ArticleScientific reports2025
A novel hybrid metaheuristic algorithm and response surface methodology approach for predictive modeling and optimization of cementitious compressive strength.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Compressive strength is a fundamental property for evaluating cementitious materials, yet its prediction and optimization remain challenging due to the complex and nonlinear interactions of chemical composition, curing time, and processing conditions. This study presents a hybrid framework that integrates Response Surface Methodology (RSM) with the Genetic Algorithm (GA) to enhance predictive modeling and optimize cement mixtures. RSM was used to construct polynomial models-linear, linear + square, linear + interaction, and full quadratic-to capture the relationships between eight oxide compositions (Silo₂, Allow₃, Foo₃, Cano, Mg, SO₃, K₂O, NaoSiO
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