Quantification of Bridge Performance Variability due to Modeling Uncertainties

There are nearly 610,000 public road bridges across the United States; approximately 25000 of existing bridges are locate in California–a high seismic zone. Observations from previous seismic events reveal that earthquakes have a significant damaging effect on bridges leading to major consequence on the economy of the affected area. In light of these effects, various studies have focused on quantifying the seismic vulnerability of bridges aiming at improving bridge design codes accordingly. The research effort presented herein intends to develop a comprehensive and efficient model that includes the coupling of bridge critical components such as shear keys, backfill passive pressure, and soil-pile-structure interaction; the missing piece of previous research in bridge seismic demand assessment. This research fills the gap between the recommended modeling approaches by seismic design guidelines–which are rather simple–and the current state-of-the-art bridge component modeling techniques that are mostly developed based on experimental data or advanced continuum finite element models. Meanwhile, we expand PEER’s Performance-Base Earthquake Engineering (PBEE) framework to include the impact of progressive deterioration during the life-time of the bridge as well as contribution of aftershock in seismic response assessment. The sensitivity of important engineering demand parameters associated with shear key, backfill, and deep foundation behaviors are quantified. We demonstrate that shear key behavior, and foundation model have a significant effect on the seismic response of ordinary bridges. The failure modes of bridges exhibit a high sensitivity to the type of backfill soil–and their adopted models–coupled with the behavior of the shear keys. Finally, we show that the bridge seismic response is not sensitive to the uniform corrosion of column rebars; however, excluding the aftershock loading results in underestimating bridge demands significantly