Methods, media, and equipment for predicting static and dynamic irregularities in long-span railway bridges

Abstract

This invention relates to the field of railway bridge engineering technology, and particularly to a method, medium, and equipment for predicting static and dynamic irregularities in long-span railway bridges. The method first establishes a deformation mapping analysis model between the track and the long-span railway bridge, calculating the vertical deformation curves under multiple temperature conditions. Then, the midpoint chord measurement method is used to calculate the chord measurement values ​​under each temperature condition. The corresponding vehicle acceleration response is solved using the dynamic time-domain integration method. Finally, temperature-chord measurement fitting functions and chord measurement-vehicle acceleration fitting functions are constructed sequentially for each discrete monitoring point along the bridge's longitudinal direction, and then combined to obtain the temperature-vehicle acceleration fitting function. This invention achieves refined evaluation across the entire bridge, accurately locating the over-limit positions of temperature deformation sensitive areas, ensuring traffic safety and track smoothness.

Description

Technical Field

[0001] This invention relates to the field of railway bridge engineering technology, and in particular to a method, medium and equipment for predicting static and dynamic irregularities in long-span railway bridges. Background Technology

[0002] Current long-span railway bridges have large spans and complex systems, making them sensitive to changes in ambient temperature. Temperature fluctuations can cause significant deformation of the bridge structure, which is transmitted to the tracks, resulting in noticeable vertical deformation and disrupting track smoothness. Track irregularities alter wheel-rail dynamics, inducing abnormal vehicle vibrations, significantly reducing ride comfort, and also exacerbating wheel-rail wear and increasing structural loads, seriously threatening the operational safety of long-span bridge lines.

[0003] Regarding track smoothness, existing engineering techniques typically employ the midpoint chord measurement method for static irregularities and actual data from track inspection vehicles for dynamic irregularities, assessing track irregularities separately. However, in practical engineering, especially in the analysis of complex structures such as bridge systems and vehicle components, there is still significant room for improvement in the selection of chord lengths for the midpoint chord measurement method for static irregularities in long-span railway cable-stayed bridges, and in the analysis of vehicle speeds during dynamic irregularities. Furthermore, there is a lack of research on models for large-span bridges under overall temperature fluctuations, making it impossible to quickly and accurately quantify the impact of temperature on the bridge-track-train coupling system.

[0004] Therefore, it is necessary to provide a new method, medium, and equipment for predicting static and dynamic irregularities in long-span railway bridges to solve the above-mentioned technical problems. Summary of the Invention

[0005] The main objective of this invention is to provide a method, medium, and equipment for predicting static and dynamic irregularities in long-span railway bridges, aiming to solve the problem that existing methods cannot quickly and accurately quantify the impact of temperature on the coupled system of long-span bridge-track-train.

[0006] To achieve the above objectives, the present invention proposes a method for predicting static and dynamic irregularities in long-span railway bridges, comprising the following steps: Several monitoring points were set up on the track bridge system to be tested to determine the element type, cross-sectional parameters, material parameters, constraints and boundary conditions of the track bridge system to be tested; and a deformation mapping analysis model of track-long-span railway bridge and a dynamic interaction model of train-track-long-span bridge were established using finite element software. Based on the deformation mapping analysis model of track-long-span railway bridge, the midpoint chord measurement method is introduced to calculate the chord measurement values ​​of track vertical deformation at each monitoring point under different temperature conditions, which are used to characterize the static irregularity of the track. Based on the dynamic interaction model of train-track-long-span bridge, the static track irregularity is used as the wheel-rail geometric excitation. The dynamic time-domain integration method is used to solve the car body acceleration under different temperature conditions to characterize the dynamic track irregularity. Based on the static and dynamic track irregularities under different temperature conditions, temperature-chord measurement fitting functions and temperature-vehicle acceleration fitting functions are constructed at different monitoring points. By using temperature-chord measurement fitting functions and temperature-vehicle acceleration fitting functions, the static and dynamic track irregularities at any monitoring point can be predicted based on the actual temperature conditions of the track-bridge system under test.

