Bridge temperature gradient mode evaluation method based on average condition transcendental function

Abstract

The invention provides a bridge temperature gradient mode evaluation method based on an average condition transcendental function. The method comprises the following steps: acquiring long-term monitoring historical structure temperature of a to-be-measured section of a bridge through a health monitoring system; carrying out difference operation on the acquired temperature data of different parts of the cross section at the same moment, and taking the maximum value of the difference value by day to obtain a daily temperature difference maximum value sample of the vertical temperature difference of the top plate and the bottom plate and the transverse temperature difference of the web plate; statistical analysis is carried out on daily temperature difference maximum value sample data, and a corresponding temperature gradient mode is obtained through curve fitting; performing mean value operation on the statistical index parameters in all the temperature gradient functions to obtain uniform index parameters; establishing an average condition transcendental function of daily temperature difference data correlation, and obtaining a temperature extreme value in a bridge design period by utilizing tail data fitting extrapolation; and substituting the uniform index parameter and the temperature extreme value into a temperature gradient function of the corresponding part for processing to obtain a temperature gradient distribution mode of different sections of the bridge.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge temperature measurement, and more specifically, to a bridge temperature gradient pattern evaluation method based on an average conditional exceedance rate function. Background Art

[0002] In the prior art, since the bridge structure is located in a space surrounded by the atmosphere, it is inevitably affected by various environmental factors, such as sunlight radiation, ambient temperature changes, etc., which causes the temperature of the structure to change, thereby generating a non-uniform temperature field inside the bridge structure. In addition, due to the thermal expansion and contraction properties of concrete materials, the structure undergoes temperature deformation. When the deformation is constrained by the internal fiber and statically indeterminate constraints of the structure, the structure will produce considerable temperature stress. Theoretical and experimental studies have shown that in large-span concrete box beams, especially in statically indeterminate structural systems such as continuous beam bridges and continuous rigid frame bridges, temperature stress can reach or even exceed the stress of vehicle loads, which has been considered to be the main cause of cracks in the main beam.

[0003] In addition, although the existing domestic and international bridge specifications have provided temperature gradient models to consider the thermal effects of the structure itself, the existing temperature gradient models are based on climate data accumulated in certain regions in the last few decades of the last century. These proposed specifications are not sufficient to accurately describe the temperature gradients of bridges in different regions. In addition, bridges are affected by different temperature gradients in different design periods, and the temperature data are correlated. With the development of bridge health monitoring (SHM) systems, more and more scholars have studied the distribution of temperature gradient patterns in box girder sections based on SHM monitoring data. Studies have shown that the temperature distribution along the cross section of the box girder is roughly in the form of an exponential function. At the same time, the extreme value extrapolation is performed using the classical extreme value theory method to obtain the extreme value of the temperature difference with a recurrence period of 50 or 100 years. The assumption of the classical extreme value theory method is that the sample data satisfies independent and identical distribution. However, the monitoring data in actual projects are usually not independent and identically distributed. Therefore, the representative value of the temperature gradient estimated based on the classical extreme value theory method may produce errors, and the accuracy of the obtained temperature gradient pattern distribution of the box girder section is also questionable.

[0004] In summary, in view of the problems existing in the existing technology, it is crucial to propose a method for accurately evaluating the temperature gradient pattern considering the bridge design period and temperature correlation for the design and maintenance of long-span bridges. Summary of the invention

[0005] The present invention aims to overcome at least one defect (shortcoming) of the above-mentioned prior art and provide a bridge temperature gradient model evaluation method based on the average conditional exceedance rate function, which is used to solve the problem that the specifications proposed by the existing temperature gradient model are not sufficient to accurately describe the bridge temperature gradient in different regions, and the representative value of the temperature gradient estimated based on the classical extreme value theory method will produce errors, resulting in low accuracy of the obtained bridge temperature gradient.

