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Embodied carbon saving potential of using recycled materials as cement substitute in Singapore’s buildings

Material production and construction activities are key contributors to global carbon footprints, necessitating sustainable alternatives. This study aims to investigate the potential of integrating recycled materials as Supplementary Cementitious Materials (SCMs) in concrete production to mitigate the substantial carbon emissions of Singapore’s building and construction sector. The research focuses on Ground Granulated Blast-furnace Slag (GGBFS), waste glass powder, and calcined marine clay as alternative SCMs, aiming to reduce environmental impact and waste disposal emissions in Singapore. Employing a cradle-to-gate Life Cycle Assessment (LCA) methodology for 1 m3 of concrete with different grades, this study quantifies embodied carbon savings and assesses the feasibility of substituting these SCMs in concrete. The results reveal that substituting Ordinary Portland Cement (OPC) with GGBFS in concrete offers the most significant reduction, up to 56%, in 1 m3 of concrete. In contrast, the use of calcined marine clay and glass powder in concrete results in reductions of up to 21% and 16%, respectively. Two case studies were used to exemplify the impact of using SCM concrete at the project scale. Results indicate that up to 31% of the total embodied carbon could be saved in the building. Additionally, scenario analysis suggests that the total emissions from cementitious materials in Singapore could decrease by 20% through the incorporation of locally recycled marine clay and glass powder. This reduction could potentially reach 56% if the GGBFS supply is not constrained. To further enhance sustainability in Singapore’s construction sector, the study proposes sourcing GGBFS from neighboring countries to minimize transportation emissions and localizing the production and usage of calcined marine clay and glass powder. These measures can improve material circularity and significantly contribute to achieving carbon reduction targets.

Constructing multicomponent cluster expansions with machine-learning and chemical embedding

Cluster expansions are commonly employed as surrogate models to link the electronic structure of an alloy to its finite-temperature properties. Using cluster expansions to model materials with several alloying elements is challenging due to a rapid increase in the number of fitting parameters and training set size. We introduce the embedded cluster expansion (eCE) formalism that enables the parameterization of accurate on-lattice surrogate models for alloys containing several chemical species. The eCE model simultaneously learns a low dimensional embedding of site basis functions along with the weights of an energy model. A prototypical senary alloy comprised of elements in groups 5 and 6 of the periodic table is used to demonstrate that eCE models can accurately reproduce ordering energetics of complex alloys without a significant increase in model complexity. Further, eCE models can leverage similarities between chemical elements to efficiently extrapolate into compositional spaces that are not explicitly included in the training dataset. The eCE formalism presented in this study unlocks the possibility of employing cluster expansion models to study multicomponent alloys containing several alloying elements.

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