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    <title language="deu">Bestandteile Digitaler Zwillinge im Erhaltungsmanagement von Verkehrsbrücken</title>
    <title language="eng">Components of digital twins in the operation and maintenance management of traffic bridges</title>
    <abstract language="deu">Digitale Zwillinge werden zukünftig ein integraler Bestandteil des Erhaltungsmanagements von Verkehrsbrücken sein. In diesem Beitrag wird argumentiert, dass sie nicht nur als digitale Abbilder physikalischer Bauwerke verstanden werden sollten, sondern als eine umfassende digitale Methode, die durch die Integration von Datenerfassung, Erhaltungsmaßnahmen, Datenmanagement, Bauwerksbewertung und Entscheidungsunterstützung die Bauwerksüberwachung und ‐erhaltung verbessert. In diesem Zusammenhang wird betont, dass der Übergang von der reaktiven zur prädiktiven Erhaltung durch den Einsatz von Digitalen Zwillingen nur dann realisierbar ist, wenn neben den erforderlichen diagnostischen und prognostischen Zustandsanalysen auch Methoden zur Optimierung von Entscheidungen über Datenerfassung und Erhaltungsmaßnahmen implementiert werden. Zur Veranschaulichung der Diskussion werden in diesem Beitrag exemplarisch zwei Bestandteile eines Digitalen Zwillings für das Erhaltungsmanagement von Verkehrsbrücken am Beispiel einer Eisenbahnbrücke demonstriert. Dabei wird zum einen gezeigt, wie Monitoringdaten mittels eines Datenmanagementsystems strukturiert verwaltet und für angeknüpfte Analysen bereitgestellt werden. Zum anderen erfolgt im Rahmen einer bauwerksspezifischen Einwirkungsermittlung eine Zugidentifikation anhand von gemessenen Schwellenschwingungen.</abstract>
    <abstract language="eng">Digital twins will become an integral part of the operation and maintenance management of traffic bridges in the future. This paper argues that they should not only be understood as digital representations of physical structures but as a digital methodology that enhances the operation and maintenance of bridges through the integration of data collection, maintenance actions, data management, structural assessment, and decision support. In this context, it is emphasized that the transition from reactive to predictive maintenance using digital twins can only be achieved if, in addition to the necessary diagnostic and prognostic condition analyses, methods for optimizing decisions on data collection and maintenance actions are also implemented. To illustrate this discussion, two key components of a digital twin for the operation and maintenance management of traffic bridges are demonstrated using a railway bridge as an example. First, it is shown how monitoring data can be systematically managed and made available for subsequent analyses through a data management system. Second, train identification based on measured sleeper vibrations is conducted as part of an object-specific load assessment.</abstract>
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    <author>Ronald Schneider</author>
    <author>Patrick Simon</author>
    <author>Ralf Herrmann</author>
    <author>Falk Hille</author>
    <author>Matthias Baeßler</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Digitale Zwillinge</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Erhaltung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Inspektion</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Monitoring</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Brücken</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="themenfelder" number="">Verkehrsinfrastrukturen</collection>
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    <publisherName>Ernst &amp; Sohn</publisherName>
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    <title language="deu">Maintalbrücke Gemünden: Bauwerksmonitoring und -identifikation aus einem Guss</title>
    <title language="eng">Maintalbrücke Gemünden – Integrated structural health monitoring and UAS diagnostics</title>
    <abstract language="deu">Die Infrastruktursysteme der Industriestaaten erfordern heute und in Zukunft ein effizientes Management bei alternder Bausubstanz, steigenden Lasten und gleichbleibend hohem Sicherheitsniveau. Digitale Technologien bieten ein großes Potenzial zur Bewältigung der aktuellen und künftigen Herausforderungen im Infrastrukturmanagement. Im BMBF-geförderten Projekt Bewertung alternder Infrastrukturbauwerke mit digitalen Technologien (AISTEC) wird untersucht, wie unterschiedliche Technologien und deren Verknüpfung gewinnbringend eingesetzt werden können. Am Beispiel der Maintalbrücke Gemünden werden ein sensorbasiertes Bauwerksmonitoring, bildbasierte Inspektion mit durch Kameras ausgestatteten Drohnen (UAS) und die Verknüpfung digitaler Bauwerksmodelle umgesetzt. Die aufgenommenen Bilder dienen u. a. als Grundlage für spätere visuelle Anomaliedetektionen und eine 3D-Rekonstruktion, welche wiederum für die Kalibrierung und Aktualisierung digitaler Tragwerksmodelle genutzt werden. Kontinuierlich erfasste Sensordaten werden ebenfalls zur Kalibrierung und Aktualisierung der Tragwerksmodelle herangezogen. Diese Modelle werden als Grundlage für Anomaliedetektionen und perspektivisch zur Umsetzung von Konzepten der prädiktiven Instandhaltung verwendet. Belastungsfahrten und historische Daten dienen in diesem Beitrag der Validierung von kalibrierten Tragwerksmodellen.</abstract>
    <abstract language="eng">Infrastructure systems of industrialised countries today and in the future require efficient management with an ageing stock, increasing loads while simultaneously maintaining a high level of safety. Digital technologies offer great potential for the current and future challenges in infrastructure management. The BMBF-funded project AISTEC is investigating how the individual technologies and their interconnection can be used beneficially. With the Maintalbrücke in Gemünden as an exemplary application, sensor-based structural monitoring, image-based inspection using unmanned aircraft systems (UAS) equipped with cameras and the integration of digital structural models are being implemented. The recorded images serve, among others, as basis for subsequent anomaly detection and a 3D reconstruction, which in turn are used for updating digital structural models. Continuously recorded sensor data is used to update the parameters of the structural models, which in turn provide the basis for predictive maintenance. Load tests are used to validate the models.</abstract>
    <parentTitle language="deu">Bautechnik</parentTitle>
    <identifier type="issn">0932-8351</identifier>
    <identifier type="doi">10.1002/bate.202100102</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-554924</identifier>
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    <enrichment key="date_peer_review">16.08.2022</enrichment>
    <enrichment key="PaperofMonth">1</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Patrick Simon</author>
    <author>M. Helmrich</author>
    <author>Ralf Herrmann</author>
    <author>Ronald Schneider</author>
    <author>Matthias Baeßler</author>
    <author>S. Lorelli</author>
    <author>G. Morgenthal</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Bauwerksüberwachung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Strukturmonitoring</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Structural Health Monitoring</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Modell-Update</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>UAS</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Belastungstest</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural system identification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural health monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Model update</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>UAS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Load tests</value>
