Vertical temperature gradient fatigue load spectrum and construction method of steel box girder bridge with wing plate

Abstract

A vertical temperature gradient fatigue load spectrum and construction method for steel box girder bridges with wing plates, comprising five vertical temperature sub-gradients and their occurrence probabilities. In view of the structural characteristics of steel box girder with wing plates and the time and space distribution characteristics of temperature field, temperature measurement points are arranged and temperature data are monitored, and EM algorithm is used for cluster analysis of temperature representative values, so as to construct a vertical temperature gradient fatigue load spectrum for steel box girder bridges with wing plates, which is adapted to the design service life of 100 years, 150 years and 200 years. The vertical temperature gradient fatigue load spectrum constructed for steel box girder bridges with wing plates can provide technical basis for temperature fatigue damage analysis and temperature-vehicle coupled fatigue damage analysis.

Description

Technical Field

[0001] The present invention belongs to the technical field of bridge engineering, and in particular relates to a vertical temperature gradient fatigue load spectrum and a construction method of a steel box girder bridge with flanges. Background Art

[0002] During operation, steel box girder bridges with flanges are inevitably subject to the combined effects of vehicle loads and temperature loads. Under the coupled cyclic effects of temperature loads and vehicle loads, the fatigue detail damage of steel box girders with flanges will increase significantly, and the fatigue damage caused by the individual effects of temperature and vehicle loads cannot be simply superimposed. Analysis of the temperature field data of steel box girders with flanges shows that the temperature loads they bear include the cyclic effects of various types of temperature differences, such as daily temperature differences and seasonal temperature differences. Therefore, it is necessary to use the temperature field monitoring data of steel box girders with flanges to establish a temperature gradient fatigue load spectrum, calculate the temperature fatigue stress history, and couple it with vehicle fatigue stress to conduct fatigue damage analysis. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a vertical temperature gradient fatigue load spectrum and a construction method for a steel box girder bridge with wing plates.

[0004] The technical solution adopted to solve the above technical problems is: a vertical temperature gradient fatigue load spectrum for steel box girder bridges with flanges. The load spectrum consists of five vertical temperature sub-gradients and the probability of occurrence of the sub-gradients within the design service life, specifically:

[0005]

[0006] In formula (1), T Gi (y) is the temperature of the ith vertical temperature sub-gradient at position y, h is the cross-sectional height of the steel box girder bridge with flange, Gi is the ith vertical temperature sub-gradient, T Gi,1 、T Gi,2 、T Gi,3 are the representative values ​​of the temperature at the top plate, at a height of 0.86h from the bottom plate, and at the bottom plate in the i-th vertical temperature sub-gradient, respectively. G1,1 、T G1,2 、T G1,3 are the representative temperature values ​​at the top plate, at a height of 0.86h from the bottom plate, and at the bottom plate in the first vertical temperature sub-gradient, respectively. T G1,1 The value range is [5.3,6.2], the unit is ℃, T G1,2 The value range is [4.8,5.4], the unit is ℃, T Gi,j is the temperature at the jth typical height in the ith vertical temperature sub-gradient, N d is the design service life, T G1,j is the temperature representative value at the jth typical height in the first vertical temperature sub-gradient, P(TG1 (y))、P(T G2 (y))、P(T G3 (y))、P(T G4 (y))、P(T G5 (y)) are the occurrence probabilities of the five vertical temperature sub-gradients within the design service life, T G1,1 、T G2,1 、T G3,1 、T G4,1 、T G5,1 These are the representative temperature values ​​at the top plate of the five vertical temperature sub-gradients.

