Multiresolution classification with semi-supervised learning for indirect bridge structural health monitoring

Siheng Chen, Fernando Cerda, Jia Guo, Joel B. Harley, Qing Shi, Piervincenzo Rizzo, Jacobo Bielak, James H. Garrett, Jelena Kovacevic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a multiresolution classification framework with semi-supervised learning for the indirect structural health monitoring of bridges. The monitoring approach envisions a sensing system embedded into a moving vehicle traveling across the bridge of interest to measure the modal characteristics of the bridge. To enhance the reliability of the sensing system, we use a semi-supervised learning algorithm and a semi-supervised weighting algorithm within a multiresolution classification framework. We show that the proposed algorithm performs significantly better than supervised multiresolution classification.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages3412-3416
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • bridge structural health monitoring
  • multiresolution classification
  • semi-supervised learning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Chen, S., Cerda, F., Guo, J., Harley, J. B., Shi, Q., Rizzo, P., Bielak, J., Garrett, J. H., & Kovacevic, J. (2013). Multiresolution classification with semi-supervised learning for indirect bridge structural health monitoring. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings (pp. 3412-3416). [6638291] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2013.6638291