[1] Liu Chengyin, Ren Lichen, Jiang Zhaoshuo, et al. Multihazard risk assessment of sea-crossing suspension bridges based on an improved Bayesian network method [J]. Journal of Southeast University (English Edition), 2024, 40 (2): 155-164. [doi:10.3969/j.issn.1003-7985.2024.02.006]

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Multihazard risk assessment of sea-crossing suspension bridges based on an improved Bayesian network method()

基于改进贝叶斯网络方法的跨海悬索桥多灾害风险评估

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

40

Issue:

2024 2

Page:

155-164

Research Field:

Civil Engineering

Publishing date:

2024-06-13

Info

Title:

Multihazard risk assessment of sea-crossing suspension bridges based on an improved Bayesian network method

基于改进贝叶斯网络方法的跨海悬索桥多灾害风险评估

Author(s):

Liu Chengyin^{1, 2}, Ren Lichen^{1, 2}, Jiang Zhaoshuo³, Fang Qiyang^{1, 2}

¹School of Civil and Environmental Engineering, Harbin Institute of Technology(Shenzhen), Shenzhen 518055, China
²Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering, Harbin Institute of Technology(Shenzhen), Shenzhen 518055, China
³School of Engineering, San Francisco State University, San Francisco 94132, USA

柳成荫^{1, 2}, 任立辰^{1, 2}, 江兆烁³, 方其样^{1, 2}

¹哈尔滨工业大学(深圳)土木与环境工程学院, 深圳518055; ²哈尔滨工业大学(深圳)广东省土木工程智能韧性结构重点实验室, 深圳518055; ³School of Engineering, San Francisco State University, San Francisco 94132, USA

Keywords:

sea-crossing suspension bridge;  risk assessment;  Bayesian network;  damage index;  structural reliability

跨海悬索桥;  风险评估;  贝叶斯网络;  致灾因子;  结构可靠度

PACS:

TU448.25

DOI:

10.3969/j.issn.1003-7985.2024.02.006

Abstract:

To assess the combined risks of long-span suspension bridges under continuous wind loads and occasional earthquakes, a risk assessment framework for cross-sea suspension bridges based on improved Bayesian networks was proposed by combining the quantitative analysis of the structural damage probability and the qualitative assessment of the damage consequences during bridge operation. First, the damage degree of each component was obtained according to the characteristics of the suspension bridge and the results of wind and earthquake analyses. Then, the failure probability of the bridge structure was calculated using the theory of structural reliability. Finally, the risk assessment model of the suspension bridge based on improved Bayesian networks was proposed to evaluate the risk during bridge operation. The results show that considering the varying impacts of different bridge components, the bridge damage level can be categorized into four degrees based on its disaster resilience. Taking the Lingdingyang Bridge as an example, the maximum risk level under multihazard risks is level 3 according to the proposed method, which requires traffic restrictions and maintenance. Therefore, this method can guide the emergency management strategy of sea-crossing bridges in response to multihazard risks.

为评估大跨度悬索桥在持续风荷载和偶发地震下的综合风险, 将桥梁运营期间结构损伤概率的定量分析和损伤后果的定性评估相结合, 提出了一种基于改进贝叶斯网络的跨海悬索桥风险评估框架.首先, 分析桥位处的风灾和地震灾害, 根据悬索桥的特点获取各构件的损伤程度;其次, 利用结构可靠度理论, 计算桥梁结构失效概率;最后, 提出了基于改进贝叶斯网络的悬索桥风险评估模型, 并对桥梁运营期风险进行评估.结果表明, 考虑不同构件对桥梁的影响程度, 根据桥梁的承灾能力将桥梁损伤分为4级.以伶仃洋大桥为例, 采用所提方法得到多灾害风险下该桥最大风险等级为3级, 需限制通行并进行检修, 说明该方法能够指导跨海桥梁应对多风险灾害的应急管理策略.

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Memo

Memo:

Biographies: Liu Chengyin(1978—), male, doctor, professor;Jiang Zhaoshuo(corresponding author), male, doctor, associate professor, zsjiang@sfsu.edu.
Foundation items: The National Key Research and Development Program of China(No.2022YFB2602105), the National Natural Science Foundation of China(No.52378295), Guangdong Province Natural Science Foundation(No.2024A1515010296), Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering(No.2023B1212010004), Shenzhen Science and Technology Program(No.KQTD20210811090112003).
Citation: Liu Chengyin, Ren Lichen, Jiang Zhaoshuo, et al.Multihazard risk assessment of sea-crossing suspension bridges based on an improved Bayesian network method[J].Journal of Southeast University(English Edition), 2024, 40(2):155-164.DOI:10.3969/j.issn.1003-7985.2024.02.006.

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