Bridge driving performance evaluation method and system based on ambient temperature

Abstract

The application provides a bridge driving performance evaluation method and system based on ambient temperature, and relates to the technical field of railway engineering. The method comprises the following steps: calculating chord measurement values and vehicle body accelerations at each detection position according to track vertical deformation data under different temperature conditions; performing correlation analysis on the chord measurement values and the vehicle body accelerations under different chord lengths to determine an optimal chord length; based on the optimal chord length, constructing a first mapping relationship between temperature and the chord measurement values, and a second mapping relationship between the chord measurement values and the vehicle body accelerations, and substituting the first mapping relationship into the second mapping relationship to obtain a third mapping relationship between temperature and the vehicle body accelerations; and evaluating driving performance under any temperature according to the third mapping relationship to obtain an evaluation result. The method can improve the efficiency of driving performance evaluation of long-span railway bridges under temperature change conditions.

Description

Technical Field

[0001] This application relates to the field of railway engineering technology, specifically to a method and system for evaluating bridge traffic performance based on ambient temperature. Background Technology

[0002] Long-span railway bridges are characterized by their large spans and complex systems, making them highly sensitive to changes in ambient temperature. Temperature fluctuations cause significant deformation in the main bridge structure, which, when transmitted to the track structure laid on top, alters the track's geometry and affects track smoothness. Current technologies for evaluating the performance of long-span bridges under temperature variations typically employ finite element method (FEM) simulation. This involves establishing a separate track-bridge deformation analysis model for each temperature condition requiring evaluation and performing calculations for each condition to obtain track deformation data. This data is then input into a train-track-bridge dynamic interaction model for dynamic simulation, ultimately yielding performance evaluation indicators. However, due to the complexity and computational demands of this modeling and calculation process, when evaluating a large number of temperature conditions or requiring real-time prediction of performance at a specific temperature, existing methods necessitate repeated modeling and calculations across multiple temperature conditions, hindering the rapid acquisition of performance data.

[0003] Furthermore, track deformation caused by temperature changes is a static geometric deviation, while vehicle vibration generated during train operation is a dynamic response; the two have fundamentally different physical characteristics. Existing evaluation methods, when analyzing the impact of temperature on train performance, typically calculate track deformation and train dynamic response independently under temperature conditions, without establishing a direct quantitative mapping relationship between temperature change, track geometry, and train vibration response. When the ambient temperature changes, the entire simulation calculation process needs to be restarted to obtain new evaluation results; it is impossible to directly output corresponding train performance indicators based on temperature input. This condition-by-condition repetitive calculation evaluation mode results in a lengthy train performance evaluation process, making it difficult to meet the needs of rapid assessment under different temperature conditions in operation and maintenance. Therefore, existing technologies suffer from low efficiency in evaluating the train performance of long-span railway bridges under temperature variation conditions. Summary of the Invention

[0004] This application provides a method and system for evaluating the driving performance of bridges based on ambient temperature, which can improve the efficiency of evaluating the driving performance of long-span railway bridges under temperature variation conditions.

[0005] In a first aspect, this application provides a method for evaluating bridge driving performance based on ambient temperature, including:

[0006] Based on the track vertical deformation data under different temperature conditions, the chord measurement value and vehicle acceleration at each detection location were calculated respectively; Correlation analysis was performed on the chord measurement values ​​and the vehicle body acceleration under different chord length conditions to determine the optimal chord length; Based on the optimal chord length, a first mapping relationship between temperature and the chord measurement value, and a second mapping relationship between the chord measurement value and the vehicle acceleration are constructed. The first mapping relationship is then substituted into the second mapping relationship to obtain a third mapping relationship between temperature and the vehicle acceleration. Based on the third mapping relationship, the driving performance at any temperature is evaluated, and the evaluation results are obtained.

[0007] Optionally, the step of calculating the chord measurement value and vehicle acceleration at each detection location based on the track vertical deformation data under different temperature conditions includes: Establish a track-bridge deformation mapping analysis model; Multiple overall temperature rise and fall conditions were set up to simulate and calculate the track-bridge deformation mapping analysis model. The vertical deformation of the track at each position along the longitudinal direction under each working condition is extracted to form the track vertical deformation data.

[0008] Optionally, the chord measurement value at each detection location is calculated, including: The vertical deformation data of the track is filtered to remove interference components below a preset wavelength; The midpoint chord measurement method was used to calculate the filtered vertical deformation data of the track using multiple sets of different chord lengths, and the chord measurement values ​​at each detection position under each chord length condition were obtained.

[0009] Optionally, the vehicle acceleration at each detection location is calculated, including: Establish a dynamic interaction model of train-track-bridge; The vertical deformation data of the track is used as the wheel-rail geometric excitation, and the vehicle body acceleration is obtained by using the dynamic time-domain integration method.

