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Computer Vision based In-plane Strain Field Monitoring for Identification of Local Damages in Structural Members



For monitoring local damages like thickness reduction due to corrosion and failure at bearings in steel plate-girder bridge, the in-plane strain field variation measurement can be utilized. It is known that, conducting the strain measurement in actual bridges especially near the ends of girders and bearings are difficult. In this research, computer vision based two dimensional digital image correlation (2D-DIC) technique is applied to obtain the strain field data for monitoring local damages. In order to validate the camera employed for computer vision, tensile test experiments are conducted on aluminum plates, with and without hole, for multipoint strain data acquisition and results are verified using conventional strain gauges and 3D-DIC results. Next, bending test experiments are carried out for a typical aluminum plate-girder bridge model specimen at laboratory level, under different loading conditions. Camera vision based monitoring is utilized for estimating the in-plane strain field variation near the ends of girder and the results are validated using the strain gauge measurements. As the applied load is minimal, the captured strain values are of small order, however, the camera based 2DDIC results are in good agreement with the strain gauge data.


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