CaPrM: Carbonation prediction model for reinforced concrete using machine learning methods

    Research output: Contribution to journalArticleScientificpeer-review

    83 Citations (Scopus)


    Reliable carbonation depth prediction of concrete structures is crucial for optimizing their design and maintenance. The challenge of conventional carbonation prediction models is capturing the complex relationship between governing parameters. To improve the accuracy and methodology of the prediction a machine learning based carbonation prediction model which integrates four learning methods is introduced. The model developed considers parameters influencing the carbonation process and enables the user to choose the best alternative of the machine based methods. The applicability of the method is demonstrated by an example where the carbonation depths are estimated using the developed model and verified with unseen data. The evaluation proofs that the model predicts the carbonation depth with a high accuracy.
    Original languageEnglish
    Pages (from-to)70-82
    Number of pages13
    JournalConstruction and Building Materials
    Publication statusPublished - 15 Dec 2015
    MoE publication typeA1 Journal article-refereed


    • bagged decision tree
    • boosted decision tree
    • carbonation
    • concrete
    • decision tree
    • machine learning
    • model
    • neural network


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