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A systematic approach to find the hyperparameters of artificial neural networks applied to damage detection in composite materials

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Abstract

Artificial Neural Networks applied to Structural Health Monitoring (SHM) have been used to detect damage in composite structures. However, tuning the ANN architecture and its hyperparameters is difficult although fundamental for the success of ANN-based SHM models. This work proposes a new measure, the Expected Accuracy, allowing for the comparison of different general sets of hyperparameters. A new methodology to create an ensemble of architectures and to compare their performance is also proposed. The number of neurons at each layer is parameterized by polynomial functions, which are smoothly adjusted among architectures. For each topology, a set of models were independently created by randomly initializing their weights and biases at the beginning of the training phase. Then, the performance of both models and topologies can be described by the accuracy measure proposed in this work. A discussion about the impact of different compressing techniques, like PCA and LDA, is also presented in the context of ANN-based SHM using vibration data. The best performing topology reached 100% accuracy in all data sets when applied to a practical binary classification SHM problem of Glass Fiber-Reinforced Plastic composites.

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Data Availability

The data that support the findings of this study are available on request from the corresponding author.

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Acknowledgements

The authors acknowledge the financial support of the Santa Catarina State Research and Innovation Funding Agency, Brazil (FAPESC process number: 2017TR1747, 2019TR779, 2021TR843, 48/2021), Coordination for the Improvement of the Higher Level Personnel, Brazil (CAPES Finance Code 001), PROMOP (Programa de Bolsas de Monitoria de Pós-Graduação) of the Santa Catarina State University, Brazil, and the National Council for Scientific and Technological Development (CNPq), Brazil. Eduardo Lenz Cardoso acknowledges the financial support of the National Council for Scientific and Technological Development, Brazil (CNPq process number: 303900/2020-2). Ricardo De Medeiros received the financial support from the National Council for Scientific and Technological Development (CNPq process number: 304795/2022-4).

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Author notes

  1. Eduardo Lenz Cardoso and Ricardo de Medeiros have contributed equally to this work.

Authors and Affiliations

  1. Mechanical Engineering Department, State University of Santa Catarina, Rua Paulo Malschitzki, 200, Joinville, 89219-710, Santa Catarina, Brazil

    Matheus Janczkowski Fogaça, Eduardo Lenz Cardoso & Ricardo de Medeiros

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  1. Matheus Janczkowski Fogaça
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Contributions

Matheus Janczkowski Fogaça: Conceptualization, Methodology, Validation, Software, Writing – original draft. Eduardo Lenz Cardoso: Investigation, Validation, Writing – review & editing, Visualization. Ricardo De Medeiros: Conceptualization, Investigation, Validation, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.

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Correspondence to Matheus Janczkowski Fogaça.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Technical Editor: Mauricio Vicente Donadon.

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Fogaça, M.J., Cardoso, E.L. & de Medeiros, R. A systematic approach to find the hyperparameters of artificial neural networks applied to damage detection in composite materials. J Braz. Soc. Mech. Sci. Eng. 45, 496 (2023). https://doi.org/10.1007/s40430-023-04371-y

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