[0007] Optionally, based on the deformation mapping analysis model of track-long-span railway bridges, the midpoint chord measurement method is introduced to calculate the chord measurement values ​​of the track vertical deformation at each monitoring point under different temperature conditions, which are used to characterize the static irregularities of the track, specifically including: The vertical deformation curves of the track on the long-span railway bridge under different temperature conditions were obtained by simulation calculation based on the deformation mapping analysis model of track-long-span railway bridge. The vertical deformation curve is processed by high-pass filtering, and the chord measurement value of the track vertical deformation at each detection position under different temperature conditions is calculated by the midpoint chord measurement method with different chord lengths, that is, the track static irregularity.

[0008] Optionally, based on the static and dynamic track irregularities under different temperature conditions, temperature-chord measurement fitting functions and temperature-vehicle acceleration fitting functions are constructed at different monitoring points, specifically including: The correlation between the measured values ​​of the chord and the vehicle acceleration corresponding to different chord lengths under the same temperature conditions was analyzed by the Pearson correlation coefficient method, and the chord length with the highest correlation was taken as the optimal chord length. Using temperature change as the independent variable and the chord measurement value corresponding to the optimal chord length as the dependent variable, a linear fitting function of temperature-chord measurement value at different monitoring points is obtained. Using the chord measurement value corresponding to the optimal chord length as the independent variable and the vehicle acceleration as the dependent variable, a linear fitting was performed to obtain the chord measurement value-vehicle acceleration fitting function at different monitoring points; Substituting the temperature-chord measurement fitting function into the chord measurement-vehicle acceleration fitting function yields the temperature-vehicle acceleration fitting functions at different monitoring points.

[0009] Optionally, the specific formula for the temperature-chord measurement linear fitting function is as follows: ; in: Temperature change The chord measurement value corresponding to the optimal chord length. and For monitoring points The two fitting coefficients of the linear fitting function of the temperature-chord measurement are as follows: This is the monitoring point number.

[0010] Optionally, the specific formula for the linear fitting model of chord measurement-vehicle acceleration is as follows: ; in: for The corresponding vehicle acceleration, The chord measurement value corresponding to the optimal chord length. and For monitoring points The two fitting coefficients of the linear fitting function of the chord measurement value and the vehicle body acceleration.

[0011] Optionally, the specific formula for the temperature-vehicle acceleration fitting function is as follows: ; in: Temperature change The acceleration of the vehicle body under the vehicle, and For monitoring points The two fitting coefficients of the linear fitting function of the temperature-chord measurement are as follows: , .

[0012] Optionally, different temperature conditions include overall heating and overall cooling. Overall heating includes multiple heating conditions where the unit temperature value increases sequentially, and overall cooling includes multiple cooling conditions where the unit temperature value decreases sequentially.

[0013] In addition, the present invention also provides a readable storage medium storing computer program instructions, which, when executed by a processor, implement the method for predicting static and dynamic irregularities of long-span railway bridges as described above.

[0014] In addition, the present invention also provides an electronic device, including: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the computer program instructions are executed by the processor as described above in the method for predicting static and dynamic irregularities of railway long-span bridges.

