Abstract
Vehicle-assisted monitoring is a promising alternative for rapid and low-cost bridge health monitoring compared to direct instrumentation of bridges. In recent years, centralized management systems for fleets of heavy vehicles have been adopted in transportation networks for logistics and other applications. These vehicles can be instrumented and easily integrated with the existing fleet management systems to provide information that can be used for bridge health monitoring. In this study, a numerical investigation is carried out to evaluate the feasibility of an indirect bridge monitoring system considering responses from several vehicles under operational conditions. The proposed method uses the vertical acceleration responses from a fleet of vehicles passing over a healthy bridge to train a deep autoencoder model (DAE) for bridge damage sensitive features. It is shown that the error in signal reconstruction from the trained DAE is sensitive to damage, when considering the distribution or results from several separate vehicle-crossing events. The bridge damage is quantified with a damage index based on the Kullback-Leibler divergence that evaluates the change in the distributions of the reconstruction errors. The performance of the proposed method is evaluated for two numerical scenarios of vehicle populations, for different damage cases including the effect of operational uncertainties (road profile, measurement noise, and variability in vehicle properties). The proposed method is also evaluated for more realistic multi-span continuous bridge for different damage cases in the presence of random traffic. The result show that the proposed method can detect damage under operational conditions and that it has the potential to become a new tool for cost-effective bridge health monitoring.
Highlights
The maintenance of ageing infrastructure is taking large parts of the total budget available to transport network owners
Malekjafarian et al [26] and Locke et al [35] proposed the idea of using artificial neural network (ANN) and deep learning respectively for damage detection using multiple vehicles measurement responses based on numerically generated vehicle-bridge interaction (VBI) data
This study proposed a damage assessment technique based on deep learning and a statistical distribution-based damage index
Summary
The maintenance of ageing infrastructure is taking large parts of the total budget available to transport network owners. The measured vibration responses are analysed with some signal pro cessing method to provide the information about the structure and possible damage state These methods generally require mul tiple sensors installed on the bridge, which increases installation and maintenance costs of the monitoring system. Malekjafarian et al [26] and Locke et al [35] proposed the idea of using artificial neural network (ANN) and deep learning respectively for damage detection using multiple vehicles measurement responses based on numerically generated vehicle-bridge interaction (VBI) data. The above-mentioned methods demonstrated that multiple vehicle responses analysed with different tools (signal processing, ANN and/or statistical analysis) can be successfully employed in indirect SHM Generally these studies are based on numerical simulations of simple vehicle models (mainly quarter-car).
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