[0010] Optionally, the step of performing correlation analysis between the chord measurement values ​​and the vehicle body acceleration under different chord length conditions to determine the optimal chord length includes: Under the same temperature conditions, calculate the Pearson correlation coefficient between the chord measurement sequence and the corresponding vehicle acceleration sequence under each chord length condition; The string length with the largest absolute value of the Pearson correlation coefficient is determined as the optimal string length.

[0011] Optionally, constructing a first mapping relationship between temperature and the measured chord length based on the optimal chord length includes: Using temperature as the independent variable and the chord measurement value at each detection position under the optimal chord length condition as the dependent variable, linear fitting is performed at each detection position to obtain the first mapping relationship at each position:

[0012] in, The change in temperature For the first Each detection location is subject to temperature changes. The fitted chord measurement value, For the first The slope of the linear fit between the temperature and chord measurements at each detection location. For the first The intercept of the linear fit between the temperature and the measured values ​​at each detection location.

[0013] Optionally, constructing the second mapping relationship between the chord measurement value and the vehicle acceleration includes: Using the chord measurement values ​​at each detection position under the optimal chord length condition as the independent variable and the vehicle acceleration at each detection position as the dependent variable, linear fitting is performed at each detection position to obtain the second mapping relationship at each position:

[0014] in, For chord measurement, For the first Each detection location corresponds to a chord measurement value. The fitted vehicle body acceleration, For the first The slope of the linear fit between the measured chord values ​​and the vehicle acceleration at each detection location. For the first The intercept of the linear fit between the chord measurement value and the vehicle acceleration at each detection location.

[0015] Optionally, substituting the first mapping relationship into the second mapping relationship to obtain the third mapping relationship between temperature and vehicle acceleration includes: Substituting the first mapping relationship at each position into the second mapping relationship at the same position, we obtain the third mapping relationship at each position:

[0016] in, The change in temperature For the first Each detection location is subject to temperature changes. The fitted vehicle body acceleration is as follows. , The first The slope and intercept of the linear fit between the temperature at each detection location and the measured chord value. , The first The slope and intercept of the linear fit between the chord measurement value and the vehicle acceleration at each detection location.

[0017] Optionally, evaluating driving performance at any temperature based on the third mapping relationship includes: Receive the target temperature value; The chord measurement value under the target temperature value is calculated according to the first mapping relationship, and the chord measurement value is compared with the preset smoothness limit value to output the line smoothness evaluation result. The vehicle body acceleration at the target temperature value is calculated based on the third mapping relationship. The vehicle body acceleration is compared with the preset comfort limit, and the driving comfort evaluation result is output.

[0018] Secondly, this application provides a bridge traffic performance evaluation system based on ambient temperature, comprising: The calculation module is used to calculate the chord measurement value and vehicle acceleration at each detection location based on the track vertical deformation data under different temperature conditions. The correlation analysis module is used to perform correlation analysis between the measured chord values ​​and the vehicle acceleration under different chord length conditions to determine the optimal chord length; The mapping construction module is used to construct a first mapping relationship between temperature and the measured value of the chord, and a second mapping relationship between the measured value of the chord and the vehicle body acceleration based on the optimal chord length, and substitute the first mapping relationship into the second mapping relationship to obtain a third mapping relationship between temperature and the vehicle body acceleration; The evaluation output module is used to evaluate the driving performance at any temperature based on the third mapping relationship and obtain the evaluation result.

[0019] Compared with existing technologies, the beneficial effects of this application are as follows: Based on the parallel calculation of chord measurement values ​​and vehicle acceleration using track vertical deformation data, correlation analysis is performed on the two under different chord length conditions to determine the optimal chord length, thus establishing a quantitative correlation between the chord measurement value (a ride comfort index) and the vehicle acceleration (a comfort index). Based on the optimal chord length, a first mapping relationship between temperature and chord measurement values ​​and a second mapping relationship between chord measurement values ​​and vehicle acceleration are constructed. Substituting the first mapping relationship into the second mapping relationship yields a third mapping relationship between temperature and vehicle acceleration, forming a full-chain quantitative mapping system of "temperature-ride comfort-comfort". Through the above mapping relationships, the corresponding chord measurement value and vehicle acceleration prediction results can be directly output after inputting any target temperature value. By constructing an analytical mapping relationship between temperature and driving performance indicators, this application can quickly obtain driving performance evaluation results under any temperature condition without re-modeling and dynamic simulation for each temperature condition, thereby improving the efficiency of driving performance evaluation for long-span railway bridges under temperature variation conditions. Attached Figure Description

[0020] Figure 1 A schematic diagram illustrating the steps of the bridge driving performance evaluation method based on ambient temperature provided in this application embodiment.

