Issue |
MATEC Web Conf.
Volume 364, 2022
International Conference on Concrete Repair, Rehabilitation and Retrofitting (ICCRRR 2022)
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Article Number | 03006 | |
Number of page(s) | 8 | |
Section | Condition Assessment of Concrete Structures - Degradation and Condition Assessment | |
DOI | https://doi.org/10.1051/matecconf/202236403006 | |
Published online | 30 September 2022 |
Evaluation of corrosion damage in reinforced concrete structures in terms of the rebar’s residual cross-section
School of Civil and Environmental Engineering, University of the Witwatersrand, Johannesburg, South Africa
* Corresponding author: victor.gilayeneh1@students.wits.ac.za
Corroding reinforced concrete structures are frequently assessed to determine the rate of corrosion propagation and the level of deterioration, which might compromise the structure’s reliability. Appropriate measures should be considered in deciding when and how to implement maintenance if safety must be ensured. However, the influential factor that governs or informs such decisions is corrosion damage quantification. Nonetheless, the current non-destructive methods of corrosion damage quantification often lead to ambiguity, and most do not evaluate corrosion damage in terms of the rebar’s cross-section loss, which is the primary effect of corrosion. To address these shortcomings, this paper proposes an effective, reliable, and less-complicated model for quantifying corrosion damage in reinforced concrete structures based on the cross-sectional area of the corroding bar. The study was conducted through experimental (laboratory-based) and numerical investigations of the relationship between the level of corrosion and the corrosion-induced crack width. Based on the investigations’ findings, appropriate relationships and essential parameters were identified, and the model was derived analytically. The derived model assesses corrosion damage in terms of the corroding bar’s residual cross-section and requires only a few input parameters, which can be obtained by non-destructive testing if not known. The model was tested against data obtained from the laboratory experiment and against other experimental and analytical data from the literature, and the results showed a good correlation.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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