Indirect structural health monitoring of a simplified laboratory-scale bridge model

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Sandryhaila

Lederman

et al. 2014

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We propose a novel recovery algorithm for signals with complex, irregular structure that is commonly represented by graphs. Our approach is a generalization of the signal inpainting technique from classical signal processing. We formulate corresponding minimization problems and demonstrate that in many cases they have closed-form solutions. We discuss a relation of the proposed approach to regression, provide an upper bound on the error for our algorithm and compare the proposed technique with other existing algorithms on realworld datasets.

Section: Bridge Condition Identificationmentioning

confidence: 99%

Signal inpainting on graphs via total variation minimization

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Sandryhaila

Lederman

et al. 2014

Self Cite

“…put a sensor on a vehicle, and let it move across the bridge. We collected 30 samples for each of 13 different bridge damage scenarios, 8 different speeds and 2 different vehicles [13]. Experimental Setup.…”

Section: Resultsmentioning

confidence: 99%

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

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Cerda

Guo

et al. 2013

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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.

“…We capture vibration characteristics of the bridge from the vibration of the traversing vehicle through the acceleration signal. We collected 30 acceleration signals for each of 13 [8], [12]. Experimental Setup.…”

Section: B Damage Detection In Bridge Structure Monitoringmentioning

confidence: 99%

Adaptive graph filtering: Multiresolution classification on graphs

2013 IEEE Global Conference on Signal and Information Processing

Sandryhaila

Moura

et al. 2013

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Abstract-We present an adaptive graph filtering approach to semi-supervised classification. Adaptive graph filters combine decisions from multiple graph filters using a weighting function that is optimized in a semi-supervised manner. We also demonstrate the multiresolution property of adaptive graph filters by connecting them to the diffusion wavelets. In our experiments, we apply the adaptive graph filters to the classification of online blogs and damage identification in indirect bridge structural health monitoring.