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Instrumentation, digital image correlation, and modeling to monitor bridge behavior and condition assessment.

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English


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  • Abstract:
    Bridge managers have historically relied on visual inspection reports and field observation, including ; photographs, to assess bridge health. The inclusion of instrumentation, including strain gauges, along ; with a structural model can enhance bridge management. This combination of instrumentation and ; modeling is commonly classified as Structural Health Monitoring (SHM). Traditional SHM measurements ; are reference-independent, such as strain gauges. These sensors can be easily installed and provided ; valuable information for bridge condition assessment, including neutral axis location. The collection of ; global deflection of a bridge, a reference-dependent measurement, is more difficult to collect as the ; connection between the bridge structure and a fixed reference is geometrically challenging. A ; measurement technique that alleviates this issue is digital image correlation. Through recent advances ; in digital photography and the computational capability of personal computers, Digital Image Correlation ; (DIC) is a non-contact measurement technique that can be cost-effectively deployed to collect global ; deflection measurements of a bridge structure. DIC uses multiple digital cameras to photograph a target ; object to provide structural response information. This project incorporates a DIC system into the bridge ; instrumentation and testing program for a concrete deck-steel girder composite bridge, Bagdad Road ; over US Route 4, in Durham, New Hampshire. This report presents the instrumentation selection, ; sensors location planning and deployment, structural testing, DIC measurement for displacement, and ; strain measurements in both laboratory and field experiments. This report also includes a detailed ; structural model that is calibrated with collected field structural response data for bridge decision-making ; and management.
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    Filetype[PDF - 7.41 MB]
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    urn:sha256:dd3c8ce8f807c134c44f97babd7fcd651d540ccc2bace0ac8afb847011714546
File Language:
English
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