<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>8999</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>28</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-02-23</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">3d imaging and analysis of cracks in loaded concrete samples. 12th Conference on Industrial Computed Tomography (iCT) 2023, 27 February - 2 March 2023 in Fürth, Germany</title>
    <abstract language="eng">Concrete plays a central role as the standard building material in civil engineering. Experimental characterization of the concrete&#13;
microstructure and a description of failure mechanisms are important to understand the concrete’s mechanical properties. Computed tomography is a powerful source of information as it yields 3d images of concrete specimens. However, complete visual&#13;
inspection is often infeasible due to very large image sizes. Hence, automatic methods for crack detection and segmentation are&#13;
needed. A region-growing algorithm and a 3d U-Net showed promising results in a previous study. Cracks in normal concrete&#13;
and high-performance concrete that were initiated via tensile tests were investigated. Here, the methods are validated on a more&#13;
diverse set of concrete types and crack characteristics. Adequate adaptions of the methods are necessary to deal with the complex&#13;
crack structures. The segmentation results are assessed qualitatively and compared to those of a template matching algorithm&#13;
which is well-established in industry.</abstract>
    <parentTitle language="eng">e-Journal of Nondestructive Testing</parentTitle>
    <identifier type="doi">10.58286/27721</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2022-10-04</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Christian Jung</author>
    <submitter>Olaf Paetsch</submitter>
    <author>Anna Nowacka</author>
    <author>Tin Barisin</author>
    <author>Dietmar Meinel</author>
    <author>Olaf Paetsch</author>
    <author>Szymon Grzesiak</author>
    <author>Michael Salamon</author>
    <author>Katja Schladitz</author>
    <author>Claudia Redenbach</author>
    <author>Matthias Pahn</author>
    <collection role="persons" number="paetsch">Paetsch, Olaf</collection>
    <collection role="projects" number="no-project">no-project</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
</export-example>
