Research Article
Applying Adaptive Neural Fuzzy Inference System to Improve Concrete Strength Estimation in Ultrasonic Pulse Velocity Tests
Table 2
Artificial neural network model training result.
| One-input (single entry) | Two-input (double entry) | Network type | Hidden layer | Number of training | R2 | MAPE (%) | Network type | Hidden layer | Number of training | R2 | MAPE (%) |
| 1-1-1 | 1 | 200 | 0.73 | 14.79 | 2-1-1 | 1 | 200 | 0.73 | 12.85 | 1-1-1 | 1 | 500 | 0.74 | 11.37 | 2-1-1 | 1 | 500 | 0.74 | 10.63 | 1-1-1 | 1 | 1000 | 0.74 | 14.52 | 2-1-1 | 1 | 1000 | 0.74 | 12.43 | 1-3-1 | 1 | 2000 | 0.73 | 10.48 | 2-3-1 | 1 | 2000 | 0.75 | 10.46 | 1-5-1 | 1 | 2000 | 0.75 | 10.98 | 2-2-1 | 1 | 2000 | 0.74 | 10.58 | 1-1-1 | 1 | 2000 | 0.74 | 10.93 | 2-7-1 | 1 | 2000 | 0.73 | 10.96 | 1-1-1 | 1 | 3000 | 0.74 | 12.91 | 2-1-1 | 1 | 3000 | 0.74 | 10.61 | 1-1-1 | 1 | 5000 | 0.74 | 13.49 | 2-1-1 | 1 | 5000 | 0.73 | 14.79 | 1-1-1 | 1 | 7500 | 0.74 | 13.08 | 2-1-1 | 1 | 7500 | 0.74 | 12.84 | 1-1-1 | 1 | 15000 | 0.74 | 14.31 | 2-1-1 | 1 | 15000 | 0.75 | 13.23 | 1-1-1 | 1 | 20000 | 0.74 | 11.45 | 2-1-1 | 1 | 20000 | 0.74 | 11.01 |
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