[0015] The technical solution of the present invention has the following beneficial effects: The technical solution of this invention first establishes a deformation mapping analysis model of track-long-span railway bridge using finite element software, and calculates the vertical deformation curves of the track on the long-span bridge under multiple temperature conditions (covering both heating and cooling). High-pass filtering is applied to the vertical deformation curves to retain low-frequency static deformation caused by temperature, and the midpoint chord measurement method is used to calculate the chord measurement values ​​under each temperature condition. Simultaneously, a dynamic interaction model of train-track-long-span bridge is established, using the static irregularities of the track under each temperature condition as the wheel-rail geometric excitation, and the corresponding vehicle acceleration response is solved using the dynamic time-domain integration method. This invention independently performs linear fitting at each discrete monitoring point along the longitudinal direction of the bridge, sequentially constructing a temperature-chord measurement fitting function and a chord measurement-vehicle acceleration fitting function, and then combining them to obtain the temperature-vehicle acceleration fitting function. The fitting function of this invention can be determined by modeling and calculation only once. Then, for any given ambient temperature, the fitting coefficient of the corresponding monitoring point can be substituted to quickly obtain the prediction results of static and dynamic irregularities at that location, realize the fine evaluation of the entire bridge range, accurately locate the over-limit location of temperature deformation sensitive areas (such as beam ends, bridge towers, steel-concrete joints, etc.), and ensure driving safety and track smoothness. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the method for predicting static and dynamic irregularities of long-span railway bridges in an embodiment of the present invention. Figure 2 This is a schematic diagram of the track irregularity chord measurement values ​​under different temperature conditions corresponding to a chord length of 40m in an embodiment of the present invention. Figure 3 This is a diagram showing the vehicle body acceleration distribution during overall temperature rise in an embodiment of the present invention. Figure 4 This is a comparison chart of the predicted values ​​of the vehicle body acceleration during overall cooling predicted by the static and dynamic irregularity prediction method for railway long-span bridges in this embodiment of the invention and the calculation results of existing models. Figure 5 This is a comparison chart of the predicted values ​​of the vehicle body acceleration during overall temperature rise predicted by the static and dynamic irregularity prediction method for railway long-span bridges in this embodiment of the invention and the calculation results of existing models.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0021] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0022] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0023] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0024] This invention proposes a method, medium, and equipment for predicting static and dynamic irregularities in long-span railway bridges. It aims to address the problem that existing methods cannot quickly and accurately quantify the impact of temperature on the coupled system of long-span bridges, tracks, and trains. Specifically, it provides a rapid prediction method based on chain-like quantization mapping. By constructing a three-level recursive fitting function of "temperature → static irregularity (sine measurement) → dynamic irregularity (vehicle acceleration)," it achieves simultaneous prediction of static and dynamic irregularities at any ambient temperature and any bridge mileage location. This embodiment is entirely based on finite element simulation and dynamic coupling calculation, without relying on field measurement data. It is applicable to both the design and operation phases, providing efficient technical support for monitoring track smoothness, predicting ride comfort, and refining maintenance of long-span railway bridges.

[0025] See Figure 1 This embodiment provides a method for predicting static and dynamic irregularities in long-span railway bridges, including the following steps: Several monitoring points were set up on the track bridge system to be tested to determine the element type, cross-sectional parameters, material parameters, constraints and boundary conditions of the track bridge system to be tested; and a deformation mapping analysis model of track-long-span railway bridge and a dynamic interaction model of train-track-long-span bridge were established using finite element software. Based on the deformation mapping analysis model of track-long-span railway bridges, the midpoint chord measurement method is introduced to calculate different temperature conditions (i.e., temperature). x The vertical deformation of the track at each monitoring point is measured using chord measurements to characterize the static irregularities of the track; the vertical deformation of the track is... Figure 1 Deformation value in Specifically, it includes: ① Based on the deformation mapping analysis model of track-long-span railway bridge, simulation calculations were performed to obtain the vertical deformation curves of the track on the long-span bridge under different temperature conditions. In this embodiment, the different temperature conditions were set as follows: +18℃, +23℃, +28℃, +33℃, and +38℃. The obtained track vertical deformation curves are shown below. Figure 2 As shown.

[0026] ② The vertical deformation curve is subjected to high-pass filtering. The midpoint chord measurement method with different chord lengths is used to calculate the chord measurement values ​​of the track vertical deformation at each detection location under different temperature conditions, i.e., the static irregularity of the track. In this embodiment, the obtained track vertical deformation curve is first subjected to 200m high-pass filtering to filter out high-frequency interference components such as track structural vibration and measurement errors, and retain the low-frequency static deformation signal caused by temperature. Then, the midpoint chord measurement method is used, with different chord lengths (such as 10m, 20m, ..., 70m, covering the commonly used chord length range for track smoothness detection), and the longitudinal positions of the track (i.e., each monitoring point) are traversed to calculate the chord measurement values ​​of the track vertical deformation at each detection location under different temperature conditions.

