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Engineering characteristics of nanosilica/polymer-modified bitumen and predicting their rheological properties using multilayer perceptron neural network model

This study examines the effect of mixing varying percentages of nano-silica (NS), i.e. 2, 4 and 6% (by weight of polymer-modified bitumen, PMB) with PMB, in unaged and aged conditions. The Fourier transform infrared spectroscopy, x-ray diffraction, scanning electron microscopy and dynamic shear rheo...

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Main Authors: Md. Yusoff, Nur Izzi, Alhamali, Dhawo Ibrahim, Ibrahim, Ahmad Nazrul Hakimi, Rosyidi, Sri Atmaja P., Abdul Hassan, Norhidayah
Format: Article
Published: Elsevier Ltd. 2019
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/88008/
http://dx.doi.org/10.1016/j.conbuildmat.2019.01.203
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spelling my.utm.880082020-12-14T22:58:33Z http://eprints.utm.my/id/eprint/88008/ Engineering characteristics of nanosilica/polymer-modified bitumen and predicting their rheological properties using multilayer perceptron neural network model Md. Yusoff, Nur Izzi Alhamali, Dhawo Ibrahim Ibrahim, Ahmad Nazrul Hakimi Rosyidi, Sri Atmaja P. Abdul Hassan, Norhidayah TA Engineering (General). Civil engineering (General) This study examines the effect of mixing varying percentages of nano-silica (NS), i.e. 2, 4 and 6% (by weight of polymer-modified bitumen, PMB) with PMB, in unaged and aged conditions. The Fourier transform infrared spectroscopy, x-ray diffraction, scanning electron microscopy and dynamic shear rheometer were used to determine chemical, microstructure and rheological properties of the binders, respectively. An artificial neural network (ANN) model, known as the multilayer perceptron neural networks model with three different algorithms namely; Levenberg-Marquardt (LM), scaled conjugate gradient (SCG), and gradient descent with adaptive back propagation (GDA) were used to predict the rheological properties of binders. The results indicate that adding NS to PMB may weaken the binders and delay their ageing. The amorphous structures of NS-PMBs remain unchanged and no new crystalline phase was formed when varying percentages of NS was added to PMB. Extreme heat caused a marked increase in the complex modulus of NS-PMB6 while low temperatures reduced its complex modulus. This resulted in enhanced resistance to the rutting and fatigue parameters. Adding higher amounts of NS particles to PMB also improved the viscoelastic properties and resistance to the ageing conditions of NS-PMB6. In terms of modeling, it was found that the most suitable algorithms and neurons number in the hidden layer for the ANN-Unaged model is LM algorithm and 11 neurons. For ANN-RTFOT and ANN-PAV models, the optimum algorithms and neurons number in hidden layer is SGC algorithm with 11 neurons and LM with 9 neurons respectively. The R-value (>0.95) for all models show a good agreement between measured and predicted data. It was concluded that the ANNs could be used as an accurate, fast and practical method for researchers and engineers to predict the phase angle and complex modulus of NS-PMBs. Elsevier Ltd. 2019-04-20 Article PeerReviewed Md. Yusoff, Nur Izzi and Alhamali, Dhawo Ibrahim and Ibrahim, Ahmad Nazrul Hakimi and Rosyidi, Sri Atmaja P. and Abdul Hassan, Norhidayah (2019) Engineering characteristics of nanosilica/polymer-modified bitumen and predicting their rheological properties using multilayer perceptron neural network model. Construction and Building Materials, 204 . pp. 781-799. ISSN 0950-0618 http://dx.doi.org/10.1016/j.conbuildmat.2019.01.203 DOI:10.1016/j.conbuildmat.2019.01.203
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Md. Yusoff, Nur Izzi
Alhamali, Dhawo Ibrahim
Ibrahim, Ahmad Nazrul Hakimi
Rosyidi, Sri Atmaja P.
Abdul Hassan, Norhidayah
Engineering characteristics of nanosilica/polymer-modified bitumen and predicting their rheological properties using multilayer perceptron neural network model
description This study examines the effect of mixing varying percentages of nano-silica (NS), i.e. 2, 4 and 6% (by weight of polymer-modified bitumen, PMB) with PMB, in unaged and aged conditions. The Fourier transform infrared spectroscopy, x-ray diffraction, scanning electron microscopy and dynamic shear rheometer were used to determine chemical, microstructure and rheological properties of the binders, respectively. An artificial neural network (ANN) model, known as the multilayer perceptron neural networks model with three different algorithms namely; Levenberg-Marquardt (LM), scaled conjugate gradient (SCG), and gradient descent with adaptive back propagation (GDA) were used to predict the rheological properties of binders. The results indicate that adding NS to PMB may weaken the binders and delay their ageing. The amorphous structures of NS-PMBs remain unchanged and no new crystalline phase was formed when varying percentages of NS was added to PMB. Extreme heat caused a marked increase in the complex modulus of NS-PMB6 while low temperatures reduced its complex modulus. This resulted in enhanced resistance to the rutting and fatigue parameters. Adding higher amounts of NS particles to PMB also improved the viscoelastic properties and resistance to the ageing conditions of NS-PMB6. In terms of modeling, it was found that the most suitable algorithms and neurons number in the hidden layer for the ANN-Unaged model is LM algorithm and 11 neurons. For ANN-RTFOT and ANN-PAV models, the optimum algorithms and neurons number in hidden layer is SGC algorithm with 11 neurons and LM with 9 neurons respectively. The R-value (>0.95) for all models show a good agreement between measured and predicted data. It was concluded that the ANNs could be used as an accurate, fast and practical method for researchers and engineers to predict the phase angle and complex modulus of NS-PMBs.
format Article
author Md. Yusoff, Nur Izzi
Alhamali, Dhawo Ibrahim
Ibrahim, Ahmad Nazrul Hakimi
Rosyidi, Sri Atmaja P.
Abdul Hassan, Norhidayah
author_facet Md. Yusoff, Nur Izzi
Alhamali, Dhawo Ibrahim
Ibrahim, Ahmad Nazrul Hakimi
Rosyidi, Sri Atmaja P.
Abdul Hassan, Norhidayah
author_sort Md. Yusoff, Nur Izzi
title Engineering characteristics of nanosilica/polymer-modified bitumen and predicting their rheological properties using multilayer perceptron neural network model
title_short Engineering characteristics of nanosilica/polymer-modified bitumen and predicting their rheological properties using multilayer perceptron neural network model
title_full Engineering characteristics of nanosilica/polymer-modified bitumen and predicting their rheological properties using multilayer perceptron neural network model
title_fullStr Engineering characteristics of nanosilica/polymer-modified bitumen and predicting their rheological properties using multilayer perceptron neural network model
title_full_unstemmed Engineering characteristics of nanosilica/polymer-modified bitumen and predicting their rheological properties using multilayer perceptron neural network model
title_sort engineering characteristics of nanosilica/polymer-modified bitumen and predicting their rheological properties using multilayer perceptron neural network model
publisher Elsevier Ltd.
publishDate 2019
url http://eprints.utm.my/id/eprint/88008/
http://dx.doi.org/10.1016/j.conbuildmat.2019.01.203
_version_ 1687393517808123904
score 13.211869

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