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Knowledge graph and CBR-based approach for automated analysis of bridge operational accidents: Case representation and retrieval

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  • Hui Xu
  • Yuxi Wei
  • Yonggang Cai
  • Bin Xing

Abstract

Bridge operational accident analysis is a critical process in bridge operational risk management. It provides valuable knowledge support for responding to newly occurring accidents. However, there are three issues: (1) research specifically focused on the past bridge operational accidents is relatively scarce; (2) there is a lack of mature research findings regarding the bridge operational accidents knowledge representation; and (3) in similar case retrieval, while case-based reasoning (CBR) is a valuable approach, there are still some challenges and limitations associated with its usage. To tackle these problems, this research proposed an automated analysis approach for bridge operational accidents based on a knowledge graph and CBR. The approach includes case representation and case retrieval, leveraging advancements in computer science and artificial intelligence. In the proposed approach, the case representation involves the adoption of a knowledge graph to construct multi-dimensional networks. The knowledge graph captures the relationships between various factors and entities, allowing for a comprehensive representation of accidents domain knowledge. In the case retrieval, a multi-circle layer retrieval strategy was innovatively proposed to enhance retrieval efficiency. Three target cases were randomly selected to verify the validity of the proposed methodology. The combination of a knowledge graph and CBR can indeed provide useful tools for the automated analysis of bridge operational accidents. Additionally, the proposed methodology can serve as a reference for intelligent risk management in other types of infrastructures.

Suggested Citation

  • Hui Xu & Yuxi Wei & Yonggang Cai & Bin Xing, 2023. "Knowledge graph and CBR-based approach for automated analysis of bridge operational accidents: Case representation and retrieval," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-21, November.
    • Handle: RePEc:plo:pone00:0294130
    DOI: 10.1371/journal.pone.0294130
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    References listed on IDEAS

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    3. Meng, Yangyang & Tian, Xiangliang & Li, Zhongwen & Zhou, Wei & Zhou, Zhijie & Zhong, Maohua, 2020. "Comparison analysis on complex topological network models of urban rail transit: A case study of Shenzhen Metro in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
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