首页|Multi-level deformation behavior monitoring of flexural structures via vision-based continuous boundary tracking: proof-of-concept study
Multi-level deformation behavior monitoring of flexural structures via vision-based continuous boundary tracking: proof-of-concept study
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NSTL
Elsevier
The concept of measuring the multi-level deformation behavior of flexural structures via the extraction of continuous structural boundaries using a computer vision technique is proposed. The feasibility of using a salient-object-detection method to estimate the deformation and curvature profile of target structures from the image frames of a video recording structural vibrations is investigated. A framework is proposed for performing this salient-object-detection-based vibration measurement technique via the aid of a pre-trained deep neural network. A method for determining the curvature estimated from the boundary extracted from the deep neural network is then introduced. The accuracy of the proposed technique is validated via two experiments. The first experiment measures the curvature of a semi-circular plate under rigid body motions. The second experiment tracks the deformation of reinforced concrete beams under impact loads. Both experiments verify that the proposed method is feasible for accurately measuring the vibration profile of the target structure.
Structural health monitoringVision-based measurementBending curvatureDeep neural networkSalient object detectionMODAL IDENTIFICATION
NSTL
Elsevier
