The use of accelerometers in UAVs for bridge health monitoring

Unmanned Aerial Vehicles (UAVs) technology has gained considerable popularity in bridge structural health monitoring for its strengths, such as low cost, safety and high energy efficiency. This paper envisions a scenario in which accelerometers are mounted onto UAVs, which then are able to gather acceleration signals by self-attaching to the bridge. However, battery life is an issue in UAVs with the subsequent limitation in the duration of the measurements. Therefore, this paper carries out a simulation on mode shape extraction from a short data burst by utilising an output only technique, the so-called frequency domain decomposition (FDD). Modal assurance criterion (MAC) is used as a statistical indicator to check differences between the estimated mode shapes and the eigenvectors from finite element analysis. The short acceleration response is generated using a planar vehicle-bridge interaction system where the moving load is modelled as two quarter-cars and the bridge is modelled as a simply supported beam. The impact of signal noise, vehicle speed and signal duration on the accuracy of the estimated mode shapes is investigated. FDD is shown to achieve high values of MAC even for short data bursts. Damping ratio is identified as a significant source of MAC discrepancy in the extraction of mode shapes. The stiffness loss due to a crack is introduced in the beam to evaluate how damage affects the mode shape compared to operational effects. How the MAC values vary with crack location and damage severity is discussed for the first three mode shapes.