Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement
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2022
Autori
Botella, RamonLo Presti, Davide
Vasconcelos, Kamilla
Bernatowicz, Kinga
Martínez, Adriana
Miró, Rodrigo
Specht, Luciano
Arámbula Mercado, Edith
Menegusso Pires, Gustavo
Pasquini, Emiliano
Ogbo, Chibuike
Preti, Francesco
Pasetto, Marco
Jiménez del Barco Carrión, Ana
Roberto, Antonio
Orešković, Marko
Kuna, Kranthi
Guduru, Gurunath
Epps Martin, Amy
Carter, Alan
Giancontieri, Gaspare
Abed, Ahmed
Dave, Eshan
Tebaldi, Gabriele
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
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 R...eclaimed 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.
Izvor:
Materials and Structures, 2022, 55, 112-Izdavač:
- Springer