TY  - JOUR 
AU  - Escuela Superior y Tecnica de Ingenieros de Minas
AU  - Palencia Coto, María Covadonga
AU  - Martínez García, Rebeca
AU  - De Prado Gil, Jesús
AU  - Silva Monteiro, Neemias
AU  - Explotacion de Minas
DA  - 2022
SN  - 2214-5095
UR  - https://hdl.handle.net/10612/20677
AB  - [EN] This study aims to apply machine learning methods to predict the compression strength of self-compacting recycled aggregate concrete. To obtain this goal, the ensemble methods: Random Forest (RF), K-Nearest Neighbor (KNN), Extremely Randomized...
LA  - eng
PB  - Science Direct
KW  - Ingeniería de minas
KW  - Compression Strength
KW  - Machine Learning
KW  - Self-compacting Concrete With
KW  - Recycled Aggregates
KW  - Prediction
KW  - Sensitivity Analysis
TI  - To predict the compressive strength of self compacting concrete with recycled aggregates utilizing ensemble machine learning models
DO  - 10.1016/J.CSCM.2022.E01046
T2  - Case Studies in Construction Materials
VL  - 16
M2  - e01046
ER  -