2026-04-17T06:30:25Zhttps://recercat.cat/oai/request

oai:recercat.cat:11351/83062025-10-25T05:36:59Zcom_2072_378072com_2072_378040col_2072_378100

Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement Botella, Ramon Lo Presti, Davide Vasconcelos, Kamilla Martínez, Adriana H. Miró, Rodrigo Bernatowicz, Kinga Aprenentatge automàtic Intel·ligència artificial - Aplicacions a la medicina PHENOMENA AND PROCESSES::Mathematical Concepts::Algorithms::Artificial Intelligence::Machine Learning DISCIPLINES AND OCCUPATIONS::Natural Science Disciplines::Mathematics::Data Analysis FENÓMENOS Y PROCESOS::conceptos matemáticos::algoritmos::inteligencia artificial::aprendizaje automático DISCIPLINAS Y OCUPACIONES::disciplinas de las ciencias naturales::matemáticas::análisis de datos Recycling; Machine learning; Artificial neural networks Reciclaje; Aprendizaje automático; Redes neuronales artificiales Reciclatge; Aprenentatge automàtic; Xarxes neuronals artificials 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. Open access funding provided by Università degli Studi di Palermo within the CRUI-CARE Agreement. 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. 2025-10-25T05:36:59Z 2025-10-25T05:36:59Z 2025-10-25T05:36:59Z 2022-10-18T08:48:37Z 2022-10-18T08:48:37Z 2022-04-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/11351/8306 Materials and Structures;55 https://doi.org/10.1617/s11527-022-01933-9 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Springer Scientia