[0021] Figure 2 This is a graph showing the vertical deformation curve of the track on a long-span bridge under overall heating, as shown in the embodiments of this application.

[0022] Figure 3 This is a graph showing the results of the overall temperature rise of 28°C and the vehicle body acceleration in the embodiments of this application.

[0023] Figure 4 This is a graph showing the correlation coefficients between chord measurement values ​​of different chord lengths and vehicle acceleration in the embodiments of this application.

[0024] Figure 5 This is a schematic diagram comparing and verifying the temperature-chord measurement fitting function and the calculated chord measurement in the embodiments of this application. Detailed Implementation

[0025] The present application will now be described in further detail with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the subject matter of the present application to the following embodiments. All technologies implemented based on the content of the present application fall within the scope of protection of the present application.

[0026] In the description of the embodiments of this application, technical terms such as "first" and "second" only distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary or secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] Please refer to Figure 1 , Figure 1 A schematic diagram illustrating the steps of a bridge traffic performance evaluation method based on ambient temperature provided in this application embodiment. The method may include: S1. Calculate the chord measurement value and vehicle acceleration at each detection location based on the track vertical deformation data under different temperature conditions.

[0029] S2. Conduct correlation analysis between chord measurement values ​​and vehicle acceleration under different chord length conditions to determine the optimal chord length.

[0030] S3. Based on the optimal chord length, construct the first mapping relationship between temperature and chord measurement value, and the second mapping relationship between chord measurement value and vehicle acceleration. Substitute the first mapping relationship into the second mapping relationship to obtain the third mapping relationship between temperature and vehicle acceleration.

[0031] S4. Based on the third mapping relationship, evaluate the driving performance at any temperature and obtain the evaluation results.

[0032] In this embodiment, the track vertical deformation data refers to the data set showing the vertical displacement of the track at various positions along the longitudinal direction of the track after the superstructure of a long-span bridge deforms due to temperature changes under different overall temperature rise and fall conditions. This data originates from a track-bridge deformation mapping analysis model established using Midas Civil finite element software. Simulation calculations are performed under multiple different overall temperature rise and fall conditions to extract the vertical displacement of each node along the longitudinal direction of the track, thus obtaining the track vertical deformation data corresponding to different temperature conditions. The detection position refers to the discrete node positions along the longitudinal direction of the track, divided at certain intervals.

[0033] Please refer to Figure 2 , Figure 2This is a graph showing the vertical deformation curve of the track on a long-span bridge under overall temperature rise, as described in this embodiment of the application. The chord measurement value is a quantitative index of track smoothness obtained by analyzing the vertical deformation data of the track using the midpoint chord measurement method. Specifically, this embodiment first performs a 200m high-pass filter on the track's vertical deformation data to remove high-frequency interference components introduced by the track's own structural vibration and measurement errors, retaining the low-frequency static deformation signal caused by temperature changes. Then, using seven different chord lengths (10m, 20m, 30m, 40m, 50m, 60m, and 70m), the midpoint chord measurement method is performed on the filtered track's vertical deformation data. That is, under each chord length condition, the detection positions along the longitudinal direction of the track are traversed to obtain the chord measurement value at each detection position. Please refer to... Figure 3 , Figure 3 This is a graph showing the results of the overall temperature rise of 28°C and the vehicle body acceleration in the embodiments of this application.

[0034] Vehicle body acceleration refers to the acceleration value of the vertical vibration of the vehicle body when the train is traveling on the bridge. In this embodiment, a dynamic interaction model of train-track-long-span bridge is first established. The vertical deformation data of the track obtained above is used as the wheel-rail geometric excitation input into the model. Then, the dynamic time-domain integration method is used to solve the model to obtain the vertical acceleration of the vehicle body when the train is traveling under the corresponding temperature conditions.

[0035] Please refer to Figure 4 , Figure 4 This embodiment of the application presents a correlation coefficient diagram between chord measurement values ​​and vehicle acceleration under different chord lengths. After obtaining the chord measurement values ​​and vehicle acceleration at each detection location under different chord length conditions, this embodiment performs a correlation analysis on the chord measurement values ​​and vehicle acceleration under different chord length conditions. The correlation analysis refers to calculating the degree of statistical correlation between the data sequence of chord measurement values ​​at each detection location under a certain chord length condition and the data sequence of vehicle acceleration at the corresponding detection location, under the same temperature conditions.

