Modeling properties of recycled aggregate concrete using gene expression programming and artificial neural network techniques

Soft computing techniques have become popular for solving complex engineering problems and developing models for evaluating structural material properties. There are limitations to the available methods, including semi-empirical equations, such as overestimating or underestimating outputs, and, more importantly, they do not provide predictive mathematical equations. Using gene expression programming (GEP) and artificial neural networks (ANNs), this study proposes models for estimating recycled aggregate concrete (RAC) properties. An experimental database compiled from parallel studies, and a large amount of literature was used to develop the models. For compressive strength prediction, GEP yielded a coefficient of determination (R²) value of 0.95, while ANN achieved an R² value of 0.93, demonstrating high reliability. The proposed predictive models are both simple and robust, enhancing the accuracy of RAC property estimation and offering a valuable tool for sustainable construction.