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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2023, Vol. 57 Issue (8): 1551-1561    DOI: 10.3785/j.issn.1008-973X.2023.08.008
    
Failure probability estimation for structures based on health monitoring data and Bayesian network
Zhi MA1,2,3(),Yao-zhi LUO4,*(),Hui-bin GE4,Hua-ping WAN4,Wen-wei FU5,Yan-bin SHEN4
1. Department of Civil Engineering, Hangzhou City University, Hangzhou 310015, China
2. Key Laboratory of Safe Construction and Intelligent Maintenance for Urban Shield Tunnels of Zhejiang Province, Hangzhou City University, Hangzhou 310015, China
3. Zhejiang Engineering Research Center of Intelligent Urban Infrastructure, Hangzhou City University, Hangzhou 310015, China
4. Space Structure Research Center, Zhejiang University, Hangzhou 310058, China
5. Department of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
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Abstract  

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.

Key words： structural health monitoring      Bayesian network      structural failure probability assessment      Bayesian dynamic linear model      time-varying reliability index     
Received: 26 September 2022      Published: 31 August 2023
CLC:  TU 391  
Fund:  浙江省自然科学基金资助项目（LQ22E080013）；浙江省空间结构重点实验室资助项目（202107）；浙江省重点研发计划资助项目（2021C03154）
Corresponding Authors: Yao-zhi LUO     E-mail: mazhi@hzcu.edu.cn;luoyz@zju.edu.cn
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Zhi MA
Yao-zhi LUO
Hui-bin GE
Hua-ping WAN
Wen-wei FU
Yan-bin SHEN
Cite this article:

Zhi MA,Yao-zhi LUO,Hui-bin GE,Hua-ping WAN,Wen-wei FU,Yan-bin SHEN. Failure probability estimation for structures based on health monitoring data and Bayesian network. Journal of ZheJiang University (Engineering Science), 2023, 57(8): 1551-1561.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2023.08.008     OR     https://www.zjujournals.com/eng/Y2023/V57/I8/1551


基于健康监测数据和贝叶斯网络的结构失效概率评估

基于健康监测数据和贝叶斯网络(BN)，提出结构体系失效概率的实时评估方法，用于结构整体安全状态的动态评价. 根据结构响应监测数据建立贝叶斯动态线性模型(BDLM)，估计结构荷载效应的概率分布，同时结合结构抗力分布，计算构件的时变可靠指标；根据结构主要失效模式构建贝叶斯网络，以描述构件失效与结构整体失效间的依赖关系；通过贝叶斯网络的概率递推从构件可靠指标求得结构体系失效概率，实现结构整体安全状态的量化评估. 利用三杆桁架模型的数值模拟数据和某单层网壳结构静力破坏试验过程的实测数据对该方法进行验证. 结果表明，所提出的结构失效概率评估方法较好地量化了结构的安全状态，并成功预警了结构体系的破坏.


关键词： 结构健康监测,  贝叶斯网络,  结构失效概率评估,  贝叶斯动态线性模型,  时变可靠指标 
Fig.1 Bayesian network of reliability calculation for simple system
Fig.2 Structural diagram of three-bar truss and its possible failure modes
Fig.3 Simulated strain data of three-bar truss structure during loading process
Fig.4 Bayesian network for reliability assessment of three-bar truss structure
$ {M_1} $ $ {M_2} $ $ {M_3} $ $ P(A = 1|M_i) $
1 1 1 1.0
0 1 1 1.0
1 0 1 1.0
1 1 0 1.0
其他 0
Tab.1 System node conditional probability table of Bayesian network for three-bar truss structure
Fig.5 Prediction distribution results of BDLM for strains of three-bar truss structure
Fig.6 Component reliability index of three-bar truss structure
Fig.7 System reliability index and failure probability of three-bar truss structure
Fig.8 Diagram and photo of static failure test model for single layer reticulated shell
Fig.9 Main buckling modes of structure and corresponding main buckling sections
Fig.10 Size and measuring point layout of section
Fig.11 Original stress data of section 3
Fig.12 Bayesian network for reliability assessment of single-layer reticulated shell structure
$ {J_1} $ $ {J_2} $ $ {J_3} $ $ {J_4} $ $ {J_5} $ $ P(A = 1|J_i) $
1 0, 1 0, 1 0, 1 0, 1 1.0
0 1 1 1 1 1.0
其他 0
Tab.2 Conditional probability table of system node A of single-layer reticulated shell structure
网络类型 $ {M_1} $ $ {M_2} $ $ {M_3} $ $ {M_4} $ $P({J_1} = 1|M_i)$
网络1 1 1 1 1 1.0
其他 0
网络2 1 1 1 1 1.0
1 1 1 0 1.0
1 0 1 1 1.0
0 1 1 1 1.0
其他 0
网络3 1 1 1 1 1.0
1 1 1 0 1.0
1 0 1 1 1.0
0 1 1 1 1.0
1 1 0 0 1.0
1 0 1 0 1.0
1 0 0 1 1.0
0 1 1 0 1.0
0 1 0 1 1.0
0 0 1 1 1.0
其他 0
Tab.3 Conditional probability table for node $ {J_1} $
Fig.13 Prediction distribution results of BDLM for equivalent stresses of section 1 and 3
Fig.14 Component reliability index of single-layer reticulated shell structure
Fig.15 System reliability index and failure probability of single-layer reticulated shell structure
Fig.16 Actual failure situation of single-layer reticulated shell structure
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