A Comparative Study of Random Forest and Genetic Engineering Programming for the Prediction of Compressive Strength of High Strength Concrete (HSC)

Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties of concrete. This study uses an ensemble random forest (RF) and gene expression programming (GEP) algorithm for the compressive strength prediction of high strength concrete. The parameters...

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Vydané v:	Applied sciences Ročník 10; číslo 20; s. 7330
Hlavní autori:	Farooq, Furqan, Nasir Amin, Muhammad, Khan, Kaffayatullah, Rehan Sadiq, Muhammad, Faisal Javed, Muhammad Faisal, Aslam, Fahid, Alyousef, Rayed
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.10.2020
Predmet:	Algorithms Civil engineering Concrete Construction industry Datasets Gene expression Genetic engineering genetic engineering programming High strength concrete Machine learning Mechanical properties prediction Prediction theory Research methodology strength concrete Structure Pakistan
ISSN:	2076-3417, 2076-3417
On-line prístup:	Získať plný text
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