Prediction of Bitumen Ageing States Using the Kernel Ridge Regression

Habassi, K.; Coulon, L.; Ayadi, A.; Samet, A.; Chazallon, C.

doi:10.34726/11000

Record link:

http://hdl.handle.net/20.500.12708/219653
https://doi.org/10.34726/11000

Title:

Prediction of Bitumen Ageing States Using the Kernel Ridge Regression

Citation:

Habassi, K., Coulon, L., Ayadi, A., Samet, A., & Chazallon, C. (2025). Prediction of Bitumen Ageing States Using the Kernel Ridge Regression. 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. 555–558). TU Wien, E230-03 Road Engineering. https://doi.org/10.34726/11000

reposiTUm DOI:

10.34726/11000

Publication Type:

Inproceedings - Full-Paper Contribution

Language:

English

Authors:

Habassi, K.
Coulon, L.
Ayadi, A.
Samet, A.
Chazallon, C.

Organisational Unit:

E000 - Technische Universität Wien

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

ISBN:

978-3-901912-99-3

Volume:

DOI of the book:

10.34726/9259

Date (published):

2025

Event name:

Advances in Materials and Pavement Performance Prediction 2025 (AM3P 2025)

Event date:

7-May-2025 - 9-May-2025

Event place:

Wien, Austria

Number of Pages:

Publisher:

TU Wien, E230-03 Road Engineering, Wien

Keywords:

bitumen ageing; Kernel Ridge Regression; Machine Learning

Abstract:

The study presents the first results of a supervised machine learning model used to predict the complex modulus of both neat and aged binders recovered from asphalt concrete. Trained on data combining complex modulus measurements and chemical properties, the model captures the intricate relationships between bitumen composition and mechanical behaviour. The model successfully predicts both known and unknown ageing states.

Research Areas:

Materials Characterization: 50%
Modeling and Simulation: 50%

Science Branch:

2011 - Bauingenieurwesen: 30%
2013 - Verkehrswesen: 70%

License:

CC BY 4.0