失效非线性相关的桥梁截面可靠性Vine-Copula数据融合
CSTR:
作者:
作者单位:

兰州大学

中图分类号:

TU391; TU392.5

基金项目:

国家自然科学基金();甘肃省自然科学基金()


Data Fusion about Vine-Copula for Bridge Section Reliability Considering Nonlinear Correlation of Failure Modes
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [18]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    为合理融合健康监测数据分析在役桥梁截面可靠性,首先应用桥梁截面多个监测点的极值应力数据,建立监测变量非线性相关的VineCopula模型,实现极值应力数据的融合分析;然后结合多个监测点的功能函数,进行桥梁截面失效模式非线性相关的VineCopula建模分析,并融合一次二阶矩(FOSM)方法,分析失效非线性相关的桥梁截面可靠性;最后进行了在役桥梁截面监测数据的验证分析.研究表明,考虑失效模式非线性相关性所得桥梁截面可靠性较不考虑失效模式相关性所得结果小,说明不考虑失效模式相关性所得结果偏保守.

    Abstract:

    Bridge section reliability analysis method is reasonably carried on with the fusion of structural health monitoring data. Firstly, the vinecopula models considering the nonlinear correlation of multiple monitored variables were established based on the extreme stress data at the multiple monitored points of bridge section, which make the extreme stress data fusion achieved. Secondly, the vine copula models considering the nonlinear correlation of failure modes about bridge section were built with the performance functions about the multiple monitored points, further, through combining the built vine copula models with first order second moment (FOSM) method, the bridge section reliability considering the nonlinear correlation of failure modes was analyzed. Finally, the monitored data of an existing bridge was provided to illustrate the proposed model and method. The results show that the obtained bridge section reliability with considering the nonlinear correlation of failure modes is bigger than that without considering the correlation of failure modes. It is illustrated that the obtained results without considering the correlation of failure modes are conservative.

    参考文献
    [1]Ni YQ, Hua XG, Ko JM. Reliability-based assessment of bridges using long-term monitoring data [J]. Key Engineering Materials, 2006, 321~323: 217.
    [2]Frangopol DM, Strauss A, Kim SY. Bridge reliability assessment based on monitoring [J]. Journal of Bridge Engineering, ASCE, 2008, 13(3): 258.
    [3]Catbas FN, Susoy M, Frangopol DM. Structural health monitoring and reliability estimation: Long span truss bridge application with environmental monitoring data [J]. Engineering Structures, 2008, 30: 2347.
    [4]Frangopol DM, Strauss A, Kim SY. Use of monitoring extreme data for the performance prediction of structures: General approach [J]. Engineering Structures, 2008, 30: 3644.
    [5]Strauss A, Frangopol DM, Kim SY. Use of monitoring extreme data for the performance prediction of structures: Bayesian updating [J]. Engineering Structures, 2008, 30: 3654.
    [6]Dissanayake PBR, Karunananda PAK. Reliability index for structural health monitoring of aging bridges [J]. Structural Health Monitoring-An International Journal, 2008, 7(2): 175.
    [7]Pourali M, Mosleh A. A Bayesian approach to sensor placement optimization and system reliability monitoring [J]. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2013, 227(3): 327.
    [8]李顺龙. 基于健康监测技术的桥梁结构状态评估和预警方法研究 [D]. 哈尔滨: 哈尔滨工业大学博士学位论文, 2009.LI Shunlong. Approaches of Condition Assessment and Damage Alarming of Bridges based on Structural Health Monitoring [D]. Harbin: Harbin Institute of Technology, 2009.
    [9]焦美菊, 孙利民, 李清富. 基于监测数据的桥梁结构可靠度评估[J]. 同济大学学报(自然科学版), 2011, 39(10): 1452.JIAO Meiju, SUN Limin, LI Qingfu. Bridge structural reliability assessment based on health monitoring data [J]. Journal of Tongji University (Natural Science). 2011, 39(10): 1452.
    [10]赵卓. 基于ARMA模型的伊通河桥监测数据建模与可靠度分析[D]. 哈尔滨: 哈尔滨工业大学硕士学位论文, 2012.ZHAO Zhuo. Health monitoring data modeling and reliability analysis for Yitong river bridge based on ARMA model [D]. Harbin: Harbin Institute of Technology, 2012.
    [11]陈志为. 基于健康监测系统的大跨多荷载桥梁的疲劳可靠度评估[J]. 工程力学, 2014, 31(7): 99.CHEN Zhiwei. Fatigue reliability assessment of multi-loading suspension bridges based on SHMs [J]. Engineering Mechanics, 2014, 31(7): 99.
    [12]樊学平. 基于验证荷载和监测数据的桥梁可靠性修正与贝叶斯预测[D]. 哈尔滨: 哈尔滨工业大学博士学位论文, 2014.FAN Xueping. Bridge reliability updating and Bayesian prediction based on proof loads and monitored data [D]. Harbin: Harbin Institute of Technology, 2014.
    [13]Liu YF,SFan XP, Lu DG.STime-dependent uncertainty analysis of structures based on copula functions [C]. Vulnerability, Uncertainty, and Risk ?ASCE. Liverpool, 2014, 1875.
    [14]Liu YF, Fan XP. 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]Bedford T, Cooke RM. Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines [J]. Annals of Mathematics Artificial Intelligence, 2001, 32(1-4): 245-268.
    [16]Bedford T, Cooke RM. Vines: A new graphical model for dependent random variables [J]. Annals of Statistics, 2002, 30(4): 1031.
    [17]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: 哈尔滨工业大学博士学位论文, 2015.LIU Yuefei. System reliability analysis of bridge structures considering correlation of failure modes and proof modes [D]. Harbin Institute of Technology, 2015.
    [18]Nelsen R B. An Introduction to Copulas[M]. Springer Series in Statistics, 2006.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

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(03):0315~0321

复制
分享
文章指标
  • 点击次数:1070
  • 下载次数: 832
  • HTML阅读次数: 866
  • 引用次数: 0
历史
  • 收稿日期:2018-03-13
  • 最后修改日期:2018-12-26
  • 录用日期:2018-12-13
  • 在线发布日期: 2019-04-03
文章二维码