Issue 70

T. Pham-Bao et alii, Frattura ed Integrità Strutturale, 70 (2024) 55-70; DOI: 10.3221/IGF-ESIS.70.03

testing, with 80%, 10%, and 10% distribution, respectively. The Levenberg-Marquardt back-propagation algorithm is employed to update the weights and biases during the training process. The training is limited to a maximum of 50 epochs, and the learning rate is set to 0.001. If there are six consecutive validation failures, the training is terminated. As shown in Fig. 9, the optimal validation performance is achieved at the 15th epoch, and training concludes at the 21st epoch due to meeting the validation criterion.

Figure 9: The graph displays MSE values for training, validation, and testing, highlighting the epoch with the best performance.

Results After completing the training process, we test the remaining 765 samples in the common data bank to verify the accuracy of ANN. As shown in Fig. 10, Fig. 11, Fig. 12, and Fig. 13, we present the retest results with a total of 10 representative samples for each damage state due to the large number of samples used for testing.

Figure 10: The chart illustrates the probability of damage occurring at eight locations ( l 1 to l 8) in a scenario where there is no damage.

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