Record link: 
http://hdl.handle.net/20.500.12708/219305
https://doi.org/10.34726/10796
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Title: 
Exploring Machine Learning Approaches for Resilient Modulus in Rejuvenated Reclaimed Asphalt Pavement
en
Citation: 
Ayazi, M. F., Singh, M., & Kumar, R. (2025). Exploring Machine Learning Approaches for Resilient Modulus in Rejuvenated Reclaimed Asphalt Pavement. In L. Eberhardsteiner, B. Hofko, & R. Blab (Eds.), 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 (pp. 539–542). TU Wien, E230-03 Road Engineering. https://doi.org/10.34726/10796
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reposiTUm DOI: 
10.34726/10796
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CatalogPlus: 
AC17644060
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Publication Type: 
Inproceedings - Full-Paper Contribution
en
Language: 
English
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Authors: 
Ayazi, M. F. 
Singh, M. 
Kumar, R. 
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Organisational Unit: 
E000 - Technische Universität Wien 
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Published in: 
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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ISBN: 
978-3-901912-99-3
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Volume: 
IV
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DOI of the book: 
10.34726/9259
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Date (published): 
2025
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Event name: 
Advances in Materials and Pavement Performance Prediction 2025 (AM3P 2025) 
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Event date: 
7-May-2025 - 9-May-2025
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Event place: 
Wien, Austria
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Number of Pages: 
4
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Publisher: 
TU Wien, E230-03 Road Engineering, Wien
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Peer reviewed: 
Yes
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Keywords: 
Machine Learning; Resilient Modulus; RAP; Flexible Pavement; Rejuvenator
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Research Areas: 
Modeling and Simulation: 35%
Structure-Property Relationsship: 30%
Computational System Design: 35%
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Science Branch: 
2011 - Bauingenieurwesen: 30%
2013 - Verkehrswesen: 70%
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License: 
CC BY 4.0 CC BY 4.0
de
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