<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>55493</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1061</pageFirst>
    <pageLast>1068</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>CRC Press</publisherName>
    <publisherPlace>Boca Raton</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Research Data Management of Structural Health Monitoring Projects and Subsequent Applications of Artificial Intelligence Methods</title>
    <abstract language="eng">Structural health monitoring (SHM) intends to improve the management of engineering structures. The number of successful SHM projects – especially SHM research projects – is ever growing, yielding added value and more scientific insight into the management of infrastructure asset. With the advent of the data age, the value of accessible data becomes increasingly evident. In SHM, many new data-centric methods are currently being developed at a high pace. A consequent application of research data management (RDM) concepts in SHM projects enables a systematic management of raw and processed data, and thus facilitates the development and application of artificial intelligence (AI) and machine learning (ML) methods to the SHM data. In this contribution, a case study based on an institutional RDM framework is presented. Data and metadata from monitoring the structural health of the Maintalbrücke Gemünden for a period of 16 months are managed with the RDM system BAM Data Store, which makes use of the openBIS data management software. An ML procedure is used to classify the data. Feature engineering, feature training and resulting data are performed and modelled in the RDM system.</abstract>
    <parentTitle language="eng">Bridge safety, maintenance, management, life-cycle, resilience and sustainability - Proceedings of the eleventh International Conference on Bridge Maintenance, Safety and Management (IABMAS 2022), Barcelona, Spain, July 11-15, 2022</parentTitle>
    <identifier type="isbn">978-1-032-35623-5</identifier>
    <identifier type="isbn">978-1-003-32264-1</identifier>
    <identifier type="doi">10.1201/9781003322641-127</identifier>
    <enrichment key="eventName">11th International Conference on Bridge Maintenance, Safety and Management (IABMAS2022)</enrichment>
    <enrichment key="eventPlace">Barcelona, Spain</enrichment>
    <enrichment key="eventStart">11.07.2022</enrichment>
    <enrichment key="eventEnd">15.07.2022</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="RelatedIdentifier">https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/55494</enrichment>
    <author>Patrick Simon</author>
    <author>Ralf Herrmann</author>
    <author>Ronald Schneider</author>
    <author>Falk Hille</author>
    <author>Matthias Baeßler</author>
    <author>Rukeia El-Athman</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Research data management</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural health monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial intelligence</value>
    </subject>
    <collection role="ddc" number="624">Ingenieurbau</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
    <collection role="institutes" number="">7.2 Ingenieurbau</collection>
    <collection role="themenfelder" number="">Infrastruktur</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
  </doc>
  <doc>
    <id>55494</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>lecture</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Research Data Management of Structural Health Monitoring Projects and Subsequent Applications of Artificial Intelligence Methods</title>
    <abstract language="eng">Structural health monitoring (SHM) intends to improve the management of engineering structures. The number of successful SHM projects – especially SHM research projects – is ever growing, yielding added value and more scientific insight into the management of infrastructure asset. With the advent of the data age, the value of accessible data becomes increasingly evident. In SHM, many new data-centric methods are currently being developed at a high pace. A consequent application of research data management (RDM) concepts in SHM projects enables a systematic management of raw and processed data, and thus facilitates the development and application of artificial intelligence (AI) and machine learning (ML) methods to the SHM data. In this contribution, a case study based on an institutional RDM framework is presented. Data and metadata from monitoring the structural health of the Maintalbrücke Gemünden for a period of 16 months are managed with the RDM system BAM Data Store, which makes use of the openBIS data management software. An ML procedure is used to classify the data. Feature engineering, feature training and resulting data&#13;
are performed and modelled in the RDM system.</abstract>
    <enrichment key="eventName">11th International Conference on Bridge Maintenance, Safety and Management (IABMAS2022)</enrichment>
    <enrichment key="eventPlace">Barcelona, Spain</enrichment>
    <enrichment key="eventStart">11.07.2022</enrichment>
    <enrichment key="eventEnd">15.07.2022</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="RelatedIdentifier">https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/55493</enrichment>
    <enrichment key="InvitedTalks">0</enrichment>
    <author>Patrick Simon</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Research data management</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural health monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial intelligence</value>
    </subject>
    <collection role="ddc" number="624">Ingenieurbau</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
    <collection role="institutes" number="">7.2 Ingenieurbau</collection>
    <collection role="themenfelder" number="">Infrastruktur</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Präsentation</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
  </doc>
</export-example>