[0007] The present invention also provides a method for constructing a temperature gradient fatigue load spectrum of a steel box girder bridge with flanges, comprising the following steps:

[0008] Step 1: Arrange a measuring point on the bottom plate of the steel box girder bridge with flanges, and use this measuring point as the coordinate origin. Arrange measuring points on the middle web and top plate along the vertical height direction. Arrange K measuring points in total. Collect the temperature value of each measuring point at a certain interval to obtain the measured temperature gradient time history curve at each height position; expand the measured temperature gradient time history curve to the design service life N through the long-short memory recurrent neural network. d The temperature history at the typical position, calculate the daily temperature difference between each measuring point on the middle web and top plate and the bottom plate measuring point and select the maximum and minimum values ​​of the daily temperature difference, and obtain the daily temperature difference extreme value matrix Q = [S1, S2,,…, S K-1 ], S m is the daily temperature difference extreme value matrix of the mth measuring point, m∈[1,K-1],

[0009]

[0010] Where, t is the tth day within the design service life, y m is the position coordinate of the mth measuring point, y m The temperature maximum on day t is y m The temperature is at its minimum on day t;

[0011] Step 2: Use the expectation maximization algorithm EM to obtain the daily temperature difference extreme value matrix S of the mth measuring point m Clustering results

[0012] 1) Initialize the mean parameter μ, variance parameter σ, and prior parameter π of the expectation maximization algorithm EM

[0013] Set the number of clusters c l The initial value is 2, and the mean parameter μ is 2×c l, the value range of element in mean parameter μ is [0, 1], variance parameter σ is c l ×2×2 space type unit diagonal matrix, prior parameter π is a vector with length c l , the initial value of element in prior parameter π is 1 / c l ;

[0014] 2) According to the daily temperature difference extreme value matrix S m , the posterior probability matrix Γ of daily temperature difference extreme value is obtained ztk

[0015] The vector composed of the mth measuring point tth daily temperature difference extreme value is defined as the kth cluster family z tk , k ∈ [1, c l ], the coordinate of the cluster center C of the kth cluster family is μ k,: , the mixed multivariate normal joint distribution probability density function is constructed by using the set mean parameter μ, variance parameter σ and prior parameter π, and the initial daily temperature difference extreme value posterior probability of the mth measuring point is obtained

[0016]

[0017] In the formula, , the posterior probability , the kth column data, , the kth data in the updated mean parameter π, , the kth row data in the updated mean parameter μ, , the kth group c l ×c l plane matrix data, N(*) is a two-dimensional normal distribution satisfying , the mean value is , and the covariance matrix is S m t，: , the tth row vector of S m matrix;

[0018] 3) According to the posterior probability of the initial daily temperature difference extreme value of the mth measuring point The parameters of the expectation maximization algorithm EM are updated according to the following formula;

[0019]

[0020] In the formula, (*) T is the transpose of the matrix, is the updated parameter of the expectation maximization algorithm EM, N k is the sum of the posterior probability;

[0021] 4) repeatedly step 2), 3), until the difference between the updated parameters of the expectation maximization algorithm EM and the corresponding parameters before updating is less than 0.01, to obtain the final mth measuring point daily temperature difference extreme value posterior probability;

[0022] 5) according to the final mth measuring point daily temperature difference extreme value posterior probability, the mth measuring point daily temperature difference extreme value matrix S m is classified;

[0023] The daily temperature difference extreme value data has a posterior probability for each cluster family, and the mth measuring point daily temperature difference extreme value data is divided into the cluster family with the highest posterior probability, to obtain the mth measuring point c l cluster family data, wherein the cluster center point coordinates of the kth cluster family are (π 1,k ,π 2,k );

[0024] 6) based on the c l cluster families of the mth measuring point obtained in step 5), one cluster family is added, and steps 2), 3), 4) and 5) are repeated, to obtain the optimal number of cluster families of the mth measuring point c l = 5 according to the Akaike information criterion;

