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Prediction of resisting force and tensile load reduction in GFRP composite materials using Artificial Neural Network-Enhanced Jaya Algorithm
Prediction of resisting force and tensile load reduction in GFRP composite materials using Artificial Neural Network-Enhanced Jaya Algorithm
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Date
2022
Authors
Noureddine Fahem, Idir Belaidi, Abdelmoumin Oulad Brahim, Mohammad Noori, Samir Khatir, Magd Abdel Wahab
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Abstract
"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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Keywords
GFRP,
Experimental test,
FEM,
ANN-E JAYA,
ANN-JAYA,
ANN-PSO