Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21579
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dc.contributor.authorAkinosho, T-
dc.contributor.authorOyedele, L-
dc.contributor.authorBilal, M-
dc.contributor.authorAjayi, A-
dc.contributor.authorDelgado, M-
dc.contributor.authorAkinade, O-
dc.contributor.authorAhmed, A-
dc.date.accessioned2020-09-16T00:33:07Z-
dc.date.available2020-09-16T00:33:07Z-
dc.date.issued2020-
dc.identifier.issn2352-7102-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/21579-
dc.description.sponsorshipEPSRC; InnovateUKen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectDeep learningen_US
dc.subjectConstruction Industryen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectAutoencodersen_US
dc.subjectGenerative Adversarial Networksen_US
dc.titleDeep Learning in the Construction Industry: A Review of Present Status and Future Innovationsen_US
dc.typeArticleen_US
dc.relation.isPartOfJournal of Building Engineering-
pubs.publication-statusAccepted-
Appears in Collections:Dept of Mechanical Aerospace and Civil Engineering Embargoed Research Papers

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