Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (6): 1592-1600.doi: 10.13229/j.cnki.jdxbgxb.20230145

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Bridge scour depth identification based on dynamic characteristics and improved particle swarm optimization algorithm

Guo-jin TAN1(),Qing-wen KONG1,Xin HE1(),Pan ZHANG2,Run-chao YANG3,Yang-jun CHAO4,Zhong YANG4   

  1. 1.College of Transportation,Jilin University,Changchun 130022,China
    2.Changchun Construction Project Safety Supervision Station,Changchun 130012,China
    3.Jilin Provincial Highway Administration,Changchun 130021,China
    4.Jilin Traffic Planning and Design Institute,Changchun 130021,China
  • Received:2023-02-28 Online:2023-06-01 Published:2023-07-23
  • Contact: Xin HE E-mail:tgj@jlu.edu.cn;hexin@jlu.edu.cn

Abstract:

A bridge scour identification method based on dynamic characteristics of bridge structure and improved particle swarm optimization algorithm was proposed, in order to identify the position and degree of bridge scour damage quickly and accurately. The dynamic characteristics of the bridge structure and the static displacement of the pier top are taken as the scour identification parameters of the bridge in this method, and the quantitative correlation between the scour identification parameters and the scour depth of different piers was obtained by using multiple nonlinear regression. Therefore, the uneven scour depth of the pier can be identified according to the dynamic characteristics obtained during the actual detection of the bridge. Finally, the numerical simulation analysis of a simply-supported beam bridge was carried out, and the concrete application steps of the proposed bridge scour identification method based on the improved particle swarm optimization algorithm were demonstrated. The results show that the proposed method can identify the bridge scour location and scour depth, which has a certain accuracy. In practical engineering, scour damage identification of bridge foundation can be realized by using this method through conventional bridge detection, which avoids operational difficulties brought by underwater operation, and can evaluate the scour state quickly and accurately.

Key words: bridge engineering, dynamic characteristics, identification method, improved particle swarm optimization algorithm, depth of scour

CLC Number: 

  • U446

Fig.1

Schematic diagram of the n-span bridges"

Fig.2

L-PSO algorithm flowchart"

Fig.3

Structure diagram of bridge (unit: cm)"

Fig.4

Numerical analysis model of bridge structure"

Table 1

Numerical analysis results of simple-supported girder bridge under 66 cases"

工况编号h1/mh2/mc2/c1φ2/φ1f/Hz工况编号h1/mh2/mc2/c1φ2/φ1f/Hz
10.000.001.000 0001.000 0003.818 8341.503.001.149 8921.002 7673.406 3
20.000.501.016 7791.000 2703.796 5351.503.501.213 0911.003 8493.332 2
30.001.001.045 3891.000 7583.758 3361.504.001.281 1851.004 9473.257 4
40.001.501.083 4461.001 4263.708 2371.504.501.351 8141.006 0143.185 1
50.002.001.129 8591.002 2423.648 7381.505.001.421 4861.006 9993.118 6
60.002.501.184 1281.003 1833.581 5392.002.001.000 0001.000 0003.485 2
70.003.001.245 8461.004 2183.508 6402.002.501.048 0321.000 9303.421 1
80.003.501.314 3181.005 3173.432 2412.003.001.102 6561.001 9493.351 6
90.004.001.388 0951.006 4343.355 0422.003.501.163 2591.003 0263.278 8
100.004.501.464 6171.007 5213.2803432.004.001.228 5561.004 1163.205 4
110.005.001.540 1031.008 5263.211 5442.004.501.296 2831.005 1773.134 4
120.500.501.000 0001.000 0003.774 3452.005.001.363 0941.006 1533.069 2
130.501.001.028 1381.000 4873.736 3462.502.501.000 0001.000 0003.358 2
140.501.501.065 5661.001 1533.686 4472.503.001.052 1201.001 0173.290 1
150.502.001.111 2131.001 9693.627 1482.503.501.109 9461.002 0893.218 9
160.502.501.164 5871.002 9073.560 3492.504.001.172 2501.003 1753.147 1
170.503.001.225 2861.003 9403.487 8502.504.501.236 8731.004 2273.077 7
180.503.501.292 6291.005 0353.411 9512.505.001.300 6221.005 1973.013 8
190.504.001.365 1881.006 1463.335 1523.003.001.000 0001.000 0003.223 7
200.504.501.440 4471.007 2293.260 9533.003.501.054 9611.001 0693.154 2
210.505.001.514 6881.008 2283.192 5543.004.001.114 1791.002 1513.084 1
221.001.001.000 0001.000 0003.698 6553.004.501.175 6001.003 1983.016 3
231.001.501.036 4041.000 6653.649 1563.005.001.236 1911.004 1622.953 9
241.002.001.080 8021.001 4793.590 4573.503.501.000 0001.000 0003.086 5
251.002.501.132 7151.002 4143.524 2583.504.001.056 1331.001 0793.018 2
261.003.001.191 7531.003 4413.452 5593.504.501.114 3551.002 1212.952 1
271.003.501.257 2521.004 5303.377 3603.505.001.171 7891.003 0802.891 4
281.004.001.327 8261.005 6353.301 4614.004.001.000 0001.000 0002.951 7
291.004.501.401 0251.006 7103.228 0624.004.501.055 1271.001 0412.887 4
301.005.001.473 2341.007 7033.160 4634.005.001.109 5091.001 9962.828 3
311.501.501.000 0001.000 0003.600 3644.504.501.000 0001.000 0002.824 9
321.502.001.042 8381.000 8113.542 3654.505.001.051 5401.000 9532.767 3
331.502.501.092 9281.001 7443.477 0665.005.001.000 0001.000 0002.711 2