[0006] The technical solution adopted by the present invention is a bridge temperature gradient pattern evaluation method based on an average conditional exceedance rate function, the method comprising:

[0007] S1: The long-term monitoring history structural temperature of the bridge section to be tested is collected through the health monitoring system;

[0008] S2: Perform difference calculation on the temperature data collected at different parts of the cross section at the same time, and take the maximum value on a daily basis to obtain the maximum daily temperature difference samples of the vertical temperature difference between the top plate and the bottom plate and the transverse temperature difference of the web;

[0009] S3: Statistical analysis is performed on the sample data of the maximum daily temperature difference, and the corresponding temperature gradient pattern is obtained by curve fitting;

[0010] S4: performing mean operation on the statistical index parameters in all temperature gradient functions in the temperature gradient mode to obtain a uniform index parameter;

[0011] S5: Establish the average conditional exceedance rate function of the daily temperature difference data correlation, use the tail data fitting extrapolation to obtain the temperature extreme value within the bridge design period;

[0012] S6: Substitute the uniform index parameter obtained in step S4 and the temperature extreme value within the bridge design period obtained in step S5 into the temperature gradient function of the corresponding parts for processing, so as to obtain the temperature gradient distribution pattern of different cross sections of the bridge.

[0013] The present invention obtains the temperature extremes within the bridge design period by fitting and extrapolating the average conditional exceedance rate function of temperature correlation, and proposes a bridge temperature gradient determination method in combination with the uniform index parameters obtained by averaging the statistical index parameters corresponding to all temperature gradient functions. The method takes into account both the bridge design period and the influence of temperature correlation, is suitable for temperature assessment tasks in all parts of beam bridges, and provides theoretical guidance for subsequent beam bridge design.

[0014] Preferably, in step S5, it also includes demarcating equidistant intervals to obtain the event function A kj (θ) and B kj (θ), and the average conditional exceedance rate function of the daily temperature difference data correlation is established through the event function and the definition of confidence interval.

[0015] In this scheme, the average conditional exceedance function method considering the correlation of temperature data is used to estimate the extreme value of the structural temperature gradient, which can make the calculation results of the extreme value of the structural temperature gradient more accurate, thereby improving the accuracy of the bridge temperature gradient and ensuring that the bridge can remain safe and stable under various environmental conditions.

[0016] Preferably, the average conditional exceedance function is:

[0017]

[0018] Among them, A kj (θ) = I{X j >θ,X j-1 ≤θ,…,X j-k+1 ≤θ}, j = k, ..., N; k = 2, 3 ...; k is the correlation order, N is the data length, θ is the starting data of the event function, X j is the jth random variable, and the function I{τ} is used to determine whether the random event τ occurs. If the event τ occurs, I{τ}=1, otherwise I{τ}=0. When the exceedance level θ takes a large value, Therefore, its average conditional exceedance function is defined as:

[0019]

[0020] Preferably, the correlation order k of the temperature difference data is determined by the average conditional exceedance function graph. If the average conditional exceedance function ∈ k (θ)≠∈ k+1 (θ)≈∈ k+2 (θ)≈∈ k+3 (θ)≈..., the average conditional exceedance rate function of the tail data tends to converge, and the k+1-order average conditional exceedance rate function is selected to predict the temperature extreme value.

[0021] Preferably, in step S5, based on the subasymptotic extreme value theory, the fitting function of the tail data is:

[0022] ∈ k (θ)′≈qexp{-a(θ-b) c}, θ ≥ γ

[0023] Among them, ∈ k (θ)′ represents the average conditional exceedance rate of the maximum daily temperature difference, γ represents the starting data for tail trend fitting, that is, the starting point of the data for establishing the tail trend model, and the parameters a, b, c and q are the coefficients in the tail data fitting function, and their values ​​are determined according to the fitting curve of the maximum daily temperature difference tail data.

