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Autonomous corrosion assessment of reinforced concrete structures : Feasibility study

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Taffese, Woubishet Zewdu
dc.contributor.author Nigussie, Ethiopia
dc.date.accessioned 2020-12-31T08:37:39Z
dc.date.available 2020-12-31T08:37:39Z
dc.date.issued 2020-12-01
dc.identifier.citation Taffese , W Z & Nigussie , E 2020 , ' Autonomous corrosion assessment of reinforced concrete structures : Feasibility study ' , Sensors (Switzerland) , vol. 20 , no. 23 , 6825 , pp. 1-25 . https://doi.org/10.3390/s20236825 en
dc.identifier.issn 1424-8220
dc.identifier.other PURE UUID: 1ad159dc-2c82-45be-932e-b9394b058182
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/1ad159dc-2c82-45be-932e-b9394b058182
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85096938679&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/53823180/sensors_20_06825_v2.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/101415
dc.description.abstract In this work, technological feasibility of autonomous corrosion assessment of reinforced concrete structures is studied. Corrosion of reinforcement bars (rebar), induced by carbonation or chloride penetration, is one of the leading causes for deterioration of concrete structures throughout the globe. Continuous nondestructive in-service monitoring of carbonation through pH and chloride ion (Cl−) concentration in concrete is indispensable for early detection of corrosion and making appropriate decisions, which ultimately make the lifecycle management of RC structures optimal from resources and safety perspectives. Critical state-of-the-art review of pH and Cl− sensors revealed that the majority of the sensors have high sensitivity, reliability, and stability in concrete environment, though the experiments were carried out for relatively short periods. Among the reviewed works, only three attempted to monitor Cl− wirelessly, albeit over a very short range. As part of the feasibility study, this work recommends the use of internet of things (IoT) and machine learning for autonomous corrosion condition assessment of RC structures. en
dc.format.extent 25
dc.format.extent 1-25
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Multidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofseries Sensors (Switzerland) en
dc.relation.ispartofseries Volume 20, issue 23 en
dc.rights openAccess en
dc.title Autonomous corrosion assessment of reinforced concrete structures : Feasibility study en
dc.type A2 Katsausartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Building Design and Construction
dc.contributor.department University of Turku
dc.contributor.department Department of Civil Engineering en
dc.subject.keyword Autonomous corrosion assessment
dc.subject.keyword Corrosion
dc.subject.keyword Deep learning
dc.subject.keyword Intelligent data analytics
dc.subject.keyword Internet of things
dc.subject.keyword Machine learning
dc.subject.keyword Reinforced concrete
dc.subject.keyword Sensors
dc.identifier.urn URN:NBN:fi:aalto-2020123160236
dc.identifier.doi 10.3390/s20236825
dc.type.version publishedVersion

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