Predicting coastal infrastructure reliability during hurricane events is important for risk-based design and disaster planning, including delineating viable emergency response routes. Previous research has focused on either infrastructure vulnerability to sea-level rise and coastal flooding, or the impact of changing sea level and landforms on surge dynamics. This paper represents a multidisciplinary effort to provide an integrative model of the combined impacts of sea-level rise, landscape changes, and coastal flooding on the vulnerability of highway bridges—the only access points between barrier islands and mainland communities—during extreme storms. Coastal flooding is forward modeled for static projections of geomorphic change. First-order parameters that are adjusted include sea level and land surface elevation. These parameters are varied for each storm simulation to evaluate relative impact on the performance of bridges surrounding Freeport, Texas. Vulnerability is estimated by evaluating both the probability of structural failure given surge and wave loads as well as the time inundated. The probability of bridge failure is found to increase with storm intensity and sea level because bridge fragility increases with storm surge height. The impact of a shifting landscape on bridge accessibility is more complex; barrier island erosion and transgression can increase, decrease, or produce no change in inundation times for storms of different intensity due to changes in wind-setup and back-bay interactions. These results suggest that tying down bridge spans and elevating low-lying roadways approaching bridges may enhance efforts aimed at protecting critical infrastructure.

Bridge restoration models are essential for assessing the functionality of bridges after extreme events, emergency response planning, and evaluating resilience and indirect losses. Most of the existing restoration models lack explicit evaluation of anticipated bridge functionality in terms of traffic restrictions on bridges, which is necessary for realistic road network modeling. Therefore, this paper proposes a decision tree based approach to determine potential traffic restrictions and their durations using empirical and expert opinion survey data on restrictions imposed on bridges after extreme events. The proposed methodology is applied in this paper to obtain seismic restoration models for bridges. Herein, three separate decision trees are developed to determine various traffic restrictions, anticipated for different levels of damage to bridge components, including bridge closure, lane closure, and speed/load restriction. Corresponding to each of the three traffic restriction decision trees, another decision tree is developed which determines the duration of the traffic restriction. The probabilistic seismic performance of a regional highway network in Memphis, Tennessee, is studied using the proposed decision trees. Additionally, HAZUS bridge restoration functions are also applied and the differences in the time evolving bridge level functionality estimates and their impacts on network performance are highlighted.