Improved ANN for Damage Identification in Laminated Composite Plate

Mohand Slimani; Samir Tiachacht; Amar Behtani; Tawfiq Khatir; Samir Khatir; Brahim Benaissa; Mohamed Kamel Riahi

doi:10.1007/978-3-031-24041-6_15

Improved ANN for Damage Identification in Laminated Composite Plate

Mohand Slimani, Samir Tiachacht, Amar Behtani, Tawfiq Khatir, Samir Khatir, Brahim Benaissa, Mohamed Kamel Riahi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

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 (MSE_cr) 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.

Original language	British English
Title of host publication	Proceedings of the International Conference of Steel and Composite for Engineering Structures - ICSCES 2022
Editors	Roberto Capozucca, Samir Khatir, Gabriele Milani
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	186-198
Number of pages	13
ISBN (Print)	9783031240409
DOIs	https://doi.org/10.1007/978-3-031-24041-6_15
State	Published - 2023
Event	International Conference on Steel and Composite for Engineering Structures, ICSCES 2022 - Ancona, Italy Duration: 12 Sep 2022 → 13 Sep 2022

Publication series

Name	Lecture Notes in Civil Engineering
Volume	317 LNCE
ISSN (Print)	2366-2557
ISSN (Electronic)	2366-2565

Conference

Conference	International Conference on Steel and Composite for Engineering Structures, ICSCES 2022
Country/Territory	Italy
City	Ancona
Period	12/09/22 → 13/09/22

Keywords

Artificial intelligence
Damage detection
Inverse problem
Metaheuristic optimization
Structural quantification

Access to Document

10.1007/978-3-031-24041-6_15

Cite this

Slimani, M., Tiachacht, S., Behtani, A., Khatir, T., Khatir, S., Benaissa, B., & Riahi, M. K. (2023). Improved ANN for Damage Identification in Laminated Composite Plate. In R. Capozucca, S. Khatir, & G. Milani (Eds.), Proceedings of the International Conference of Steel and Composite for Engineering Structures - ICSCES 2022 (pp. 186-198). (Lecture Notes in Civil Engineering; Vol. 317 LNCE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-24041-6_15

Slimani, Mohand ; Tiachacht, Samir ; Behtani, Amar et al. / Improved ANN for Damage Identification in Laminated Composite Plate. Proceedings of the International Conference of Steel and Composite for Engineering Structures - ICSCES 2022. editor / Roberto Capozucca ; Samir Khatir ; Gabriele Milani. Springer Science and Business Media Deutschland GmbH, 2023. pp. 186-198 (Lecture Notes in Civil Engineering).

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title = "Improved ANN for Damage Identification in Laminated Composite Plate",

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.",

keywords = "Artificial intelligence, Damage detection, Inverse problem, Metaheuristic optimization, Structural quantification",

author = "Mohand Slimani and Samir Tiachacht and Amar Behtani and Tawfiq Khatir and Samir Khatir and Brahim Benaissa and Riahi, {Mohamed Kamel}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Steel and Composite for Engineering Structures, ICSCES 2022 ; Conference date: 12-09-2022 Through 13-09-2022",

year = "2023",

doi = "10.1007/978-3-031-24041-6_15",

language = "British English",

isbn = "9783031240409",

series = "Lecture Notes in Civil Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

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}

Slimani, M, Tiachacht, S, Behtani, A, Khatir, T, Khatir, S, Benaissa, B & Riahi, MK 2023, Improved ANN for Damage Identification in Laminated Composite Plate. in R Capozucca, S Khatir & G Milani (eds), Proceedings of the International Conference of Steel and Composite for Engineering Structures - ICSCES 2022. Lecture Notes in Civil Engineering, vol. 317 LNCE, Springer Science and Business Media Deutschland GmbH, pp. 186-198, International Conference on Steel and Composite for Engineering Structures, ICSCES 2022, Ancona, Italy, 12/09/22. https://doi.org/10.1007/978-3-031-24041-6_15

Improved ANN for Damage Identification in Laminated Composite Plate. / Slimani, Mohand; Tiachacht, Samir; Behtani, Amar et al.
Proceedings of the International Conference of Steel and Composite for Engineering Structures - ICSCES 2022. ed. / Roberto Capozucca; Samir Khatir; Gabriele Milani. Springer Science and Business Media Deutschland GmbH, 2023. p. 186-198 (Lecture Notes in Civil Engineering; Vol. 317 LNCE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Improved ANN for Damage Identification in Laminated Composite Plate

AU - Slimani, Mohand

AU - Tiachacht, Samir

AU - Behtani, Amar

AU - Khatir, Tawfiq

AU - Khatir, Samir

AU - Benaissa, Brahim

AU - Riahi, Mohamed Kamel

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N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Damage detection

KW - Inverse problem

KW - Metaheuristic optimization

KW - Structural quantification

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SN - 9783031240409

T3 - Lecture Notes in Civil Engineering

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BT - Proceedings of the International Conference of Steel and Composite for Engineering Structures - ICSCES 2022

A2 - Capozucca, Roberto

A2 - Khatir, Samir

A2 - Milani, Gabriele

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Steel and Composite for Engineering Structures, ICSCES 2022

Y2 - 12 September 2022 through 13 September 2022

ER -

Slimani M, Tiachacht S, Behtani A, Khatir T, Khatir S, Benaissa B et al. Improved ANN for Damage Identification in Laminated Composite Plate. In Capozucca R, Khatir S, Milani G, editors, Proceedings of the International Conference of Steel and Composite for Engineering Structures - ICSCES 2022. Springer Science and Business Media Deutschland GmbH. 2023. p. 186-198. (Lecture Notes in Civil Engineering). doi: 10.1007/978-3-031-24041-6_15

Improved ANN for Damage Identification in Laminated Composite Plate

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Keywords

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