Trial lecture: 11:00
Defend the dissertation: 13:15
https://hiof.zoom.us/j/63690188255?pwd=bS2Tb9LETdEV12zahx0YWcaf4v8yDG.1
Mostafa Aliyari’s PhD research explores application of emerging technologies like remotely operated unmanned aerial vehicles (UAVs) and machine learning (ML), focusing on deep learning (DL) for data collection and data analysis of critical infrastructure systems, particularly in bridge inspections and power load forecasting. As technological and demographic changes heighten the complexity and vulnerability of infrastructure, innovative methods for data collection and analysis are essential.
One part of the research investigates hazard identification and risk assessment in bridge inspections utilizing UAVs. The images captured during these inspections are used to develop DL methods, such as mask region-based convolutional neural networks and transfer learning techniques for failure detection, specifically crack detection. Empirical validation through case studies of the Grimsøy and Håkenby bridges in Norway illustrates the practical application of UAV-assisted bridge inspections. The results indicate that integrating UAVs with advanced DL techniques significantly improves structural failure detection, enhancing prediction accuracy and overall safety.
The second part of the study focuses on time series data related to power load forecasting. This section includes a comparative analysis of the effectiveness of various time series prediction DL models. Additionally, a novel ant colony optimization algorithm is developed to optimize the parameters of the multi-head attention layer, a main component of transformers, for power load forecasting. The findings demonstrate notable improvements in power load prediction accuracy and optimized resource allocation within power systems.
Overall, this research highlights how critical infrastructures can benefit from the integration of new technologies and advanced machine learning predictive techniques for effective risk and performance assessment, ultimately enhancing asset management strategies.