A Machine Learning Led Investigation Predicting the Thermos-mechanical Properties of Novel Waste-based Composite in Construction

The study explores the potential of machine learning (ML) in predicting the thermal and mechanical properties of earth-based composites reinforced with natural Borassus fruit fiber. The limited availability of large datasets for accurate predictions is a challenge in material science research, which...

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Veröffentlicht in:	Waste and biomass valorization 2024-09, Vol.15 (9), p.5445-5461
Hauptverfasser:	Mahamat, Assia Aboubakar, Boukar, Moussa Mahamat, Leklou, Nordine, Obianyo, Ifeyinwa Ijeoma, Stanislas, Tido Tiwa, Bih, Numfor Linda, Ayeni, Olugbenga, Ibrahim, Nurudeen Mahmud, Savastano, Holmer
Format:	Artikel
Sprache:	eng
Schlagworte:	Building materials Competitive materials Composite materials Compressive strength Construction industry Construction materials Data collection Datasets Decision trees Engineering Engineering Sciences Environment Environmental Engineering/Biotechnology Flexural strength Heat conductivity Heat transfer Industrial Pollution Prevention Learning algorithms Machine learning Mechanical properties Original Paper Performance assessment Predictions Regression analysis Renewable and Green Energy Root-mean-square errors Thermal conductivity Thermodynamic properties Waste Management/Waste Technology
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