TY - CONF A1 - Simon, Patrick A1 - Schneider, Ronald A1 - Herrmann, Ralf A1 - Hille, Falk A1 - Baeßler, Matthias T1 - Sensorbasierte Systemidentifikation N2 - Die aktuelle Instandhaltungsstrategien von Ingenieurbauwerken arbeiten zustandsbasiert und stützen sich auf visuelle Inspektionen in kurzen, starren Intervallen. Beim Übergang zu Predicitive-Maintenance-Strategien können Sensordaten eigesetzt werden um Prognosemodelle der Bauwerke zu aktualisieren. Ein erster Schritt hierzu ist die sensorbasierte Systemidentifikation. T2 - Abschlussveranstaltung AISTEC CY - Berlin, Germany DA - 02.05.2022 KW - Systemidentifikation KW - Strukturmonitoring PY - 2022 AN - OPUS4-55495 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald T1 - Risikoanalyse technischer Systeme: Aktivitäten an der BAM N2 - Die Risikoanalyse ist ein formales Verfahren zur Unterstützung von Entscheidungen unter Unsicherheit und Risiko. Es ermöglicht eine Optimierung von Maßnahmen zur Verhinderung unerwünschter Konsequenzen. Der Vortrag stellt die aktuellen Aktivitäten an der BAM in dem Feld Risikoanalyse technischer System vor. T2 - 4. Sitzung des Beirates „Infrastruktur“ der Bundesanstalt für Materialforschung und –prüfung (BAM) CY - Berlin, Germany DA - 19.04.2018 KW - Risiko KW - Risikoanalyse PY - 2018 AN - OPUS4-46436 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald A1 - Simon, Patrick A1 - Herrmann, Ralf A1 - Baeßler, Matthias T1 - Sensorbasiertes Monitoring (AP4 + AP7): Ein Überblick N2 - Im AISTEC Projekt erforscht der FB 7.2 Verfahren zur Bewertung von Verkehrsbrücken auf der Gruandlage von sensorbasierten Bauwerksmessungen. In diesem Vortrag wird ein Überlick über die Forschungsarbeiten des FB 7.2 präsentiert. Des Weiteren wird ein Ausblick zur quantitativen Integration von sensorbasierten Bauwerksmessungen in die risiko-basierte prädiktive Planung von Inspektionen und Reparaturen von Ingenieurbauwerken gegeben. T2 - AISTEC 5. Verbundtreffen CY - Online meeting DA - 24.06.2021 KW - AISTEC KW - Structural Health Monitoring KW - Brücken PY - 2021 AN - OPUS4-52982 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald A1 - Herrmann, Ralf A1 - Simon, Patrick A1 - Hille, Falk A1 - Said, Samir A1 - Baeßler, Matthias T1 - Sensorbasiertes Monitoring der Maintalbrücke Gemünden N2 - Monitoringsysteme erfassen kontinuierlich Bauwerksdaten wie z.B. Bauwerksbeschleunigungen, auf deren Grundlage Bauwerksschäden mit Hilfe von SHM-Methoden quantifiziert werden können. Mit den gewonnenen Informationen über den aktuellen Bauwerkszustand können Vorhersagen des Bauwerkszustandes und der Bauwerkszuverlässigkeit aktualisiert und erforderliche Inspektionen und Instandhaltungsmaßnahmen vorausschauend geplant werden. Im BMBF-Forschungsvorhaben AISTEC entwickeln der Fachbereich 7.2 „Ingenieurbau“ innovative Monitoringverfahren zur Systemidentifikation und automatischen Detektion, Lokalisierung und Quantifizierung von Schäden an Infrastrukturbauwerken anhand von gemessenen dynamischen und statischen Bauwerksdaten. Im Rahmen dieses Projektes werden die Verfahren an der Maintalbrücke bei Gemünden angewendet, welche Teil der ICE-Strecke Hannover-Würzburg ist. In diesem Vortrag wird das für die Maintalbrücke Gemünden geplante und umgesetzte Monitoingsystem vorgestellt. T2 - 4. Verbundtreffen AISTEC CY - Weimar, Germany DA - 24.09.2020 KW - Zivile Sicherheit KW - Brücken KW - Structural Health Monitoring PY - 2020 AN - OPUS4-51333 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Simon, Patrick A1 - Schneider, Ronald A1 - Herrmann, Ralf A1 - Viefhues, Eva A1 - Baeßler, Matthias T1 - Experimente in der Klimakammer N2 - Im Rahmen des Verbundtreffens des Vorhabens AISTEC werden akutelle Versuche aus der Großklimakammer des FB 7.2 präsentiert. T2 - 5. Verbundtreffen AISTEC CY - Online meeting DA - 24.06.2021 KW - Klimakammer KW - Stochastic Subspace Damage Detection KW - Model Update KW - Asphalt PY - 2021 AN - OPUS4-52869 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald T1 - Structural integrity management research at BAM N2 - This presentation provides an overview on the structural integrity management research at BAM. In addition, a framework for monitoring and risk-informed inspection and maintenance planning for offshore steel structures is presented. T2 - Structural Health Monitoring Using Statistical Pattern Recognition CY - Berlin, Germany DA - 20.03.2023 KW - Structural integrity KW - Monitoring KW - Maintenance KW - Inspeciton KW - Bridges KW - Offshore wind PY - 2023 AN - OPUS4-57864 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald A1 - Thöns, S. A1 - Rogge, Andreas 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 - Simon, Patrick A1 - Schneider, Ronald A1 - Baeßler, Matthias T1 - Untersuchungsmethoden klimatisch belasteter Bauteile - Projektupdate BAM N2 - Kontinuierliche Sensorbasierte Bauwerksmessungen leisten einen wichtigen Beitrag zur Sicherheit von Verkehrsbauwerken. Hierzu werden im Vorhaben AISTEC Referenzbauwerke und Referenzverfahren untersucht mit Schwerpunkt auf den Einfluss klimatischer Bedingungen. Der Vortrag stellt den aktuellen Projektstand des FB 7.2 vor. T2 - Erstes Verbundtreffen AISTEC CY - Weimar, Germany DA - 21.03.2019 KW - Structural Health Monitoring KW - Environmental Effects KW - Climate Chamber KW - Brücken KW - SHM KW - Klimakammer PY - 2019 AN - OPUS4-47702 LA - deu 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 AN - OPUS4-55494 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Herrmann, Ralf A1 - Simon, Patrick A1 - Schneider, Ronald A1 - Hille, Falk A1 - Baeßler, Matthias T1 - SHM system integration and experiments at a high speed railway bridge N2 - The long-term preservation of our infrastructure requires not only intelligent sensor technology and highly developed monitoring procedures, but also innovative digital tools for analyzing, evaluating and utilizing the results. This includes mathematical and, in particular, probabilistic methods for damage detection and tracking as well as for calculating service life and maintenance cycles and data management. The example project Maintal Bridge Gemuenden as part of the AISTEC project shows the workflow for the implementation of structural health monitoring and experimental tests with a train of Deutsche Bahn. The influence lines, as one possible way for damage detection, were measured with a highly accurate GNSS System to locate the trains position when crossing the bridge. The results were compared to measurements from 1987 just before the bridge went in operation. T2 - Structural Health Monitoring Using Statistical Pattern Recognition CY - Berlin, Germany DA - 20.03.2023 KW - SHM KW - Maintal Bridge Gemuenden KW - Load Test KW - Damage Detection KW - Railway PY - 2023 AN - OPUS4-57242 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald A1 - Straub, D. T1 - Cumulative failure probability of deteriorating structures: Can it drop? N2 - The reliability of deteriorating structures at time t is quantified by the probability that failure occurs within the period leading up to time t. This probability is often referred to as cumulative failure probability and is equal to the cumulative distribution function of the time to failure. In structural reliability, an estimate of the cumulative failure probability is obtained based on probabilistic engineering models of the deterioration processes and structural performance. Information on the condition and the loading contained in inspection and monitoring data can be included in the probability estimate through Bayesian updating. Conditioning the probability of failure on the inspection or monitoring outcomes available at time t (e.g. detections or no detection of damages) can lead to a reduction in that probability. Such a drop in the cumulative failure probability might seem counterintuitive since the cumulative failure probability is a non-decreasing function of time. In this paper, we illustrate—with the help of a numerical example—that such a drop is possible because the cumulative probability before and after the updating is not based on the same information, hence not on the same probabilistic model. T2 - 18th Internatinoal Probabilistic Workshop (IPW 2020) CY - Online Meeting DA - 12.05.2021 KW - Deterioration KW - Structural systems KW - Time-variant reliability KW - Bayesian updating KW - Inspection KW - Monitoring PY - 2021 AN - OPUS4-52770 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Artinov, Antoni T1 - Modeling the Relationship between Weld Pool Dynamics and Centerline Solidification Cracking in High Power Laser Beam Welding N2 - The present work deals with the recently confirmed widening of the weld pool interface, known as a bulging effect, and its relevance in high power laser beam welding. A combined experimental and numerical approach is utilized to study the influence of the bulge on the hot cracking formation and the transport of alloying elements in the molten pool. A technique using a quartz glass, a direct-diode laser illumination, a high-speed camera, and two thermal imaging cameras is applied to visualize the weld pool geometry in the longitudinal section. The study examines the relevance of the bulging effect on both, partial and complete penetration, as well as for different sheet thicknesses ranging from 8 mm to 25 mm. The numerical analysis shows that the formation of a bulge region is highly dependent on the penetration depth and occurs above 10 mm penetration depth. The location of the bulge correlates strongly with the cracking location. The obtained experimental and numerical results reveal that the bulging effect increases the hot cracking susceptibility and limits the transfer of alloying elements from the top of the weld pool to the weld root. T2 - Gastvortrag an der Universität Innsbruck, Institut für Konstruktion und Materialwissenschaften CY - Innsbruck, Austria DA - 18.06.2023 KW - Laser beam welding KW - Centerline solidification cracking KW - Bulging KW - Numerical simulation PY - 2024 AN - OPUS4-60333 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald T1 - Bayes‘sche Analyse von Ingenieurmodellen N2 - Der Vortrag zeigt, dass Unsicherheiten in Ingenieurmodellen quantitativ mit Methoden der Wahrscheinlichkeitstheorie modelliert werden können. Zusätzlich können durch eine Bayes‘sche Analyse probabilistische Ingenieurmodelle konsistent anhand von Daten „gelernt“ werden. T2 - Workshop "Digitaler Zwilling" des BAM Koptenzzentrums „Modellierung und Simulation“ CY - Berlin, Germany DA - 04.06.2018 KW - Probabilitische Ingenieurmodelle KW - Bayes'sche Analyse PY - 2018 AN - OPUS4-46437 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF 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, Netherlands DA - 02.07.2023 KW - Bridge KW - Safety KW - Fatigue KW - Modal system identification KW - Model updating PY - 2023 AN - OPUS4-57863 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Baeßler, Matthias A1 - Schneider, Ronald A1 - Hille, Falk A1 - Herrmann, Ralf A1 - Said, Samir T1 - Anwendung von kontinuierlichen sensor-basierten Monitoringverfahren zur Bewertung von Infrastrukturbauwerken N2 - Kontinuierliche Sensorbasierte Bauwerksmessungen leisten einen wichtigen Beitrag zur Sicherheit von Verkehrsbauwerken. Hierzu werden im Vorhaben AISTec Referenzbauwerke und Referenzverfahren untersucht. Der Vortrag stellt die Expertise des FB 7.2, Herangehensweisen und Projektziele vor´. T2 - Kick-Off AIStec CY - Weimar, Germany DA - 17.10.2018 KW - Zivile Sicherheit KW - Brücken KW - SHM PY - 2018 AN - OPUS4-46325 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Simon, Patrick A1 - Schneider, Ronald A1 - Baeßler, Matthias A1 - Recknagel, Christoph T1 - Enhancing structural models with material tests and static response data - a case study considering a steel beam with asphalt layer subject to temperature variations N2 - Gradual or sudden changes in the state of structural systems caused, for example, by deterioration or accidental load events can influence their