Filtern
Dokumenttyp
- Beitrag zu einem Tagungsband (13) (entfernen)
Referierte Publikation
- nein (13) (entfernen)
Schlagworte
Organisationseinheit der BAM
- 7 Bauwerkssicherheit (11)
- 7.2 Ingenieurbau (9)
- 7.0 Abteilungsleitung und andere (2)
- 7.1 Baustoffe (1)
- VP Vizepräsident (1)
- VP.1 eScience (1)
Inspections are an efficient means of enhancing the reliability of redundant structural Systems subjected to fatigue. To investigate the effect of such inspections, we represent the deterioration state of a Daniels System by means of a probabilistic fatigue crack growth model of all elements, which considers stochastic dependence among element fatigue behavior. We include inspection results in the calculation of the System collapse probability through Bayesian updating of the System deterioration state. Based on this approach, we calculate the collapse probability of a deteriorating Daniels System conditional on different inspection strategies in terms of inspection coverage and inspection times. The acceptability of an inspection strategy is verified by comparing the calculated collapse probabilities with maximum acceptable System failure probabilities. This study is a Step towards identifying optimal inspection strategies for redundant structural Systems subjected to fatigue.
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.
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.