6 September 2017 Investigation on the thermographic detection of corrosion in RC structures
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Proceedings Volume 10444, Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017); 104441M (2017) https://doi.org/10.1117/12.2281183
Event: Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017), 2017, Paphos, Cyprus
Abstract
Corrosion of the steel reinforcement is the main problem of reinforced concrete (RC) structures. Over the past decades, several methods have been developed aiming to detect the corrosion process early in order to minimise the structural damage and consequently the repairing costs. Emphasis was given in developing methods and techniques of non-destructive nature providing fast on-the-spot detection and covering large areas rather that concentrating on single locations. This study, investigates a non-destructive corrosion detection technique for reinforced concrete, which is based on infrared thermography and the difference in thermal characteristics of corroded and non-corroded steel rebars. The technique is based on the principle that corrosion products have poor heat conductivity, and they inhibit the diffusion of heat that is generated in the reinforcing bar due to heating. For the investigation RC specimens, have been constructed in the laboratory using embedded steel bars of different corrosion states. Afterward, one surface of the specimens was heated using an electric device while thermal images were captured at predefined time instants on the opposite surface with an IR camera. The test results showed a clear difference between the thermal characteristics of the corroded and the non-corroded samples, which demonstrates the potential of using thermography in corrosion detection in RC structures.
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Elia A. Tantele, Renos A. Votsis, Nicholas Kyriakides, Panagiota G. Georgiou, Fotia G. Ioannou, "Investigation on the thermographic detection of corrosion in RC structures", Proc. SPIE 10444, Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017), 104441M (6 September 2017); doi: 10.1117/12.2281183; https://doi.org/10.1117/12.2281183
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