    </subject>
    <collection role="ddc" number="624">Ingenieurbau</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
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    <issue>18</issue>
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    <title language="eng">Vibration-based system identification of a large steel box girder bridge</title>
    <abstract language="eng">The Bundesanstalt für Materialforschung und -prüfung (BAM) collaborates with TNO to develop a software framework for automated calibration of structural models based on monitoring data. The ultimate goal is to include such models in the asset management process of engineering structures. As a basis for developing the framework, a multi-span road bridge consisting of ten simply supported steel box girders was selected as a test case. Our group measured output-only vibration data from one box girder under ambient conditions. From the data, we determined eigenfrequencies and mode shapes. In parallel, we developed a preliminary structural model of the box girder for the purpose of predicting its modal properties. In this contribution, we provide an overview of the measurement campaign, the operational modal analysis, the structural modeling and qualitatively compare the identified with the predicted modes. As an outlook, we discuss the further steps in the calibration process and future applications of the calibrated model.</abstract>
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    <author>Ronald Schneider</author>
    <author>Patrick Simon</author>
    <author>Falk Hille</author>
    <author>Ralf Herrmann</author>
    <author>Matthias Baeßler</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Verkehrsinfrastukturen</value>
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      <value>SHM</value>
    </subject>
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      <value>System identification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Operational modal analysis</value>
    </subject>
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    <title language="eng">A Bayesian Probabilistic Framework for Building Models for Structural Health Monitoring of Structures Subject to Environmental Variability</title>
    <abstract language="eng">Managing aging engineering structures requires damage identification, capacity reassessment, and prediction of remaining service life. Data from structural health monitoring (SHM) systems can be utilized to detect and characterize potential damage. However, environmental and operational variations impair the identification of damages from SHM data. Motivated by this, we introduce a Bayesian probabilistic framework for building models and identifying damage in monitored structures subject to environmental variability. The novelty of our work lies (a) in explicitly considering the effect of environmental influences and potential structural damages in the modeling to enable more accurate damage identification and (b) in proposing a methodological workflow for model‐based structural health monitoring that leverages model class selection for model building and damage identification. The framework is applied to a progressively damaged reinforced concrete beam subject to temperature variations in a climate chamber. Based on deflections and inclinations measured during diagnostic load tests of the undamaged structure, the most appropriate modeling approach for describing the temperature‐dependent behavior of the undamaged beam is identified. In the damaged state, damage is characterized based on the identified model parameters. The location and extent of the identified damage are consistent with the cracks observed in the laboratory. A numerical study with synthetic data is used to validate the parameter identification. The known true parameters lie within the 90% highest density intervals of the posterior distributions of the model parameters, suggesting that this approach is reliable for parameter identification. Our results indicate that the proposed framework can answer the question of damage identification under environmental variations. These findings show a way forward in integrating SHM data into the management of infrastructures.</abstract>
    <parentTitle language="eng">Structural Control and Health Monitoring</parentTitle>
    <identifier type="doi">10.1155/2024/4204316</identifier>
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    <author>Patrick Simon</author>
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    <author>Guido Morgenthal</author>
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      <value>Verkehrsinfrastukturen</value>
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      <value>Environmental and Operational Variability</value>
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      <value>Damage Identification</value>
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      <value>Model Building</value>
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    <title language="eng">Optimal vibration sensor placement for jacket support structures of offshore wind turbines based on value of information analysis</title>
    <abstract language="eng">Information on the condition and reliability of an offshore jacket structure provided by a vibration-based structural health monitoring system can guide decisions on inspection and maintenance. When selecting the sensor setup, the designer of the monitoring system must assess its overall benefit compared to its costs before installation. The potential benefit of continuously monitoring the dynamic response of a jacket structure can be formally quantified through a value of information analysis from Bayesian decision theory. In this contribution, we present a framework for optimizing the placement of vibration sensors on offshore jacket structures by maximizing the value of information of the monitoring system. To solve the resulting discrete optimization problem, we adapt a genetic algorithm. The framework is demonstrated in a numerical example considering a redundant jacket-type steel frame. The numerical study shows that monitoring the vibration response of the frame is beneficial. Good sensor setups consist of relatively few sensors located towards the upper part of the&#13;
frame. The adapted genetic algorithm performs similarly well as established sequential sensor placement algorithms and holds substantial promise for application to real jacket structures.</abstract>
    <parentTitle language="eng">Ocean Engineering</parentTitle>
    <identifier type="doi">10.1016/j.oceaneng.2023.115407</identifier>
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    <author>Lukas Eichner</author>
    <author>Ronald Schneider</author>
    <author>Matthias Baeßler</author>
    <subject>
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      <type>uncontrolled</type>
      <value>Optimal sensor placement</value>
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    <subject>
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      <type>uncontrolled</type>
      <value>Value of information</value>
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    <subject>
