7.2 Ingenieurbau
Filtern
Dokumenttyp
- Beitrag zu einem Tagungsband (23)
- Vortrag (23)
- Zeitschriftenartikel (8)
- Buchkapitel (1)
- Posterpräsentation (1)
Schlagworte
- Structural Health Monitoring (7)
- Offshore (5)
- SHM (5)
- Structural health monitoring (5)
- Gründungsstrukturen (4)
- Grout (3)
- Leichtbau (3)
- Offshore Windenergieanlagen (3)
- Repair (3)
- Automatisierte schweißtechnische Fertigung (2)
Organisationseinheit der BAM
- 7 Bauwerkssicherheit (56)
- 7.2 Ingenieurbau (56)
- 7.4 Baustofftechnologie (11)
- 9 Komponentensicherheit (6)
- 9.3 Schweißtechnische Fertigungsverfahren (5)
- 3 Gefahrgutumschließungen; Energiespeicher (2)
- 3.3 Sicherheit von Transportbehältern (2)
- 7.1 Baustoffe (2)
- 8 Zerstörungsfreie Prüfung (2)
- 5 Werkstofftechnik (1)
- 5.3 Polymere Verbundwerkstoffe (1)
- 7.6 Korrosion und Korrosionsschutz (1)
- 8.0 Abteilungsleitung und andere (1)
- 8.6 Faseroptische Sensorik (1)
- 9.4 Integrität von Schweißverbindungen (1)
- VP Vizepräsident (1)
- VP.1 eScience (1)
Paper des Monats
- ja (2)
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.
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.
Support structures of wind turbines in German offshore wind farms are regularly inspected. Currently, inspection outcomes are generally not systematically recorded. This prevents their consistent handling and processing, which is a key requirement to enable an efficient structural integrity management. As part of the DiMoWind-Inspect project, a data model and reference designation system for such inspection results is developed to facilitate their identification, localization, quantification, tracing and linking throughout the lifetime of a wind farm and beyond. The inspection results together with structural health monitoring results and information on repairs form the basis for assessing and predicting the structural condition, estimating the remaining lifetime, and planning of future inspections, structural health monitoring, and repairs. As a basis for developing a digital structural integrity management, a generic framework is proposed in this paper, which describes the stakeholders, data, models, processes and workflows of the integrity management of support structures in offshore wind farms and their interrelations. The framework adopts a building information modelling approach to describe the information relevant to the structural integrity management. The central digital space of the framework is a common data environment. An implementation of the framework will enable a digital structural integrity management in which inspection, structural health monitoring, repair, assessment and planning outcomes generated throughout the life cycle of a wind farm can be consistently collected, managed, shared and processed by the relevant shareholders.
The response of many geotechnical systems, whose structural behavior depends on shearing effect, is closely related to soil structure interaction phenomenon. Experimentally it is found that the localisation of these effect happens at a narrow soil layer next to the structure. Numerically, this behavior can be modelled through inter-face elements and adequate constitutive models. In this work, a constitutive model in the framework of Gen-eralized Plasticity for sandy soils has been chosen to be adapted for the interface zone. From the direct shear experiments a sandy soil at loose and dense states under different normal pressures is considered. The adapted constitutive model is able to reproduce contraction and dilatation of the soil according to its relative density and it shows a good agreement with the experimental data.
Right from the beginning of applying SHM to bridge structures it was obvious that environmental based perturbations on the measurement significantly influence the ability to identify structural damage.
Strategies are needed to classify such effects and consider them appropriately in SHM. Various methods have been developed and analyzed to separate environmental based effects from damage induced changes in the measures. Generally, two main approaches have emerged from research activity in this fields: (a) statistics based tools analyzing patterns in the data or in computed parameters and (b) methods, utilizing the structural model of the bridge taking into account environmental as well as damage based changes of stiffness values.
With the back-ground of increasing affordability of sensing and computing technology, effort should be made to increase sensitivity, reliability and robustness of procedures, separating environmental from damage caused changes in SHM measures. The contribution describes both general strategies and points out their Advantages and drawbacks. As basis, a review on relevant methods was conducted. The aim of the study is to classify approaches for separating damage describing information from environmental based perturbations in dependency of the SHM objective. And such, it is intended to describe a best practice in designing concepts for Monitoring infrastructure, naturally effected by environmental influences.
The shaft bearing capacity often plays a dominant role for the overall structural behaviour of axially loaded piles in offshore deep foundations. Under cyclic loading, a narrow zone of soil at the pile-soil interface is subject to cyclic shearing solicitations. Thereby, the soil may densify and lead to a decrease of confining stress around the pile due to microphenomena such as particle crushing, migration and rearrangement. This reduction of radial stress has a direct impact on the shaft capacity, potentially leading in extreme cases to pile failure. An adequate interface model is needed in order to model this behaviour numerically. Different authors have proposed models that take typical Interface phenomena in account such as densification, grain breakage, normal pressure effect and roughness. However, as the models become more complex, a great number of material parameters need to be defined and calibrated. This paper proposes the adoption and transformation of an existing soil bulk model (Pastor- Zienkiewicz) into an interface model. To calibrate the new interface model, the results of an experimental campaign with the ring shear device under cyclic loading conditions are here presented. The constitutive model shows a good capability to reproduce typical features of sand behaviour such as cyclic compaction and dilatancy, which in saturated partially-drained conditions may lead to liquefaction and cyclic mobility phenomena.
Changes in the measured response of structural systems can be an indication of structural damages. However, such changes can also be caused by the effect of varying environmental conditions. To detect, localize and quantify changes or damages in structural systems subject to varying environmental conditions, physics-based models of the structural systems have to be applied which explicitly account for the influence of ambient conditions on the structural behavior. Data obtained from the structural systems should be used to calibrate the models and update predictions. Bayesian system identification is an effective framework for this task. In this paper, we apply this framework to learn the parameters of two competing structural models of a reinforced concrete beam subject to varying temperatures based on static response data. The models describe the behavior of the beam in the uncracked and cracked condition. The data is collected in a series of load tests in a climate chamber. Bayesian model class selection is then applied to infer the most plausible condition of the beam conditional on the available data.