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Damage detection in GFRP composite structures by improved artificial neural network using new optimization techniques
Zara, A.; Belaidi, I.; Khatir, S.; Brahim, A.O.; Boutchicha, D.; Wahab, M.A. (2023). Damage detection in GFRP composite structures by improved artificial neural network using new optimization techniques. Composite Structures 305: 116475. https://dx.doi.org/10.1016/j.compstruct.2022.116475
In: Composite Structures. Elsevier: Oxford. ISSN 0263-8223; e-ISSN 1879-1085, meer
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| Author keywords |
GFRP; Experimental tests; FEM; E-Jaya; ANN; Crack length identification |
| Auteurs | | Top |
- Zara, A.
- Belaidi, I.
- Khatir, S.
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- Brahim, A.O.
- Boutchicha, D.
- Wahab, M.A., meer
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| Abstract |
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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