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Movable bridge maintenance monitoring.

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English


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  • Abstract:
    Movable bridges have particular maintenance issues, which cost considerably more than those of fixed bridges, ; mostly because of the complex interaction of the mechanical, electrical and structural components. In order to track ; maintenance and operational performance, a comprehensive monitoring system was implemented on Sunrise ; Bridge (Ft. Lauderdale) to track the behavior and condition of several critical mechanical, electrical and structural ; components. In this project, a number of statistical analysis and machine learning-based methods were developed ; and employed to track the operation of the mechanical components. After the completion of the previous phases ; of the project, the bridge was scheduled for painting; however, the monitoring system was significantly damaged ; during the preparation, sandblasting and painting despite the considerable efforts of FDOT personnel to protect the ; system. The research team focused on repairing the monitoring system, which was affected by the painting ; operation, collecting and analyzing more data and preparing the system for FDOT. In this phase, the monitoring ; system was maintained. Details of the field work conducted to repair the damaged monitoring system are presented. ; Then, analysis of data that were collected after the monitoring system was repaired is presented for different ; mechanical components. The baseline response and the thresholds for acceptable behavior were established. ; During this phase of the project, unanticipated behaviors were observed for two components (one at the span locks ; and one at the gearbox) at two different times. These findings indicating the unanticipated behavior using the ; monitoring system are also corroborated with the independent maintenance reports. These changes in behavior ; required maintenance work at the span lock and gearbox as noted in the maintenance logs. Finally, ; recommendations are provided based on the findings and experiences from this project.
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    PDF
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    Filetype[PDF - 3.76 MB]
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  • Main Document Checksum:
    urn:sha256:377b6145774c6165901af098fb0a8a62094a301c75465aaae9e45cab37e955a2
File Language:
English
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