[0025] Step 3: according to the traversal in step 2, the daily temperature difference extreme value matrix of each measuring point in the daily temperature difference extreme value matrix Q of the web plate and the top plate is obtained, 5 cluster families of each measuring point are obtained, the 5 cluster families of each measuring point are sorted in ascending order according to the values of the cluster center points, the cluster families of the K measuring points form a measuring point cluster family matrix with 5 columns and K rows, the cluster families in the same column of the measuring point cluster family matrix are merged, and the new cluster family obtained is a sub-gradient of the temperature gradient fatigue load spectrum, the value of the cluster center point of the temperature measuring point cluster family at each height is the representative temperature value of each height in the sub-gradient, and the ratio of the number of the daily temperature difference extreme value vector in the original top plate measuring point cluster family to the total number of the daily temperature difference extreme value vector in the sub-gradient is the occurrence probability of the sub-gradient in the design service life.

[0026] Preferably, the arrangement method of the K measuring points in step 1 is that one measuring point is arranged on the bottom plate of the steel box girder bridge with wing plates, and the measuring point is taken as the coordinate origin, the measuring points are arranged on the web plate and the top plate along the vertical height direction, and a total of K=8 measuring points are arranged, and the positions of the measuring points are represented by the vertical distances from the lower surface of the bottom plate of the steel box girder bridge with wing plates as 0.0 m, h-1.20 m, h-0.60 m, h-0.30 m, h-0.20 m, h-0.10 m, h-0.05 m and h.

[0027] Preferably, the temperature value interval time of each measuring point in step 1 is 60-1200 seconds.

[0028] The application has the following beneficial effects:

[0029] 1、The vertical temperature gradient fatigue load spectrum of the steel box girder bridge with wing plates is established according to the structural characteristics and the space-time distribution characteristics of the temperature field of the steel box girder bridge with wing plates, the temperature load action on the bridge in the actual operation process can be more accurately reflected, the long-life fatigue design and fatigue life evaluation of the bridge for 100 years, 150 years and 200 years are suitable, and a scientific design basis for the long-term performance of the bridge is provided.

[0030] 2、The EM algorithm is used to propose a construction method of the corresponding temperature gradient fatigue load spectrum, each measuring point can be reasonably sorted and clustered according to probability, the accuracy and efficiency of the load spectrum construction are improved, and a technical basis is provided for temperature fatigue damage analysis and temperature-vehicle coupled fatigue damage analysis.

[0031] 3、The unnecessary design conservatism is reduced by the scientific method, so that the bridge design is optimized while the safety of the bridge is ensured, and the cost is reduced. Meanwhile, the cyclic action of various types of temperature differences such as daily temperature difference and seasonal temperature difference is considered, so that the load spectrum can adapt to different environmental conditions. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 It is a three-dimensional structural schematic diagram of the steel box girder bridge with wing plates.

[0033] Figure 2 It is a vertical temperature sub-gradient mode of the temperature gradient fatigue load spectrum of the steel box girder bridge with wing plates.

[0034] Figure 3 It is a sub-gradient occurrence probability of the temperature gradient fatigue load spectrum of the steel box girder bridge with wing plates.

[0035] Figure 4 It is a flowchart of the temperature gradient fatigue load spectrum construction method of the steel box girder bridge with wing plates in embodiment 1.

[0036] Figure 5 It is a temperature measuring point arrangement diagram of the steel box girder bridge with wing plates.

[0037] Figure 6 It is a temperature time history curve at a typical height of the steel box girder bridge with wing plates.

[0038] Figure 7 It is an AIC information amount criterion analysis result of the temperature data of the steel box girder bridge with wing plates.

[0039] Figure 8 It is an EM clustering analysis result of the steel box girder bridge with wing plates. DETAILED DESCRIPTION

[0040] The application will be further described in detail below in combination with the drawings and embodiments, but the application is not limited to these embodiments.

