Machine learning in concrete technology - A review of current researches, trends, and applications

Aug 2, 2026

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Machine learning techniques are applied across concrete technology to characterize materials using image processing, optimize mix design from historical records, and predict fresh, hardening, and hardened concrete properties from laboratory data. Research also extends to evaluating durability, predicting or detecting cracks during service life, and forecasting erosion and chemical attachment effects. The review consolidates current applications and trends, showing that ML models learn from experimental and industrial datasets to provide timely, cost-effective predictions with acceptable accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

Machine learning overview and model development

Applications of ML in concrete technology

Concrete complexity and the need for data-driven prediction

TYPE Review PUBLISHED 23 February 2023 DOI 10.3389/fbuil.2023.1145591 OPEN ACCESS EDITED BY Matteo Pelliciari, University of Modena and Reggio Emilia, Italy REVIEWED BY Marco Martino Rosso, Polytechnic University of Turin, Italy Yuri De Santis, University of L’Aquila, Italy *CORRESPONDENCE Yaser Gamil, yaser. gamil@ltu.se SPECIALTY SECTION This article was submitted to Computational Methods in Structural Engineering, a section of the journal Frontiers in Built Environment RECEIVED 16 January 2023 ACCEPTED 30 January 2023 PUBLISHED 23 February 2023 CITATION Gamil Y (2023), Machine learning in concrete technology: A review of current researches, trends, and applications. Front. Built Environ. 9:1145591 . doi: 10.3389/fbuil.2023.1145591 COPYRIGHT © 2023 Gamil. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Machine learning in concrete technology: A review of current researches, trends, and applications Yaser Gamil* Building Materials, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Luleå, Sweden Machine learning techniques have been used in different ﬁelds of concrete technology to characterize the materials based on image processing techniques, develop the concrete mix design based on historical data, and predict the behavior of fresh concrete, hardening, and hardened concrete properties based on laboratory data. The methods have been extended further to evaluate the durability and predict or detect the cracks in the service life of concrete, It has even been applied to predict erosion and chemical attaches. This article offers areview of current applications and trends of machine learning techniques and applications in concrete technology. The ﬁndings showed that machine learning techniques can predict the output based on historical data and are deemed to be acceptable to evaluate, model, and predict the concrete properties from its fresh state, to its hardening and hardened state to service life. The ﬁndings suggested more applications of machine learning can be extended by utilizing the historical data acquitted from scientiﬁc laboratory experiments and the data acquitted from the industry to provide a comprehensive platform to predict and evaluate concrete properties. It was found modeling with machine learning saves time and cost in obtaining concrete properties while offering acceptable accuracy. KEYWORDS machine learning, concrete, mix optimization, performance, data, crack detection Introduction Machine learning (ML) is part of a subset of artiﬁcial intelligence which is the study of computer algorithms that can learn and develop on their own with experience and historical data (Mitchell et al., 2013; Jordan and Mitchell, 2015; Bonaccorso, 2017). It produces a model based on training data to make predictions or judgments without having to be explicitly programmed to do so (Ghahramani, 2015) . It offers rapid solutions in modeling complex systems (Khambra and Shukla, 2021) . ML algorithms are used in a broad range of applications, including business optimization (Apte, 2010), agriculture (Liakos et al., 2018), medicine (Rajkomar et al., 2019), email ﬁltering (Dada et al., 2019), speech recognition (Padmanabhan and Johnson Premkumar, 2015), and computer vision (Sebeet al., 2005), while using traditional algorithms to do the required tasks is difﬁcult or impossible and time-consuming (Wang et al., 2009) . ML has been extended to be used in the built environment in different areas (Rachele et al., 2021) either to

FAQs

How is machine learning used to predict concrete properties?

It learns from training datasets to predict concrete output from historical and laboratory measurements, covering fresh, hardening, and hardened states.

What concrete technology applications are highlighted in the review?

Applications include image-based material characterization, mix design optimization, durability evaluation, and crack detection or prediction during service life.

What advantages does machine learning offer compared with traditional approaches?

ML modeling can reduce time and cost for obtaining concrete properties while maintaining acceptable accuracy.

Aug 2, 2026

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