[0036] In this embodiment, the Pearson correlation coefficient method is used to perform the analysis. Specifically, for each set of chord length conditions, the chord measurement sequence and vehicle acceleration sequence at all detection locations are used as inputs, and the Pearson correlation coefficient between them is calculated. The closer the absolute value of the correlation coefficient is to 1, the stronger the linear correlation. In this embodiment, the correlation coefficients corresponding to each of the seven sets of chord length conditions are calculated, and then the chord length with the largest absolute value of the Pearson correlation coefficient is selected as the optimal chord length from the seven sets of chord lengths. The optimal chord length refers to the chord length with the highest degree of linear correlation between its chord measurement value and vehicle acceleration among all the different chord lengths participating in the test. The chord measurement values ​​calculated under this chord length condition can most effectively reflect the track smoothness characteristics that affect driving comfort.

[0037] After determining the optimal chord length, this embodiment constructs a first mapping relationship, a second mapping relationship, and a third mapping relationship based on the optimal chord length. The first mapping relationship is a functional correspondence between the temperature change and the measured chord value. This embodiment uses the temperature change as the independent variable and the measured chord value at each detection location under the optimal chord length condition as the dependent variable. Linear fitting is performed at each detection location to obtain a linear fitting function between the temperature and the measured chord value at each detection location. Please refer to... Figure 5 , Figure 5 This is a schematic diagram comparing and verifying the temperature-chord measurement fitting function and the calculated chord measurement in the embodiments of this application.

[0038] The second mapping relationship is the functional correspondence between the chord measurement value and the vehicle acceleration. In this embodiment, the chord measurement value at each detection position under the optimal chord length condition is used as the independent variable, and the vehicle acceleration at each detection position is used as the dependent variable. Linear fitting is performed at each detection position to obtain the linear fitting function between the chord measurement value and the vehicle acceleration at each detection position.

[0039] The third mapping relationship is the functional correspondence between temperature change and vehicle acceleration. In this embodiment, the first mapping relationship at each detection location is substituted into the second mapping relationship at the same detection location. That is, the temperature-sine measurement fitting function at each detection location is substituted into the sine measurement-vehicle acceleration fitting function at the corresponding location for composite operation to obtain the linear fitting function between temperature change and vehicle acceleration at each detection location.

[0040] After constructing the first and third mapping relationships, this embodiment evaluates the train performance at any temperature based on the third mapping relationship. The train performance evaluation includes two dimensions: track smoothness evaluation and train comfort evaluation. The former corresponds to the evaluation of track geometry, and the latter corresponds to the evaluation of train running quality. This embodiment receives a specified target temperature value as input, calculates the chord measurement value at each detection location under the target temperature value according to the first mapping relationship, compares the chord measurement value with a preset smoothness limit, and determines that the track smoothness at that detection location does not meet the requirements when the chord measurement value exceeds the smoothness limit; otherwise, it is determined that the requirements are met, and the track smoothness evaluation result is output accordingly.

[0041] Meanwhile, this embodiment calculates the vehicle acceleration at each detection location under the target temperature value based on the third mapping relationship, compares the vehicle acceleration with a preset comfort limit, and determines that the driving comfort under that temperature condition does not meet the requirements when the vehicle acceleration exceeds the comfort limit; otherwise, it is determined that the requirements are met, and the driving comfort assessment result is output accordingly. This assessment result provides a basis for subsequent operation and maintenance decisions and track geometry control of long-span railway bridges under temperature change conditions.

[0042] In some implementations, the step of calculating the chord measurement value and vehicle acceleration at each detection location based on the track vertical deformation data under different temperature conditions includes: A track-bridge deformation mapping analysis model is established; multiple overall temperature rise and fall conditions are set, and the track-bridge deformation mapping analysis model is simulated and calculated; the vertical deformation of the track at each longitudinal position under each condition is extracted to form the track vertical deformation data.

[0043] In this embodiment of the application, the track-bridge deformation mapping analysis model can be established by constructing a numerical analysis model in the Midas Civil finite element software that reflects the deformation transfer relationship between the track and the bridge, based on the actual track structure and bridge structure type of the long-span railway bridge.

[0044] Setting multiple overall temperature rise and fall conditions refers to setting multiple different temperature change ranges as calculation conditions based on the range of ambient temperature changes that the area where the long-span railway bridge is located. For example, there are overall temperature rise conditions and overall temperature fall conditions. Each condition corresponds to a specific temperature change amount, which is used to comprehensively cover the temperature load conditions that the bridge may experience in actual operation.

[0045] The simulation calculation of the track-bridge deformation mapping analysis model specifically refers to applying temperature loads corresponding to each overall temperature rise and fall condition in the Midas Civil software, running the finite element numerical solver to perform static calculations, thereby obtaining the vertical displacement response transmitted to the track after the deformation of the main bridge structure under each temperature condition.

