Publications Scientifiques

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Author: search.filters.author.Khatir, SamirHas files: No

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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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    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
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    A modified transmissibility indicator and Artificial Neural Network for damage identification and quantification in laminated composite structures
    (Elsevier, 2020) Zenzen, Roumaissa; Khatir, Samir; Belaidi, Idir; Thanh, CuongLe; MagdAbdel, Wahab
    Recently, more attention has been paid to Artificial Neural Network (ANN) in the field of damage identification of engineering structures based on modal analysis. This paper proposes a new modified damage indicator, using transmissibility technique to improve Local Frequency Response Ratio (LFCR), combined with ANN. The main objective of the proposed damage indicator is to reduce the number of collected data for fast prediction and with higher accuracy instead of collecting all modal analysis data, i.e. natural frequencies, damping ratios, and mode shapes, or using inverse analysis for damage quantification. The suggested approach is tested using three layers laminated cross-ply [0°/90°/0°] composite beam and plate having single and multiple damage(s). The reliability and accuracy of the proposed application are demonstrated by predicting the severity of damages in the considered composite structures after analysing four damage scenarios