MCDA-based Genetic Algorithms for Developing Disaster Resilient Designs of Critical Supply Networks

The electric power system is one of the most important critical infrastructures of a country. Recently, the number of natural and man‐made disasters is increased, which can impose extensive damages and costs to the power system. A resilient power system can withstand against, adapt to and recover from these disasters. Power system resilience is quantified by mathematical tools which are called “resilience metrics”. Currently, a lot of resilience metrics are proposed in the power system literature. In this paper, based on the extensive research in the critical infrastructure resilience literature which specifically concentrates on the “area‐based” resilience metrics, a new area‐based resilience metric is proposed which can measure the power system resilience considering the government policymaker criteria, which are rarely noticed before. The proposed and conventional area‐based resilience metrics are evaluated based on the real data from the 2012 Superstorm Sandy in the USA, which led to significant damage to the power distribution system. The simulation results show that the proposed area‐based resilience metric is very simple, can successfully address actual power system performance curves and is more meaningful and tangible than the conventional area‐based metrics for the government policymaker. The proposed area‐based resilience metric has also a general form and can be used for other critical infrastructures.

Link-level resilience analysis for real-world networks using crowd-sourced data

Resilient teams overcome sudden, dynamic changes by enacting rapid, adaptive responses that maintain system effectiveness. We analyzed two experiments on human-autonomy teams (HATs) operating a simulated remotely piloted aircraft system (RPAS) and correlated dynamical measures of resilience with measures of team performance. Across both experiments, HATs experienced automation and autonomy failures, using a Wizard of Oz paradigm. Team performance was measured in multiple ways, using a mission-level performance score, a target processing efficiency score, a failure overcome score, and a ground truth resilience score. Novel dynamical systems metrics of resilience measured the timing of system reorganization in response to failures across RPAS layers, including vehicle, controls, communications layers, and the system overall. Time to achieve extreme values of reorganization and novelty of reorganization were consistently correlated with target processing efficiency and ground truth resilience across both studies. Correlations with mission-level performance and the overcome score were apparent but less consistent. Across both studies, teams displayed greater system reorganization during failures compared to routine task conditions. The second experiment revealed differential effects of team training focused on coordination coaching and trust calibration. These results inform the measurement and training of resilience in HATs using objective, real-time resilience analysis.

Background/Objectives: The resilience of safety-critical systems is gaining importance due to the rise in cyber and physical threats, especially within critical infrastructure. Traditional static resilience metrics may not capture dynamic system states, leading to inaccurate assessments and ineffective responses to cyber threats. This work aims to develop a data-driven, adaptive method for resilience metric learning. Methods: We propose a data-driven approach using inverse reinforcement learning (IRL) to learn a single, adaptive resilience metric. The method infers a reward function from expert control actions. Unlike previous approaches using static weights or fuzzy logic, this work applies adversarial inverse reinforcement learning (AIRL), training a generator and discriminator in parallel to learn the reward structure and derive an optimal policy. Results: The proposed approach is evaluated on multiple scenarios: optimal communication network rerouting, power distribution network reconfiguration, and cyber–physical restoration of critical loads using the IEEE 123-bus system. Conclusions: The adaptive, learned resilience metric enables faster critical load restoration in comparison to conventional RL approaches.

This quantitative research investigates the integration of artificial intelligence (AI) into climate risk modeling to enhance national energy resilience and inform security planning. Climate risk modeling quantifies the probabilistic impacts of temperature anomalies, precipitation variability, and extreme weather events on energy systems, while AI provides the computational capacity to process multidimensional climatic and infrastructural datasets. Using a multi-site, longitudinal design and federated learning architecture, this study models the dynamic interdependencies among climate variables, grid performance, and defense-critical infrastructures. The results reveal strong correlations between temperature anomalies and outage frequency (r = 0.82, p < 0.01), as well as inverse relationships between precipitation variability and hydropower efficiency (r = –0.64, p < 0.05). Regression analysis indicates that climate predictors explain 71% of the variance in energy reliability outcomes (Adjusted R² = 0.71), which increases to 0.84 with the inclusion of AI-derived variables. Mediation testing demonstrates that energy resilience accounts for 42% of the indirect effect of climatic stressors on national security readiness. Reliability and validity assessments confirm strong internal consistency (Cronbach’s α > 0.85) and predictive accuracy exceeding 93%. These findings substantiate that AI-enhanced models outperform traditional statistical methods in forecasting energy system disruptions and quantifying resilience metrics. The research concludes that AI-driven climate modeling provides a scientifically verifiable foundation for anticipatory energy governance and national defense planning. Policy recommendations emphasize institutionalizing AI-based predictive analytics, establishing federated data-sharing infrastructures, and standardizing resilience metrics across critical energy networks. The study contributes to the evolving discipline of computational climate-security analytics by demonstrating how AI transforms environmental uncertainty into actionable intelligence for sustainable energy and national security systems.

