Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/9309
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTripathy, Rashmi Ranjan-
dc.contributor.authorMaity, Damodar-
dc.date.accessioned2010-06-01T06:34:05Z-
dc.date.available2010-06-01T06:34:05Z-
dc.date.issued2004-10-
dc.identifier.issn0975-1017 (Online); 0971-4588 (Print)-
dc.identifier.urihttp://hdl.handle.net/123456789/9309-
dc.description369-377en_US
dc.description.abstractThis paper presents a neural network based approach to detect and assess the structural damage. The basic strategy applied in this study is to train a neural network to recognize the behaviour of the undamaged structure as well as the structure with various possible damaged states. Curvature damage factor (CDF) is used as a possible candidate for the damage identification by error back-propagation training algorithm (EBPTA). When this trained network is subjected to the measured response, it should be able to detect any existing damage. This idea is applied on a cantilever beam and a plane frame. The results show the efficiency of the developed algorithm.en_US
dc.language.isoen_USen_US
dc.publisherCSIRen_US
dc.relation.ispartofseriesInt. Cl.7 G 06 N 3/06en_US
dc.sourceIJEMS Vol.11(5) [October 2004]en_US
dc.titleDamage assessment of structures from changes in curvature damage factor using artificial neural networken_US
dc.typeArticleen_US
Appears in Collections:IJEMS Vol.11(5) [October 2004]

Files in This Item:
File Description SizeFormat 
IJEMS 11(5) 369-377.pdf427.52 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.