Publication
Detection of exposed steel rebars based on deep-learning techniques and unmanned aerial vehicles
| dc.contributor.author | Santos, R. | |
| dc.contributor.author | Ribeiro, Diogo | |
| dc.contributor.author | Lopes, Patrícia | |
| dc.contributor.author | Cabral, R. | |
| dc.contributor.author | Calçada, R. | |
| dc.date.accessioned | 2022-12-21T12:09:50Z | |
| dc.date.available | 2022-12-21T12:09:50Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | In recent years deep-learning techniques have been developed and applied to inspect cracks in RC structures. The accuracy of these techniques leads to believe that they may also be applied to the identification of other pathologies. This article proposes a technique for automated detection of exposed steel rebars. The tools developed rely on convolutional neural networks (CNNs) based on transfer-learning using AlexNet. Experiments were conducted in large-scale structures to assess the efficiency of the method. To circumvent limitations on the proximity access to structures as large as the ones used in the experiments, as well as increase cost efficiency, the image capture was performed using an unmanned aerial system (UAS). The final goal of the proposed methodology is to generate orthomosaic maps of the pathologies or structure 3D models with superimposed pathologies. The results obtained are promising, confirming the high adaptability of CNN based methodologies for structural inspection. | pt_PT |
| dc.description.sponsorship | This work was financially supported by: Base Funding - UIDB/04708/2020 and Programmatic Funding - UIDP/04708/2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Construções funded by national funds through the FCT/MCTES (PIDDAC). Additionally, the author Rafael Cabral acknowledges the support provided by the doctoral grant UI/BD/150970/2021 - Portuguese Science Foundation, FCT/MCTES. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1016/j.autcon.2022.104324 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/21229 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Elsevier | pt_PT |
| dc.relation | Institute of R&D in Structures and Construction | |
| dc.relation | Institute of R&D in Structures and Construction | |
| dc.relation | Structural Integrity Self-Assessment Applied to Railway Assets based on Physical Twin Models | |
| dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0926580522001972 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Remote inspection | pt_PT |
| dc.subject | Reinforced concrete (RC) | pt_PT |
| dc.subject | Concrete structures | pt_PT |
| dc.subject | Exposed rebar | pt_PT |
| dc.subject | Unmanned aerial vehicles (UAVs) | pt_PT |
| dc.subject | Convolutional neural network (CNN) | pt_PT |
| dc.title | Detection of exposed steel rebars based on deep-learning techniques and unmanned aerial vehicles | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Institute of R&D in Structures and Construction | |
| oaire.awardTitle | Institute of R&D in Structures and Construction | |
| oaire.awardTitle | Structural Integrity Self-Assessment Applied to Railway Assets based on Physical Twin Models | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04708%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04708%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/POR_NORTE/UI%2FBD%2F150970%2F2021/PT | |
| oaire.citation.startPage | 104324 | pt_PT |
| oaire.citation.title | Automation in Construction | pt_PT |
| oaire.citation.volume | 139 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | POR_NORTE | |
| person.familyName | Ribeiro | |
| person.familyName | Lopes | |
| person.givenName | Diogo | |
| person.givenName | Patrícia | |
| person.identifier | 277594 | |
| person.identifier.ciencia-id | 2318-666E-AA75 | |
| person.identifier.ciencia-id | 7E1F-9D1D-2E42 | |
| person.identifier.orcid | 0000-0001-8624-9904 | |
| person.identifier.orcid | 0000-0001-7171-499X | |
| person.identifier.scopus-author-id | 24476782300 | |
| person.identifier.scopus-author-id | 25653663700 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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