Predicting rutting performance of carbon nano tube (CNT) asphalt binders using regression models and neural networks

Predicting rutting performance of carbon nano tube (CNT) asphalt binders using regression models... •The effect of CNT, loading frequency and temperature on complex modulus and rutting resistance were investigated.•The addition of CNT to the neat binder lead to improve rutting resistance and mechanical behavior.•ANN techniques to be more effective in predicting the rutting properties of the CNT modified binder.•R2 values in ANN, multiple and linear regression the data set are 0.997, 0.819, and 0.420, respectively.•The proposed ANN model of the rutting performance of nano modified binders is quite accurate, fast and practical. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Construction and Building Materials Elsevier

Predicting rutting performance of carbon nano tube (CNT) asphalt binders using regression models and neural networks

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier Ltd
ISSN
0950-0618
eISSN
1879-0526
D.O.I.
10.1016/j.conbuildmat.2017.11.071
Publisher site
See Article on Publisher Site

Abstract

•The effect of CNT, loading frequency and temperature on complex modulus and rutting resistance were investigated.•The addition of CNT to the neat binder lead to improve rutting resistance and mechanical behavior.•ANN techniques to be more effective in predicting the rutting properties of the CNT modified binder.•R2 values in ANN, multiple and linear regression the data set are 0.997, 0.819, and 0.420, respectively.•The proposed ANN model of the rutting performance of nano modified binders is quite accurate, fast and practical.

Journal

Construction and Building MaterialsElsevier

Published: Jan 30, 2018

References

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