[0046] Extracting the vertical deformation of the track at various longitudinal positions under different operating conditions to form the track vertical deformation data involves extracting the vertical displacement values ​​of each node position along the longitudinal direction of the track at certain intervals after the simulation calculation for each temperature condition. The vertical displacements of all nodes are then arranged in order of track mileage to form the track vertical deformation data corresponding to that temperature condition. This data, when represented as a curve, is the track vertical deformation curve. Therefore, through simulation calculations under multiple temperature conditions, multiple sets of track vertical deformation data under different temperature conditions can be obtained, providing a data foundation for subsequent calculations of chord measurements and vehicle acceleration.

[0047] In some implementations, the specific method for calculating the chord measurement values ​​at each detection location may include: The track vertical deformation data is filtered to remove interference components below a preset wavelength; the midpoint chord measurement method is used to calculate the filtered track vertical deformation data with multiple sets of different chord lengths to obtain the chord measurement values ​​at each detection position under each chord length condition.

[0048] In this embodiment, filtering the track vertical deformation data to remove interference components below a preset wavelength involves inputting the track vertical deformation data obtained in step one into a high-pass filter, setting the cutoff wavelength to 200m, and filtering to remove high-frequency components with wavelengths less than or equal to 200m from the track vertical deformation curve. The interference components mixed in the track vertical deformation data mainly originate from local vibrations of the track structure itself, deformation of the fastener system, short-wave irregularities on the rail surface, and random errors introduced by the measuring equipment. These components have high frequencies and short wavelengths, differing from the overall deformation characteristics of the bridge caused by temperature changes. High-pass filtering at 200m effectively removes these high-frequency interference components, retaining the low-frequency static deformation signal caused by temperature changes, allowing the subsequently calculated chord measurement values ​​to more accurately reflect the true impact of temperature on track smoothness.

[0049] The midpoint chord measurement method is used to calculate the vertical deformation of the filtered track using multiple sets of chord lengths. This involves using the midpoint chord measurement method as a track smoothness feature extraction tool after filtering, setting multiple sets of different chord length parameters, and calculating the chord measurement values ​​for each set of filtered data. The basic principle of the midpoint chord measurement method is as follows: at any longitudinal detection position on the track, taking that position as the midpoint, half the chord length is taken forward and backward as the two endpoints. Connecting these two endpoints forms a reference chord line. Then, the deviation of the actual track elevation at the midpoint position from this reference chord line is calculated; this deviation is the chord measurement value at that position. A larger chord measurement value indicates a more severe local geometric deviation of the track at that position, and a poorer track smoothness.

[0050] Multiple sets of different chord lengths refer to seven sets of chord lengths (10m, 20m, 30m, 40m, 50m, 60m, and 70m) set according to the engineering practice needs of track smoothness testing, covering the commonly used chord length range for track smoothness testing in railway engineering. Different chord lengths have different sensitivities to track deformation; shorter chord lengths are more sensitive to local short-wave irregularities, while longer chord lengths can better reflect the long-wave vertical deformation characteristics of the track. Under each set of chord lengths, the computing device traverses all detection positions along the longitudinal direction of the track, substituting the filtered vertical deformation data at each position into the midpoint chord measurement method calculation formula to obtain the chord measurement value at each detection position under that chord length condition. Through the calculation of the above seven sets of chord lengths, a sequence of chord measurement values ​​under seven different chord length conditions can be obtained, providing a data foundation for subsequent correlation analysis and determination of the optimal chord length.

[0051] In some implementations, the method for calculating the vehicle acceleration at each detection location specifically includes: A dynamic interaction model of train-track-bridge is established; the vertical deformation data of the track is used as the wheel-rail geometric excitation, and the acceleration of the vehicle body is obtained by using the dynamic time-domain integration method.

[0052] In this embodiment of the application, establishing a train-track-bridge dynamic interaction model means that, based on the established Midas Civil finite element model of a long-span bridge, a train subsystem and a track subsystem are further introduced to construct a coupled dynamic model that can simulate the dynamic interaction between the vehicle, track and bridge when the train is traveling on a long-span bridge.

[0053] Using the track vertical deformation data as wheel-rail geometric excitation means that when solving the train-track-bridge dynamic interaction model, the track vertical deformation data under different temperature conditions obtained by the Midas Civil finite element simulation calculation in step one is input into the model as the external geometric excitation at the wheel-rail contact interface.

[0054] The vehicle body acceleration is obtained by using the dynamic time-domain integration method. This involves establishing a complete train-track-bridge dynamic interaction model and using track vertical deformation data as the wheel-rail geometric excitation input. Then, a numerical integration method is used to solve the model's dynamic equations step-by-step in the time domain. Through iterative calculations of the above time-domain integration process, the time history data of the vehicle body's vertical vibration acceleration under corresponding temperature conditions is finally obtained. The magnitude of this vehicle body acceleration directly reflects the smoothness of train operation and the level of passenger comfort. By performing the above solution process on track vertical deformation data obtained under different overall temperature rise and fall conditions, the vehicle body acceleration corresponding to each temperature condition can be obtained.

