Vertical temperature gradient fatigue load spectrum of composite plate girder bridge and construction method

Abstract

The invention discloses a vertical temperature gradient fatigue load spectrum of a composite plate girder bridge and a construction method. The vertical temperature gradient fatigue load spectrum comprises four vertical temperature sub-gradients and occurrence probabilities thereof. The inventor puts forward a simplified calculation formula for the temperature stress of the composite plate girder bridge, analyzes the distribution characteristics of a vertical temperature field of the composite plate girder bridge, obtains the temperature sub-gradient of the composite plate girder bridge by using a density clustering analysis method with noise, and calculates the occurrence probability of the sub-gradient in combination with a mathematical statistics basic principle. The vertical temperature gradient fatigue load spectrum of the composite plate girder bridge suitable for 100-year, 150-year and 200-year design service life is provided, and the constructed vertical temperature gradient fatigue load spectrum of the composite plate girder bridge can be used for long-life anti-fatigue design and fatigue damage evaluation of the composite plate girder bridge.

Description

Technical Field

[0001] The invention belongs to the technical field of bridge engineering, and in particular relates to a vertical temperature gradient fatigue load spectrum of a composite plate girder bridge and a construction method thereof. Background Art

[0002] Composite plate girder bridges usually use I-section steel beams with large vertical temperature gradients. Under the action of solar radiation, periodic temperature fatigue stress will be generated in the details. Under the coupled cyclic action of temperature load and vehicle load, the fatigue cumulative fatigue damage of the details of composite plate girder bridges will increase significantly. Since the temperature effect includes the cyclic effect of daily temperature difference and seasonal temperature difference, the temperature gradient fatigue load spectrum can be used to characterize various periodic temperature fatigue effects. In-depth research on the temperature effect and structural behavior of composite steel plate girders in natural environments, construct a temperature gradient fatigue load spectrum suitable for composite plate girder bridges with a design service life of 200 years. 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 of a composite plate girder bridge and a construction method thereof.

[0004] The technical solution adopted to solve the above technical problems is: a vertical temperature gradient fatigue load spectrum of a composite plate girder bridge, which consists of four vertical temperature sub-gradients and the probability of occurrence of the sub-gradients within the design service life, specifically:

[0005]

[0006] Where, T Gi (y) is the temperature of the ith vertical temperature sub-gradient at position y, h is the height of the composite plate girder bridge, h c is the height of the combined layer, h s is the height of the steel plate beam, h = h c +h s , Gi is the i-th vertical temperature sub-gradient, T Gi,j is the temperature representative value at the jth typical height in the ith vertical temperature sub-gradient, N d is the design service life, T G1,1 、T G1,2 、T G1,3 、T G1,4 are the top of the combined layer in the ith vertical temperature sub-gradient, and the distance h from the top plate. c 0.5h from the bottom plate s The temperature at the bottom plate of the lower wing is represented by T G1,1 The value range is [4.1,5.2], the unit is ℃, T G1,2 The value range is [5.5,7.4], the unit is ℃, T Gi,jis the temperature representative value 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)) are the occurrence probabilities of the four vertical temperature sub-gradients within the design service life, T G1,1 、T G2,1 、T G3,1 、T G4,1 They are the representative temperature values ​​at the top plate of the four vertical temperature sub-gradients.

[0007] The method for constructing the vertical temperature gradient fatigue load spectrum of a composite plate girder bridge includes the following steps:

[0008] Step 1: Collect temperature data of composite plate girder bridge

[0009] m temperature measuring points are arranged inside the concrete slab, steel top plate, bottom plate and web of the composite slab-girder bridge. The temperature values ​​of each measuring point are collected at regular intervals to obtain the measured temperature gradient time history curve at each position. The measured temperature gradient time history curve is extended to the design service life N by using the long short-term memory recursive neural network algorithm. d The temperature history at typical locations is used to obtain the maximum daily temperature at each measuring point every day within the design service life, and the design service life daily temperature extreme value matrix Q = [M1, M2, …, M t ,…,M Nd×365 ],M t is the daily temperature maximum vector of each measuring point on the tth day;

