Foundation Models for Structural Health Monitoring
<|reference_start|>Foundation Models for Structural Health Monitoring: Structural Health Monitoring (SHM) is a critical task for ensuring the safety and reliability of civil infrastructures, typically realized on bridges and viaducts by means of vibration monitoring. In this paper, we propose for the first time the use of Transformer neural networks, with a Masked Auto-Encoder architecture, as Foundation Models for SHM. We demonstrate the ability of these models to learn generalizable representations from multiple large datasets through self-supervised pre-training, which, coupled with task-specific fine-tuning, allows them to outperform state-of-the-art traditional methods on diverse tasks, including Anomaly Detection (AD) and Traffic Load Estimation (TLE). We then extensively explore model size versus accuracy trade-offs and experiment with Knowledge Distillation (KD) to improve the performance of smaller Transformers, enabling their embedding directly into the SHM edge nodes. We showcase the effectiveness of our foundation models using data from three operational viaducts. For AD, we achieve a near-perfect 99.9% accuracy with a monitoring time span of just 15 windows. In contrast, a state-of-the-art method based on Principal Component Analysis (PCA) obtains its first good result (95.03% accuracy) only considering 120 windows. On two different TLE tasks, our models obtain state-of-the-art performance on multiple evaluation metrics (R$^2$ score, MAE% and MSE%). On the first benchmark, we achieve an R$^2$ score of 0.97 and 0.85 for light and heavy vehicle traffic, respectively, while the best previous approach stops at 0.91 and 0.84. On the second one, we achieve an R$^2$ score of 0.54 versus the 0.10 of the best existing method.<|reference_end|>
@article{benfenati2024foundation,
    title={Foundation Models for Structural Health Monitoring},
    author={Luca Benfenati, Daniele Jahier Pagliari, Luca Zanatta, Yhorman
  Alexander Bedoya Velez, Andrea Acquaviva, Massimo Poncino, Enrico Macii, Luca
  Benini, Alessio Burrello},
    journal={arXiv preprint arXiv:2404.02944},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02944},
    primaryClass={cs.LG cs.AI cs.SY eess.SY}
}