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

Liu Chengyin1,2, Ren Lichen1,2, Jiang Zhaoshuo3, Fang Qiyang1,2

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Journal of Southeast University (English Edition) ›› 2024, Vol. 40 ›› Issue (2) : 155-164. DOI: 10.3969/j.issn.1003-7985.2024.02.006

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

  • Liu Chengyin1,2, Ren Lichen1,2, Jiang Zhaoshuo3, Fang Qiyang1,2
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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.

Keywords

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

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

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Funding
Jiang Zhaoshuo, male, doctor, associate professor, zsjiang@sfsu.edu.
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