Method, system, medium and device for identifying transverse connection performance of hollow slab bridge based on grey correlation

Abstract

The present application provides a method, system, medium and equipment for identifying the transverse connection performance of hollow slab bridges based on grey correlation, which belongs to the technical field of municipal highway bridges. Establish a finite element model of the bridge to be evaluated; select a vehicle model, and substitute the selected vehicle model into the established finite element model for calculation, and extract the deflection values ​​of adjacent hollow slabs under each step length of each vehicle working condition from the calculation results; calculate the grey correlation of the deflection of two adjacent hollow slabs according to the deflection values ​​under each step length of each vehicle working condition; establish a bridge health monitoring system, and use the bridge health monitoring system to collect data of bridges under multiple working conditions in real time; calculate the grey correlation between two adjacent hollow slabs according to the measured data; compare and analyze the measured grey correlation with the reference grey correlation, so as to identify the transverse connection performance of the hollow slab bridge. This scheme avoids dependence on model assumptions, and the judgment result is simple and direct, with wide applicability.

Description

Technical Field

[0001] The present application relates to the technical field of municipal highway bridges, and in particular to a method, system, medium and equipment for identifying the transverse connection performance of hollow slab bridges based on grey correlation. Background Art

[0002] Prefabricated hollow slab beam bridges are widely used in the construction of small and medium span bridges in my country due to their simple structure, easy transportation and installation, and short construction period. However, during the operation period, it was found that this type of bridge had different degrees of damage problems, among which the damage to the transverse hinge joint was the most serious. As a key component that connects the hollow slab beams into a whole, the transverse hinge joint undertakes the important task of lateral load transmission. Once it fails, it will reduce the integrity of the structure. In severe cases, it will cause the disease of "single plate stress" and endanger the safety of the structure. Therefore, it is an important task to evaluate hollow slab bridges to judge whether the force transmission of the hollow slab hinge joint is normal and whether the transverse connection is good.

[0003] The commonly used methods for judging the transverse connection of hollow slabs are to judge from the relative deflection, relative rotation angle, and transverse influence line between two adjacent hollow slabs. These methods start from the mechanical concept of hinges and study the working conditions of hinge joints and adjacent hollow slabs. However, these methods ignore the difference between the mechanical model assumptions and the actual operation of bridges. For early small tongue-and-groove joint bridges, the hinge joint concrete between hollow slabs fell off seriously, and the outer side of the hollow slab web was smooth, and the bonding between the hinge joint concrete and the hollow slab was poor, which was quite different from the theoretical assumptions on site. Therefore, these methods have certain limitations in the actual application of bridges.

[0004] At the same time, the commonly used evaluation methods for hollow slab hinge joints mostly adopt the cumulative deflection or absolute deflection method. From the perspective of force, this method has corresponding defects. The lateral connection of the hollow slab is related to the position of the vehicle traveling on the hollow slab bridge, and has a great relationship with the form of the loaded vehicle. The method of simply considering the cumulative deflection or absolute deflection has certain theoretical defects.

[0005] Therefore, it is necessary to provide an improved technical solution to address the above-mentioned deficiencies in the prior art. Summary of the invention

[0006] In view of the shortcomings of the above-mentioned identification methods, the present invention proposes a method for identifying the transverse connection performance of hollow slab bridges based on grey correlation, which is suitable for identifying the transverse connection performance of various hollow slab bridge types, including hinged joint damage, hinged joint detachment of various degrees, and hollow slabs of various cross-sectional forms. The problem of large deviations between the measured results and the theoretical assumptions can be avoided. At the same time, the transverse connection is judged by comparing the dynamic deflection change rate, avoiding the defects of the same problem in the absolute cumulative value deflection judgment. In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0007] In a first aspect, the present application provides a method for identifying the lateral connection performance of a hollow slab bridge based on grey correlation, comprising:

[0008] Step S101: establishing a finite element model of the bridge to be evaluated;

[0009] Step S102: Select a vehicle model, and substitute the selected vehicle model into the established finite element model for calculation, and extract the deflection values ​​of adjacent hollow slabs under each step length of each vehicle working condition from the calculation results;

[0010] Step S103: Calculate the grey correlation degree of the deflections of two adjacent hollow slabs according to the deflection values ​​at each step length of each vehicle working condition, and record it as a reference grey correlation degree;

[0011] Step S104: establishing a bridge health monitoring system, and using the bridge health monitoring system to collect data of the bridge under multiple working conditions in real time to obtain measured data, wherein the measured data at least includes dynamic deflection data and dynamic inclination data of two consecutive hollow slabs;

[0012] Step S105: calculating the grey correlation degree between two adjacent hollow slabs according to the measured data, and recording it as the measured grey correlation degree;

[0013] Step S106: Compare and analyze the measured grey correlation degree with the reference grey correlation degree to identify the transverse connection performance of the hollow slab bridge.

[0014] In some possible implementations, in step S102, a vehicle model with more than three axes is selected as a representative vehicle and substituted into an established finite element model for calculation.

[0015] In some possible implementations, in step S102, the vehicle operating condition at least includes operating conditions at different vehicle driving positions and operating conditions at different vehicle driving step lengths.

[0016] In some possible implementations, step S103 further includes: performing statistical analysis on the reference grey correlation degree to determine a value range of the reference correlation degree of the bridge;

[0017] Accordingly, in step S106, the measured grey relational degree is compared and analyzed with the reference grey relational degree, specifically:

[0018] It is determined whether the measured grey correlation degree is within the value range of the reference correlation degree. If so, it is determined that the transverse connection of the hollow slab bridge is normal. If not, it is determined that there is a problem with the transverse connection of the hollow slab bridge, and suggestions for detailed inspection and reinforcement are put forward for the next step.

[0019] In some possible implementations, in step S104, the bridge health monitoring system includes at least a dynamic deflection meter and a dynamic rotation angle, and the dynamic deflection meter and the dynamic rotation angle synchronously collect the dynamic deflection data and the dynamic inclination data;

[0020] After the bridge health monitoring system is used to collect data of the bridge under multiple working conditions in real time and obtain the measured data, the following steps are also included: determining data abnormalities and eliminating them, which are specifically as follows:

[0021] The dynamic deflection data collected in real time is compared with the deflection value under the design load. If the dynamic deflection data collected in real time is greater than 2.5 times the deflection value under the design load, it is determined that the dynamic deflection data collected in real time is suspected to be abnormal;

[0022] If the dynamic deflection data collected in real time is suspected to be abnormal, the dynamic deflection data is cross-checked with the dynamic inclination data collected synchronously at the same position to obtain the verification result;

[0023] If the verification results show that the data is still abnormal, the abnormal dynamic deflection data will be eliminated.

[0024] In some possible implementations, in step S104, after the step of determining data abnormality and eliminating the data, a step of re-checking is further included, which is specifically as follows:

[0025] A separation algorithm is used to separate the dynamic deflection data after removing abnormal data, so as to obtain the deflection caused by the vehicle and the deflection caused by temperature; the separation algorithm is any one of the wavelet method and the variational mode decomposition method;

[0026] Calculate the grey correlation degree of the deflection caused by temperature, recorded as the first grey correlation degree. If the deviation between the first grey correlation degree and the constant 1 is greater than a preset deviation threshold, it is determined that the deflection data caused by the separated vehicle is abnormal, and the separation algorithm is replaced.

[0027] Use the replaced separation algorithm to perform data separation again on the dynamic deflection data after removing abnormal data, and calculate the grey correlation degree of the deflection data caused by temperature again, which is recorded as the second grey correlation degree. If the deviation between the second grey correlation degree and the constant 1 is still greater than the preset deviation threshold, check the operation of the bridge health monitoring system, re-collect data and replace the separation algorithm until reasonable data is obtained.

[0028] If the deviation between the first grey relational degree or the second grey relational degree and the constant 1 is less than a preset deviation threshold, the deflection data caused by the separated vehicle is provided as the final measured data to step S105 for use.

[0029] In some possible implementations, the sampling frequency of the dynamic deflection meter and the dynamic rotation angle is greater than 20 Hz.

[0030] In a second aspect, this embodiment provides a system for identifying the lateral connection performance of a hollow slab bridge based on grey correlation, comprising:

[0031] a model building unit configured to build a finite element model of the bridge to be evaluated;

[0032] A model calculation unit is configured to select a vehicle model, substitute the selected vehicle model into the established finite element model for calculation, and extract the deflection value at each step length of each vehicle working condition from the calculation result;

[0033] A reference value calculation unit is configured to calculate the grey correlation degree of the deflections of two adjacent hollow slabs according to the deflection values ​​at each step length of each vehicle working condition, and record it as a reference grey correlation degree;

[0034] A real-time monitoring unit is configured to establish a bridge health monitoring system and use the bridge health monitoring system to collect data of the bridge under multiple working conditions in real time to obtain measured data, wherein the measured data at least includes dynamic deflection data and dynamic inclination data of two consecutive hollow slabs;

[0035] A measured value calculation unit is configured to calculate the grey correlation degree between two adjacent hollow slabs according to the measured data, which is recorded as the measured grey correlation degree;

[0036] The comparison and analysis unit is configured to compare and analyze the measured grey correlation degree with the reference grey correlation degree, so as to identify the transverse connection performance of the hollow slab bridge.

[0037] In a third aspect, the present embodiment provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for identifying the lateral connection performance of a hollow slab bridge based on grey correlation as provided in any of the above embodiments is implemented.

[0038] In a fourth aspect, this embodiment provides an electronic device, comprising: a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for identifying the lateral connection performance of a hollow slab bridge based on grey correlation as provided in any of the above embodiments is implemented.