[0027] Based on the dynamic interaction model of train-track-long-span bridge, and taking static track irregularities as wheel-rail geometric excitation, the dynamic time-domain integration method is used to solve the car body acceleration under different temperature conditions to characterize the dynamic track irregularities; see the car body acceleration distribution diagram during overall temperature rise. Figure 3 .

[0028] Based on static and dynamic track irregularities under different temperature conditions, temperature-chord measurement fitting functions and temperature-vehicle acceleration fitting functions are constructed at different monitoring points; specifically including: The correlation between the measured values ​​of the chord and the vehicle acceleration corresponding to different chord lengths under the same temperature conditions was analyzed by the Pearson correlation coefficient method, and the chord length with the highest correlation was taken as the optimal chord length. Using temperature change as the independent variable and the chord measurement value corresponding to the optimal chord length as the dependent variable, a linear fitting method is performed to obtain the temperature-chord measurement fitting function at different monitoring points; the specific formula for the temperature-chord measurement linear fitting function is as follows: ; in: Temperature change The chord measurement value corresponding to the optimal chord length. and For monitoring points The two fitting coefficients of the linear fitting function of the temperature-chord measurement are as follows: This is the monitoring point number.

[0029] In the linear fitting process of this embodiment, the average coefficient of determination is used. To verify the fitting effect, specifically: If there is Groups of temperature samples, each group of samples contains The monitoring point, then the first The determination coefficient of each monitoring point for: ; in, For the first The monitoring point, the first The true chord measurement values ​​of the static deformation of the track under the set temperature; For the first The monitoring point, the first Fitted chord measurements of static deformation of the track under a set of temperatures; For the first All monitoring points are in the whole The average value of the true chord measurements of the static deformation of the track under the set temperature; This refers to the number of temperature conditions.

[0030] The average coefficient of determination for each monitoring point is: ; The average coefficient of determination in this embodiment The closer the value is to 1, the better the model fit.

[0031] In this embodiment, the fitting coefficient of the temperature and the chord measurement function during overall heating is... and See Table 1 for the fitting coefficients of the temperature and chord measurement function during overall cooling. and See Table 2.

[0032] Table 1. Fitting coefficients of the temperature-sine measurement fitting function during overall heating.

[0033] Table 2. Fitting coefficients of the temperature-sine measurement function during overall cooling.

[0034] Using the chord measurement value corresponding to the optimal chord length as the independent variable and the vehicle acceleration as the dependent variable, a linear fitting was performed to obtain the chord measurement value-vehicle acceleration fitting function at different monitoring points; the specific formula of the chord measurement value-vehicle acceleration linear fitting model is as follows: ; in: for The corresponding vehicle acceleration, The chord measurement value corresponding to the optimal chord length. and For monitoring points The two fitting coefficients of the linear fitting function of the chord measurement value and the vehicle body acceleration.

[0035] This embodiment uses a novel, independent temperature condition to verify the accuracy and engineering applicability of the fitted function of chord measurement-vehicle acceleration. By substituting the temperature condition not involved in the fitting into the established functional relationship, the predicted vehicle acceleration value is calculated and then compared with the actual acceleration value obtained from the simulation of the train-track-long-span bridge dynamic interaction model. The reliability of the function is comprehensively judged from three aspects: numerical error, curve trend, and spatial distribution. Specifically: 1. Substitute the changes in chord measurement values ​​caused by temperature changes in the verification working conditions into the chord measurement value-vehicle acceleration function at each location to obtain the predicted vehicle acceleration curves for each longitudinal mileage of the entire bridge.

