Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review

Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have become of great interest in recent years owing to their superior ability to detect damage and deficiencies in civil engineering structures. With the advent of the Internet of Things, big data and the colossal...

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Veröffentlicht in:Archives of computational methods in engineering Jg. 28; H. 4; S. 2621 - 2643
Hauptverfasser: Flah, Majdi, Nunez, Itzel, Ben Chaabene, Wassim, Nehdi, Moncef L.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.06.2021
Springer Nature B.V
Schlagworte:
Algorithms
Clustering
Damage detection
Engineering
Internet of Things
Machine learning
Mathematical and Computational Engineering
Original Paper
Structural damage
Structural health monitoring
Systematic review
Wind turbines
ISSN:1134-3060, 1886-1784
Online-Zugang:Volltext
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Zusammenfassung:Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have become of great interest in recent years owing to their superior ability to detect damage and deficiencies in civil engineering structures. With the advent of the Internet of Things, big data and the colossal and complex backlog of aging civil infrastructure assets, such applications will increase very rapidly. ML can efficiently perform several analyses of clustering, regression and classification of damage in diverse structures, including bridges, buildings, dams, tunnels, wind turbines, etc. In this systematic review, the diverse ML algorithms used in this domain have been classified into two major subfields: vibration-based SHM and image-based SHM. The efficacy of deploying ML algorithms in SHM has been discussed and detailed critical analysis of ML applications in SHM has been provided. Accordingly, practical recommendations have been made and current knowledge gaps and future research needs have been outlined.
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ISSN:1134-3060
1886-1784
DOI:10.1007/s11831-020-09471-9