      <language>eng</language>
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      <value>Jacket support structure</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Offshore wind turbine</value>
    </subject>
    <subject>
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      <value>Monitoring-informed inspection and maintenance planning</value>
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    <title language="eng">Optimal sensor placement for vibration-based structural health monitoring obtained via value of information analysis as part of a digital structural integrity management of offshore structures</title>
    <abstract language="eng">A digital structural integrity management of offshore structures enables an optimized planning of inspections and repairs with risk-based methods. In a risk-based approach, the inspection and repair strategy that minimizes the expected lifetime costs consisting of the expected inspection, repair and failure costs is determined. In addition to inspections, information on the structural condition can be continuously obtained by monitoring the vibration response of the structural system. Changes in the vibration characteristics quantified in terms of modal properties can be an indication of structural damage. In risk-based inspection and repair planning, the effect of monitoring results is determined via Bayesian updating of the structural condition and reliability. This information should be applied to inform decisions on inspections and may result in a reduced inspection effort. The benefit of continuously monitoring the structural health can be quantified in terms of the value of information, which corresponds to the difference between the expected lifetime costs with and without monitoring. In this work, we demonstrate in a numerical example how an optimized sensor placement for a vibration-based structural health monitoring system can be determined by maximizing the value of information.</abstract>
    <parentTitle language="eng">3rd International Conference on Health Monitoring of Civil &amp; Maritime Structures (HeaMES 2022) - Proceedings</parentTitle>
    <identifier type="isbn">978-1-8383226-7-0</identifier>
    <identifier type="url">https://www.researchgate.net/publication/362569906_Optimal_sensor_placement_for_vibration-based_structural_health_monitoring_obtained_via_value_of_information_analysis_as_part_of_a_digital_structural_integrity_management_of_offshore_structures</identifier>
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    <author>Lukas Eichner</author>
    <author>Ronald Schneider</author>
    <author>Patrick Simon</author>
    <author>Matthias Baeßler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural Health Monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Value of Information</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Offshore Wind Energy</value>
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    <subject>
      <language>eng</language>
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      <value>Optimal Sensor Placement</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayesian System Identification</value>
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    <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>
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    <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>
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    <subject>
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      <value>Artificial intelligence</value>
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    <pageFirst>111</pageFirst>
    <pageLast>117</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Research Publishing</publisherName>
    <publisherPlace>Singapore</publisherPlace>
    <creatingCorporation>ISRERM Organizers</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">A framework for data and structural integrity management for support structures in offshore wind farms based on building information modelling</title>
    <abstract language="eng">Support structures of wind turbines in German offshore wind farms are regularly inspected. Currently, inspection outcomes are generally not systematically recorded. This prevents their consistent handling and processing, which is a key requirement to enable an efficient structural integrity management. As part of the DiMoWind-Inspect project, a data model and reference designation system for such inspection results is developed to facilitate their identification, localization, quantification, tracing and linking throughout the lifetime of a wind farm and beyond. The inspection results together with structural health monitoring results and information on repairs form the basis for assessing and predicting the structural condition, estimating the remaining lifetime, and planning of future inspections, structural health monitoring, and repairs. As a basis for developing a digital structural integrity management, a generic framework is proposed in this paper, which describes the stakeholders, data, models, processes and workflows of the integrity management of support structures in offshore wind farms and their interrelations. The framework adopts a building information modelling approach to describe the information relevant to the structural integrity management. The central digital space of the framework is a common data environment. An implementation of the framework will enable a digital structural integrity management in which inspection, structural health monitoring, repair, assessment and planning outcomes generated throughout the life cycle of a wind farm can be consistently collected, managed, shared and processed by the relevant shareholders.</abstract>
    <parentTitle language="deu">Proc. of the 8th Intl. Symp. on Reliability Engineering and Risk Management (ISRERM 2022)</parentTitle>
    <identifier type="doi">10.3850/978-981-18-5184-1_MS-04-161-cd</identifier>
    <enrichment key="eventName">8th International Symposium on Reliability Engineering and Risk Management</enrichment>
    <enrichment key="eventPlace">Hannover, Germany</enrichment>
    <enrichment key="eventStart">04.09.2022</enrichment>
    <enrichment key="eventEnd">07.09.2022</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Lukas Eichner</author>
    <author>Paul Gerards</author>
    <author>Ralf Herrmann</author>
    <author>Ronald Schneider</author>
    <author>Falk Hille</author>
    <author>Matthias Baeßler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Offshore wind energy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>support structures</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>structural health monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>building information modelling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>structural integrity management</value>
    </subject>
    <collection role="ddc" number="621">Angewandte Physik</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
    <collection role="institutes" number="">7.2 Ingenieurbau</collection>
    <collection role="themenfelder" number="">Energie</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
  </doc>
  <doc>
    <id>62879</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>175</pageFirst>
    <pageLast>188</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>8</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Probabilistische Systemidentifikation einer Versuchsstruktur für Substrukturen von Offshore-Windenergieanlagen mit statischen und dynamischen Messdaten</title>
