MACHINE LEARNING APPLICATIONS FOR CHLORIDE INGRESS PREDICTION IN CONCRETE: INSIGHTS FROM RECENT LITERATURE.

Chloride corrosion significantly impacts the durability of reinforced concrete (RC) structures. Traditional evaluation methods are time-consuming and expensive. Machine Learning (ML) offers a promising alternative, providing efficient and accurate predictions. This review explores recent ML advancements in assessing corrosion in RC structures. Various algorithms, such as Artificial Neural Networks (ANNs), Gene Expression Programming (GEP), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Ensemble Learning, have shown potential in estimating corrosion processes, predicting material properties, and evaluating structural durability. Future research should focus on integrating ML with physical models to enhance robustness and reliability in service life prediction. This review summarizes current trends, challenges, and the future potential of ML in predicting chloride ingress and its impact on concrete durability. [ABSTRACT FROM AUTHOR]