PSI - Issue 42

Chahboub Yassine et al. / Procedia Structural Integrity 42 (2022) 1025–1032 Author name / Structural Integrity Procedia 00 (2019) 000 – 000

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specimen. However, with this and the development of machining techniques, more importance is given to subsidized specimens to check their validity. They can be used with ANN to predict huge equipment failure in a very short time. 3. Conclusion As proved in many studies, the GTN model is a powerful and applicable tool in the research and industry areas In the conclusion of this work, we predicted the pipeline's failure using two different approaches, the direct method, and the backpropagation method; the reason for choosing two approaches is to show the importance of integrating the artificial neural network. The results showed that predicting the pipeline's failure using the direct method was successful. However, it took 30 days; on the other hand, the integration of ANN reduced the prediction time to six hours; more studies have to be done to elaborate a clear approach to integrating the ANN in more complicated geometries based on subsized specimens Acknowledgements This work was carried out as part of the Advanced Structural Integrity Assessment Tools for Safe Long Term (ATLA+). References PeterTrampus, 2019 Role and importance of NDE in nuclear power plant life extension,Procedia Structural Integrity Volume 16, 2019, Pages 161-168 AiméLay-Ekuakille, Vito Telesca, 2020 Flow distribution imaging and sensing for leaks in pipelines using decimated signal diagonalization Measurement: Sensors Volumes 7 – 9, October 2020, 100014 Jairo Alberto Muñoza, Tarek Khelfa, Alexander Komissarov, José-María Cabrera 2021 Ductility and plasticity of ferritic-pearlitic steel after severe plastic deformation Materials Science and Engineering: A Volume 805, 23 February 2021, 140624 A.Alaswad, K.Y.Benyounis, A.G.Olabi 2016, Optimization Techniques in Material Processing, Reference Module in Materials Science and Materials Engineering Taslim D.Shikalgar, B.K.Dutta, J.Chattopadhyay, 2020 Analysis of p-SPT specimens using Gurson parameters ascertained by Artificial Neural Network Engineering Fracture Mechanics Volume 240, December 2020, 107324 E.J.Pérez-Pérez, F.R.López-Estrada, G.Valencia-Palomo, L.Torres, V.Puig, J .D.Mina-Antoniod, 2021 Leak diagnosis in pipelines using a combined artificial neural network approach ☆ Control Engineering Practice Volume 107, February 2021, 104677 Gurson, A.L, 1975 Plastic Flow and Fracture Behavior of Ductile Materials Incorporating Void Nucleation, Growth and Interaction. Ph.D. Thesis,\ Rice, J.R., Tracey, D.M., 1969. On the ductile enlargement of voids in triaxial stress fields. J. Mech. Phys. Solids 17, 201 – 217 Needleman, A., Tvergaard, V., 1984. An analysis of ductile rupture in notched bars. J. Mech. Phys. Solids 32, 461 – 490. Bauvineau L, Burlet H, Eripret C, Pineau A (1996) Modelling ductile stable crack growth in a C-Mn steel with local approaches. J Phys IV France 06(C6):C6-33 – C36-42 Decamp K, Bauvineau L, Besson J, Pineau A (1997) Size and geometry effects on ductile rupture of notched bars in a CMn steel: experiments and modelling. Int J Fract 88(1):1 – 18 Schmitt W, Sun DZ, Blauel JG (1997) Damage mechanics analysis (Gurson model) and experimental verification of the behaviour of a crack in a weld-cladded component. Nucl Eng Des 174(3):237 – 246 Skallerud B, Zhang ZL (1997) A 3D numerical study of ductile tearing and fatigue crack growth under nominal cyclic plasticity. Int J Solids Struct 34(24):3141 – 3161. Benseddiq N, Imad A (2008) A ductile fracture analysis using a local damage model. Int J Press Vesel Pip [85(4):219 – 227 Hamdi Aguir, Haykel Marouani (2010) Gurson-Tvergaard-Needleman parameters identification using artificial neural networks in sheet metal blanking International Journal of Material Forming 3:113-116 · April Moinereau, D., et al, "STYLE project: a large scale ductile tearing experiment on a cladded ferritic pipe", Proceedings of ASME 2014 PV&P Conference, paper PVP2014-28077