Condition assessment of civil engineering infrastructure systems is of growing importance as they face aging and degradation due to both human-made activities and environmental factors. Nevertheless, challenges persist in data collection, leading to "data scarcity", and the need for frequent site visits in inspections, presenting significant obstacles in the assessment of the civil infrastructure systems. This dissertation aims to overcome these challenges by exploring the potential of two emerging technologies: Generative Artificial Intelligence (AI) and Virtual Reality (VR). In tackling the issue of data scarcity, the research question revolves around how Generative AI can be utilized to mitigate data collection-related constraints and increase data availability, thus facilitating health monitoring applications of infrastructure systems. For that, using various Generative AI models, the dissertation works on acceleration response data generation, including data augmentation and domain translation applications on different structures. In addressing the site visit challenge, the dissertation focuses on the use of VR to bring the infrastructure to the experts in a single collaborative immersive environment and investigate its impact on decision-making in inspections. For that, using VR technology, the dissertation develops a Virtual Meeting Environment (VME) integrated with the infrastructure data and models presented through novel immersive visualization techniques. The dissertation further investigates the impact of VME on decision-making in infrastructure inspections through experimentation with engineers. These investigations of the use of Generative AI and VR demonstrate various contributions. Generative AI effectively tackles the need for vast datasets in data-intensive damage detection applications. It also demonstrates its potential in estimating representative response data for various structural conditions across dissimilar infrastructures. VME, on the other hand, offers an increased understanding of the material along with a safer, practical, and cost-effective complementing alternative to traditional in-person site visits. It further reveals how VME improves decision-making in infrastructure inspections.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2023-1190 |
| Date | 01 January 2024 |
| Creators | Luleci, Furkan |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Thesis and Dissertation 2023-2024 |
| Rights | In copyright |
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