Publications Scientifiques

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Author: search.filters.author.Khatir, SamirSubject: search.filters.subject.ANN

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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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    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