TY  - JOUR
AU  - Botella, Ramon
AU  - Lo Presti, Davide
AU  - Vasconcelos, Kamilla
AU  - Bernatowicz, Kinga
AU  - Martínez, Adriana
AU  - Miró, Rodrigo
AU  - Specht, Luciano
AU  - Arámbula Mercado, Edith
AU  - Menegusso Pires, Gustavo
AU  - Pasquini, Emiliano
AU  - Ogbo, Chibuike
AU  - Preti, Francesco
AU  - Pasetto, Marco
AU  - Jiménez del Barco Carrión, Ana
AU  - Roberto, Antonio
AU  - Orešković, Marko
AU  - Kuna, Kranthi
AU  - Guduru, Gurunath
AU  - Epps Martin, Amy
AU  - Carter, Alan
AU  - Giancontieri, Gaspare
AU  - Abed, Ahmed
AU  - Dave, Eshan
AU  - Tebaldi, Gabriele
PY  - 2022
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2659
AB  - 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.
PB  - Springer
T2  - Materials and Structures
T1  - Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement
SP  - 112
VL  - 55
DO  - https://doi.org/10.1617/s11527-022-01933-9
ER  - 

@article{
author = "Botella, Ramon and Lo Presti, Davide and Vasconcelos, Kamilla and Bernatowicz, Kinga and Martínez, Adriana and Miró, Rodrigo and Specht, Luciano and Arámbula Mercado, Edith and Menegusso Pires, Gustavo and Pasquini, Emiliano and Ogbo, Chibuike and Preti, Francesco and Pasetto, Marco and Jiménez del Barco Carrión, Ana and Roberto, Antonio and Orešković, Marko and Kuna, Kranthi and Guduru, Gurunath and Epps Martin, Amy and Carter, Alan and Giancontieri, Gaspare and Abed, Ahmed and Dave, Eshan and Tebaldi, Gabriele",
year = "2022",
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.",
publisher = "Springer",
journal = "Materials and Structures",
title = "Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement",
pages = "112",
volume = "55",
doi = "https://doi.org/10.1617/s11527-022-01933-9"
}

Botella, R., Lo Presti, D., Vasconcelos, K., Bernatowicz, K., Martínez, A., Miró, R., Specht, L., Arámbula Mercado, E., Menegusso Pires, G., Pasquini, E., Ogbo, C., Preti, F., Pasetto, M., Jiménez del Barco Carrión, A., Roberto, A., Orešković, M., Kuna, K., Guduru, G., Epps Martin, A., Carter, A., Giancontieri, G., Abed, A., Dave, E.,& Tebaldi, G.. (2022). Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement. in Materials and Structures
Springer., 55, 112.
https://doi.org/https://doi.org/10.1617/s11527-022-01933-9

Botella R, Lo Presti D, Vasconcelos K, Bernatowicz K, Martínez A, Miró R, Specht L, Arámbula Mercado E, Menegusso Pires G, Pasquini E, Ogbo C, Preti F, Pasetto M, Jiménez del Barco Carrión A, Roberto A, Orešković M, Kuna K, Guduru G, Epps Martin A, Carter A, Giancontieri G, Abed A, Dave E, Tebaldi G. Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement. in Materials and Structures. 2022;55:112.
doi:https://doi.org/10.1617/s11527-022-01933-9 .

Botella, Ramon, Lo 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, "Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement" in Materials and Structures, 55 (2022):112,
https://doi.org/https://doi.org/10.1617/s11527-022-01933-9 . .