Mechanical performance prediction of asphalt mixtures: a baseline study of linear and non-linear regression compared with neural network modeling

Nicola Baldo; Fabio Rondinella; Fabiola Daneluz; Pavla Vacková; Jan Valentin; Marcin Gajewski; Jan Król

doi:10.7409/rabdim.025.001

Published: 2025-03-05

Vol. 24 No. 1 (2025)

Mechanical performance prediction of asphalt mixtures: a baseline study of linear and non-linear regression compared with neural network modeling

Nicola Baldo

, Fabio Rondinella

, Fabiola Daneluz

, Pavla Vacková

, Jan Valentin

, Marcin Gajewski

, Jan Król

Section: Articles

https://doi.org/10.7409/rabdim.025.001

Abstract

Accurate predictions of asphalt mixtures’ mechanical performance are crucial to improve the conventional mix-design procedures and to optimize both pavements’ performance and service life. This research explores this issue by means of a comparative analysis between different modeling approaches: conventional regressions, both linear and non-linear, and artificial neural networks. The former are widely used but may lack the flexibility to capture complex relationships between testing conditions and the corresponding mechanical behavior. The latter represent promising alternatives due to their capability to successfully model non-linear interactions between variables. This research compares the predictive accuracy of these different modeling approaches using experimental data resulting from 4-point bending tests carried out under several temperatures and loading frequencies. The outcomes suggest that neural networks outperform conventional regression models in capturing complex relationships, highlighting the strengths and limitations of each modeling approach and providing insights for selecting optimal models in road pavement engineering applications.

Keywords:

artificial neural networks, asphalt mixtures, linear regression, machine learning, mechanical behaviour, non-linear regression

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Baldo, N., Rondinella, F., Daneluz, F., Vacková, P., Valentin, J., Gajewski, M., & Król, J. (2025). Mechanical performance prediction of asphalt mixtures: a baseline study of linear and non-linear regression compared with neural network modeling. Roads and Bridges – Drogi I Mosty, 24(1), 27–35. https://doi.org/10.7409/rabdim.025.001

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Nicola Baldo

nicola.baldo@uniud.it
https://orcid.org/0000-0002-5627-9763

Affiliation:

University of Udine, Polytechnic Department of Engineering and Architecture, Via del Cotonificio 114, 33100 Udine Italy

Fabio Rondinella

https://orcid.org/0000-0002-8702-5555

Affiliation:

University of Udine, Polytechnic Department of Engineering and Architecture, Via del Cotonificio 114, 33100 Udine Italy

Fabiola Daneluz

https://orcid.org/0000-0002-8305-3911

Affiliation:

University of Udine, Polytechnic Department of Engineering and Architecture, Via del Cotonificio 114, 33100 Udine Italy

Pavla Vacková

https://orcid.org/0000-0003-4636-8356

Affiliation:

Czech Technical University, Faculty of Civil Engineering, Thákurova 7, 166 29 Prague Czechia

Jan Valentin

https://orcid.org/0000-0001-9155-1921

Affiliation:

Czech Technical University, Faculty of Civil Engineering, Thákurova 7, 166 29 Prague Czechia

Marcin Gajewski

https://orcid.org/0000-0002-3171-8504

Affiliation:

Warsaw University of Technology, Faculty of Civil Engineering, 16 Armii Ludowej Av., 00-637 Warsaw Poland

Jan Król

https://orcid.org/0000-0001-8728-3530

Affiliation:

Warsaw University of Technology, Faculty of Civil Engineering, 16 Armii Ludowej Av., 00-637 Warsaw Poland

Mechanical performance prediction of asphalt mixtures: a baseline study of linear and non-linear regression compared with neural network modeling

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Nicola Baldo

Fabio Rondinella

Fabiola Daneluz

Pavla Vacková

Jan Valentin

Marcin Gajewski

Jan Król