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

Previous Articles    

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

RICH HTML

 2   

PDF (PC)

121

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
1 刘月飞, 樊学平. 失效非线性相关的桥梁截面可靠性Vine-Copula数据融合[J]. 同济大学学报:自然科学版, 2019. 47(3): 315-321.
Liu Yue-fei, Fan Xue-ping. Data fusion about Vine-Copula for bridge section reliability considering nonlinear correlation of failure modes[J]. Journal of Tongji University (Natural Science), 2019, 47(3): 315-321.
2 Fan X P, Liu Y F. Time-variant reliability prediction of bridge system based on BDGCM and SHM data[J]. Structural Control and Health Monitoring, 2018, 25(7): 1-16.
3 Liu Y F, Fan X P. Gaussian Copula-Bayesian dynamic linear model-based time-dependent reliability prediction of bridge structures considering nonlinear correlation between failure modes[J]. Advances in Mechanical Engineering, 2016, 8(11): 1-15.
4 刘月飞. 考虑失效模式和验证模式相关性的桥梁结构体系可靠度分析[D]. 哈尔滨: 哈尔滨工业大学航天学院, 2015.
Liu Yue-fei. System reliability analysis of bridge structures considering correlation of failure modes and proof modes[D]. Harbin: Aerospace Academy,Harbin Institute of Technology, 2015.
5 Liu Y F, Fan X P. Dynamic reliability prediction for the steel box girder based on multivariate Bayesian dynamic gaussian copula model and SHM extreme stress data[J]. Structural Control and Health Monitoring, 2020,27(6): 1-17.
6 樊学平, 杨光红, 肖青凯, 等. 考虑安全性的桥梁主梁体系可靠性动态藤Copula预测方法[J]. 同济大学学报:自然科学版, 2020, 48(2): 165-175.
Fan Xue-ping, Yang Guang-hong, Xiao Qing-kai, et al. Dynamic Vine-Copula prediction approach of bridge girder system reliability considering structural safety[J]. Journal of Tongji University (Natural Science), 2020, 48(2): 165-175.
7 Liu H B, Wang X R, Tan G J, et al. System reliability evaluation of a bridge structure based on multivariate copulas and the AHP-EW method that considers multiple failure criteria[J]. Applied Sciences, 2020, 10(4): 1-19.
8 Bedford T, Cooke R M. Probability density decomposition for conditionally dependent random variables modeled by vines[J]. Annals of Mathematics & Artificial Intelligence, 2001, 32(1-4): 245-268.
9 Bedford T, Cooke R M. Vines: a new graphical model for dependent random variables[J]. Annals of Statistics, 2002, 30(4): 1031-1068.
10 Nápoles O M, Cooke R M, Kurowicka D. About the number of vines and regular vines on n nodes [R/OL]. [2010-12-01]. .
11 Dissmann J, Brechmann E C, Czado C, et al. Selecting and estimating regular vine copulae and application to financial returns[J]. Computational Statistics & Data Analysis, 2013, 59(1): 52-69.
12 Pearson K. Mathematical contributions to the theory of evolution. III. regression, heredity, and panmixia[J]. Philosophical Transactions of the Royal Society of London A, 1895, 186(4): 253-318.
13 Melchers R E, Beck A T. Structural Reliability Analysis and Prediction[M]. New York: John Wiley & Sons, 2018.
14 Ang A H S, Tang W H. Probability Concepts in Engineering Planning and Design (Vol. II) [M]. New York: John Wiley and Sons, 1984.
15 李英华. 基于长期健康监测的连续刚构梁桥的性能分析与演化规律研究[D]. 广州: 华南理工大学土木与交通学院, 2012.
Li Ying-hua. Performance analysis and evolution of continuous rigid frame bridge based on long-term health monitoring[D]. Guangzhou: School of Civil Engineering and Transportation, South China University of Technology, 2012.
16 王震. 基于桥梁长期健康监测的数据特征分析与可靠度计算[D]. 广州: 华南理工大学机械与汽车工程学院, 2014.
Wang Zhen. Characteristics analysis and reliability calculation based on long-term bridge health monitoring data[D]. Guangzhou: School of Mechanical and Automotive Engineering, South China University of Technology, 2014.
[1] Yong-chun CHENG,He LI,Li-ding LI,Hai-tao WANG,Yun-shuo BAI,Chao CHAI. Analysis of mechanical properties of asphalt mixture affected by aggregate based on grey relational degree [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 925-935.
[2] Jiang YU,Zhi-hao ZHAO,Yong-jun QIN. Damage of reinforced concrete shear beams based on acoustic emission and fractal [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 620-630.
[3] Er-gang XIONG,Han XU,Ci TAN,Jing WANG,Ruo-yu DING. Shear strength of reinforced concrete beams based on elastoplastic stress field theory [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(1): 259-267.
[4] Zhao LIU,Jiang-lin CHENG,Yu-tian ZHU,Li-hui ZHENG. Vertical vibration modeling and motion correlation analysis of rail vehicles [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1600-1607.
[5] Xue-ping FAN,Guang QU,Yue-fei LIU. Bridge extreme stress prediction based on new data assimilation algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 572-580.
[6] Xiu-zhen WANG,Yong-jiu QIAN,Chang-jiang SHAO,Shuai SONG. Seismic vulnerability analysis of frame structure considering floor correlation [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 202-209.
[7] De-lei YANG,Le-wei TONG. Calculation formula of SCF for CHS⁃CFSHS welded T⁃joints with brace under axial tension [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 1891-1899.
[8] Yi JIA,Ren-da ZHAO,Yong-bao WANG,Fu-hai LI. Sensitivity analysis of viscous damper parameters for multi⁃span and long⁃unit continuous girder bridges [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 1871-1883.
[9] Lun-hua BAI,Rui-li SHEN,Xing-biao ZHANG,Lu WANG. In-plane stability of self-anchored suspension bridge [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1500-1508.
[10] Yu-mei LIU,Ning-guo QIAO,Jiao-jiao ZHUANG,Peng-cheng LIU,Ting HU,Li-jun CHEN. Anomaly detection of rail vehicle gearbox based on multi⁃sensor data fusion [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1465-1470.
[11] Da-yi QU,Yan-feng JIA,Dong-mei LIU,Jing-ru YANG,Wu-lin WANG. Dynamic partitioning method for road network intersection considering multiple factors [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1478-1483.
[12] Yin⁃juan FU,Yong LI,Li⁃qin XU,Kun⁃hui ZHANG. Design and analysis of NLFM⁃Costas RF stealth radar signal [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 994-999.
[13] Zhi⁃hui LI,Tao ZHONG,Yong⁃hua ZHAO,Yong⁃li HU,Hai⁃tao LI,Jing⁃wei ZHAO. Pedestrian tracking algorithm for autonomous driving [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 680-687.
[14] Jing WANG,Xiang LYU,Xiao⁃long QU,Chun⁃ling ZHONG,Yun⁃long ZHANG. Analysis of relationship between subgrade soil shear strength and chemical and minerals component [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 766-772.
[15] LIU Zhao-hui, WANG Chao, LYU Wen-hong, GUAN Xin. Identification of data characteristics of vehicle running status parameters by nonlinear dynamic analysis [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1405-1410.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd