| Summary: | Carbonation is one of the critical durability issues in reinforced concrete structures in terms of their structural integrity and safety and may cause the fatal deterioration and corrosion of steel reinforcement if ignored. Many researchers have performed a considerable number of studies to predict the carbonation of concrete structures. However, it is still challenging to predict the carbonation depth or carbonation coefficient, as they depend on various factors. Therefore, creating a model that can learn from available data using Data Driven Techniques (DDT) is a step forward in this research field. This study provides new approaches to predict the carbonation coefficient of concrete through Model Tree (MT), Random Forest (RF) and Multi-Gene Genetic Programming (MGGP) approaches. With 827 case studies, the predicted models can be seen as a function of a set of conditioning factors, which are statistically significant in explaining the carbonation mechanism. The results obtained through MT, RF and MGGP were compared with those obtained through Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs) and Genetic Programming (which were previously developed). The results reveal that the MT, RF and MGGP perform better than the previous models. Moreover, the MT technique displays its output in terms of series of equations, RF as multiple trees and MGGP in form of a single equation, which are more user-friendly and applicable in practice.
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