Revolutionizing sustainable construction through predictive modeling of green concrete

Concrete is the most commonly used building material in the world, yet because of its enormous carbon emissions, other greenhouse gas emissions, and resource depletion during production, it has a major negative impact on the environment. In response, academic and business leaders have been creating cutting-edge substitutes for conventional concrete. Among them, green concrete has become a prominent sustainable option. This study presents a comparative modeling framework for predicting the compressive strength development of normal and green concrete using both linear and exponential regression techniques. Experimental strength data at 7, 14, and 28 days have been used to develop regression models for both concrete types. A linear regression model has been constructed for normal concrete, yielding a strong correlation (R² = 0.844), whereas green concrete, characterized by the delayed pozzolanic activity of supplementary cementitious materials, has been best represented by an exponential model. The exponential regression provided an excellent fit to green concrete strength data, capturing the nonlinear strength gain pattern typical of mixes incorporating fly ash and recycled aggregates. In addition, a Pareto analysis has been performed to identify the most critical curing periods contributing to strength development. Results show that approximately 86 % of the 28-day compressive strength in green concrete has been achieved within the first 14 days, emphasizing the importance of early-age curing and mix optimization. Overall, the study demonstrates how predictive modeling of strength development in green concrete not only supports more efficient mix optimization but also contributes to advancing sustainable construction practices by promoting the use of eco-friendly materials and data-driven decision-making.