Exploring indirect vehicle-bridge interaction for bridge SHM

Cerda, Fernando; Garrett, James H.; Bielak, Jacobo; Bhagavatula, Ramamurthy; Kovačević, Jelena

doi:10.1201/b10430-99

Cited by 8 publications

(12 citation statements)

References 34 publications

(24 reference statements)

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“…Table 2 compares the performance of different classifiers with the low labeling ratio of 10%. We use V for vehicle, S for speed, SMRC for supervised MRC, LR for logistic regression, NB for naive Bayes, KSVM for kernel SVM, LP for label propagation, SSMRC for semi-supervised MRC and LP-W for label propagation with a semi-supervised weighting algorithm in (2). We see that when the labeling ratio is low, supervised MRC performs poorly, label propagation works well, and semi-supervised MRC works the best.…”

Section: Resultsmentioning

confidence: 99%

“…While we can labelq (2) as Class 1 but cannot make a decision forq (1) , the entropy measure tells us that we can labelq (1) with higher confidence (less uncertainty) because its entropy is lower. To resolve this issue, we define a new uncertainty measure, s be the uncertainty of the sth subband to label the ith sample.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…The drawback of such direct approaches is that they require a sophisticated and expensive electronic infrastructure with installation, maintenance and power support. Recently, indirect approaches have been proposed [1,2,3], suggesting the use of moving vehicles to collect data from accelerometers inside the vehicles, a far less expensive and complex solution (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Chen

Cerda

Guo

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Chen

Cerda

Guo

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…The indirect approach, on the other hand, overcomes this limit by installing sensors on a moving vehicle that, by crossing the bridge of interest, can capture the dynamic vehicle-bridge interaction and thus, indirectly, the dynamic characteristics of the bridge itself (Lin et al 2005;Cerda et al 2010). Using sophisticated signal processing algorithms, information about the bridge condition can be extracted from the vehicle-bridge interaction signals, which may allow for the detection and location of structural damage.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Sparse Representation and Fourier Discriminant Methods: Damage Location Classification in Indirect Lab-scale Bridge Structural Health Monitoring

Wang

Chen

Lederman

et al. 2013

Structures Congress 2013

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This paper presents a novel method for interpreting data to improve the indirect structural health monitoring (SHM) of bridges. The research presented in the study is part of an ongoing study aimed at developing a novel SHM paradigm for the health assessment of bridges. In this paradigm, we envision the use of an instrumented vehicle that assesses a bridge's dynamic characteristics while traveling across the bridge. These characteristics are then correlated to the health of the structure by means of advanced signal processing and pattern recognition approaches. In this paper, we present and compare two classification algorithms that locate the presence of damages at well-defined locations on the structure: sparse representation and the Fourier discriminant methods, and find that the sparse representation method provides superior classification accuracy.

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“…The signals have relevant information in the localized time-frequency regions. A classification framework has been developed for indirect bridge health monitoring (Cerda et al 2010;Chen et al 2014) which combines the multi-resolution classification with semi-supervised learning. A multiresolution decomposition technique, such as wavelet transform, was used to extract hidden features in the localized time-frequency regions from signals taken from the vehicle.…”

Section: Pattern Recognition Algorithmsmentioning

confidence: 99%

Structural Health Monitoring Based on Vehicle-Bridge Interaction: Accomplishments and Challenges

Zhu

Law

2015

Advances in Structural Engineering

134

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The use of the vehicle-bridge interaction (VBI) data for structural health monitoring has received considerable interest in the last decade. Compared with the traditional bridge health monitoring, the VBI based approach allows the target bridges to be monitored or assessed under operating conditions. The VBI system has time-variant features and the vehicle can serve as a moving exciter and a mobile sensor in the system. Many bridge damage identification techniques based on VBI have been developed, and they could be divided into three categories, namely, technique based on the bridge responses, technique based on the vehicle responses and technique based on both the vehicle and bridge responses. This paper presents a review on the structural health monitoring based on VBI and the challenges for its general implementation in practice.

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Exploring indirect vehicle-bridge interaction for bridge SHM

Cited by 8 publications

References 34 publications

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

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

Comparison of Sparse Representation and Fourier Discriminant Methods: Damage Location Classification in Indirect Lab-scale Bridge Structural Health Monitoring

Structural Health Monitoring Based on Vehicle-Bridge Interaction: Accomplishments and Challenges

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