AUTHOR=Awoyera Paul O. , Bahrami Alireza , Oranye Chukwufumnanya , Bendezu Romero Lenin M. , Mansouri Ehsan , Mortazavi Javad , Hu Jong Wan 

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

JOURNAL=Frontiers in Built Environment

VOLUME=Volume 10 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2024.1447800

DOI=10.3389/fbuil.2024.1447800

ISSN=2297-3362

ABSTRACT=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.