Issue 45

S. Harzallah et al, Frattura ed Integrità Strutturale, 45 (2018) 147-155; DOI: 10.3221/IGF-ESIS.45.12 150 shown in Fig. 2, with rectangular crack. The coil is placed along the crack length direction, moving in the XOY Plan, parallel to the x-axis[9]. Figure 3 : Impedance Z vs. the crack’s width. Figure 4 : Impedance Z vs. the crack’s depth. R ESULTS INTERPRETATION he results of simulation obtained in the case of a non-magnetic plate without defects are illustrated in the following Figures; - Fig. 1 represents the distribution of the currents induced on the surface of target. It is noticed that their values are high because the conductivity of the non-magnetic target is large (j = σ.2π.f.A) but are relatively weak compared to the primary currents. - Fig. 2 indicates the distribution of magnetic field. That explains the strong concentration of the vectors of magnetic induction on the level of the cracks without the possibility to penetrate inside the plate due to the characteristics of material and the effect of the frequency. - In Figs. 3 and 4, one can notice that when the width decreases then, the value of impedance ΔZ decreases at the same rate. The width of the defect has a great influence on the variation of impedance meanwhile the variation depth of the defect has a light influence on ΔZ. It is noticed that the difference of impedance ΔZ has a dependency on the width of the defect, indeed the width of defect increases ΔZ. On the other side, when the width decreases then, ΔZ decreases. The depth of defect has an effect on the impedance. The variation depth of the defect has a light influence on ΔZ. It is noticed that the difference of impedance ΔZ dependents on the width of the defect. The width of defect increases ΔZ and also one can remark that when the width decreases then, ΔZ decreases at the same rate. It is obvious that the depth of defect does not influence the impedance. A RTIFICIAL N EURAL N ETWORKS lthough this is an oversimplified model of the biological brain, the organization and the information processing strategies of an ANN are based on the features of their biological counterparts. The neurons combine the input impulses in several ways, operating in parallel with other neurons to perform a variety of functions. In artificial neural nets, each simple node performs a weighted sum of the inputs and computes a nonlinear function of the results [17]. The prediction methodology by artificial intelligence or machine learning is based on the following key steps [1]: 1. Selection of the variable to predict, e.g. global, diffuse or beam solar radiation on horizontal or tilted surface; 2. Selection of the input parameters and data collection, e.g. climatologic data, geographical coordinates, past time series, etc.; 3. Definition of the training and testing sets; 4. Development of the ANN model and training phase assessment; 0.04 0.045 0.05 0.055 0.06 0.065 0.07 -1 0 1 2 3 4 5 6 7 8 9 W=1mm W=1.25mm W=1.5mm W=2mm -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 -1 0 1 2 3 4 5 6 7 8 w=2mm w=3mm w=4mm w=5mm w=7mm T A