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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Detalles Bibliográficos
Autores: Botella Nieto, Ramón|||0000-0003-0801-3247, Lo Presti, Davide, Vasconcelos, Kamilla, Bernatowicz, Kinga, Martínez Reguero, Adriana Haydée|||0000-0002-3709-0463, Miró Recasens, José Rodrigo|||0000-0003-2843-6626, Specht, Luciano, Arámbula Mercado, Edith, Menegusso Pires, Gustavo, Pasquini, Emiliano, Ogbo, Chibuike, Preti, Francesco, Pasetto, Marco, del Barco Carrión, Ana Jiménez, Roberto, Antonio, Oreskovic, Marko, Kuna, Kranthi K., Guduru, Gurunath, Epps Martin, Amy, Carter, Alan, Giancontieri, Gaspare, Abed, Ahmed, Dave, Eshan, Tebaldi, Gabrielle
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/371182
Acceso en línea:https://hdl.handle.net/2117/371182
https://dx.doi.org/10.1617/s11527-022-01933-9
Access Level:acceso abierto
Palabra clave:Asphalt pavements
Hot mix asphalt
Recycling
Reclaimed asphalt pavement
Degree of binder activity
Machine learning
Artificial neural networks
Random forest
Indirect tensile strength
Asfalt
Àrees temàtiques de la UPC::Enginyeria civil::Infraestructures i modelització dels transports::Transport per carretera
Descripción
Sumario: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.

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