Issue 45

S. Harzallah et al, Frattura ed Integrità Strutturale, 45 (2018) 147-155; DOI: 10.3221/IGF-ESIS.45.12 155 [4] Zaoui, A., Menana, H., Feliachi, M. and Berthiau, G. (2010). Inverse problem in nondestructive testing using arrayed eddy current sensors, Journal of Sensors, 10(9), pp. 8696-8704. [5] Helifa, B., Filiachi, M., Lefkaier, I.K., Boubenider, F., Zaoui, A. and Lagraa, N. (2016). Characterization of surface cracks using eddy current NDT simulation by 3D-FEM and inversion by neural network, ACES Journal, 31(2). [6] Rao, B.P.C., Raj, B. and Kröning, M. (1999). Artificial Neural Network for On-Line Eddy Current Testing. In: Thompson D.O., Chimenti D.E. (eds) Review of Progress in Quantitative Nondestructive Evaluation. Review of Progress in Quantitative Nondestructive Evaluation, Springer, Boston, MA, 18 A. [7] Harzallah, S., Mimouni, M. L., Benissad, S. and Chabaat, M. (2018). 3D-FEM Computational and Inverse Problem in Nondestructive Evaluation Using Neural Networks for Detection of Cracks, Transylvanian Review, 26, pp. 1-13. [8] Uchimoto, T., Takagi, T., Ichihara, T. and Dobmann, G. (2015). Evaluation of fatigue cracks by an angle beam EMAT– ET dual probe, NDT & E International, 72, pp. 10–16. [9] Harzallah, S. and Chabaat, M. (2016). 3-D Eddy current modeling for evaluating the Fracture parameters by a new method based on the variation of the impedance, International Journal of Applied Electromagnetics and Mechanics, 53, pp. 1–15. [10] Sakagami, T. (2015). Remote nondestructive evaluation technique using infrared thermography for fatigue cracks in steel bridges, Fracture of Engineering Materials & Structures, 38(7). [11] Notghi, B. and Brigham, J. C. A. (2015). Computational approach for robust nondestructive test design maximizing characterization capabilities for solids and structures subject to uncertainty, International Journal for Numerical Methods in Engineering 104(4). [12] Zaidi, H., Santandre, L., Krebsa, G. and Le Bihan, Y. (2014). Finite element simulation of the probe displacement in eddy current testing, International Journal of Applied Electromagnetics and Mechanics, 45, pp. 887–893. [13] Garcia-Martin, J., Gomez-Gil, J. and Vazquez-Sanchez, E. (2011). Non-Destructive Techniques based on eddy current testing, Sensors, 11(3), pp. 2525-2565. [14] Hughes, R., Fan, Y. and Dixon, S. (2014). Near electrical resonance signal enhancement (NERSE) in eddy current crack detection, Journal of Nondestructive Testing and Evaluation, 66, pp. 82–89. [15] Babbar, V.K., Underhill, P.R., Stott, C. and Krause, T.W. (2014). Finite element modelling of second layer crack detection in aircraft bolt holes with ferrous fasteners present, 65, pp. 64–71. [16] Xu, P., Huang, S. and Zhao, W. (2011). A new differential eddy current testing sensor used for detecting crack extension direction, NDT&E International, 44, pp. 339–343. [17] Bortolini, M., Gamberi, M. and Regattieri, A. (2016). Artificial neural network optimisation for monthly average daily global solar radiation prediction. Energy Conversion and Management, 120, pp. 320-329. [18] Moghaddamnia, A., Remesan, R., Kashani, M. H., Mohammadi, M., Han, D. and Piri, J. (2009). Comparison of LLR, MLP, Elman, NNARX and ANFIS Models—with a case study in solar radiation estimation. Journal of Atmospheric and Solar-Terrestrial Physics, 71(8), pp. 975-982. [19] Hassanpour Kashani, M. (2008). Flood estimation at ungauged sites using a new hybrid model. Journal of Applied Sciences 9, pp. 1744–1749. [20] Guermoui, M., Rabehi, A., Benkaciali, S. and Djafer, D. (2016). Daily global solar radiation modelling using multi-layer perceptron neural networks in semi-arid region. Leonardo Electronic Journal of Practices and Technologies, 28, pp. 35-46. [21] Rabehi, A., Guermoui, M., Djafer, D. and Zaiani, M. (2015). Radial basis function neural networks model to estimate global solar radiation in semi-arid area. Leonardo Electronic Journal of Practices and Technologies, 27, pp. 177-184.