Prediction of chloride resistance level of concrete using machine learning for durability and service life assessment of building structures
Woubishet Zewdu Taffese, Leonardo Espinosa-Leal
Abstract
The resistance of concrete to chloride penetration determines the durability and service life of reinforced concrete building structures in coastal or chloride-laden environments. This work adopted five machine learning algorithms, naïve bayes, k-nearest neighbors, decision trees, support vector machine, and random forests, to predict the chloride resistance level of concrete based on its ingredients, considering two scenarios. The first scenario considers all features describing the mix components, whereas the second scenario considers only a subset of the features. All models are validated by performing intensive evaluation matrices using unseen data. The validation results confirm that the developed models predict the level of chloride resistance of concrete with high accuracy. Of all the algorithms, the support vector machine performed best, with 89% and 88% accuracy in the first and second scenarios, respectively.