Experimental Investigation and Machine Learning Prediction of Mechanical Properties of Rubberized Concrete for Sustainable Construction

Concrete is widely used in civil engineering applications and the natural aggregates which used in concrete are scarce, but its demand is increasing. The disposal of rubber tyres poses a significant environmental challenge, as their decomposition releases harmful chemicals into the soil and water bodies over many years. Decomposition of tyres should be done in a smart way and hence came the emergence of mixing recycled rubber crumbs into concrete as Rubberised Concrete (RC). This paper provides an in-depth analysis of the mechanical properties of concrete such as Compressive Strength (f_ck), Tensile Strength (f_t), Flexural Strength (f_cr) of 7, 14, 28 days in replacement of fine aggregate with fine rubber (FR), and Coarse Aggregate with Coarse Rubber (CR). The results indicate that RC is more suitable for structural applications, including Reinforced Concrete columns, beams, slabs, than conventional concrete. The primary objective of the article is to explore the potential use of recycled rubber crumbs in concrete, referred to as Rubberised Concrete (RC), and to analyze its mechanical properties such as compressive strength, tensile strength, and flexural strength over different curing periods. Additionally, machine learning (ML) based prediction model has been developed for various strength characteristics of concrete mixtures at 28 days. The hyperparameter optimization using Grid Search CV with fivefold cross-validation have been performed to obtain the best hyperparameters. The model's performance is evaluated using metrics like MAE, MSE, RMSE, and R-squared values. Results reveal varying performances among different ML algorithms for predicting flexural, tensile, and compressive strengths.