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Machine learning in concrete technology: A review of current researches, trends, and applications
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.ORCID iD: 0000-0002-0036-8417
2023 (English)In: Frontiers in Built Environment, E-ISSN 2297-3362, Frontiers in Built Environment, E-ISSN 2297-3362, Vol. 9, article id 1145591Article, review/survey (Refereed) Published
Abstract [en]

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 a review 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.

Place, publisher, year, edition, pages
Frontiers Media S.A. , 2023. Vol. 9, article id 1145591
Keywords [en]
concrete, crack detection, data, machine learning, mix optimization, performance
National Category
Civil Engineering Computer Sciences
Research subject
Building Materials
Identifiers
URN: urn:nbn:se:ltu:diva-96270DOI: 10.3389/fbuil.2023.1145591ISI: 000948646100001Scopus ID: 2-s2.0-85150064166OAI: oai:DiVA.org:ltu-96270DiVA, id: diva2:1747707
Note

Validerad;2023;Nivå 2;2023-03-30 (hanlid)

Available from: 2023-03-30 Created: 2023-03-30 Last updated: 2023-03-30Bibliographically approved

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Gamil, Yaser

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