@misc{10481/74651, year = {2022}, month = {4}, url = {http://hdl.handle.net/10481/74651}, 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}, organization = {Spanish Government RTI2018-096224-J-I00}, organization = {European Commission}, organization = {Ministry of Education, Universities and Research (MIUR) PRIN 2017 USR342}, publisher = {Springer}, keywords = {Hot mix asphalt}, keywords = {Recycling}, keywords = {Reclaimed asphalt pavement}, keywords = {Degree of binder activity}, keywords = {Machine learning}, keywords = {Artificial neural networks}, keywords = {Random forest}, keywords = {Indirect tensile strength}, title = {Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement}, doi = {10.1617/s11527-022-01933-9}, author = {Botella, Ramón and Jiménez Del Barco Carrión, Ana}, }