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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Bibliographic Details
Published in:	Multiscale and Multidisciplinary Modeling, Experiments and Design Vol. 7; no. 3; pp. 2901 - 2928
Main Authors:	Parhi, Suraj Kumar, Panda, Soumyaranjan, Dwibedy, Saswat, Panigrahi, Saubhagya Kumar
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.07.2024
Subjects:	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
Online Access:	Get full text
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