Universitätsbibliothek
Weimar Open Access

Das ständig steigende Mobilitätsbedürfnis erfordert den Neu- und Ausbau von Verkehrswegen, da diese den heute auftretenden Verkehrsbelastungen und –stärken häufig nicht mehr gewachsen sind. In dieser Arbeit wird an Hand eines Bauvorhabens die Entscheidungsfindung zur Auswahl eines geeigneten Bauverfahrens gezeigt. Dazu wird, nach einer allgemeinen Beschreibung des Problems, eine Übersicht möglicher Verfahren zur Baugrundverbesserung gegeben. Es wurden mehrere Ausführungsvarianten vorentwurfsmäßig ausgearbeitet und miteinander verglichen. Die so gefundene Vorzugsvariante wurde anschließend für die Bauaufgabe dimensioniert und planerisch aufbereitet. Abschließend wurden Probefelder zur Überprüfung der Bemessung und zur Optimierung der Ausführung beschrieben und ausgewertet.

Different types of data provide different type of information. The present research analyzes the error on prediction obtained under different data type availability for calibration. The contribution of different measurement types to model calibration and prognosis are evaluated. A coupled 2D hydro-mechanical model of a water retaining dam is taken as an example. Here, the mean effective stress in the porous skeleton is reduced due to an increase in pore water pressure under drawdown conditions. Relevant model parameters are identified by scaled sensitivities. Then, Particle Swarm Optimization is applied to determine the optimal parameter values and finally, the error in prognosis is determined. We compare the predictions of the optimized models with results from a forward run of the reference model to obtain the actual prediction errors. The analyses presented here were performed calibrating the hydro-mechanical model to 31 data sets of 100 observations of varying data types. The prognosis results improve when using diversified information for calibration. However, when using several types of information, the number of observations has to be increased to be able to cover a representative part of the model domain. For an analysis with constant number of observations, a compromise between data type availability and domain coverage proves to be the best solution. Which type of calibration information contributes to the best prognoses could not be determined in advance. The error in model prognosis does not depend on the error in calibration, but on the parameter error, which unfortunately cannot be determined in inverse problems since we do not know its real value. The best prognoses were obtained independent of calibration fit. However, excellent calibration fits led to an increase in prognosis error variation. In the case of excellent fits; parameters' values came near the limits of reasonable physical values more often. To improve the prognoses reliability, the expected value of the parameters should be considered as prior information on the optimization algorithm.

For the safe and efficient operation of dams, frequent monitoring and maintenance are required. These are usually expensive, time consuming, and cumbersome. To alleviate these issues, we propose applying a wave-based scheme for the location and quantification of damages in dams. To obtain high-resolution “interpretable” images of the damaged regions, we drew inspiration from non-linear full-multigrid methods for inverse problems and applied a new cyclic multi-stage full-waveform inversion (FWI) scheme. Our approach is less susceptible to the stability issues faced by the standard FWI scheme when dealing with ill-posed problems. In this paper, we first selected an optimal acquisition setup and then applied synthetic data to demonstrate the capability of our approach in identifying a series of anomalies in dams by a mixture of reflection and transmission tomography. The results had sufficient robustness, showing the prospects of application in the field of non-destructive testing of dams.