[0041] Example 1

[0042] This example takes Shenyang Houdingxiang Ring Expressway Steel Box Girder Bridge as an example, the girder height h is 3.00 m, and the three-dimensional structure is as shown in Figure 1 The temperature gradient fatigue load spectrum model of the highway steel box girder bridge with a wing plate in this example is composed of five vertical temperature sub-gradients and the occurrence probability of the sub-gradients within the design service life, specifically:

[0043]

[0044] In formula (1), T Gi (y) is the temperature of the i-th vertical temperature sub-gradient at position y, h is the height of the steel box girder bridge with a wing plate, Gi is the i-th vertical temperature sub-gradient, T Gi,1 , T Gi,2 , and T Gi,3 are the representative values of the temperature at the top plate, at a height of 0.86h from the bottom plate, and at the bottom plate in the i-th vertical temperature sub-gradient, respectively, T G1,1 , T G1,2 , and T G1,3 are the representative values of the temperature at the top plate, at a height of 0.86h from the bottom plate, and at the bottom plate in the first vertical temperature sub-gradient, respectively, T G1,1 , T G1,2 , and T Gi,j are the representative values of the temperature at the top plate, at a height of 0.86h from the bottom plate, and at the bottom plate in the first vertical temperature sub-gradient, respectively, T d is the design service life, T G1,j is the representative value of the temperature at the j-th typical height in the first vertical temperature sub-gradient, P(T G1 (y)), P(T G2 (y)), P(T G3 (y)), P(T G4 (y)), and P(T G5 (y)) are the occurrence probabilities of the five vertical temperature sub-gradients within the design service life, T G1,1 , T G2,1 , T G3,1 , T G4,1 , and T G5,1 are the representative values of the temperature at the top plate of the five vertical temperature sub-gradients, respectively.

[0045] Among them, the temperature value T G1,1 at the top plate in the first vertical temperature sub-gradient is 5.9℃, the temperature value T G1,2 at a height of 0.86h from the bottom plate is 5.2℃, and the temperature value T G1,3= 3.7℃. The representative temperature values of the typical positions in each sub-gradient of the temperature gradient fatigue load spectrum constructed for the design service life of 100 years, 150 years, and 200 years are shown in Table 1, and the linear gradient mode of the vertical typical measuring point temperatures of the sub-gradient is shown in Figure 2 . Among them, 150 years and 200 years are long-life design service life, and the occurrence probability of T G1 ~ T G5 is 11.9%, 30.5%, 21.6%, 23.2%, and 12.8%, as shown in Figure 3 .

[0046] Table 1 Representative temperature values of sub-gradient of temperature gradient fatigue load spectrum of steel box girder bridge with wing plate

[0047]

[0048] In Figure 4 , the method for constructing a temperature gradient fatigue load spectrum model of a steel box girder bridge with wing plates according to the present embodiment includes the following steps:

[0049] Step 1: Arrange 1 measuring point on the bottom plate of the steel box girder bridge with wing plates, and take the measuring point as the coordinate origin. Arrange measuring points on the middle web and top plate along the vertical height direction, a total of 9 measuring points, denoted as K1~K9. The vertical distance from the bottom surface of the steel box girder bottom plate is denoted as y. Different measuring point positions can be denoted as 0.00m, 0.90m, 1.80m, 2.40m, 2.70m, 2.80m, 2.90m, 2.95m, and 3.00m respectively. The y=0.00m measuring point is arranged on the steel box girder bottom plate, i.e. the coordinate origin, and the y=3.00m measuring point is arranged on the steel box girder top plate. The y=0.90m, 1.80m, 2.40m, 2.70m, 2.80m, 2.90m, and 2.95m measuring points are arranged on the middle web of the steel box girder, respectively. The specific measuring point arrangement diagram is shown in Figure 5 . Collect long-term temperature data for these measuring points with an interval of 1 second, obtain effective temperature data for the past year, calculate the measured temperature gradient time history curve, and the measured temperature gradient time history curve is shown in Figure 6 . Extend the measured temperature gradient time history curve to the design service life N d of the typical position by long-short memory recurrent neural network, calculate the daily temperature difference between each measuring point on the middle web and top plate and the bottom plate measuring point, and select the maximum and minimum daily temperature difference to obtain the daily temperature difference extreme value matrix Q = [S1, S2, …, S K-1 ] of the measuring points on the middle web and top plate, S m is the daily temperature difference extreme value matrix of the mth measuring point, m ∈ [1, K-1]. The collection interval in the present embodiment can also be 1800 seconds;

