Damage Identification on a Numerical Two-Storey Framed Structure using Ambient Vibration Response Analysis and Artificial Neural Networks

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dc.contributor.author Dackermann, U
dc.contributor.author Li, J
dc.contributor.author Samali, B
dc.contributor.editor Law, S
dc.contributor.editor Cheng, L
dc.contributor.editor Xia, Y
dc.contributor.editor SU, Z
dc.date.accessioned 2012-10-12T03:36:50Z
dc.date.issued 2011-01
dc.identifier.citation Proceeding of The 14th Asia Pacific Conferance, 2011, pp. 338 - 347
dc.identifier.isbn 978-962-367-731-8
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/19367
dc.description.abstract This paper presents a damage identification method based on ambient floor vibration measurements in multi-storey buildings. The proposed method uses ambient response vibration data to fannulate a damage index based on Frequency Response Functions (FRFs), which is used as input parameter to artificial neural networks (ANNs), to identify locations and severities of damage in a two-storey framed structure. By adopting principal component analysis (PCA) techniques, the Size of the derived damage index is reduced in order to obtain suitable patterns for ANN training. A hierarchy of neural network ensembles is designed to take advantage of individual characteristics of measurements from different floor locations. The proposed method is tested on finite element models of a complex two-storey framed structure inflicted with notch-type damage of different locations and severities (in total six damage cases). The results of the study show that the proposed algorithm is capable of accurately and reliably identifying damage in complex multi-storey structures based on response-only ambient floor vibration measurements.
dc.format Ryan Stoker
dc.publisher The Hong Kong Polytechnic University
dc.relation.isreplacedby 10453/33147
dc.relation.isreplacedby http://hdl.handle.net/10453/33147
dc.subject Damage Identification, Ambient Vibration, Artificial Neural Networks, Neural Network Ensembles, Structural Health Monitoring, Frequency Response Functions, Principal Component Analysis
dc.title Damage Identification on a Numerical Two-Storey Framed Structure using Ambient Vibration Response Analysis and Artificial Neural Networks
dc.type Conference Proceeding
dc.parent Proceeding of The 14th Asia Pacific Conferance
dc.journal.number en_US
dc.publocation Hong Kong en_US
dc.identifier.startpage 338 en_US
dc.identifier.endpage 347 en_US
dc.cauo.name FEIT.School of Civil and Environmental Engineering en_US
dc.conference Verified OK en_US
dc.conference Dynamics for Sustainable Engineering
dc.for 1202 Building
dc.for 1201 Architecture
dc.personcode 995216 en_US
dc.personcode 930859 en_US
dc.personcode 870186 en_US
dc.percentage 50 en_US
dc.classification.name Architecture en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom Dynamics for Sustainable Engineering en_US
dc.date.activity 20111205 en_US
dc.date.activity 2011-12-05
dc.location.activity Hong Kong en_US
dc.location.activity ISI000248151100080
dc.description.keywords Damage Identification, Ambient Vibration, Artificial Neural Networks, Neural Network Ensembles, Structural Health Monitoring, Frequency Response Functions, Principal Component Analysis en_US
dc.description.keywords 3-D magnetic property, vector hysteresis loci, B and H search coils
dc.staffid en_US
dc.staffid 870186 en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group /University of Technology Sydney/Strength - Built Infrastructure
utslib.copyright.status Open Access
utslib.copyright.date 2015-04-15 12:23:47.074767+10
utslib.collection.history General (ID: 2)


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