[0024] By fitting and extrapolating the tail data of the bridge, the future structural change trend of the bridge within the design cycle can be accurately predicted, providing a more scientific basis for bridge management. The proposed bridge temperature gradient determination method takes into account both the bridge design cycle and the influence of temperature correlation. It is suitable for temperature assessment tasks in all parts of beam bridges, providing theoretical guidance for subsequent beam bridge design, and can take corresponding preventive maintenance measures to extend the service life of the bridge. It can also optimize maintenance polarization and strategies based on preset results to improve maintenance efficiency and cost-effectiveness. At the same time, through timely prediction and extrapolation, the risks of bridge emergencies and accidents caused by unknown factors can be reduced, thereby improving the safety and reliability of bridges.

[0025] Preferably, in step S5, optimization is also performed when fitting and extrapolating the tail data, and the optimization objective function used is:

[0026]

[0027] Among them, ρ j =(logCI + (θ j )-logCI - (θ j )) -2 Represents weight coefficient; CI + and CI - They represent the upper and lower bounds of the confidence interval respectively; θ represents the starting data of the event function.

[0028] In this scheme, the tail data fitting and extrapolation are optimized, and the results of fitting and extrapolation are made more accurate through optimization, thereby further improving the accuracy of the temperature gradient of the bridge.

[0029] Preferably, in the step S6, it also includes replacing the statistical index parameter in the temperature gradient function with the uniform index parameter.

[0030] In this scheme, the uniform index parameter replaces the statistical index parameter so that the obtained temperature gradient distribution pattern has a stronger correlation with the temperature, further improving its accuracy.

[0031] Preferably, in step S1, the historical structural temperature includes structural temperature data of the top plate, bottom plate and left and right web plates of the measured bridge section.

[0032] Since temperature is one of the important reasons for deformation and stress of bridge structures, by measuring the temperature data of different parts, we can fully understand the temperature conditions of various bridge structures and obtain a more accurate bridge temperature gradient, so as to formulate effective maintenance strategies and safety management measures to ensure the long-term operation safety and reliability of the bridge.

[0033] Preferably, in step S2, the number of days n of temperature data collected at different parts of the cross section at the same time is greater than 365 days, and the sampling frequency is 50 Hz.

[0034] In this solution, the number of days for temperature collection is greater than one year, and the corresponding sampling frequency is set to ensure the data completeness of the collected temperature data.

[0035] Preferably, in step S3, the temperature gradient function includes the vertical temperature gradient distribution of the bridge and the transverse temperature gradient distribution of the web; the vertical temperature gradient distribution of the bridge and the transverse temperature gradient distribution of the web are both curve-fitted using an exponential function.

[0036] The vertical and lateral temperature gradient distribution is usually not a simple linear or stable function, but shows an exponential change trend with the change of distance or height. The exponential function can more accurately reflect this nonlinear change characteristic, thereby predicting the temperature change trend in the future time period. By understanding the actual distribution of vertical and lateral temperature gradients, it can provide important technical support and data basis for the design, construction, maintenance and safety assessment of bridges.

[0037] On the other hand, the present application also provides a bridge temperature gradient pattern evaluation device based on the average condition exceedance method, the device comprising:

[0038] Temperature data acquisition module: used to collect the long-term monitoring historical structural temperature of the bridge section to be tested through the health monitoring system;

[0039] Data processing module: used to perform difference calculation on the temperature data collected at different parts of the cross section at the same time, and take the maximum value on a daily basis to obtain the maximum daily temperature difference samples of the vertical temperature difference between the top plate and the bottom plate and the transverse temperature difference of the web;

[0040] Curve fitting module: used to perform statistical analysis on the sample data of the maximum daily temperature difference obtained by the data processing module, and obtain the corresponding temperature gradient function type by using curve fitting;

[0041] Mean operation module: used to perform mean operation on the statistical index parameters in all temperature gradient functions to obtain uniform index parameters;

[0042] Fitting and extrapolation module: used to establish the average conditional exceedance rate function of the daily temperature difference data correlation, and use the tail data fitting and extrapolation to obtain the temperature extreme value within the bridge design period;

[0043] Temperature gradient distribution pattern acquisition module: used to input the uniform index parameters obtained by the mean operation module and the temperature extreme values ​​obtained by the fitting extrapolation module into the temperature gradient function of the corresponding parts for processing, so as to obtain the temperature gradient distribution patterns of different sections of the bridge.

[0044] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for evaluating a bridge temperature gradient pattern based on an average conditional exceedance rate function as described above is implemented.

[0045] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the bridge temperature gradient pattern evaluation method based on the average conditional exceedance rate function as described in any one of the above is implemented.

[0046] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned bridge temperature gradient pattern evaluation methods based on the average condition exceedance rate function.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. Based on the measured temperature data collected by the health monitoring system, the average conditional exceedance rate method considering the correlation of temperature data is used to estimate the extreme value of the structural temperature gradient, which can make the calculation result of the extreme value of the structural temperature gradient more accurate.

[0049] 2. The bridge temperature gradient pattern assessment method based on the average conditional exceedance rate method can be applied to bridges of different structural types in different regions and has high universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of the framework flow of a bridge temperature gradient pattern evaluation method based on the average conditional exceedance rate method provided by the present invention.

[0051] Figure 2 It is a schematic diagram of the physical structure of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0052] The drawings of the present invention are only for illustrative purposes and should not be construed as limiting the present invention. In order to better illustrate the following embodiments, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; it is understandable to those skilled in the art that some well-known structures and their descriptions in the drawings may be omitted.

[0053] Example 1

[0054] like Figure 1 As shown, this embodiment provides a bridge temperature gradient mode evaluation method based on an average condition exceedance rate function, the method comprising:

[0055] S1: The long-term monitoring history structural temperature of the bridge section to be tested is collected through the health monitoring system;

[0056] S2: Perform difference calculation on the temperature data collected at different parts of the cross section at the same time, and take the maximum value on a daily basis to obtain the maximum daily temperature difference samples of the vertical temperature difference between the top plate and the bottom plate and the transverse temperature difference of the web;

[0057] S3: Statistical analysis is performed on the sample data of the maximum daily temperature difference, and the corresponding temperature gradient function type is obtained by curve fitting;

[0058] S4: performing mean operation on the statistical index parameters in all temperature gradient functions to obtain uniform index parameters;

[0059] S5: Establish the average conditional exceedance rate function of the daily temperature difference data correlation, use the tail data fitting extrapolation to obtain the temperature extreme value within the bridge design period;

[0060] S6: Substitute the uniform index parameter obtained in step S4 and the temperature extreme value within the bridge design period obtained in step S5 into the temperature gradient function of the corresponding parts to obtain the temperature gradient distribution pattern of different cross sections of the bridge.

[0061] This embodiment obtains the temperature extremes within the bridge design period by establishing an average conditional exceedance rate function of temperature correlation for fitting and extrapolation, and proposes a bridge temperature gradient determination method in combination with the uniform index parameter obtained by averaging the statistical index parameters corresponding to all temperature gradient functions. This method takes into account both the bridge design period and the influence of temperature correlation, is suitable for temperature assessment tasks in all parts of beam bridges, and provides theoretical guidance for subsequent beam bridge design.

[0062] Preferably, taking a three-span continuous rigid frame bridge as an example, in step S1, the temperature monitoring data of the mid-span section of the main beam of the three-span continuous rigid frame bridge is selected, and the historical structural temperature monitored for a long time by the health monitoring system includes the structural temperature data of the top plate, bottom plate and left and right webs of the bridge section of the measured three-span continuous rigid frame bridge.

[0063] Since temperature is one of the important reasons for deformation and stress of bridge structures, by measuring the temperature data of different parts, we can fully understand the temperature conditions of various bridge structures and obtain a more accurate bridge temperature gradient, so as to formulate effective maintenance strategies and safety management measures to ensure the long-term operation safety and reliability of the bridge.

[0064] Preferably, in step S2 of this embodiment, the time length of the monitored temperature data of the bridge top plate, bottom plate and left and right web plates collected at the same time is from February 2021 to May 2022, the number of days collected is greater than 365 days, and the sampling frequency is set to 50Hz. In this embodiment, the number of days for temperature collection is set to be greater than one year, and the corresponding sampling frequency is also set to ensure the data completeness of the collected temperature data.

[0065] Further preferably, in this embodiment, analysis is also performed based on the temperature data collected daily to obtain the vertical daily temperature difference between the top plate and the bottom plate, and the horizontal daily temperature difference between the web plate, the maximum daily temperature difference is extracted on a daily basis, and then statistical analysis is performed to draw a temperature gradient distribution diagram of the top plate, web plate and bottom plate, and the corresponding temperature gradient function is obtained by fitting.

[0066] Among them, in the three-span continuous rigid frame bridge monitored by this embodiment, the temperature gradient function also includes the vertical temperature gradient distribution of the bridge and the transverse temperature gradient distribution of the web; wherein, the vertical temperature gradient distribution of the bridge includes the vertical temperature gradient distribution of the top plate and the vertical temperature gradient distribution of the bottom plate, the vertical temperature gradient distribution of the top plate and the transverse temperature gradient distribution of the web are exponentially distributed, and the vertical temperature gradient distribution of the bottom plate is linearly distributed, and then the temperature difference is used as the horizontal coordinate and the measuring point spacing is used as the vertical coordinate to perform curve fitting on the above vertical and transverse temperature gradient distributions, so as to obtain the corresponding temperature gradient function types respectively.

[0067] It can be seen that in practical applications, the vertical and lateral temperature gradient distribution is usually not a simple linear or stable function, but presents an exponential change trend with changes in distance or height. The use of exponential function can more accurately reflect this nonlinear change characteristic, thereby predicting the temperature change trend in the future time period. By understanding the actual distribution of vertical and lateral temperature gradients, it can provide important technical support and data basis for the design, construction, maintenance and safety assessment of bridges.

[0068] Preferably, in this embodiment, the statistical exponential parameters in all the obtained temperature gradient functions are also subjected to probability statistics, and all the statistical exponential parameters are subjected to mean calculation to obtain the uniform exponential parameter corresponding to the temperature gradient distribution pattern of the three-span continuous rigid frame bridge described in this embodiment, and it is defined as the exponential parameter in the desired temperature gradient distribution pattern.

[0069] Preferably, in step S5 of this embodiment, if Figure 1 As shown, it also includes demarcating equidistant intervals to obtain the event function A kj (θ) and B kj(θ), and the average conditional exceedance rate function of the daily maximum temperature difference data of different parts of the bridge is established through the event function and the definition of confidence interval. By using the average conditional exceedance rate function method considering the correlation of temperature data to estimate the extreme value of the structural temperature gradient, the calculation results of the extreme value of the structural temperature gradient can be made more accurate, thereby improving the accuracy of the bridge temperature gradient and ensuring that the bridge can remain safe and stable under various environmental conditions.

[0070] Further preferably, the average conditional exceedance function is:

[0071]

[0072] Among them, A kj (θ) = I{X j >θ,X j-1 ≤θ,…,X j-k+1 ≤θ}, j = k, ..., N; k = 2, 3 ...; k is the correlation order, N is the data length, θ is the starting data of the event function, X j is the jth random variable, and the function I{τ} is used to determine whether the random event τ occurs. If the event τ occurs, I{τ}=1, otherwise I{τ}=0. When the exceedance level θ takes a large value, Therefore, its average conditional exceedance function is defined as:

[0073]

[0074] Preferably, the correlation order k of the temperature difference data is determined by the average conditional exceedance function graph. If the average conditional exceedance function ∈ k (θ)≠∈ k+1 (θ)≈∈ k+2 (θ)≈∈ k+3 (θ)≈..., the average conditional exceedance rate function of the tail data tends to converge, and the k+1-order average conditional exceedance rate function is selected to predict the temperature extreme value.

