Résumé:
This thesis explores the use of machine learning algorithms to predict the compressive and
flexural strength of aluminum waste-based fiber-reinforced concrete as a green alternative for
the development of mechanical properties at reduced environmental expense. Smart
prediction models are to be created using three main algorithms: Artificial Neural Networks
(ANN), Random Forest, and XGBoost. The procedure entailed experimental data collection,
normalization as preprocessing, correlation analysis, and partitioning data into training,
validation, and testing sets. Results indicated that XGBoost performed best with R² equal to
approximately 0.80 for compressive strength and 0.97 for flexural strength. The study
concludes that the use of AI in concrete mix design reduces reliance on costly experimental
testing and sustainable building practices. Future studies are encouraged to expand data
sources and explore hybrid model approaches for greater accuracy.
