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What is Machine Learning-Based Prediction of Concrete Compressive about?

This study explores the use of machine learning to predict the compressive strength of concrete incorporating Ground Granulated Blast Furnace Slag (GGBS) using a dataset of 560 experimental values. Various machine learning models, including XGBoost, Random Forest, ANN, and SVM, were evaluated, with XGBoost demonstrating the highest predictive accuracy and lowest error rates. The research highlights the potential of machine learning in optimizing concrete mix design for enhanced sustainability and performanc

Author

Don John

Language

EN