Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring
This addresses the problem of efficient, low-cost bridge monitoring for infrastructure management, though it appears incremental as it builds on existing vehicle-vibration-based methods.
The paper tackles the challenge of extracting damage-sensitive and domain-invariant features from vehicle vibration data for bridge health monitoring, achieving the best damage quantification and localization results in five out of six simulated experiments compared to conventional methods.
We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle traveling over a bridge to assess bridge health. Motivated by indirect sensing methods' benefits, such as low-cost and low-maintenance, vehicle-vibration-based bridge health monitoring has been studied to efficiently monitor bridges in real-time. Yet applying this approach is challenging because 1) physics-based features extracted manually are generally not damage-sensitive, and 2) features from machine learning techniques are often not applicable to different bridges. Thus, we formulate a vehicle bridge interaction system model and find a physics-guided DS & DI feature, which can be extracted using the synchrosqueezed wavelet transform representing non-stationary signals as intrinsic-mode-type components. We validate the effectiveness of the proposed feature with simulated experiments. Compared to conventional time- and frequency-domain features, our feature provides the best damage quantification and localization results across different bridges in five of six experiments.