Predictive modeling of precast concrete compressive strength using artificial neural networks in a data-driven engineering framework: enhancing structural durability and sustainable construction

This study addresses the critical need for sustainable construction by developing a predictive model for the compressive strength of Grade C40 precast concrete using Artificial Neural Networks (ANN). The purpose of the research is to enhance construction efficiency, reduce material waste, and promote sustainability. Employing a dataset of 1503 entries, the methodology involved analyzing 16 variables—such as water-to-cement ratio, aggregate properties, and curing conditions—to account for the complex interactions affecting concrete strength. The ANN model was chosen for its superior ability to uncover intricate, nonlinear relationships between input parameters and outputs, outperforming other machine learning models like XGBoost and Gradient Boosting in capturing these complexities. Key findings highlight the significant influence of factors like ambient and curing temperatures, water content, and mix proportions, with Gradient Boosting achieving the highest prediction accuracy for both 7- and 28-day compressive strengths (R² scores of 0.92 and 0.90, respectively). The ANN model contributed nuanced insights into these interactions, making it indispensable for understanding concrete behavior under varying conditions. Practical implications of the research are profound: by minimizing reliance on trial-and-error methods, this approach not only reduces costs and time but also supports the development of optimized concrete mixes, thereby decreasing carbon emissions and material waste. The findings align with the goals of sustainable construction and climate resilience, enabling a shift toward data-driven, resource-efficient practices. Furthermore, this model empowers local construction industries to adopt autonomous, AI-driven frameworks, fostering both technological self-reliance and sustainable infrastructure development.