Browsing by Author "Capozucca, Roberto"

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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
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    A new methodology to predict the sequence of GFRP layers using machine learning and JAYA algorithm
    (Elsevier, 2023) Fahem, Noureddine; Belaidi, Idir; Oulad Brahim, Abdelmoumin; Capozucca, Roberto; Thanh, Cuong Le; Khatir, Samir; Abdel Wahab, Magd M.
    In this paper, the best stacking sequence using experimental tests of GFRP composites is investigated. The main objective of this work is to determine the main specification of GFRP composite material, which is represented by its physics-mechanical properties, weight, and cost, before performing a series of experimental tests based on various stacking sequences. Our methodology is divided into three stages. The first stage is characterized by extracting the bending data from mechanical tests of some GFRP composites. In the second stage, the validated numerical model is used to simulate numerous cases of stacking sequences. In the last stage, the extracted data is used to determine the parameters for different stacking sequences using an inverse technique based on ANN and JAYA algorithm. The results provide a good prediction of parameters as well as a good orientation to make decisions on the best GFRP stacking sequence to be used, according to the required specifications of the manufacturer.
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    Optimal Prediction for Patch Design Using YUKI-RANDOM-FOREST in a Cracked Pipeline Repaired with CFRP
    (Springer Nature, 2024) Oulad Brahim, Abdelmoumin; Capozucca, Roberto; Khatir, Samir; Fahem, Noureddine; Benaissa, Brahim; Cuong-Le, Thanh
    This paper presents the effectiveness of a hybrid YUKI-RANDOM-FOREST, Particle Swarm Optimization-YUKI (PSO-YUKI), and balancing composite motion optimization algorithm (BCMO) based on artificial neural networks (ANN) for the best prediction of patch design considering the maximum principal stress. The study compares the maximum principal stress in a damaged pipe under different composite patch designs. Robust models have been developed and utilized in various applications. The research investigates the influence of cracks on the mechanical characteristics of API X70 steel in a test pipe under critical pressure. The numerical model employs the extended finite element method (XFEM) to simulate notches. Extending the optimization technique, the study examines the effect of crack presence in a pipeline section under internal pressure without and with composite repairs on the maximum principal stress. The sensitivity of stress is analyzed with respect to the design parameters of the composite patch. Finally, YUKI-RANDOM-FOREST, NN-PSO-YUKI, and NN-BCMO, with different parameters and hidden layer sizes are employed to predict the maximum principal stress under different composite patch designs, and yielding minimal error. Once the database was built, our model was prepared to predict various situations at the composite patch level. Compared to other methods, the obtained results with hybrid YUKI-RANDOM-FOREST are effective. The investigation technique is relevant to real-world engineering applications, structural safety control, and design processes.
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    The Optimal Values of Hashin Damage Parameters Predict Using Inverse Problem in a CFRP Composite Material
    (Springer, 2024) Fahem, Noureddine; Belaidi, Idir; Aribi, Chouaib; Zara, Abdeldjebar; Khatir, Tawfiq; Oulad Brahim, Abdelmoumin; Capozucca, Roberto
    The ever-increasing demand for advanced composite materials in industries like aerospace and automotive has spurred the drive to address their inherent weaknesses. This pursuit is facilitated by the availability of numerical simulations and artificial intelligence, offering a cost-effective means to comprehensively study various phenomena without excessive reliance on experimentation. While existing models in the scientific realm provide a foundation for composite material modeling, achieving results closely aligned with experimental data is often challenging due to the variation of the parameters and conditions. This present study introduces an innovative approach aimed at optimizing composite material performance and minimizing discrepancies between experimental and numerical outcomes. This approach leverages sophisticated optimization algorithms to fine-tune the Hashin damage parameters, resulting in a highly accurate model. Furthermore, the incorporation of an Artificial Neural Network (ANN) via an inverse problem based on Jaya’s algorithm solving strategy facilitates the prediction of optimal parameters, ensuring a significant reduction in error. This novel methodology presents a promising avenue for elevating the efficiency and reliability of CFRP composite materials in practical applications.
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    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
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    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