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AuthorAvci O.
AuthorAbdeljaber O.
AuthorKiranyaz, Mustafa Serkan
AuthorHussein M.
AuthorGabbouj M.
AuthorInman D.J.
Available date2022-04-26T12:31:18Z
Publication Date2021
Publication NameMechanical Systems and Signal Processing
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.ymssp.2020.107077
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85087280293&doi=10.1016%2fj.ymssp.2020.107077&partnerID=40&md5=8fe402ba41df4001a0ab283e3ee9d60c
URIhttp://hdl.handle.net/10576/30592
AbstractMonitoring structural damage is extremely important for sustaining and preserving the service life of civil structures. While successful monitoring provides resolute and staunch information on the health, serviceability, integrity and safety of structures; maintaining continuous performance of a structure depends highly on monitoring the occurrence, formation and propagation of damage. Damage may accumulate on structures due to different environmental and human-induced factors. Numerous monitoring and detection approaches have been developed to provide practical means for early warning against structural damage or any type of anomaly. Considerable effort has been put into vibration-based methods, which utilize the vibration response of the monitored structure to assess its condition and identify structural damage. Meanwhile, with emerging computing power and sensing technology in the last decade, Machine Learning (ML) and especially Deep Learning (DL) algorithms have become more feasible and extensively used in vibration-based structural damage detection with elegant performance and often with rigorous accuracy. While there have been multiple review studies published on vibration-based structural damage detection, there has not been a study where the transition from traditional methods to ML and DL methods are described and discussed. This paper aims to fulfill this gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.
Languageen
PublisherAcademic Press
SubjectDamage detection
Learning systems
Structural analysis
Structural health monitoring
Computing power
Detection approach
Safety of structures
Sensing technology
Structural damage detection
Structural damages
Vibration response
Vibration-based damage detection
Deep learning
TitleA review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications
TypeArticle Review
Volume Number147
dc.accessType Abstract Only


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