Multiscale 1D-CNN for Damage Severity Classification and Localization Based on Lamb Wave in Laminated Composites

Olivier Munyaneza and Jung Woo Sohn ()
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Olivier Munyaneza: Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Graduated School, Kumoh National Institute of Technology, Daehak-ro 61, Gumi 39177, Gyeongbuk, Republic of Korea
Jung Woo Sohn: School of Mechanical System Engineering, Kumoh National Institute of Technology, Daehak-ro 61, Gumi 39177, Gyeongbuk, Republic of Korea

Mathematics, 2025, vol. 13, issue 3, 1-18

Abstract: Lamb-wave-based structural health monitoring is widely employed to detect and localize damage in composite plates; however, interpreting Lamb wave signals remains challenging due to their dispersive characteristics. Although convolutional neural networks (CNNs) demonstrate a significant capability for pattern recognition within these signals relative to other machine learning models, CNNs frequently encounter difficulties in capturing all the underlying patterns when the damage severity varies. To address this issue, we propose a multiscale, one-dimensional convolutional neural network (MS-1D-CNN) to assess the damage severity and localize damage in laminated plates. The MS-1D-CNN is capable of learning both low- and high-level features, enabling it to distinguish between minor and severe damage. The dataset was obtained experimentally via a sparse array of four lead zirconate titanates, with signals from twelve paths fused and downsampled before being input into the model. The efficiency of the model was evaluated using accuracy, precision, recall, and F1-score metrics for severity identification, along with the mean squared error, mean absolute error, and R 2 for damage localization. The experimental results indicated that the proposed MS-1D-CNN outperformed support vector machine and artificial neural network models, achieving higher accuracy in both identifying damage severity and localizing damage with minimal error.

Keywords: lamb wave; damage classification; damage localization; multiscale 1D-CNN; artificial neural networks (ANNs); support vector machine (SVM); fully convolutional networks (FCNs) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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