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Modal Strain Energy and Convolutional Neural Network-Based Damage Identification in Plate-Like Structures

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Ngoc-Tuan-Hung Bui
Thanh-Cao Le
Van-Sy Bach
Tran-Huu-Tin Luu
Manh-Hung Tran
Chi-Khai Nguyen
Duc-Duy Ho
Published in
4th International Conference on Structural Health Monitoring and Engineering Structures (SHM&ES 2025)
Publisher
Springer Nature Switzerland

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Abstract

This chapter explores the innovative combination of modal strain energy (MSE) and convolutional neural networks (CNN) for damage identification in plate-like structures. The study introduces a one-step process that leverages the sensitivity of MSE to structural damage and the robustness of CNN algorithms. Through finite element analysis, the MSE of an aluminum plate is determined, with damage severity measured as a percentage of thickness reduction. The proposed CNN architecture, featuring 2D convolutional layers and dense layers, is trained on a dataset of random damage scenarios. The results demonstrate the method's high accuracy in identifying single and multiple damages, with identification capacity indicators exceeding 80%. The study also investigates the effectiveness of combining MSE data from six bending mode shapes, further enhancing prediction performance. This research highlights the potential of integrating deep learning with traditional structural health monitoring techniques, offering a promising approach for efficient and accurate damage identification in plate-like structures.
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previous chapter Forecasting the Ultimate Load Capacity of Flat Slabs with Artificial Neural Networks
next chapter Detecting Multiple Damages in I-Section Steel Beams Using an Improved Mode Shape Curvature Change-Based Method
Title
Modal Strain Energy and Convolutional Neural Network-Based Damage Identification in Plate-Like Structures
Authors
Ngoc-Tuan-Hung Bui
Thanh-Cao Le
Van-Sy Bach
Tran-Huu-Tin Luu
Manh-Hung Tran
Chi-Khai Nguyen
Duc-Duy Ho
Copyright Year
2026
Book
4th International Conference on Structural Health Monitoring and Engineering Structures (SHM&ES 2025)

Print ISBN: 978-3-032-04644-4

Electronic ISBN: 978-3-032-04645-1

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-04645-1_3
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