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

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 o...

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Bibliographic Details
Published in:Materials and structures 2022-05, Vol.55 (4), Article 112
Main 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, Dave, Eshan, Tebaldi, Gabrielle
Format: Article
Language:English
Subjects:
Artificial neural networks
Asphalt pavements
Bonding strength
Building construction
Building Materials
Civil Engineering
Data analysis
Engineering
Machine learning
Machines
Manufacturing
Materials Science
Original Article
Polynomials
Processes
Reclamation
Regularization
Solid Mechanics
Tensile strength
Theoretical and Applied Mechanics
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Summary: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.
ISSN:1359-5997
1871-6873
DOI:10.1617/s11527-022-01933-9