Resilience Quantification of Transportation Infrastructure Subjected to Hazards

Abstract

Evaluating the resilience of transportation infrastructures, including bridges, roads, and tunnels, is a critical aspect of ensuring the ongoing functionality and reliability of urban or regional areas in the face of various disruptive events. Such infrastructures are susceptible to a range of disruptions which can have significant impacts on their ability to function effectively. Resilience refers to the capacity of an infrastructure or a system to withstand and recover from these disruptions. This research presents a framework to evaluate the resilience surface for assessing the resilience of various transportation infrastructure components. This comprehensive approach involves several steps. First, the framework identifies unique damage configurations by performing a fragility analysis. This analysis allows for a better understanding of how susceptible the infrastructure is to different hazards. Next, the framework focuses on the restoration of the affected infrastructure by developing recovery curves for each identified damage configuration. This is done by taking into account relevant restoration data and considering the specific characteristics of each configuration. Additionally, the framework acknowledges the inherent uncertainty that exists within various aspects of infrastructure resilience assessment. These uncertainties include hazard intensity, modeling uncertainty, and the restoration process itself. By incorporating these uncertainties into the framework, a more accurate and reliable assessment can be achieved. The utility of this framework is demonstrated through its application to a real-world case study involving a highway bridge located in Canada. The goal of this research is to offer decision-makers a valuable tool for evaluating the resilience of transportation infrastructure. This can contribute to more robust and reliable transportation infrastructures, capable of withstanding and recovering from a wide range of disruptive events.