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Damage Classification of Steel Frames Using Long Short-Term Memory and Fully Convolutional Network Models

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Truong Thanh Chung
Tran Tien Son
Le Van Vu
Luong Nguyen-Duc
Tran Ngoc Hoa
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 application of a hybrid deep learning model, SE-1DCNN-LSTM, for classifying structural damage in steel frames using acceleration data. The model integrates a one-dimensional convolutional neural network (1DCNN) with a Squeeze-and-Excitation (SE) mechanism and a Long Short-Term Memory (LSTM) module to capture both spatial and temporal features effectively. The study utilizes the QUGS acceleration dataset, collected from a steel frame structure under various damage scenarios, to train and evaluate the model. The results show that the SE-1DCNN-LSTM model achieves higher accuracy and improved generalization compared to a conventional 1DCNN-LSTM model. The SE mechanism enhances feature discrimination by adaptively assigning weights to different data channels, focusing on the most critical signals for damage detection. The model's performance is optimized through automated hyperparameter tuning, ensuring robust and reliable damage classification. This innovative approach offers a promising solution for structural health monitoring, enabling early damage detection and more practical maintenance planning.
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previous chapter Suggesting a Procedure of Technical Diagnosing a Thin-Walled Horizontally Curved Steel Bridge
next chapter Reliable and Interpretable AI for CFST Column Safety Assessment
Title
Damage Classification of Steel Frames Using Long Short-Term Memory and Fully Convolutional Network Models
Authors
Truong Thanh Chung
Tran Tien Son
Le Van Vu
Luong Nguyen-Duc
Tran Ngoc Hoa
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_14
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