Vehicle collisions with bridge piers can result in significant damage to the support pier and potentially lead to catastrophic failure of the whole structure. The Nation’s aging infrastructure suggests that many structures no longer meet current design standards, placing many bridge susceptible to failure if subjected to an extreme loading event. This research aims to study the structural response of reinforced concrete bridge piers subjected to vehicle collisions. A sensitivity analysis is conducted to observe the causes of shear and bending failures of bridge piers subjected to vehicle collision. Parameters, such as pier diameter, transverse reinforcement spacing, vehicle impact velocity, pile cap height, and multi-pier configuration, are investigated in this study.
The finite element code LS-DYNA is utilized to simulate and analyze the vehicle collisions to obtain accurate and detailed results. The vehicle models offered by the National Crash Analysis Center and the National Transportation Research Center, Inc. are used to conduct this research. The finite element modeling controls and material properties are validated by conducting an impact drop hammer experiment. The bridge pier collision models are validated by comparing vehicle damage and impact forces with published research results. Conservation of energy is also checked to assure stability within the impact simulation.
A sensitivity analysis suggests that different pier parameters have a profound effect on failure modes and distribution of impact forces. Piers with large stiffness result in high impact forces, low lateral displacements, and high resistance to shear forces and bending moments. A performance-based analysis shows that bridge piers can be designed using damage ratios associated with particular damage states.
Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:masters_theses_2-1062 |
Date | 29 August 2014 |
Creators | Gomez, Nevin L |
Publisher | ScholarWorks@UMass Amherst |
Source Sets | University of Massachusetts, Amherst |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | Masters Theses |
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