[0055] In some embodiments, the step of performing correlation analysis between the chord measurement values ​​and the vehicle body acceleration under different chord length conditions to determine the optimal chord length includes: Under the same temperature conditions, the Pearson correlation coefficient between the chord measurement sequence and the corresponding vehicle acceleration sequence under each chord length condition is calculated; the chord length with the largest absolute value of the Pearson correlation coefficient is determined as the optimal chord length.

[0056] Pearson correlation coefficient between string measurement and vehicle acceleration for:

[0057] in, For chord measurement sequence, An acceleration sequence, , These are their respective mean values.

[0058] Correlation coefficient The range of values ​​for is [ [1, 1], the closer the absolute value is to 1, the stronger the linear correlation, and the closer it is to 0, the weaker the linear correlation.

[0059] In some implementations, constructing a first mapping relationship between temperature and the measured chord length based on the optimal chord length includes: Using temperature as the independent variable and the chord measurement value at each detection position under the optimal chord length condition as the dependent variable, linear fitting is performed at each detection position to obtain the first mapping relationship at each position:

[0060] in, The change in temperature For the first Each detection location is subject to temperature changes. The fitted chord measurement value, For the first The slope of the linear fit between the temperature and chord measurements at each detection location. For the first The intercept of the linear fit between the temperature and the measured values ​​at each detection location.

[0061] Table 1 is a coefficient table of the first mapping relationship provided in the embodiments of this application.

[0062] Table 1

[0063] If there is Groups of temperature samples, each group of samples contains The nth measuring point, then the nth The determination coefficient of each measuring point for:

[0064] in, For the first The measuring point, the first The true chord measurement values ​​of the static deformation of the track under the set temperature. For the first The measuring point, the first The fitted chord measurements of the static deformation of the track under a set of temperatures. For the first Each measuring point in all The average value of the true chord measurements of the static deformation of the track under the set temperature. The average coefficient of determination of each measuring point The closer the value is to 1, the better the model fit.

[0065] In some implementations, constructing a second mapping relationship between the chord measurement value and the vehicle acceleration includes: Using the chord measurement values ​​at each detection position under the optimal chord length condition as the independent variable and the vehicle acceleration at each detection position as the dependent variable, linear fitting is performed at each detection position to obtain the second mapping relationship at each position:

[0066] in, For chord measurement, For the first Each detection location corresponds to a chord measurement value. The fitted vehicle body acceleration, For the first The slope of the linear fit between the measured chord values ​​and the vehicle acceleration at each detection location. For the first The intercept of the linear fit between the chord measurement value and the vehicle acceleration at each detection location.

[0067] Table 2 is a coefficient table of the second mapping relationship provided in the embodiments of this application.

[0068] Table 2

[0069] In some embodiments, substituting the first mapping relationship into the second mapping relationship to obtain a third mapping relationship between temperature and vehicle acceleration includes: Substituting the first mapping relationship at each position into the second mapping relationship at the same position, we obtain the third mapping relationship at each position:

[0070] in, The change in temperature For the first Each detection location is subject to temperature changes. The fitted vehicle body acceleration is as follows. , The first The slope and intercept of the linear fit between the temperature at each detection location and the measured chord value. , The first The slope and intercept of the linear fit between the chord measurement value and the vehicle acceleration at each detection location.

[0071] Table 3 is a coefficient table of the third mapping relationship provided in the embodiments of this application.

[0072] Table 3

[0073] Furthermore, the evaluation of driving performance at any temperature based on the third mapping relationship includes: Receive the target temperature value; calculate the chord measurement value under the target temperature value according to the first mapping relationship, compare the chord measurement value with the preset smoothness limit, and output the line smoothness evaluation result; calculate the vehicle body acceleration under the target temperature value according to the third mapping relationship, compare the vehicle body acceleration with the preset comfort limit, and output the driving comfort evaluation result.

[0074] The system can obtain the user-specified target temperature change from an external input interface. This target temperature value represents the ambient temperature condition required for vehicle performance evaluation and prediction. The received target temperature value is then substituted into the previously constructed temperature-chord measurement linear fitting function for each detection location. Specifically, for each detection location, the target temperature value is used as the independent variable and substituted into the corresponding first mapping relationship for calculation, yielding the predicted chord measurement value at that location under the target temperature condition. By calculating for each detection location, the chord measurement value distribution at each location along the entire route under the target temperature condition can be obtained.

