Resilience Of Infrastructure Systems Research Articles

Using functional decomposition to bridge the design gap between desired emergent multi‐agent‐system resilience and individual agent design

AbstractIncreasing the resilience of modern infrastructure systems is recognized as a priority by both the International Council on Systems Engineering and the National Academy of Engineering. Resilience answers the key stakeholder need for a stable and predictable system by withstanding, adapting to, and recovering from unexpected faults. Increasing resilience in multi‐agent systems is especially challenging because resilience is an emergent system‐level property rather than the sum of individual agent functions. This paper uses biological systems as a source of inspiration for resilient functions, examining the central question How can biologically inspired design be used to increase the emergent property of resilience in multi‐agent systems? The paper uses functional decomposition to break down the individual functions that result in resilience and transfer the properties to generalized systems. Accordingly, the central hypothesis examined in this article is If functional decomposition is performed on eusocial insect colonies, then generalizable approaches to increase the emergent property of multi‐agent system resilience can be identified. The results provide two contributions. The first contribution is the identification of six general functions based on eusocial insect behavior that influence resilience. The second contribution is a description of the process of identifying and transferring insect behaviors into generalized design‐for‐resilience guidance. To support these contributions, a case study applies biologically inspired functions to an emergency power service system and proposes tactics for the power system to improve its resilience. Thus, this article provides a key step towards our goal of using biologically inspired design to influence the emergent property of resilience in multi‐agent systems.

Unraveling fundamental properties of power system resilience curves using unsupervised machine learning

Power system is vital to modern societies, while it is susceptible to hazard events. Thus, analyzing resilience characteristics of power system is important. The standard model of infrastructure resilience, the resilience triangle, has been the primary way of characterizing and quantifying resilience in infrastructure systems for more than two decades. However, the theoretical model provides a one-size-fits-all framework for all infrastructure systems and specifies general characteristics of resilience curves (e.g., residual performance and duration of recovery). Little empirical work has been done to delineate infrastructure resilience curve archetypes and their fundamental properties based on observational data. Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance. There is a dire dearth of empirical studies in the field, which hindered our ability to fully understand and predict resilience characteristics in infrastructure systems. To address this gap, this study examined more than two hundred power-grid resilience curves related to power outages in three major extreme weather events in the United States. Through the use of unsupervised machine learning, we examined different curve archetypes, as well as the fundamental properties of each resilience curve archetype. The results show two primary archetypes for power grid resilience curves, triangular curves, and trapezoidal curves. Triangular curves characterize resilience behavior based on three fundamental properties: 1. critical functionality threshold, 2. critical functionality recovery rate, and 3. recovery pivot point. Trapezoidal archetypes explain resilience curves based on 1. duration of sustained function loss and 2. constant recovery rate. The longer the duration of sustained function loss, the slower the constant rate of recovery. The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructure in extreme weather events.

Unraveling the dynamics of the cyber threat landscape: Major shifts examined through the recent societal events

The continuous expansion of cyberspace offers numerous advantages but also broadens the cybersecurity attack surface, thereby affecting the resilience of critical infrastructure and societal systems. Given the complexity of factors affecting cybersecurity, a data-centric approach is needed, which involves leveraging existing datasets on cyber incidents and utilizing advanced data analytics techniques for enhanced preparedness against evolving cyber threats. In this study, we optimize past cyberattack data for statistical analysis and introduce a methodology for detecting significant shifts in cyberattack patterns through intervention analysis. By applying this approach, we correlate major shifts in the cyber threat landscape over the past six years with critical societal events like COVID-19 and the Russia–Ukraine War, offering insights meaningful for both society and policy formulation.