[0050]

[0051] where t is the tth day in the design life, y m is the coordinate of the mth observation point, is the maximum temperature at the tth day, m is the minimum temperature at the tth day, is the maximum temperature at the tth day, m is the minimum temperature at the tth day;

[0052] Step 2, obtain the clustering result of the matrix S m of the daily temperature range extreme values by using the expectation maximization algorithm EM

[0053] 1) initialize the mean parameter μ, variance parameter σ, and prior parameter π of the expectation maximization algorithm EM, and set the number of clustering families c l The initial value is 2, the mean parameter μ is a 2×c l matrix, the value range of the elements in the mean parameter μ is [0, 1], the variance parameter σ is a c l ×2×2 space type unit diagonal matrix, and the prior parameter π is a vector with a length of c l The initial value of the elements in the prior parameter π is 1 / c l ;

[0054] 2) obtain the posterior probability matrix Γ m of the daily temperature range extreme values according to the matrix S ztk

[0055] Define the vector composed of the tth daily temperature range extreme values of the mth observation point as the kth clustering family z tk , k ∈ [1, c l ], and the coordinate of the clustering center C of the kth clustering family is μ k,: , construct a mixed multivariate normal joint distribution probability density function by using the set mean parameter μ, variance parameter σ, and prior parameter π, and obtain the initial daily temperature range extreme value posterior probability of the mth observation point

[0056]

[0057] where, is the posterior probability of the kth column data, is the kth data in the prior mean parameter π, is the kth row data in the prior mean parameter μ, is the kth group c l ×c l plane matrix data in the prior variance parameter σ, N(*) is a two-dimensional normal distribution satisfying is the mean, and is the covariance matrix, S m t，: is S mThe tth row vector of the matrix;

[0058] 3) The posterior probability of the initial daily temperature difference extreme value of the mth measuring point The parameters of the expectation maximization algorithm EM are updated according to the following formula;

[0059]

[0060] In the formula, (*) T is the transpose of the matrix, is the updated parameter of the expectation maximization algorithm EM, N k is the sum of the posterior probabilities;

[0061] 4) Steps 2), 3) are repeatedly performed until the difference between the updated parameter of the expectation maximization algorithm EM and the corresponding parameter before the update is less than 0.01, and the final posterior probability of the daily temperature difference extreme value of the mth measuring point is obtained;

[0062] 5) The daily temperature difference extreme value data in the matrix S m of the daily temperature difference extreme value of the mth measuring point are classified according to the final posterior probability of the daily temperature difference extreme value of the mth measuring point;

[0063] The daily temperature difference extreme value data has a posterior probability for each cluster family, and the daily temperature difference extreme value data of the mth measuring point is divided into the cluster family with the highest posterior probability, to obtain c l cluster family data of the mth measuring point, wherein the cluster center point coordinates of the kth cluster family are (π 1,k , π 2,k );

[0064] 6) One cluster family is added to the c l cluster families of the mth measuring point obtained in step 5), and steps 2), 3), 4), and 5) are repeated, and the optimal number of cluster families c l = 5 of the mth measuring point is obtained according to the Akaike information criterion, as shown in Figure 7 ;

[0065] Step 3: According to the traversal in step 2, the daily temperature difference extreme value vector of each measuring point in the daily temperature difference extreme value matrix Q of the measuring points on the web plate and the top plate is obtained, 5 cluster families of each measuring point are obtained, and the 5 cluster families of each measuring point are sorted in ascending order according to the values of the cluster center points. The cluster families of the 8 measuring points form a measuring point cluster family matrix with 5 columns and 8 rows, the cluster families in the same column of the measuring point cluster family matrix are merged, the new cluster family obtained is a sub-gradient of the temperature gradient fatigue load spectrum model, the value of the cluster center point of the original top plate measuring point cluster family is the temperature representative value of the sub-gradient, the ratio of the number of the daily temperature difference extreme value vector of the original top plate measuring point cluster family in the sub-gradient to the total number of the daily temperature difference extreme value vectors in the sub-gradient is the occurrence probability of the sub-gradient in the design service life, and the clustering result is shown in Figure 8 .