[0036] 2. The measured / simulated curves of vehicle body acceleration under the same verification temperature conditions were calculated using the train-track-long-span bridge dynamic interaction model as a true reference.

[0037] 3. Plot a curve comparing predicted and actual values ​​to intuitively judge the consistency of trends; use error curves to show the distribution of deviations at various locations across the entire bridge and identify sensitive sections.

[0038] The overall cooling acceleration predicted by the chord measurement-vehicle acceleration model is compared with the acceleration calculated by dynamics software. The comparison between the predicted vehicle acceleration during overall cooling in this embodiment and the results calculated by existing models is shown below. Figure 4 As shown, by Figure 4 It can be seen that under the overall cooling condition of -37℃, the maximum prediction error of the model is 0.15 m / s. 2 And only a few locations produced speeds greater than 0.1 m / s. 2 The error is small. The predicted values ​​at all locations on the bridge deviate little from the actual values, with no obvious abnormal deviations. The overall numerical agreement meets the engineering calculation requirements, and the extrapolation prediction capability is stable. The fitting coefficient of the chord measurement-vehicle acceleration fitting function during overall cooling is... and See Table 3.

[0039] Table 3. Fitting coefficients of the chord measurement-vehicle body acceleration fitting function during overall cooling.

[0040] The overall temperature rise prediction of the railcar body during the temperature rise, based on the chord measurement-car body acceleration fitting function, is compared with the railcar body acceleration calculated by dynamics software. The comparison between the predicted car body acceleration during the overall temperature rise in this embodiment and the calculation results of existing models is shown below. Figure 5 As shown, by Figure 5 It can be seen that under the condition of an overall temperature rise of 43℃, the maximum prediction error is 0.18m / s. 2 And only a few locations produced speeds greater than 0.05 m / s. 2 The error is small. The predicted values ​​at all locations on the entire bridge deviate little from the actual values, with no obvious abnormal deviations. The overall numerical agreement meets the engineering calculation requirements, and the extrapolation prediction capability is stable. The fitting coefficients of the chord measurement-vehicle acceleration fitting function during overall temperature rise are... and See Table 4.

[0041] Table 4. Fitting coefficients of the chord measurement-vehicle body acceleration fitting function during overall temperature rise.

[0042] The above test results show that the temperature-vehicle acceleration function established in this embodiment exhibits good prediction accuracy under new temperature conditions. The predicted curve is highly consistent with the simulated curve, and the peak acceleration locations and variation patterns at the mid-span of the main span, near the bridge towers, and in the steel-concrete composite section are completely consistent. The predicted values ​​at all locations on the entire bridge deviate little from the actual values, and the maximum absolute error is controlled within the allowable range, resulting in a high overall consistency. The errors are mainly concentrated in the stiffness abrupt change region, but overall, the calculation requirements are still met, and no significant abnormal deviations are observed.

[0043] Substituting the temperature-chord measurement fitting function into the chord measurement-vehicle acceleration fitting function yields the temperature-vehicle acceleration fitting functions at different monitoring points. The specific formula for the temperature-vehicle acceleration fitting function is as follows: ; in: Temperature change The acceleration of the vehicle body under the vehicle, and For monitoring points The two fitting coefficients of the linear fitting function of the temperature-chord measurement are as follows: , .

[0044] In this embodiment, the fitting coefficient of the chord measurement-vehicle body acceleration fitting function during overall temperature rise is... and See Table 5 for the fitting coefficients of the chord measurement-vehicle body acceleration fitting function during overall cooling. and See Table 6.

[0045] Table 5. Fitting coefficients of the chord measurement-vehicle body acceleration fitting function during overall temperature rise.

[0046] Table 6. Fitting coefficients of the chord measurement-vehicle body acceleration fitting function during overall cooling.

[0047] By using temperature-chord measurement fitting functions and temperature-vehicle acceleration fitting functions, the static and dynamic track irregularities at any monitoring point can be predicted based on the actual temperature conditions of the track-bridge system under test.

