Research Article
Extreme Gradient Boosting Algorithm for Predicting Shear Strengths of Rockfill Materials
Table 1
Statistics of parameters of the training and testing datasets.
| | Statistical parameter | Dataset | Input variable | Output variable | | D10 (mm) | D30 (mm) | D60 (mm) | D90 (mm) | C C | C U | GM | FM | R | UCSmin (MPa) | UCSmax (MPa) | γ (kN/m3) | σn (MPa) | τ (MPa) |
| | Minimum | Total data | 0.010 | 0.560 | 1.200 | 2.600 | 0.100 | 1.360 | 0.200 | 3.000 | 1.000 | 1.000 | 5.000 | 9.320 | 0.002 | 0.005 | | Training | 0.010 | 0.560 | 1.200 | 2.600 | 0.100 | 1.360 | 0.200 | 3.000 | 1.000 | 1.000 | 5.000 | 9.320 | 0.002 | 0.005 | | Testing | 0.010 | 0.560 | 1.200 | 2.600 | 0.100 | 1.470 | 0.200 | 3.000 | 1.000 | 1.000 | 5.000 | 9.320 | 0.021 | 0.024 |
| | Maximum | Total data | 33.900 | 42.400 | 80.100 | 100.000 | 22.270 | 1040.000 | 6.000 | 8.800 | 6.000 | 250.000 | 400.000 | 38.900 | 4.205 | 3.921 | | Training | 33.900 | 42.400 | 80.100 | 100.000 | 22.270 | 1040.000 | 6.000 | 8.800 | 6.000 | 250.000 | 400.000 | 38.900 | 4.205 | 3.921 | | Testing | 33.900 | 42.400 | 50.000 | 99.000 | 22.270 | 1040.000 | 6.000 | 8.800 | 5.000 | 100.000 | 250.000 | 38.900 | 3.223 | 2.492 |
| | Mean | Total data | 4.463 | 7.860 | 18.280 | 39.927 | 2.404 | 69.561 | 2.903 | 6.142 | 4.327 | 73.691 | 168.455 | 20.799 | 0.734 | 0.662 | | Training | 4.867 | 8.465 | 19.287 | 40.386 | 2.199 | 53.324 | 2.788 | 6.250 | 4.364 | 75.045 | 170.682 | 20.766 | 0.729 | 0.660 | | Testing | 2.887 | 5.442 | 14.252 | 38.091 | 3.226 | 134.510 | 3.365 | 5.709 | 4.182 | 68.273 | 159.545 | 20.932 | 0.756 | 0.668 |
| | Standard deviation | Total data | 8.875 | 10.335 | 14.420 | 22.432 | 3.414 | 193.628 | 1.278 | 1.298 | 0.957 | 37.975 | 87.844 | 4.861 | 0.785 | 0.652 | | Training | 9.179 | 10.577 | 15.135 | 22.018 | 3.075 | 156.064 | 1.243 | 1.261 | 0.910 | 39.230 | 88.010 | 4.605 | 0.780 | 0.662 | | Testing | 7.453 | 9.050 | 10.349 | 24.289 | 4.492 | 194.958 | 1.331 | 1.374 | 1.131 | 32.444 | 87.967 | 5.854 | 0.816 | 0.619 |
|
|
