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Optimization of sustainable mix design for alkali-activated materials using machine learning methods
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| Title: | Optimization of sustainable mix design for alkali-activated materials using machine learning methods |
| Authors: | Kong, Yukun Browse this author |
| Keywords: | Alkali-activated materials | | Strength | | Workability | | Drying shrinkage | | Machine learning | | Life-cycle assessment |
| Issue Date: | 28-Jun-2024 |
| Publisher: | Hokkaido University |
| Conffering University: | 北海道大学 |
| Degree Report Number: | 甲第16047号 |
| Degree Level: | 博士 |
| Degree Discipline: | 工学 |
| Examination Committee Members: | (主査) 准教授 胡桃澤 清文, 教授 佐藤 努, 教授 杉山 隆文, 教授 廣吉 直樹 |
| Degree Affiliation: | 工学院（環境循環システム専攻） |
| (Relation)haspart: | Chapter 2 is published as (1) Y.K. Kong, K. Kurumisawa, Fresh properties and characteristic testing methods for alkali-activated materials: A review, J. Build. Eng. 75 (2023) 106830. | | (2) Y.K. Kong, M. Kato, K. Kurumisawa, Recent advances in x-ray computed tomography for alkali-activated materials: A review, J. Adv. Concr. Technol. 21 (2023) 573-595. | | Chapter 3 is published as (1) Y.K. Kong, K. Kurumisawa, Application of machine learning in predicting workability for alkali-activated materials, Case Stud. Constr. Mater. 18 (2023) e02173. | | Chapter 4 is published as (1) S.H. Chu, Y.K. Kong, Mathematical model for strength of alkali-activated materials, J. Build. Eng. 44 (2021) 103189. | | Chapter 5 is published as (1) Y.K. Kong, K. Kurumisawa, Prediction of the drying shrinkage of alkali-activated materials using artificial neural networks, Case Stud. Constr. Mater. 17 (2022) e01166. |
| Type: | theses (doctoral) |
| URI: | http://hdl.handle.net/2115/92803 |
| Appears in Collections: | 学位論文 (Theses) > 博士 （工学）
課程博士 (Doctorate by way of Advanced Course) > 工学院(Graduate School of Engineering)
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