    <abstract language="deu">In diesem Beitrag wird ein probabilistischer Ansatz zur Systemidentifikation für Tragstrukturen von Offshore-Windkraftanlagen vorgestellt. Der Schwerpunkt der Forschung liegt auf der Integration von globalen Systemantworten in Form von Eigenfrequenzen und -formen sowie Verschiebungen und Dehnungen als lokale Messdaten. Die unterschiedlichen Daten werden kombiniert für die Aktualisierung der Parameter eines Finite-Elemente-Modells genutzt. Zu diesem Zweck wird ein probabilistischer Ansatz nach Bayes verfolgt, um Vorwissen sowie Unsicherheiten einzubeziehen. Die Methodik wird bei einer Versuchsstruktur angewandt, die eine Jacket-Substruktur von Offshore-Windenergieanlagen nachbildet. Eine Systemidentifikation mit Hilfe von Überwachungsdaten ist wertvoll für Jacket-Substrukturen, da eine Zustandsanalyse für die Gewährleistung der strukturellen Integrität unerlässlich ist, aber hinsichtlich der schwierigen Offshore-Bedingungen möglichst effizient sein muss. In diesem Zusammenhang schafft diese Arbeit die Grundlage für eine Schadenserkennung, eine verbesserte Vorhersage der Ermüdungslebensdauer und optimierte Instandhaltungsstrategien. Während das Modell hinsichtlich der statischen Messdaten erfolgreich aktualisiert werden kann, sind Schwierigkeiten bei der Identifizierung der dynamischen Systemeigenschaften erkennbar.</abstract>
    <parentTitle language="deu">8. VDI-Fachtagung Baudynamik 2025</parentTitle>
    <enrichment key="eventName">8. VDI-Fachtagung Baudynamik 2025</enrichment>
    <enrichment key="eventPlace">Würzburg, Germany</enrichment>
    <enrichment key="eventStart">02.04.2025</enrichment>
    <enrichment key="eventEnd">03.04.2025</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Lukas Eichner</author>
    <author>Ronald Schneider</author>
    <author>Matthias Baeßler</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Systemidentifikation</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Versuchsstruktur</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Jacket</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Offshore-Windenergie</value>
    </subject>
    <collection role="ddc" number="621">Angewandte Physik</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
    <collection role="institutes" number="">7.2 Ingenieurbau</collection>
    <collection role="themenfelder" number="">Energie</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
    <collection role="themenfelder" number="">Windenergie</collection>
  </doc>
  <doc>
    <id>51590</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1254</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Institute of Structural Analysis and Antiseismic Research, School of Civil Engineering, National Technical University of Athens (NTUA)</publisherName>
    <publisherPlace>Athens, Greece</publisherPlace>
    <creatingCorporation>EASD - European Association for Structural Dynamics</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Vibration-based Structural Health Monitoring of a Reinforced Concrete Beam Subjected to Varying Ambient Conditions Using Bayesian Methods</title>
    <abstract language="eng">Inspection and maintenance contribute significantly to the lifetime cost of bridges. There is significant potential in using information obtained through structural health monitoring to update predictive models of the condition and performance of such structures, and thus ena-ble an improved decision-making regarding inspection and maintenance activities. Within the AISTEC project funded by the German Federal Ministry of Education and Research, we develop vibration-based structural health monitoring systems aimed at continuously provid-ing information on the structural condition of bridges. Environmental variations such as changing ambient temperatures can significantly influence the dynamic characteristics of bridges and thus mask the effect of structural changes and damages. It remains a challenge to account for such influences in structural health monitoring. To study the effect of ambient temperatures on the dynamic characteristics of beam structures, we monitor the vibration response of a reinforced concrete beam in the uncracked and cracked state at varying tem-peratures in a climate chamber. We postulate a set of competing parameterized probabilistic structural models, which explicitly account for the effect of varying ambient temperatures on the mechanical properties of the system. We then combine the information provided by the structural models with the information contained in the recorded vibration data to learn the parameters of the temperature-dependent structural models and infer the plausible state of the beam using Bayesian system identification and model class selection.</abstract>
    <parentTitle language="eng">Proceedings of EURODYN 2020, XI International Conference on Structural Dynamics</parentTitle>
    <identifier type="isbn">978-618-85072-2-7</identifier>
    <identifier type="isbn">978-618-85072-0-3</identifier>
    <enrichment key="eventName">EURODYN 2020, XI International Conference on Structural Dynamics</enrichment>
    <enrichment key="eventPlace">Online meeting</enrichment>
    <enrichment key="eventStart">23.11.2020</enrichment>
    <enrichment key="eventEnd">26.11.2020</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Patrick Simon</author>
    <author>Ronald Schneider</author>
    <author>Eva Viefhues</author>
    <author>Samir Said</author>
    <author>Ralf Herrmann</author>
    <author>Matthias Baeßler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural health monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayesian methods</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environmental effects</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="">Graue Literatur</collection>
  </doc>
  <doc>
    <id>52809</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>934</pageFirst>
    <pageLast>941</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>CRC Press</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Bayesian system identification of a reinforced concrete beam subject to temperature variations based on static response data</title>
    <abstract language="eng">Changes in the measured response of structural systems can be an indication of structural damages. However, such changes can also be caused by the effect of varying environmental conditions. To detect, localize and quantify changes or damages in structural systems subject to varying environmental conditions, physics-based models of the structural systems have to be applied which explicitly account for the influence of ambient conditions on the structural behavior. Data obtained from the structural systems should be used to calibrate the models and update predictions. Bayesian system identification is an effective framework for this task. In this paper, we apply this framework to learn the parameters of two competing structural models of a reinforced concrete beam subject to varying temperatures based on static response data. The models describe the behavior of the beam in the uncracked and cracked condition. The data is collected in a series of load tests in a climate chamber. Bayesian model class selection is then applied to infer the most plausible condition of the beam conditional on the available data.</abstract>
    <parentTitle language="eng">Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations</parentTitle>
