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http://hdl.handle.net/10773/41613
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DC Field | Value | Language |
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dc.contributor.author | Rodrigues, Fernanda | pt_PT |
dc.contributor.author | Cotella, Victoria | pt_PT |
dc.contributor.author | Rodrigues, Hugo | pt_PT |
dc.contributor.author | Rocha, Eugénio | pt_PT |
dc.contributor.author | Freitas, Felipe | pt_PT |
dc.contributor.author | Matos, Raquel | pt_PT |
dc.date.accessioned | 2024-04-18T14:53:58Z | - |
dc.date.available | 2024-04-18T14:53:58Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://hdl.handle.net/10773/41613 | - |
dc.description.abstract | Currently, 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.iso | eng | pt_PT |
dc.publisher | MDPI | pt_PT |
dc.relation | info:eu-repo/grantAgreement/FCT/POR_CENTRO/SFRH%2FBD%2F147532%2F2019/PT | pt_PT |
dc.relation | UIDB/ECI/04450/2020 | pt_PT |
dc.rights | openAccess | pt_PT |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | pt_PT |
dc.subject | Survey metadata | pt_PT |
dc.subject | R-CNN neural network | pt_PT |
dc.subject | Database design | pt_PT |
dc.subject | Cultural heritage | pt_PT |
dc.subject | Information management | pt_PT |
dc.title | Application of deep learning approach for the classification of buildings’ degradation state in a BIM methodology | pt_PT |
dc.type | article | pt_PT |
dc.description.version | published | pt_PT |
dc.peerreviewed | yes | pt_PT |
degois.publication.issue | 15 | pt_PT |
degois.publication.title | Applied Sciences | pt_PT |
degois.publication.volume | 12 | pt_PT |
dc.identifier.doi | 10.3390/app12157403 | pt_PT |
dc.identifier.essn | 2076-3417 | pt_PT |
dc.identifier.articlenumber | 7403 | pt_PT |
Appears in Collections: | CIDMA - Artigos FAAG - Artigos |
Files in This Item:
File | Description | Size | Format | |
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applsci-12-07403-v2.pdf | 25.8 MB | Adobe PDF | View/Open |
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