This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Faculty of Civil Engineering, University of Montenegro , Podgorica , Montenegro
The increasing availability of experimental data in civil engineering has created new opportunities for the application of artificial intelligence techniques in structural engineering research. Machine learning approaches provide powerful tools for identifying complex relationships between material composition and mechanical properties. In this study, data-driven methods are applied to investigate the prediction of concrete compressive strength using a publicly available experimental dataset. The considered parameters include the main components of concrete mixtures as well as the age of concrete specimens. The objective is to explore the capability of machine learning models to capture nonlinear relationships between mixture composition and the resulting compressive strength. The presented framework represents a preliminary step toward broader applications of artificial intelligence in structural engineering problems, including reinforced concrete elements such as beam–column joints.
machine learning, concrete compressive strength prediction, artificial intelligence in structural engineering, data-driven modelling, reinforced concrete structures
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.
