<div class="csl-bib-body">
<div class="csl-entry">Zhang, F., Wang, D., Sun, Y., & Falchetto, A. C. (2025). Machine learning-aided rheological prediction models of asphalt binders based on chemical properties. In L. Eberhardsteiner, B. Hofko, & R. Blab (Eds.), <i>Advances in Materials and Pavement Performance Prediction IV : Contributions to the 4th International Conference on Advances in Materials and Pavement Performance Prediction (AM3P 2025), 7-9 May 2025, Vienna, Austria</i> (pp. 583–586). TU Wien, E230-03 Road Engineering. https://doi.org/10.34726/11001</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/219654
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dc.identifier.uri
https://doi.org/10.34726/11001
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dc.description.abstract
This work aims to provide rapid rheological characterization of asphalt binders through their chemical properties based on advanced machine learning tools. With this objective, Fourier transform infrared spectroscopy (FTIR) and dynamic shear rheometer (DSR) are adopted to measure the chemical and rheological properties. Results indicate that the raw six FTIR features can be reduced to two principal components (PC 1 and PC 2), and the variance and role of PC 1 are more significant than PC 2. Multiple linear regression models can predict the phase angle accurately but not for modulus. Gaussian process regression model with higher R2 and lower RMSE values can accurately predict both modulus and phase angle.
en
dc.language.iso
en
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dc.relation.ispartofseries
Advances in Materials and Pavements Performance Prediction
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
asphalt binders
en
dc.subject
rheological properties
en
dc.subject
chemical composition
en
dc.subject
machine learning
en
dc.title
Machine learning-aided rheological prediction models of asphalt binders based on chemical properties
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.identifier.doi
10.34726/11001
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dc.contributor.affiliation
Aalto University, Finland
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dc.contributor.affiliation
Aalto University, Finland
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dc.contributor.affiliation
Aalto University, Finland
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dc.contributor.affiliation
Aalto University, Finland
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dc.relation.isbn
978-3-901912-99-3
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dc.relation.doi
10.34726/9259
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dc.description.startpage
583
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dc.description.endpage
586
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dc.rights.holder
TU Wien, E230-03 Road Engineering
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Advances in Materials and Pavement Performance Prediction IV : Contributions to the 4th International Conference on Advances in Materials and Pavement Performance Prediction (AM3P 2025), 7-9 May 2025, Vienna, Austria
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tuw.container.volume
IV
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tuw.book.ispartofseries
Advances in Materials and Pavements Performance Prediction
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tuw.relation.publisher
TU Wien, E230-03 Road Engineering
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tuw.relation.publisherplace
Wien
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tuw.researchTopic.id
C6
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tuw.researchTopic.id
M8
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tuw.researchTopic.id
C3
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tuw.researchTopic.name
Modeling and Simulation
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tuw.researchTopic.name
Structure-Property Relationsship
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tuw.researchTopic.name
Computational System Design
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tuw.researchTopic.value
35
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tuw.researchTopic.value
30
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tuw.researchTopic.value
35
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tuw.publication.orgunit
E000 - Technische Universität Wien
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dc.description.numberOfPages
4
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dc.rights.identifier
CC BY 4.0
de
dc.rights.identifier
CC BY 4.0
en
tuw.editor.orcid
0000-0003-2153-9315
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tuw.editor.orcid
0000-0002-8329-8687
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tuw.editor.orcid
0000-0003-4101-1964
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tuw.event.name
Advances in Materials and Pavement Performance Prediction 2025 (AM3P 2025)
