Abstract

The utilization of supplementary cementitious materials (SCMs) subjected to carbonation processing represents a viable strategy to mitigate anthropogenic CO2 emissions associated with concrete production, potentially contributing to the achievement of carbon neutrality. However, existing studies have limitations in effectively predicting the varying carbonation capacities of different SCMs, a gap that this research aims to address. Recent research efforts focused on the carbonation of waste-material-sourced SCMs are reviewed, along with a comparative discussion on diverse carbonation methods. A detailed data set encapsulating the properties of SCMs, and carbonation configurations was compiled. At the same time, six ensemble learning models were developed and evaluated, with a particular emphasis on the CatBoost model due to its exemplary performance in predicting the carbonation capacity of SCMs. This study suggests a promising direction for optimizing carbonation processes across different types of SCMs, underscoring their potential in sustainable concrete production.

Department(s)

Economics

Second Department

Civil, Architectural and Environmental Engineering

Third Department

Electrical and Computer Engineering

Fourth Department

Computer Science

Comments

Missouri University of Science and Technology, Grant None

Keywords and Phrases

carbonation; data sets; ensemble learning; machine learning; supplementary cementitious materials

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2024

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