Broadening resilience: An evaluation of policy and planning for drinking water resilience in 100 US cities

Around the world, drinking water systems provide safe, accessible drinking water to the communities they serve. While they are faced with a growing number of short and long-term challenges, assessing the resilience of drinking water systems—or their ability to cope with disturbances and surprise and continuously adapt to stress and change—is an ongoing challenge. Many drinking water resilience assessment methodologies focus narrowly on the technical dimensions of the resilience of infrastructure systems, ignoring the human or environmental dimensions, and consider resilience to the present, ignoring resilience to future change. To fill this gap, we developed a conceptual framework and scoring methodology for evaluating municipal-scale policy and planning for drinking water system resilience. Our approach considers social, technical, and environmental elements of resilience at broad spatial and temporal scales. We then used this methodology to assess policy and planning for drinking water resilience in 100 U.S. cities. We found that municipalities are at very different stages in their policy and planning for drinking water resilience, particularly in terms of the attention they give to climate change and their consideration of the broader social dimensions of resilience. Overall, larger cities and those with more liberal populations are likely to have higher policy and planning scores. The findings highlight the variation in municipal policy and planning for drinking water system resilience, and the importance of community characteristics as drivers of resilience planning. Our approach is transferable to assessing resilience for drinking water systems within and beyond the U.S.

A Proposal for a Federated Learning Protocol for Mobile and Management Systems

In this research paper, we introduce a federated learning communication protocol tailored for emergency management applications. Our primary objective is to tackle the communication challenges that arise in such critical scenarios. In order to overcome the limitations associated with centralized server architectures, we present an innovative communication protocol. This protocol empowers the framework to effectively cooperate with multiple centralized servers, fostering efficient knowledge sharing and model training while ensuring the utmost data privacy and security. By harnessing this protocol, our framework elevates the performance and resilience of vital infrastructure systems operating on the Android platform, thereby facilitating real-time operational scenarios. This research makes a substantial contribution to the field of emergency management applications, as we offer a comprehensive solution that optimizes communication and enables seamless collaboration with numerous centralized servers.

ResQ-IOS: An iterative optimization-based simulation framework for quantifying the resilience of interdependent critical infrastructure systems to natural hazards

Critical Infrastructure Systems are highly complex and interdependent. Growing complexity and interdependency between infrastructure systems and frequent exposure to extreme events have inevitably increased the probability of cascading failures and the prolonged lack of serviceability in urban communities, especially so for energy systems. The resilience analysis of interdependent infrastructure systems against natural hazards provides stakeholders with a comprehensive outlook on recovery strategies to minimize the damage costs and losses caused by extreme events. This paper introduces the ResQ-IOS, a Resilience Quantification Iterative Optimization-based Simulation (IOS) framework for quantifying the resilience of interdependent infrastructure systems to natural hazards with the capability of considering the real-world conditions for the status of infrastructure systems' components. The ResQ-IOS framework consists of five modules: risk assessment, simulation, optimization, database, and controller. To evaluate the capabilities of this framework, the seismic resilience of interdependent energy infrastructure networks (power, natural gas, and water) in Shelby County (TN), USA, was assessed. The results of the resilience analysis of the case study suggest that the water network is the best candidate for implementing pre-disaster Resilience Enhancement Measures (REMs), like increasing the supply capacity. Due to the controlling role of the power network in the community's recovery process, it is recommended that post-disaster REMs, such as increasing the number of Repair and Maintenance (R&M) teams, should be applied to the power network to speed up the restoration of failed components in that network and consequently, shorten the recovery duration of the community. The ResQ-IOS can be employed as a useful computational tool for planning the resilience-oriented sustainable development of urban communities by, for example, deploying Renewable Energy (RE)-based strategies to enhance their disaster resilience.

Interconnectedness enhances network resilience of multimodal public transportation systems for Safe-to-Fail urban mobility

The growing interconnectedness of urban infrastructure networks presents challenges to their ability to handle unforeseen disruptions, particularly in the context of extreme weather events resulting from climate change. Understanding the resilience of interconnected infrastructure systems is imperative to effectively manage such disruptions. This study investigates the role of interconnectedness in enhancing the resilience of public transportation systems in Hong Kong, a city heavily reliant on public transit. Our results demonstrate that interconnected transportation systems improve resilience by reducing topological vulnerabilities, increasing attack tolerance, and enhancing post-disruption interoperability. Findings also identify the potential to integrate vulnerable systems for greater robustness and highlight the marginal benefits of enhancing intermodal transfer. Strengthening interconnectedness among modes of urban public transit fosters a safe-to-fail system, presenting a distinct resilience-by-design approach. This complements conventional resilience-by-intervention approaches that focus on improving individual systems or introducing entirely new systems.

