TY - CONF A1 - Schneider, Ronald T1 - Modeling in support of offshore wind farm end-of-life decision making N2 - The EU member states have set out ambitious long-term goals for deploying offshore wind energy. The installed offshore wind capacity is set to increase from 14.6 GW in 2021 to around 320 GW in 2050. This signifies the role of offshore wind energy as a major contributor to reaching the EU’s climate and energy goals. To ensure that the defined targets are met, a significant number of new wind farms has to be installed and existing wind farms reaching the end of their planned life need to be reused efficiently. Some of the relevant reuse alternatives are lifetime extension, repowering based on the existing support structures and repowering with new turbines. As a basis of the decision-making regarding the reuse of existing offshore wind farm, the expected utility of each relevant option should be determined based on the associated expected rewards, costs and risks. The optimal concept maximizes the utility of the decision-maker and fulfills the existing constraints and requirements. To facilitate such a quantitative decision-making, models and methods have to be developed. In particular, models are required that enable predictions of (a) the condition and performance of the turbines and support structures and (b) the renumeration, costs and consequences of adverse events. These predictions have to consider (a) the governing uncertainties, (b) the available information from the planning, construction, installation and operating phase, (b) potential repair, retrofitting and strengthening schemes and (c) possible monitoring, inspection and maintenance regimes for the future operating phase. Over the past years, several models, methods and tools have been developed at the Bundesanstalt für Materialforschung und -prüfung (BAM) to support the structural integrity management of offshore wind turbine substructures. These include: (a) a prototype for reliability-based, system-wide, adaptive planning of inspections of welded steel structures in offshore wind farms, (b) a method for monitoring and risk-informed optimization of inspection and maintenance strategies for jacket structures subject to fatigue, and (c) a probabilistic cost model of inspection and maintenance of welded steel structures in offshore wind farms. This contribution provides an overview on these works and discusses how they can be adapted and extended to support the decision-making regarding lifetime extensions and repowering of offshore wind farms. T2 - 12th International Forum on Engineering Decision Making (12th IFED) CY - Stoos, Switzerland DA - 05.12.2023 KW - Windenergie KW - Offshore Wind KW - Lifetime Extension KW - Repowering PY - 2023 AN - OPUS4-60430 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald T1 - Zuverlässigkeitsbasierte Bemessung und Monte Carlo Simulation N2 - Am 19. und 20.11.2024 veranstaltete die BAM und der DGGT AK 2.15 „Zuverlässigkeitsbasierte Methoden in der Geotechnik“ in Zusammenarbeit mit der DGGT-Akademie einen zweitägigen Workshop zur Einführung in die zuverlässigkeitsbasierte Bemessung. Am ersten Tag wurden in einem Short Course die Grundlagen der Zuverlässigkeitsanalyse in der Geotechnik vermittelt, wobei den Teilnehmenden die Möglichkeit geboten wurde, unter Anleitung Aufgaben mit Hilfe geeigneter Software am PC selbstständig zu bearbeiten. In diesem Vortrag wurde die Grundlagen der zuverlässigkeitsbasierten Bemessung und die Monte Carlo Simulation zur Abschätzung der Versagenswahrscheinlichkeit dargestellt. T2 - 2. Workshop des DGGT AK 2.15 „Zuverlässigkeitsbasierte Methoden in der Geotechnik“ CY - Berlin, Germany DA - 19.11.2024 KW - Zuverlässigkeitsanalyse KW - Geotechnik KW - Monte Carlo Simulation PY - 2024 AN - OPUS4-61905 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald A1 - Schwiersch, Niklas T1 - Betrieb und Planung - Ausgewählte Vorteile von Risikoanalysen im Ingenieur- und Wasserbau N2 - Am 19. und 20.11.2024 veranstaltete die BAM und der DGGT AK 2.15 „Zuverlässigkeitsbasierte Methoden in der Geotechnik“ in Zusammenarbeit mit der DGGT-Akademie einen zweitägigen Workshop zur Einführung in die zuverlässigkeitsbasierte Bemessung. Im