Browsing by Author "Magd, Abdel Wahab"

Now showing 1 - 6 of 6

Damage detection and localization in composite beam structures based on vibration analysis
(2015) Khatir, Samir; Belaidi, Idir; Serra, Roger; Magd, Abdel Wahab; Khatir, Tawfiq
This paper presents an approach of inverse dam-age detection and localization based on model reduction. The problem is formulated as an inverse problem where an optimization algorithm is used to minimize the cost func-tion expressed as the normalized difference between a fre-quency vector of the tested structure and its numerical model. A finite element model of bi-dimensional monolith-ic composite beam reinforced by a graphite-epoxy is used to define a numerical model of the tested structure in which different scenarios of damage are considered by stiffness reduction. Then, calculations are made on a re-duced model built by the technique of proper orthogonal decomposition coupled by radial basis functions. The accu-racy of the method is verified through different damage configurations. The results show that the developed algo-rithm is a feasible methodology of predicting damage in short computing time and with high accuracy. The effect of noise on the accuracy of the results is investigated in some cases for the structure under consideration
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
Numerical study for single and multiple damage detection and localization in beam-like structures using BAT algorithm
(2016) Khatir, Samir; Belaidi, Idir; Serra, Roger; Magd, Abdel Wahab; Khatir, Tawfiq
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
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
Structural Health Monitoring of Beam-Like and Truss Structures Using Frequency Response and Particle Swarm Optimization: Volume 2: Numerical Modelling in Mechanical and Materials Engineering, NME 2018, 28-29 August 2018, Ghent University, Belgium
(2019) Zenzen, Roumaissa; Khatir, Samir; Belaidi, Idir; Magd, Abdel Wahab
In this paper, non-destructive damage identification in beam-like and truss structures using Frequency Response (FR) data is presented. This approach is to formulate an inverse problem using Particle Swarm Optimization (PSO) and Finite Element Method (FEM) to identify the presence, location and quantification of the damage. PSO is one of the most efficient bio-inspired methods. It is used to minimize the objective function, which is based on FR data. The damage in structure is caused by loss of rigidity at a specific location. The capability and efficiency of this application to identify the location and severity of damage are demonstrated by means of several numerical examples. The results of the proposed approach show good accuracy.