Issue 45

S. Harzallah et al, Frattura ed Integrità Strutturale, 45 (2018) 147-155; DOI: 10.3221/IGF-ESIS.45.12 153 Evaluation of the depth or length independently does not lead to a complete characterization of the geometry of such a crack. This is why an evaluation of two sizes (depth and length) will be established at the same time exploiting the previous architecture; but this time the database must contain two vectors of inputs and two vectors for output to estimate. Fig. (6) shows a block diagram of a network (MLP) with two inputs and two outputs for the fault characterization. After repeated several times the learning algorithm (for each iteration biases reset, obtained results are different), Fig. (7) is drawn showing the cost function (MSE). One can notice that it converges to the imposed optimum of 5. 10 -7 after 122 iterations. Curves in Figs. 8 and 9 give an acceptable correlation between the rectangular geometry of a crack by the direct model using the finite element method and those obtained by the neural network and mean absolute error (MAE). Figure 8 : The correlation between the desired exit and the exit of the network. Figure 9 : Mean absolute error (MAE). 0.04 0.045 0.05 0.055 0.06 0.065 0.07 0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018