Rahmen der Vortragsveranstaltung am zweiten Tag wurden zum einen die unterschiedlichen Sicherheits- und Bemessungsphilosophien nach dem bewährten Teilsicherheitskonzept sowie nach dem zuverlässigkeitsbasierten Konzept vorgestellt. Zum anderen werden verschiedene Anwendungen zuverlässigkeitsbasierter Verfahren aus der Ingenieurpraxis und Wissenschaft präsentiert. In diesem Vortrag wurden aus Sicht des Ingenieur- und Wasserbaus Vorteile von Risikoanalysen u. a. für die Planung von Inspektionszyklen, die Ursachenforschung im Versagensfall und die Konzeption von Hochwasserrisikomanagement-Maßnahmen präsentiert. T2 - 2. Workshop des DGGT AK 2.15 „Zuverlässigkeitsbasierte Methoden in der Geotechnik“ CY - Berlin, Germany DA - 19.11.2024 KW - Risikoanalyse KW - Offshore-Strukturen KW - Hochwasserschutz PY - 2024 AN - OPUS4-61906 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hille, Falk A1 - Herrmann, Ralf A1 - Schneider, Ronald A1 - Pitters, S. A1 - Wedel, F. A1 - Hindersmann, I. T1 - Developing a guideline for structural health monitoring of road bridges in Germany N2 - In recent years, Structural Health Monitoring (SHM) has become a useful and increasingly widely used tool for supporting lifetime extensions of existing bridges with known structural deficiencies or indications of potentially critical damages or damage processes. At the same time, methods and tools are emerging, which enable monitoring-informed predictive maintenance of new and existing bridges based on digital twins. The monitoring process – starting from the definition of monitoring actions and ending with decisions based on monitoring outcomes – is complex and requires expertise in structural engineering, operation and maintenance of bridges, metrology, and data analytics. To support German road authorities, engineering consultancies, building contractors and other stakeholders of the bridge management, the Federal Highway Research Institute (BASt) has initiated the development of a new guideline for applying SHM as part of the management of road bridges. The guideline will present various use cases and for each identified use case, it will propose a proven monitoring scheme. In addition, the guideline will provide guidance on assessing the benefits of SHM as well as a common approach to managing monitoring data as a systematic basis for integrating monitoring data in the bridge management. This contribution discusses the motivation, objectives, and scope of the guideline, describes its use case centric structure and outlines the proposed data management. T2 - IABMAS 2024 CY - Kopenhagen, Danmark DA - 24.06.2024 KW - Guideline KW - Structural health monitoring KW - Road bridges KW - Infrastructure PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-613947 SN - 978-1-032-77040-6 DO - https://doi.org/10.1201/9781003483755-236 SP - 2009 EP - 2017 PB - CRC Press AN - OPUS4-61394 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Wedel, F. A1 - Pitters, S. A1 - Herrmann, Ralf A1 - Schneider, Ronald A1 - Hille, Falk A1 - Hindersmann, I. T1 - Guideline for the strategic application of monitoring of road bridges in Germany N2 - Engineering structures are an important part of our transport infrastructure. Their failure is associated with high safety risks and economic damage. Ensuring the availability of these constructions and guaranteeing their operational safety are thus important tasks. Currently, maintenance of engineering structures is a reactive process and therefore not yet a predictive process. Every 3 to 6 years, the structures are visually inspected, all damages are documented and a condition grade is assigned to the structure based on the detected damages. Continuous data-providing methods such as monitoring are used only very occasionally. With the help of monitoring, condition data about the structures are continuously collected, which can be used for a better assessment of the structures. On the one hand, this would increase safety of engineering structures, and on the other hand, it makes the shift towards predictive maintenance management based on real-time data and predictions, where the onset of damage is detected before it occurs, possible in the first place. Monitoring plays