[0075] For example, if the average conditional exceedance rate function ∈1(θ)≠∈2(θ)≈∈3(θ)≈∈4(θ) is obtained from the average conditional exceedance rate function graph, that is, the average conditional exceedance rate function of the tail data tends to converge at the second order, so the second-order average conditional exceedance rate function can be selected for temperature extreme value prediction.

[0076] Preferably, in step S5, the fitting function of the tail data is:

[0077] ∈ k (θ)′≈qexp{-a(θ-b) c}, θ ≥ γ

[0078] Among them, ∈ k (θ)′ represents the average conditional exceedance rate of the maximum daily temperature difference, γ represents the starting data for tail trend fitting, that is, the starting point of the data for establishing the tail trend model; parameters a, b, c and q are coefficients in the tail fitting function, and their values ​​are determined according to the fitting curve of the maximum daily temperature difference tail data.

[0079] By fitting and extrapolating the tail data of the bridge, the future structural change trend of the bridge within the design cycle can be accurately predicted, providing a more scientific basis for bridge management. The proposed bridge temperature gradient determination method takes into account both the bridge design cycle and the influence of temperature correlation. It is suitable for temperature assessment tasks in all parts of beam bridges, providing theoretical guidance for subsequent beam bridge design, and can take corresponding preventive maintenance measures to extend the service life of the bridge. It can also optimize maintenance polarization and strategies based on preset results to improve maintenance efficiency and cost-effectiveness. At the same time, through timely prediction and extrapolation, the risks of bridge emergencies and accidents caused by unknown factors can be reduced, thereby improving the safety and reliability of bridges.

[0080] Preferably, in step S5, optimization is also performed when fitting and extrapolating the tail data, and the optimization objective function used is:

[0081]

[0082] Among them, ρ j =(logCI + (θ j )-logCI - (θ j )) -2 Represents weight coefficient; CI + and CI - They represent the upper and lower bounds of the confidence interval respectively; θ represents the starting data of the event function.

[0083] In this embodiment, the tail data is optimized during fitting and extrapolation, so that the fitting and extrapolation results are more accurate, thereby further improving the accuracy of the bridge temperature gradient.

[0084] In this embodiment, based on the above steps, the average conditional exceedance rate function is used to extrapolate the extreme temperature difference of different parts of the three-span continuous rigid frame bridge during the design period of 50 years, including the extreme temperature difference of the bridge top plate and the web plate, so as to accurately predict the future change trend of the structure, which can help bridge engineers better understand the behavior of the bridge during long-term use, and based on this, carry out reasonable management and maintenance, so as to ensure the safety and sustainable development of the bridge.

[0085] Preferably, in step S6 of this embodiment, it also includes replacing the statistical index parameter in the temperature gradient function with the uniform index parameter, and substituting the uniform index parameter and the obtained temperature difference extreme value into the temperature gradient function of the corresponding part of the bridge to obtain its temperature gradient distribution pattern, including the vertical temperature gradient distribution pattern of the bridge top plate and the horizontal temperature gradient distribution pattern of the web, and verifying the accuracy of the desired temperature gradient distribution pattern by comparing with the temperature gradient pattern in the specification. Thereby enhancing the correlation between the obtained temperature gradient distribution pattern and temperature, and also considering the bridge design cycle, further improving the accuracy of the temperature gradient distribution pattern.

