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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Automated Bridge Inspection using Digital Image Correlation and other Vision-based Methods</title>
      <link>https://rip.trb.org/View/2341501</link>
      <description><![CDATA[Building on the work previously performed as part of this study, the methods developed for fatigue crack characterization using digital image correlation (DIC) will be applied to full-scale structures prone to both fatigue and fracture failures. This will include full-scale sign structure components experiencing fatigue loading and subsequent cracking, as well as representative large-scale girder specimens prone to constraint-induced fracture failure. Additionally, optical data will be generated and collected to use in evaluating the potential for machine learning and artificial intelligence methods in fatigue crack identification and characterization. This phase of the project represents deployment of the previously-developed methodologies while still looking forward to other enhanced vision-based tools. Deployment mechanisms will include various hand-held and stationary cameras, unmanned aerial vehicles (UAVs), and/or augmented reality devices such as the Microsoft HoloLens2. It is anticipated this research program will lead to vision-based inspection tools that can potentially be used in automated bridge inspections.]]></description>
      <pubDate>Sat, 17 Feb 2024 16:13:37 GMT</pubDate>
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