Structural health monitoring using video stream, influence lines, and statistical analysis

    Authors

    R. Zaurin;F. N. Catbas

    Comments

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    Abbreviated Journal Title

    Struct. Health Monit.

    Keywords

    SHM; monitoring; computer vision; image; damage; influence lines; statistical analysis; experiment; Engineering, Multidisciplinary; Instruments & Instrumentation

    Abstract

    Civil infrastructure systems experience damage, overloading, aging due to normal operations, severe environmental conditions, and extreme events. These effects change the structural behavior and performance. Novel structural health monitoring (SHM) strategies are increasingly becoming more important to objectively determine the actual condition and these changes. The main objective of this study is to demonstrate the integration of video images and sensor data as promising techniques for the safety of bridges in the context of SHM. The UCF 4-span bridge model is used to demonstrate the method. Image and sensing data are analyzed to obtain unit influence line (UIL) as an index for monitoring the bridge behavior under loading conditions identified using computer vision techniques. UILs are extracted for several different moving loads. In addition to the analysis of UILs in a comparative fashion, a new method based on statistical outlier detection from UIL vector sets is proposed and demonstrated. The new method is applied to detect and identify some of the most common damage scenarios for bridges such as changes in boundary conditions and loss of connectivity between composite sections. Successful results are obtained from the experimental studies.

    Journal Title

    Structural Health Monitoring-an International Journal

    Volume

    10

    Issue/Number

    3

    Publication Date

    1-1-2011

    Document Type

    Article

    Language

    English

    First Page

    309

    Last Page

    332

    WOS Identifier

    WOS:000290144100006

    ISSN

    1475-9217

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