Machine learning driven transformation of industrial steel waste into waterproof concrete blocks using polypropylene fiber for effective waste management
摘要
The increasing production of industrial steel waste poses significant environmental challenges, necessitating innovative reuse strategies. This study investigates the transformation of steel slag into waterproof concrete blocks reinforced with polypropylene fibers. Fine aggregates were partially replaced with steel slag at varying proportions, and fibers were incorporated to enhance tensile strength, crack resistance, and impermeability. At the optimal mix (15% slag and 0.5% fiber), the 28-day compressive strength increased by 38% compared with the control, while flexural strength improved by 42%, tensile strength by 19%, and water absorption decreased by 22%. These findings demonstrate the feasibility of producing durable, waterproof blocks with substantial potential for waste valorization. Experimental tests were complemented with Random Forest and XGBoost machine learning models; XGBoost performed best, achieving a high predictive accuracy (R2 > 0.95) across all mechanical and durability properties. Feature importance analysis revealed curing age as the most influential factor, followed by fiber content and slag dosage, highlighting the predictive capabilities of this approach. The synergy of steel slag and polypropylene fibers enhances mechanical performance and provides practical benefits for moisture-prone structures, including basements, tunnels, and foundations. Overall, this research contributes to sustainable construction practices by recycling industrial waste into high-value materials and demonstrates the potential of machine learning to optimize concrete performance efficiently. Future work will focus on long-term durability under aggressive exposures and life-cycle environmental assessment for large-scale applications.