Sustainable concrete performance prediction using machine learning for mechanical and durability properties with Construction and Demolition waste

This research explored the use of Construction and Demolition (C&D) waste as a partial replacement for natural aggregates in concrete and its influence on fresh, mechanical, and durability properties. Results revealed that workability, slump, and setting time decreased with higher C&D content; however, slump showed only a 12% reduction at a 40% substitution rate, remaining within acceptable industry limits. Compressive strength experienced a minimal 4% decline at this level, still meeting construction standards. Durability properties showed some variation: water absorption doubled, sulfate resistance decreased by 27%, and chloride penetration increased by 15%. To predict these behaviors, four machine learning (ML) techniques—Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Network (ANN), and XGBoost—were employed. Among them, the RF model demonstrated superior predictive accuracy for compressive strength, water absorption, and resistance parameters, achieving an R² value of 0.985. Overall, the study concludes that C&D waste can effectively replace natural aggregates without significantly compromising concrete performance. Moreover, integrating ML-based prediction models enables data-driven optimization of concrete mix designs, fostering the production of sustainable, high-performance, and durable concrete with reduced environmental impact. An equation-based numerical model was developed and validated with the experimental and machine learning methodologies to predict the mechanical properties of recycled aggregate concrete. The integrated approach shows the reliable prediction of strength and proof-of-concept for the suitability of recycled aggregates in sustainable concrete construction.

C&D waste; Recycled aggregate concrete; Compressive strength; Durability; Machine learning; Random Forest; XGBoost; Predictive modeling; Sustainable concrete