Equation-driven strength prediction of GGBS concrete: a symbolic machine learning approach for sustainable development

The growing demand for low-carbon construction materials highlights the need to predict and optimise the performance of Ground Granulated Blast-Furnace Slag (GGBS) concrete with high accuracy. This study aims to develop reliable and interpretable predictive models for estimating the compressive stre...

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Autores principales:	Amin Muhammad Nasir, Naeem Asad, Faraz Muhammad Iftikhar, Qadir Muhammad Tahir
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	De Gruyter 2025-12-01
Colección:	Reviews on Advanced Materials Science
Materias:	ground granulated blast-furnace slag-based concrete compressive strength machine learning prediction models
Acceso en línea:	https://doi.org/10.1515/rams-2025-0183
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