TY - JOUR A1 - Thibaux, Philippe A1 - Thiele, Marc A1 - Van Wittenberghe, Jeroen A1 - Baeßler, Matthias T1 - Comparison of resonance and hydraulic testing on large scale fatigue tests of welded tubular joints for offshore wind turbine foundations N2 - Jackets structures as foundations for offshore wind energy converters are efficient solutions. But these structures require the welding of a large number of joints. The design of the Jacket structures is typically driven by fatigue. Therefore, consequently the fatigue strength of the joints is a primary parameter for an optimized design. The present paper investigates if tubular joints produced by manual welding using the current techniques have an improved performance compared to the relevant standards that are applied for the design of the foundations. To investigate this, 4 full-scale tests in geometry representative of a structure were performed, 2 using a resonance method and 2 using a three-point bending method with hydraulic actuators. The results are similar, with cracks initiated early and extending extensively before failure. The results are very close to the current T-curve from DNV RP-C203 applied for tubular joints. KW - Fatigue KW - Tubular joints KW - Resonance testing KW - Steel welds PY - 2025 DO - https://doi.org/10.1016/j.ijfatigue.2024.108797 SN - 0142-1123 VL - 193 SP - 1 EP - 16 PB - Elsevier Ltd. AN - OPUS4-62373 LA - eng 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 - 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 - JOUR A1 - Baeßler, Matthias A1 - Simon, Patrick T1 - Ballasted track on vibrating bridge decks: physical mechanisms, empirical findings, and a proposal for assessment N2 - This paper summarizes the key findings and physical mechanisms and provides information on open questions and the assessment of railway bridge superstructure vibrations. Bridges are classic disruption points on a railway track. If bridge superstructures are dynamically excited by train traffic, the vertical accelerations of the track must be considered. For a ballasted track, this can lead to the destabilization of the ballast track, as the bridge superstructure acts like a vibrating table. In this respect, the paper explains in more detail what is meant by destabilization, when this destabilization occurs and how various influencing parameters such as acceleration amplitude, the vibration sequence and frequency affect its occurrence. In the InBridge4EU project, gaps in knowledge such as the effect of single impulse loads are being investigated experimentally. A new test facility has been set up for this purpose, the initial results of which are presented here. An essential element in the assessment of this scenario is the stability of the track under high compression forces with simultaneous dynamic excitation of the superstructure. A new approach for the assessment of bridge vibrations with respect to lateral stability is presented. KW - Railway bridge dynamics KW - Bridge deck acceleration KW - Ballast destabilization KW - Lateral track stability KW - Track buckling PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-630972 DO - https://doi.org/10.1142/S0219455425400243 SN - 0219-4554 SN - 1793-6764 IS - 2540024 SP - 1 EP - 27 PB - World Scientific CY - Singapore AN - OPUS4-63097 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Herrmann, Ralf A1 - Ramasetti, Eshwar Kumar A1 - Degener, Sebastian A1 - Hille, Falk A1 - Baeßler, Matthias T1 - A living lab for Structural Health Monitoring at the Nibelungen Bridge Worms for Transfer Learning of Structural Dynamics N2 - The Nibelungen Bridge in Worms, Germany has been selected as a national demonstration structure for advanced non-destructive testing (NDT) and structural health monitoring concepts to extend the lifetime of civil structures and to optimize O&M actions. Parts of the research that involves this bridge as a demonstrator belong to the focus area program SPP100+. In this program, the bridges SHM System has been extended and combined with an additional setup of vibration sensors. The used digital smart sensor with pre-processing functions, the arrangement of the sensors at the structure and additional edge computing capability allows the investigation of transfer learning and other methods directly into the real structure. The living lab with seven triaxial accelerometers can be reconfigured in real-time and adjusted to the needs of AI models for classification. The comparison with the existing conventional SHM sensors has been made possible by hardware synchronization to the existing SHM System and collocating sensors at similar positions, so that a hardware exchange can be an investigated use-case for the transfer learning. During idle times, the system collects vibration data like a conventional SHM system. T2 - 10th European Workshop on Structural Health Monitoring (EWSHM 2024) CY - Potsdam DA - 10.06.2024 KW - Nibelungen Bridge KW - Living Lab KW - Transfer Learning KW - Transfer Structures KW - Modal Analysis PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-612810 UR - https://www.ndt.net/search/docs.php3?id=29853 DO - https://doi.org/10.58286/29853 SN - 1435-4934 VL - 29 IS - 7 SP - 1 EP - 8 PB - NDT.net GmbH & Co. KG CY - Mayen AN - OPUS4-61281 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ramasetti, Eshwar Kumar A1 - Herrmann, Ralf A1 - Degener, Sebastian A1 - Baeßler, Matthias T1 - Development of generic AI models to predict the movement of vehicles on bridges N2 - For civil, mechanical, and aerospace structures to extend operation times and to remain in service, structural health monitoring (SHM) is vital. SHM is a method to examining and monitoring the dynamic behavior of essential constructions. Because of its versatility in detecting unfavorable structural changes and enhancing structural dependability and life cycle management, it