Artificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review
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2025-05-17
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Nature Research
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.
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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.
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© 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/).