Machine learning predictions of high-strength RCA concrete utilizing chemically activated fly ash and nano-silica.

This study explores the potential of RCA combined with nano silica and chemically activated fly ash to produce sustainable and high strength concrete. The research addresses the challenges posed by RCA's inferior mechanical and durability properties by incorporating SCM. A comprehensive experimental program includes 420 and 240 samples for compressive strength and acid resistance. Machine learning algorithms such as Decision Trees, Random Forest, XG-Boost, and Ada Boost are used to predict RCA concrete performance metrics, with XG-Boost achieve the highest predictive accuracy (R = 0.995) for compressive strength while random forest performance is better for acid resistance (R = 0.909). The findings demonstrate substantial improvement in mechanical performance and durability, under scoring the effectiveness of SCMs in optimizing RCA- based concrete. The integration of machine learning provides a robust framework for performance predictions, contributing to the advancement of sustainable and resilient construction materials.