Claims

1. A vertical temperature gradient fatigue load spectrum for a steel box girder bridge with flanges, characterized by: The load spectrum consists of five vertical temperature sub-gradients and the probability of occurrence of the sub-gradients within the design service life, specifically: In formula (1), T Gi (y) is the temperature of the ith vertical temperature sub-gradient at position y, h is the cross-sectional height of the steel box girder bridge with flange, Gi is the ith vertical temperature sub-gradient, T Gi,1 、T Gi,2 、T Gi,3 are the representative values ​​of the temperature at the top plate, at a height of 0.86h from the bottom plate, and at the bottom plate in the i-th vertical temperature sub-gradient, respectively. G1,1 、T G1,2 、T G1,3 are the representative temperature values ​​at the top plate, at a height of 0.86h from the bottom plate, and at the bottom plate in the first vertical temperature sub-gradient, respectively. T G1,1 The value range is [5.3,6.2], the unit is ℃, T G1,2 The value range is [4.8,5.4], the unit is ℃, T Gi,j is the temperature at the jth typical height in the ith vertical temperature sub-gradient, N d is the design service life, T G1,j is the temperature representative value at the jth typical height in the first vertical temperature sub-gradient, P(T G1 (y))、P(T G2 (y))、P(T G3 (y))、P(T G4 (y))、P(T G5 (y)) are the occurrence probabilities of the five vertical temperature sub-gradients within the design service life, T G1,1 、T G2,1 、T G3,1 、T G4,1 、T G5,1 These are the representative temperature values ​​at the top plate of the five vertical temperature sub-gradients.

