TY  - JOUR 
AU  - Botella, Ramón
AU  - Jiménez Del Barco Carrión, Ana
PY  - 2022
UR  - http://hdl.handle.net/10481/74651
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...
LA  - eng
PB  - Springer
KW  - Hot mix asphalt
KW  - Recycling
KW  - Reclaimed asphalt pavement
KW  - Degree of binder activity
KW  - Machine learning
KW  - Artificial neural networks
KW  - Random forest
KW  - Indirect tensile strength
TI  - Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement
DO  - 10.1617/s11527-022-01933-9
ER  -