Research Article
Smart Aggregate-Based Concrete Stress Monitoring via 1D CNN Deep Learning of Raw Impedance Signals
Table 5
Specifications of the four 1D CNN architectures (M1 − M4).
| Layers | Type | Depth | Filter | Stride |
| Model M1 | 1 | Conv | 4 | 1 × 6 | 1 | 2 | ReLU | — | — | — | 3 | Maxpool | — | 1 × 2 | 2 | 4 | Conv | 4 | 1 × 4 | 1 | 5 | ReLU | — | — | — | 6 | Maxpool | — | 1 × 2 | 2 | 7 | Conv | 8 | 1 × 5 | 1 | 8 | ReLU | — | — | — | 9 | Maxpool | — | 1 × 2 | 2 | 10 | Fc1 | 48 | — | — | 11 | Fc2 | 32 | — | — | 12 | Fc3 | 1 | — | — | 13 | Regression | — | — | — |
| Model M2 (see Table 1) |
| Model M3 | 1 | Conv | 4 | 1 × 6 | 1 | 2 | ReLU | — | — | — | 3 | Maxpool | — | 1 × 2 | 2 | 4 | Conv | 4 | 1 × 4 | 1 | 5 | ReLU | — | — | — | 6 | Maxpool | — | 1 × 2 | 2 | 7 | Conv | 8 | 1 × 5 | 1 | 8 | ReLU | — | — | — | 9 | Maxpool | — | 1 × 2 | 2 | 10 | Conv | 8 | 1 × 5 | 1 | 11 | ReLU | — | — | — | 12 | Maxpool | — | 1 × 2 | 2 | 13 | Conv | 8 | 1 × 5 | 1 | 14 | ReLU | — | — | — | 15 | Maxpool | — | 1 × 2 | 2 | 16 | Fc1 | 48 | — | — | 17 | Fc2 | 32 | — | — | 18 | Fc3 | 1 | — | — | 19 | Regression | | — | — |
| Model M4 | 1 | Conv | 4 | 1 × 6 | 1 | 2 | ReLU | — | — | — | 3 | Maxpool | — | 1 × 2 | 2 | 4 | Conv | 4 | 1 × 4 | 1 | 5 | ReLU | — | — | — | 6 | Maxpool | — | 1 × 2 | 2 | 7 | Conv | 8 | 1 × 5 | 1 | 8 | ReLU | — | — | — | 9 | Maxpool | — | 1 × 2 | 2 | 10 | Conv | 8 | 1 × 5 | 1 | 11 | ReLU | — | — | — | 12 | Maxpool | — | 1 × 2 | 2 | 13 | Conv | 8 | 1 × 5 | 1 | 14 | ReLU | — | — | — | 15 | Maxpool | — | 1 × 2 | 2 | 16 | Conv | 8 | 1 × 5 | 1 | 17 | ReLU | — | — | — | 18 | Maxpool | — | 1 × 2 | 2 | 19 | Fc1 | 48 | — | — | 20 | Fc2 | 32 | — | — | 21 | Fc3 | 1 | — | | 22 | Regression | | — | — |
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