AUTHOR=Gamil Yaser 

TITLE=Machine learning in concrete technology: A review of current researches, trends, and applications

JOURNAL=Frontiers in Built Environment

VOLUME=Volume 9 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2023.1145591

DOI=10.3389/fbuil.2023.1145591

ISSN=2297-3362

ABSTRACT=Machine learning techniques have been used in different fields of concrete technology to characterize the materials, develop the concrete mix design, and predict the behavior of fresh concrete, hardening and hardened concrete properties. The methods have been extended further to evaluate the durability and predict or detect the cracks in the service life of concrete. This article offers a review of current applications and trends of machine learning techniques in concrete technology.  The findings showed that machine learning techniques can predict the output based on historical data and deemed to be acceptable to evaluate, model and predict the concrete properties from its young life to service life. The findings suggested more applications of machine learning by utilizing the historical data acquitted from the scientific laboratory experiments and the data acquitted from the industry to provide a comprehensive platform to predict and evaluate the concrete properties. It was found modelling with machine learning saves time and cost for obtaining concrete properties.