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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 Chi-squared interaction detection (CHAID) to predict the residual flexural capacity of corroded RC structures. The predicting results are compared to the surveyed data including 120 corroded RC beams from the projects built before 1975 to rank the efficiency of single models. Some combined models are applied to investigate the improvement in predicting the flexural capacity of corroded RC structures compared to the single models. The result shows that LR and GENLIN models give almost the same results and the best efficiency. The combined models can not improve the efficiency compared to the two best single models