Artificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review

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dc.contributor.author Luo, Dayou
dc.contributor.author Wang, Kejin
dc.contributor.author Wang, Dongming
dc.contributor.author Sharma, Anuj
dc.contributor.author Li, Wengui
dc.contributor.author Choi, In Ho
dc.contributor.department Department of Civil, Construction and Environmental Engineering
dc.date.accessioned 2025-05-28T19:59:46Z
dc.date.available 2025-05-28T19:59:46Z
dc.date.issued 2025-05-17
dc.description.abstract Artificial Intelligence (AI) is transforming concrete research. This review explores various AI techniques that drive cutting-edge solutions across all stages of concrete lifecycle, from material, mixture, and process optimization to quality control and performance prediction. Meta-analysis shows that XGBoost model excels in predicting workability (R2 = 0.98), while ensemble models provide the best strength predictions (R2 = 0.93). The study highlights trends, gaps, and future AI opportunities in concrete technology.
dc.description.comments This article is published as Luo, Dayou, Kejin Wang, Dongming Wang, Anuj Sharma, Wengui Li, and In Ho Choi. "Artificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review." npj Materials Sustainability 3, no. 1 (2025): 1-35. https://doi.org/10.1038/s44296-025-00058-8.
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/jrl8636r
dc.language.iso en
dc.publisher Nature Research
dc.rights © The Author(s) 2025. This article is licensed under a Creative Commons Attribution-Non Commercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material (http://creativecommons.org/licenses/by-nc-nd/4.0/).
dc.source.uri https://doi.org/10.1038/s44296-025-00058-8 *
dc.subject.disciplines DegreeDisciplines::Engineering::Materials Science and Engineering::Structural Materials
dc.subject.disciplines DegreeDisciplines::Physical Sciences and Mathematics::Computer Sciences::Artificial Intelligence and Robotics
dc.subject.disciplines DegreeDisciplines::Engineering::Civil and Environmental Engineering::Construction Engineering and Management
dc.title Artificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review
dc.type Article
dspace.entity.type Publication
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relation.isAuthorOfPublication 717eae32-77e8-420a-b66c-a44c60495a6b
relation.isOrgUnitOfPublication 933e9c94-323c-4da9-9e8e-861692825f91
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