Filtern
Dokumenttyp
- Vortrag (2) (entfernen)
Sprache
- Englisch (2)
Referierte Publikation
- nein (2) (entfernen)
Schlagworte
- Reinforced concrete (2) (entfernen)
Organisationseinheit der BAM
- 7 Bauwerkssicherheit (1)
- 7.2 Ingenieurbau (1)
Eingeladener Vortrag
- nein (2)
The concreting of prefabricated concrete structures can lead to insufficient bonding or even to remaining cavities. Honeycombs (aggregate clusters without cement) represent potential weakening of the structure and need to be detected non-destructively. In our study we tested the capability of ground penetrating radar (GPR) techniques for this purpose. We applied GPR in reflection mode and zero-offset profiling (ZOP) transmission mode on a precast concrete twin wall with built-in honeycombs. GPR measurements were performed as two channel measurement with ground coupled antennas with centre frequencies of 1.5 GHz and 2.6 GHz mounted to an automated scanner system.
Our findings show that ZOP transmission measurements are a more efficient method to detect voids in reinforced concrete structures compared to reflection mode measurements. This holds for both the effort needed for the measurement and the evaluation as well as the validity of the data. Honeycombs (basically representing voids) are usually characterized by strongly reduced amplitudes and earlier arrivals of the transmitted wave.
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.