Experimental and Artificial Intelligence-Based Framework for Performance Prediction of Rubberized Concrete Incorporating Waste Tyre Rubber

The accumulation of waste tyres presents a significant environmental challenge owing to their non-biodegradable nature and limited recycling options. The incorporation of tyre-derived rubber into concrete offers a promising strategy to reduce landfill waste and lower the consumption of natural aggregates. This study presents an integrated experimental and machine learning-based framework for evaluating and predicting the performance of rubberized concrete. M25-grade concrete mixtures were prepared with partial replacement of coarse aggregates by waste tyre rubber at proportions of 0%, 10%, 20%, and 30% by volume. Mechanical performance was assessed through compressive and split-tensile strength tests, whereas durability was evaluated using water absorption measurements. Microstructural characterization was conducted using scanning electron microscopy and X-ray diffraction analysis. In parallel, predictive models based on artificial neural networks, adaptive neuro-fuzzy inference systems, and fuzzy logic were developed and validated using statistical measures. The results showed that increasing rubber content reduced mechanical strength and increased water absorption due to weaker interfacial bonding and higher porosity. Nevertheless, concrete containing a 10% rubber replacement retained approximately 90% of the control strength while maintaining satisfactory durability. The machine learning models demonstrated strong predictive accuracy for estimating concrete properties. Overall, the findings suggest that limited incorporation of waste tyre rubber can contribute to the development of sustainable and low-carbon concrete materials with reduced embodied energy and environmental impact.