Application of Ensemble Machine Learning Methods to Estimate the Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete

In this study, compressive strength (CS) of fiber-reinforced nano-silica concrete (FRNSC) was anticipated using ensemble machine learning (ML) approaches. Four types of ensemble ML methods were employed, including gradient boosting, random forest, bagging regressor, and AdaBoost regressor, to achiev...

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Detalles Bibliográficos
Autores principales: Anjum, Madiha, Khan, Kaffayatullah, Ahmad, Waqas, Ahmad, Ayaz, Amin, Muhammad Nasir, Nafees, Afnan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9506242/
https://www.ncbi.nlm.nih.gov/pubmed/36146051
http://dx.doi.org/10.3390/polym14183906

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9506242/
https://www.ncbi.nlm.nih.gov/pubmed/36146051
http://dx.doi.org/10.3390/polym14183906

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