7.2 Ingenieurbau
Filtern
Dokumenttyp
- Vortrag (9)
- Beitrag zu einem Tagungsband (6)
Referierte Publikation
- nein (15) (entfernen)
Schlagworte
- Structural health monitoring (8)
- Structural Health Monitoring (4)
- Environmental effects (3)
- Artificial intelligence (2)
- Bayesian methods (2)
- Bayesian updating (2)
- Brücken (2)
- Digital twin (2)
- Klimakammer (2)
- Material tests (2)
Organisationseinheit der BAM
- 7.2 Ingenieurbau (15) (entfernen)
Eingeladener Vortrag
- nein (9)
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.
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.
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.
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.