[0039] The technical solution of the embodiment of the present application has the following beneficial effects:

[0040] 1. Traditional finite element analysis and bridge design often rely on a series of model assumptions (such as material properties, load distribution, etc.). These assumptions may not be completely consistent with the actual situation, resulting in differences between the assumptions of the finite element model and the actual situation. By introducing grey correlation analysis, more attention is paid to the actual performance and change trend of the data, reducing the strict requirements on model assumptions, so that in actual operation, even if the assumptions are not completely consistent with the actual situation, reliable evaluation results can still be obtained. Therefore, based on grey correlation to identify the transverse connection performance of hollow slab bridges, there is no need to consider the problem that the commonly used bridge calculation assumptions are inconsistent with the actual situation, and it has a wider applicability.

[0041] 2. By combining the finite element calculation results (theoretical data) with the actual monitoring data, and integrating the bridge theoretical calculation results with the data analysis results, it is possible to make up for the difference between the assumptions of the finite element model and the actual situation, provide a more realistic evaluation, avoid the limitations that may be caused by relying solely on the theoretical model, and improve the reliability of the results.

[0042] 3. In the solution provided in this application, the grey correlation focuses on the relative change of the dynamic deflection of two adjacent hollow slabs rather than the absolute value. It can capture the overall correlation through fewer measuring point data. Even if there are fewer monitoring points, the grey correlation analysis can still provide useful information, and thus the lateral connection between the hollow slabs can be judged with fewer monitoring points.

[0043] 4. By calculating the grey correlation of deflection data, the connection performance between adjacent hollow slabs can be directly measured. The judgment result is direct, simple and obvious. The obtained result can provide current data support for the next reinforcement treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic flow chart of a method for identifying the transverse connection performance of a hollow slab bridge based on grey correlation according to some embodiments of the present application.

[0045] Figure 2 A schematic flow chart of a method for identifying the transverse connection performance of a hollow slab bridge based on grey correlation provided according to another embodiment of the present application.

[0046] Figure 3A schematic diagram of the system structure for identifying the lateral connection performance of hollow slab bridges based on grey correlation according to some embodiments of the present application.

[0047] Figure 4 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application.

[0048] Figure 5 The hardware structure diagram of the electronic device provided according to the embodiment of the present application. DETAILED DESCRIPTION

[0049] The embodiments of the present application are described below in conjunction with the accompanying drawings.

[0050] Embodiment 1:

[0051] This embodiment provides a method for identifying the transverse connection performance of hollow slab bridges based on grey correlation. Figure 1 , Figure 2 As shown, the method includes:

[0052] Step S101: Establish a finite element model of the bridge to be evaluated.

[0053] The finite element model of the bridge can be established using professional finite element analysis software, such as ANSYS, ABAQUS, MIDAS, etc. These software can simulate complex structural behaviors and handle large-scale calculations. It is recommended to use the beam grid method to build the model. During the construction, the bridge structure can be simplified into a grid system composed of a series of interconnected beam units. Each beam represents a component in the bridge, and the lateral connection between the hollow slabs is considered.

[0054] In this embodiment, the finite element model of the bridge should be established strictly in accordance with the assumptions made during the design calculation. The assumptions made during the design calculation may include the material properties, geometric properties, boundary conditions, load conditions, etc. of the bridge structure. When establishing the finite element model of the bridge, strictly following the assumptions made during the design calculation can ensure the accuracy of the grey correlation results of the theoretical calculation and the reliability of the analysis results.

[0055] Step S102: Select a vehicle model, and substitute the selected vehicle model into the established finite element model for calculation, and extract the deflection values ​​of adjacent hollow slabs at each step length in each vehicle working condition from the calculation results.

[0056] In this embodiment, the vehicle type can be selected according to the analysis target, the design purpose of the bridge, and the actual traffic conditions, and the specific selected vehicle type can be, for example, a small car, a medium-sized car, a heavy-duty car, etc. As an example, statistical analysis can be performed based on the data of the highway toll station or the data of the installed dynamic weighing system to obtain the real traffic flow and load distribution data, and then representative bridges and vehicle types are selected for calculation to determine the representative vehicle, and then a detailed vehicle load model is constructed through software based on the parameters such as the number of axles, axle weight, wheelbase, and wheelbase of the selected representative vehicle.

[0057] After the representative vehicle model is determined, it can be substituted into the established bridge finite element model.

[0058] In a specific implementation, in step S102, a vehicle model with more than three axles is selected as a representative vehicle and substituted into the established finite element model for calculation. The advantage of selecting a vehicle model with more than three axles as a representative vehicle in this embodiment is that vehicles with more than three axles (such as heavy trucks, trucks, buses, etc.) usually represent the most important traffic load type during the use of the bridge. Compared with light vehicles, such heavy-loaded vehicles have a more significant impact on the bridge. By selecting a vehicle model with more than three axles, the finite element analysis can more realistically simulate the actual traffic conditions and traffic loads, and reflect the performance of the bridge under heavy load conditions. After substituting into the vehicle model with more than three axles, since these types of vehicles have a larger total weight, they cause more obvious lateral forces on the bridge, and can simulate larger loads and complex load distributions, and then can simulate the behavior of the bridge under the most unfavorable load, capture the real stress state of the bridge under the load, and play an important role in the ultimate bearing capacity analysis and lateral connection performance of the bridge.

[0059] After the vehicle model is built, the finite element simulation can be carried out. In the finite element simulation calculation, multiple vehicle conditions can be considered, such as different driving positions, different load change conditions, different driving steps, etc. Among them, the driving step refers to the time step in the finite element analysis, that is, in the finite element analysis, the vehicle's travel process is divided into multiple time segments (steps), and each time step represents the specific position of the vehicle on the bridge deck at a certain moment and the state of the bridge. Through multiple time steps, the deflection changes at different positions of the bridge during the vehicle movement can be obtained, and then the deflection values ​​of all hollow slabs can be extracted.

[0060] In this embodiment, by substituting a representative vehicle model into the finite element model of the bridge and considering conditions such as different driving positions and different forms of step lengths, the response of the bridge under various working conditions can be calculated, comprehensively considering various stress conditions of the hollow slab.

[0061] Finally, the deflection values ​​of adjacent hollow slabs at each step size for each vehicle operating condition are extracted from the calculation results.

[0062] Step S103: Calculate the grey correlation degree of the deflections of two adjacent hollow slabs according to the deflection values ​​at each step length of each vehicle working condition, and record it as a reference grey correlation degree.

[0063] Here, deflection refers to dynamic deflection; grey correlation (absolute grey correlation, generalized grey correlation) refers to the correlation between adjacent hollow slabs calculated based on the finite element analysis simulation results. Since it is obtained through finite element simulation analysis and calculation, it can also be called theoretical grey correlation.

[0064] As an example, when selecting indicators, the dynamic deflection change rate can be used as an indicator to calculate the grey correlation degree, that is, the dynamic deflection change rate is calculated first, and then the strength of the lateral connection of the hollow slab is judged by comparing the dynamic deflection change rate. The specific calculation steps are as follows:

[0065] Assume that the time series of the deflection of two adjacent hollow slabs under any working condition is X 1 and X 2 :

[0066] X 1 ={x 1 (1), x 1 (2), …x 1 (n)},

[0067] X 2 ={x 2 (1), x 2 (2), …x 2 (n)},

[0068] Where n is the total number of steps.

[0069] According to the above deflection time series X 1 and X 2 , the calculation method of subtracting the deflection value of the next step from the deflection value of the previous step (except the first step, if it is the first step, the deflection value of the first step is subtracted from itself, and finally 0 is taken), and the dynamic deflection change rate sequence of the two adjacent hollow slabs is constructed respectively and

[0070]

[0071]

[0072] in, Each element in the sequence is calculated as follows:

[0073]

[0074] Each element in the sequence is calculated as follows:

[0075]

[0076] The dynamic deflection change rate series of two adjacent hollow slabs are and Sum and take the absolute value:

[0077]

[0078] Calculation sequence and The difference between the corresponding position elements in the sequence and , and then sum and take the absolute value of the difference sequence:

[0079]

[0080] in, Sequence and The kth element of .

[0081] Then the grey correlation degree ε of the deflection of two adjacent hollow slabs is 12 The calculation method is as follows:

[0082]

[0083] Furthermore, step S103 also includes: statistically analyzing the calculation results of the reference grey correlation degree under different working conditions to determine the value range of the reference correlation degree of the bridge. The upper and lower limits of the reference correlation degree can be obtained by statistically calculating the maximum and minimum values ​​of the grey correlation degree, or can be defined by the mean and standard deviation. For example, the value range can be defined as 2 standard deviations or 3 standard deviations to ensure the reliability of the value range.

[0084] Step S104: Establish a bridge health monitoring system, and use the bridge health monitoring system to collect data of the bridge under multiple working conditions in real time to obtain measured data, wherein the measured data at least includes dynamic deflection data and dynamic inclination data of two consecutive hollow slabs.

[0085] Among them, the bridge health monitoring system may include the following modules: sensor network: arranged at key positions of the bridge, real-time collection of structural response data, such as stress, deflection, acceleration, displacement, temperature, etc.; data acquisition and transmission system: used to collect sensor data and transmit it to the central control system; central control system: including data storage, processing and analysis modules, used to store and analyze the collected data; remote monitoring platform: through the Internet or a private network, the monitoring data and analysis results are transmitted to the manager for remote monitoring. The detailed structure of each of the above modules can be implemented with reference to the existing technology, and this application will not be repeated here.

[0086] The bridge response data under various working conditions are collected in real time through various sensors in the bridge health monitoring system to obtain measured data. Among them, the bridge health monitoring system includes at least dynamic deflection monitoring items and dynamic angle monitoring items of two consecutive hollow slabs to obtain the dynamic deflection data and dynamic inclination data of the two consecutive hollow slabs.

