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Enhancing Vibration-Based Failure Identification in Beam Structures Using Statistical Features and Machine Learning

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Long Viet Ho
Ba Ho-Xuan
Toan Vu-Van
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 enhancement of vibration-based failure identification in beam structures through the integration of statistical features and machine learning. The study introduces a novel statistically-based modal curvature index (SBMCI) for accurate damage localization, which outperforms traditional methods like the Modal Curvature Method (MCM) and the Mode Shape Curvature Squares Method (MSCS) by reducing false positives. The research also presents a hybrid approach combining the artificial hummingbird algorithm (AHA) with artificial neural networks (ANN) for precise damage quantification, even under noisy conditions. Case studies involving a simply supported steel beam and a three-span continuous concrete girder demonstrate the effectiveness of the proposed methods. The results show that the SBMCI index improves damage localization accuracy, while the AHA-ANN model achieves high precision in damage quantification with errors under 2%. This comprehensive approach offers a robust solution for structural health monitoring, making it a valuable resource for professionals in the field.
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next chapter Forecasting the Ultimate Load Capacity of Flat Slabs with Artificial Neural Networks
Title
Enhancing Vibration-Based Failure Identification in Beam Structures Using Statistical Features and Machine Learning
Authors
Long Viet Ho
Ba Ho-Xuan
Toan Vu-Van
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_1
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