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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