    <identifier type="doi">10.1201/9780429279119-125</identifier>
    <enrichment key="eventName">Tenth International Conference on Bridge Maintenance, Safety and Management (IABMAS 2020)</enrichment>
    <enrichment key="eventPlace">Online meeting</enrichment>
    <enrichment key="eventStart">11.04.2021</enrichment>
    <enrichment key="eventEnd">15.04.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">10.10.2022</enrichment>
    <author>Patrick Simon</author>
    <author>Ronald Schneider</author>
    <author>Matthias Baeßler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayesian system identification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Damage identification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environmental effects</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural health monitoring</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>
  </doc>
  <doc>
    <id>54126</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1537</pageFirst>
    <pageLast>1544</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation>International Society for Structural Health Monitoring of Intelligent Infrastructure (ISHMII)</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Enhancing structural models with material tests and static response data - a case study considering a steel beam with asphalt layer subject to temperature variations</title>
    <abstract language="eng">Gradual or sudden changes in the state of structural systems caused, for example, by deterioration or accidental load events can influence their load-bearing capacity. Structural changes can be inferred from static and/or dynamic response data measured by structural health monitoring systems. However, they may be masked by variations in the structural response due to varying environmental conditions. Particularly, the interaction of nominally load-bearing components with nominally non-load bearing components exhibiting characteristics that vary as a function of the environmental conditions can significantly affect the monitored structural response. Ignoring these effects may hamper an inference of structural changes from the monitoring data. To address this issue, we adopt a probabilistic model-based framework as a basis for developing digital twins of structural systems that enable a prediction of the structural behavior under varying ambient condition. Within this framework, different types of data obtained from real the structural system can be applied to update the digital twin of the structural system using Bayesian methods and thus enhance predictions of the structural behavior. In this contribution, we implement the framework to develop a digital twin of a simply supported steel beam with an asphalt layer. It is formulated such that it can predict the static response of the beam in function of its temperature. In a climate chamber, the beam was subject to varying temperatures and its static response wass monitored. In addition, tests are performed to determine the temperature-dependent properties of the asphalt material. Bayesian system identification is applied to enhance the predictive capabilities of the digital twin based on the observed data.</abstract>
    <parentTitle language="eng">International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-10)</parentTitle>
    <identifier type="issn">2564-3738</identifier>
    <enrichment key="eventName">International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-10)</enrichment>
    <enrichment key="eventPlace">Online meeting</enrichment>
    <enrichment key="eventStart">30.06.2021</enrichment>
    <enrichment key="eventEnd">02.07.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Patrick Simon</author>
    <author>Ronald Schneider</author>
    <author>Matthias Baeßler</author>
    <author>Christoph Recknagel</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital twin</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural health monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Material tests</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayesian updating</value>
    </subject>
    <collection role="ddc" number="624">Ingenieurbau</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
    <collection role="institutes" number="">7.1 Baustoffe</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="">Graue Literatur</collection>
  </doc>
  <doc>
    <id>52810</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>66</pageFirst>
    <pageLast>76</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>55</volume>
    <type>conferenceobject</type>
    <publisherName>Technische Universität Dresden</publisherName>
    <publisherPlace>Dresden</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Structural Health Monitoring der Maintalbrücke Gemünden zur Unterstützung einer prädiktiven Instandhaltung</title>
    <abstract language="deu">Nach den bisherigen Regeln werden Bauwerksprüfungen von Brücken in starr definierten Intervallen durchgeführt. Diese starre, periodische, zustandsbasierte Instandhaltungsstrategie soll zukünftig durch eine flexible und prädiktive Instandhaltung ersetzt werden. Hierbei sollen Inspektionen und Instandhaltungsmaßnahmen unterstützt durch autonome Systeme auf der Grundlage von Monitoringdaten geplant und zusätzlich bei unvorhergesehenen Ereignissen ausgelöst werden. Im Rahmen des Verbundforschungsvorhabens AISTEC wird ein Vorgehen für Großbauwerke und kleinere Regelbauwerke zur Umsetzung eines kombinierten globalen, schwingungsbasierten und quasistatischen Monitorings entworfen. An der Maintalbrücke Gemünden – einer semiintegralen Rahmenbrücke aus Spannbeton auf der Schnellfahrstrecke Hannover–Würzburg – wurde als Demonstrator ein Dauermonitoring realisiert.</abstract>
    <parentTitle language="deu">Schriftenreihe Konstruktiver Ingenieurbau Dresden</parentTitle>
    <identifier type="url">https://tu-dresden.de/bu/bauingenieurwesen/imb/ressourcen/dateien/veranstaltungen/seub/11-seub-2021/2021_SEUB11_06_Herrmann_et_al.pdf?lang=de</identifier>
    <enrichment key="eventName">11. Symposium Experimentelle Untersuchungen von Baukonstruktionen (SEUB)</enrichment>
    <enrichment key="eventPlace">Online meeting</enrichment>
    <enrichment key="eventStart">08.03.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Ralf Herrmann</author>
    <author>Ronald Schneider</author>
    <author>Patrick Simon</author>
    <author>Falk Hille</author>
    <author>Samir Said</author>
    <author>Matthias Baeßler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural Health Monitoring</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Umwelteinflüsse</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Prädiktive Instandhaltung</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>
  </doc>
  <doc>
    <id>60765</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>558</pageFirst>
    <pageLast>567</pageLast>
    <pageNumber/>
    <edition/>
    <issue>10</issue>
    <volume>101</volume>
    <type>article</type>
    <publisherName>Ernst &amp; Sohn</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Digitales Datenmanagement für die Instandhaltung von Offshore-Windparks</title>
    <title language="eng">Digital data management for maintenance in offshore wind farms</title>
