Publications Scientifiques

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Author: search.filters.author.Khatir, Samir

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    Prediction of the peak load and absorbed energy of dynamic brittle fracture using an improved artificial neural network
    (Elsevier, 2022) Oulad Brahim, Abdelmoumin; Belaidi, Idir; Fahem, Noureddine; Khatir, Samir; Mirjalili, Seyedali Jamal; Abdel Wahab, Magd M.
    In this paper, a robust technique is presented to predict the peak load and crack initiation energy of dynamic brittle fracture in X70 steel pipes using an improved artificial neural network (IANN). The main objective is to investigate the behaviour of API X70 steel based on two experimental tests, namely Drop Weight Tear Test (DWTT) and the Charpy V-notch impact (CVN), for steel pipe specimens. The mechanical properties in the brittle fracture behaviour of API X70 steel pipes are predicted utilizing numerical approaches with different crack lengths. Next, to simulate the impact of API X70 steel pipes at lower temperatures through a numerical approach, a cohesive approach using the extended Finite Element Method (XFEM) is used. The data obtained are used as input for the proposed IANN using Balancing Composite Motion Optimization (BCMO), Particle Swarm Optimization (PSO) and Jaya optimization algorithms, to predict the peak load values and crack initiation energy of dynamic brittle fractures in API X70 steel with different crack lengths. The results show the effectiveness of ANN-PSO and ANN-BCMO based on the convergence of the results and the accuracy of the prediction of the peak load and crack initiation energy.
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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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    Damage detection in GFRP composite structures by improved artificial neural network using new optimization techniques
    (Elsevier, 2023) Zara, Abdeldjebar; Belaidi, Idir; Khatir, Samir; Oulad Brahim, Abdelmoumin; Boutchicha, Djilali; Abdel Wahab, Magd
    Structural damage identification has been researched for a long time and continues to be an active research topic. This paper proposes the use of the natural frequencies of a novel composite structures made of glass fibre reinforced polymer (GFRP). The proposed methodology consists of an improved Artificial Neural Network (ANN) using optimization algorithms to detect the exact crack length. In the first step, the characterization of fabricated material is provided to determine Young's modulus using an experimental static bending test, tensile test and modal analysis test. Next, numerical validation is performed using commercial software ABAQUS to extract more data for different crack locations in the structure. The comparison between experimental and numerical results shows a good agreement. ANN has been improved using recent optimization techniques such as Jaya, enhanced Jaya (E-Jaya), Whale Optimization Algorithm (WOA) and Arithmetic Optimization Algorithm (AOA) to calibrate the influential parameters during training. After considering several scenarios, the results show that the accuracy of E-Jaya is better than other optimization techniques. This study on crack identification using improved ANN can be used to investigate the safety and soundness of composite structures
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    Strength prediction of a steel pipe having a hemi-ellipsoidal corrosion defect repaired by GFRP composite patch using artificial neural network
    (Elsevier, 2023) Oulad Brahim, Abdelmoumin; Belaidi, Idir; Khatir, Samir; Le Thanh, Coung; Mirjalili, Seyedali; Magd, Abdel Wahab
    Local stress concentration occurs when faults are present in pipelines under pressure. An example of such defects is the problem of corrosion caused by the environment in the field of pipeline installation. In the first part of this paper, we attempt to model the corrosion in the hemi-ellipsoidal form in order to study the locations of stress concentration in the specimens by several experimental cases and their influence on the stress resistance. The Gurson-Tvergaard-Needleman (GTN) mesoscopic damage model is used to simulate the specimens with good accuracy. In the second part, the investigation is extended to a pipe under static pressure with and without the presence of a glass fibre reinforced polymer (GFRP) composite patch. The maximum stress and percent stress reduction in a defected pipe with a hemi-ellipsoidal defect are determined using a 3D finite element model. This part examines the impact of the geometry of the composite patches on the percentage reduction of the maximum stresses in a section of pipeline subjected to static pressure. In the third part, the stresses and the percentage reduction in the maximum stresses are predicted using an artificial neural network (ANN). An inverse problem using ANN and Jaya algorithm is proposed to predict the group level of different sizes of defects under composite patches based on the maximum stress and percentage reduction of stress that the pipe withstands. The new method relates directly to real-world pipeline construction and repair applications. It could be also used for structural safety monitoring
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    Prediction of resisting force and tensile load reduction in GFRP composite materials using Artificial Neural Network-Enhanced Jaya Algorithm
    (Elsevier, 2023) Fahem, Noureddine; Belaidi, Idir; Oulad Brahim, Abdelmoumin; Noori, Mohammad; Khatir, Samir; Magd, Abdel Wahab
    This work presents an experimental and a numerical studies on the effect of the phenomenon of porosity on the mechanical properties of Glass Fiber Reinforced Polymer (GFRP). In a first part, material elaboration, as well as its characterization using a three-point bending test to extract the basic mechanical properties of the material, is considered. In a second part, a finite element model is created to simulate the problem of air bubbles broadly. Several cases of different shapes and sizes are simulated. The results show a significant effect on the reduction of load in both tensile and bending cases as the size of the bubbles increases. Furthermore, the second part includes the application of the Artificial Neural Network-Enhanced Jaya Algorithm (ANN-E JAYA) to predict the reduction of the tensile load as a function of different crack lengths obtained from an Extended Finite Element Method (XFEM) model. Next, to verify the accuracy of provided application, a comparison is made with two other applications such as Artificial Neural Network-Jaya Algorithm (ANN-JAYA) and Artificial Neural Network-Particle Swarm Optimization (ANN-PSO). The results of the three algorithms show good convergence, with a slight increase in accuracy for ANN-E JAYA. MATLAB code and data used in this article can be found at https://github.com/Samir-Khatir/GFRP-ANN-E-JAYA.git
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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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    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
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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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    Damage Detection in Truss Structures Using Transmissibility Combined with Optimization Techniques
    (Springer link, 2020) Zenzen, Roumaissa; Khatir, Samir; Belaidi, Idir; Magd, Abdel Wahab
    The paper presents an effective approach based on Modal Assurance Criterion (MAC) formulation, transmissibility function and Particle Swarm Optimization (PSO) for damage assessment in truss structures. The Finite Element Method (FEM) is used to build the structures using Matlab. The main purpose of this study is to apply the transmissibility technique as an objective function based on MAC formulation to predict the damage location and severity. The objective function used in the proposed approach is based on transmissibly using MAC formulation (TMAC). The results show that the present methodology can reliably identify damage scenarios with higher accuracy even in case of complex structures