[0010] Step 2: Use the density clustering algorithm with noise to preprocess the daily temperature extreme value matrix Q of the design service life

[0011] Define the initial parameters of the density clustering algorithm, namely the minimum number of points MP and the neighborhood radius r e , select each vector in the daily temperature extreme value matrix Q of the design service life as a high-dimensional coordinate point A in turn, calculate the Euclidean distance between other vectors and the high-dimensional coordinate point A, and set the Euclidean distance ≤ the neighborhood radius r e The vector is returned to the neighborhood of the high-dimensional coordinate point A, and N d ×365 neighborhoods, filter out the neighborhoods whose number of vectors is ≥ the minimum number of points MP, and take each of the filtered neighborhoods as a temporary cluster family T. The high-dimensional coordinate point A of each temporary cluster family T is taken as the core point of the cluster family, and all temporary cluster families T constitute a temporary cluster family set R;

[0012] Step 3: Merge temporary clusters to get formal clusters:

[0013] Step 3.1: Randomly select a temporary cluster T from the temporary cluster set R, query other temporary clusters in turn, and take out other temporary clusters whose core points are in the temporary cluster T from the temporary cluster set R and merge them into the temporary cluster to obtain the formal cluster T. / ;

[0014] Step 3.2: Repeat step 3.1 until all temporary clusters in the temporary cluster set R are taken, and S formal clusters T are obtained. / ;

[0015] Step 4: Determine the temperature gradient fatigue load spectrum of the concrete box girder bridge

[0016] Each formal cluster family T / is the vertical temperature sub-gradient T Gi (y), each formal clustering family T / The sample points in the clusters are divided into S formal clusters T / The ratio of all sample points in is the probability of occurrence of each sub-gradient within the design service life P(T Gi ), thus obtaining the temperature fatigue load spectrum of the concrete box girder bridge.

[0017] Preferably, in step 2, the minimum number of points MP≤25 and the neighborhood radius r e It is 2.5 to 7.5.

[0018] Preferably, the Euclidean distance is obtained according to the following formula:

[0019]

[0020] Where, d e is the Euclidean distance, t A Date k The temperature maximum at the location, y k is the kth typical measuring point position, t A is the date of the temporary cluster family, and t is the date of the daily temperature extreme value vector other than the cluster center.

[0021] Preferably, the arrangement method of the m measuring points in step 1 is: arrange a measuring point on the bottom plate of the composite plate girder bridge, and take the measuring point as the coordinate origin, arrange measuring points on the web and the top plate along the vertical height direction, and arrange a total of m=9 measuring points. The positions of the measuring points are expressed as vertical distances from the lower surface of the bottom plate of the composite plate girder bridge as 0.00m, H-0.96m, H-0.51m, H-0.36m, H-0.26m, H-0.21m, H-0.20m, H-0.12m, H, where H is the height of the composite steel plate girder.

[0022] Preferably, the temperature values ​​of each measuring point in step 1 are collected at intervals of 60 to 1200 seconds.

[0023] The beneficial effects of the present invention are as follows:

[0024] 1. The present invention establishes a vertical temperature gradient fatigue load spectrum of a long-life composite plate girder bridge by analyzing the measured temperature field data of the composite plate girder bridge. The temperature fatigue load spectrum can be used for the long-life anti-fatigue design and fatigue damage assessment of the composite plate girder bridge.

[0025] 2. The present invention proposes a method for constructing the thermal fatigue load spectrum of a composite plate-girder bridge using a density clustering algorithm with noise. The method can self-identify and cluster the number of clusters according to the statistical characteristics of the temperature data, thereby improving the accuracy and reliability of the load spectrum construction.

[0026] 3. The load spectrum constructed by the present invention can be directly used for the long-life anti-fatigue design and fatigue damage assessment of composite plate-girder bridges, providing a practical tool for bridge design and maintenance.

