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Machine Learning-Based Distraction-Free Method for Measuring the Optical Displacement of Long-Span Bridge Structures

Auteur(s): ORCID
ORCID
Médium: article de revue
Langue(s): anglais
Publié dans: Structural Control and Health Monitoring, , v. 2024
Page(s): 1-14
DOI: 10.1155/2024/1824559
Abstrait:

A photogrammetric displacement measurement method based on machine learning was proposed to improve the robustness to environmental disturbances. (1) To reduce the target positioning error caused by environmental vibration especially atmospheric turbulence, a machine learning-based weighted location algorithm combined with an adaptive window selection strategy was developed. In an outdoor displacement table experiment, the proposed method’s root mean squared error (RMSE) is 0.04 mm when the distance is 50 m, showing better accuracy and stability. (2) To complement/correct the missing or anomalous data caused by adverse external conditions, such as severe occlusion or camera shaking, a fast data self-diagnosis using a correlation vector machine was performed to make full use of the full-field measurement results obtained from the images. The applicability of the proposed method in extreme cases was demonstrated in designed experiments and an actual displacement measurement task of a long-span bridge subjected to vortex-induced vibration.

Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1155/2024/1824559.
  • Informations
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  • Reference-ID
    10749396
  • Publié(e) le:
    14.01.2024
  • Modifié(e) le:
    14.01.2024
 
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