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

Abstract

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.

Publication DOI: https://doi.org/10.1617/s11527-022-01933-9
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Engineering Systems and Supply Chain Management
Additional Information: 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 http://creativecommons.org/licenses/by/4.0/. 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.
Uncontrolled Keywords: Artificial neural networks,Degree of binder activity,Hot mix asphalt,Indirect tensile strength,Machine learning,Random forest,Reclaimed asphalt pavement,Recycling,Civil and Structural Engineering,Building and Construction,Materials Science(all),Mechanics of Materials
Publication ISSN: 1359-5997
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://link.sp ... 527-022-01933-9 (Publisher URL)
PURE Output Type: Article
Published Date: 2022-05
Published Online Date: 2022-04-16
Accepted Date: 2022-03-01
Authors: Botella, Ramon
Lo Presti, Davide
Vasconcelos, Kamilla
Bernatowicz, Kinga
Martínez, Adriana H.
Miró, Rodrigo
Specht, Luciano
Mercado, Edith Arámbula
Pires, Gustavo Menegusso
Pasquini, Emiliano
Ogbo, Chibuike
Preti, Francesco
Pasetto, Marco
del Barco Carrión, Ana Jiménez
Roberto, Antonio
Orešković, Marko
Kuna, Kranthi K.
Guduru, Gurunath
Martin, Amy Epps
Carter, Alan
Giancontieri, Gaspare
Abed, Ahmed (ORCID Profile 0000-0002-6822-5519)
Dave, Eshan
Tebaldi, Gabrielle

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