Old wine in new bottles? Understanding infrastructure resilience: Foundations, assessment, and limitations

While the trend towards building resilience in infrastructure systems for disaster risk reduction is accelerating, the application of infrastructure resilience is largely conceptual or an embellishment to established modelling techniques. In response, this paper sets out the theoretical foundations of infrastructure resilience, which broadly applies across critical infrastructure networks. This is followed by reviewing system-based and network-based approaches to resilience assessment with a focus on transport infrastructure, where there is an emerging body of studies. It spotlights critical issues in conflating resilience with other concepts of infrastructure safety management or merely tagging resilience on established methods, indicating a phenomenon of “old wine in new bottles”. Also, oversimplifying disruption scenarios does not provide a sufficient basis for informed resilience planning. This paper reminds readers to look beyond resilience as a mere buzzword and offers guidance on how to do so by recognising the theoretical and methodological cornerstones of infrastructure resilience.

Modelling principal-agent dilemma for management of resilience in interdependent infrastructure systems

Temporal dilemmas are observed between the short-term incentives of the asset managers, as agents, versus the long-term resilience driven goals of the organisations, who own and operate the infrastructure systems as principals. That is, the incentive mechanisms designed based on the short-term tenure of an asset manager may structurally deprive the infrastructure owner/operator organisations from directing resources towards their long-term resilience driven goals. Lack of long-term incentives by asset managers adds another layer to managment of resilience in the context of infrastructure systems in their life cycle. We proposed a framework for integrating resilience into asset management of infrastructure systems and modelled it to explore the impact of such temporal dilemmas in principal-agent relations and potential strategic responses. The model reflects the interdependency of the sub-systems of infrastructures to assess each sub-system's periodic coupled resilience. The proposed integrated simulation reflects coupled impacts of shocks and stressors within regular asset management regimes. The simulation results provide quantitative evidence of how managerial and political dimensions can impact management of resilience in the context of infrastructure systems and open another discourse towards considering the complexities of organising for resilience in socio-technical systems.

Designing resilient and economically viable water distribution systems: a multi-dimensional approach

Enhancing the resilience of critical infrastructure systems requires substantial investment and entails trade-offs between environmental and economic benefits. To this aim, we propose a methodological framework that combines resilience and economic analyses and assesses the economic viability of alternative resilience designs for a Water Distribution System (WDS) and its interdependent power and transportation systems. Flow-based network models simulate the interdependent infrastructure systems and Global Resilience Analysis (GRA) quantifies three resilience metrics under various disruption scenarios. The economic analysis monetizes the three metrics and compares two resilience strategies involving the installation of remotely controlled shutoff valves. Using the Micropolis synthetic interdependent water-transportation network as an example, we demonstrate how our framework can guide infrastructure stakeholders and utility operators in measuring the value of resilience investments. Overall, our approach highlights the importance of economic analysis in designing resilient infrastructure systems.

Probabilistic Assessment of Power Systems Resilience Under Natural Disasters

Extreme weather can have a substantial influence on a power system's operational resilience since they are high-impact, low-probability (HILP) events. Therefore, Power systems must be resilient to HILP incidents in addition to being reliable against widely spread and credible threats. Despite the rarity of such events, the severity of their potential impact necessitates the development of appropriate resilience assessment tools to capture their implications and enhance the resilience of energy infrastructure systems. In this paper, a probabilistic strategy is proposed to assess and evaluate the operational resilience of power distribution networks against the impacts of HILP events depending on value-at-risk and conditionally value-at-risk quantitative risk-based assessments. With several scenarios built on sequentially Monte Carlo simulations, the consequence of a windstorm on a distribution network can be assessed using a probability-based resilience assessment methodology. The presented method is examined on an IEEE 37-bus system. Different case studies based on detailed data are presented and analyzed to demonstrate the proposed method's usefulness.

