Table 6 Comparison of the adopted ML models for different CS ranges.
From: Machine learning and interactive GUI for concrete compressive strength prediction
CS range | Performance indices | MLR | MNLR | SVR | GEP | ANN | ANFIS | RF | AdaBoost | XGBoost | CatBoost |
---|---|---|---|---|---|---|---|---|---|---|---|
Low CS | R2 | 0.248 | 0.173 | 0.271 | 0.529 | 0.731 | 0.411 | 0.827 | 0.519 | 0.842 | 0.854 |
RMSE (MPa) | 10.05 | 11.66 | 9.98 | 6.10 | 3.26 | 6.16 | 2.73 | 8.46 | 2.53 | 2.37 | |
MAPE | 0.614 | 0.778 | 0.609 | 0.364 | 0.111 | 0.339 | 0.111 | 0.615 | 0.109 | 0.098 | |
Moderate CS | R2 | 0.211 | 0.204 | 0.205 | 0.450 | 0.862 | 0.596 | 0.925 | 0.571 | 0.922 | 0.932 |
RMSE (MPa) | 9.65 | 8.97 | 10.16 | 6.61 | 3.19 | 5.61 | 2.19 | 5.48 | 2.24 | 2.08 | |
MAPE | 0.197 | 0.178 | 0.195 | 0.134 | 0.042 | 0.117 | 0.040 | 0.113 | 0.043 | 0.041 | |
High CS | R2 | 0.125 | 0.001 | 0.093 | 0.249 | 0.744 | 0.505 | 0.801 | 0.414 | 0.886 | 0.925 |
RMSE (MPa) | 13.28 | 15.91 | 13.32 | 10.58 | 4.40 | 8.11 | 3.99 | 8.46 | 2.95 | 2.32 | |
MAPE | 0.168 | 0.203 | 0.167 | 0.137 | 0.035 | 0.103 | 0.039 | 0.113 | 0.029 | 0.026 |