    <abstract language="deu">Im Forschungsprojekt DiMoWind-Inspect wurde untersucht, welche Grundvoraussetzungen geschaffen werden müssen, um digitale Methoden für die Instandhaltung von Offshore-Windenergieanlagen einsetzen zu können. Daten aus allen Lebensphasen der Anlagen werden derzeit nur selten effizient dafür genutzt, um die sinnvollsten Instandhaltungsmaßnahmen am richtigen Ort, zum richtigen Zeitpunkt und mit den geringsten Kosten durchzuführen. Eine im Vorhaben entwickelte einheitliche Strukturierung der bisher häufig unstrukturierten Bau- und Instandhaltungsdaten ermöglicht ihre übergreifende, anwenderspezifische Verfügbarkeit. Hierfür werden Prinzipien des Referenzkennzeichnungssystems RDS-PP adaptiert. Es wird aufgezeigt, wie neben Bauteilen auch zusätzliche Informationen wie Instandhaltungsmaßnahmen oder Mängel strukturiert werden können. Dem Ansatz des Building Information Modeling folgend werden die Bauwerkskomponenten direkt mit den Informationen aus Inspektionen und weiteren Instandhaltungsmaßnahmen verknüpft. So können Informationen aus dem Betrieb über den Zustand der Struktur verarbeitet und für die maßgebenden Berechnungen und Nachweise zur Verfügung gestellt werden. Als Anwendungsfall wird ein zweistufiges Konzept vorgestellt, das zur Bewertung der Ermüdungslebensdauer eines korrosionsgeschädigten Konstruktionsdetails der Gründungsstruktur einer Offshore-Windenergieanlage mit Informationen aus der Instandhaltung eingesetzt wird.</abstract>
    <abstract language="eng">The DiMoWind-Inspect research project explored the essential requirements for implementing digital methods in the maintenance of offshore wind turbines. Currently, data from all stages of the turbines' lifecycles are underutilized, leading to suboptimal maintenance actions being taken in terms of location, timing, and cost. A consistent structuring of previously often unstructured construction and maintenance data developed in the project enables their cross-disciplinary, user-specific availability. To accomplish this, the principles of the Reference Designation System for Power Plants RDS-PP are applied. In this way, additional information, such as maintenance measures or defects, can be structured alongside components. The components of the structures are directly linked with information from inspections and other maintenance activities, following the Building Information Modeling approach. This allows for processing operational information about the condition of the structure and providing it for relevant calculations and assessments. As a use case, a two-stage concept is presented, utilizing maintenance information to assess the fatigue life of a corrosion-damaged structural detail in the support structure of an offshore wind turbine.</abstract>
    <parentTitle language="deu">Bautechnik</parentTitle>
    <identifier type="doi">10.1002/bate.202400026</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">23.05.2025</enrichment>
    <author>Lukas Eichner</author>
    <author>Paul Gerards-Wünsche</author>
    <author>Karina Happel</author>
    <author>Sigurd Weise</author>
    <author>Gerrit Haake</author>
    <author>Lars Sieber</author>
    <author>Holger Flederer</author>
    <author>Ronald Schneider</author>
    <author>Ralf Herrmann</author>
    <author>Falk Hille</author>
    <author>Matthias Baeßler</author>
    <author>Holger Huhn</author>
    <author>Andreas Küchler</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Building Information Modeling</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Datenmanagement</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Instandhaltung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Offshore-Windenergie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Referenzkennzeichnungssystem</value>
    </subject>
    <collection role="ddc" number="621">Angewandte Physik</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
    <collection role="institutes" number="">7.2 Ingenieurbau</collection>
    <collection role="themenfelder" number="">Energie</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="themenfelder" number="">Windenergie</collection>
  </doc>
  <doc>
    <id>57244</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>15</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A framework for assessing the reliability of crack luminescence – an automated fatigue crack detection system</title>
    <abstract language="eng">The new crack luminescence method offers the possibility of making fatigue surface cracks in metallic materials more visible during inspections through a special coating system. This coating system consists of two layers, whereby the first layer has fluorescent properties and emits visible light as soon as it is irradiated by UV light. The top layer is black and is designed to prevent the fluorescent layer from emitting if no crack develops in the underlying material. The technique proved particularly useful in a wide variety of fatigue tests of steel components under laboratory conditions. Moreover, it has the potential to be used in various industrial applications. To enable industrial deployment and integration into maintenance strategies, a concept study is developed in this contribution, resulting in a qualification framework that can serve as a foundation for determining the reliability of the crack luminescence system in terms of a probability of detection curve. Within this study, factors causing measurement variability and uncertainty are being determined and their influences assessed. Due to the extension of the system by a moving computer vision system for automated crack detection using artificial intelligence, additional long-term effects associated with structural health monitoring systems need to be incorporated into an extended probability of detection study as part of the technical justification. Finally, important aspects and findings related to design of experiments are discussed, and a framework for reliability assessment of a new optical crack monitoring method is presented, emphasizing the influence of various uncertainty parameters, including long-term effects such as system ageing.</abstract>
    <parentTitle language="eng">SPIE Conference Smart Structures + Nondestructive Evaluation 2023</parentTitle>
    <identifier type="doi">10.1117/12.2658390</identifier>
    <identifier type="isbn">978-1-5106-6086-1</identifier>
    <enrichment key="eventName">SPIE Conference Smart Structures + Nondestructive Evaluation 2023</enrichment>
    <enrichment key="eventPlace">Long Beach, CA, USA</enrichment>
    <enrichment key="eventStart">24.03.2023</enrichment>
    <enrichment key="eventEnd">28.03.2023</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">18.09.2023</enrichment>
    <author>Paul Gerards-Wünsche</author>
    <author>Mirjana Ratkovac</author>
    <author>Ronald Schneider</author>
    <author>Falk Hille</author>
    <author>Matthias Baeßler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural Health Monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Non-Destructive Evaluation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Probability of Detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reliability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Computer Vision</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Crack Luminescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wind</value>
    </subject>
    <collection role="ddc" number="621">Angewandte Physik</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
    <collection role="institutes" number="">7.2 Ingenieurbau</collection>
    <collection role="themenfelder" number="">Energie</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
  </doc>
  <doc>
