1. Modern infrastructure systems are highly interconnected and comprised of geographically extensive networks (Chang, 2016). Such interconnections lead to interdependencies among infrastructure sys...@en

Purpose: Resource allocation is essential to infrastructure management. The purpose of this study is to develop a methodological framework for resource allocation that takes interdependencies among infrastructure systems into consideration to minimize the overall impact of infrastructure network disruptions due to extreme events. Design/methodology/approach: Taking advantage of agent-based modeling techniques, the proposed methodology estimates the interdependent effects of a given infrastructure failure which are then used to optimize resource allocation such that the network-level resilience is maximized. Findings: The findings of the study show that allocating resources with the proposed methodology, where optimal infrastructure reinforcement interventions are implemented, can improve the resilience of infrastructure networks with respect to both direct and interdependent risks of extreme events. These findings are also verified by the results of two case studies. Practical implications: As the two case studies have shown, the proposed methodological framework can be applied to the resource allocation process in asset management practices. Social implications: The proposed methodology improves the resilience of the infrastructure network, which can alleviate the social and economic impact of extreme events on communities. Originality/value: Capitalizing on the combination of agent-based modeling and simulation-based optimization techniques, this study fulfills a critical gap in infrastructure asset management by incorporating infrastructure interdependence and resilience concepts into the resource allocation process.@en

Traffic operations vary drastically before-, during- and after natural disasters due to several reasons, such as the movement of affected populations, road failures, and heavy precipitation. The disaster-induced effects on road links and their subsequent recovery to the pre-disaster state are functions of the designed network resilience. Thus, the investigation of traffic operations including speed and volume fluctuations during such disasters can provide useful insights into the resilience of the roadway network. Furthermore, identifying road links and corridors which are most affected during natural disasters and estimating the extent of the effect on traffic are crucial steps in devising traffic management strategies for mitigating future hazards. A key challenge to the realization of the above objectives is the lack of data pertaining to roadway and traffic conditions during natural disasters. While modern sensing technologies based on non-Dedicated Short-Range (wireless) Communications such as Bluetooth® have been widely adopted to track traffic performance, they need to be corroborated with supplementary data for a complete characterization of the prevalent traffic conditions. In this study, an alternative method is presented for identifying the traffic fluctuations induced by a natural disaster (Hurricane Harvey) on an urban traffic network (Houston) by studying the characteristics of extreme travel time observations. The study relies on time series decomposition and anomaly detection algorithms to investigate the spatiotemporal effects of the hurricane on the traffic conditions. The results of the case study suggest that the metrics developed are effective in quantifying the resilience of traffic networks against natural disasters by capturing both the initial impact and recovery. The study showed that the Houston freeway network was not completely recovered from the effects of Hurricane Harvey, even three weeks after the occurrence of the hurricane.@en

Over the past years, the frequency and scope of disasters affecting the United States have significantly increased. Government agencies have made efforts in improving the nation's disaster response framework to minimize fatalities and economic loss due to disasters. Disaster response has evolved with the emergency management agencies incorporating systematic changes in their organization and emergency response functions to accommodate lessons learned from past disaster events. Technological advancements in disaster response have also improved the agencies' ability to prepare for and respond to natural hazards. The transportation and logistics sector has a primary role in emergency response during and after disasters. In this light, this paper seeks to identify how effective policy changes and new technology have aided the transportation and logistics sector in emergency response and identify gaps in current practices for further improvement. Specifically, this study compares and contrasts the transportation and logistical support to emergency relief efforts during and after two major Hurricane events in the U.S., namely Hurricane Katrina (which affected New Orleans in 2005) and Hurricane Harvey (which affected Houston in 2017). This comparison intends to outline the major steps taken by the government and the private entities in the transportation and logistics sector to facilitate emergency response and the issues faced during the process. Finally, the paper summarizes the lessons learned from both the Hurricane events and provides recommendations for further improvements in transportation and logistical support to disaster response.@en

Among natural disasters, hurricanes pose significant threat to port infrastructure in the United States, especially to those along the coasts of the Atlantic Ocean and the Gulf of Mexico. The operational continuity of ports is critical because of the growing reliance of domestic and international businesses on ports and their significance in the growth of national and regional economies. While direct physical damages to ports are not rare, a shutdown of port operations is more likely declared by authorities as a precautionary and preparatory measure during hurricanes. Considering the enormous economic importance of ports and their vulnerability to hurricanes, the current study proposes a framework and methodology to analyze the economic risks of such hurricane-related shutdowns using hurricane- and port-related determinants. The risks of shutdowns are modeled using regression analysis based on historical port shutdown data and are combined with extensions of the well-known input–output model to predict the operational and economic risks of ports to hurricanes. The application of the methodology is demonstrated by conducting a case study based on the Texas Port System to evaluate the economic risks of hurricane-related port disruptions on the U.S. economy.@en