therefore an important role in the management of the infrastructure. It is a crucial a step towards the digitalisation of our infrastructure and existing processes. Although there are many use cases for monitoring that have already been successfully implemented, monitoring is not yet widely used by German road authorities due to several challenges that were also identified within the scope of the project. Therefore, a guide has been developed in which the current obstacles related to monitoring are identified and possibilities for integrating monitoring into existing processes are offered. To ensure optimal alignment with the needs of road authorities, both an online survey and a workshop have been conducted. The results are summarised in a brochure that is made available to authorities to improve the use of monitoring in Germany. This paper presents the guideline and the brochure. T2 - 10th European Workshop on Structural Health Monitoring (EWSHM 2024) CY - Potsdam, Germany DA - 10.06.2024 KW - Verkehrsinfrastrukturen KW - Engineering Structures KW - Bridges KW - Structural Health Monitoring KW - Guideline PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-612821 UR - https://www.ndt.net/search/docs.php3?id=29582 DO - https://doi.org/10.58286/29582 SN - 1435-4934 VL - 29 IS - 7 SP - 1 EP - 8 PB - NDT.net GmbH & Co. KG CY - Mayen AN - OPUS4-61282 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Herrmann, Ralf A1 - Hille, Falk A1 - Pitters, S. A1 - Ramasetti, Eshwar Kumar A1 - Schneider, Ronald A1 - Wedel, F. A1 - Hindersmann, I. ED - Rogge, Andreas ED - Meng, Birgit T1 - Föderiertes Datenmanagement von Monitoringdaten aus Structural Health Monitoring Anwendungen und daraus gewonnenen Trainingsdaten bei Spannbetonbrücken N2 - Beim Structural Health Monitoring entstehen eine Vielzahl von Daten, Metadaten und Modellen mit hohem Wert für die Beurteilung des Bauwerkszustands und der Prognose von Veränderungen. Kontinuierlich anwachsende Datenbestände müssen nachhaltig geteilt, genutzt und über die Bauwerkslebensdauer archiviert werden. Dabei stellen die Vielfalt der Messaufgaben, die Heterogenität der Daten, die dezentrale Erfassung und z. T. der Umfang eine Herausforderung für die beteiligten Akteure dar. Für den Datentransfer der Monitoringdaten zwischen der Straßenbauverwaltung und den Monitoringausführenden wird ein flexibles Abstimmungsverfahren vorgeschlagen und ein Lösungskonzept für ein föderiertes Datenmanagement skizziert. Eine weitere Herausforderung stellt die bauwerksübergreifende Zusammenstellung von Trainingsdaten für konkrete KI-Anwendungen dar. T2 - 11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM Green Intelligent Building CY - Berlin, Germany DA - 16.10.2024 KW - Structural Health Monitoring PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-612902 SN - 978-3-9818564-7-7 SP - 178 EP - 185 PB - Bundesanstalt für Materialforschung und -prüfung (BAM) CY - Berlin AN - OPUS4-61290 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hille, Falk A1 - Herrmann, Ralf A1 - Pitters, S. A1 - Hindersmann, I. A1 - Schneider, Ronald A1 - Wedel, F. ED - Rogge, Andreas ED - Meng, Birgit T1 - Leitfaden für den strategischen Einsatz von Monitoring an Straßenbrücken N2 - Für die Anwendung von Structural Health Monitoring (SHM) im Rahmen des Managements von Straßenbrü-cken wurde zur Unterstützung von Straßenbauverwaltungen, Ingenieurbüros, Bauunternehmen und weiteren Akteuren die Erstellung eines Leitfadens initiiert. Dieser Leitfaden stellt typische Anwendungsfälle für Monito-ring vor und schlägt für diese bewährte Herangehensweisen vor. Darüber hinaus bietet der Leitfaden Empfeh-lungen für Ausschreibung und Vergabe von Monitoringleistungen, eine Anleitung zur Bewertung des wirt-schaftlichen Nutzens von SHM, Hinweise zur optimal einsetzbaren Messtechnik sowie einen Ansatz für das Datenmanagement zur Integration der Monitoringdaten in das Brückenmanagement. T2 - 11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM Green Intelligent Building CY - Berlin, Germany DA - 16.10.2024 KW - Leitfaden KW - Monitoring KW - Straßenbrücke PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-612931 SN - 978-3-9818564-7-7 SP - 186 EP - 191 PB - Bundesanstalt