Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/14807
Full metadata record
DC FieldValueLanguage
dc.contributor.authorEdward C.
dc.contributor.authorBalu A.S.
dc.date.accessioned2021-05-05T10:15:48Z-
dc.date.available2021-05-05T10:15:48Z-
dc.date.issued2020
dc.identifier.citationIOP Conference Series: Materials Science and Engineering , Vol. 936 , 1 , p. -en_US
dc.identifier.urihttps://doi.org/10.1088/1757-899X/936/1/012044
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/14807-
dc.description.abstractColumns are one of the most vital segments in bridgessince its post-seismic behaviour is of much importance. The retrofitting methods and rehabilitation strategies of bridges mainly rely on the identification of the failure mode of columns. It has been witnessed in various studies on columns that the mode of failure highly depends on section and material properties and there is no specific boundary between the modes, which makes their identification more sophisticated. This paper uses an artificial neural network to predict the modes of failure by analysing the effects of such soft computing methods. In this study, machine- learning models were generated from the experimental data of 253 columns of rectangular cross-section and its accuracy of failure mode prediction was evaluated by considering failure modes mainly flexure, flexure-shear, and shear. The optimal input parameters have also been evaluated for the machine-learning algorithm that enhances the efficiency of failure mode prediction. © Published under licence by IOP Publishing Ltd.en_US
dc.titleFailure Mode Recognition of Columns Using Artificial Neural Networken_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.


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