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• W4310553658 abstract "The inclusion of microencapsulated phase change materials (MPCM) in construction materials is a promising solution for increasing the energy efficiency of buildings and reducing their carbon emissions. Although MPCMs provide thermal energy storage capability in concrete, they typically decrease its compressive strength. A unified framework for the mixture design of concrete incorporating MPCM is yet to be developed to facilitate practical applications. This study proposes a mix design procedure using a novel ternary machine learning (ML) paradigm. For this purpose, the tabular generative adversarial network (TGAN) was utilized to generate large synthetic mixture design data based on the limited available experimental observations. The synthetic data is then employed to construct robust predictive ML models. The gradient boosting regressor (GBR) model trained with synthetic data outperformed the model trained with real data, achieving a testing coefficient of determination (R2) of 0.963 and mean absolute error (MAE) of 2.085 MPa. The TGAN-GBR model was ultimately integrated with the particle swarm optimization (PSO) algorithm to construct a powerful recommendation system for optimizing the mixture design of concrete and mortar incorporating different types of MPCMs. Extensive parametric analyses along with the employed optimization procedure accomplished the mixture design of latent heat thermal energy storage concrete with maximum MPCM inclusion and minimum cement content for various compressive strength classes. The proposed framework enables energy conservation technology in the design of eco-friendly building materials with acceptable mechanical performance." @default.
• W4310553658 created "2022-12-12" @default.
• W4310553658 creator A5039665873 @default.
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• W4310553658 date "2023-02-01" @default.
• W4310553658 modified "2023-10-16" @default.
• W4310553658 title "Design of concrete incorporating microencapsulated phase change materials for clean energy: A ternary machine learning approach based on generative adversarial networks" @default.
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• W4310553658 doi "https://doi.org/10.1016/j.engappai.2022.105652" @default.
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