DSpace Repository

RESTATE Project: Real-time decision support system for safety assessment of large concrete dams. The action cycle: Data-Information-Knowledge-Decision Making

Show simple item record

dc.contributor.author Mata, J. pt_BR
dc.date.accessioned 2022-04-01T10:20:54Z pt_BR
dc.date.accessioned 2022-04-08T09:05:44Z
dc.date.available 2022-04-01T10:20:54Z pt_BR
dc.date.available 2022-04-08T09:05:44Z
dc.date.issued 2017-07 pt_BR
dc.identifier.uri https://repositorio.lnec.pt/jspui/handle/123456789/1014789
dc.description.abstract To 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.iso eng pt_BR
dc.publisher University of Deusto Rovira i Virgili University pt_BR
dc.rights openAccess pt_BR
dc.subject Concrete dam pt_BR
dc.subject Decision support system pt_BR
dc.subject Machine Learning pt_BR
dc.title RESTATE Project: Real-time decision support system for safety assessment of large concrete dams. The action cycle: Data-Information-Knowledge-Decision Making pt_BR
dc.type conferenceObject pt_BR
dc.description.pages 1 pt_BR
dc.identifier.local Bilbao, Spain pt_BR
dc.description.sector DBB/NO pt_BR
dc.identifier.conftitle nternational Summer School on Deep Learning 2017 pt_BR
dc.contributor.peer-reviewed SIM pt_BR
dc.contributor.academicresearchers NAO pt_BR
dc.contributor.arquivo SIM pt_BR


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account