    <id>63368</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>26</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Sage</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Parallelized adaptive Bayesian updating with structural reliability methods for inference of large engineering models</title>
    <abstract language="eng">The reassessment of engineering structures, such as bridges, now increasingly involve the integration of models with realworld data. This integration aims to achieve accurate ‘as-is’ analysis within a digital twin framework. Bayesian model updating combines prior knowledge and data with models to enhance the modelling accuracy while consistently handling uncertainties. When updating large engineering models, numerical methods for Bayesian analysis present significant computational challenges due to the need for a substantial number of likelihood evaluations. The novelty of this contribution is to parallelize adaptive Bayesian Updating with Structural reliability methods combined with subset simulation (aBUS) to improve its computational efficiency. To demonstrate the efficiency and practical applicability of the proposed approach, we present a case study on the Maintalbrücke Gemünden, a large railway bridge. We leverage modal property data to update a linear-elastic dynamic structural model of the bridge. The parallelized aBUS approach significantly reduces computational time, making Bayesian updating of large engineering models feasible within reasonable timeframes. The improved efficiency allows for a wider implementation of Bayesian model updating in structural health monitoring and maintenance decision support systems.</abstract>
    <parentTitle language="eng">Advances in Structural Engineering</parentTitle>
    <identifier type="issn">1369-4332</identifier>
    <identifier type="issn">2048-4011</identifier>
    <identifier type="doi">10.1177/13694332251346848</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-633686</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">23.06.2025</enrichment>
    <licence>Creative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International</licence>
    <author>Patrick Simon</author>
    <author>Ronald Schneider</author>
    <author>Matthias Baeßler</author>
    <author>Guido Morgenthal</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayesian model updating</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayesian updating with structural reliability methods</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural health monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Parallelization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Modal analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Railway bridge</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>
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    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="themenfelder" number="">Verkehrsinfrastrukturen</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/63368/Simon et al. - 2025 - Parallelized adaptive Bayesian updating with struc.pdf</file>
  </doc>
  <doc>
    <id>63441</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>505</pageFirst>
    <pageLast>510</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>International Society for Soil Mechanics and Geotechnical Engineering</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
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    <title language="eng">3D spatial modelling of CPT data for probabilistic preliminary assessment of potential pile tip damage upon collision with boulders</title>
    <abstract language="eng">Tip damage of monopiles from boulder collisions during installation has emerged as a critical design issue, primarily due to the combination of the widespread use of large-diameter piles and the complexity of the ground conditions at the available sites. Recently, a framework relating potential pile tip damage to cone tip resistance from Cone Penetration Tests (CPTs) has been proposed in the literature. Gaussian processes are powerful stochastic models that enable probabilistic spatial interpolation of soil data at any location within a site. On this basis, this study utilizes sparse CPT data from a site in the North Sea, to first develop an efficient Gaussian process regression model, which is used to derive a three-dimensional (3D) probabilistic predictive map of the cone tip resistance. Assuming deterministic loading conditions and a factual collision with a boulder of pre-defined properties, the cone tip resistance predictive model is subsequently used for a probabilistic preliminary assessment of potential pile tip damage. Results of the analysis are realistic 3D probability maps of potential damage that aim to support engineering judgment and contribute towards cost-effective site investigation planning and offshore wind farm design.</abstract>
    <parentTitle language="eng">Proceedings of the fifth International Symposium on Frontiers in Offshore Geotechnics (ISFOG 2025)</parentTitle>
    <identifier type="doi">10.53243/ISFOG2025-323</identifier>
    <identifier type="isbn">978-2-85782-758-0</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-634417</identifier>
    <enrichment key="eventName">5th International Symposium on Frontiers in Offshore Geotechnics (ISFOG 2025)</enrichment>
    <enrichment key="eventPlace">Nantes, France</enrichment>
    <enrichment key="eventStart">09.06.2025</enrichment>
    <enrichment key="eventEnd">13.06.2025</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <licence>Allgemeines Deutsches Urheberrecht</licence>
    <author>Orestis Zinas</author>
    <author>Iason Papaioannou</author>
    <author>Ronald Schneider</author>
    <author>Pablo Cuéllar</author>
    <author>Matthias Baeßler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CPT data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gaussian process regression</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Boulder impact</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Preliminary pile tip damage assessment</value>
    </subject>
    <collection role="ddc" number="621">Angewandte Physik</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
    <collection role="institutes" number="">7.2 Ingenieurbau</collection>
    <collection role="themenfelder" number="">Energie</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
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    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="themenfelder" number="">Windenergie</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/63441/Zinas et al. ISFOG2025-323.pdf</file>
  </doc>
  <doc>
    <id>54531</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>9</pageFirst>
    <pageLast>17</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
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    <creatingCorporation>ASRANet Ltd</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Probabilistic approaches to the design and assessment of offshore foundations considering spatially varying site conditions</title>
