Prediction of Bending Beam Rheometer Test Outputs Using Artificial Neural Networks

The major objective of this study is to investigate the possibility of using Artificial Neural Networks in creating prediction models capable of estimating Bending Beam Rheometer outputs; namely creep stiffness, and m-value based on test temperature, modifier content; in our case waste vegetable oil...

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Vydáno v:	Key Engineering Materials IX - 9th International Conference on Key Engineering Materials (9th ICKEM 2019) Ročník 821; s. 1
Hlavní autoři:	Aljarrah, Mohammad Fuad, Alshorman, Mohammad Emad, Khasawneh, Mohammad Ali, Al-Omari, Aslam Ali
Médium:	Kapitola Journal Article
Jazyk:	angličtina
Vydáno:	Zurich Trans Tech Publications 2019 Trans Tech Publications Ltd
Témata:	Algorithms Artificial neural networks Back propagation General Engineering & Project Administration Materials Neural networks Prediction models Rheometers Stiffness Testing time Vegetable oils Bending Beam Rheometer Creep Stiffness m-Value Artificial Neural Networks Waste Vegetable Oil
ISBN:	9783035714807, 3035714800
ISSN:	1013-9826, 1662-9795, 1662-9795
On-line přístup:	Získat plný text
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