Exploring Machine Learning to Study and Predict the Chloride Threshold Level for Carbon Steel Reinforcement

Abstract

Chloride-induced corrosion of steel reinforcing bar (rebar) is the primary cause of deterioration in reinforced concrete structures, posing a significant infrastructure challenge. The chloride threshold level (CTL) of rebar, which represents the critical amount of chloride needed to initiate active corrosion, is crucial in corrosion and service life prediction models. However, substantial uncertainties and a multitude of influencing factors, along with the absence of a universally accepted testing framework, hinder the achievement of a consistent CTL range for service life models and complicate comparisons of published values. This study addresses these challenges by developing multiple machine learning models to predict CTL, considering 21 carefully selected features. A comprehensive database of 423 data points was compiled from an exhaustive literature review. Seven machine learning models—linear regression, decision tree, random forest, K-nearest neighbors, support vector machine, artificial neural network, and an ensemble model—were developed and optimized. The ensemble model achieved superior prediction performance, with a mean absolute error of 0.218% by weight of binder, root mean square error of 0.321%, and a coefficient of determination of 0.751 on unseen CTL data. Partial dependence plots generated using the support vector machine model quantified the effect of each feature on CTL. The random forest model identified SiO₂ binder content and exposed rebar area to chlorides as the most influential factors. The study also examined the impact of supplementary cementitious materials (SCMs), finding that only blast furnace slag positively affected CTL.