AbstractMany factors are considered when inspecting and evaluating the overall condition of a bridge. Of particular consideration here is load testing of bridges to evaluate the existing load-carrying capacity. Sensor systems are often mounted directly to girders for this assessment; however, installing sensors and data acquisition systems can be an expensive and time-consuming process, particularly given that load testing does not warrant long-term monitoring. As an alternative, noncontact remote sensing techniques have been developed for measuring structural deformations, and have the potential to be used for static load test applications. These approaches do not require sophisticated instrumentation installations, and can provide a denser array of measurements, compared with conventional sensors. A particular focus has been on techniques that use video recordings, tracking the motion between subsequent video frames via computer vision methods. There are now commercial offerings for such measurement systems, as well as an array of techniques that can be used for custom applications. While such methods have seen significant testing under laboratory conditions, there are only a limited number of studies that provide comparative methodological analyses under full-scale field conditions. This paper presents a case study on the use of vision-based methods for bridge load testing, and provides a comparison of a digital image correlation (DIC) approach with a phase-based optical flow method. Two sets of field experiments were performed on bridges in the state of Delaware. The results show that vision-based methods can provide comparable results to conventional sensor installations, given sufficient consideration of the unique technical demands of these methods, as well as operational logistics. In most cases, the DIC and phase-based methods provided comparable results, though the DIC system yielded generally better accuracy, owing to a combination of algorithmic differences and additional signal postprocessing.

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