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YU Zhong-ru, SHAN De-shan, SUN Rong-hui. Population-based structural health monitoring of bridges: Review and challenges[J]. Journal of Traffic and Transportation Engineering, 2025, 25(5): 1-22. doi: 10.19818/j.cnki.1671-1637.2025.05.001
Citation: YU Zhong-ru, SHAN De-shan, SUN Rong-hui. Population-based structural health monitoring of bridges: Review and challenges[J]. Journal of Traffic and Transportation Engineering, 2025, 25(5): 1-22. doi: 10.19818/j.cnki.1671-1637.2025.05.001
shu

Population-based structural health monitoring of bridges: Review and challenges

doi: 10.19818/j.cnki.1671-1637.2025.05.001

  • School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

Funds:

National Natural Science Foundation of China 51978577

More Information
  • Corresponding author: SHAN De-shan (1968-), male, professor, PhD, dsshan@163.com
  • Received Date: 2024-05-09
  • Accepted Date: 2025-08-07
  • Rev Recd Date: 2025-06-24
  • Publish Date: 2025-10-28
    Abstract
  • To solve the practical challenges faced in bridge structural health monitoring (BSHM), such as data scarcity, label deficiency, large operational environment variations, and high monitoring computational costs, the framework of population-based structural health monitoring (PBSHM) was systematically reviewed and summarized. A brief overview of the fundamental concepts of PBSHM was provided by thoroughly analyzing relevant studies reported in recent years. The definitions of different structural population types, including homogeneous populations and heterogeneous populations, were elaborated on from three perspectives: geometry, material, and topology. Key issues that PBSHM aims to solve were also summarized. Research status in the field of PBSHM was reviewed and analyzed in detail from two aspects: structural similarity measurement, and knowledge transfer methods and applications under different population types. Specific application scenarios and solution approaches under this framework were summarized, and the functions and connections of different technical methods were analyzed. In light of the practical engineering problems faced in the field of BSHM, the challenges and potential solutions for PBSHM of bridges were discussed. Review results indicate that structural populations form the basis of the PBSHM framework. Utilizing indicators of structural mechanical behavior similarity and data distribution similarity can effectively determine the knowledge transferability among bridge population members. Explicit transfer methods show good performance in anomaly monitoring and feature alignment for bridge structural populations, while implicit transfer methods based on deep neural network models exhibit stronger adaptability, which can automatically extract more representative features, meeting the end-to-end requirements in practical BSHM applications. Based on leveraging knowledge transfer technology, the PBSHM framework shows great potential in addressing issues such as differences in complex operational conditions, data imbalance impacts, damage identification, data prediction and generation, feature alignment, and normalization in bridge structural health monitoring tasks.

     

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