A Damage Detection Method of Bridges Utilizing Vehicle Vibration Time History Signal

Yu, Zhongru; Shao, Shuai; Deng, Gaofeng; Zhou, Zhixiang

doi:10.1007/978-3-030-64594-6_37

Cited by 1 publication

(1 citation statement)

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“…The numerical analysis and laboratory experiment results showed that the approach could identify the existence of damage and provide useful information about its severity. Subsequently, the aforementioned adapted MFCC was applied to a drive-by damage detection framework [63]. The method's feasibility was verified numerically with a single DOFs sprung mass model and by a laboratory experiment.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Applying Mel-Frequency Cepstral Coefficients in a Drive-by Damage Detection Methodology for High-Speed Railway Bridges

et al. 2022

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In this paper, a drive-by damage detection methodology for high-speed railway (HSR) bridges is addressed, to appraise the application of Mel-frequency cepstral coefficients (MFCC) to extract the Damage Index (DI). A finite element (FEM) 2D VTBI model that incorporates the train, ballasted track and bridge behavior is presented. The formulation includes track irregularities and a damaged condition induced in a specified structure region. The feasibility of applying cepstrum analysis components to the indirect damage detection in HSR by on-board sensors is evaluated by numerical simulations, in which dynamic analyses are performed through a code implemented in MATLAB. Different damage scenarios are simulated, as well as external excitations such as measurement noises and different levels of track irregularities. The results show that MFCC-based DI are highly sensitive regarding damage detection, and robust to the noise. Bridge stiffness can be recognized satisfactorily at high speeds and under different levels of track irregularities. Moreover, the magnitude of DI extracted from MFCC is related to the relative severity of the damage. The results presented in this study should be seen as a first attempt to link cepstrum-based features in an HSR drive-by damage detection approach.

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Section: Introductionmentioning

confidence: 99%