[0075] The predicted chord measurement values ​​calculated at each detection location are compared one by one with preset smoothness limits. A judgment is made at each detection location: if the chord measurement value at a location does not exceed the smoothness limit, the track smoothness at that location is deemed to meet the requirements; if the chord measurement value exceeds the smoothness limit, the track smoothness at that location is deemed not to meet the requirements, i.e., that location is a weak area in track geometry. The evaluation device summarizes the judgment results of all detection locations, generates and outputs the track smoothness evaluation result. This evaluation result specifically includes whether the smoothness at each detection location is qualified and the specific distribution of unqualified locations, providing a precise positioning basis for track maintenance. The preset smoothness limit can be derived from the allowable deviation value of track static smoothness specified in relevant railway engineering specifications. This limit is a quantitative threshold for judging whether the track geometry meets the requirements for safe operation.

[0076] The received target temperature value is substituted into the temperature-vehicle acceleration linear fitting function constructed above for each detection location. For each detection location, the target temperature value is used as the independent variable and substituted into the corresponding third mapping relationship for calculation to obtain the predicted vehicle acceleration at that detection location under the target temperature condition. By calculating for all detection locations one by one, the vehicle acceleration distribution at each location when the train is running under the target temperature condition can be obtained.

[0077] Finally, the predicted vehicle acceleration results calculated at each detection location are compared with the preset comfort limits. When the vehicle acceleration at a certain location does not exceed the comfort limit, the driving comfort at that location is determined to meet the requirements; when the vehicle acceleration exceeds the comfort limit, the driving comfort at that location is determined to not meet the requirements. The evaluation device summarizes the judgment results of all detection locations, generates and outputs the driving comfort evaluation results, which specifically include whether the comfort at each detection location is qualified and the distribution of locations that do not meet the comfort requirements. The preset comfort limits are derived from the maximum allowable vertical acceleration of the vehicle body specified in relevant railway engineering specifications. These limits are quantitative standards for judging whether the train's running smoothness and passenger comfort meet the requirements.

[0078] In the above implementation process, the chord measurement value and vehicle acceleration are calculated in parallel based on the track vertical deformation data. Correlation analysis is performed on the two under different chord lengths to determine the optimal chord length, establishing a quantitative correlation between the chord measurement value (a ride comfort index) and the vehicle acceleration (a comfort index). Based on the optimal chord length, a first mapping relationship between temperature and chord measurement value and a second mapping relationship between chord measurement value and vehicle acceleration are constructed. Substituting the first mapping relationship into the second mapping relationship yields a third mapping relationship between temperature and vehicle acceleration, forming a full-chain quantitative mapping system of "temperature-ride comfort-comfort". Through these mapping relationships, the corresponding chord measurement value and vehicle acceleration prediction results can be directly output after inputting any target temperature value. This application, by constructing an analytical mapping relationship between temperature and driving performance indicators, can quickly obtain driving performance evaluation results under any temperature condition without re-modeling and dynamic simulation for each temperature condition, thus improving the efficiency of driving performance evaluation for long-span railway bridges under temperature variation conditions.

[0079] Based on the same concept, embodiments of this application also provide a bridge traffic performance evaluation system based on ambient temperature, including: The calculation module is used to calculate the chord measurement value and vehicle acceleration at each detection location based on the track vertical deformation data under different temperature conditions. The correlation analysis module is used to perform correlation analysis between the measured chord values ​​and the vehicle acceleration under different chord length conditions to determine the optimal chord length; The mapping construction module is used to construct a first mapping relationship between temperature and the measured value of the chord, and a second mapping relationship between the measured value of the chord and the vehicle body acceleration based on the optimal chord length, and substitute the first mapping relationship into the second mapping relationship to obtain a third mapping relationship between temperature and the vehicle body acceleration; The evaluation output module is used to evaluate the driving performance at any temperature based on the third mapping relationship and obtain the evaluation result.

[0080] It should be understood that when the various modules of the system provided in the above embodiments are working, the division of each functional module in the above description is only used as an example. In actual applications, the above functions can be assigned to different functional modules as needed. That is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0081] The functional modules in the above embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the embodiments of this application.

[0082] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for evaluating bridge traffic performance based on ambient temperature, characterized in that, include: Based on the track vertical deformation data under different temperature conditions, the chord measurement value and vehicle acceleration at each detection location were calculated respectively; Correlation analysis was performed on the chord measurement values ​​and the vehicle body acceleration under different chord length conditions to determine the optimal chord length; Based on the optimal chord length, a first mapping relationship between temperature and the chord measurement value, and a second mapping relationship between the chord measurement value and the vehicle acceleration are constructed. The first mapping relationship is then substituted into the second mapping relationship to obtain a third mapping relationship between temperature and the vehicle acceleration. Based on the third mapping relationship, the driving performance at any temperature is evaluated, and the evaluation results are obtained.

