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Predicting Carbonation by payalkarnuke27 is a document available to read on EtoBox.
This study develops machine learning models to predict carbonation depth in fly ash concrete, utilizing five ensemble-based algorithms. The gradient boosting regressor (GBR) outperformed other models with an R2 value exceeding 0.94 and identified curing and exposure time as key influencing factors through SHAP analysis. The findings aim to enhance the durability and sustainability of concrete structures by improving prediction accuracy compared to traditional methods.
- Author
- payalkarnuke27
- Language
- EN