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 - Simon, Patrick A1 - Baeßler, Matthias ED - Rogge, Andreas ED - Meng, Birgit T1 - Temperatureinfluss auf Strukturmonitoring – Aktuelle Versuche N2 - Strukturmonitoring kann wertvolle Daten für die Zustandsbewertung und Schadensdetektion von Infrastrukturbauwerken liefern. Umgebungsbedingungen wie die Temperatur beeinflussen die Bauwerke und somit die Messdaten jedoch erheblich. Um Methoden für den Umgang mit Temperatureinflüssen zu entwickeln, wurden an der BAM Versuche an Stahlbeton- und Asphaltbalken unter kontrollierten Temperaturen von -40 °C bis 60 °C und definierten Schädigungen durchgeführt. Die Daten ermöglichen die Erforschung und Validierung neuer, auch unter Temperatureinfluss zuverlässiger Methoden des Strukturmonitorings. T2 - 11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM Green Intelligent Building CY - Berlin, Germany DA - 16.10.2024 KW - Strukturmonitoring KW - Temperatureinfluss KW - Infrastrukturbauwerke KW - Stahlbeton- und Asphaltbalken PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-613289 SN - 978-3-9818564-7-7 SP - 294 EP - 302 PB - Bundesanstalt für Materialforschung und -prüfung (BAM) CY - Berlin AN - OPUS4-61328 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 - 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 - CONF 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. T2 - SMAR 2024 - 7th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures CY - Salerno, Italy DA - 04.09.2024 KW - Machine learning KW - Structural Health Monitoring (SHM) PY - 2024 AN - OPUS4-61375 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 - CONF A1 - Eichner, Lukas A1 - Thiele, Marc A1 - Hille, Falk A1 - Baeßler, Matthias T1 - Digitale Modellierungsprozesse in der wiederkehrenden Prüfung von Windenergieanlagen - DiMoWind-Inspect - Abschlusstreffen N2 - Dieses Dokument enthält die Präsentationsfolien des BAM-Teams beim Abschlusstreffen des Forschungsprojekts DiMoWind-Inspect. Es wurden die Arbeitspakete Datenmanagement, Referenzkennzeichnungssystem, Grundlagen der Schadensbewertung, Risikobasierte Inspektions- und Instandhaltungsplanung und Schadensdetektion mittels Risslumineszenz sowie eine abschließende Bewertung des Projekts vorgestellt. T2 - DiMoWind-Inspect - Abschlusstreffen CY - Berlin, Germany DA - 12.09.2024 KW - Building Information Modeling (BIM) KW - Referenzkennzeichnungssystem KW - Risslumineszenz (Riluminati) KW - Risikobasierte Inspektionsplanung (RBI) PY - 2024 AN - OPUS4-61054 LA - deu 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 - 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 - TY - CONF A1 - Baeßler, Matthias A1 - Weise, Sigurd T1 - Digitalization of Maintenance Processes N2 - Main aspects of Digitalization of Maintenance Processes are summarized and discussed. Introduction to the Windforce2024 session organised by BAM. T2 - Windforce 2024 CY - Bremerhaven, Germany DA - 11.06.2024 KW - Maintenance Digital Twin Offshore Wind Energy PY - 2024 AN - OPUS4-60350 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Baeßler, Matthias A1 - Haake, Gerrit T1 - ReNEW - Entscheidungstools für die Nutzungsverlängerung und das Repowering von Windenergieanlagen N2 - Der Vortrag gibt einen Überblick über gängige Nachnutzungskonzepte, die im Rahmen des Forschungsvorhabens ReNEW untersucht werden. Hierzu wir eine probabilistisches Entscheidungsmodell eingeführt. Die Möglichkeiten von Bauwerksertüchtigungen werden erörtert. T2 - RAVE ROWD Workshop 2024 CY - Hannover, Germany DA - 10.12.2024 KW - Windenergieanlagen KW - Bauwerksertüchtigung KW - Offshore-Windparks KW - Repowering PY - 2024 AN - OPUS4-62237 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Baeßler, Matthias A1 - Eichner, Lukas A1 - Weise, Sigurd T1 - Digital building management