[0048] In this embodiment, different temperature conditions include overall heating and overall cooling. Overall heating includes multiple heating conditions where the unit temperature value increases sequentially, and overall cooling includes multiple cooling conditions where the unit temperature value decreases sequentially.

[0049] The technical solution of this embodiment has the following advantages compared with the prior art: 1. Construct a chained quantization mapping method for "temperature → static imperfection → dynamic imperfection"; specifically: Unlike traditional case-by-case simulation methods that require repeated modeling for each new temperature, this embodiment constructs a temperature-chord measurement fitting function and a chord measurement-vehicle acceleration fitting function sequentially, and then combines them to obtain a temperature-vehicle acceleration fitting function. Once this chain model is established, static and dynamic irregularities at any temperature can be directly calculated using the formula, eliminating the need for repeated finite element calculations and dynamic simulations, thus improving computational efficiency.

[0050] 2. Achieve refined independent prediction at any mileage along the bridge; specifically: Existing technologies typically only output the maximum vehicle acceleration for the entire bridge or a specific cross-section, which is insufficient to guide precise maintenance. This invention constructs an independent fitting function for each discrete location along the longitudinal direction of the bridge (e.g., a cross-section every 30m, with each cross-section corresponding to a monitoring point). This function can output chord measurement values ​​and predicted vehicle acceleration values ​​for any cross-section at any mileage, accurately locating the out-of-limit positions in temperature-sensitive areas such as beam ends, bridge towers, and steel-concrete joints, providing a direct basis for "repairing only where out-of-limit conditions are found."

[0051] 3. The parametric model based on finite element method and dynamic simulation does not rely on field measurement data and has strong universality; specifically: All fitting functions in this embodiment are constructed based on the finite element model and vehicle-track-bridge dynamic coupling simulation, without relying on long-term measured data of existing bridges. Therefore, this embodiment is not only suitable for the rapid evaluation of bridges in operation, but also applicable to the design stage or newly built bridges (without historical data), allowing for early prediction of track smoothness and driving comfort under different temperature conditions. The model parameters can be flexibly adjusted according to the bridge structure, making it suitable for various types of long-span railway bridges.

[0052] This embodiment also includes a readable storage medium storing computer program instructions, which, when executed by a processor, implement the static and dynamic irregularities prediction method for long-span railway bridges as described above.

[0053] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0054] This embodiment also includes an electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the computer program instructions are executed by the processor to perform the static and dynamic irregularity prediction method for railway long-span bridges as described above.

[0055] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0056] The electronic device can be a mobile phone, desktop computer, laptop, handheld computer, cloud server, or other computing device. The electronic device may include, but is not limited to, processors and memory. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0057] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0058] The memory can be used to store the computer program and / or modules. The processor implements the computer program by running or executing the computer program and / or modules stored in the memory, and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0059] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0060] The above description is only a preferred embodiment of the present invention and does not limit the scope of the present invention. All equivalent structural transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the protection scope of the present invention.

Claims

1. A method for predicting static and dynamic irregularities in long-span railway bridges, characterized in that, Includes the following steps: Several monitoring points were set up on the track bridge system to be tested to determine the unit type, cross-sectional parameters, material parameters, constraints and boundary conditions of the track bridge system to be tested. A deformation mapping analysis model of track-long-span railway bridge and a dynamic interaction model of train-track-long-span bridge were established using finite element software. Based on the deformation mapping analysis model of track-long-span railway bridge, the midpoint chord measurement method is introduced to calculate the chord measurement values ​​of track vertical deformation at each monitoring point under different temperature conditions, which are used to characterize the static irregularity of the track. Based on the dynamic interaction model of train-track-long-span bridge, the static track irregularity is used as the wheel-rail geometric excitation. The dynamic time-domain integration method is used to solve the car body acceleration under different temperature conditions to characterize the dynamic track irregularity. Based on the static and dynamic track irregularities under different temperature conditions, temperature-chord measurement fitting functions and temperature-vehicle acceleration fitting functions are constructed at different monitoring points. By using temperature-chord measurement fitting functions and temperature-vehicle acceleration fitting functions, the static and dynamic track irregularities at any monitoring point can be predicted based on the actual temperature conditions of the track-bridge system under test.