    <abstract language="eng">In this contribution, we consider two applications in which probabilistic approaches can potentially complement or enhance the design and assessment of offshore wind turbine foundations. First, we illustrate in a numerical example that probabilistic modelling can be helpful in dealing with chang-es in turbine locations during the planning phase of an offshore wind farm. In this case, spatial probabilistic modelling of the ground conditions enables (a) an inference of the soil properties at the modified turbine location from field data collected at different locations across an offshore wind farm site and (b) an optimisation of further site investigations. Second, we discuss the uncer-tainties and risks associated with the installation of large diameter monopiles in soils with hetero-geneities such as strong layers and/or embedded boulders. Subsequently, we present a concept for modelling, understanding, and managing these risks based on a probabilistic model of the subgrade conditions, monopile, and subgrade-pile-interaction.</abstract>
    <parentTitle language="eng">Proceedings of the 3rd International Conference on Structural Integrity for Offshore Energy Industry (SI 2021)</parentTitle>
    <identifier type="isbn">978-1-8383226-3-2</identifier>
    <enrichment key="eventName">3rd International Conference on Structural Integrity for Offshore Energy Industry (SI 2021)</enrichment>
    <enrichment key="eventPlace">Online meeting</enrichment>
    <enrichment key="eventStart">15.11.2021</enrichment>
    <enrichment key="eventEnd">16.11.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Ronald Schneider</author>
    <author>Pablo Cuéllar</author>
    <author>Matthias Baeßler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Probabilistic modelling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Spatially varying ground conditions</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Foundation reliability analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Monopile installation risks</value>
    </subject>
    <collection role="ddc" number="621">Angewandte Physik</collection>
    <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="">Energie</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>
  </doc>
  <doc>
    <id>64327</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>245</pageFirst>
    <pageLast>251</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Verlag der Technischen Universität Graz</publisherName>
    <publisherPlace>Graz, Austria</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">On potentials and challenges of physics-informed SHM for civil engineering structures</title>
    <abstract language="eng">Physics-informed structural health monitoring, which integrates realistic physical models of material behavior, structural response, damage mechanisms, and aging processes, offers a promising approach to improve monitoring capabilities and inform operation and maintenance planning. However, the associated technical challenges and model requirements are context-specific and vary widely across applications. To illustrate the relevance and potential of the topic, two application examples are presented. The first focuses on monitoring the modal characteristics of a prestressed road bridge, where strong sensitivity to temperature variations limits the diagnostic capabilities of conventional vibration-based global monitoring. The discussion highlights how environmental influences can obscure structural changes, and emphasizes that purely data-based approaches are inherently limited to detecting anomalies and do not enable comprehensive condition diagnostics. The second example explores a physics-informed monitoring approach for prestressed concrete bridges affected by hydrogen-induced stress corrosion cracking.</abstract>
    <parentTitle language="eng">Proceedings of SHMII-13</parentTitle>
    <identifier type="isbn">978-3-99161-057-1</identifier>
    <identifier type="doi">10.3217/978-3-99161-057-1-039</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-643271</identifier>
    <enrichment key="eventName">SHMII-13</enrichment>
    <enrichment key="eventPlace">Graz, Austria</enrichment>
    <enrichment key="eventStart">01.09.2025</enrichment>
    <enrichment key="eventEnd">05.09.2025</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Matthias Baeßler</author>
    <author>Gino Ebell</author>
    <author>Ralf Herrmann</author>
    <author>Falk Hille</author>
    <author>Ronald Schneider</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Hydrogen Stress Corrosion Cracking</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>SHM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Physics informed</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="institutes" number="">7.6 Korrosion und Korrosionsschutz</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="themenfelder" number="">Verkehrsinfrastrukturen</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/64327/shmii_13_039.pdf</file>
  </doc>
  <doc>
    <id>64416</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>114</pageFirst>
    <pageLast>121</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>International Group of Operational Modal Analysis</publisherName>
    <publisherPlace>Gijón, Spain</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">System identification and model calibration of a steel road  bridge</title>
    <abstract language="eng">The Bundesanstalt für Materialforschung und -prüfung (BAM), in cooperation with the Netherlands Organization for Applied Scientific Research (TNO), is working on a framework for integrating frequently updated structural models into an asset management process for bridge structures. A multi-span steel road bridge was selected as a test case for the development of this framework. In order for the structural model to represent the real behavior of the bridge with sufficient accuracy, model calibration is required. In this case, we have planned to calibrate the model based on the dynamic response of the bridge. To determine its dynamic properties, a multi-setup operational modal analysis was performed on one of the bridge spans. In parallel, a structural model of the span was developed based on the available design and service life information. Both eigenfrequencies and mode shapes were used as reference parameters to calibrate the model. A sensitivity analysis was performed to identify the most influential design parameters. Subsequently, a genetic algorithm was applied for minimizing the difference between measured and simulated characteristic responses. In the proposed paper, we summarize the measurements as well as the determination of the modal response of the bridge and describe the process of calibration of the structural model using the identified dynamic response.</abstract>
    <parentTitle language="eng">Proceedings of the 11th International Operational Modal Analysis Conference (IOMAC 2025)</parentTitle>
    <identifier type="isbn">978-84-09-75120-4</identifier>
    <enrichment key="eventName">11th International Operational Modal Analysis Conference (IOMAC 2025)</enrichment>
    <enrichment key="eventPlace">Rennes, France</enrichment>
    <enrichment key="eventStart">20.05.2025</enrichment>
    <enrichment key="eventEnd">23.05.2025</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">01.12.2025</enrichment>
    <author>Falk Hille</author>
    <author>Ronald Schneider</author>
    <author>Patrick Simon</author>
    <author>Ralf Herrmann</author>
    <author>Matthias Baeßler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bridge structure</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Operational modal analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Model calibration</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="">Graue Literatur</collection>
    <collection role="themenfelder" number="">Verkehrsinfrastrukturen</collection>
  </doc>
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