has been extensively used in many engineering domains, especially in civil bridges. Due to the recent technical developments in sensors, high-speed internet, and cloud computing, data-driven approaches to structural health monitoring are gaining appeal. Since artificial intelligence (AI), especially in SHM, was introduced into civil engineering, these modern and promising methods have attracted significant research attention. In this work, a large dataset of acceleration time series using digital sensors was collected by installing a structural health monitoring (SHM) system on Nibelungen Bridge located in Worms, Germany. In this paper, a deep learning model is developed for accurate classification of different types of vehicle movement on the bridge from the data obtained from accelerometers. The neural network is trained with key features extracted from the acceleration dataset and classification accuracy of 98 % was achieved. KW - Structural Health Monitoring KW - Artifical Intelligence KW - Machine Learning KW - Nibelungen Bridge PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-620289 DO - https://doi.org/10.1016/j.prostr.2024.09.307 VL - 64 SP - 557 EP - 564 PB - Elsevier B.V. AN - OPUS4-62028 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 - JOUR A1 - Hüsken, Götz A1 - Shamsuddoha, Md A1 - Pirskawetz, Stephan A1 - Hofmann, Detlef A1 - Baeßler, Matthias A1 - Kühne, Hans-Carsten T1 - Potential of a Repair System for Grouted Connections in Offshore Structures: Development and Experimental Verification N2 - Grouted connections are intensively used in offshore rigs, platforms as well as jacket and monopile offshore wind turbine structures. Being located in remote offshore conditions, these connections can experience considerable adverse loading during their lifetimes. Degradation was reported inside similar connections, which were installed in the last three decades. Grouting in the offshore sites may often be proven difficult, which eventually leads to reduced load-bearing capacity of connections in the long run. Thus, repair and rehabilitation of such connections should be planned ahead to minimize operational delays and costs. In this study, scaled grouted connections were manufactured using a novel mould, whose integrity were monitored using digital image correlation (DIC). The connections were loaded under static load to visualize the main failure pattern using distributed fibre optic sensors and acoustic emission (AE) analysis. Grouted connections were then repaired using a cementitious injectable grout. The effectiveness of the grout injection was monitored using dye penetration technique. Finally, specimens are reloaded to identify the potential of such repair for grouted connections. KW - Offshore KW - Grouted connection KW - Fibre optic sensors KW - Acoustic emission analysis KW - Cracks KW - Repair KW - Rehabilitation KW - Static loading PY - 2021 DO - https://doi.org/10.1016/j.marstruc.2021.102934 VL - 77 SP - 102934 PB - Elsevier Ltd. AN - OPUS4-52059 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Eichner, Lukas A1 - Gerards-Wünsche, Paul A1 - Happel, Karina A1 - Weise, Sigurd A1 - Haake, Gerrit A1 - Sieber, Lars A1 - Flederer, Holger A1 - Schneider, Ronald A1 - Herrmann, Ralf A1 - Hille, Falk A1 - Baeßler, Matthias A1 - Huhn, Holger A1 - Küchler, Andreas T1 - Digitales Datenmanagement für die Instandhaltung von Offshore-Windparks T1 - Digital data management for maintenance in offshore wind farms N2 - 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. N2 - 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. KW - Building Information Modeling KW - Datenmanagement KW - Instandhaltung KW - Offshore-Windenergie KW - Referenzkennzeichnungssystem PY - 2024 DO - https://doi.org/10.1002/bate.202400026 VL - 101 IS - 10 SP - 558 EP - 567 PB - Ernst & Sohn CY - Berlin AN - OPUS4-60765 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 T1 - Parallelized adaptive Bayesian updating with structural reliability methods for inference of large engineering models N2 - 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. KW - Bayesian model updating KW - Bayesian updating with structural reliability methods KW - Structural health monitoring KW - Parallelization KW - Modal analysis KW - Railway bridge PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-633686 DO - https://doi.org/10.1177/13694332251346848 SN - 1369-4332 SN - 2048-4011 SP - 1 EP - 26 PB - Sage AN - OPUS4-63368 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Balscheit, Hagen A1 - Geißler, Peter A1 - Cuéllar, Pablo A1 - Basedau, Frank A1 - Baeßler, Matthias T1 - Large-scale field tests on plastic pile tip failure upon monopile installation N2 - Large-diameter monopiles are the most common foundation structures for offshore wind turbines. One relevant failure mode during installation is plastic failure of the pile tip which may increase progressively during further driving (pile tip buckling; extrusion buckling). This paper presents the details and results of a large-scale field-test campaign with dynamic pile installation for the validation and calibration of different numerical approaches concerning pile-tip buckling phenomena. The phenomenology of observed pile-tip failures is here described in detail and a first quantitative approach is evaluated based on the field-test data. As the number of new projects continues to grow and the necessity to construct wind farms in challenging terrain increases, such field data-sets will become increasingly relevant. KW - Monopile KW - Offshore Windenergy KW - Pfahlfußbeulen KW - Pile Tip Buckling KW - Reference Tests PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-611651 DO - https://doi.org/10.1016/j.oceaneng.2024.119322 VL - 313 SP - 1 EP - 14 PB - Elsevier B.V. AN - OPUS4-61165 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -