements, and the tension and compression supports are finely modeled using solid elements and connected through multi-point constraints. a stress time history data acquisition module, configured to apply a fatigue vehicle load of a preset unit to the modified multi-scale finite element model of the bridge to obtain a stress influence line, and to obtain stress time history data of the tension and compression bearings based on the load spectrum and the stress influence line; a fatigue life assessment module for calculating the stress amplitude, number of loading cycles, and average stress of the stress time history data according to the rainflow counting method, performing equivalent processing on the stress amplitude and the number of loading cycles based on the fatigue damage equivalence principle, obtaining the daily average equivalent stress amplitude and the equivalent daily number of cycles, and combining the Miner criterion to obtain the fatigue life of the tension and compression bearing; The life warning module is used to trigger an alarm signal according to the relationship between the fatigue life of the tension and compression support and the service life threshold, and generate a priority list for replacing bolts in the tension and compression support based on the alarm signal.

9. An electronic device, characterized in that: It includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of a method for evaluating the life of tension and compression bearings of a cable-stayed bridge under random traffic loads as described in any one of claims 1 to 7 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the method for evaluating the life of the tension and compression bearings of a cable-stayed bridge under random traffic loads as described in any one of claims 1-7 are implemented.

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