Electrical Resistivity-Based Prediction of Corrosion-Affected Areas in Reinforced Concrete

This study investigates the development of a predictive model in simulations for assessing steel corrosion in determining corrosion-affected zones in reinforced concrete. A series of reinforced concrete cubes with varying degrees of corrosion were tested using a four-probe Wenner configuration. The experimental data showed a clear inverse relationship between ER and steel mass loss, with a strong negative correlation, highlighting the potential of ER as a corrosion indicator. A third-degree polynomial model was developed to predict the diameter of the corrosion-affected region based on steel mass loss and concrete cover, achieving high predictive accuracy. This model was validated using numerical simulation conducted in COMSOL Multiphysics, which replicated the experimental setup under steady-state conditions. Parametric studies further examined the effects of electrical conductivity (σ) and electrode spacing on the simulated results. The findings confirm that while σ has a moderate impact, electrode spacing significantly influences the measured ER values. The study underscores the importance of incorporating variable parameters into simulation models to improve the accuracy and field applicability of ER-based corrosion assessments. Furthermore, the simulation framework developed in this study demonstrates how numerical modeling can enhance the interpretive value of ER measurements, supporting the advancement of non-destructive testing techniques aimed at improving corrosion monitoring and maintenance strategies.