Academic Journal
Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement
| العنوان: | Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement |
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| المؤلفون: | Botella Nieto, Ramón, Lo Presti, Davide, Vasconcelos, Kamilla, Bernatowicz, Kinga, Martínez Reguero, Adriana Haydée, Miró Recasens, José Rodrigo, 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 |
| المساهمون: | Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental, Universitat Politècnica de Catalunya. MATCAR - Materials de Construcció i Carreteres |
| سنة النشر: | 2022 |
| المجموعة: | Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge |
| مصطلحات موضوعية: | Àrees temàtiques de la UPC::Enginyeria civil::Infraestructures i modelització dels transports::Transport per carretera, Asphalt pavements, Hot mix asphalt, Recycling, Reclaimed asphalt pavement, Degree of binder activity, Machine learning, Artificial neural networks, Random forest, Indirect tensile strength, Asfalt |
| الوصف: | 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. ; art 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. ; Peer Reviewed ; Postprint (published version) |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1871-6873 |
| Relation: | https://link.springer.com/article/10.1617/s11527-022-01933-9; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096224-J-I00/ES/NUEVO PROCEDIMIENTO UNIVERSAL PARA EVALUAR LA DUCTILIDAD DE TODO TIPO DE LIGANTES ASFALTICOS EN UN AMPLIO RANGO DE TEMPERATURAS. ENSAYO DE DUCTILIDAD DE ASFALTOS (DAST)/; Botella, R. [et al.]. Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement. "Materials and structures", Maig 2022, vol. 55, núm. 4, p. 112:1.; http://hdl.handle.net/2117/371182 |
| DOI: | 10.1617/s11527-022-01933-9 |
| الاتاحة: | http://hdl.handle.net/2117/371182 https://doi.org/10.1617/s11527-022-01933-9 |
| Rights: | Attribution 4.0 International ; http://creativecommons.org/licenses/by/4.0/ ; Open Access |
| رقم الانضمام: | edsbas.F95FCB28 |
| قاعدة البيانات: | BASE |
| تدمد: | 18716873 |
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| DOI: | 10.1617/s11527-022-01933-9 |