Research Article
Prediction of Compressive Strength of Concrete and Rock Using an Elementary Instance-Based Learning Algorithm
Table 3
Correlation matrices of concrete datasets.
| | Dataset 1 | ssa | SF | W | ca | age | TCM | HRWRA | FA | CS |
| | ssa | 1 | 0.040 | −0.854 | −0.279 | 0 | 0.479 | 0.682 | −0.511 | 0.584 | | SF | — | 1 | 0 | 0 | 0 | 0 | 0.030 | −0.203 | 0.082 | | W | — | — | 1 | 0.334 | 0 | −0.568 | −0.917 | 0 | −0.485 | | ca | — | — | — | 1 | 0 | −0.965 | −0.581 | 0 | −0.484 | | age | — | — | — | — | 1 | 0 | 0 | 0 | 0.578 | | TCM | — | — | — | — | — | 1 | 0.761 | 0 | 0.556 | | HRWRA | — | — | — | — | — | — | 1 | 0.166 | 0.477 | | FA | — | — | — | — | — | — | — | 1 | −0.342 | | CS | — | — | — | — | — | — | — | — | 1 |
| | Dataset 2 | W/P | SP | ca | FA | C | ssa | CS | | |
| | W/P | 1 | 0.138 | −0.306 | −0.518 | 0.461 | −0.350 | −0.466 | | | | SP | — | 1 | 0.059 | −0.236 | −0.124 | −0.028 | −0.442 | | | | ca | — | — | 1 | 0.052 | −0.379 | 0.141 | −0.027 | | | | FA | — | — | — | 1 | −0.612 | −0.215 | 0.214 | | | | C | — | — | — | — | 1 | −0.163 | 0.288 | | | | ssa | — | — | — | — | — | 1 | 0.375 | | | | CS | — | — | — | — | — | — | 1 | | |
| | Dataset 3 | W/B | s/a | W | FA | AE | SP | CS | | |
| | W/B | 1 | 0.572 | −0.032 | −0.016 | −0.958 | −0.898 | −0.909 | | | | s/a | — | 1 | −0.382 | −0.122 | −0.622 | −0.482 | −0.333 | | | | W | — | — | 1 | −0.014 | 0.208 | −0.209 | −0.286 | | | | FA | — | — | — | 1 | 0.012 | 0.024 | −0.068 | | | | AE | — | — | — | — | 1 | 0.863 | 0.841 | | | | SP | — | — | — | — | — | 1 | 0.922 | | | | CS | — | — | — | — | — | — | 1 | | |
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The parameters listed in the first row of datasets 1 to 3 are defined in Table 2. |
