Research Article
A New Procedure for Damage Assessment of Prestressed Concrete Beams Using Artificial Neural Network
Table 3
Summary of the results for various input cases.
| Input parameter to the ANN | Number of inputs | Expected damage level (%) ED | Minimum predicted damage level | Maximum predicted damage level | Average predicted damage level | No. of epochs taken for training the ANN | Performance accuracy | Predicted damage value (OD) | Difference (ED–OD) | Predicted damage value (OD) | Difference (ED–OD) | Predicted damage value (OD) | Difference (ED–OD) |
| p-f | 2 | 40 50 70 | 32.19 33.37 68.09 | −7.81 −16.63 −1.91 | 47.98 66.10 72.17 | +7.98 +16.10 +2.17 | 37.69 57.56 70.40 | −2.31 +7.56 +0.40 | 1203 | 1 | s-f | 2 | 40 50 70 | 33.01 31.77 58.98 | −6.99 −18.23 −11.02 | 52.95 77.68 77.68 | +12.95 +27.68 +7.68 | 42.05 48.63 71.44 | +2.05 −1.37 +1.44 | 423 | 0.06 | f | 1 | 40 50 70 | 20.13 52.59 57.60 | −19.87 +2.59 −12.40 | 58.77 58.82 59.00 | +18.77 +8.82 −11.00 | 35.82 56.09 59.00 | −4.18 +6.09 −11.00 | 5000 | 0.25 |
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