The occurrence of natural disasters such as cyclones, earthquakes, and floods has increased worldwide, and their effect is most intense in localized regions. These events expose weaknesses in the power system infrastructures and show how well-prepared the system is to operate its services in a resilient way. During and after an extreme event, the outage and service disruption happens due to the inability of the affected part of the grid to cope with disruptions, which leads to insufficient resilience of the system. It is becoming more critical to enhance the resilience of electrical networks to extreme weather occurrences through suitable hardening processes and smart operational techniques. A reliable prevention approach necessitates a quantitative resilience metric that can estimate the effects of the future extreme events on distribution systems and assess the possible benefits of various planning strategies. This study aims to address the issue of lack of resilience metrics and proposes probabilistic metrics for evaluating the performance of distribution systems resilience in case of extreme weather events. Specifically, this study introduces active and passive resilience concepts to provide insights into system response. The proposed resilience metrics for various weather scenarios are quantified for the IEEE 33-bus system. The simulation framework also analyzes the effects of different operational and structural resilience improvement approaches on the proposed resilience metrics.

A synthesis of disaster resilience measurement methods and indices

Cyber-physical systems are an integral component of weapons, sensors, and autonomous vehicles, as well as cyber assets directly supporting tactical forces. Mission resilience of tactical networks affects command and control, which is important for successful military operations. Traditional engineering methods for mission assurance will not scale during battlefield operations. Commanders need useful mission resilience metrics to help them evaluate the ability of cyber assets to recover from incidents to fulfill mission essential functions. We develop 6 cyber resilience metrics for tactical network architectures. We also illuminate how psychometric modeling is necessary for future research to identify resilience metrics that are both applicable to the dynamic mission state and meaningful to commanders and planners.

Many development practitioners, academicians, development institutions and organizations, are attempting to discover the characteristic of resilience in Nepal. The issue of disaster resilience got its prominence in Nepali scholarship after the 2015 earthquake. The people of Nepal today, are visible to perpetual disaster events and profound vulnerability to the disaster, which was already there. Disaster incidents are increasing day by day, due to natural hazards such as landslides, floods, and human-induced activities like road accidents, fire, etc. But these consequences are not merely natural events; they are social events as well. The deaths of people, injuries, and property damage are related to the social side of the disaster. Similarly, natural hazards such as earthquakes are also creating disasters because of vulnerability and the absence of disaster resilience in Nepalese people. Rising incidents of disaster vulnerability and resilience is increasing concern of the state, academia and local level, since Nepal is 4th and 11th vulnerable to the risk of climate change and earthquakes in the world. Apart from that, other chances of disaster are equally mounting, and the capacity to cope (Resilience), or coming back to usual conditions is not enough for surviving and moving forward.
 
 This article tries to explore the relationship between vulnerability and disaster resilience, and the research questions of this article are, what types of vulnerability are creating the barrier for a resilient household and what are the attributes of a resilient family? Dhugin, Lamatar is the field for this research, and answers are dug out based on the Nepal earthquake 2015 as a major disaster of Nepal. Field data are collected after finalizing the purposive sampling. The interview method is used to take the depth information. Face-to-face interviews with informants remained the primary sources for data collection. And this research has been conducted using a qualitative method. My finding is: adopting capacity with the help of resources and assets and absorbing capacity by shifting occupation; after a disaster are the attributes of resilient households, whereas geographically vulnerability, exclusion, poverty remained barriers for resilient families.

Chapter 10 - Robust Backbone Design

Water distribution networks (WDNs) play a crucial role in the well-being of human populations and economic prosperity. It is essential that they cope with abnormal operating conditions and recover functionality quickly. Traditionally, WDNs are designed using cost and reliability objectives, but there is a lack of consensus on the definition and quantification of reliability which typically is a computationally intense process. Subsequently, various reliability-like metrics, called resilience indices, have been developed and demonstrated in the design of WDNs. Few studies exist that thoroughly evaluate the performance of the previously developed resilience metrics. This paper investigates three resilience metrics by evaluating their performance on three benchmark WDNs for several simulated mechanical failure states. The metrics studied are: (a) resilience index, (b) network resilience index (NRI), and (c) modified resilience index (MRI). The metric MRI performed better overall but NRI produced cheaper designs that performed better in the case of WDN-I. It is recommended that a better metric that incorporates different dimensions of resilience, such as robustness and redundancy, should be developed in the future.