Table 2

Calculation results of regression model coefficients"

系数拟合值系数拟合值
t10.006 197p10.000 004
t20.074 700p2-0.000 010
t3-0.060 807p3-0.000 042
t40.214 000p40.005 620
t50.041 420p50.000 009
t6-2.044 000p60.030 260
t7-0.114 580p70.000 122
t85.578 000p80.975 290

Fig.5

Prediction effect of natural frequency regression model"

Fig.6

Prediction effect of right/left mode ratio regression model for bridge pier top position"

Fig.7

Convergence diagram of the objective function of different algorithms"

Table 3

Calculation results of regression model coefficients"

算法工况11工况30
适应度收敛次数适应度收敛次数
GWO1.669 7e-5730.001 303 461
PSO0.005 063 5704.440 9e-1692
L-PSO4.440 9e-16512.220 4e-1675

Fig.8

Prediction renderings of 66 cases of simple supported beam bridges"

Table 4

Calculation results of regression model coefficients"

工况理论值

L-PSO

预测值

工况理论值

L-PSO

预测值

h1/mh2/mh1/mh2/mh1/mh2/mh1/mh2/m
10.00.00.000.20341.53.01.503.01
20.00.50.010.56351.53.51.503.54
30.01.00.050.92361.54.01.514.04
40.01.50.071.36371.54.51.514.49
50.02.00.071.87381.55.01.524.91
60.02.50.052.39392.02.02.001.83
70.03.00.032.92402.02.52.002.42
80.03.50.003.43412.03.02.003.00
90.04.00.003.92422.03.52.003.54
100.04.50.004.44432.04.02.004.04
110.05.00.004.98442.04.52.014.49
120.50.50.460.59452.05.02.014.88
130.51.00.490.95462.52.52.512.35
140.51.50.501.40472.53.02.502.95
150.52.00.511.91482.53.52.503.53
160.52.50.512.44492.54.02.504.04
170.53.00.502.97502.54.52.504.49
180.53.50.493.50512.55.02.504.87
190.54.00.494.01523.03.03.012.88
200.54.50.494.50533.03.53.003.49
210.55.00.515.00543.04.03.004.02
221.01.00.980.94553.04.53.004.49
231.01.51.001.40563.05.03.004.87
241.02.01.001.92573.53.53.513.42
251.02.51.012.47583.54.03.503.99
261.03.01.003.01593.54.53.504.49
271.03.51.003.53603.55.03.504.89
281.04.01.004.03614.04.04.013.94
291.04.51.014.50624.04.54.014.47
301.05.01.024.96634.05.04.004.89
311.51.51.501.36644.54.54.514.43
321.52.01.501.90654.55.04.504.89
331.52.51.502.46665.05.05.014.87
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