2. The method for constructing a vertical temperature gradient fatigue load spectrum for a steel box girder bridge with flanges according to claim 1 is characterized in that: The following steps are involved: Step 1: Arrange a measuring point on the bottom plate of the steel box girder bridge with flanges, and use this measuring point as the coordinate origin. Arrange measuring points on the middle web and top plate along the vertical height direction. Arrange K measuring points in total. Collect the temperature value of each measuring point at a certain interval to obtain the measured temperature gradient time history curve at each height position. Extend the measured temperature gradient time history curve to the design service life N through the long-short memory recurrent neural network. d The temperature history at the typical position, calculate the daily temperature difference between each measuring point on the middle web and top plate and the bottom plate measuring point and select the maximum and minimum values ​​of the daily temperature difference, and obtain the daily temperature difference extreme value matrix Q = [S1, S2,,…, S K-1 ], S m is the daily temperature difference extreme value matrix of the mth measuring point, m∈[1,K-1], Where, t is the tth day within the design service life, y m is the position coordinate of the mth measuring point, y m The temperature maximum on day t is y m The temperature is at its minimum on day t; Step 2: Use the expectation maximization algorithm EM to obtain the daily temperature difference extreme value matrix S of the mth measuring point m Clustering results 1) Initialize the mean parameter μ, variance parameter σ, and prior parameter π of the expectation maximization algorithm EM and set the number of clusters c l The initial value is 2, and the mean parameter μ is 2×c l The matrix, the value range of the elements in the mean parameter μ is [0,1], and the variance parameter σ is c l ×2×2 spatial unit diagonal matrix, the prior parameter π is the length of c l The initial value of the element in the prior parameter π is 1 / c l ; 2) According to the daily temperature difference extreme value matrix S m Get the posterior probability matrix Γ of the daily temperature difference extreme value ztk Define the vector of temperature difference extreme values ​​at the mth measuring point on the tth day as the kth cluster z tk ,k∈[1,c l ], the coordinates of the cluster center C of the kth cluster family are μ k,: , using the set mean parameter μ, variance parameter σ, and prior parameter π, a mixed multivariate normal joint distribution probability density function is constructed to obtain the posterior probability of the initial daily temperature difference extreme value at the mth measuring point Where, is the posterior probability The kth column data, is the kth data in the mean parameter π before updating, is the k-th row data of the mean parameter μ before updating, To update the kth group c in the forward error parameter σ l ×c l Plane matrix data, N(*) is to meet the following is the mean and is the two-dimensional normal distribution with covariance matrix, S mt，: For S m The t-th row vector of the matrix; 3) According to the posterior probability of the initial daily temperature difference extreme value of the mth measuring point Update the parameters of the expectation maximization algorithm EM according to the following formula; In the formula, (*) T is the transpose of the matrix, is the updated parameter of the expectation maximization algorithm EM, N k is the sum of the posterior probabilities; 4) Repeat steps 2) and 3) until the difference between the updated parameters of the expectation maximization algorithm EM and the corresponding parameters before the update is less than 0.01, and obtain the final posterior probability of the extreme value of the daily temperature difference at the mth measuring point; 5) According to the final posterior probability of the extreme value of the daily temperature difference at the mth measuring point, the extreme value matrix S of the daily temperature difference at the mth measuring point is calculated. m Classify the daily temperature difference extreme data; The daily temperature difference extreme value data has a posterior probability for each cluster. The daily temperature difference extreme value data of the mth measuring point is divided into the cluster with the highest corresponding posterior probability, and the mth measuring point c is obtained. l Cluster data, where the coordinates of the cluster center of the kth cluster are (π 1,k ,π 2,k ); 6) The mth measuring point c obtained in step 5) l Add one cluster group to the mth cluster group, repeat steps 2), 3), 4), and 5), and obtain the optimal number of cluster groups c for the mth measurement point according to the Akaike information criterion. l =5; Step 3. According to step 2, traverse the daily temperature difference extreme value matrix of each measuring point in the daily temperature difference extreme value matrix Q of the measuring points on the middle web and top plate to obtain 5 cluster groups for each measuring point. Sort the 5 cluster groups of each measuring point from small to large according to the value of the cluster center point. The cluster groups of K measuring points constitute a measuring point cluster group matrix with 5 columns and K rows. The cluster groups in the same column of the measuring point cluster group matrix are merged to obtain a new cluster group as the sub-gradient of the temperature gradient fatigue load spectrum. The value of the cluster center point of the temperature measuring point cluster group at each height is the representative temperature value at each height of the sub-gradient. The ratio of the number of daily temperature difference extreme value vectors in the original top plate measuring point cluster group in the sub-gradient to the total number of daily temperature difference extreme value vectors in the sub-gradient is the occurrence probability of the sub-gradient in the design service life.

3. The method for constructing a vertical temperature gradient fatigue load spectrum for a steel box girder bridge with flanges according to claim 2 is characterized in that: The arrangement method of the K measuring points in step 1 is as follows: one measuring point is arranged on the bottom plate of the steel box girder bridge with flanges, and the measuring point is used as the coordinate origin, and measuring points are arranged on the middle web and the top plate along the vertical height direction, with a total of K=8 measuring points. The positions of the measuring points are expressed as vertical distances from the lower surface of the bottom plate of the steel box girder bridge with flanges as 0.00m, h-1.20m, h-0.60m, h-0.30m, h-0.20m, h-0.10m, h-0.05m, and h.

4. The method for constructing a vertical temperature gradient fatigue load spectrum for a steel box girder bridge with flanges according to claim 2 is characterized in that: The temperature value interval of each measuring point in step 1 is 60 to 1200 seconds.

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