2. The method for predicting static and dynamic irregularities of long-span railway bridges according to claim 1, characterized in that, Based on the deformation mapping analysis model of track-long-span railway bridges, the midpoint chord measurement method is introduced to calculate the chord measurement values ​​of track vertical deformation at various monitoring points under different temperature conditions. These values ​​are used to characterize the static irregularities of the track, specifically including: The vertical deformation curves of the track on the long-span railway bridge under different temperature conditions were obtained by simulation calculation based on the deformation mapping analysis model of track-long-span railway bridge. The vertical deformation curve is processed by high-pass filtering, and the chord measurement value of the track vertical deformation at each detection position under different temperature conditions is calculated by the midpoint chord measurement method with different chord lengths, that is, the track static irregularity.

3. The method for predicting static and dynamic irregularities of long-span railway bridges according to claim 2, characterized in that, Based on the static and dynamic track irregularities under different temperature conditions, temperature-chord measurement fitting functions and temperature-vehicle acceleration fitting functions are constructed at different monitoring points, specifically including: The correlation between the measured values ​​of the chord and the vehicle acceleration corresponding to different chord lengths under the same temperature conditions was analyzed by the Pearson correlation coefficient method, and the chord length with the highest correlation was taken as the optimal chord length. Using temperature change as the independent variable and the chord measurement value corresponding to the optimal chord length as the dependent variable, a linear fitting function of temperature-chord measurement value at different monitoring points is obtained. Using the chord measurement value corresponding to the optimal chord length as the independent variable and the vehicle acceleration as the dependent variable, a linear fitting was performed to obtain the chord measurement value-vehicle acceleration fitting function at different monitoring points; Substituting the temperature-chord measurement fitting function into the chord measurement-vehicle acceleration fitting function yields the temperature-vehicle acceleration fitting functions at different monitoring points.

4. The method for predicting static and dynamic irregularities of long-span railway bridges according to claim 3, characterized in that, The specific formula for the linear fitting function of temperature-chord measurements is as follows: ； in: Temperature change The chord measurement value corresponding to the optimal chord length. and For monitoring points The two fitting coefficients of the linear fitting function of the temperature-chord measurement are as follows: This is the monitoring point number.

5. The method for predicting static and dynamic irregularities of long-span railway bridges according to claim 4, characterized in that, The specific formula for the linear fitting model of chord measurement-vehicle acceleration is as follows: ； in: for The corresponding vehicle acceleration, The chord measurement value corresponding to the optimal chord length. and For monitoring points The two fitting coefficients of the linear fitting function of the chord measurement value and the vehicle body acceleration.

6. The method for predicting static and dynamic irregularities of long-span railway bridges according to claim 5, characterized in that, The specific formula for the temperature-vehicle acceleration fitting function is as follows: ； in: Temperature change The acceleration of the vehicle body under the vehicle, and For monitoring points The two fitting coefficients of the linear fitting function of the temperature-chord measurement are as follows: , .

7. The method for predicting static and dynamic irregularities of long-span railway bridges according to any one of claims 1 to 6, characterized in that, Different temperature conditions include overall heating and overall cooling. Overall heating includes multiple heating conditions where the unit temperature value increases sequentially, and overall cooling includes multiple cooling conditions where the unit temperature value decreases sequentially.

8. A readable storage medium, characterized in that, It stores computer program instructions, which, when executed by a processor, implement the method for predicting static and dynamic irregularities of long-span railway bridges as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, include: The method for predicting static and dynamic irregularities of a long-span railway bridge as described in any one of claims 1 to 7 includes at least one processor, at least one memory, and computer program instructions stored in the memory.

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