Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming...

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Detalles Bibliográficos
Autores principales: Khan, Mohsin Ali, Farooq, Furqan, Javed, Mohammad Faisal, Zafar, Adeel, Ostrowski, Krzysztof Adam, Aslam, Fahid, Malazdrewicz, Seweryn, Maślak, Mariusz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8746218/
https://www.ncbi.nlm.nih.gov/pubmed/35009206
http://dx.doi.org/10.3390/ma15010058

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8746218/
https://www.ncbi.nlm.nih.gov/pubmed/35009206
http://dx.doi.org/10.3390/ma15010058

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