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<p>With the extended service life of these structures and increasingly challenging service environments, ensuring the structural safety and durability of in-service large-span structures, as well as extending their operational lifespan, has become a critical concern. To promote the advancement of health monitoring technologies for large-span structures and enhance their operational safety, this paper systematically reviews the research progress in this field over the past two decades, both domestically and internationally. The review emphasizes the suitability of health monitoring systems, methods for preprocessing monitoring data, damage identification algorithms, and the integration of digital twin technology in structural health monitoring (SHM). The analysis reveals a growing trend towards the application of machine learning (ML) techniques and digital twin technologies in the monitoring of structures, highlighting their potential to improve monitoring accuracy and efficiency.</p>

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Research status and prospects of health monitoring methods and applications for large-span structures

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月1日

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  • Shengxuan Shao,
  • Ye Yuan,
  • Zechen Xue,
  • Xia Feng,
  • Yongjun Qiao,
  • Bo Huang

摘要

With the extended service life of these structures and increasingly challenging service environments, ensuring the structural safety and durability of in-service large-span structures, as well as extending their operational lifespan, has become a critical concern. To promote the advancement of health monitoring technologies for large-span structures and enhance their operational safety, this paper systematically reviews the research progress in this field over the past two decades, both domestically and internationally. The review emphasizes the suitability of health monitoring systems, methods for preprocessing monitoring data, damage identification algorithms, and the integration of digital twin technology in structural health monitoring (SHM). The analysis reveals a growing trend towards the application of machine learning (ML) techniques and digital twin technologies in the monitoring of structures, highlighting their potential to improve monitoring accuracy and efficiency.