The ability to predict the reduction in capacity of a structure after an earthquake is vital in the process of assessing a structure after a main-shock or an after-shock. Main-shocks are normally followed by a few aftershocks in a short period of time. Researchers in the past have focused for the most part on the effects of main-shocks on buildings. Very little research has been performed on the ability to predict the reduction in capacity of bridges in aftershocks. This thesis focuses on providing a way of assessing the reduction in capacity for main-shocks as compared to aftershocks and the effects and importance of both in a bridge. The reduction in capacity was defined using three different ratios: ultimate force, stiffness, and strain energy ratio. The ratios were computed relative to an undamaged state following both the main-shock scenario and the main-shock combined with aftershock scenario. The force, stiffness, and strain energy quantities were obtained from lateral pushover analyses along the two lateral bridge axes. Probabilistic demand models describing the loss in capacity were formulated by pairing intensity measures, based on real ground motions obtained from previous earthquakes, for the main-shock and aftershock with the capacity ratios, obtained from nonlinear dynamic time history analysis. Additionally, the reduction in capacity was conditioned on residual displacement and intensity measure in an attempt to discover the reduction in capacity ratio due to the contribution of residual displacement and therefore separate contributions from geometrical and material nonlinearities. This thesis demonstrates that the usage of strain energy ratio provides a definition of capacity that ultimately provides the best correlation between capacity and intensity measure.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-3123 |
| Date | 01 January 2012 |
| Creators | Espinosa, Diego Francisco |
| 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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