Review of AI-assisted design of low-carbon cost-effective concrete toward carbon neutrality
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10462_2025_Article_11182.pdf
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5.74 MB
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Adobe PDF
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a32b1b5e5c414bea36cc4818e11984ec
Author(s)
Mahjoubi, Soroush
Barhemat, Rojyar
Meng, Weina
Bao, Yi
Date Issued
May 3, 2025
Journal
Artificial Intelligence Review
Publisher
Springer Netherlands
Citation
Mahjoubi, S., Barhemat, R., Meng, W. et al. Review of AI-assisted design of low-carbon cost-effective concrete toward carbon neutrality. Artif Intell Rev 58, 225 (2025).
Version
Final published version
Abstract
Decarbonizing concrete production is a critical step toward achieving carbon neutrality by 2050. This paper highlights the advancements in artificial intelligence-assisted design of low-carbon cost-effective concrete, focusing on integrating machine learning-based property prediction with multi-objective optimization. Data collection and processing techniques, such as automatic data extraction, artificial data generation, and anomaly detection, are first discussed to address the importance of dataset quality. Strategies that capture physicochemical information of ingredients, including by-product supplementary cementitious materials and recycled aggregates, are then examined to enhance model generalizability. Various machine learning models—from individual regression approaches to heterogeneous ensemble methods—are compared for their predictive accuracy and robustness. Methods for hyperparameter tuning, model evaluation, and interpretation to ensure reliable and interpretable predictions are reviewed. Design optimization approaches are then highlighted, ranging from grid/random searches to more sophisticated gradient-based and metaheuristic algorithms, aimed at minimizing carbon footprint, embodied energy, and cost. Future research avenues encompass (1) application-specific design frameworks that integrate critical objectives—mechanical performance, durability, fresh-state behavior, and time-dependent properties such as creep and shrinkage—tailored to specific structural and environmental requirements; (2) holistic design optimization that simultaneously refines mixture design and structural parameters; and (3) probabilistic approaches to systematically manage uncertainties in materials, structural performance, and loading conditions systematically.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1007/s10462-025-11182-1
