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Adaptive Network-Based Fuzzy Inference for Damage Detection in Rectangular Laminated Composite Plates Using Vibrational Data

Morteza Saadatmorad, Ramazan-Ali Jafari-Talookolaei, Mohammad-Hadi Pashaei, Samir Khatir (UGent) and Magd Abdel Wahab (UGent)
(2022) Proceedings of the 2nd International Conference on Structural Damage Modelling and Assessment (SDMA 2021). In Lecture Notes in Civil Engineering 204. p.179-196
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Abstract
A novel model is developed to identify damages severities in rectangular laminated composite plates (RLCPs) based on the finite element method (FEM) and adaptive neuro-fuzzy inference system (ANFIS). Empirical data are generated via FEM, and then the ANFIS algorithm is applied to identify damages’ severities of single damage scenarios. In this study, the networks’ inputs and outputs are the first five natural frequencies and damages’ severities. Also, a multilayer perceptron neural network (MLP) is developed to compare results obtained from ANFIS. Results show that the MLP model can identify the severity of every single damage scenario in RLCPs with a high precision (R > 0.9). Also, results obtained indicate that ANFIS can identify and predict the severity of every single damage scenario in RLCPs better than MLP with perfect precision (R = 0.99999).

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MLA
Saadatmorad, Morteza, et al. “Adaptive Network-Based Fuzzy Inference for Damage Detection in Rectangular Laminated Composite Plates Using Vibrational Data.” Proceedings of the 2nd International Conference on Structural Damage Modelling and Assessment (SDMA 2021), edited by Magd Abdel Wahab, vol. 204, Springer, 2022, pp. 179–96, doi:10.1007/978-981-16-7216-3_14.
APA
Saadatmorad, M., Jafari-Talookolaei, R.-A., Pashaei, M.-H., Khatir, S., & Abdel Wahab, M. (2022). Adaptive Network-Based Fuzzy Inference for Damage Detection in Rectangular Laminated Composite Plates Using Vibrational Data. In M. Abdel Wahab (Ed.), Proceedings of the 2nd International Conference on Structural Damage Modelling and Assessment (SDMA 2021) (Vol. 204, pp. 179–196). https://doi.org/10.1007/978-981-16-7216-3_14
Chicago author-date
Saadatmorad, Morteza, Ramazan-Ali Jafari-Talookolaei, Mohammad-Hadi Pashaei, Samir Khatir, and Magd Abdel Wahab. 2022. “Adaptive Network-Based Fuzzy Inference for Damage Detection in Rectangular Laminated Composite Plates Using Vibrational Data.” In Proceedings of the 2nd International Conference on Structural Damage Modelling and Assessment (SDMA 2021), edited by Magd Abdel Wahab, 204:179–96. Springer. https://doi.org/10.1007/978-981-16-7216-3_14.
Chicago author-date (all authors)
Saadatmorad, Morteza, Ramazan-Ali Jafari-Talookolaei, Mohammad-Hadi Pashaei, Samir Khatir, and Magd Abdel Wahab. 2022. “Adaptive Network-Based Fuzzy Inference for Damage Detection in Rectangular Laminated Composite Plates Using Vibrational Data.” In Proceedings of the 2nd International Conference on Structural Damage Modelling and Assessment (SDMA 2021), ed by. Magd Abdel Wahab, 204:179–196. Springer. doi:10.1007/978-981-16-7216-3_14.
Vancouver
1.
Saadatmorad M, Jafari-Talookolaei R-A, Pashaei M-H, Khatir S, Abdel Wahab M. Adaptive Network-Based Fuzzy Inference for Damage Detection in Rectangular Laminated Composite Plates Using Vibrational Data. In: Abdel Wahab M, editor. Proceedings of the 2nd International Conference on Structural Damage Modelling and Assessment (SDMA 2021). Springer; 2022. p. 179–96.
IEEE
[1]
M. Saadatmorad, R.-A. Jafari-Talookolaei, M.-H. Pashaei, S. Khatir, and M. Abdel Wahab, “Adaptive Network-Based Fuzzy Inference for Damage Detection in Rectangular Laminated Composite Plates Using Vibrational Data,” in Proceedings of the 2nd International Conference on Structural Damage Modelling and Assessment (SDMA 2021), Ghent, Belgium, 2022, vol. 204, pp. 179–196.
@inproceedings{8729908,
  abstract     = {{A novel model is developed to identify damages severities in rectangular laminated composite plates (RLCPs) based on the finite element method (FEM) and adaptive neuro-fuzzy inference system (ANFIS). Empirical data are generated via FEM, and then the ANFIS algorithm is applied to identify damages’ severities of single damage scenarios. In this study, the networks’ inputs and outputs are the first five natural frequencies and damages’ severities. Also, a multilayer perceptron neural network (MLP) is developed to compare results obtained from ANFIS. Results show that the MLP model can identify the severity of every single damage scenario in RLCPs with a high precision (R > 0.9). Also, results obtained indicate that ANFIS can identify and predict the severity of every single damage scenario in RLCPs better than MLP with perfect precision (R = 0.99999).}},
  author       = {{Saadatmorad, Morteza and Jafari-Talookolaei, Ramazan-Ali and Pashaei, Mohammad-Hadi and Khatir, Samir and Abdel Wahab, Magd}},
  booktitle    = {{Proceedings of the 2nd International Conference on Structural Damage Modelling and Assessment (SDMA 2021)}},
  editor       = {{Abdel Wahab, Magd}},
  isbn         = {{9789811672156}},
  issn         = {{2366-2557}},
  language     = {{eng}},
  location     = {{Ghent, Belgium}},
  pages        = {{179--196}},
  publisher    = {{Springer}},
  title        = {{Adaptive Network-Based Fuzzy Inference for Damage Detection in Rectangular Laminated Composite Plates Using Vibrational Data}},
  url          = {{http://doi.org/10.1007/978-981-16-7216-3_14}},
  volume       = {{204}},
  year         = {{2022}},
}
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