TY  - JOUR 
AU  - Escuela Superior y Tecnica de Ingenieros de Minas
AU  - Martínez García, Rebeca
AU  - Palencia Coto, María Covadonga
AU  - Jagadesh, P
AU  - De Prado Gil, Jesús
AU  - Ingenieria Mecanica
DA  - 2022
UR  - https://hdl.handle.net/10612/20584
AB  - [EN] Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting...
LA  - eng
PB  - MDPI
KW  - Ingeniería de minas
KW  - Machine Learning
KW  - Splitting Tensile Strength
KW  - Self-compacting Concrete
KW  - Recycled Aggregates
KW  - Prediction
TI  - A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete
DO  - 10.3390/MA15124164
T2  - Materials
VL  - 15
M2  - 4164
ER  -