Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (4): 1296-1305.doi: 10.13229/j.cnki.jdxbgxb20200387

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Optimal R-vine copula information fusion for failure probability analysis of long-span bridge girder

Xue-ping FAN1,2(),Guang-hong YANG2,Qing-kai XIAO3,Yue-fei LIU1,2   

  1. 1.Key Laboratory of Mechanics on Disaster and Environment in Western China of the Ministry of Education，Lanzhou University，Lanzhou 730030，China
    2.School of Civil Engineering and Mechanics，Lanzhou University，Lanzhou 730030，China
    3.School of Civil Engineering and Transportation，South China university of Technology，Guangzhou 510641，China
  • Received:2020-06-03 Online:2021-07-01 Published:2021-07-14
  • Contact: Xue-ping FAN E-mail:fxp_2004@163.com

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Abstract:

To reasonably analyze the failure probability of the long-span bridge girder, considering the correlation among the failure modes of the multiple control monitoring points, a new data fusion method about the failure probability analysis for the long-span bridge girder is presented. With the extreme strain information, the optimal R-Vine copula model considering the correlation among the failure modes of the multiple control monitoring points is built with the corresponding performance functions, bivariate pair-copula model and the optimal R-vine. Further, with the first order second moment (FOSM) method, the failure probability of the long-span bridge girder considering the correlation among the failure modes is analyzed , the feasibility of which is compared with the other analysis method using the monitoring data of the existing bridge. The results show that the optimal R-Vine copula information fusion method for the failure probability analysis of long-span bridge girder considering the correlation among failure modes is more reasonable.

Key words: structural engineering, girder, correlation, R-Vine Copula model, first order second moment method, reliability analysis

CLC Number: 

  • TU391

Fig.1

R-Vine structure of fine-dimensional randon variables"

Fig.2

Monitored section layout of Xijiang bridge"

Fig.3

Sensor layouts about section A， B， C， D and E"

Fig.4

Monitored extreme strain curves of five sections"

Table 1

Pearson correlation coefficients between monitored points"

监测点Sensor1Sensor2Sensor3Sensor4Sensor5Sensor6Sensor7Sensor8Sensor9Sensor10
Sensor 1—0.670.880.600.760.320.850.650.860.62
Sensor 2——0.910.990.430.790.940.990.940.98
Sensor 3———0.880.680.560.990.880.990.88
Sensor 4————0.370.830.910.980.910.98
Sensor 5—————-0.020.640.360.650.40
Sensor 6——————0.610.840.620.80
Sensor 7———————0.910.990.91
Sensor 8————————0.910.98
Sensor 9—————————0.91
Sensor10——————————

Fig.5

The first tree of R-Vine structure"

Table 2

Correlation/partial correlation coefficients and failure probability of multiple Pair-Copula"

树连接边Pearson系数失效概率连接边Pearson系数失效概率
T11，50.761.36e-064，100.984.57e-05
1，30.883.15e-076，80.846.87e-09
3，70.993.22e-072，80.996.13e-05
7，901.78e-062，40.994.57e-05
2，90.943.67e-05
T23，5|10.021.34e-124，9|2-0.451.12e-13
1，7|3-0.472.74e-172，10|40.351.70e-05
3，9|70.416.66e-092，6|8-0.469.26e-16
2，7|90.092.01e-114，8|20.379.34e-06
T35，7|1，3-0.131.47e-138，9|2，4-0.142.18e-09
1，9|3，70.041.14e-084，6|2，80.421.13e-09
2，3|7，9-0.606.59e-178，10|2，40.244.59e-05
4，7|2，90.242.48e-08
T45，9|1，3，7-0.143.42e-127，8|2，4，9-0.164.55e-11
1，2|3，7，9-0.702.50e-146，9|2，4，8-0.532.65e-25
3，4|2，7，90.075.17e-106，10|2，4，8-0.173.76e-12
T52，5|1，3，7，9-0.482.44e-126，7|2，4，8，9-0.251.09e-18
1，4|2，3，7，9-0.302.61e-109，10|2，4，6，8-0.052.42e-07
3，8|2，4，7，90.065.38e-10
T64，5|1，2，3，7，9-0.092.45e-103，6|2，4，7，8，9-0.291.27e-20
1，8|2，3，4，7，90.501.09e-057，10|2，4，6，8，9-0.124.40e-09
T75，8|1，2，3，4，7，9-0.379.45e-143，10|2，4，6，7，8，90.079.03e-09
1，6|2，3，4，7，8，90.416.55e-10
T85，6|1，2，3，4，7，8，9-0.251.63e-181，10|2，3，4，6，7，8，9-0.248.57e-08
T95，10|1，2，3，4，6，7，8，90.202.43e-07
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