Bridge-ballastless track structure extreme temperature prediction method and system

Abstract

The present invention relates to the technical field of probability statistical analysis and prediction of concrete temperature fields, and discloses a bridge-ballastless track structure extreme temperature prediction method and system, so as to avoid subjectivity of the conventional experience distribution method and the cumbersome derivation process of the first-order second-moment method and to obtain a relatively preciser temperature extremum in the case of a certain amount of data. The method disclosed by the present invention comprises: forming a statistical sample by means of measured bridge-ballastless track structure temperature data; calculating firt to fourth moments of the stastical sample to obtain a statistical feature of the sample; constructing virtual distribution by means of standard regular distribution to indirectly describe a distribution condition of the sample; solving a key parameter and feature estimation of the virtual distribution; constructing an extreme state function considering an extreme temperature, and giving an exceedance probability; and calculating first to fourth moments of the extreme state function by means of a reliability indicator formula, and reversing an extreme temperature value corresponding to the exceedance probability and giving a corresponding reoccurrence period.

Description

technical field

[0001] The invention relates to the technical field of probability statistics analysis and prediction of concrete temperature field, in particular to a method and system for predicting the limit temperature of a bridge-ballastless track structure. Background technique

[0002] Under the influence of external temperature such as solar radiation, convective heat transfer and radiation heat transfer, linear and nonlinear temperature fields are easy to form in the bridge-ballastless track structure, and the temperature effect will cause temperature stress and deformation of the bridge-ballastless track structure, seriously It affects the durability of concrete bridges and track structures, and threatens the safety of train operation. The bridge-ballastless track is a concrete structure, and its overall limit temperature prediction calculation, temperature field simulation and prediction are the keys to controlling the temperature deformation of the bridge-ballast...

Examples

Embodiment 1

[0033] The embodiment of the present invention first discloses a bridge-ballastless track structure limit temperature prediction method, such as figure 1 shown, including the following steps:

[0034] Step S1, forming a statistical sample through the measured temperature data of the bridge-ballastless track structure.

[0035] In this step, the temperature data can come from actual measurement data or existing meteorological data, which is easy to obtain, simple and practical. Among them, since the structural temperature change is mainly affected by natural factors, its distribution is closer to the normal distribution, but due to the complex and changeable influencing factors, its real distribution is often difficult to determine, so the virtual distribution constructed by the normal distribution is the most suitable for the sample actual situation. The temperature probability statistics are generally divided into two methods: the fitted distribution method and the method o...

Embodiment 2

[0105] Corresponding to the above method embodiments, this embodiment discloses a bridge-ballastless track structure limit temperature prediction system, including:

[0106] The first module is used to form a statistical sample through the measured temperature data of the bridge-ballastless track structure;

[0107] The second module is used to calculate the first to fourth moments of statistical samples, and obtain the statistical feature value of the sample as the sample mean μ G , standard deviation σ G , skewness α 3G with kurtosis α 4G ; and calculate the characteristic point u of the standard normal distribution mean and the first three standard deviations around the mean 0 , u 1± , u 2± , u 3± , and the probability density value P of the corresponding feature point 0 ,P 1+ ,P 1- ,P 2+ ,P 2- ,P 3+ ,P 3- ;

[0108] The third module is used to construct virtual distribution through Fleishman polynomial normal transformation: T(X)=Φ(U)=a 1 +a 2 U+a 3 u 2 +...

technical field

[0001] The invention relates to the technical field of probability statistics analysis and prediction of concrete temperature field, in particular to a method and system for predicting the limit temperature of a bridge-ballastless track structure. Background technique

[0002] Under the influence of external temperature such as solar radiation, convective heat transfer and radiation heat transfer, linear and nonlinear temperature fields are easy to form in the bridge-ballastless track structure, and the temperature effect will cause temperature stress and deformation of the bridge-ballastless track structure, seriously It affects the durability of concrete bridges and track structures, and threatens the safety of train operation. The bridge-ballastless track is a concrete structure, and its overall limit temperature prediction calculation, temperature field simulation and prediction are the keys to controlling the temperature deformation of the bridge-ballast...

Examples

Embodiment 1

[0033] The embodiment of the present invention first discloses a bridge-ballastless track structure limit temperature prediction method, such as figure 1 shown, including the following steps:

[0034] Step S1, forming a statistical sample through the measured temperature data of the bridge-ballastless track structure.

[0035] In this step, the temperature data can come from actual measurement data or existing meteorological data, which is easy to obtain, simple and practical. Among them, since the structural temperature change is mainly affected by natural factors, its distribution is closer to the normal distribution, but due to the complex and changeable influencing factors, its real distribution is often difficult to determine, so the virtual distribution constructed by the normal distribution is the most suitable for the sample actual situation. The temperature probability statistics are generally divided into two methods: the fitted distribution method and the method o...

Embodiment 2

[0105] Corresponding to the above method embodiments, this embodiment discloses a bridge-ballastless track structure limit temperature prediction system, including:

[0106] The first module is used to form a statistical sample through the measured temperature data of the bridge-ballastless track structure;

[0107] The second module is used to calculate the first to fourth moments of statistical samples, and obtain the statistical feature value of the sample as the sample mean μ G , standard deviation σ G , skewness α 3G with kurtosis α 4G ; and calculate the characteristic point u of the standard normal distribution mean and the first three standard deviations around the mean 0 , u 1± , u 2± , u 3± , and the probability density value P of the corresponding feature point 0 ,P 1+ ,P 1- ,P 2+ ,P 2- ,P 3+ ,P 3- ;

[0108] The third module is used to construct virtual distribution through Fleishman polynomial normal transformation: T(X)=Φ(U)=a 1 +a 2 U+a 3 u 2 +...

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