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Purpose – This work seeks to present a systematic study that aimed to provide quantitative understanding of the fundamental factors that influence the chloride threshold of pitting corrosion of steel in concrete, by conducting a set of laboratory tests to assess the corrosion potential (E corr ) and pitting potential (E pit ) of steel coupons in simulated concrete pore solutions. Design/methodology/approach – With the aid of artificial neural network, the laboratory data were used to establish a phenomenological model correlating the influential factors (total chloride concentration, chloride binding, solution pH, and dissolved oxygen (DO) concentration) with the pitting risk (characterized by E corr −E pit ). Three‐dimensional response surfaces were then constructed to illustrate such predicted correlations and to shed light on the complex interactions between various influential factors. Findings – The results indicate that the threshold (Cl − )/(OH − ) of steel rebar in simulated concrete pore solutions is a function of DO concentration, pH and chloride binding, instead of a unique value. Research limitations/implications – The limitations and implications of the research findings were also discussed. Practical implications – This research could have significant practical implications in predicting the service life of new or existing reinforced concrete in chloride‐laden environments. Originality/value – This study further advances the knowledge base relevant to the chloride‐induced corrosion of steel rebar in concrete.
Anti-Corrosion Methods and Materials – Emerald Publishing
Published: Jun 28, 2011
Keywords: Corrosion; Modelling; Predictive process; Concrete; Steel
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