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dc.contributor.authorMata, J.pt_BR
dc.date.accessioned2022-04-01T10:20:54Zpt_BR
dc.date.accessioned2022-04-08T09:05:44Z-
dc.date.available2022-04-01T10:20:54Zpt_BR
dc.date.available2022-04-08T09:05:44Z-
dc.date.issued2017-07pt_BR
dc.identifier.urihttps://repositorio.lnec.pt/jspui/handle/123456789/1014789-
dc.description.abstractTo show how quality control of data, statistics, Machine Learning, and Artificial Intelligence will change Infrastructure Safety and Risk Management and Public Safety Forever is expected with the RESTATE project. This work aims to develop methodologies and procedures to support decision-making for the timely safety control of large infrastructures under operating conditions. This project aims to address new methodologies based on Deep Learning to create value in three main activities: quality control of monitoring data, analysis and interpretation of the structural behaviour, and safety assessment.pt_BR
dc.language.isoengpt_BR
dc.publisherUniversity of Deusto Rovira i Virgili Universitypt_BR
dc.rightsopenAccesspt_BR
dc.subjectConcrete dampt_BR
dc.subjectDecision support systempt_BR
dc.subjectMachine Learningpt_BR
dc.titleRESTATE Project: Real-time decision support system for safety assessment of large concrete dams. The action cycle: Data-Information-Knowledge-Decision Makingpt_BR
dc.typeconferenceObjectpt_BR
dc.description.pages1pt_BR
dc.identifier.localBilbao, Spainpt_BR
dc.description.sectorDBB/NOpt_BR
dc.identifier.conftitlenternational Summer School on Deep Learning 2017pt_BR
dc.contributor.peer-reviewedSIMpt_BR
dc.contributor.academicresearchersNAOpt_BR
dc.contributor.arquivoSIMpt_BR
Appears in Collections:DBB/NO - Comunicações a congressos e artigos de revista

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