Dynamic-based damage identification using neural network ensembles and damage index method

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dc.contributor.author Dackermann, U
dc.contributor.author Li, J
dc.contributor.author Samali, B
dc.date.accessioned 2011-02-07T06:23:34Z
dc.date.issued 2010-12-01
dc.identifier.citation Advances in Structural Engineering, 2010, 13 (6), pp. 1001 - 1016
dc.identifier.issn 1369-4332
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/13660
dc.description.abstract This paper presents a vibration-based damage identification method that utilises a "damage fingerprint" of a structure in combination with Principal Component Analysis (PCA) and neural network techniques to identify defects. The Damage Index (DI) method is used to extract unique damage patterns from a damaged beam structure with the undamaged structure as baseline. PCA is applied to reduce the effect of measurement noise and optimise neural network training. PCA-compressed DI values are, then, used as inputs for a hierarchy of neural network ensembles to estimate locations and severities of various damage cases. The developed method is verified by a laboratory structure and numerical simulations in which measurement noise is taken into account with different levels of white Gaussian noise added. The damage identification results obtained from the neural network ensembles show that the presented method is capable of overcoming problems inherent in the conventional DI method. Issues associated with field testing conditions are successfully dealt with for numerical and the experimental simulations. Moreover, it is shown that the neural network ensemble produces results that are more accurate than any of the outcomes of the individual neural networks.
dc.language eng
dc.relation.isbasedon 10.1260/1369-4332.13.6.1001
dc.title Dynamic-based damage identification using neural network ensembles and damage index method
dc.type Journal Article
dc.description.version Published
dc.parent Advances in Structural Engineering
dc.journal.volume 6
dc.journal.volume 13
dc.journal.number 6 en_US
dc.publocation United Kingdom en_US
dc.identifier.startpage 1001 en_US
dc.identifier.endpage 1016 en_US
dc.cauo.name FEIT.School of Civil and Environmental Engineering en_US
dc.conference Verified OK en_US
dc.conference 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)
dc.for 0905 Civil Engineering
dc.personcode 930859
dc.personcode 870186
dc.personcode 995216
dc.percentage 100 en_US
dc.classification.name Civil Engineering en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.date.activity 2010-12-07
dc.location.activity en_US
dc.location.activity Singapore, SINGAPORE
dc.description.keywords artificial neural network
dc.description.keywords damage identification
dc.description.keywords damage index method
dc.description.keywords modal strain energy
dc.description.keywords neural network ensemble
dc.description.keywords principal component analysis
dc.description.keywords structural health monitoring
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
pubs.consider-herdc true
utslib.collection.history General (ID: 2)

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