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A hybrid PSO and Grey Wolf Optimization algorithm for static and dynamic crack identification

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Abstract
This paper introduces an inverse problem for crack identification in two-dimensional structures using eXtend Finite Element Method (XFEM) associated with original Grey Wolf Optimization (GWO) and improved GWO using Particle Swarm Optimization (PSO) (IGWO). Static analysis with different boundary conditions and experimental modal analysis of cracked plates with varying crack length, positions, and orientation are used to test the accuracy of IGWO compared with the original GWO. The objective function is based on vertical measured strain and is computed at each iteration. The obtained results indicate that IGWO provides more accurate results than GWO based on convergence study and the error between exact and estimated results. Next, another application based on dynamic experimental cracked plates is used to improve Artificial Neural Network (ANN) parameters using GWO and IGWO. The frequencies and crack lengths are used as input and output for vertical and horizontal cracks in the plates. Thus, the model can be used for the prediction of crack length. IGWO can select the best parameters for better prediction compared with GWO.
Keywords
Applied Mathematics, Mechanical Engineering, Condensed Matter Physics, General Materials Science, Crack identification, Inverse problem, GWO, IGWO, ANN, Machine learning, PSO, XFEM, DAMAGE DIAGNOSTIC-TECHNIQUE, POD, SEARCH

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MLA
Al Thobiani, Faisal, et al. “A Hybrid PSO and Grey Wolf Optimization Algorithm for Static and Dynamic Crack Identification.” THEORETICAL AND APPLIED FRACTURE MECHANICS, vol. 118, 2022, doi:10.1016/j.tafmec.2021.103213.
APA
Al Thobiani, F., Khatir, S., Benaissa, B., Ghandourah, E., Mirjalili, S., & Abdel Wahab, M. (2022). A hybrid PSO and Grey Wolf Optimization algorithm for static and dynamic crack identification. THEORETICAL AND APPLIED FRACTURE MECHANICS, 118. https://doi.org/10.1016/j.tafmec.2021.103213
Chicago author-date
Al Thobiani, Faisal, Samir Khatir, Brahim Benaissa, Emad Ghandourah, Seyedali Mirjalili, and Magd Abdel Wahab. 2022. “A Hybrid PSO and Grey Wolf Optimization Algorithm for Static and Dynamic Crack Identification.” THEORETICAL AND APPLIED FRACTURE MECHANICS 118. https://doi.org/10.1016/j.tafmec.2021.103213.
Chicago author-date (all authors)
Al Thobiani, Faisal, Samir Khatir, Brahim Benaissa, Emad Ghandourah, Seyedali Mirjalili, and Magd Abdel Wahab. 2022. “A Hybrid PSO and Grey Wolf Optimization Algorithm for Static and Dynamic Crack Identification.” THEORETICAL AND APPLIED FRACTURE MECHANICS 118. doi:10.1016/j.tafmec.2021.103213.
Vancouver
1.
Al Thobiani F, Khatir S, Benaissa B, Ghandourah E, Mirjalili S, Abdel Wahab M. A hybrid PSO and Grey Wolf Optimization algorithm for static and dynamic crack identification. THEORETICAL AND APPLIED FRACTURE MECHANICS. 2022;118.
IEEE
[1]
F. Al Thobiani, S. Khatir, B. Benaissa, E. Ghandourah, S. Mirjalili, and M. Abdel Wahab, “A hybrid PSO and Grey Wolf Optimization algorithm for static and dynamic crack identification,” THEORETICAL AND APPLIED FRACTURE MECHANICS, vol. 118, 2022.
@article{8737645,
  abstract     = {{This paper introduces an inverse problem for crack identification in two-dimensional structures using eXtend Finite Element Method (XFEM) associated with original Grey Wolf Optimization (GWO) and improved GWO using Particle Swarm Optimization (PSO) (IGWO). Static analysis with different boundary conditions and experimental modal analysis of cracked plates with varying crack length, positions, and orientation are used to test the accuracy of IGWO compared with the original GWO. The objective function is based on vertical measured strain and is computed at each iteration. The obtained results indicate that IGWO provides more accurate results than GWO based on convergence study and the error between exact and estimated results. Next, another application based on dynamic experimental cracked plates is used to improve Artificial Neural Network (ANN) parameters using GWO and IGWO. The frequencies and crack lengths are used as input and output for vertical and horizontal cracks in the plates. Thus, the model can be used for the prediction of crack length. IGWO can select the best parameters for better prediction compared with GWO.}},
  articleno    = {{103213}},
  author       = {{Al Thobiani, Faisal and Khatir, Samir and Benaissa, Brahim and Ghandourah, Emad and Mirjalili, Seyedali and Abdel Wahab, Magd}},
  issn         = {{0167-8442}},
  journal      = {{THEORETICAL AND APPLIED FRACTURE MECHANICS}},
  keywords     = {{Applied Mathematics,Mechanical Engineering,Condensed Matter Physics,General Materials Science,Crack identification,Inverse problem,GWO,IGWO,ANN,Machine learning,PSO,XFEM,DAMAGE DIAGNOSTIC-TECHNIQUE,POD,SEARCH}},
  language     = {{eng}},
  pages        = {{11}},
  title        = {{A hybrid PSO and Grey Wolf Optimization algorithm for static and dynamic crack identification}},
  url          = {{http://dx.doi.org/10.1016/j.tafmec.2021.103213}},
  volume       = {{118}},
  year         = {{2022}},
}

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