Research Article
A Deep Learning Prediction Model for Structural Deformation Based on Temporal Convolutional Networks
Table 3
Orthogonal experiment results.
| Test number | Types and levels of factors | RMSE | MAPE | MAE | Running time (min) | A | B | C | D | E |
| 1 | 5 | 8 | 8 | 8 | 0.0001 | 1.08 | 1.13 | 0.66 | 7.73 | 2 | 5 | 16 | 16 | 12 | 0.001 | 1.05 | 0.86 | 0.53 | 38.73 | 3 | 5 | 24 | 32 | 16 | 0.01 | 2.26 | 5.61 | 1.70 | 97.90 | 4 | 5 | 32 | 64 | 20 | 0.05 | 9.09 | 9.93 | 7.26 | 247.30 | 5 | 6 | 8 | 16 | 16 | 0.05 | 1.08 | 1.07 | 0.58 | 45.34 | 6 | 6 | 16 | 8 | 20 | 0.01 | 1.10 | 0.84 | 0.57 | 47.70 | 7 | 6 | 24 | 64 | 8 | 0.001 | 1.05 | 0.64 | 0.49 | 75.61 | 8 | 6 | 32 | 32 | 12 | 0.0001 | 2.41 | 1.83 | 1.69 | 87.78 | 9 | 7 | 8 | 32 | 20 | 0.001 | 1.22 | 0.63 | 0.47 | 77.55 | 10 | 7 | 16 | 64 | 16 | 0.0001 | 1.20 | 1.47 | 0.74 | 129.25 | 11 | 7 | 24 | 8 | 12 | 0.05 | 1.17 | 1.73 | 0.74 | 36.68 | 12 | 7 | 32 | 16 | 8 | 0.01 | 1.08 | 0.76 | 0.51 | 39.23 | 13 | 8 | 8 | 64 | 12 | 0.01 | 1.08 | 0.89 | 0.57 | 68.86 | 14 | 8 | 16 | 32 | 8 | 0.05 | 0.98 | 0.34 | 0.41 | 41.04 | 15 | 8 | 24 | 16 | 20 | 0.0001 | 1.18 | 1.5648 | 0.73 | 98.53 | 16 | 8 | 32 | 8 | 16 | 0.001 | 1.28 | 1.3177 | 0.83 | 63.74 |
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The bold values represent the best prediction result of the model when the TCN model takes this set of parameters.
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