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dc.contributor.advisorOtero, Luis Daniel
dc.contributor.authorAvendano Arbelaez, Juan Camilo
dc.date.accessioned2020-12-21T16:06:45Z
dc.date.available2020-12-21T16:06:45Z
dc.date.created2020-12
dc.date.issued2020-12
dc.date.submittedDecember 2020
dc.identifier.urihttp://hdl.handle.net/11141/3213
dc.descriptionThesis (Ph.D.) - Florida Institute of Technology, 2020.en_US
dc.description.abstractThis dissertation presents the design and development of a structural health monitoring (SHM) system specifically tailored for transportation infrastructure components, such as bridges. The proposed system collects data by using contactless sensors and performs health characterization and failure prediction. It is capable of simulating multiple load conditions on structures, identifying possible failure points, and detecting and predicting failure scenarios. Both hardware and software implementations of a model of a bridge were performed as a pilot project in order to validate the proposed system. Computer simulation in ANSYS and the application of gradient boosting neural networks were performed to produce a comparative and predictive analysis of the behavior of transportation infrastructures, which can be used to understand the health of the structure and make informed decisions.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.rightsCopyright held by author.en_US
dc.titleDevelopment of a Deformation-Based Structural Health System with Contactless Sensors and Machine Learning for Health Characterization and Failure Predictionen_US
dc.typeDissertationen_US
dc.date.updated2020-12-16T14:47:59Z
thesis.degree.nameDoctor of Philosophy in Systems Engineeringen_US
thesis.degree.levelDoctoralen_US
thesis.degree.disciplineSystems Engineeringen_US
thesis.degree.departmentComputer Engineering and Sciencesen_US
thesis.degree.grantorFlorida Institute of Technologyen_US
dc.type.materialtext


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