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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Veröffentlicht in:	Applied sciences Jg. 10; H. 20; S. 7330
Hauptverfasser:	Farooq, Furqan, Nasir Amin, Muhammad, Khan, Kaffayatullah, Rehan Sadiq, Muhammad, Faisal Javed, Muhammad Faisal, Aslam, Fahid, Alyousef, Rayed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.10.2020
Schlagworte:	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
Online-Zugang:	Volltext
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