The growing risk of natural and artificial or manufactured hazards combined with a lack of community preparedness have revealed the necessity for comprehensive and effective metrics for evaluating and improving a community’s resilience, i.e., the ability of communities to prepare for, withstand, and recover from disasters. In this paper, the authors review existing community resilience metrics and tools, classifying them into one of three main categories: community-level, sector-specific, or sociological. The paper provides short descriptions of each metric and comparisons across metrics within the three main categories and across classes. The authors assess the strengths and limitations of these metrics, discuss challenges in improving community resilience, and provide recommendations for the development of new measures of resilience. The paper concludes with an outlook on the future of community resilience, particularly the need for metrics that apply across hazards, geographic areas, and factor...

PurposeTo be able to continuously provide affordable services to consumers, managers of critical infrastructure (CI) maintenance supply networks have to balance investments in resilience with costs. At the same time, CI providers need to consider factors that influence resilience such as the geographical spread or the location of the network. This study aims to contextualize supply chain resilience knowledge by exploring how maintenance resource configurations impact resilience and costs in CI supply networks.Design/methodology/approachAn in-depth longitudinal single case study of a representative CI provider that has centralized its maintenance supply network is used. Data were collected before and after the change to evaluate the effect of the changes on the maintenance supply network.FindingsThis study shows that in this specific CI maintenance context, structural resource choices such as the quantity or location of spare parts and tools, the creation and exploitation of tacit knowledge and staff motivation impact both resilience and costs due to geographical spread, network location and other network properties.Originality/valueThis study extends general supply chain resilience knowledge to a new setting (i.e. CI) and shows how existing insights apply in this context. More specifically, it is shown that even in engineered supply networks there is a need to consider the effect of human agency on resilience as the creation and exploitation of tacit knowledge are of immense importance in managing the network. In addition, the relationship between normal accidents theory and high reliability theory (HRT) is revisited as findings indicate that HRT is also important after a disruption has taken place.

Summary: The UK Health Security Agency (UKHSA) has provided the Global Hazard Weekly Bulletin (GHWB) since 2012 to contribute to event-based surveillance (EBS) for all hazards. EBS is the organized and rapid capture of information about events that are a potential risk to public health (WHO 2008). A total of 445 bulletins were produced and released from October 8, 2012, to November 8, 2024. In accordance with GDPR, which governs the personal data of individuals, registration to receive the bulletin indicates that 20,741 subscribers (as of November 8, 2024) can now access this resource. The definition of ‘hazard’ is ‘a process, phenomenon or human activity that may cause loss of life, injury or other health impacts, property damage, social and economic disruption or environmental degradation’ and that has had a significant human impact, adopted by the United Nations General Assembly in February 2017. Each week, the GHWB team members work to identify hazardous events and disasters that have had a significant human impact that have occurred in the preceding seven days, which is collaboratively compiled and sent by UKHSA once a week with information collected from a variety of sources, including international media outlets, peer-reviewed literature, and reports from international organizations including United Nations (UN). The process of compiling, analyzing, and reviewing articles for inclusion in the bulletin has benefited the responsible team. The current team consists of members from several different teams across UKHSA, some of which are not focused on global hazards. Generating the GHWB provides some members of this group with the opportunity to enhance their understanding of the global hazards landscape. In addition, the process allows for the review and testing of the UNDRR/ISC Hazard Information Profiles (2021), which are currently being updated for the Global Platform for Disaster Risk Reduction in June 2025.

Although earthquakes have been recognised as major natural hazards with the potential to cause loss of life, property damage, and social and economic disruption in Canada, most risk and emergency managers still lack the necessary tools and guidance to adequately undertake rigorous risk assessments. Recently, Natural Resources Canada (NRCan) has adopted Hazus, a standardized best-practice methodology developed by the US Federal Emergency Management Agency (FEMA) for estimating potential losses from common natural hazards, such as earthquakes, floods, and hurricanes. Hazus combines science, engineering knowledge, and mathematical modelling with geographic information systems technology to estimate physical damage and economic and social losses. Besides the ground shaking, the earthquake model considers landslide, liquefaction, and fault rupture susceptibilities. Depending on the severity of the resulting transient ground motion and permanent ground deformation, five potential damage states (none, slight, moderate, extensive, complete) are employed to estimate the amount of structural damage and consequent economic and social losses. This note reports some of the typical features of the recently adapted Hazus earthquake model, with an emphasis on the considerations of earthquake-induced hazards, and overviews the ongoing activities and potential challenges in implementing this model in Canada.