TR2020-053
Damage-Sensitive and Domain-Invariant Feature Extraction for Vehicle-Vibration-Based Bridge Health Monitoring
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- "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}
- }
,
- "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.
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Research Area:
Abstract:
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.
Related News & Events
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NEWS MERL presenting 13 papers and an industry talk at ICASSP 2020 Date: May 4, 2020 - May 8, 2020
Where: Virtual Barcelona
MERL Contacts: Petros T. Boufounos; Chiori Hori; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Yanting Ma; Hassan Mansour; Philip V. Orlik; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
Research Areas: Computational Sensing, Computer Vision, Machine Learning, Signal Processing, Speech & AudioBrief- MERL researchers are presenting 13 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held virtually from May 4-8, 2020. Petros Boufounos is also presenting a talk on the Computational Sensing Revolution in Array Processing (video) in ICASSP’s Industry Track, and Siheng Chen is co-organizing and chairing a special session on a Signal-Processing View of Graph Neural Networks.
Topics to be presented include recent advances in speech recognition, audio processing, scene understanding, computational sensing, array processing, and parameter estimation. Videos for all talks are available on MERL's YouTube channel, with corresponding links in the references below.
This year again, MERL is a sponsor of the conference and will be participating in the Student Job Fair; please join us to learn about our internship program and career opportunities.
ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year. Originally planned to be held in Barcelona, Spain, ICASSP has moved to a fully virtual setting due to the COVID-19 crisis, with free registration for participants not covering a paper.
- MERL researchers are presenting 13 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held virtually from May 4-8, 2020. Petros Boufounos is also presenting a talk on the Computational Sensing Revolution in Array Processing (video) in ICASSP’s Industry Track, and Siheng Chen is co-organizing and chairing a special session on a Signal-Processing View of Graph Neural Networks.