Predicting the flexural capacity of corroded reinforced concrete beams using artificial intelligence models

Predicting the residual flexural capacity of corroded reinforced concrete (RC) structures is to help civil engineers decide to repair or strengthen the structures. This study presents the application of six single algorithm-based models of artificial intelligence, such as artificial neural network (ANN), support vector machine (SVM), classification and regression trees (CART), linear regression (LR), general linear model (GENLIN), and automatic Chisquared interaction detection (CHAID) to predict the residual flexural capacity of corroded RC structures.
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Predicting the flexural capacity of corroded reinforced concrete beams using artificial intelligence models