Predicting carbonation depth of concrete using a hybrid ensemble model

This study aims to develop a robust and accurate machine learning-based model to improve the prediction accuracy of carbonation depth in complex concrete structures. Two hybrid ensemble methods, the inverse variance method and the artificial neural network-based ensemble method, were proposed to integrate multiple algorithms. The models were trained on a dataset of 532 data points with 6 input variables. Performance evaluation metrics and the Taylor diagram were used to compare the models. The results showed that the hybrid ensemble models outperformed the single models, with the inverse variance-based model achieving the highest performance (R = 0.975, RMSE = 2.978 mm). Additionally, the contribution analysis indicated that carbonation time, CO2 concentration, and the amount of binder were the most influential factors. This study provides an efficient prediction model for carbonation depth and valuable insights for carbonation-resistant design in concrete structures.