2. The bridge driving performance evaluation method based on ambient temperature according to claim 1, characterized in that, The calculation of chord measurement values ​​and vehicle acceleration at each detection location based on track vertical deformation data under different temperature conditions includes: Establish a track-bridge deformation mapping analysis model; Multiple overall temperature rise and fall conditions were set up to simulate and calculate the track-bridge deformation mapping analysis model. The vertical deformation of the track at each position along the longitudinal direction under each working condition is extracted to form the track vertical deformation data.

3. The bridge driving performance evaluation method based on ambient temperature according to claim 1, characterized in that, Calculate the chord measurement values ​​at each detection location, including: The vertical deformation data of the track is filtered to remove interference components below a preset wavelength; The midpoint chord measurement method was used to calculate the filtered vertical deformation data of the track using multiple sets of different chord lengths, and the chord measurement values ​​at each detection position under each chord length condition were obtained.

4. The bridge driving performance evaluation method based on ambient temperature according to claim 1, characterized in that, Calculate the vehicle acceleration at each detection location, including: Establish a dynamic interaction model of train-track-bridge; The vertical deformation data of the track is used as the wheel-rail geometric excitation, and the vehicle body acceleration is obtained by using the dynamic time-domain integration method.

5. The bridge driving performance evaluation method based on ambient temperature according to claim 1, characterized in that, The correlation analysis between the chord measurement values ​​and the vehicle acceleration under different chord length conditions to determine the optimal chord length includes: Under the same temperature conditions, calculate the Pearson correlation coefficient between the chord measurement sequence and the corresponding vehicle acceleration sequence under each chord length condition; The string length with the largest absolute value of the Pearson correlation coefficient is determined as the optimal string length.

6. The bridge driving performance evaluation method based on ambient temperature according to claim 1, characterized in that, The step of constructing a first mapping relationship between temperature and the measured chord length based on the optimal chord length includes: Using temperature as the independent variable and the chord measurement value at each detection position under the optimal chord length condition as the dependent variable, linear fitting is performed at each detection position to obtain the first mapping relationship at each position: in, The change in temperature For the first Each detection location is subject to temperature changes. The fitted chord measurement value, For the first The slope of the linear fit between the temperature and chord measurements at each detection location. For the first The intercept of the linear fit between the temperature and the measured values ​​at each detection location.

7. The bridge driving performance evaluation method based on ambient temperature according to claim 1, characterized in that, The construction of the second mapping relationship between the chord measurement value and the vehicle acceleration includes: Using the chord measurement values ​​at each detection position under the optimal chord length condition as the independent variable and the vehicle acceleration at each detection position as the dependent variable, linear fitting is performed at each detection position to obtain the second mapping relationship at each position: in, For chord measurement, For the first Each detection location corresponds to a chord measurement value. The fitted vehicle body acceleration, For the first The slope of the linear fit between the measured chord values ​​and the vehicle acceleration at each detection location. For the first The intercept of the linear fit between the chord measurement value and the vehicle acceleration at each detection location.

8. The bridge driving performance evaluation method based on ambient temperature according to claim 1, characterized in that, The step of substituting the first mapping relationship into the second mapping relationship to obtain the third mapping relationship between temperature and vehicle acceleration includes: Substituting the first mapping relationship at each position into the second mapping relationship at the same position, we obtain the third mapping relationship at each position: in, The change in temperature For the first Each detection location is subject to temperature changes. The fitted vehicle body acceleration is as follows. , The first The slope and intercept of the linear fit between the temperature at each detection location and the measured chord value. , The first The slope and intercept of the linear fit between the chord measurement value and the vehicle acceleration at each detection location.

9. The bridge driving performance evaluation method based on ambient temperature according to claim 1, characterized in that, The evaluation of driving performance at any temperature based on the third mapping relationship includes: Receive the target temperature value; The chord measurement value under the target temperature value is calculated according to the first mapping relationship, and the chord measurement value is compared with the preset smoothness limit value to output the line smoothness evaluation result. The vehicle body acceleration at the target temperature value is calculated based on the third mapping relationship. The vehicle body acceleration is compared with the preset comfort limit, and the driving comfort evaluation result is output.

10. A bridge traffic performance evaluation system based on ambient temperature, characterized in that, include: The calculation module is used to calculate the chord measurement value and vehicle acceleration at each detection location based on the track vertical deformation data under different temperature conditions. The correlation analysis module is used to perform correlation analysis between the measured chord values ​​and the vehicle acceleration under different chord length conditions to determine the optimal chord length; The mapping construction module is used to construct a first mapping relationship between temperature and the measured value of the chord, and a second mapping relationship between the measured value of the chord and the vehicle body acceleration based on the optimal chord length, and substitute the first mapping relationship into the second mapping relationship to obtain a third mapping relationship between temperature and the vehicle body acceleration; The evaluation output module is used to evaluate the driving performance at any temperature based on the third mapping relationship and obtain the evaluation result.

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