[0087] The dynamic deflection obtained from the bridge health monitoring system is extracted, and the dynamic deflection values ​​of the adjacent hollow slabs for which the lateral distribution coefficient needs to be determined are selected. The extraction time length should be determined according to the actual situation, and the extracted data should include monitoring data under various vehicle models and various driving trajectories.

[0088] Furthermore, the bridge health monitoring system includes at least a dynamic deflection meter and a dynamic rotation angle, and the dynamic deflection meter and the dynamic rotation angle synchronously collect dynamic deflection data and dynamic inclination data.

[0089] Among them, the sampling frequency of the dynamic deflection meter and dynamic rotation angle should be greater than 20HZ, and the sampling of the two devices should be synchronized. A sampling frequency greater than 20Hz can ensure that the high-frequency components of bridge vibration, deflection and rotation angle can be captured, avoid data omission or distortion, and improve the time domain accuracy of the data. The dynamic deflection meter and dynamic rotation angle device are sampled synchronously to ensure that the data collected by the two sensors are consistent in time, avoid data dislocation or time lag, and facilitate subsequent analysis.

[0090] After the bridge health monitoring system is used to collect data of the bridge under multiple working conditions in real time and obtain the measured data, the following steps are also included: determining data abnormalities and eliminating them, which are specifically as follows:

[0091] Step S1041: Compare the dynamic deflection data collected in real time with the deflection value under the design load. If the dynamic deflection data collected in real time is greater than 2.5 times the deflection value under the design load, it is determined that the dynamic deflection data collected in real time is suspected to be abnormal.

[0092] Step S1042: if the dynamic deflection data collected in real time is suspected to be abnormal, the dynamic deflection data is cross-checked with the dynamic inclination data collected synchronously at the same position to obtain a check result;

[0093] Step S1043: If the verification result shows that the data is still abnormal, the abnormal dynamic deflection data will be eliminated.

[0094] That is to say, the maximum measured deflection value should not exceed 2.5 times the deflection under the design load. When an abnormality is suspected, the inclination data at the same position should be cross-checked to jointly determine the abnormal situation. When it is judged to be abnormal, it should be eliminated. By setting the deflection value threshold (i.e. not exceeding 2.5 times the deflection under the design load) and combining the inclination data verification, it is possible to reduce erroneous judgments caused by equipment failure, environmental noise or errors in the data collection process, and improve the accuracy of abnormality detection.

[0095] Considering that the change of bridge deflection may be caused by vehicle load or temperature effect, especially under extreme weather conditions, the influence of temperature factor is significantly enhanced, and the temperature and load effects are confused, which in turn affects the accuracy of the final result. In order to more accurately identify the actual dynamic response caused by vehicle load, after the step of judging data abnormality and eliminating it, it also includes: the step of re-checking (data processing), which is as follows:

[0096] Step S1044: using a separation algorithm to separate the dynamic deflection data after removing abnormal data, to obtain the deflection caused by the vehicle and the deflection caused by temperature; the separation algorithm is any one of the wavelet method and the variational mode decomposition method (VMD).

[0097] By separating the dynamic deflection data, it is possible to ensure that only the bridge deflection caused by vehicles is obtained, thus preparing for the next step of analysis.

[0098] Step S1045: Calculate the grey correlation of the deflection caused by temperature, recorded as the first grey correlation. If the deviation between the first grey correlation and the constant 1 is greater than a preset deviation threshold, it is determined that the deflection data caused by the separated vehicle is abnormal, and the separation algorithm is replaced.

[0099] If the grey correlation degree deviates greatly from the constant 1 (i.e., complete correlation), that is, the deviation is greater than the preset deviation threshold (the specific threshold can be given based on expert experience), it means that the deflection of the temperature effect does not match the actual situation, indicating that there may be a separation error. At this time, the separation algorithm should be replaced to reduce the risk of confusion between temperature and load effects, ensure the accuracy and reliability of the overall separation results, and avoid making incorrect assessments and judgments on the bridge.

[0100] Step S1046: Use the replaced separation algorithm to perform data separation again on the dynamic deflection data after eliminating the abnormal data, and recalculate the grey correlation of the deflection data caused by temperature, recorded as the second grey correlation. If the deviation between the second grey correlation and the constant 1 is still greater than the preset deviation threshold, check the operation of the bridge health monitoring system, recollect data and replace the separation algorithm until reasonable data is obtained.

[0101] In short, under normal circumstances, the deflection deformation caused by the temperature of adjacent hollow slabs should be basically the same. If the difference is large (the generalized grey correlation degree is significantly different from 1), the data is abnormal and the previous operation should be recalibrated. The advantage of doing this is that the signal collected by the bridge health monitoring system needs to be highly accurate. If the deviation of the second grey correlation degree is still large, it indicates that there may be problems with the currently collected data or the algorithm used. By recollecting the data or checking the system operation, the reliability and accuracy of the data source can be ensured. At the same time, different algorithms may have different processing capabilities for specific situations. By replacing the algorithm, the result deviation caused by the inability of a specific algorithm to accurately separate can be avoided, ensuring that the most appropriate algorithm is selected to process the current data. Re-calibration can also improve the flexibility and adaptability to complex working conditions.

