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Movable bridge maintenance monitoring.

File Language:
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.

  • Format:
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  • Main Document Checksum:
    urn:sha256:377b6145774c6165901af098fb0a8a62094a301c75465aaae9e45cab37e955a2
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  • File Type:
    Filetype[PDF - 3.76 MB ]
File Language:
English
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