für Materialforschung und -prüfung (BAM) CY - Berlin AN - OPUS4-61293 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schneider, Ronald A1 - Simon, Patrick A1 - Herrmann, Ralf A1 - Hille, Falk A1 - Baeßler, Matthias T1 - Bestandteile Digitaler Zwillinge im Erhaltungsmanagement von Verkehrsbrücken T1 - Components of digital twins in the operation and maintenance management of traffic bridges N2 - 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. N2 - 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. KW - Digitale Zwillinge KW - Erhaltung KW - Inspektion KW - Monitoring KW - Brücken PY - 2025 DO - https://doi.org/10.1002/bate.202400101 SN - 1437-0999 SP - 1 EP - 11 PB - Ernst & Sohn CY - Berlin AN - OPUS4-62837 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Simon, Patrick A1 - Helmrich, M. A1 - Herrmann, Ralf A1 - Schneider, Ronald A1 - Baeßler, Matthias A1 - Lorelli, S. A1 - Morgenthal, G. T1 - Maintalbrücke Gemünden: Bauwerksmonitoring und -identifikation aus einem Guss T1 - Maintalbrücke Gemünden – Integrated structural health monitoring and UAS diagnostics N2 - 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. N2 - 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. KW - Bauwerksüberwachung KW - Strukturmonitoring KW - Structural Health Monitoring KW - Modell-Update KW - UAS KW - Belastungstest KW - Structural system identification KW - Structural health monitoring KW - Model update KW - UAS KW - Load tests PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-554924 DO - https://doi.org/10.1002/bate.202100102 SN - 0932-8351 VL - 99 IS - 3 SP - 163 EP - 172 PB - Ernst & Sohn CY - Berlin AN - OPUS4-55492 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schneider, Ronald A1 - Simon, Patrick A1 - Hille, Falk A1 - Herrmann, Ralf A1 - Baeßler, Matthias T1 - Vibration-based system identification of a large steel box girder bridge N2 - 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. T2 - XII International Conference on Structural Dynamics (EURODYN 2023) CY - Delft, The Netherlands DA - 02.07.2023 KW - Verkehrsinfrastukturen KW - SHM KW - Model updating KW - System identification KW - Operational modal analysis PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-605041 DO - https://doi.org/10.1088/1742-6596/2647/18/182039 SN - 1742-6596 VL - 2647 IS - 18 SP - 1 EP - 9 PB - IOP Publishing CY - Bristol AN - OPUS4-60504 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald T1 - Digitaler Zwilling im Erhaltungsmanagement von Straßenbrücken N2 - Digitale Zwillinge werden zukünftig ein integraler Bestandteil des Erhaltungsmanagements von Straßenbrücken 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 T2 - Workshop „Digitaler Zwilling im Lebenszyklusmanagement" der AG BIM in der Bauwerkserhaltung des Koordinierungsausschusses Erhaltung (KoA-Erhaltung) der B/L-DB Konstruktiver Ingenieurbau CY - Potsdam, Germany DA - 09.04.2025 KW - Digitale Zwillinge KW - Erhaltung KW - Inspektion KW - Monitoring KW - Brücken PY - 2025 AN - OPUS4-62951 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Simon, Patrick A1 - Schneider, Ronald A1 - Baeßler, Matthias A1 - Morgenthal, Guido ED - Cha, Young-Jin T1 - A Bayesian Probabilistic Framework for Building Models for Structural Health Monitoring of Structures Subject to Environmental Variability N2 - 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. KW - Verkehrsinfrastukturen KW - SHM KW - Model Updating KW - Environmental and Operational Variability KW - Damage Identification KW - Model Building PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-605027 DO - https://doi.org/10.1155/2024/4204316 SN - 1545-2255 VL - 2024 IS - 1 SP - 1 EP - 23 PB - Wiley AN - OPUS4-60502 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald T1 - SysPark: a software tool for system-wide adaptive planning of inspections of turbine support structures in offshore wind farms N2 - Fatigue is one of the main deterioration processes affecting the performance of welded steel support structures of wind turbines in offshore wind farms. In this contribution, we present a probabilistic physics-based fatigue deterioration model of a wind farm that accounts for the stochastic dependence among the fatigue