[0027] 4. The present invention expands the measured temperature data to the designed service life through the long short-term memory recursive neural network algorithm, effectively processes a large amount of temperature data, and improves the efficiency of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is the vertical temperature sub-gradient model of composite plate-girder bridge.

[0029] Figure 2 Frequency distribution diagram of each vertical temperature sub-gradient of the composite plate girder bridge.

[0030] Figure 3 A flow chart for constructing vertical temperature fatigue load spectra of the composite plate girder bridge of the present invention.

[0031] Figure 4 This is the layout diagram of temperature measurement points for composite slab-girder bridge.

[0032] Figure 5 This is the temperature history curve at a typical location of a composite plate girder bridge.

[0033] Figure 6 This is the clustering result of the density clustering algorithm with noise. DETAILED DESCRIPTION

[0034] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments, but the present invention is not limited to these embodiments.

[0035] Example 1

[0036] Take a composite plate girder bridge in Xi'an with a beam height of 1.40m as an example. The temperature gradient fatigue load spectrum of the bridge consists of four vertical temperature sub-gradients and the probability of occurrence of the sub-gradients within the design service life, specifically:

[0037]

[0038] Where, T Gi (y) is the temperature of the ith vertical temperature sub-gradient at position y, h is the height of the composite plate girder bridge, h c is the height of the combined layer, h s is the height of the steel plate beam, h = h c +h s , Gi is the i-th vertical temperature sub-gradient, T Gi,j is the temperature representative value at the jth typical height in the ith vertical temperature sub-gradient, N d is the design service life, T G1,1 、T G1,2 、T G1,3 、T G1,4 are the top of the combined layer in the ith vertical temperature sub-gradient, and the distance h from the top plate. c 0.5h from the bottom plate s The temperature at the bottom plate of the lower wing is represented by T G1,1 The value range is [4.1,5.2], the unit is ℃, T G1,2 The value range is [5.5,7.4], the unit is ℃, T Gi,j is the temperature representative value 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)) are the occurrence probabilities of the four vertical temperature sub-gradients within the design service life, T G1,1 、T G2,1 、T G3,1 、T G4,1 They are the representative temperature values ​​at the top plate of the four vertical temperature sub-gradients.

[0039] In this embodiment, T G1,1 =4.8℃, T G1,2 =6.9℃. According to the thermal fatigue load spectrum of the composite plate girder bridge, the representative temperature values ​​of each sub-gradient at different heights corresponding to the design service life of 100 years, 150 years and 200 years are shown in Table 1. The vertical temperature sub-gradient is as follows: Figure 1 Vertical temperature sub-gradient T G1 ~T G4 The probability of occurrence is 23.6%, 36.1%, 26.1%, and 14.2%, see Figure 2 .

[0040] Table 1 Representative values ​​of gradient temperature of vertical temperature fatigue load spectrum of highway composite steel plate beams

[0041]

[0042] exist Figure 3 In the present embodiment, the method for constructing the temperature fatigue load spectrum of the composite plate girder bridge comprises the following steps:

[0043] Step 1: Long-term temperature data monitoring;

[0044] A measuring point is arranged on the bottom plate of the composite plate girder bridge, and the measuring point is used as the coordinate origin. Measuring points are arranged on the web and top plate along the vertical height direction. A total of m = 9 measuring points are arranged. The positions of the measuring points are expressed as 0.00m, 0.45m, 0.90m, 1.05m, 1.15m, 1.20m, 1.21m, 1.29m, 1.41m from the vertical distance from the lower surface of the bottom plate of the composite plate girder bridge. The measuring points are arranged as follows: Figure 4 As shown, the temperature value of each measuring point can be collected once every 300 seconds, once every 1 second, or once every 1800 seconds;

[0045] The measured temperature gradient time history curves at various locations of the composite plate girder bridge are obtained, such as Figure 5 As shown in the figure, the measured temperature gradient time history curve is extended to the design service life N using the long short-term memory recursive neural network algorithm. d The temperature history at typical locations is used to obtain the maximum daily temperature at each measuring point every day within the design service life, and the design service life daily temperature extreme value matrix Q = [M1, M2, …, M t ,…,M Nd×365 ],M t is the daily temperature maximum vector of each measuring point on the tth day;

[0046] Step 2: Use the density clustering algorithm with noise to preprocess the daily temperature extreme value matrix Q of the design service life

[0047] Define the initial parameters of the density clustering algorithm with noise, namely the minimum number of points MP and the neighborhood radius r e , minimum number of points MP≤25, neighborhood radius r e is 2.5 to 7.5. In this embodiment, the minimum number of points MP is 15, and the neighborhood radius r e is 5;

[0048] In the daily temperature extreme value matrix Q of the design service life, each vector is selected as a high-dimensional coordinate point A in turn, and the Euclidean distance between other vectors and the high-dimensional coordinate point A is calculated. e ≤neighborhood radius r e The vector is returned to the neighborhood of the high-dimensional coordinate point A, and N d ×365 neighborhoods, filter out the neighborhoods whose number of vectors is ≥ the minimum number of points MP, and take each of the filtered neighborhoods as a temporary cluster family T. The high-dimensional coordinate point A of each temporary cluster family T is taken as the core point of the cluster family, and all temporary cluster families T constitute a temporary cluster family set R;

[0049] Among them, the Euclidean distance is obtained according to the following formula;

[0050]

[0051] Where, d e is the Euclidean distance, t A Date k The temperature maximum at the location, y k is the kth typical measuring point position, t A is the date of the temporary cluster core point, and t is the date of the daily temperature extreme value vector other than the cluster center point;

[0052] Step 3: Merge temporary clusters to get formal clusters:

[0053] Step 3.1: Randomly select a temporary cluster T from the temporary cluster set R, query other temporary clusters in turn, and take out other temporary clusters whose cores are in the temporary cluster T from the temporary cluster set R and merge them into the temporary cluster to obtain the formal cluster T. / ;

[0054] Step 3.2: Repeat step 3.1 until all temporary clusters in the temporary cluster set R are taken, and S formal clusters T are obtained. / The cluster analysis results are as follows Figure 6 shown.

[0055] Step 4: Determine the thermal fatigue load spectrum of the concrete box girder bridge

[0056] Each formal cluster family T / is the vertical temperature sub-gradient T of the model Gi (y), each formal clustering family T / The sample points in the clusters are divided into S formal clusters T / The ratio of all sample points in is the probability of occurrence of each sub-gradient within the design service life P(T Gi ), thus obtaining the temperature fatigue load spectrum of the concrete box girder bridge.

Claims

1. A vertical temperature gradient fatigue load spectrum of a composite plate girder bridge, characterized in that: The load spectrum consists of four vertical temperature sub-gradients and the probability of occurrence of the sub-gradients within the design service life, specifically: Where, T Gi (y) is the temperature of the ith vertical temperature sub-gradient at position y, h is the height of the composite plate girder bridge, h c is the height of the combined layer, h s is the height of the steel plate beam, where h = h c +h s , Gi is the i-th vertical temperature sub-gradient, T Gi,j is the temperature representative value at the jth typical height in the ith vertical temperature sub-gradient, N d is the design service life, T G1,1 , T G1,2 , T G1,3 , T G1,4 are the top of the combined layer in the ith vertical temperature sub-gradient, and the distance h from the top plate. c 0.5h from the bottom plate s The temperature at the bottom plate of the lower wing is represented by T G1,1 The value range is [4.1,5.2], the unit is ℃, T G1,2 The value range is [5.5,7.4], the unit is ℃, T Gi,j is the temperature representative value 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)) are the occurrence probabilities of the four vertical temperature sub-gradients within the design service life, T G1,1 , T G2,1 , T G3,1 , T G4,1 They are the representative temperature values ​​at the top plate of the four vertical temperature sub-gradients.

