Data‑Driven Insights into Controlling the Reactivity of Supplementary Cementitious Materials in Hydrated Cement

• Published in: International journal of concrete structures and materials Vol. 18; no. 5; pp. 839 - 850
• Main Authors: Degefa, Aron Berhanu, Jeon, Geonyeol, Choi, Sooyung, Bak, JinYeong, Park, Seunghee, Yoon, Hyungchul, Park, Solmoi
• Format: Journal Article
• Language: English
• Published: Singapore 한국콘크리트학회 28.06.2024; Springer Nature Singapore; Springer; Springer Nature B.V; SpringerOpen
• Subjects: Analysis; Building Materials; Cement; Comminution; Concrete properties; Decision trees; Degree of reaction; Emissions; Engineering; Gaussian process; Green buildings; Machine learning; Optimization; Parameters; Particle size; Particle size distribution; Prediction; Regression; Silicon dioxide; Solid Mechanics; Specific gravity; Structural Materials; Supplementary cementitious materials; Support vector machines; Water-cement ratio; 토목공학; South Korea; Degree of reaction; Supplementary cementitious materials; Machine learning; Prediction
• ISSN: 1976-0485; 2234-1315; 2234-1315
• DOI: 10.1186/s40069-024-00677-w

Supplementary cementitious materials (SCMs) play an essential role in sustainable construction due to their potential to reduce carbon emissions, promote circular economy principles, and enhance the properties of concrete. However, the inherent diversity of SCMs makes it challenging to predict their degree of reaction (DOR). This study applies machine learning techniques to predict DOR while exploring key parameters affecting it. Five machine learning models are utilized: linear regression, Gaussian process regression (GPR), decision tree regression, support vector machine and extreme gradient boosting, with GPR providing the most accurate and adaptable prediction. The study delves into the impact of various parameters on DOR, revealing their significance. Silica content emerges as the most critical, followed by particle size distribution, specific gravity, and water-to-cement (W/C) ratio. Optimizing DOR requires extending curing time, reducing particle size distribution, and considering optimal silica content and W/C ratio. This research emphasizes the importance of understanding the relationships between parameters and the DOR of SCMs, providing insights to enhance the efficiency of SCMs in cementitious systems through machine learning and data-driven analysis.