Determination of rheological properties of bio-asphalt binders through experimental and multilayer feed-forward neural network methods

This study seeks to determine the rheological properties of unaged and RTFO-aged bio-asphalt binders using experimental and modelling methods. Crude palm oil (CPO) was used as a bio-oil at varying percentages of 0, 5, 10 and 15% by total weight of asphalt binder. The dynamic shear rheometer (DSR) wa...

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Autores principales:	Abdulnaser M Al-Sabaeei, Madzlan B Napiah, Muslich H Sutanto, Suzielah Rahmad, Nur Izzi Md Yusoff, Wesam S Alaloul
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Bio-asphalt Crude palm oil Rheological properties Artificial neural network Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/d1cf9b8937c443f7a423cdb3acfde582
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spelling	oai:doaj.org-article:d1cf9b8937c443f7a423cdb3acfde5822021-11-22T04:21:28ZDetermination of rheological properties of bio-asphalt binders through experimental and multilayer feed-forward neural network methods2090-447910.1016/j.asej.2021.04.003https://doaj.org/article/d1cf9b8937c443f7a423cdb3acfde5822021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2090447921001623https://doaj.org/toc/2090-4479This study seeks to determine the rheological properties of unaged and RTFO-aged bio-asphalt binders using experimental and modelling methods. Crude palm oil (CPO) was used as a bio-oil at varying percentages of 0, 5, 10 and 15% by total weight of asphalt binder. The dynamic shear rheometer (DSR) was used to investigate the rheological properties of bio-asphalt binders. The multilayer feed-forward neural network method was used to predict the complex modulus and phase angle of bio-asphalt binders by virtue of its ability to learn and adapt. Result of the DSR analysis showed that the complex modulus of bio-asphalt with 5% CPO is almost similar as that of the base asphalt binder, and that higher CPO content resulted in reduced complex modulus and higher phase angle. Result of the modelling shows that all models have an R2 value greater than 0.99, thus indicating the good agreement between the predicted and the experimental results.Abdulnaser M Al-SabaeeiMadzlan B NapiahMuslich H SutantoSuzielah RahmadNur Izzi Md YusoffWesam S AlaloulElsevierarticleBio-asphaltCrude palm oilRheological propertiesArtificial neural networkEngineering (General). Civil engineering (General)TA1-2040ENAin Shams Engineering Journal, Vol 12, Iss 4, Pp 3485-3493 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bio-asphalt Crude palm oil Rheological properties Artificial neural network Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Bio-asphalt Crude palm oil Rheological properties Artificial neural network Engineering (General). Civil engineering (General) TA1-2040 Abdulnaser M Al-Sabaeei Madzlan B Napiah Muslich H Sutanto Suzielah Rahmad Nur Izzi Md Yusoff Wesam S Alaloul Determination of rheological properties of bio-asphalt binders through experimental and multilayer feed-forward neural network methods
description	This study seeks to determine the rheological properties of unaged and RTFO-aged bio-asphalt binders using experimental and modelling methods. Crude palm oil (CPO) was used as a bio-oil at varying percentages of 0, 5, 10 and 15% by total weight of asphalt binder. The dynamic shear rheometer (DSR) was used to investigate the rheological properties of bio-asphalt binders. The multilayer feed-forward neural network method was used to predict the complex modulus and phase angle of bio-asphalt binders by virtue of its ability to learn and adapt. Result of the DSR analysis showed that the complex modulus of bio-asphalt with 5% CPO is almost similar as that of the base asphalt binder, and that higher CPO content resulted in reduced complex modulus and higher phase angle. Result of the modelling shows that all models have an R2 value greater than 0.99, thus indicating the good agreement between the predicted and the experimental results.
format	article
author	Abdulnaser M Al-Sabaeei Madzlan B Napiah Muslich H Sutanto Suzielah Rahmad Nur Izzi Md Yusoff Wesam S Alaloul
author_facet	Abdulnaser M Al-Sabaeei Madzlan B Napiah Muslich H Sutanto Suzielah Rahmad Nur Izzi Md Yusoff Wesam S Alaloul
author_sort	Abdulnaser M Al-Sabaeei
title	Determination of rheological properties of bio-asphalt binders through experimental and multilayer feed-forward neural network methods
title_short	Determination of rheological properties of bio-asphalt binders through experimental and multilayer feed-forward neural network methods
title_full	Determination of rheological properties of bio-asphalt binders through experimental and multilayer feed-forward neural network methods
title_fullStr	Determination of rheological properties of bio-asphalt binders through experimental and multilayer feed-forward neural network methods
title_full_unstemmed	Determination of rheological properties of bio-asphalt binders through experimental and multilayer feed-forward neural network methods
title_sort	determination of rheological properties of bio-asphalt binders through experimental and multilayer feed-forward neural network methods
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/d1cf9b8937c443f7a423cdb3acfde582
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Determination of rheological properties of bio-asphalt binders through experimental and multilayer feed-forward neural network methods

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