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Identification of crack in a structural member using improved radial basis function (IRBF) neural networks

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cris.author.scopus-author-id56693009600
cris.author.scopus-author-id35976256700
dc.contributor.authorMachavaram, Rajendra
dc.contributor.authorKrishnapillai, Shankar
dc.date.accessioned2023-09-20T05:10:00Z
dc.date.available2023-09-20T05:10:00Z
dc.date.issued01-05-2013
dc.description.abstractPurpose: The purpose of this paper is to provide an effective and simple technique to structural damage identification, particularly to identify a crack in a structure. Artificial neural networks approach is an alternative to identify the extent and location of the damage over the classical methods. Radial basis function (RBF) networks are good at function mapping and generalization ability among the various neural network approaches. RBF neural networks are chosen for the present study of crack identification. Design/methodology/approach: Analyzing the vibration response of a structure is an effective way to monitor its health and even to detect the damage. A novel two-stage improved radial basis function (IRBF) neural network methodology with conventional RBF in the first stage and a reduced search space moving technique in the second stage is proposed to identify the crack in a cantilever beam structure in the frequency domain. Latin hypercube sampling (LHS) technique is used in both stages to sample the frequency modal patterns to train the proposed network. Study is also conducted with and without addition of 5% white noise to the input patterns to simulate the experimental errors. Findings: The results show a significant improvement in identifying the location and magnitude of a crack by the proposed IRBF method, in comparison with conventional RBF method and other classical methods. In case of crack location in a beam, the average identification error over 12 test cases was 0.69 per cent by IRBF network compared to 4.88 per cent by conventional RBF. Similar improvements are reported when compared to hybrid CPN BPN networks. It also requires much less computational effort as compared to other hybrid neural network approaches and classical methods. Originality/value: The proposed novel IRBF crack identification technique is unique in originality and not reported elsewhere. It can identify the crack location and crack depth with very good accuracy, less computational effort and ease of implementation. © Emerald Group Publishing Limited.
dc.identifier.doi10.1108/IJICC-May-2012-0025
dc.identifier.issn1756378X
dc.identifier.scopus2-s2.0-84880592440
dc.identifier.urihttps://apicris.irins.org/handle/IITM2023/37135
dc.relation.ispartofseriesInternational Journal of Intelligent Computing and Cybernetics
dc.sourceInternational Journal of Intelligent Computing and Cybernetics
dc.subjectBeams
dc.subjectCrack identification
dc.subjectFrequency domain
dc.subjectImproved radial basis function neural networks
dc.subjectLatin hypercube sampling
dc.subjectMechanical behaviour of materials
dc.subjectReduced search space moving technique
dc.subjectStress (materials)
dc.subjectStructural damage
dc.subjectStructural members
dc.subjectStructures
dc.titleIdentification of crack in a structural member using improved radial basis function (IRBF) neural networks
dc.typeJournal
dspace.entity.typePublication
oaire.citation.endPage211
oaire.citation.issue2
oaire.citation.startPage182
oaire.citation.volume6
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
person.affiliation.cityChennai
person.affiliation.id60025757
person.affiliation.nameIndian Institute of Technology Madras
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