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

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journal contribution
posted on 2013-05-01, 00:00 authored by Siheng Chen, Fernando Cerda, Jia Guo, Joel B. Harley, Qing Shi, Piervincenzo Rizzo, Jacobo BielakJacobo Bielak, James GarrettJames Garrett, Jelena KovacevicJelena Kovacevic

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.


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