Elemental Design of Alkali-Activated Materials with Solid Wastes Using Machine Learning Article Swipe
Junfei Zhang
,
Shenyan Shang
,
Zehui Huo
,
Junlin Chen
,
Yuhang Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ma17184573
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ma17184573
Understanding the strength development of alkali-activated materials (AAMs) with fly ash (FA) and granulated blast furnace slag (GBFS) is crucial for designing high-performance AAMs. This study investigates the strength development mechanism of AAMs using machine learning. A total of 616 uniaxial compressive strength (UCS) data points from FA-GBFS-based AAM mixtures were collected from published literature to train four tree-based machine learning models. Among these models, Gradient Boosting Regression (GBR) demonstrated the highest prediction accuracy, with a correlation coefficient (R-value) of 0.970 and a root mean square error (RMSE) of 4.110 MPa on the test dataset. The SHapley Additive exPlanations (SHAP) analysis revealed that water content is the most influential variable in strength development, followed by curing periods. The study recommends a calcium-to-silicon ratio of around 1.3, a sodium-to-aluminum ratio slightly below 1, and a silicon-to-aluminum ratio slightly above 3 for optimal AAM performance. The proposed design model was validated through laboratory experiments with FA-GBFS-based AAM mixtures, confirming the model’s reliability. This research provides novel insights into the strength development mechanism of AAMs and offers a practical guide for elemental design, potentially leading to more sustainable construction materials.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ma17184573
- OA Status
- gold
- Cited By
- 2
- References
- 114
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4402600437Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/ma17184573Digital Object Identifier
- Title
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Elemental Design of Alkali-Activated Materials with Solid Wastes Using Machine LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-18Full publication date if available
- Authors
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Junfei Zhang, Shenyan Shang, Zehui Huo, Junlin Chen, Yuhang WangList of authors in order
- Landing page
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https://doi.org/10.3390/ma17184573Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/ma17184573Direct OA link when available
- Concepts
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Compressive strength, Fly ash, Mean squared error, Ground granulated blast-furnace slag, Curing (chemistry), Coefficient of determination, Materials science, Regression analysis, Machine learning, Computer science, Mathematics, Composite material, Algorithm, StatisticsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2024-09-18 |
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