Optimization of recycled rubber self-compacting concrete: Experimental findings and machine learning-based evaluation

This research aims to assess the rheological and mechanical characteristics of Self-compacting concrete (SCC) incorporating waste tire rubber aggregates (WRTA) as an interim substitute for coarse aggregates. However, the standard experimental modeling approach has significant obstacles when it comes to overcoming the nonlinearity and environmental susceptibility of concrete parts. Therefore, linear regression (LR) and extreme gradient boosting (XGBoost) were used as two standard single machine learning (ML) models to predict the aforementioned rubberized SCC features. In this study, conventional coarse aggregates were supplanted with WRTA at 0%, 5%, 10%, and 20% to uncover the optimal proportion of coarse aggregates substituting rubber. To find the optimum amount of WRTA to use as a substitute, the study follows the impacts of rubber on the self-compacting rubberized concrete's (SCRC) rheological and mechanical characteristics. The consequences on fresh properties were investigated by the slump flow, J-ring, and V-funnel tests, while compressive and splitting tensile strengths tests were conducted to assess mechanical properties. Increasing WRTA test outputs indicated a deterioration in workability and hardened qualities. While a 10% swapping ratio is deemed feasible for producing SCRC, optimal results were achieved by reducing environmental impacts and efficiently managing a significant volume of rubber tire waste with a 5% substitution of rubber within the coarse aggregates. The research findings indicated a noticeable decrease in fresh properties as the WRTA content increased. Notably, after 28 days, a 10% WRTA substitution led to a 34% reduction in compressive strength and a 28% decrease in splitting tensile strength, satisfying ACI standards. Furthermore, XGBoost demonstrated superior predictive performance with the highest R² values, outperforming the LR model and affirming its efficacy in delivering more accurate predictions.