Dynamic-Based Damage Identification Using Neural Network Ensembles and Damage Index Method

UTSePress Research/Manakin Repository

Search UTSePress Research

Advanced Search


My Account

Show simple item record

dc.contributor.author Dackermann, Ulrike en_US
dc.contributor.author Li, Jianchun en_US
dc.contributor.author Samali, Bijan en_US
dc.contributor.editor en_US
dc.date.accessioned 2011-02-07T06:23:34Z
dc.date.available 2011-02-07T06:23:34Z
dc.date.issued 2010 en_US
dc.identifier 2010000170 en_US
dc.identifier.citation Dackermann Ulrike, Li Jianchun, and Samali Bijan 2010, 'Dynamic-Based Damage Identification Using Neural Network Ensembles and Damage Index Method', Multi Science Publishing, vol. 13, no. 6, pp. 1001-1016. en_US
dc.identifier.issn 1369-4332 en_US
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. en_US
dc.language en_US
dc.publisher Multi Science Publishing en_US
dc.relation.isbasedon http://dx.doi.org/10.1260/1369-4332.13.6.1001 en_US
dc.title Dynamic-Based Damage Identification Using Neural Network Ensembles and Damage Index Method en_US
dc.parent Advances In Structural Engineering en_US
dc.journal.volume 13 en_US
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.for 090500 en_US
dc.personcode 995216 en_US
dc.personcode 930859 en_US
dc.personcode 870186 en_US
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.location.activity en_US
dc.description.keywords damage identification, artificial neural network, neural network ensemble, structural health monitoring, damage index method, modal strain energy, principal component analysis en_US
dc.staffid en_US
dc.staffid 870186 en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record