Research Article
Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods
Table 1
Properties of the constituents.
| | | Fineness modulus, k (−) | Particle density, ρ (kg/m3) | Water absorption, μ (kg/kg) | Compressive strength, fcc (MPa) | Blaine specific surface, σ (m2/kg) |
| | Aggregate | | Basalt | Crushed stone II | 10.456 | 2872 | 0.0100 | — | — | | Crushed stone I | 9.129 | 2878 | 0.0130 | — | — | | Crushed stone sand | 5.198 | 2845 | 0.0220 | — | — |
| | Limestone | Crushed stone II | 10.181 | 2600 | 0.0120 | | | | Crushed stone I | 7.107 | 2590 | 0.0170 | | | | Crushed stone sand | 4.791 | 2550 | 0.0260 | — | — |
| | | Sand | 3.770 | 2600 | 0.0140 | — | — |
| | Binding material | | Cement | CEM V/A (S-P) 32.5 N | — | 2990 | 0.0000 | 34.4 | 416.0 | | SDC 32.5 R | — | 3160 | 0.0000 | 44.75 | 339.0 | | CEM I 42.5 R | — | 3140 | 0.0000 | 55.1 | 379.0 |
| | Admixture | | Super plasticizer | — | — | 1100 | 0.0000 | — | — |
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