Review of anomaly detection in large span bridges: available methods, recent advancements and future trends

During the life-cycle service of the constructed large span bridges, they face various threats every day due to the sophisticated operational environments. To ensure the structural safety, it is necessary to detect potential anomaly. Based on different inspection, monitoring and analysis technique,...

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Published inAdvances in bridge engineering Vol. 5; no. 1; pp. 2 - 26
Main Authors Fan, Ziyuan, Tang, Xiaoli, Chen, Yang, Ren, Yuan, Deng, Chao, Wang, Zihang, Peng, Ying, Shi, Chenghong, Huang, Qiao
Format Journal Article
LanguageEnglish
Published Singapore Springer Nature Singapore 01.12.2024
Springer Nature B.V
SpringerOpen
Subjects
Anomalies
Anomaly detection
Bridges
Cables
Civil Engineering
Construction
Design specifications
Engineering
Intelligent algorithm
Large Scale Bridges: Design
Large span bridge
Maintenance
Management
Parameters
Review
Structural health monitoring
Structural safety
Time series
Trends
Intelligent algorithm
Large span bridge
Structural health monitoring
Anomaly detection
Time series
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ISSN2662-5407
2662-5407
DOI10.1186/s43251-024-00113-9

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Summary:During the life-cycle service of the constructed large span bridges, they face various threats every day due to the sophisticated operational environments. To ensure the structural safety, it is necessary to detect potential anomaly. Based on different inspection, monitoring and analysis technique, huge amounts of data that direct or indirect reflect structural characteristics can be obtained, and hence the anomaly detection methods developed. In order to provide a summary of relevant information needed by researchers to realize what is concerned about and how current practices deal with these issues, then further promote the application, this paper reviews understanding of anomaly detection in large span bridges. It starts with an analysis of concerned parameters, including dynamic and static structural parameters of a bridge. The various data sources are then commented. Next, existing anomaly detection methods are reviewed and classified. Finally, this paper concisely provides recent progress and discusses future research trends based on the identified knowledge gaps. We hope that this review will help development in this field.
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ISSN:2662-5407
2662-5407
DOI:10.1186/s43251-024-00113-9