dc.creatorAyele, Yonas Zewdu
dc.creatorAliyari, Mostafa
dc.creatorGriffiths, David
dc.creatorLópez Droguett, Enrique
dc.date.accessioned2021-06-09T20:08:35Z
dc.date.available2021-06-09T20:08:35Z
dc.date.created2021-06-09T20:08:35Z
dc.date.issued2020
dc.identifierEnergies 2020, 13, 6250
dc.identifier10.3390/en13236250
dc.identifierhttps://repositorio.uchile.cl/handle/2250/180068
dc.description.abstractBridges are a critical piece of infrastructure in the network of road and rail transport system. Many of the bridges in Norway (in Europe) are at the end of their lifespan, therefore regular inspection and maintenance are critical to ensure the safety of their operations. However, the traditional inspection procedures and resources required are so time consuming and costly that there exists a significant maintenance backlog. The central thrust of this paper is to demonstrate the significant benefits of adapting a Unmanned Aerial Vehicle (UAV)-assisted inspection to reduce the time and costs of bridge inspection and established the research needs associated with the processing of the (big) data produced by such autonomous technologies. In this regard, a methodology is proposed for analysing the bridge damage that comprises three key stages, (i) data collection and model training, where one performs experiments and trials to perfect drone flights for inspection using case study bridges to inform and provide necessary (big) data for the second key stage, (ii) 3D construction, where one built 3D models that offer a permanent record of element geometry for each bridge asset, which could be used for navigation and control purposes, (iii) damage identification and analysis, where deep learning-based data analytics and modelling are applied for processing and analysing UAV image data and to perform bridge damage performance assessment. The proposed methodology is exemplified via UAV-assisted inspection of Skodsberg bridge, a 140 m prestressed concrete bridge, in the Viken county in eastern Norway.
dc.languageen
dc.publisherMDPI
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile
dc.sourceEnergies
dc.subjectDrone-assisted bridge inspection
dc.subjectCrack detection
dc.subjectCrack segmentation
dc.subjectDamage assessment
dc.subjectUAV
dc.subjectPerformance analysis
dc.titleAutomatic Crack Segmentation for UAV-Assisted Bridge Inspection
dc.typeArtículo de revista


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