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We introduce a physics-guided signal processing approach to extract adamage-sensitive and domain-invariant (DS & DI) feature from accelerationresponse data of a vehicle traveling over a bridge to assess bridge health.Motivated by indirect sensing methods' benefits, such as low-cost andlow-maintenance, vehicle-vibration-based bridge health monitoring has beenstudied to efficiently monitor bridges in real-time. Yet applying this approachis challenging because 1) physics-based features extracted manually aregenerally not damage-sensitive, and 2) features from machine learningtechniques are often not applicable to different bridges. Thus, we formulate avehicle bridge interaction system model and find a physics-guided DS & DIfeature, which can be extracted using the synchrosqueezed wavelet transformrepresenting non-stationary signals as intrinsic-mode-type components. Wevalidate the effectiveness of the proposed feature with simulated experiments.Compared to conventional time- and frequency-domain features, our featureprovides the best damage quantification and localization results acrossdifferent bridges in five of six experiments.

Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring