Research Article
Evolution of Deep Neural Network Architecture Using Particle Swarm Optimization to Improve the Performance in Determining the Friction Angle of Soil
Table 11
Comparison with previous studies.
| Author | Model | Parameter | Number of samples | R | R2 | RMSE (o) | MAE (o) |
| Khanlari et al. [60] | ANN | Internal friction angle | 200 | 0.89 | 0.792 | 2.26 | 1.92 | MVR | 0.89 | 0.79 | 2.385 | 1.929 | Mohammadi et al. [61] | MLP | Internal friction angle | 108 | 0.902 | 0.814 | 9.285 | 6.137 | MLR | 0.719 | 0.517 | 5.082 | 3.954 | Iyeke et al. [62] | ANN | Internal friction angle | 83 | 0.897 | 0.805 | 4.77 | 4.34 | Present study | DNN | Internal friction angle | 245 | 0.958 | 0.918 | 1.936 | 1.425 | PSO-DNN | 0.967 | 0.935 | 1.77 | 1.34 |
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