In infrastructure networks, each of the constituent infrastructure components (nodes and links) has its own significance. The extent to which each node is important to the network largely varies based on the resource or service it provides and the type of infrastructure nodes that are dependent on it. Similarly, the links are essential for the uninterrupted supply of resources to their respective destinations. To enhance the resilience of infrastructure networks, it is of vital importance to identify and quantify the consequences of infrastructure component failures on other components in the network, for which researchers have proposed several methods. Though all infrastructure nodes may be vulnerable to some kind of external hazards, resource and time constraints make it impossible for infrastructure managers and policymakers to adopt measures to enhance the resilience of every component in a network. Hence, it is necessary to address the issue of node and link prioritization to develop effective resilience strategies for networks. This study proposes two resilience indexes, a node criticality index and a node susceptibility index, to deal with two types of node ranking relevant to infrastructure network resilience. Then the node ranking indexes are used in a heuristic algorithm to rank and prioritize infrastructure links. The indexes can support decision-making for designing and managing resilience in interdependent infrastructure networks prior to a disaster, for identifying infrastructure components that warrant immediate restoration during or after a disaster, and for devising additional resilience strategies to handle heightened disaster risks during recovery.@en

Multiple criteria decision analysis (MCDA) is a decision support tool widely used by government agencies for evaluating, assessing, and prioritizing project alternatives in circumstances where conflicting and competing objectives are to be achieved. MCDA techniques offer a systematic procedure for treating a complex project selection problem into a group of simpler subproblems for identifying the best project alternative from an available pool of options while ensuring that the concerns of stakeholders are adequately addressed. MCDA is implemented in several stages, focusing on identification of project alternatives, definition of relevant criteria, assessment of performance of alternatives based on those objectives, and identification of the best alternative(s). There are several techniques available for implementing MCDA; however, each of these techniques varies in the manner stakeholder inputs are gathered and handled. Hence, the selection of a method must be based on a comprehensive analysis of the advantages and limitations of available MCDA techniques.@en

Unanticipated events such as natural disasters, terrorist attacks, cyber-attacks, and so forth, could cause prolonged disruptions in major utility service networks including, for example, water and electricity, in urban areas. Owing to the presence of complex interdependencies among infrastructure systems in an urban network, the disruption of one system may trigger a chain of events that degrades the proper functioning of several other dependent systems. Consequently, many parts of the city may not have access to multiple utility services and amenities. Identifying the most vulnerable communities exposed to such utility disruptions is key to performing immediate relief operations. In this paper, the concept of Priority Index, introduced as a measure of the susceptibility of communities to the event, is presented to rank urban regions based on the extent of the impact of disruptions (both cascading and interdependent impacts) caused by an event, as well as the social vulnerability of communities. Agent-based models are employed to simulate the consequences of a disruptive event on a semi-realistic urban infrastructure network. Later, the extent of impact on communities is evaluated using the simulation results and the American Community Survey data. The proposed Priority Index could help city administrations and utility service agencies identify the regions in a city that require immediate attention after a disruptive event occurs in the infrastructure network. A case study based on a semi-realistic infrastructure network in Austin, Texas is presented to demonstrate the implementation of the concept of Priority Index and the methodological framework.@en

Extreme weather events may disrupt port and coastal freight operations, resulting in direct and indirect economic losses to ports, supporting infrastructure, and reliant industry systems. Therefore, understanding the existing resilience capacity of the Texas port system to extreme weather events is necessary. To accomplish this, the weather hazards present along the Texas Gulf Coast are quantified. Stakeholder workshops, surveys, and interviews inform our understanding of the current status of resilience practice in Texas ports. Frameworks for assessing the criticality, vulnerability, exposure, risk, and resilience of the Texas port system are developed. The economic impacts of port disruptions from hurricanes are quantified using input-output tables. A tool, PortRESECO, is developed to be used by port stakeholders to assess the resilience of a port facility and view the results of the economic analysis. Finally, recommendations are made for implementation to increase the resilience of coastal freight operations in Texas.@en