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dc.rights.licenseopenen_US
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorBAUDRIT, Cedric
IDREF: 095921443
dc.contributor.authorTAILLANDIER, Franck
dc.contributor.authorCURT, Corinne
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorHOANG, Q. A.
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorSBARTAÏ, Zoubir-Mehdi
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorBREYSSE, Denys
dc.date.accessioned2022-03-09T09:10:25Z
dc.date.available2022-03-09T09:10:25Z
dc.date.issued2022-04
dc.identifier.issn1474-0346en_US
dc.identifier.urioai:crossref.org:10.1016/j.aei.2022.101551
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/136396
dc.description.abstractEnThe capitalization and the analysis of historical information is nowadays a prerequisite for any effective risk management and assessment in a wide range of domains. Despite the development of mathematical models, procedures, support decision systems and databases, some engineering disciplines, such as civil engineering, remain resistant to the use of new digital technology due to the gap between the expectations of the engineers and the support that the tools may really provide. It is essential to propose a tool able to process both cross disciplinary and interdisciplinary knowledge flux and feedback from experience in a common and convenient unifying framework. The aim is to assist and to support engineering work and to make the task of knowledge modelling easier. The domain of dam systems is no exception to the rule. Dam failures are still commonplace. These failures stem from a lack of understanding about the complex relationships between three different factors: random hazards, the limit states of dam structures along with human activities and decisions. No generic and holistic approach is currently available that permits the processing of both knowledge and data, performs inferences and is easily usable for all types of users. This paper proposes the basic principles of a convenient design methodology for capitalizing, learning and predicting based on the formalism of conceptual graphs. The aim is to provide an easily usable tool able to (1) capitalise heterogeneous knowledge and store a database about dams, (2) issue alerts on current projects, (3) draw lessons from past dam failures and (4) tackle key issues in forensic civil engineering.
dc.language.isoENen_US
dc.sourcecrossref
dc.subject.enKnowledge representation
dc.subject.enConceptual graph
dc.subject.enDam failure
dc.subject.enForensic engineering
dc.title.enGraph based knowledge models for capitalizing, predicting and learning: A proof of concept applied to the dam systems
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/j.aei.2022.101551en_US
dc.subject.halSciences de l'ingénieur [physics]/Matériauxen_US
bordeaux.journalAdvanced Engineering Informaticsen_US
bordeaux.page101551en_US
bordeaux.volume52en_US
bordeaux.hal.laboratoriesInstitut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionCNRSen_US
bordeaux.institutionINRAEen_US
bordeaux.institutionArts et Métiersen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcedissemin
hal.identifierhal-03602408
hal.version1
hal.date.transferred2022-03-09T09:10:27Z
hal.exporttrue
workflow.import.sourcedissemin
dc.rights.ccPas de Licence CCen_US
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