Research Article
Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network
Table 1
Initial statistical analysis of the dataset.
| Variable | Cement | Water content | Air foam | Waste fishing net | Pu |
| Unit | % by weight | % by weight | % by weight | % by weight | kN/m2 | Role | Input | Input | Input | Input | Output | Count | 51.0 | 51.0 | 51.0 | 51.0 | 51.0 | Mean | 12.5 | 168.2 | 2.4 | 0.1 | 38.0 | Stda | 2.5 | 32.5 | 1.0 | 0.1 | 26.5 | Min | 8.0 | 125.0 | 1.0 | 0.0 | 7.9 | Q25 | 12.0 | 156.0 | 2.0 | 0.0 | 15.7 | Q50 | 12.0 | 156.0 | 2.0 | 0.1 | 27.2 | Q75 | 12.0 | 171.5 | 2.0 | 0.1 | 60.4 | Max | 20.0 | 250.0 | 5.0 | 0.2 | 100.7 |
|
|
aStandard deviation.
|