Incorporating the human factor in modeling the operational resilience of interdependent infrastructure systems

Incorporating the human factor in modeling the operational resilience of interdependent infrastructure systems

Resilience and risk-based restoration strategies for critical infrastructure under uncertain disaster scenarios

Improving the resilience of critical infrastructure (CI) systems to natural disasters is essential for society's long-term development. Considering the uncertainty in disaster scenarios, this study first proposes a two-stage risk-neutral stochastic model to develop restoration resource deployment and allocation strategies with the objective of maximizing the expected value of resilience (EVR) of a CI system. Secondly, a risk metric is applied to assess the risk on the value of resilience imposed by the uncertainty. And a multi-objective optimization model for risk management is proposed, in which minimizing the value of the risk metric is formulated as a secondary objective to the EVR. The trade-offs between the EVR and the risk objective are assessed via the ε-constraint method. Finally, the utility of proposed models is illustrated using the electric power system of the Greater Toronto Area (GTA). The results show that: (i) there is a significant advantage in using risk-neutral strategies to improve system resilience over strategies without considering the characteristics of disaster scenarios; (ii) the value of the risk metric can be significantly reduced at the expense of decreasing EVR; and (iii) if the use cost of the resources is considered, there are optimal amounts of available resources for risk-neutral and risk-averse CI system operators. The models can assist CI system operators in selecting effective emergency response strategies for disasters with uncertain scenarios.

Using impact chains for assessing local climate risk—A case study on impacts of extended periods of fluvial low waters and drought on a metropolitan region

As the climate crisis accelerates, the resilience of Europe's aging critical infrastructure systems shifts more and more into focus. However, the rising interconnectedness of critical infrastructure systems and the dependency of their operation on multiple stakeholders makes approaches that target the resilience of isolated infrastructures insufficient and might even result in a decrease of the resilience of the whole system. This need for more resilience thinking in interconnected infrastructure systems has resulted in advances in risk analyses of supply chains and analyses of interdependencies in infrastructure systems from a Critical Infrastructure Protection/Resilience perspective. However, results from such analyses on the level of interconnected infrastructure systems have seldomly be broken down to the level of individual corporate value chains, a necessity as national/regional resilience efforts need to be supported on the local level. In this paper we therefore propose a novel approach for value chain climate risk and vulnerability analysis that combines a participatory, indicator-based approach with a semi-quantitative risk matrix approach to allow linking analyses from national to local scale and supports economic assessment of climate change impacts for individual businesses. This approach has been developed and prototypically applied in a case study in a German metropolitan area located at the Rhine River. The results allow to identify where along the dependency chains of interconnected infrastructure systems, hazards and impacts might manifest, which cascading (economic) impacts result on the level of individual infrastructure operators, and where resilience measures should be taken to be most effective and (cost) efficient.

The Adaptive Seismic Resilience of Infrastructure Systems: A Bayesian Networks Analysis

Earthquakes pose a significant threat to infrastructure systems. However, improving the seismic resilience of infrastructure systems in earthquake-prone regions is fraught with obstacles. First, this article reviews the current status of earthquake resilience research, points out the gaps of existing research, and then focuses on the adaptability in resilience. Secondly, five groups of influencing factors of infrastructure system adaptability are identified and clustered through literature review and expert knowledge. Thirdly, the structure and conditional probability table of the Bayesian network model are given in detail, and the evaluation model of Bayesian network adaptability is created. A Chinese earthquake-prone county was used to verify the applicability of the model. The research uses forward propagation analysis to calculate the adaptability of the case and obtains the probability of the case’s adaptability. The backward propagation to obtain the ranking of the influence degree of the critical influencing factors on the adaptability and the top three factors are respectively earthquake history, relevant information and contingency mechanisms. Finally, the research suggests measures to improve adaptability.

Considering curriculum, content, and delivery for adaptive pathways: higher education and disaster resilient infrastructure in the Indian urban context

ABSTRACT In the context of urbanisation in the Global South and increasing climate-induced disaster events, fostering resilience in infrastructure systems is critical to delivering on goals of economic development, poverty reduction, and climate action. Adaptive pathways, given its inherent consideration of uncertainty and an embedded feedback mechanism, becomes a necessary conceptual underpinning to deliver on the resilient infrastructure challenge. We argue that knowledge and iterative learning are key components that enable the flexibility of adaptive pathways. Higher education (HE) plays a critical role in influencing knowledge that is adaptive and dynamic to respond to this challenge. This study adopts a qualitative approach with a case study design to identify gaps in how urban resilient infrastructure is conceptualised and taught in HE institutions. . The study finds that interdisciplinarity, when reflected in the elements of content, pedagogy, and delivery would foster substantial critical thinking and reflexivity required to address the resilient infrastructure challenge.

