Strategies for Prioritizing Needs for Accelerated Construction after Hazard Events

There is a need for rapid and responsive infrastructure repair and construction after natural disaster events such as hurricanes, wildfires, and tornadoes. These natural disasters often shut down basic infrastructure systems, as experienced recently in several Region 6 states as well as in other states around the country. Accelerated construction practices are often used in these situations to speed up the traditional, and often slow, project delivery process. However, after a natural disaster, several and different types of transportation infrastructure components are in need of inspection, rehabilitation or reconstruction, and transportation agencies are challenged with the task of prioritizing these accelerated projects. This study conducted an extensive literature review of current accelerated methods, infrastructure prioritization practices, and institutional barriers. Interviews with professionals from the transportation industry, including both private and public services, were conducted. Significant input from the railroad industry was used to compare private and public transportation systems responses after disasters. The results of this survey were used to quantify the importance of the accelerate methods and prioritization criteria, and which are the barriers to implement a prioritization model. Lastly, a decision support tool for prioritizing needs for accelerated construction after disaster events, specifically hurricanes and flooding, which commonly affect Region 6, was developed using the data collected

GIS-based methodology for prioritization of preparedness interventions on road transport under wildfire events

Climate change is leading to a rise in the occurrence and intensity of wildfires, exacerbated by the growing encroachment of communities into the natural environment, posing challenges to our global capacity to respond to wildfires. During wildfire events, road transport infrastructure becomes crucial for the evacuation of people and accessibility to an emergency by first responders. Nevertheless, resilience management of transportation infrastructure affected by wildfires is poorly considered, despite its relevant role and high exposure to wildfires. Therefore, this study proposes a new methodology to estimate the priority level for wildfire preparation by combining exposure and criticality of road transportation infrastructure to wildfire hazards with consideration of different wildfire categories. The analysis is conducted at the system level considering interdependencies and redundancies among infrastructure components and using a geographic information system (GIS) to automate the modelling process and visualization of results. The proposed methodology is applied to a case study in the Leiria region of Portugal, demonstrating its utility in prioritizing economic resources and decision-making for areas requiring preparation. This approach can serve as a resilience-based tool for decision-making, supporting the implementation of effective adaptation strategies to enhance wildfire resilience.This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020. This work is financed by national funds through the Foundation for Science and Technology (Fundaç˜ao para a ciˆencia e tecnologia, FCT, Portugal), under grant agreement 2020.05755.BD attributed to the first author

Safe-To-Fail Infrastructure for Resilient Cities under Non-Stationary Climate

abstract: Motivated by the need for cities to prepare and be resilient to unpredictable future weather conditions, this dissertation advances a novel infrastructure development theory of “safe-to-fail” to increase the adaptive capacity of cities to climate change. Current infrastructure development is primarily reliant on identifying probable risks to engineered systems and making infrastructure reliable to maintain its function up to a designed system capacity. However, alterations happening in the earth system (e.g., atmosphere, oceans, land, and ice) and in human systems (e.g., greenhouse gas emission, population, land-use, technology, and natural resource use) are increasing the uncertainties in weather predictions and risk calculations and making it difficult for engineered infrastructure to maintain intended design thresholds in non-stationary future. This dissertation presents a new way to develop safe-to-fail infrastructure that departs from the current practice of risk calculation and is able to manage failure consequences when unpredicted risks overwhelm engineered systems. This dissertation 1) defines infrastructure failure, refines existing safe-to-fail theory, and compares decision considerations for safe-to-fail vs. fail-safe infrastructure development under non-stationary climate; 2) suggests an approach to integrate the estimation of infrastructure failure impacts with extreme weather risks; 3) provides a decision tool to implement resilience strategies into safe-to-fail infrastructure development; and, 4) recognizes diverse perspectives for adopting safe-to-fail theory into practice in various decision contexts. Overall, this dissertation advances safe-to-fail theory to help guide climate adaptation decisions that consider infrastructure failure and their consequences. The results of this dissertation demonstrate an emerging need for stakeholders, including policy makers, planners, engineers, and community members, to understand an impending “infrastructure trolley problem”, where the adaptive capacity of some regions is improved at the expense of others. Safe-to-fail further engages stakeholders to bring their knowledge into the prioritization of various failure costs based on their institutional, regional, financial, and social capacity to withstand failures. This approach connects to sustainability, where city practitioners deliberately think of and include the future cost of social, environmental and economic attributes in planning and decision-making.Dissertation/ThesisDoctoral Dissertation Sustainability 201

Critical Infrastructure Protection Metrics and Tools Papers and Presentations

Contents: Dr. Hilda Blanco: Prioritizing Assets in Critical Infrastructure Systems; Christine Poptanich: Strategic Risk Analysis; Geoffrey S. French/Jin Kim: Threat-Based Approach to Risk Case Study: Strategic Homeland Infrastructure Risk Assessment (SHIRA); William L. McGill: Techniques for Adversary Threat Probability Assessment; Michael R. Powers: The Mathematics of Terrorism Risk Stefan Pickl: SOA Approach to the IT-based Protection of CIP; Richard John: Probabilistic Project Management for a Terrorist Planning a Dirty Bomb Attack on a Major US Port; LCDR Brady Downs: Maritime Security Risk Analysis Model (MSRAM); Chel Stromgren: Terrorism Risk Assessment and Management (TRAM); Steve Lieberman: Convergence of CIP and COOP in Banking and Finance; Harry Mayer: Assessing the Healthcare and Public Health Sector with Model Based Risk Analysis; Robert Powell: How Much and On What? Defending and Deterring Strategic Attackers; Ted G. Lewis: Why Do Networks Cascade

Quantifying restoration costs in the aftermath of an extreme event using system dynamics and dynamic mathematical modeling approaches

Extreme events such as earthquakes, hurricanes, and the like, lead to devastating effects that may render multiple supply chain critical infrastructure elements inoperable. The economic losses caused by extreme events continue well after the emergency response phase has ended and are a key factor in determining the best path for post-disaster restoration. It is essential to develop efficient restoration and disaster management strategies to ameliorate the losses from such events. This dissertation extends the existing knowledge base on disaster management and restoration through the creation of models and tools that identify the relationship between production losses and restoration costs. The first research contribution is a system dynamics inoperability model that determines inputs, outputs, and flows for roadway networks. This model can be used to identify the connectivity of road segments and better understand how inoperability contributes to economic consequences. The second contribution is an algorithm that integrates critical infrastructure data derived from bottom-up cost estimation technique as part of an object-oriented software tool that can be used to determine the impact of system disruptions. The third contribution is a dynamic mathematical model that establishes a framework to estimate post-disaster restoration costs from a whole system perspective. Engineering managers, city planners, and policy makers can use the methodologies developed in this research to develop effective disaster planning schemas and to prioritize post-disaster restoration operations --Abstract, page iv

Group-privacy threats for geodata in the humanitarian context

The role of geodata technologies in humanitarian action is arguably indispensable in determining when, where, and who needs aid before, during, and after a disaster. However, despite the advantages of using geodata technologies in humanitarianism (i.e., fast and efficient aid distribution), several ethical challenges arise, including privacy. The focus has been on individual privacy; however, in this article, we focus on group privacy, a debate that has recently gained attention. We approach privacy through the lens of informational harms that undermine the autonomy of groups and control of knowledge over them. Using demographically identifiable information (DII) as a definition for groups, we first assess how these are derived from geodata types used in humanitarian DRRM. Second, we discuss four informational-harm threat models: (i) biases from missing/underrepresented categories, (ii) the mosaic effect—unintentional sensitive knowledge discovery from combining disparate datasets, (iii) misuse of data (whether it is shared or not); and (iv) cost–benefit analysis (cost of protection vs. risk of misuse). Lastly, borrowing from triage in emergency medicine, we propose a geodata triage process as a possible method for practitioners to identify, prioritize, and mitigate these four group-privacy harms

A Survey on the Application of Evolutionary Algorithms for Mobile Multihop Ad Hoc Network Optimization Problems

Evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been applied to many optimization problems in a high number of scientific areas. In this survey paper, we focus on the application of evolutionary algorithms to solve optimization problems related to a type of complex network likemobilemultihop ad hoc networks. Since its origin, mobile multihop ad hoc network has evolved causing new types of multihop networks to appear such as vehicular ad hoc networks and delay tolerant networks, leading to the solution of new issues and optimization problems. In this survey, we review the main work presented for each type of mobile multihop ad hoc network and we also present some innovative ideas and open challenges to guide further research in this topic