7.2 Ingenieurbau
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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.
A significant number of new wind farms has to be installed and, at the same time, existing wind farms reaching the end of their planned life need to be reused efficiently to ensure that the ambitous goals for deploying offshore wind are met. Some relevant reuse alternatives for offshore wind farms are lifetime extension, repowering utilizing existing substructures and full replacement. In this presentation - starting from experience gained from extending the lifetime of the of the U1 metro viaduct in Berlin - we discuss end-of-life decision making in offshore wind. We focus particularly on issues concerning substructures and highlight existing challenges and opprtunities in research and development.
Monopiles are currently the predominant foundation type for offshore wind turbines in Europe. Due to the increasing dimensions of the turbines, pile diameters beyond 10m become necessary. A design-relevant failure mode of monopiles is the local buckling of the pile wall in the embedded sections. Relevant buckling guidelines do not consider the soil-structure interaction specifically, although the embedment may allow for a reduction of wall thickness. However, Eurocode-based design concepts require a validation with comparative buckling cases for validation, either in terms of buckling curve parameters for both the algebraic stress-based and semi-numerical LBA/MNA design concept or as a calibration factor kGMNIA for fully numerical GMNIA calculations. These parameters are not yet available for embedded shells. To close this gap, we have conducted experiments on piles embedded in sand to investigate local buckling under soil-structure-interaction. The results will be used to calibrate numerical models. This research was carried out as part of the VERBATIM research project, funded by PTJ/BMWK and supported by the Carbon Trust's Offshore Wind Accelerator consortium.
The research project Verification of Buckling Assessment and Behaviour in Large Monopiles (VERBATIM) focuses on challenges arising due to the ongoing increase of dimensions of monopiles as foundation for offshore wind turbines. This project is focused on two main topics:
- Monopile tip buckling during pile installation
- Local buckling of embedded piles during operation
It is known from previous investigations and experiences in practice that large deformations of a monopile tip during offshore installation works can occur. These large deformations initiated at the tip of the monopile are commonly referred to as “pile tip buckling”. Own research focuses on optimising monopile dimensions to reduce the cost of monopile foundations for offshore wind turbines while preventing pile tip buckling.
Buckling of embedded cylindrical shells for offshore structures is a common engineering task. Structures can be analysed using standard software and have been done so in recent studies e.g., Gottschalk. However, the trend towards larger monopile and optimised structures has led to a fundamental need for better and verified models. A main challenge is the submodelling of the structure-soil-interaction. Developing such complex models is one main goal of the VERBATIM project.
For both main topics model tests were performed at the laboratories of BAM and TU Berlin. These experimental results were used to verify related numerical models and simulations.
The new crack luminescence method offers the possibility of making fatigue surface cracks in metallic materials more visible during inspections through a special coating system. This coating system consists of two layers, whereby the first layer has fluorescent properties and emits visible light as soon as it is irradiated by UV light. The top layer is black and is designed to prevent the fluorescent layer from emitting if no crack develops in the underlying material. The technique proved particularly useful in a wide variety of fatigue tests of steel components under laboratory conditions. Moreover, it has the potential to be used in various industrial applications. To enable industrial deployment and integration into maintenance strategies, a concept study is developed in this contribution, resulting in a qualification framework that can serve as a foundation for determining the reliability of the crack luminescence system in terms of a probability of detection curve. Within this study, factors causing measurement variability and uncertainty are being determined and their influences assessed. Due to the extension of the system by a moving computer vision system for automated crack detection using artificial intelligence, additional long-term effects associated with structural health monitoring systems need to be incorporated into an extended probability of detection study as part of the technical justification. Finally, important aspects and findings related to design of experiments are discussed, and a framework for reliability assessment of a new optical crack monitoring method is presented, emphasizing the influence of various uncertainty parameters, including long-term effects such as system ageing.
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.
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.
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.