2. The method for constructing vertical temperature gradient fatigue load spectrum of composite plate girder bridge according to claim 1 is characterized by: The following steps are involved: Step 1: Collect temperature data of composite plate girder bridge m temperature measuring points are arranged inside the concrete slab, steel top plate, bottom plate and web of the composite slab-girder bridge. The temperature values ​​of each measuring point are collected at regular intervals to obtain the measured temperature gradient time history curve at each position. The measured temperature gradient time history curve is extended to the design service life N by using the long short-term memory recursive neural network algorithm. d The temperature history at typical locations is used to obtain the maximum daily temperature at each measuring point every day within the design service life, and the design service life daily temperature extreme value matrix Q = [M1, M2, …, M t ,…,M Nd×365 ],M t is the daily temperature maximum vector of each measuring point on the tth day; Step 2: Use the density clustering algorithm with noise to preprocess the daily temperature extreme value matrix Q of the design service life Define the initial parameters of the density clustering algorithm, namely the minimum number of points MP and the neighborhood radius r e , select each vector in the daily temperature extreme value matrix Q of the design service life as a high-dimensional coordinate point A in turn, calculate the Euclidean distance between other vectors and the high-dimensional coordinate point A, and set the Euclidean distance ≤ the neighborhood radius r e The vector is returned to the neighborhood of the high-dimensional coordinate point A, and N d ×365 neighborhoods, filter out the neighborhoods whose number of vectors is ≥ the minimum number of points MP, and take each of the filtered neighborhoods as a temporary cluster family T. The high-dimensional coordinate point A of each temporary cluster family T is taken as the core point of the cluster family, and all temporary cluster families T constitute a temporary cluster family set R; Step 3: Merge temporary clusters to get formal clusters: Step 3.1: Randomly select a temporary cluster T from the temporary cluster set R, query other temporary clusters in turn, and take out other temporary clusters whose core points are in the temporary cluster T from the temporary cluster set R and merge them into the temporary cluster to obtain the formal cluster T. / ; Step 3.2: Repeat step 3.1 until all temporary clusters in the temporary cluster set R are taken, and S formal clusters T are obtained. / ; Step 4: Determine the temperature gradient fatigue load spectrum of the concrete box girder bridge Each formal cluster family T / is the vertical temperature sub-gradient T Gi (y), each formal clustering family T / The sample points in the clusters are divided into S formal clusters T / The ratio of all sample points in is the probability of occurrence of each sub-gradient within the design service life P(T Gi ), thus obtaining the temperature fatigue load spectrum of the concrete box girder bridge.

3. The method for constructing the thermal fatigue load spectrum of a composite plate girder bridge according to claim 2 is characterized in that: In step 2, the minimum number of points MP≤25, the neighborhood radius r e It is 2.5 to 7.

5.

4. The method for constructing the thermal fatigue load spectrum of a composite plate girder bridge according to claim 2 is characterized in that: The Euclidean distance is obtained according to the following formula: Where, d e is the Euclidean distance, t A Date k The temperature maximum at the location, y k is the kth typical measuring point position, t A is the date of the temporary cluster family, and t is the date of the daily temperature extreme value vector other than the cluster center.

5. The method for constructing the vertical temperature gradient fatigue load spectrum of the composite plate girder bridge according to claim 2 is characterized in that: The arrangement method of the m measuring points in step 1 is: arrange a measuring point on the bottom plate of the composite plate girder bridge, and take the measuring point as the coordinate origin, arrange measuring points on the web and the top plate along the vertical height direction, and arrange m=9 measuring points in total. The positions of the measuring points are expressed as 0.00m, H-0.96m, H-0.51m, H-0.36m, H-0.26m, H-0.21m, H-0.20m, H-0.12m, H in terms of vertical distance from the lower surface of the bottom plate of the composite plate girder bridge, where H is the height of the composite steel plate girder.

6. The method for constructing the vertical temperature gradient fatigue load spectrum of a composite plate girder bridge according to claim 2 is characterized in that: In step 1, the temperature values ​​of each measuring point are collected at intervals of 60 to 1200 seconds.

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