Modeling and Optimization of Concrete Mixtures Using Machine Learning Estimators and Genetic Algorithms
Ana Isabel Oviedo Carrascal, Jorge Londoño, John F. Vargas, Carolina Zuluaga, Ana Gómez
Abstract
This study presents a methodology to optimize concrete mixtures by integrating machine learning (ML) and genetic algorithms. ML models are used to predict compressive strength, while genetic algorithms optimize the mixture cost under quality constraints. Using a dataset of over 19,000 samples from a local ready-mix concrete producer, various predictive ML models were trained and evaluated regarding cost-effective solutions. The results show that the optimized mixtures meet the desired compressive strength range and are cost-efficient, thus having 50% of the solutions yielding a cost below 98% of the test cases. CatBoost emerged as the best ML technique, thereby achieving a mean absolute error (MAE) below 5 MPa. This combined approach enhances quality, reduces costs, and improves production efficiency in concrete manufacturing.