[0086] Example 2

[0087] In this embodiment, a bridge temperature gradient pattern evaluation device based on the average condition exceedance rate method is also provided to implement the bridge temperature gradient pattern evaluation method based on the average condition exceedance rate method described in Example 1, wherein the device includes:

[0088] Temperature data acquisition module: used to collect the long-term monitoring historical structural temperature of the bridge section to be tested through the health monitoring system;

[0089] Data processing module: used to perform difference calculation on the temperature data collected at different parts of the cross section at the same time, and take the maximum value on a daily basis to obtain the maximum daily temperature difference samples of the vertical temperature difference between the top plate and the bottom plate and the transverse temperature difference of the web;

[0090] Curve fitting module: used to perform statistical analysis on the sample data of the maximum daily temperature difference obtained by the data processing module, and obtain the corresponding temperature gradient function type by using curve fitting;

[0091] Mean operation module: used to perform mean operation on the statistical index parameters in all temperature gradient functions to obtain uniform index parameters;

[0092] Fitting and extrapolation module: used to establish the average conditional exceedance rate function of the daily temperature difference data correlation, and use the tail data fitting and extrapolation to obtain the temperature extreme value within the bridge design period;

[0093] Temperature gradient distribution pattern acquisition module: used to input the uniform index parameters obtained by the mean operation module and the temperature extreme values ​​obtained by the fitting extrapolation module into the temperature gradient function of the corresponding parts for processing, so as to obtain the temperature gradient distribution patterns of different sections of the bridge.

[0094] In another aspect, this embodiment further provides an electronic device, Figure 2 is a schematic diagram of the physical structure of an electronic device provided in this embodiment, such as Figure 2As shown, the electronic device may include: a processor (processor) 210, a communication interface (Communications Interface) 220, a memory (memory) 230 and a communication bus 240, wherein the processor 210, the communication interface 220, and the memory 230 communicate with each other through the communication bus 240. The processor 210 can call the logic instructions in the memory 230 to execute the bridge temperature gradient pattern evaluation method based on the average conditional exceedance rate function, which includes: S1: collecting the long-term monitoring historical structural temperature of the bridge section to be tested through the health monitoring system; S2: performing difference calculation on the temperature data collected at different parts of the section at the same time, and taking the maximum value on a daily basis to obtain the daily temperature difference maximum value samples of the vertical temperature difference between the top plate and the bottom plate and the transverse temperature difference of the web; S3: performing statistical analysis on the daily temperature difference maximum value sample data, and obtaining the corresponding temperature gradient function type by curve fitting; S4: performing mean calculation on the statistical index parameters in all temperature gradient functions to obtain uniform index parameters; S5: establishing the average conditional exceedance rate function of the correlation of daily temperature difference data, and obtaining the temperature extreme value within the bridge design period by fitting and extrapolation of the tail data; S6: substituting the uniform index parameter obtained in step S4 and the temperature extreme value within the bridge design period obtained in step S5 into the temperature gradient function of the corresponding part to obtain the temperature gradient distribution pattern of different sections of the bridge.

[0095] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0096] This embodiment also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the bridge temperature gradient pattern evaluation method based on the average condition exceedance rate function as described above is implemented.

[0097] This embodiment also provides a computer program product, including a computer program, which, when executed by a processor, implements the bridge temperature gradient pattern assessment method based on the average condition exceedance rate function as described above.

[0098] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0099] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0100] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation methods of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the claims of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A bridge temperature gradient pattern evaluation method based on average conditional exceedance function, characterized in that: include: S1: The long-term monitoring history structural temperature of the bridge section to be tested is collected through the health monitoring system; S2: Perform difference calculation on the temperature data collected at different parts of the cross section at the same time, and take the maximum value on a daily basis to obtain the maximum daily temperature difference samples of the vertical temperature difference between the top plate and the bottom plate and the transverse temperature difference of the web; S3: Statistical analysis is performed on the sample data of the maximum daily temperature difference, and the corresponding temperature gradient pattern is obtained by curve fitting; S4: performing mean operation on the statistical index parameters in all temperature gradient functions in the temperature gradient mode to obtain a uniform index parameter; S5: Establish the average conditional exceedance rate function of the daily temperature difference data correlation, use the tail data fitting extrapolation to obtain the temperature extreme value within the bridge design period; S6: Substitute the uniform index parameter obtained in step S4 and the temperature extreme value within the bridge design period obtained in step S5 into the temperature gradient function of the corresponding parts for processing, so as to obtain the temperature gradient distribution pattern of different cross sections of the bridge.

