Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement

Ramon Botella, Davide Lo Presti*, Kamilla Vasconcelos, Kinga Bernatowicz, Adriana H. Martínez, Rodrigo Miró, Luciano Specht, Edith Arámbula Mercado, Gustavo Menegusso Pires, Emiliano Pasquini, Chibuike Ogbo, Francesco Preti, Marco Pasetto, Ana Jiménez del Barco Carrión, Antonio Roberto, Marko Orešković, Kranthi K. Kuna, Gurunath Guduru, Amy Epps Martin, Alan CarterGaspare Giancontieri, Ahmed Abed, Eshan Dave, Gabrielle Tebaldi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This paper describes the development of novel/state-of-art computational framework to accurately predict the degree of binder activity of a reclaimed asphalt pavement sample as a percentage of the indirect tensile strength (ITS) using a reduced number of input variables that are relatively easy to obtain, namely compaction temperature, air voids and ITS. Different machine learning (ML) techniques were applied to obtain the most accurate data representation model. Specifically, three ML techniques were applied: 6th-degree multivariate polynomial regression with regularization, artificial neural network and random forest regression. The three techniques produced models with very similar precision, reporting a mean absolute error ranging from 12.2 to 12.8% of maximum ITS on the test data set. The work presented in this paper is an evolution in terms of data analysis of the results obtained within the interlaboratory tests conducted by Task Group 5 of the RILEM Technical Committee 264 on Reclaimed Asphalt Pavement. Hence, despite it has strong bonds with this framework, this work was developed independently and can be considered as a natural follow-up.

Original languageEnglish
Article number112
JournalMaterials and Structures/Materiaux et Constructions
Issue number4
Early online date16 Apr 2022
Publication statusPublished - May 2022

Bibliographical note

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Funding Information:
Part of this research was funded by the project RTI2018-096224-J-I00 that has been cofounded by the Spanish Ministry of Science and Innovation, inside the National Program for Fostering Excellence in Scientific and Technical Research, National Subprogram of Knowledge Generation, 2018 call, in the framework of the Spanish National Plan for Scientific and Technical Research and Innovation 2017–2020, and by the European Union, through the European Regional Development Fund, with the main objective of Promoting technological development, innovation and quality research. Part of this work was financially supported by the Italian Ministry of University and Research with the research Grant PRIN 2017 USR342 Urban Safety, Sustainability and Resilience.


  • Artificial neural networks
  • Degree of binder activity
  • Hot mix asphalt
  • Indirect tensile strength
  • Machine learning
  • Random forest
  • Reclaimed asphalt pavement
  • Recycling


Dive into the research topics of 'Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement'. Together they form a unique fingerprint.

Cite this