Integrating Waste Rubber Tires and AI-Driven Optimization in Concrete: A Comprehensive Study on Performance, Safety, and Sustainability

Abstract

The disposal of waste rubber tires poses significant environmental challenges. Their incorporation into concrete as a partial replacement for natural aggregates presents a promising pathway towards sustainable construction. This study comprehensively investigates the performance, durability, and sustainability of rubberized concrete, with a novel integration of artificial intelligence (AI) for mix design optimization and a dedicated assessment of health and safety (H&S) procedures during production. A systematic experimental program was designed, incorporating different rubber replacement levels (0%, 5%, 10%, 15%, 20%, 25%, and 30% by volume of fine aggregate). The rubber particles were treated with a silane coating agent to enhance the rubber-cement matrix interface. The results indicate a trade-off: while compressive, tensile, and flexural strengths decrease with higher rubber content (e.g., up to 65% reduction in compressive strength at 30% replacement), significant enhancements were observed in impact resistance (up to 300% increase), ductility, and thermal/sound insulation. AI models, particularly an Artificial Neural Network (ANN), were successfully employed to predict the compressive strength with high accuracy (R² = 0.98), optimizing the mix design for target properties. The H&S risk assessment identified critical control points, notably dust inhalation from rubber crumb, and prescribed mitigation measures. The study concludes that rubberized concrete, optimized via AI and produced under strict H&S protocols, is a viable, high-performance, and sustainable material for non-structural and specialty applications, aligning with circular economy principles.