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Owners or operators of offshore wind farms perform inspections to collect information on the condition of the wind turbine support structures and perform repairs if required. These activities are costly and should be optimized. Risk-based methods can be applied to identify inspection and repair strategies that ensure an optimal balance between the expected total service life cost of inspection and repair, and the achieved risk reduction. Such an optimization requires explicit modeling of repairs. In this paper, the impact of different repair models on the results of a risk-based optimization of inspection and repair strategies is quantified in a numerical example considering a jacket-type steel frame subject to high-cycle fatigue. The example showed that, in this specific application, there is no need for detailed modeling of the behavior of repaired welded connections.
Owners or operators of offshore wind farms perform inspections to collect information on the condition of the wind turbine support structures and perform repairs if required. These activities are costly and should be optimized. Risk-based methods can be applied to identify inspection and repair strategies that ensure an optimal balance between the expected total service life cost of inspection and repair, and the achieved risk reduction. Such an optimization requires explicit modeling of repairs. In this paper, the impact of different repair models on the results of a risk-based optimization of inspection and repair strategies is quantified in a numerical example considering a jacket-type steel frame subject to high-cycle fatigue. The example showed that, in this specific application, there is no need for detailed modeling of the behavior of repaired welded connections.
Reliability analysis of deteriorating structural systems requires the solution of time-variant reliability problems.
In the general case, both the capacity of and the loads on the structure vary with time. This analysis can be approached by approximation through a series of time-invariant reliability problems, which is a potentially effective strategy for cases where direct solutions of the time-variant reliability problem are challenging, e.g. for structural systems with many elements or arbitrary load processes. In this contribution, we thoroughly Review the formulation of the equivalent time-invariant reliability problems and extend this approximation to structures for which inspection and monitoring data is available. Thereafter, we present methods for efficiently evaluating the reliability over time. In particular, we propose the combination of sampling-based methods with a FORM (first-order reliability method) approximation of the series system reliability problem that arises in the computation of the lifetime reliability. The framework and algorithms are demonstrated on a set of numerical examples, which include the computation of the reliability conditional on inspection data.
In this contribution, we consider two applications in which probabilistic approaches can potentially complement or enhance the design and assessment of offshore wind turbine foundations. First, we illustrate in a numerical example that probabilistic modelling can be helpful in dealing with chang-es in turbine locations during the planning phase of an offshore wind farm. In this case, spatial probabilistic modelling of the ground conditions enables (a) an inference of the soil properties at the modified turbine location from field data collected at different locations across an offshore wind farm site and (b) an optimisation of further site investigations. Second, we discuss the uncer-tainties and risks associated with the installation of large diameter monopiles in soils with hetero-geneities such as strong layers and/or embedded boulders. Subsequently, we present a concept for modelling, understanding, and managing these risks based on a probabilistic model of the subgrade conditions, monopile, and subgrade-pile-interaction.
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.
The current practice of operating and maintaining deteriorating structural systems ensures acceptable levels of structural reliability, but it is not clear how efficient it is. Changing the current prescriptive approach to a risk-based approach has great potential to enable a more efficient management of such systems. Risk-based optimization of operation and maintenance strategies identifies the strategy that optimally balances the cost for controlling deterioration in a structural system with the achieved risk reduction. Inspections and monitoring are essential parts of operation and maintenance strategies. They are typically performed to reduce the uncertainty in the structural condition and inform decisions on future operation and maintenance actions. In risk-based optimization of operation and maintenance strategies, Bayesian updating is used to include information contained in inspection and monitoring data in the prediction of the structural reliability. All computations need to be repeated many times for different potential inspection and monitoring outcomes. This motivates the development of robust and efficient approaches to this computationally challenging task.
The reliability of deteriorating structural systems is time-variant because the loads on them and their capacities change with time. In most practical applications, the reliability analysis of deteriorating structural systems can be approached by dividing their lifetime into discrete time intervals. The time-variant reliability problem can then be represented by a series of time-invariant reliability problems. Using this methodology as a starting point, this thesis proposes a novel approach to compute the time-variant reliability of deteriorating structural systems for which inspection and monitoring data are available. The problem is formulated in a nested way in which the prediction of the structural condition is separated from the computation of the structural reliability conditional on the structural condition. Information on the structural condition provided by inspections and monitoring is included in the reliability assessment through Bayesian updating of the system deterioration model employed to predict the structural condition. The updated system reliability is obtained by coupling the updated deterioration model with a probabilistic structural model utilized to calculate the failure probability conditional on the structural condition. This approach is the first main outcome of this thesis and termed nested reliability analysis (NRA) approach. It is demonstrated in two numerical examples considering inspected and monitored steel structures subject to high-cycle fatigue.
An alternative – recently developed – approach, which also follows the strategy of discretizing time, describes deteriorating structural systems with hierarchical dynamic Bayesian networks (DBN). DBN combined with approximate or exact inference algorithms also enable the computation of the time-variant reliability of deteriorating structural systems conditional on information provided by inspection and monitoring data. In this thesis – as a proof of concept – a software prototype is developed based on the DBN approach, which can be used to assess the reliability of a corroding concrete box girder for which half-cell potential measurements are available. This is the second main outcome of this thesis.
Both approaches presented in this thesis enable an integral reliability analysis of inspected and monitored structures that accounts for system effects arising from (a) the correlation among deterioration states of different structural elements, (b) the interaction between element deterioration and system failure, and (c) the indirect information gained on the condition of all unobserved structural elements from inspecting or monitoring the condition of some structural elements. Thus, both approaches enable a systemwide risk-based optimization of operation and maintenance strategies for deteriorating structural systems.
The NRA approach can be implemented relatively easily with subset simulation, which is a sequential Monte Carlo method suitable for estimating rare event probabilities. Subset simulation is robust and considerably more efficient than crude Monte Carlo simulation. It is, however, still sampling-based and its efficiency is thus a function of the number of inspection and monitoring outcomes, as well as the value of the simulated event probabilities. The current implementation of the NRA approach performs separate subset simulation runs to estimate the reliability at different points in time. The efficiency of the NRA approach with subset simulation can be significantly improved by exploiting the fact that failure events in different years are nested. The lifetime reliability of deteriorating structural systems can thus be computed in reverse chronological order in a single subset simulation run.
The implementation of the DBN approach is much more demanding than the implementation of the NRA approach but it has two main advantages. Firstly, the graphical format of the DBN facilitates the presentation of the model and the underlying assumptions to stakeholders who are not experts in reliability analysis. Secondly, it can be combined with exact inference algorithms. In this case, its efficiency neither depends on the number of inspection and monitoring outcomes, nor on the value of the event probabilities to be calculated. However, in contrast to the NRA approach with subset simulation, the DBN approach with exact inference imposes restrictions on the number of random variables and the dependence structure that can be implemented in the model.
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.
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.