Metaheuristic optimization of machine learning models for strength prediction of high-performance self-compacting alkali-activated slag concrete

The present study focuses on producing high-performance eco-efficient alternatives to conventional cement-based composites. The study is divided into two parts. The first part comprises of production of high-strength self-compacting alkali-activated slag concrete (SC-AASC) with GGBFS as a primary bi...

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Vydané v:	Multiscale and Multidisciplinary Modeling, Experiments and Design Ročník 7; číslo 3; s. 2901 - 2928
Hlavní autori:	Parhi, Suraj Kumar, Panda, Soumyaranjan, Dwibedy, Saswat, Panigrahi, Saubhagya Kumar
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.07.2024
Predmet:	Characterization and Evaluation of Materials Engineering Mathematical Applications in the Physical Sciences Mechanical Engineering Numerical and Computational Physics Original Paper Simulation Solid Mechanics High-strength Self-compacting Sensitivity analysis GGBFS XGBoost Machine learning Alkali-activated concrete SHO
ISSN:	2520-8160, 2520-8179
On-line prístup:	Získať plný text
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