Dissecting heterogeneous pathways to disparate household-level impacts due to infrastructure service disruptions

The objective of this study is to empirically and systematically assess the combination of inherent susceptibility factors, protective actions, and factors of hazard exposure that influence a household's level of hardship experienced due to disruptions in critical infrastructure services during disasters. Classification and regression tree (CART) decision tree models and survey data from three major hurricane events were used to: (1) identify the pathways leading to impact(s) due to service disruptions and explore the differences in pathways across vulnerable population groups; and (2) identify the points of intervention to mitigate well-being impacts in households due to disruptions in water, energy, food, and road transportation services. The results reveal how the associative pathways between these factors change between socioeconomic and demographic groups in the impacted community and for different infrastructure service system types. The findings suggest that not all vulnerable households experienced high hardship outcomes despite prolonged outages. Finally, the hardship pathways suggest recommendations for improving resilience in infrastructure systems in a more equitable manner. The findings can be used by emergency and infrastructure managers and operators to better prioritize resource allocation for hazard mitigation investments and restorations. Accordingly, this study contributes to the theory of human-centric infrastructure resilience.

Developing adaptive resilience in infrastructure systems: an approach to quantify long-term benefits

ABSTRACT To enable systems to adapt to changing future conditions, ensuring adaptive capacity in resilience planning is critical. Thispaper presents an approach to evaluate the long-term benefits of adaptive resilience in infrastructuresystems under future uncertainty. The methodology uses long timeframeassessment methods based on NPV and approaches to quantifying different levelsof uncertainty along with multi-criteria assessment methods. The approach is demonstrated usingthree case studies, where investments have focused on different aspects ofadaptive resilience in various infrastructure systems. The results demonstratethe increasing benefits of adaptive strategies over time with ongoing learningand the evolving nature of resilience needs. The presented approach can be usedby decision-makers in multiple infrastructure sectors. A flexible approach to evaluate the long-term benefits of building adaptive capacity to enhanceresilience, this methodology can be a useful tool for practitioners and policymakers topresent a business case for long-term adaptive resilience investments..

Impacts of varying network parameters on the vulnerability and resilience of interdependent critical infrastructure systems

ABSTRACT Critical infrastructure systems are complex and subjected to evolving risks and hazards, which makes anticipating their behavior difficult. To prioritize among actions that increase system resilience, it is critical to understand their impacts on parameters defining a network and on anticipated network performance. In this paper, the authors investigate the impacts of variations in three parameters on network vulnerability: component vulnerabilities, service interdependency redundancies, and system link configuration. The advances of this work compared to prior studies include: 1) The impacts of parameters varied across a range of values at the component level are evaluated considering component functionality and connectivity; 2) quantitative analyses of component performance as parameters vary are investigated based on system redundancies; and 3) probabilistic system interdependencies are analyzed through a Bayesian network that considers component pathways. Results quantify effects of changes in component vulnerabilities and dependencies and are used to discuss impacts on system resilience.

Probabilistic framework to quantify the seismic resilience of natural gas distribution networks

Natural Gas Distribution Networks (NGDNs) are complex infrastructure systems that play an essential role in ensuring the welfare and prosperity of many communities by serving their residential, commercial, industrial, and transportation sectors. Earthquakes can damage the critical components of NGDNs and disrupt their services. The disruption of NGDN services can lead to significant economic and social losses. These losses can be mitigated by adopting appropriate resilience enhancement measures. This study puts forward a probabilistic framework to quantify the seismic resilience of NGDNs. This framework comprises multiple interconnected probabilistic models that work in harmony to characterize the earthquake-induced damage to NGDN components, their post-earthquake recovery process, and the losses incurred by the community due to the disruption in the natural gas flow. It considers various uncertainties, including those associated with the earthquake location, time and intensity, the response of various NGDN components and the earthquake-induced damage they may sustain, the duration and cost of the recovery process, and the demand for natural gas during the recovery process. To appropriately model the complex behavior of the NGDNs, their dependency on power networks is also considered. A novel index for quantifying the resilience of infrastructure systems is also introduced that considers the recovery duration in addition to the losses incurred by the community. To showcase the capabilities of the proposed framework, it is used to evaluate the seismic resilience of the natural gas distribution network of a virtual community. This framework can be used to evaluate and compare various resilience enhancement strategies.