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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/10.1007/978-3-030-77143-0_10
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85118993145&doi=10.1007%2f978-3-030-77143-0_10&partnerID=40&md5=e10e21bd04ab01e18363c3e17c23c925
URIhttp://hdl.handle.net/10576/30581
AbstractThis paper presents a brief overview of vibration-based damage identification studies based on Deep Learning (DL) in civil engineering structures. The presence, type, size, and propagation of structural damage on civil infrastructure have always been a topic of research. In the last couple of decades, there has been a significant shift in the damage detection paradigm when the advancements in sensing and computing technologies met with the ever-expanding use of artificial neural network algorithms. Machine-Learning (ML) tools enabled researchers to implement more feasible and faster tools in damage detection applications. When an artificial neural network has more than three layers, it is typically considered as a ?deep? learning network. Being an important accomplishment of the ML era, DL tools enable complex systems which are made of several layers to learn implementations of data with outstanding categorization and compartmentalization capability. In fact, with proper training, a DL tool can operate directly with the unprocessed raw data and help the algorithm produce output data. Competitive capabilities like this led DL algorithms perform very well in complicated problems by dividing a relatively large problem into much smaller and more manageable portions. Specifically for damage identification and localization on civil infrastructure, Convolutional Neural Networks (CNNs) and Unsupervised Pretrained Networks (UPNs) are the known DL tools published in the literature. This paper presents an overview of these studies.
Languageen
PublisherSpringer
SubjectBackpropagation
Convolutional neural networks
Deep learning
Multilayer neural networks
Structural dynamics
Structural health monitoring
Civil engineering structures
Civil infrastructures
Damage Identification
Damage localization
Deep learning
Infrastructure health
Learning methods
Learning tool
Machine-learning
Vibration-based damage detection
Damage detection
TitleAn Overview of Deep Learning Methods Used in Vibration-Based Damage Detection in Civil Engineering
TypeConference Paper
Pagination93-98
dc.accessType Abstract Only


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