7.2 Ingenieurbau
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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.
We examine the behavior of reinforced concrete components subjected to impact induced loading conditions which might be caused by vessels collisions such as aircraft fuel tanks The concrete plates were impact damaged at TU Dresden and shipped to BAM At BAM laminar tomography as the imaging method is used to determine and quantify the damage state An automatic crack detection method based on template matching is applied to find the cracks and we aim to develop a new method using machine learning Algorithms In addition numerical models are developed to understand the experiment and to predict the damage structures based on failure mechanisms.
Current capabilities for full-scale field testing are highly resource intensive. Reliable small-scale experiments are an effective alternative. Characterization of the dynamic response and damage of RC elements to scaled blast loads was investigated in scaled-down field experiments. Spatially resolved information on the dynamic structural response using distributed fiber optic acoustic sensing (DAS) and acceleration as well as blast loading by piezoelectric pressure sensors.