Machine learning-driven innovations in green eco-environmental rubberized concrete design towards sustainability

Abstract

This research investigates the state-of-the-art developments in environmentally sustainable rubberized concrete (RC) design, with a significant focus on its eco-friendly characteristics. Novel methodologies for enhancing RC composition are currently under investigation, and machine learning, specifically CatBoost, plays a pivotal role in facilitating this exploration. The model is employed on a dataset comprising 590 RC samples. Importantly, an impressive coefficient of determination value of 0.991 is attained while using CatBoost to predict the compressive strength (CS) of RC, highlighting its exceptional predictive accuracy. Visual representations of variable interactions are generated using Partial Dependence Plots (PDPs). This study further presents design optimization results and key insights, paving the way for the development of sustainable RC materials. In addition, Pareto optimal solutions are presented, prioritizing the maximization of rubber and cementitious replacement material content while simultaneously striving for optimal CS. This approach embodies the study’s dedication to achieving a balanced and sustainable outcome where environmental benefits are maximized without compromising material performance.