Bridges are the most critical components of the transportation network. The functionality of bridges is important for hurricane aftermath recovery and emergency activities. However, past hurricane events revealed the potential susceptibility of these bridges under storm induced wave and surge loads. Coastal bridges traditionally were not designed to sustain hurricane induced wave and surge loads; and furthermore, no reliability assessment tool exists for bridges exposed to this hazard. However, such a tool is imperative for decision makers to evaluate the risk posed to the existing bridge inventory, and to decide on the retrofit measures and mitigation strategies. This dissertation offers a first attempt to quantify the structural vulnerability of bridges under coastal storms, offering a probabilistic framework, input tools, and application illustrations. To accomplish this goal, first an unbiased wave load model is developed based on the existing wave load models in the literature. The biased is removed from the load models through statistical analysis of the experimental test data. The developed wave load model is used to evaluate the response of coastal bridges employing single-physics domain Dynamic numerical models. Additionally, a high fidelity fluid-structure interaction model is developed to take into account the significant intricacies, such as turbulence, wave diffraction, and air entrapment, as well as material and geometric nonlinearities in structure. This numerical model provides insight on the influential parameters that affect the response of coastal bridges. Moreover, a Monte Carlo based Static Model methodology is developed to enable fast evaluation of the bridge deck unseating mode of failure. This methodology can be used for fast screening of vulnerable structures under hurricane induced wave and surge loads in a large bridge inventory. New statistical learning tools are used to develop fragility surfaces for coastal bridges vulnerable to storms. The performance of each of these tools is evaluated and compared. The statistical learning approaches are used to enable reliability assessment using the more rigorous finite element models such as the Dynamic and FSI Models which is important for improved confidence and retrofit assessment. Additionally, a new systematic method to evaluate the limit state capacity functions based on the post-event global performance of the bridge structure is developed. The application of the developed reliability models is illustrated by utilizing them for Houston/Galveston Bay area bridge inventory. The case study of Houston/Galveston Bay area reveals that more than 30% of bridges have a high probability of failure during an extreme hurricane scenario event. Two vulnerable bridge structures from the case study are selected to investigate the effect of different potential retrofit measures. Recommendations are made for the most appropriate retrofit measures that can prevent the deck unseating without significantly increasing the structural demands on other components.

Since the devastating hurricane seasons of 2004, 2005, and 2008, the stability and serviceability of coastal bridges during and following hurricane events have become a main public concern. Twenty coastal bridges, critical for hurricane evacuation and recovery efforts, in Texas have been identified as vulnerable to hurricane surge and wave action. To accurately assess extreme surges at these bridges, a dimensionless surge response function methodology was adopted. The surge response function defines maximum surge in terms of hurricane meteorological parameters such as hurricane size, intensity, and landfall location. The advantage of this approach is that, given a limited set of discrete hurricane surge data (either observed or simulated), all possible hurricane surges within the meteorological parameter space may be described. In this thesis, we will first present development of the surge response function methodology optimized to include the influence of regional continental shelf geometry. We will then demonstrate surge response function skill for surge prediction by comparing results with surge observations for Hurricanes Carla (1961) and Ike (2008) at several stations along the coast. Finally, we apply the improved surge response function methodology to quantify extreme surges for Texas coastal bridge probability and vulnerability assessment.