Filtern
Dokumenttyp
Sprache
- Englisch (4)
Referierte Publikation
- nein (4) (entfernen)
Schlagworte
- Monitoring (1)
- Risk (1)
- Robustness (1)
- Structural reliability (1)
- Wind energy converter (1)
This paper describes a structural reliability analysis utilizing monitoring data in the ultimate limit state with consideration of the uncertainties of the monitoring procedure. For this purpose the uncertainties of the monitoring data are modeled utilizing a new framework for the determination of measurement uncertainties. The approach is based on a process equation and Statistical models of observations for the derivation of a posterior measurement uncertainty by Bayesian updating. This facilitates the quantification of a measurement uncertainty using all available data of the measurement process. For the reliability analysis in the ultimate limit state, monitoring data can be utilized as a loading model Information and as proof loading, i.e. resistance model Information. Both approaches are discussed with generic examples and it is shown that the modeling of monitoring data in a reliability analysis can result in a reduction of uncertainties and as a consequence in the reduction of the probability of failure. Furthermore, the proof loading concept is developed further to account for the uncertain characteristic of proof loading due to the measurement uncertainties which is consistent with the framework for the determination of measurement uncertainties. These approaches and findings can be utilized for the assessment of structures for life cycle extension and the design of monitoring Systems.
Structural Health Monitoring (SHM) systems are designed for assisting owners and operators with information and forecasts concerning the fitness for purpose of structures and building systems. The benefit associated with the implementation of SHM may in some cases be intuitively anticipated or proven by past experiences but in general there appears to be no rational or systematic approach for assessing the value of SHM systems a-priory to their implementation. The present paper addresses the assessment of the value of SHM with basis in structural risk assessments and the Bayesian pre-posterior decision analysis. The quantification of the value of SHM builds upon the quantification of the value of information (VoI) or rather the benefit of monitoring. The suggested approach involves a probabilistic representation of the loads and environmental conditions acting on structures as well as their responses and performances over their life-cycle. In addition, the quality of monitoring and the performance of possible remedial actions triggered by monitoring results are modeled probabilistically.The consequences accounted for, in principle include all consequences associated with the performance of the structure over its life-cycle as well as the costs associated with monitoring and possible remedial actions. The suggested approach is illustrated through two case studies concerning the monitoring of welded details in steel structures subjected to fatigue loading. The case studies address the effect of the uncertainty associated with the performance of SHM on the value of SHM. Moreover, in order to illustrate the potential of the application of approach for monitoring of structural systems an optimal strategy for SHM is determined for a system comprised of three welded details.