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Integrating Swarm Intelligence with Neural Networks: A Combination Approach for Predicting Beam Cracks

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Proceedings of the International Conference of Steel and Composite for Engineering Structures (ICSCES 2023)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 486))

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Abstract

Detecting and locating damage is a crucial aspect of structural health monitoring. While Artificial Neural Networks (ANNs) have shown success in identifying damage in civil and mechanical structures, they come with certain limitations. However, enhancing the effectiveness of ANNs is achievable through adjustments in their architecture and training strategies. This study introduces a metaheuristic algorithm, specifically the Butterfly Optimization Algorithm (BOA), to optimize an ANN for predicting multiple damages in aluminum bars. Input parameters include natural frequencies, and output parameters consist of crack depths. The paper employs an enhanced Finite Element Model (FEM) to gather data through simulation, considering various crack depths. To gauge the dependability of this method, we gather experimental data from the examination of beams with varying crack depths. The results obtained are juxtaposed with comparable approaches employing metaheuristic algorithms like the Artificial Bee Colony Algorithm (ABC) and Genetic Algorithm (GA). The newly proposed approach demonstrates robust performance in predicting damage, showcasing its efficacy in comparison to alternative methods.

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Author information

Authors and Affiliations

  1. Structural Section DICEA, Polytechnic University of Marche, Brecci Bianche 10, 60131, Ancona, Italy

    Abdelwahhab Khatir, Roberto Capozucca, Erica Magagnini & Abdelmoumin Oulad Brahim

  2. Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, 700000, Vietnam

    Samir Khatir

  3. Civil Engineering Department, University of Sciences and Technologies Mohamed Boudiaf of Oran (USTO-MB), 1505, EL MNAOUER, 31000, Oran, Algeria

    Abdelwahhab Khatir & Amine Osmani

  4. Mechanical Engineering Department, University Center of Naama, P.O. Box 66, 45000, Naama, Algeria

    Bochra Khatir

Authors
  1. Abdelwahhab Khatir
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  2. Roberto Capozucca
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  3. Erica Magagnini
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  4. Samir Khatir
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  5. Abdelmoumin Oulad Brahim
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  6. Amine Osmani
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  7. Bochra Khatir
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Corresponding author

Correspondence to Abdelwahhab Khatir .

Editor information

Editors and Affiliations

  1. Design Engineering Laboratory, Toyota Technological Institute, Nagoya, Japan

    Brahim Benaissa

  2. DICEA, Università Politecnica delle Marche, Ancona, Italy

    Roberto Capozucca

  3. CEATS Center, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam

    Samir Khatir

  4. Dept. ABC, Politecnico di Milano, Milan, Italy

    Gabriele Milani

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Khatir, A. et al. (2024). Integrating Swarm Intelligence with Neural Networks: A Combination Approach for Predicting Beam Cracks. In: Benaissa, B., Capozucca, R., Khatir, S., Milani, G. (eds) Proceedings of the International Conference of Steel and Composite for Engineering Structures. ICSCES 2023. Lecture Notes in Civil Engineering, vol 486. Springer, Cham. https://doi.org/10.1007/978-3-031-57224-1_10

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  • DOI: https://doi.org/10.1007/978-3-031-57224-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57223-4

  • Online ISBN: 978-3-031-57224-1

  • eBook Packages: EngineeringEngineering (R0)

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