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Die B1 Brücke am Altstädter Bahnhof in Brandenburg an der Havel wurde im Dezember 2019 aufgrund von während einer turnusmäßigen Inspektion entdeckten Rissen und Hohlstellen entlang der vorgespannten Längsträger gesperrt und im Mai 2021 abgebrochen. In der Zwischenzeit wurde die Brücke detailliert überwacht. Vor dem Abriss wurden zudem umfangreiche Untersuchungen zur Bestandsaufnahme und Schadensanalyse sowie Tests moderner Sensorik vorgenommen. Dabei konnte sowohl die richtige, zuvor nicht sicher bekannte Anzahl von Spanngliedern in den Querträgern sicher ermittelt werden als auch durch moderne Varianten der Schwingungsmessungen und der faseroptischen Sensorik zusätzliche Kenntnisse püber das Bauwerksverhalten ermittelt werden . In dem Beitrag werden die Verfahren mit ihren Möglichkeiten und Grenzen vorgestellt, die Ergebnissee an der Brücke in Brandenburg erläutert und zukünftige Einsatzmöglichkeiten diskutiert.
The structural integrity of outer reinforced concrete (RC) containments of nuclear power plants provides an essential shield against external hazards. If this containment is damaged by an impact event, such as an aircraft crash, the question arises to which degree the reinforced concrete containment still has its protective capability. This concerns both purely structural protection and protection against liquids penetrating the interior of the containment. Due to the dimensions of the containment structures, it is difficult to perform real scale impact experiments, so in the past decades plate geometries at medium scale have been used for investigations. Detailed investigations on the structural behaviour of RC members or RC plates subjected to impact loading have already been presented in Just et al., Hering, Hering et al., Bracklow et al., Hille et al. and Nerger et al.
The following investigations deal with the single and multiple impact event (first hard impact and/or subsequent soft impact) on a RC specimen, which provides the basis for further investigations. A description of the test setup and the test procedure as well as a presentation of the test results from the impact tests are provided.
Furthermore, the experimental program is presented, which the damaged RC specimens are to undergo to deal with the question of how much the impact-damaged RC structure has become permeable to liquid media, such as water and kerosene, depending on the intensity of the impact. The aim of these following investigations is to develop a test setup that can be applied to investigate the liquid penetration behaviour (LPB) of small, medium, and large-scale RC members.
In addition to the liquid penetration experiments, the damaged specimens are to be examined by planar tomography to obtain the damage inside the specimen. The combination of damaging event, fluid penetration behaviour and tomography should enable a comprehensive understanding of the damage to the RC specimen.
Structural health monitoring is a promising technology to automatically detect structural changes based on permanently installed sensors. Vibration-based methods that evaluate the global system response to ambient excitation are suited to diagnose changes in boundary conditions, i.e., changes in member prestress or imposed displacements. In this paper, these changes are evaluated based on sensitivity-based statistical tests, which are capable of detecting and localizing parametric structural changes. The main contribution is the analytical calculation of sensitivity vectors for changes in boundary conditions (i.e., changes in prestress or support conditions) based on stress stiffening, and the combination with a numerically efficient algorithm, i.e., Nelson’s method. One of the main advantages of the employed damage diagnosis algorithm is that, although it uses physical models for damage detection, it considers the uncertainty in the data-driven features, which enables a reliabilitybased approach to determine the probability of detection. Moreover, the algorithm can be trained and the probability of detecting future damages can be predicted based on data and a model from the undamaged structure,
in an unsupervised learning mode, making it particularly relevant for unique structures, where no data from the damaged state is available. For proof of concept, a numerical case study is presented. The study assesses the loss of prestress in a two-span reinforced concrete beam and showcases suitable validation approaches for the sensitivity calculation.