A New Methodology for Bridge Inspections in Linear Infrastructures from Optical Images and HD Videos Obtained by UAV
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http://hdl.handle.net/10045/122031
Títol: | A New Methodology for Bridge Inspections in Linear Infrastructures from Optical Images and HD Videos Obtained by UAV |
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Autors: | Cano, Miguel | Pastor Navarro, José Luis | Tomás, Roberto | Riquelme, Adrián | Asensio, José Luis |
Grups d'investigació o GITE: | Ingeniería del Terreno y sus Estructuras (InTerEs) |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Ingeniería Civil |
Paraules clau: | Maintenance of linear infrastructures | UAV | Remote inspection | Damage structures | Bridge |
Àrees de coneixement: | Ingeniería del Terreno |
Data de publicació: | 3-de març-2022 |
Editor: | MDPI |
Citació bibliogràfica: | Cano M, Pastor JL, Tomás R, Riquelme A, Asensio JL. A New Methodology for Bridge Inspections in Linear Infrastructures from Optical Images and HD Videos Obtained by UAV. Remote Sensing. 2022; 14(5):1244. https://doi.org/10.3390/rs14051244 |
Resum: | Many bridges and other structures worldwide present a lack of maintenance or a need for rehabilitation. The first step in the rehabilitation process is to perform a bridge inspection to know the bridge′s current state. Routine bridge inspections are usually based only on visual recognition. In this paper, a methodology for bridge inspections in communication routes using images acquired by unmanned aerial vehicle (UAV) flights is proposed. This provides access to the upper parts of the structure safely and without traffic disruptions. Then, a standardized and systematized novel image acquisition protocol is applied for data acquisition. Afterwards, the images are studied by civil engineers for damage identification and description. Then, specific structural inspection forms are completed using the acquired information. Recommendations about the need of new and more detailed inspections should be included at this stage when needed. The suggested methodology was tested on two railway bridges in France. Image acquisition of these structures was performed using an UAV for its ability to provide an expert assessment of the damage level. The main advantage of this method is that it makes it possible to safely accurately identify diverse damages in structures without the need for a specialised engineer to go to the site. Moreover, the videos can be watched by as many engineers as needed with no personal movement. The main objective of this work is to describe the systematized methodology for the development of bridge inspection tasks using a UAV system. According to this proposal, the in situ inspection by a specialised engineer is replaced by images and videos obtained from an UAV flight by a trained flight operator. To this aim, a systematized image/videos acquisition method is defined for the study of the morphology and typology of the structural elements of the inspected bridges. Additionally, specific inspection forms are proposed for every type of structural element. The recorded information will allow structural engineers to perform a postanalysis of the damage affecting the bridges and to evaluate the subsequent recommendations. |
Patrocinadors: | This research was funded by the Shift2Rail Joint Undertaking under the European Union’s Horizon 2020 research and innovation program, with grant agreement No 777630, project MOMIT, “Multiscale Observation and Monitoring of railway Infrastructure Threats”. |
URI: | http://hdl.handle.net/10045/122031 |
ISSN: | 2072-4292 |
DOI: | 10.3390/rs14051244 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/article |
Drets: | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Revisió científica: | si |
Versió de l'editor: | https://doi.org/10.3390/rs14051244 |
Apareix a la col·lecció: | INV - INTERES - Artículos de Revistas Investigacions finançades per la UE |
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Cano_etal_2022_RemoteSens.pdf | 11,44 MB | Adobe PDF | Obrir Vista prèvia | |
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