What question did this study set out to answer?

This research aims to optimize the mechanical properties of sustainable concrete using a mixed model of experimental and computational methods.

June 20, 2026

Hybrid machine learning and genetic algorithm framework for performance-based optimization of sustainable concrete with industrial and polymer wastes

Key Points

This research aims to optimize the mechanical properties of sustainable concrete using a mixed model of experimental and computational methods.
Developed a hybrid model combining experimental studies and advanced machine learning methods for strength prediction.
Investigated a ternary blended system of silica fume, waste glass powder, and crumb rubber over 7, 28, and 56 days.
Utilized Genetic Algorithms for optimizing mix proportions based on machine learning predictions.
XGBoost and Ensemble models predicted concrete strengths with R2 > 0.88, marking high accuracy.
Optimal mix proportions increased compressive strength by 34%, flexural strength by 37%, and tensile strength by 46%.
Silica fume and waste glass powder improved strength, while crumb rubber negatively influenced bond integrity.

Abstract

This paper outlines a hybrid experimental and computational model to assess and optimize the mechanical behavior of sustainable concrete with silica fume (SF), waste glass powder (WGP), and crumb rubber (CR). Compared to current research, which mainly concentrates on individual or binary systems, this paper explores a ternary blended system and characterizes the intricate interactions among mix parameters with advanced machine learning methods. To establish compressive, flexural and tensile strengths of 7, 28 and 56 days, experimental investigations were carried out. The findings suggest that SF and WGP increase strength because of pozzolanic reactivity and refinement of microstructure, and CR decreases strength because of the weaker interfacial bonding. Several machine learning models, such as Random Forest, XGBoost, Support Vector Machine, Decision Tree, and Ensemble, were built on the basis of strength prediction with the highest level of accuracy being XGBoost and Ensemble (R2 > 0.88). Moreover, optimization with the help of Genetic Algorithms was used to determine the optimal mix proportions leading to the increase in compressive, flexural, and tensile strengths by 34%, 37%, and 46%, respectively. The proposed framework offers a sound and data-driven methodology of designing high-performance and environmentally friendly concrete mixtures.

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