load-bearing capacity. Structural changes can be inferred from static and/or dynamic response data measured by structural health monitoring systems. However, they may be masked by variations in the structural response due to varying environmental conditions. Particularly, the interaction of nominally load-bearing components with nominally non-load bearing components exhibiting characteristics that vary as a function of the environmental conditions can significantly affect the monitored structural response. Ignoring these effects may hamper an inference of structural changes from the monitoring data. To address this issue, we adopt a probabilistic model-based framework as a basis for developing digital twins of structural systems that enable a prediction of the structural behavior under varying ambient condition. Within this framework, different types of data obtained from real the structural system can be applied to update the digital twin of the structural system using Bayesian methods and thus enhance predictions of the structural behavior. In this contribution, we implement the framework to develop a digital twin of a simply supported steel beam with an asphalt layer. It is formulated such that it can predict the static response of the beam in function of its temperature. In a climate chamber, the beam was subject to varying temperatures and its static response wass monitored. In addition, tests are performed to determine the temperature-dependent properties of the asphalt material. Bayesian system identification is applied to enhance the predictive capabilities of the digital twin based on the observed data. T2 - International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-10) CY - Online meeting DA - 30.06.2021 KW - Digital twin KW - Structural health monitoring KW - Material tests KW - Bayesian updating PY - 2021 AN - OPUS4-54130 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zinas, Orestis A1 - Schneider, Ronald A1 - Cuéllar, Pablo A1 - Schepers, Winfried A1 - Baeßler, Matthias A1 - Papaioannou, I. T1 - Potentials of probabilistic approaches in offshore foundation installation N2 - This presentation discusses the potentials of probabilistic methods in offshore foundation installation, from the perspective of probabilistic ground models and data-driven site characterization. We discuss about methodologies for utilizing site-specific geotechnical (CPT) and geological data, aiming to construct an integrated ground model that can predict stratigraphic profiles and useful for geotechnical design parameters at any location within a 3D domain. The predicted parameters and stratigraphy are then used to predict the probability of potential pile tip damage, upon collision with a boulder. T2 - Colloquium Buckling of Offshore Wind Energy Structures CY - Berlin, Germany DA - 14.02.2024 KW - Wind KW - Offshore KW - Buckling KW - Probabilistic KW - Ground PY - 2024 AN - OPUS4-59538 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald T1 - Überwachung von Brücken mit digitalen Methoden N2 - Brücken werden durch Inspektionen und teilweise durch Monitoring überwacht, um Daten und Informationen über den Zustand und die Einwirkungen zu sammeln. Auf deren Grundlage werden Entscheidungen hinsichtlich Maßnahmen zur Gewährleistung der Sicherheit und Verfügbarkeit getroffen. In diesem Vortrag werden die Potentiale der Digitalisierung zur Unterstützung und Verbesserung der Brückenüberwachung diskutiert. Insbesondere werden die Bereiche Datenmanagement, Brückenmodellierung und Entscheidungsfindung betrachtet. T2 - BVPI Arbeitstagung 2022 CY - Berlin, Germany DA - 16.09.2022 KW - Entscheidungsfindung KW - Brücken KW - Überwachung KW - Digitalisierung KW - Datenmanagement KW - Modellierung PY - 2022 AN - OPUS4-55770 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald A1 - Simon, Patrick A1 - Herrmann, Ralf A1 - Hille, Falk A1 - Baeßler, Matthias 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 - 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 -