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

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.

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Publication Details

Journal
Discover Applied Sciences
Published
2025-09-01
DOI
https://doi.org/10.1007/s42452-025-07679-7
Primary Topic
Structural Health Monitoring Techniques
Type
article
Field-Weighted Citation Impact
0.00

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

Ye Yuan, Bo Huang, Feng Xia, Yongjun Qiao et al.
2 links
Discover Applied Sciences
Structural Health Monitoring Techniques
article

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

Ye Yuan, Bo Huang, Feng Xia, Yongjun Qiao, Zechen Xue, S Shao
article en

Abstract

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.

Discover Applied SciencesVol. 7(9)
Merchants Chongqing Communications Research and Design Institute (CN), Suzhou Thermal Engineering Research Institute (CN), Xuzhou Construction Machinery Group (China) (CN), Chongqing Jiaotong University (CN)
Chongqing Jiaotong University
Openalex Percentile: Top 76%
Structural Health Monitoring Techniques
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