MACHINE LEARNING-BASED PREDICTION OF MECHANICAL PROPERTIES OF COMPOSITE CONCRETE INCORPORATING INDUSTRIAL BY-PRODUCTS
Abstract
The increasing demand for sustainable construction materials has accelerated the incorporation of industrial by-products such as fly ash, ground granulated blast furnace slag (GGBFS), silica fume, rice husk ash, marble dust, and waste glass powder into composite concrete. These supplementary cementitious materials not only reduce environmental impacts associated with cement production but also enhance specific mechanical and durability properties of concrete. However, predicting the mechanical performance of composite concrete remains a complex challenge due to the nonlinear interactions among constituent materials, curing conditions, mix proportions, and environmental factors. Recent advancements in machine learning (ML) have provided innovative solutions for accurately predicting concrete properties while minimizing experimental costs and time. This study presents a comprehensive review and conceptual framework for machine learning-based prediction of mechanical properties of composite concrete incorporating industrial by-products. A systematic examination of recent studies published between 2018 and 2026 is conducted to evaluate the effectiveness of various ML algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting Machines (GBM), Extreme Gradient Boosting (XGBoost), and Deep Learning models. The findings reveal that ensemble learning techniques frequently outperform traditional statistical methods, achieving prediction accuracies exceeding 90% in several applications. Furthermore, the review identifies critical challenges such as data scarcity, lack of standardized datasets, model interpretability issues, and limited real-world implementation. The study proposes a conceptual framework integrating sustainable concrete design with advanced machine learning methodologies. It highlights future research opportunities involving explainable artificial intelligence, hybrid optimization algorithms, digital twins, and Internet of Things (IoT)-enabled monitoring systems. The research contributes to sustainable construction practices by demonstrating how intelligent prediction models can support material optimization, resource conservation, and reduced carbon emissions in the construction industry. Keywords: Machine Learning, Composite Concrete, Industrial By-Products, Mechanical Properties Prediction, Sustainable Construction, Artificial Intelligence, Smart Materials