TR2020-053

Damage-Sensitive and Domain-Invariant Feature Extraction for Vehicle-Vibration-Based Bridge Health Monitoring


    •  Liu, J., Chen, B., Chen, S., Berges, M., Bielak, J., Noh, H.Y., "Damage-Sensitive and Domain-Invariant Feature Extraction for Vehicle-Vibration-Based Bridge Health Monitoring", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP40776.2020.9053450, April 2020, pp. 3007-3011.
      BibTeX TR2020-053 PDF Video
      • @inproceedings{Liu2020apr,
      • author = {Liu, Jingxiao and Chen, Bingqing and Chen, Siheng and Berges, Mario and Bielak, Jacobo and Noh, Hae Young},
      • title = {Damage-Sensitive and Domain-Invariant Feature Extraction for Vehicle-Vibration-Based Bridge Health Monitoring},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2020,
      • pages = {3007--3011},
      • month = apr,
      • publisher = {IEEE},
      • doi = {10.1109/ICASSP40776.2020.9053450},
      • issn = {2379-190X},
      • isbn = {978-1-5090-6631-5},
      • url = {https://www.merl.com/publications/TR2020-053}
      • }
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  • Research Area:

    Signal Processing

We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle traveling over a bridge to assess bridge health. Motivated by indirect sensing methods' benefits, such as low-cost and low maintenance, vehicle-vibration-based bridge health monitoring has been studied to efficiently monitor bridges in real-time. Yet applying this approach is challenging because 1) physics-based features extracted manually are generally not damage-sensitive, and 2) features from machine learning techniques are often not applicable to different bridges. Thus, we formulate a vehicle bridge interaction system model and find a physics-guided DS & DI feature, which can be extracted using the synchrosqueezed wavelet transform representing non-stationary signals as intrinsic-mode-type components. We validate the effectiveness of the proposed feature with simulated experiments. Compared to conventional time and frequency-domain features, our feature provides the best damage quantification and localization results across different bridges in five of six experiments.

 

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