Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/41613
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dc.contributor.authorRodrigues, Fernandapt_PT
dc.contributor.authorCotella, Victoriapt_PT
dc.contributor.authorRodrigues, Hugopt_PT
dc.contributor.authorRocha, Eugéniopt_PT
dc.contributor.authorFreitas, Felipept_PT
dc.contributor.authorMatos, Raquelpt_PT
dc.date.accessioned2024-04-18T14:53:58Z-
dc.date.available2024-04-18T14:53:58Z-
dc.date.issued2022-
dc.identifier.urihttp://hdl.handle.net/10773/41613-
dc.description.abstractCurrently, there is extensive research focused on automatic strategies for the segmentation and classification of 3D point clouds, which can accelerate the study of a landmark and integrate it with heterogeneous data and attributes, useful to facilitate the digital management of architectural heritage data. In this work, an automated image-based survey has been exploited a Region- Based Convolutional Neural Network. The training phase has been executed providing examples of images with the anomalies to be detected. At the same time, a laser scanning process was conducted to obtain a point cloud, which acts as a reference for the BIM process. In a final step, a process of projecting information from the images onto the BIM recreates the pathology shapes on the model’s objects, which generates a decision support system for the built environment. The innovation of this research concerns the development of a workflow in which it is possible to automatize the recognition and classification of defects in historical buildings, to finally interpolate this geometric and numerical information with a BIM methodology, obtaining a representation and quantification of the information adapted to the facility management process. The use of innovative techniques such as artificial intelligence algorithms and different plug-ins becomes the main strength of this project.pt_PT
dc.language.isoengpt_PT
dc.publisherMDPIpt_PT
dc.relationinfo:eu-repo/grantAgreement/FCT/POR_CENTRO/SFRH%2FBD%2F147532%2F2019/PTpt_PT
dc.relationUIDB/ECI/04450/2020pt_PT
dc.rightsopenAccesspt_PT
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSurvey metadatapt_PT
dc.subjectR-CNN neural networkpt_PT
dc.subjectDatabase designpt_PT
dc.subjectCultural heritagept_PT
dc.subjectInformation managementpt_PT
dc.titleApplication of deep learning approach for the classification of buildings’ degradation state in a BIM methodologypt_PT
dc.typearticlept_PT
dc.description.versionpublishedpt_PT
dc.peerreviewedyespt_PT
degois.publication.issue15pt_PT
degois.publication.titleApplied Sciencespt_PT
degois.publication.volume12pt_PT
dc.identifier.doi10.3390/app12157403pt_PT
dc.identifier.essn2076-3417pt_PT
dc.identifier.articlenumber7403pt_PT
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FAAG - Artigos

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