@ARTICLE{Slimani2023186,
	author = {Slimani, Mohand and Tiachacht, Samir and Behtani, Amar and Khatir, Tawfiq and Khatir, Samir and Benaissa, Brahim and Riahi, Mohamed Kamel},
	title = {Improved ANN for Damage Identification in Laminated Composite Plate},
	year = {2023},
	journal = {Lecture Notes in Civil Engineering},
	volume = {317 LNCE},
	pages = {186 – 198},
	doi = {10.1007/978-3-031-24041-6_15},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149808068&doi=10.1007%2f978-3-031-24041-6_15&partnerID=40&md5=fe3f0ef797f6e3b78b977febe40eea71},
	abstract = {This paper presents an improved Artificial Neural Network (ANN) for structural health monitoring of composite materials. Simply supported three-ply [0∘90∘0∘] square laminated plate modeled with a 9 × 9 grid is provided and validated based on the literature review. Modal strain energy change ratio (MSEcr) is used to localize the damaged elements and eliminate the healthy elements. Next, improved ANN using the Arithmetic optimization algorithm (AOA) used for structural quantification. AOA aims to optimize the parameters of ANN for better training. Several scenarios are considered to test the accuracy of the presented approach. The results showed that the approach can localize and quantify the damage correctly. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	note = {Cited by: 5}
}

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