EVENT-DRIVEN OPERATION OF DISTRIBUTED SYSTEMS WITH ARTIFICIAL INTELLIGENCE TECHNOLOGIES AND BEHAVIOR MODELING

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dc.contributor.advisorAustin, Mark Aen_US
dc.contributor.authorMontezzo Coelho, Maria Eduardaen_US
dc.contributor.departmentCivil Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2022-06-15T05:37:10Z
dc.date.available2022-06-15T05:37:10Z
dc.date.issued2022en_US
dc.description.abstractThis dissertation aims to enhance decision making in urban settings by integrating artificial intelligence technologies with distributed behavior modeling. Today’s civil engineering systems are far more heterogeneous than their predecessors and may be connected to other types of systems in completely new ways, making the task of system design, analysis and integration of multi-disciplinary concerns much more difficult than in the past. These challenges can be addressed by combining machine learning formalisms and semantic model representations of urban systems, that work side-by-side in collecting data, identifying events, and managing city operations in real-time. We exercise the proposed approach on a problem involving anomaly detection in an urbanwater distribution system and a metrorail system.en_US
dc.identifierhttps://doi.org/10.13016/3as4-uo8k
dc.identifier.urihttp://hdl.handle.net/1903/28730
dc.language.isoenen_US
dc.subject.pqcontrolledCivil engineeringen_US
dc.subject.pqcontrolledArtificial intelligenceen_US
dc.subject.pqcontrolledOperations researchen_US
dc.subject.pquncontrolledAnomaly Detectionen_US
dc.subject.pquncontrolledDigital Twinsen_US
dc.subject.pquncontrolledGraph Autoencodersen_US
dc.subject.pquncontrolledMachine Learningen_US
dc.subject.pquncontrolledSemantic Modelingen_US
dc.subject.pquncontrolledUrban Operationsen_US
dc.titleEVENT-DRIVEN OPERATION OF DISTRIBUTED SYSTEMS WITH ARTIFICIAL INTELLIGENCE TECHNOLOGIES AND BEHAVIOR MODELINGen_US
dc.typeDissertationen_US

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