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AuthorAvci O.
AuthorAbdeljaber O.
AuthorKiranyaz, Mustafa Serkan
AuthorInman D.
Available date2022-04-26T12:31:21Z
Publication Date2020
Publication NameConference Proceedings of the Society for Experimental Mechanics Series
ResourceScopus
Identifierhttp://dx.doi.org/10.1007/978-3-030-12115-0_17
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85066808220&doi=10.1007%2f978-3-030-12115-0_17&partnerID=40&md5=1ed58e1d025e76f0f9979ca305e45689
URIhttp://hdl.handle.net/10576/30615
AbstractStructural damage detection methods available for structural health monitoring applications are based on data preprocessing, feature extraction, and feature classification. The feature classification task requires considerable computational power which makes the utilization of centralized techniques relatively infeasible for wireless sensor networks. In this paper, the authors present a novel Wireless Sensor Network (WSN) based on One Dimensional Convolutional Neural Networks (1D CNNs) for real-time and wireless structural health monitoring (SHM). In this method, each CNN is assigned to its local sensor data only and a corresponding 1D CNN is trained for each sensor unit without any synchronization or data transmission. This results in a decentralized system for structural damage detection under ambient environment. The performance of this method is tested and validated on a steel grid laboratory structure.
Languageen
PublisherSpringer New York LLC
SubjectClassification (of information)
Convolution
Damage detection
Dynamics
Feature extraction
Neural networks
One dimensional
Structural analysis
Structural dynamics
Structural health monitoring
Ambient environment
Computational power
Convolutional neural network
Decentralized system
Feature classification
Real time
Structural damage detection
Wireless structural health monitoring
Wireless sensor networks
TitleConvolutional neural networks for real-time and wireless damage detection
TypeConference Paper
Pagination129-136
dc.accessType Abstract Only


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