Failure probability estimation for structures based on health monitoring data and Bayesian network

被引:0
|
作者
Ma Z. [1 ,2 ,3 ]
Luo Y.-Z. [4 ]
Ge H.-B. [4 ]
Wan H.-P. [4 ]
Fu W.-W. [5 ]
Shen Y.-B. [4 ]
机构
[1] Department of Civil Engineering, Hangzhou City University, Hangzhou
[2] Key Laboratory of Safe Construction, Intelligent Maintenance for Urban Shield Tunnels of Zhejiang Province, Hangzhou City University, Hangzhou
[3] Zhejiang Engineering Research Center of Intelligent Urban Infrastructure, Hangzhou City University, Hangzhou
[4] Space Structure Research Center, Zhejiang University, Hangzhou
[5] Department of Civil Engineering, Suzhou University of Science and Technology, Suzhou
关键词
Bayesian dynamic linear model; Bayesian network; structural failure probability assessment; structural health monitoring; time-varying reliability index;
D O I
10.3785/j.issn.1008-973X.2023.08.008
中图分类号
学科分类号
摘要
Based on health monitoring data and Bayesian network (BN), a real-time estimation method of structural system failure probability was proposed, which was applied for assessing structural safety conditions dynamically. First, the Bayesian dynamic linear model (BDLM) was established using structural health monitoring data, and the probabilistic distribution of the load effect was calculated. Time-varying reliability of structural components was obtained, combined with distribution of structural resistance. Then, the BN was constructed according to the main failure mode of the structure, where the dependency between failure of the components and the structure can be described. Through probability inference of the BN, the failure probability of the structural system can be obtained from the component reliability, and the quantitative assessment of overall structural safety conditions was achieved. Finally, the proposed method was verified by the simulated data of a three-bar truss and the measured data of static failure test of a single layer reticulated shell. Results show that the proposed method properly quantifies the safety condition of the structure and successfully gives the early warning of the structural failure. © 2023 Zhejiang University. All rights reserved.
引用
收藏
页码:1551 / 1561
页数:10
相关论文
共 32 条
  • [1] LUO Yao-zhi, ZHAO Jing-yu, Research status and future prospects of space structure health monitoring [J], Journal of Building Structures, 43, 10, pp. 16-28, (2022)
  • [2] HAWCHAR L, SOUEIDY C E, SCHOEFS F., Principal component analysis and polynomial chaos expansion for time-variant reliability problems [J], Reliability Engineering and System Safety, 167, pp. 406-416, (2017)
  • [3] CATBAS F N, SUSOY M, FRANGOPOL D M., Structural health monitoring and reliability estimation: long span truss bridge application with environmental monitoring data [J], Engineering Structures, 30, 9, pp. 2347-2359, (2008)
  • [4] CHEN Long, HUANG Tian-li, Dynamic prediction of reliability of in-service RC bridges using the Bayesian updating and inverse gaussian process [J], Engineering Mechanics, 37, 4, pp. 186-195, (2020)
  • [5] LU Nai-wei, LUO Yuan, WANG Qin-yong, Et al., Dynamic reliability assessment for long-span bridges under vehicle load [J], Journal of Zhejiang University: Engineering Science, 50, 12, pp. 2328-2335, (2016)
  • [6] NI Y Q, CHEN R., Strain monitoring based bridge reliability assessment using parametric Bayesian mixture model, Engineering Structures, 226, (2021)
  • [7] ZHU B, FRANGOPOL D M., Incorporation of structural health monitoring data on load effects in the reliability and redundancy assessment of ship cross-sections using Bayesian updating [J], Structural Health Monitoring: An International Journal, 12, 4, pp. 377-392, (2013)
  • [8] LIU Yue-fei, System reliability analysis of bridge structures considering correlation of failure modes and proof modes, (2015)
  • [9] PEARL J., Probabilistic reasoning in intelligent systems: networks of plausible inference, (1988)
  • [10] LOU Wen-zhong, ZHAO Yue-cen, FENG Heng-zhen, Et al., Reliability analysis on MEMS S&A device based on Bayesian network [J], Transaction of Beijing Institute of Technology, 41, 9, pp. 952-960, (2021)