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...

Deep Reinforcement Learning for Rapid Communication Network Recovery with Multiple Repair Crews

Natural disasters can destroy communication network components, potentially leading to severe losses in connectivity. During those devastating events, network connectivity is crucial for rescue teams as well as anyone in need of assistance. Therefore, swift network restoration following a disaster is vital. However, post-disaster network...