Data-driven framework for prediction of mechanical properties of waste glass aggregates concrete

This research presents a novel data-driven framework for predicting the mechanical properties of waste glass aggregate concrete using six advanced metaheuristic optimization algorithms: Bat Algorithm (Bat), Cuckoo Search Algorithm (Cuckoo), Elephant Herding Optimization (Elephant), Firefly Algorithm (Firefly), Rhinoceros Optimization Algorithm (Rhino), and Gray Wolf Optimizer (Wolf). The study evaluates these models based on their ability to predict compressive strength (Fc), tensile strength (Ft), density, and slump using key statistical performance indicators such as SSE, MAE, MSE, RMSE, accuracy, R², and KGE. Sensitivity analysis was conducted using Hoffman and Gardener's method as well as the SHAP technique to determine the most influential parameter in the prediction process. Results indicate that the Firefly and Wolf algorithms exhibited the highest prediction accuracy across all four properties, with Wolf emerging as the overall best-performing model due to its superior generalization ability, lower error rates, and high correlation with experimental results. Among the input parameters, the water-to-binder ratio was identified as the most influential factor affecting the mechanical properties of waste glass aggregate concrete, as demonstrated by both sensitivity analysis methods. This highlights the critical role of optimal water content in achieving desirable strength and workability in sustainable concrete mixtures. The study's novelty lies in the comparative assessment of multiple optimization algorithms applied to waste-based concrete, an approach that has not been extensively explored in previous research. Additionally, the integration of SHAP analysis for feature importance ranking provides an interpretable machine learning approach to concrete mix design, which enhances decision-making for engineers and researchers. The practical implications of this research extend to sustainable machine learning-based concrete design, where AI-driven optimization can help reduce the reliance on conventional trial-and-error methods. By utilizing waste glass aggregates, the study supports circular economy initiatives in construction, reducing environmental impact while maintaining structural performance. The proposed models can be implemented in real-world scenarios to optimize mix designs for large-scale applications, leading to cost-effective and eco-friendly construction materials. This research advances the field of smart construction by demonstrating the effectiveness of machine learning in sustainable material engineering, paving the way for future AI-assisted innovations in the industry.