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Corrosion is a common deterioration that reduces the service life of concrete structures and steels. Particularly, corrosion behavior is a highly nonlinear problem influenced by complex characteristics. This study used advanced artificial intelligence (AI) techniques to predict pitting corrosion risk of steel reinforced concrete and marine corrosion rate of carbon steel. The AI-based models used for prediction included single and ensemble models constructed from four well-known machine learners including artificial neural networks (ANNs), support vector regression/machines (SVR/SVMs), classification and regression tree (CART), and linear regression (LR). Notably, a hybrid metaheuristic regression model was implemented by integrating a smart nature-inspired metaheuristic optimization algorithm (i.e., smart firefly algorithm) with a least squares SVR. Prediction accuracy was evaluated using two real-world datasets …