For this thesis, a study was completed on two different structures on the UCF Orlando campus through the use of both structural plans and point cloud technology. The results sought to understand the viability of point cloud technology as an accurate tool for the static and dynamic modal analysis of existing structures. For static analysis, a portion of the framing of Spectrum Stadium was rendered, modeled, analyzed and compared to a previous case study. The results emphasized how different users can render dissimilar member sizes and lengths due to human judgment on point cloud visuals. The study also found that structural plans cannot always be relied upon as the most accurate source for analysis as the new point cloud produced more accurate results than the structural plans when compared to the control model. For the pedestrian bridge, the structure was scanned, rendered and modeled for both static and dynamic modal analysis. The point cloud produced from scanning the bridge was modified twice in order to have three distinct point clouds with varying densities: fine, medium and coarse. These three cases were compared to structural plans in a static analysis. The fine point cloud produced the most accurate displacement results with an accuracy above 96%. The data sources were also compared to experimental data under dynamic modal analysis to discover how lessening the density of point clouds affect the accuracy of results. The analysis showed that point cloud technology can give you an accuracy of 88% and above for frequency while also producing MAC values exceeding 0.9 consistently. Also, changes in density were found to change the accuracy of results but the numeric values stayed within close proximity by not differing more than 10%. This thesis shines a light on the accuracy point cloud technology can ascertain and the potential it has within engineering.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-7285 |
| Date | 01 May 2019 |
| Creators | Cano, Jacob |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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