Quantifying the condition of aging structures is important to verify structural integrity and long-term reliability. Structural health monitoring plays a key role in the prevention of catastrophic failure, in improving the safety of infrastructure, and in reducing the downtime and costs associated with their maintenance. Bridges are typically designed to have a lifespan on order of 50years; therefore, bridge monitoring is important since many of them are near to or have already exceeded their design life. Conventional sensors and examination techniques such as accelerometers and strain gages produce results at only a discrete number of points. Visual inspection only provides qualitative information and is subject to human variability and inconsistencies between inspectors. Moreover, both approaches are labor intensive and time-consuming. In recent years, three-dimensional digital image correlation systems have proven their efficiency in being able to provide accurate quantitative information of structural deformations, full-field strain, and geometry profiles of large-scale structures. At the same time, unmanned aerial vehicles have emerged as valuable tools for remotely performing measurements in places, which are either difficult or dangerous to access. With regard to bridge inspection, unmanned aerial vehicles have the capability to expedite the measurement process, offer increased accessibility, and reduce interference with the structures' functionality. In this study, a novel approach that combines the use of an unmanned aerial vehicle and three-dimensional digital image correlation is developed to perform non-contact, optically based measurements to monitor the health of bridges. Extensive laboratory tests and a long-term monitoring campaign on two in-service concrete bridges demonstrated the accuracy of this system in detecting structural changes. Results show that this system is able to detect changes to the bridge geometry with an uncertainty on the order of 10(-5)m while improving accessibility. The feasibility of the approach, best practices, and lessons learned is presented.
Autonomous inspection;bridge;digital image correlation;long-term monitoring;structural health monitoring;unmanned aerial vehicle;