behavior of different hotspots at turbine and wind farm level. The dependence exists because of uncertain common influencing factors such as similar material properties, fabrication qualities and load conditions. These system effects signify that an inspection of one hotspot provides indirect information of the condition of the remaining hotspots and thus enable an optimization of the inspection effort for a wind farm. The wind farm model consists of two levels: A turbine support structure (level 1 system model) is represented by its fatigue hotspots and their stochastic dependence. The fatigue performance of a hotspot is described by a probabilistic fracture mechanics (FM) fatigue model. The probabilistic model of the parameters of the FM fatigue model is calibrated based on design data and is thus directly linked to the design of the turbine support structures. Dependence among the fatigue performances of different hotspots in a turbine support structure is modeled by introducing correlations between the stochastic parameters of the FM fatigue models. A wind farm (level 2 system model) consists of different turbine support structures (level 1 system models). Additional correlations are introduced at wind farm level to account for the dependence among the fatigue behavior of hotspots belonging to different turbine support structures. The wind farm model enables the computation of (marginal) hotspot fatigue reliabilities, system fatigue reliabilities of individual turbine support structures and the system fatigue reliability of an entire wind farm. The probabilistic model of the parameters of the two-level system model can be consistently updated with inspection outcomes using Bayesian methods. The updated probabilistic model of the model parameters then forms the basis for updating the estimates of the fatigue reliabilities. We implement the wind farm model in a software tool named SysPark. The tool provides the means for planning inspections at wind farm level using an adaptive reliability-based threshold approach. In this approach, the first inspection campaign is planned in the year before the fatigue failure rates of the hotspots with the lowest fatigue reliabilities exceed a threshold failure rate. Once inspection results become available, the probabilistic model of the parameters of the wind farm model is updated. If repairs are performed, the wind farm model is additionally modified to describe the behavior of the repaired hotspots. The updated and modified model then enables the planning of the next inspection campaign and so on. The software tool is demonstrated in a case study considering a generic wind farm consisting of turbines with jacket support structures. T2 - Wind Energy Science Conference (WESC 2021) CY - Online Meeting DA - 25.05.2021 KW - Fatigue deterioration KW - Offshore wind farm KW - Inspection planning PY - 2021 AN - OPUS4-52767 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald T1 - Integrating vibration monitoring into risk-based inspection and maintenance planning for deteriorating structural systems N2 - A reliability and risk-based framework for integrating vibration monitoring data in the planning of inspection and maintenance of deteriorating structural systems is discussed and demonstrated in a numerical example. T2 - Wind Energy Science Conference (WESC 2021) CY - Online Meeting DA - 25.05.2021 KW - Deterioration KW - Structural systems KW - Inspection KW - Vibration monitoring KW - Maintenance PY - 2021 AN - OPUS4-52766 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Eichner, Lukas A1 - Schneider, Ronald A1 - Baeßler, Matthias T1 - Optimal vibration sensor placement for jacket support structures of offshore wind turbines based on value of information analysis N2 - 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 frame. The adapted genetic algorithm performs similarly well as established sequential sensor placement algorithms and holds substantial promise for application to real jacket structures. KW - Optimal sensor placement KW - Value of information KW - Jacket support structure KW - Offshore wind turbine KW - Monitoring-informed inspection and maintenance planning PY - 2023 DO - https://doi.org/10.1016/j.oceaneng.2023.115407 SN - 0029-8018 VL - 288 IS - 2 PB - Elsevier Ltd. CY - Amsterdam AN - OPUS4-58725 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Eichner, Lukas A1 - Schneider, Ronald A1 - Simon, Patrick A1 - Baeßler, Matthias T1 - 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 N2 - 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. T2 - 3rd International Conference on Health Monitoring of Civil & Maritime Structures (HeaMES 2022) CY - Online meeting DA - 08.06.2022 KW - Structural Health Monitoring KW - Value of Information KW - Offshore Wind Energy KW - Optimal Sensor Placement KW - Bayesian System Identification PY - 2022 UR - 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 SN - 978-1-8383226-7-0 SP - 23 EP - 32 AN - OPUS4-55465 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Simon, Patrick A1 - Herrmann, Ralf A1 - Schneider, Ronald A1 - Hille, Falk A1 - Baeßler, Matthias A1 - El-Athman, Rukeia T1 - Research Data Management of Structural Health Monitoring Projects and Subsequent Applications of Artificial Intelligence Methods N2 - 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. T2 - 11th International Conference on Bridge Maintenance, Safety and Management (IABMAS2022) CY - Barcelona, Spain DA - 11.07.2022 KW - Research data management KW - Structural health monitoring KW - Artificial intelligence PY - 2022 SN - 978-1-032-35623-5 SN - 978-1-003-32264-1 DO - https://doi.org/10.1201/9781003322641-127 SP - 1061 EP - 1068 PB - CRC Press CY - Boca Raton AN - OPUS4-55493 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Eichner, Lukas A1 - Gerards, Paul A1 - Herrmann, Ralf A1 - Schneider, Ronald A1 - Hille, Falk A1 - Baeßler, Matthias T1 - A framework for data and structural integrity management for support structures in offshore wind farms based on building information modelling N2 - 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. T2 - 8th International Symposium on Reliability Engineering and Risk Management CY - Hannover, Germany DA - 04.09.2022 KW - Offshore wind energy KW - support structures KW - structural health monitoring KW - building information modelling KW - structural integrity management PY - 2022 DO - https://doi.org/10.3850/978-981-18-5184-1_MS-04-161-cd SP - 111 EP - 117 PB - Research Publishing CY - Singapore AN - OPUS4-55656 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald T1 - Belastungsversuch und Methodenvalidierung an der Maintalbrücke Gemünden N2 - Im Projekt AISTEC wurden Methoden entwickelt, die der prädiktiven Instandhaltung von Ingenieurbauwerken dienen. Zur Validierung dieser Methoden wurden an einem Referenzbauwerk - der Maintalbrücke Gemünden - Belastungstests durchgeführt. T2 - Abschlussveranstaltung AISTEC CY - Berlin, Germany DA - 02.05.2022 KW - Belastungsfahrt KW - GNSS KW - Einflusslinien PY - 2022 AN - OPUS4-55496 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald T1 - Kontinuierliche sensordaten- und modellgestützte Bewertung von Infrastrukturbauwerken in der Praxis N2 - Ein Einsatz von kontinuierlichen sensorbasierten Verfahren komplementär zu den zeitlich diskreten bildbasierten Verfahren haben ein enormes Potenzial, die bisher hauptsächlich visuellen Prüfprozesse zu transformieren. Zur Ermöglichung eines strategischen Einsatzes des sensorbasierten Monitorings im Erhaltungsmanagement von Ingenieurbauwerken ist das übergeordnete Ziel des Projektes AISTEC-Pro die Demonstration des praktischen Mehrwerts des sensorbasierten Monitorings. Dies wird durch praktische Anwendungen an der Maintalbrücke Gemünden und einem neu ausgewählten Referenzbauwerk unter Berücksichtigung von typischen Einsatzszenarien im Bereich der Bauwerkserhaltung erreicht werden. Die Einsatzszenarien werden in enger Abstimmung mit den Endanwendern entwickelt und fortgeschrieben. Hierbei wird insbesondere Bezug auf die aktuelle Neufassung der DIN 1076 genommen, in der erstmals die Anwendung von sensor- und bildbasierten Monitoringverfahren in der Bauwerksprüfung geregelt wird. T2 - AISTEC-Pro Auftakttreffen CY - Weimar, Germany DA - 02.12.2024 KW - Sensorbasiertes Monitoring KW - Erhaltung KW - Verkehrsbrücken PY - 2024 AN - OPUS4-61919 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -