Browsing by Author "Magagnini, Erica"

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Experimental crack identification of API X70 steel pipeline using improved artificial neural networks based on whale optimization algorithm
(Elsevier, 2022) Oulad Brahim, Abdelmoumin; Belaidi, Idir; Khatir, Samir; Magagnini, Erica; Capozucca, Roberto; Abdel Wahab, Magd
Intelligent systems have recently received recognition for their ability to solve extremely complicated and multidimensional problems. Artificial Neural Networks (ANN) has quite a lot of success in overcoming such issues, but some limitation can be found. The present study discusses in detail the application of the WOA-ANN hybrid model for predicting the crack length based on different input values, i.e. strains, stresses, and displacements, to test the accuracy of the presented technique. The proposed technique is compared with GA-ANN, AOA-ANN, and WOABAT-ANN. Coupled metaheuristic optimization algorithms with ANN aim to increase its effeciency. The connectivity between neurons carries some weight. Neurons are also connected to some biases. Connection weights and biases are modified to give the smallest possible error function based on the input values, and corresponding target output values supplied. Back Propagation (BP) is the usual name for this approach. The investigated approach is related to real engineering applications and controls the structures’ state. Standard ASTM test specimens are chosen to study the evolution of fracture mechanics parameters. Next, an analytical model is developed by simulating the tests using the Finite Element Method (FEM) and validated with experimental results. FEM is used to analyse the tensile failure process of the one-sided notch samples with the mesoscopic GTN damage model and extract the data required for WOA-ANN. After collecting the database, our model is ready for predicting different scenarios. The obtained results using WOA-ANN are efficient compared to other techniques
Prediction of gurson damage model parameters coupled with hardening law identification of steel X70 pipeline using neural network
(Springer, 2021) Oulad Brahim, Abdelmoumin; Belaidi, Idir; Khatir, Samir; Magagnini, Erica; Capozucca, Roberto; Wahab, Magd Abdel
The Gurson–Tvergaard–Needleman damage model (GTN) describes the three stages of ductile tearing of steel: nucleation, growth and coalescence of micro-voids. This work is divided into two main parts. In the first part, based on the inverse analysis and the comparison between the experimental and numerical data, the parameters of the GTN damage model in conjunction with the hardening law are determined. The identification is broadened to include a considerable number of experimental tests drawn from our previous works and other works done at ALFAPIPE Ghardaia laboratory. In the second part, an Artificial Neural Network model is developed to predict the parameters of the (GTN) model coupled with the harden- ing law that goes through the prediction of traction and impact properties of API X70 steel pipe depending on its chemical composition. The weight of the chemical elements in percentages is considered as the inputs and the GTN parameters are considered as the outputs. In order to validate the obtained ANNGTN parameters, traction and impact tests are simulated. The numerical results are compared with the experimental ones and revealed that the developed model is very precise and has the potential to capture the interaction of GTN parameters coupled with hardening law and chemical composition of steel pipelines
Sensitivity analysis of the gtn damage parameters at different temperature for dynamic fracture propagation in x70 pipeline steel using neural network
(Gruppo Italiano Frattura, 2021) Abdelmoumin Ouladbrahim, Abdelmoumin; Belaidi, Idir; Khatir, Samir; Magagnini, Erica; Capozucca, Roberto; Wahab, Magd Abdel
In this paper, the initial and maximum load was studied using the Finite Element Modeling (FEM) analysis during impact testing (CVN) of pipeline X70 steel. The Gurson-Tvergaard-Needleman (GTN) constitutive model has been used to simulate the growth of voids during deformation of pipeline steel at different temperatures. FEM simulations results used to study the sensitivity of the initial and maximum load with GTN parameters values proposed and the variation of temperatures. Finally, the applied artificial neural network (ANN) is used to predict the initial and maximum load for a given set of damage parameters X70 steel at different temperatures, based on the results obtained, the neural network is able to provide a satisfactory approximation of the load initiation and load maximum in impact testing of X70 Steel