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dc.contributor.authorVallamsundar, Suriyapriya
dc.date.accessioned2007-09-24 18:21:51 (GMT)
dc.date.available2007-09-24 18:21:51 (GMT)
dc.date.issued2007-09-24T18:21:51Z
dc.date.submitted2007
dc.identifier.urihttp://hdl.handle.net/10012/3289
dc.description.abstractNondestructive testing is used extensively throughout the industry for quality assessment and detection of defects in engineering materials. The range and variety of anomalies is enormous and critical assessment of their location and size is often complicated. Depending upon final operational considerations, some of these anomalies may be critical and their detection and classification is therefore of importance. Despite the several advantages of using Nondestructive testing for flaw detection, the conventional NDT techniques based on the heuristic experience-based pattern identification methods have many drawbacks in terms of cost, length and result in erratic analysis and thus lead to discrepancies in results. The use of several statistical and soft computing techniques in the evaluation and classification operations result in the development of an automatic decision support system for defect characterization that offers the possibility of an impartial standardized performance. The present work evaluates the application of both supervised and unsupervised classification techniques for flaw detection and classification in a semi-infinite half space. Finite element models to simulate the MASW test in the presence and absence of voids were developed using the commercial package LS-DYNA. To simulate anomalies, voids of different sizes were inserted on elastic medium. Features for the discrimination of received responses were extracted in time and frequency domains by applying suitable transformations. The compact feature vector is then classified by different techniques: supervised classification (backpropagation neural network, adaptive neuro-fuzzy inference system, k-nearest neighbor classifier, linear discriminate classifier) and unsupervised classification (fuzzy c-means clustering). The classification results show that the performance of k-nearest Neighbor Classifier proved superior when compared with the other techniques with an overall accuracy of 94% in detection of presence of voids and an accuracy of 81% in determining the size of the void in the medium. The assessment of the various classifiers’ performance proved to be valuable in comparing the different techniques and establishing the applicability of simplified classification methods such as k-NN in defect characterization. The obtained classification accuracies for the detection and classification of voids are very encouraging, showing the suitability of the proposed approach to the development of a decision support system for non-destructive testing of materials for defect characterization.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectNon-destructive testingen
dc.subjectflaw detectionen
dc.subjectfinite element modelingen
dc.subjectLS-DYNAen
dc.subjectclassification techniquesen
dc.subjecttools of artificial intelligenceen
dc.subjectneural networken
dc.subjectfuzzy logicen
dc.subjectlinear discriminate analysisen
dc.subjectk nearest neighboren
dc.subjectRayleigh wavesen
dc.subjectLamb sourceen
dc.subjectconfusion matrixen
dc.subjectseismic wavesen
dc.titleNumerical Evaluation of Classification Techniques for Flaw Detectionen
dc.typeMaster Thesisen
dc.pendingfalseen
dc.subject.programCivil Engineeringen
uws-etd.degree.departmentCivil and Environmental Engineeringen
uws-etd.degreeMaster of Applied Scienceen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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