for OWEC-structures N2 - The presentation summarizes the data management and Digital modeling processes in the recurring inspection of wind turbines as developed in DiMoWind-Inspect. T2 - Windforce 2024 CY - Bremerhaven, Germany DA - 10.06.2024 KW - DiMoWind RDS-PP Maintenance Digital Twin Offshore Wind Energy PY - 2024 AN - OPUS4-60351 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Baeßler, Matthias A1 - Cuéllar, Pablo A1 - Schepers, Winfried T1 - VERBATIM: Project Introduction and Large Scale Experiments N2 - The presentation summarizes the scope of the joint project VERBATIM on buckling of large Monopiles. The presented work from the authors focusses on the experimental field tests of large predented piles during driving and a numerical investigation on the observed buckling behaviour. T2 - Colloquium „Buckling of Offshore Wind Energy Structures“ CY - Berlin, Germany DA - 14.02.2024 KW - Buckling KW - Offshore KW - Wind Energy Structures KW - Monopiles KW - Pile-Tip-Buckling PY - 2024 AN - OPUS4-59535 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Baeßler, Matthias A1 - Balscheit, Hagen A1 - Schepers, Winfried A1 - Victor, Albrecht A1 - Daryaei, Reza A1 - Bianco, Marcelo A1 - Starost, Christina T1 - Close-out Webinar OWA VERBATIM N2 - The risk of pile base buckling is a major reason for high pile wall thicknesses in monopiles. The VERBATIM project - Verification of the buckling detection and behaviour of large monopiles - aimed to investigate buckling phenomena related to both the plastic deformation of the pile tip during installation and the buckling of the embedded pile near the seabed. Numerical models were developed and validated on the basis of extensive tests. This enables a better understanding of the buckling behaviour in order to reduce the wall thickness, which allows cost savings in the amount of steel and the development of safer and optimised structures. The previous design procedures were able to ensure the successful installation of the monopiles. However, as the size of monopiles continues to increase, the development of improved design methods for safe and cost-effective foundations is becoming increasingly important. The presentation provides an overview of the investigations carried out and the results. T2 - Webinar Carbon Trust Offshore Wind Accelerator CY - Online meeting DA - 22.05.2024 KW - Offshore Windenergy Pile Buckling PY - 2024 AN - OPUS4-60348 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Baeßler, Matthias A1 - Aubram, Daniel T1 - Vorstellung OWA VERBATIM N2 - Das Risiko von Pfahlfussbeulen ist ein wesentlicher Grund für hohe Pfahlwandstärken bei Monopiles. Das Projekt VERBATIM - Verifikation des Beulnachweises und –verhaltens großer Monopiles zielte darauf ab, Beulphänomene zu untersuchen, die sich sowohl auf die plastischen Verformungen der Pfahlspitze während der Installation als auch auf das Beulen des eingebetteten Pfahls in der Nähe des Seebodens beziehen. Auf der Basis aufwändiger Versuche wurden numerische Modelle entwickelt und validiert. Dies ermöglicht ein besseres Verständnis des Beulverhaltens, um die Wanddicke zu reduzieren, was Kosteneinsparungen bei der Stahlmenge sowie die Entwicklung sicherer und optimierter Strukturen erlaubt. Die bisherigen Designverfahren konnten die erfolgreiche Installation der Monopiles gewährleisten. Da die Größe von Monopiles jedoch stetig zunimmt, wird die Entwicklung verbesserter Designverfahren für sichere und kostengünstige Fundamente immer wichtiger. Der Rückenwind-Vortrag gibt eine Übersicht zu den durchgeführten Untersuchungen und Ergebnissen T2 - Rückenwind Aktuelles aus der Windenergieforschung (PTJ) CY - Online meeting DA - 21.06.2024 KW - Offshore Windenergy Pile Buckling PY - 2024 AN - OPUS4-60349 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -