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AuthorAvci O.
AuthorAbdeljaber O.
AuthorKiranyaz, Mustafa Serkan
Available date2022-04-26T12:31:17Z
Publication Date2022
Publication NameConference Proceedings of the Society for Experimental Mechanics Series
ResourceScopus
Identifierhttp://dx.doi.org/10.1007/978-3-030-75988-9_17
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85118193312&doi=10.1007%2f978-3-030-75988-9_17&partnerID=40&md5=54b7815adfb1a14bf6d0473b14249d2f
URIhttp://hdl.handle.net/10576/30582
AbstractThis paper presents a brief overview of vibration-based structural damage detection studies that are based on machine learning (ML) in civil engineering structures. The review includes both parametric and nonparametric applications of ML accompanied with analytical and/or experimental studies. While the ML tools help the system learn from the data fed into, the computer enhances the task with the learned information without any programming on how to process the relevant data. As such, the performance level of ML-based damage identification methodologies depends on the feature extraction and classification steps, especially on the classifier choices for which the characteristic nature of the acceleration signals is recorded in a feasible way. Yet, there are several issues to be discussed about the existing ML procedures for both parametric and nonparametric applications, which are presented in this paper.
Languageen
PublisherSpringer
SubjectComputer programming
Machine learning
Structural dynamics
Structural health monitoring
Structures (built objects)
'current
Civil engineering structures
Damage Identification
Damage localization
Learn+
Machine-learning
Nonparametrics
State of the art
Structural damage detection
Vibration-based method
Damage detection
TitleStructural Damage Detection in Civil Engineering with Machine Learning: Current State of the Art
TypeConference Paper
Pagination223-229
dc.accessType Abstract Only


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