[0102] Step S1047: If the deviation between the first grey correlation degree or the second grey correlation degree and the constant 1 is less than the preset deviation threshold, the separated vehicle-induced deflection data is provided as the final measured data to the next step S105 for use.

[0103] Step S105: Calculate the grey correlation degree between two adjacent hollow slabs according to the measured data, and record it as the measured grey correlation degree.

[0104] The specific calculation method of the measured grey relational degree in this step can refer to the grey relational degree calculation method described in step S103, which will not be repeated here.

[0105] Step S106: Compare and analyze the measured grey correlation degree with the reference grey correlation degree to identify the transverse connection performance of the hollow slab bridge.

[0106] Furthermore, when the value range of the reference correlation degree of the bridge is known, in step S106, the measured grey correlation degree is compared and analyzed with the reference grey correlation degree, specifically: it is determined whether the measured grey correlation degree is within the value range of the reference correlation degree. If so, it is determined that the lateral connection of the hollow slab bridge is normal (that is, the lateral connection is evaluated to be acceptable); if not, it is determined that there is a problem with the lateral connection of the hollow slab bridge, and suggestions for detailed inspection and reinforcement are proposed for the next step.

[0107] It can be understood that the absolute difference method can be used to compare and analyze the measured gray correlation degree with the reference gray correlation degree, that is, the absolute difference between the measured gray correlation degree and the reference gray correlation degree under each working condition is calculated. By calculating the difference between the two, the deviation of the lateral connection performance under the actual working condition from the ideal state can be directly quantified. In addition, the relative difference method can be used to compare and analyze the measured gray correlation degree with the reference gray correlation degree, that is, the ratio of the measured gray correlation degree to the reference gray correlation degree under each working condition is calculated to measure the degree of deviation of the measured performance from the ideal state.

[0108] In summary, the technical solution of this embodiment has the following advantages:

[0109] 1. It has wider applicability and does not need to consider the problem that the commonly used bridge calculation assumptions do not match the actual situation.

[0110] 2. Fewer monitoring points can be used to determine the horizontal connection between hollow slabs.

[0111] 3. The judgment results are direct, simple and obvious, and the obtained results can provide current data support for the next step of reinforcement treatment.

[0112] 4. Integrate bridge theoretical calculation results with data analysis results to improve the reliability of the results.

[0113] Embodiment 2:

[0114] This embodiment provides a system for identifying the lateral connection performance of hollow slab bridges based on grey correlation. Figure 3 As shown, the system includes: a model building unit 301, a model calculation unit 302, a reference value calculation unit 303, a real-time monitoring unit 304, a measured value calculation unit 305 and a comparison and analysis unit 306.

[0115] in:

[0116] A model building unit 301 is configured to build a finite element model of the bridge to be evaluated;

[0117] The model calculation unit 302 is configured to select a vehicle model, substitute the selected vehicle model into the established finite element model for calculation, and extract the deflection value at each step length of each vehicle working condition from the calculation result;

[0118] The reference value calculation unit 303 is configured to calculate the grey correlation degree of the deflections of two adjacent hollow slabs according to the deflection values ​​at each step length of each vehicle working condition, and record it as a reference grey correlation degree;

[0119] The real-time monitoring unit 304 is configured to establish a bridge health monitoring system and use the bridge health monitoring system to collect data of the bridge under multiple working conditions in real time to obtain measured data, wherein the measured data at least includes dynamic deflection data and dynamic inclination data of two consecutive hollow slabs;

[0120] A measured value calculation unit 305 is configured to calculate the grey correlation degree between two adjacent hollow slabs according to the measured data, which is recorded as the measured grey correlation degree;

[0121] The comparison and analysis unit 306 is configured to compare and analyze the measured grey correlation degree with the reference grey correlation degree, so as to identify the transverse connection performance of the hollow slab bridge.

[0122] The system for identifying the transverse connection performance of hollow slab bridges based on grey correlation provided in this embodiment can implement the process and steps of the method for identifying the transverse connection performance of hollow slab bridges based on grey correlation provided in any of the above embodiments, and achieve the same technical effects, which will not be repeated here one by one.

