Predicting the Degree of Reaction of Supplementary Cementitious Materials in Hydrated Portland Cement

Aron Berhanu Degefa, Seunghee Park, Beomjoo Yang () and Solmoi Park ()
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Aron Berhanu Degefa: Department of Civil Engineering, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan 48513, Republic of Korea
Seunghee Park: School of Civil, Architectural Engineering & Landscape Architecture, Sungkyunkwan University, Suwon 16419, Republic of Korea
Beomjoo Yang: School of Civil Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju 28644, Republic of Korea
Solmoi Park: Department of Civil Engineering, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan 48513, Republic of Korea

Sustainability, 2023, vol. 15, issue 21, 1-17

Abstract: Determination and prediction of degree of reaction (DOR) of supplementary cementitious materials (SCMs) in hydrated Portland cement are important for designing concrete with lower levels of embodied carbon dioxide. Herein, a model for predicting the DOR of SCMs in hydrated cement was developed using a set of collected data and a machine learning algorithm based on genetic programming toolbox for the identification of physical systems. The results suggest that the model reliably predicts the DOR of slag, fly ash, metakaolin, and silica fume with a coefficient of determination ( R 2 ) value of 0.89. The predicted DOR of SCMs is found to be directly proportional to water-to-cement ratio and curing time, while it is highly reliant on the oxide composition and differs amongst SCMs. For instance, the DOR of slag substantially increased with a higher alumina content, while the DOR of metakaolin remained more stable, primarily influenced by the silica-to-alumina ratio. The proposed model is particularly useful for predicting phase assemblages of SCMs-blended Portland cement when experimental data and information on SCMs are limited and properties of SCMs are highly variable. The insights gained from this study offer a pathway towards more sustainable and efficient concrete design, aligning with contemporary environmental objectives.

Keywords: supplementary cementitious materials; degree of reaction; machine learning; prediction; thermodynamic modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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