Classification of corrosion risk zones using GIS

Nikolas Georgiou; Constantina Anastasiou; Elia A. Tantele; Renos A. Votsis; Chris Danezis

doi:10.1117/12.2239553

12 August 2016 Classification of corrosion risk zones using GIS

Nikolas Georgiou, Constantina Anastasiou, Elia A. Tantele, Renos A. Votsis, Chris Danezis

Proceedings Volume 9688, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016); 968825 (2016) https://doi.org/10.1117/12.2239553
Event: Fourth International Conference on Remote Sensing and Geoinformation of the Environment, 2016, Paphos, Cyprus

Abstract

Corrosion of steel reinforcement is the major deterioration factor of the RC infrastructures. Several factors are contributing towards increasing the corrosion risk like the exposure and environmental conditions which are a function of the geographical location of the infrastructure. Information for these conditions and their affected areas can be proved valuable at design stage and/or during maintenance planning. This study aims to relate corrosion risk of RC infrastructures with their geographical location. The corrosion risk is quantified through data from NDT methods and subsequently correlated with its location. Therefore high risk areas with structures prone to corrosion deterioration are identified. The latter is implemented via GIS tools in order to create maps that describe how corrosion risk is related to the location of each structure. Two GIS methods are suggested, the grid system and the use of classified areas. Corrosion data has been collected from labs about various constructions in Cyprus and used in conjunction with GIS tools to provide useful information on corrosion identification. The outcome is a digitized map of the Limassol area which indicates the risks levels associated with corrosion of the steel reinforcement.

Citation Download Citation

Nikolas Georgiou, Constantina Anastasiou, Elia A. Tantele, Renos A. Votsis, and Chris Danezis "Classification of corrosion risk zones using GIS", Proc. SPIE 9688, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016), 968825 (12 August 2016); https://doi.org/10.1117/12.2239553

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