Multiclass deep support vector data description for structural health monitoring

Abstract

Ensuring the structural integrity of building is critical for maintaining public safety, necessitating reliable methods for the detection of internal defects. In practice, however, obtaining data from compromised structures poses significant challenges because most available data come from buildings in normal condition. This study addresses this issue by introducing a novel, real-time defect detection approach for structural health monitoring using multiclass Deep Support Vector Data Description (SVDD), a semisupervised deep learning model. The proposed method leverages only normal condition data from the target structure for training, enabling the identification of anomalies without requiring prior defect data. Kernel density estimation (KDE) was integrated, a robust statistical technique, to further enhance the model's predictive accuracy. The proposed methodology was validated using publicly available data from a three-story shake table experiment, demonstrating superior performance compared to existing anomaly detection techniques. Although traditional machine learning methods for this task typically achieve a maximum average accuracy of 58.68%, the KDE-enhanced multiclass deep SVDD developed in this study significantly outperforms these methods, demonstrating a remarkable average accuracy of 88.11%. Comprehensive parameter optimization was conducted to determine the optimal model configuration. The defect detection framework presented in this study offers a significant advancement in real-time structural health monitoring, providing a powerful tool for the proactive maintenance and safety assurance of buildings.

Cite

APA

Lee, Y. I., Lee, S. M., & Kang, T. H.-K. (2025). Multiclass deep support vector data description for structural health monitoring. Journal of Structural Engineering, 151(12). https://doi.org/10.1061/JSENDH.STENG-14823

BibTeX

@article{lee2025svdd,
  author  = {Lee, Young Il and Lee, Sang Min and Kang, Thomas H.-K.},
  title   = {Multiclass Deep Support Vector Data Description for Structural Health Monitoring},
  journal = {Journal of Structural Engineering},
  volume  = {151},
  number  = {12},
  year    = {2025},
  doi     = {10.1061/JSENDH.STENG-14823},
  url     = {https://ascelibrary.org/doi/full/10.1061/JSENDH.STENG-14823}
}