2. The bridge temperature gradient pattern evaluation method based on the average conditional exceedance function according to claim 1 is characterized in that: In the step S5, it also includes demarcating equidistant intervals to obtain an event function, and establishing an average conditional exceedance rate function of the daily temperature difference data correlation through the event function and the defined confidence interval.

3. The bridge temperature gradient pattern evaluation method based on the average conditional exceedance function according to claim 2 is characterized in that: The average conditional exceedance function is: Among them, the event function A kj (θ) = I{X j >θ,X j-1 ≤θ,…,X j-k+1 ≤θ}, j=k,…,N; k=2,3,…;X j is the jth random variable, k is the correlation order, N is the data length, and θ is the starting data of the event function.

4. The bridge temperature gradient pattern evaluation method based on the average conditional exceedance function according to claim 3 is characterized in that: The correlation order k of the temperature difference data can be obtained by the average conditional exceedance rate function graph. If the average conditional exceedance rate function ∈ k (θ)≠∈ k+1 (θ)≈∈ x+2 (θ)≈∈ k+3 (θ)≈…, the average conditional exceedance rate function of the tail data tends to converge, and the k+1-order average conditional exceedance rate function is selected to predict the temperature extremes.

5. The bridge temperature gradient pattern evaluation method based on the average conditional exceedance function according to claim 4 is characterized in that: In step S5, based on the subasymptotic extreme value theory, the fitting function of the tail data is: ∈ k (θ)′≈qexp{-a(θ-b) c },θ≥γ Among them, ∈ k (θ)′ represents the average conditional exceedance rate of the maximum daily temperature difference, γ represents the starting data for tail trend fitting, that is, the starting point of the data for establishing the tail trend model; parameters a, b, c and q are coefficients in the tail data fitting function, and their values ​​are determined by the fitting curve of the maximum daily temperature difference tail data.

6. The bridge temperature gradient pattern evaluation method based on the average conditional exceedance function according to claim 5 is characterized in that: In the step S5, optimization is also performed when fitting and extrapolating the tail data, and the optimization objective function used is: Among them, ρ j =(logCI + (θ j )-logCI - (θ j )) -2 Represents weight coefficient; CI + and CI - They represent the upper and lower bounds of the confidence interval respectively; θ represents the starting data of the event function.

7. The bridge temperature gradient pattern evaluation method based on the average conditional exceedance function according to claim 1 is characterized in that: In the step S6, it also includes replacing the statistical index parameter in the temperature gradient function with the uniform index parameter.

8. A bridge temperature gradient pattern evaluation method based on an average conditional exceedance function according to any one of claims 1 to 7, characterized in that: In step S1, the historical structural temperature includes the structural temperature data of the top plate, bottom plate and left and right web plates of the measured bridge section.

9. The bridge temperature gradient pattern evaluation method based on the average conditional exceedance function according to claim 8 is characterized in that: In step S2, the number of days n of temperature data collected at different parts of the cross section at the same time is greater than 365 days.

10. The bridge temperature gradient pattern evaluation method based on the average conditional exceedance function according to claim 9, characterized in that: In step S3, the temperature gradient function includes the vertical temperature gradient distribution of the bridge and the transverse temperature gradient distribution of the web; the vertical temperature gradient distribution of the bridge and the transverse temperature gradient distribution of the web are curve-fitted using an exponential function.

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