[0123] Embodiment three:

[0124] Figure 4 Schematic diagram of the structure of an electronic device provided according to some embodiments of the present application; Figure 4 As shown, the electronic device includes:

[0125] One or more processors 401;

[0126] The computer-readable storage medium may be configured to store one or more programs 402. When one or more processors 401 execute one or more programs 402, the following steps are implemented:

[0127] Step S101: establishing a finite element model of the bridge to be evaluated;

[0128] Step S102: Select a vehicle model, and substitute the selected vehicle model into the established finite element model for calculation, and extract the deflection values ​​of adjacent hollow slabs under each step length of each vehicle working condition from the calculation results;

[0129] Step S103: Calculate the grey correlation degree of the deflections of two adjacent hollow slabs according to the deflection values ​​at each step length of each vehicle working condition, and record it as a reference grey correlation degree;

[0130] Step S104: establishing a bridge health monitoring system, and using the bridge health monitoring system to collect data of the bridge under multiple working conditions in real time to obtain measured data, wherein the measured data at least includes dynamic deflection data and dynamic inclination data of two consecutive hollow slabs;

[0131] Step S105: calculating the grey correlation degree between two adjacent hollow slabs according to the measured data, and recording it as the measured grey correlation degree;

[0132] Step S106: Compare and analyze the measured grey correlation degree with the reference grey correlation degree to identify the transverse connection performance of the hollow slab bridge.

[0133] Figure 5 The hardware structure of the electronic device provided according to some embodiments of the present application; Figure 5 As shown, the hardware structure of the electronic device may include: a processor 501 , a communication interface 502 , a computer-readable storage medium (also called a memory) 503 and a communication bus 504 .

[0134] The processor 501 , the communication interface 502 , and the computer-readable storage medium 503 communicate with each other via a communication bus 504 .

[0135] The computer-readable storage medium 503 may be configured to store one or more programs.

[0136] Optionally, the communication interface 502 may be an interface of a communication module, such as an interface of a GSM module.

[0137] The processor 501 executes one or more programs, which implement the following steps:

[0138] Step S101: establishing a finite element model of the bridge to be evaluated;

[0139] Step S102: Select a vehicle model, and substitute the selected vehicle model into the established finite element model for calculation, and extract the deflection values ​​of adjacent hollow slabs under each step length of each vehicle working condition from the calculation results;

[0140] Step S103: Calculate the grey correlation degree of the deflections of two adjacent hollow slabs according to the deflection values ​​at each step length of each vehicle working condition, and record it as a reference grey correlation degree;

[0141] Step S104: establishing a bridge health monitoring system, and using the bridge health monitoring system to collect data of the bridge under multiple working conditions in real time to obtain measured data, wherein the measured data at least includes dynamic deflection data and dynamic inclination data of two consecutive hollow slabs;

[0142] Step S105: calculating the grey correlation degree between two adjacent hollow slabs according to the measured data, and recording it as the measured grey correlation degree;

[0143] Step S106: Compare and analyze the measured grey correlation degree with the reference grey correlation degree to identify the transverse connection performance of the hollow slab bridge.

[0144] The processor 501 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. It may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0145] The electronic device of the embodiment of the present application exists in various forms, including but not limited to:

[0146] (1) Mobile communication devices: These devices are characterized by their mobile communication functions and their main purpose is to provide voice and data communications. These terminals include: smart phones (e.g., iPhone), multimedia phones, functional phones, and low-end phones.

[0147] (2) Ultra-mobile personal computer devices: These devices fall into the category of personal computers, have computing and processing capabilities, and generally also have mobile Internet access features. These terminals include: PDA, MID and UMPC devices, such as iPad.

[0148] (3) Portable entertainment devices: These devices can display and play multimedia content. They include audio and video players (such as iPods), handheld game consoles, e-books, smart toys, and portable car navigation devices.

[0149] (4) Server: A device that provides computing services. The server consists of a processor, hard disk, memory, system bus, etc. The server is similar to a general computer architecture, but because it needs to provide highly reliable services, it has higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0150] (5) Other electronic devices with data interaction functions.

[0151] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present application.

[0152] The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or implemented as computer code originally stored in a remote recording medium or a non-temporary machine storage medium downloaded through a network and to be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method described herein for identifying the lateral connection performance of a hollow slab bridge based on grey correlation is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0153] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for identifying the transverse connection performance of hollow slab bridges based on grey correlation, characterized in that: include: Step S101: establishing a finite element model of the bridge to be evaluated; Step S102: Select a vehicle model, and substitute the selected vehicle model into the established finite element model for calculation, and extract the deflection values ​​of adjacent hollow slabs under each step length of each vehicle working condition from the calculation results; Step S103: Calculate the grey correlation degree of the deflections of two adjacent hollow slabs according to the deflection values ​​at each step length of each vehicle working condition, and record it as a reference grey correlation degree; Step S104: establishing a bridge health monitoring system, and using the bridge health monitoring system to collect data of the bridge under multiple working conditions in real time to obtain measured data, wherein the measured data at least includes dynamic deflection data and dynamic inclination data of two consecutive hollow slabs; Step S105: calculating the grey correlation degree between two adjacent hollow slabs according to the measured data, and recording it as the measured grey correlation degree; Step S106: Comparing and analyzing the measured grey correlation degree with the reference grey correlation degree, so as to identify the transverse connection performance of the hollow slab bridge; Step S103 also includes: Performing statistical analysis on the reference grey correlation degree to determine a value range of the reference correlation degree of the bridge; In step S106, the measured grey relational degree is compared and analyzed with the reference grey relational degree, specifically: It is determined whether the measured grey correlation degree is within the value range of the reference correlation degree. If so, it is determined that the transverse connection of the hollow slab bridge is normal. If not, it is determined that there is a problem with the transverse connection of the hollow slab bridge, and suggestions for detailed inspection and reinforcement are put forward for the next step.

