Active damage control using artificial intelligence: initial studies into identification and mitigation

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dc.contributor.authorKiel, David H.en
dc.contributor.committeechairRobertshaw, Harry H.en
dc.contributor.committeememberRogers, Craig A.en
dc.contributor.committeememberWicks, Alfred L.en
dc.contributor.departmentMechanical Engineeringen
dc.date.accessioned2014-03-14T21:41:33Zen
dc.date.adate2009-07-29en
dc.date.available2014-03-14T21:41:33Zen
dc.date.issued1993-06-05en
dc.date.rdate2009-07-29en
dc.date.sdate2009-07-29en
dc.description.abstractThis thesis presents an initial investigation into Active Damage Control (AD C) using Artificial Intelligence (AI). AI can alleviate the sometimes complicated task of modelling the system and also provides an adaptable solution process. The two research areas of ADC, damage identification and damage control, are studied in separate investigations. An AI technique called "rule induction" is used for the damage identification study. Velocity data from three plates (one without damage, one with damage at the center, and one with damage at the edge) are acquired using a laser data acquisition system. A set of rules is then induced from these data which accurately identifies which plates have damage and where the damage is located. With regard to the damage control, a real-time, machine-learning technique called "BOXES" is used to locally control the vibration of various systems by identifying their vibrational patterns. Using this technique, it is shown that the computer successfully learns an effective control law for various simulations using its trials and failures as the only learning information. It is also seen that the learning algorithm is somewhat less effective when experimentally applying this method to a pin-pin, aluminum beam. A discussion of possible improvements are presented in the future work section.en
dc.description.degreeMaster of Scienceen
dc.format.extentxi, 158 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-07292009-090316en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-07292009-090316/en
dc.identifier.urihttp://hdl.handle.net/10919/43980en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V855_1993.K554.pdfen
dc.relation.isformatofOCLC# 28912239en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V855 1993.K554en
dc.subject.lcshArtificial intelligenceen
dc.subject.lcshStructural control (Engineering)en
dc.titleActive damage control using artificial intelligence: initial studies into identification and mitigationen
dc.typeThesisen
dc.type.dcmitypeTexten
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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