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
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Shrnutí: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, and testing time interval. A feedforward backpropagation neural network with Bayesian Regulation training algorithm and an SSE performance function was implemented. It was found that the neural network model shows high predictive powers with training and testing performance of 99.8% and 99.2% respectively. Plots between laboratory obtained values and neural network predicted outputs were also considered, and a strong correlation between the two methods was concluded. Therefore, it was reasonable to state that using neural networks to build prediction models in order to find BBR test values is justified.
Bibliografie:Selected, peer reviewed papers from the 9th International Conference on Key Engineering Materials (9th ICKEM 2019), 29 March - 1 April, 2019, Oxford, United Kingdom
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISBN:9783035714807
3035714800
ISSN:1013-9826
1662-9795
1662-9795
DOI:10.4028/www.scientific.net/KEM.821.500