A Methodology for Damage Detection Using Unsupervised Learning in the Field of Structural Health Monitoring: Based on Gaussian Mixture Modeling

This thesis aims to investigate the feasibility of developing a successful unsupervised Structural Health Monitoring (SHM) methodology to detect damage in structures, specifically bridges. Detecting damage, especially in its earliest stages, is challenging, thus prompting the need for robust and effective methods. The success of such a...

Machine learning-based identification of precursors of extreme events in chaotic systems

Abrupt and rapid high-amplitude changes in a dynamical system’s states known as extreme events appear in many processes occurring in nature, such as drastic climate patterns, rogue waves, or avalanches. These events often entail catastrophic effects, therefore their description and prediction is of great importance. Nevertheless, because of...

Global shoreline forecasting with a clustering approach

Coastal areas around the world have always been densely populated areas. However, sea-level-rise and an increase in single extreme events due to climate change, threaten the coastal areas and their inhabitants. Governmental organizations, coastal managers and various private parties thrive for better insights into long-term shoreline behavior...

Investigating the MetOp ASCAT vegetation parameters

In this study, an unsupervised classification approach is used to investigate and characterize the spatial and temporal variability of MetOp-A ASCAT backscatter (σ◦) and the TUW SMR vegetation parameters across mainland France between 2007 and 2017. Currently, soil moisture data is retrieved from ASCAT backscatter measurements using the TU Wien...