Long-term displacement measurement of full-scale bridges using computer vision and lidar
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- Long-term displacement measurement of full-scale bridges using computer vision and lidar
- Lee, Junhwa
- Lee, Young-Joo
- Issue Date
- Graduate School of UNIST
- Bridge displacement is regarded as a key safety indicator that is widely adopted for structural health monitoring (SHM). Bridge structures deflect in response to applied loads and structural degradation. As extensive vibrations of bridges cause passenger’s discomfort and accelerate structural degradation, modern societies take the bridge displacement into account in their design codes and regular maintenance protocols to ensure serviceability and safety of the bridge structures. The short-term displacement is generally employed in bridge SHM, together with the level of load carrying capacity. Even though the long-term displacement can also provide essential safety information, in addition to the short-term data, the long-term displacement monitoring of bridges is not commonly conducted owing to practical difficulties. The long-term monitoring of displacement using conventional displacement sensors, such as a linear variable differential transformer, laser displacement sensor, and radar, or indirect estimation methods, such as an acceleration-based method or multimetric sensor-based approaches result in errors, which typically accumulate over time. A limited number of research studies have addressed long-term bridge displacement measurement; however, the sensor drift can still cause errors in those measurements. This paper proposes long-term displacement measurement methods using computer vision and LiDAR, tailored to full-scale bridge structures. The computer vision-based approach compensates for the camera motion-induced errors by using an auxiliary camera and the long-term displacement can be achieved regardless of the camera movement. A LiDAR-based method is also presented, by which the long-term time history of the bridge displacement can be tracked by a temporarily installed LiDAR, thus eliminating the need for a permanent installation in the field. These two long-term measurement approaches were cross validated on a 40 m-long full-scale railway bridge under construction. Over a span of 650 days, these two methods showed a similar trend, thus validating the applicability of each method. Important structural information, such as immediate displacement due to dead load, long-term deflection due to creep, daily fluctuation due to temperature gradient, could potentially provide long-term displacement data in bridge health monitoring.
- Department of Urban and Environmental Engineering (Urban Infrastructure Engineering)
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