2. The method according to claim 1, characterized in that In step S102, a vehicle model with more than three axes is selected as a representative vehicle and substituted into the established finite element model for calculation.

3. The method according to claim 1, characterized in that In step S102, the vehicle operating conditions at least include operating conditions at different vehicle driving positions and operating conditions at different vehicle driving step lengths.

4. The method according to claim 1, characterized in that: In step S104, the bridge health monitoring system at least includes a dynamic deflection meter and a dynamic rotation angle, and the dynamic deflection meter and the dynamic rotation angle synchronously collect the dynamic deflection data and the dynamic inclination angle data; After the bridge health monitoring system is used to collect data of the bridge under multiple working conditions in real time and obtain the measured data, the following steps are also included: determining data abnormalities and eliminating them, which are specifically as follows: The dynamic deflection data collected in real time is compared with the deflection value under the design load. If the dynamic deflection data collected in real time is greater than 2.5 times the deflection value under the design load, it is determined that the dynamic deflection data collected in real time is suspected to be abnormal; If the dynamic deflection data collected in real time is suspected to be abnormal, the dynamic deflection data is cross-checked with the dynamic inclination data collected synchronously at the same position to obtain the verification result; If the verification results show that the data is still abnormal, the abnormal dynamic deflection data will be eliminated.

5. The method according to claim 4, characterized in that In step S104, after the step of determining data abnormality and eliminating the data, the step of re-checking is also included, which is as follows: A separation algorithm is used to separate the dynamic deflection data after removing abnormal data, so as to obtain the deflection caused by the vehicle and the deflection caused by temperature; the separation algorithm is any one of the wavelet method and the variational mode decomposition method; Calculate the grey correlation degree of the deflection caused by temperature, recorded as the first grey correlation degree. If the deviation between the first grey correlation degree and the constant 1 is greater than a preset deviation threshold, it is determined that the deflection data caused by the separated vehicle is abnormal, and the separation algorithm is replaced. Use the replaced separation algorithm to perform data separation again on the dynamic deflection data after removing abnormal data, and calculate the grey correlation degree of the deflection data caused by temperature again, which is recorded as the second grey correlation degree. If the deviation between the second grey correlation degree and the constant 1 is still greater than the preset deviation threshold, check the operation of the bridge health monitoring system, re-collect data and replace the separation algorithm until reasonable data is obtained. If the deviation between the first grey relational degree or the second grey relational degree and the constant 1 is less than a preset deviation threshold, the deflection data caused by the separated vehicle is provided as the final measured data to step S105 for use.

6. The method according to claim 4 or 5, characterized in that: The sampling frequency of the dynamic deflection meter and the dynamic rotation angle is greater than 20HZ.

7. A system for identifying the transverse connection performance of hollow slab bridges based on grey correlation, characterized in that: include: a model building unit configured to build a finite element model of the bridge to be evaluated; A model calculation unit is configured to select a vehicle model, substitute the selected vehicle model into the established finite element model for calculation, and extract the deflection value at each step length of each vehicle working condition from the calculation result; A reference value calculation unit is configured to calculate the grey correlation degree of the deflections of two adjacent hollow slabs according to the deflection values ​​at each step length of each vehicle working condition, and record it as a reference grey correlation degree; A real-time monitoring unit is configured to establish a bridge health monitoring system and use the bridge health monitoring system to collect data of the bridge under multiple working conditions in real time to obtain measured data, wherein the measured data at least includes dynamic deflection data and dynamic inclination data of two consecutive hollow slabs; A measured value calculation unit is configured to calculate the grey correlation degree between two adjacent hollow slabs according to the measured data, which is recorded as the measured grey correlation degree; A comparison and analysis unit is configured to compare and analyze the measured grey correlation degree with the reference grey correlation degree, so as to identify the transverse connection performance of the hollow slab bridge; The reference value calculation unit is further used to: perform statistical analysis on the reference grey correlation degree to determine the value range of the reference correlation degree of the bridge; The comparative analysis unit is further configured to determine whether the measured grey correlation degree is within the value range of the reference correlation degree. If so, it is determined that the transverse connection of the hollow slab bridge is normal; if not, it is determined that there is a problem with the transverse connection of the hollow slab bridge, and suggestions for detailed inspection and reinforcement are put forward for the next step.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: include: A memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 6 when executing the program.

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