In recent years, fast growth of relevant fields, such as modern sensing technology, control techniques, artificial intelligence, telecommunications, materials and data analysis, lays a technical and theoretical foundation for implementing structural health monitoring (SHM) technology in more direct and more cost-effective ways. Concerning traditional structure management and maintenance detection methods, some defects can be found, including a high manual inspection cost, high subjectivity, high uncertainty, low efficiency, lack of scientifically quantified bases and failure in satisfying demands of engineering practice.
This may further affect transportation safety of bridge structures. However, bridge structures suffer different degrees of damages and deterioration far before their designed lifetime due to material performance degradation and other influencing factors (e.g., environmental erosion, vehicle loads, wind loads, earthquakes and fatigue) in their entire service life. The proposed method can serve as a foundation for further research on digital twins for large-scale structures, structural condition assessment, and intelligent damage identification.īridge engineering is not only the basis for traffic and transportation systems, but also an indispensable part of rapid progress in modern transportation. Experimental results show that the holographic visual sensor and the proposed algorithms can extract an accurate holographic full-field displacement signal, and factually and sensitively accomplish vibration measurement, while accurately reflecting the real change in structural properties under various damage/action conditions. Additionally, the feature points set of the structural holography geometry and the holography feature contours were established. According to the temporal and spatial characteristics of the series data, the holographic geometric morphology tracking algorithm was introduced. Using an automatic camera patrol experimental device, original segmental dynamic and static video monitoring data of a model bridge under various damage/activities were collected. This study validates a proposed holographic visual sensor and algorithms in a computer-vision-based full-field non-contact displacement and vibration measurement.
To further improve the precision and efficiency of structural health monitoring technology and the theory of large-scale structures, full-field non-contact structural geometry morphology monitoring is expected to be a breakthrough technology in structural safety state monitoring and digital twins, owing to its economic, credible, high frequency, and holographic advantages.
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