Resilience Metrics Research Articles

Comparative resilience of US and EU meat processing to the Covid19 pandemic

Despite the similarities in the drop and recovery patterns of US and EU meat production during the Covid19 pandemic, the literature in the two continents assessed their respective meat processing industries’ resilience differently - resilient in the EU and non-resilient in the US but offered no resilience metrics in support of their assessments. We cast the differing transatlantic views in the context of the economic resilience literature and operationalize several metrics for cattle, pig, and chicken slaughter. We find the US less resilient than the EU in cattle and pig slaughter. Resilience in chicken slaughter depends on which metric is used. We discuss possible drivers of resilience and highlight how our metrics can be used for further research to inform resilience policy on both continents.

Resilience assessment of water distribution networks exposed to substance intrusion

ABSTRACT Resilience in water distribution systems is the ability to detect, respond and recover from disruptive events and calamities, such as extreme weather, natural disasters, human errors, and malicious attacks. A system’s resilience can be influenced by its topology. This study applies a novel network generation procedure based on the C-town benchmark network to produce 296 network variants with similar structures yet unique topological features. Then, a stress-testing procedure is applied to expose these variants to a set of substance intrusion scenarios. Finally, their performance is ranked according to a set of resilience metrics, and their relationship to the network design is evaluated based on topological attributes through correlation analysis. The results suggest that specific topological attributes, often characterized as a branched network design, tend to be more resilient when exposed to quality-related failures. As such, it provides insights into the relationship between topology and resilience of water distribution systems.

Multi-stage collaborative resilient enhancement strategy for coupling faults in distribution cyber physical systems

Due to the strong interdependence of cyber systems and physical systems, cyber-physical systems (CPS) often face serious coupling faults in case of extreme natural disasters. To improve the ability of the distribution CPS to withstand extreme events and rapidly resume normal operation, it is critical to consider effective recovery processes and formulate the collaborative resilience enhancement strategy of CPS. In this paper, a sequential multi-stage collaborative recovery strategy from CPS faults caused by natural disasters is proposed, considering the interdependence of the cyber system and physical system at the layers of facilities, topologies and functions, and the coupling relationship of power grid integration in the recovery process. In the implementation of this strategy, on the cyber side, based on the demand of power grid integration recovery, the communication optical fiber aims to be repaired as quickly as possible to control the power grid Remote Controlled Switches (RCS). On the physical side, the power grid is restored with the objective to reduce loss of load, considering the whole process of degradation, response and recovery during fault handling in coordination. Resilience metrics based on the proposed method are introduced to evaluate the resilience level, as the absorption rate, adaptation rate and energy supply rate. IEEE-33 system is used as an example to show that compared with the traditional recovery strategy that considers only the power grid, the proposed strategy can significantly improve the system's ability to face disasters at the early stage, maintain power supply after the disaster, and speed up the overall load recovery.

Resilience assessment framework toward interdependent bus–rail transit network: Structure, critical components, and coupling mechanism

Understanding the interdependent nature of multimodal public transit networks (PTNs) is vital for ensuring the resilience and robustness of transportation systems. However, previous studies have predominantly focused on assessing the vulnerability and characteristics of single-mode PTNs, neglecting the impacts of heterogeneous disturbances and shifts in travel behavior within multimodal PTNs. Therefore, this study introduces a novel resilience assessment framework that comprehensively analyzes the coupling mechanism, structural and functional characteristics of bus–rail transit networks (BRTNs). In this framework, a network performance metric is proposed by considering the passengers’ travel behaviors under various disturbances. Additionally, stations and subnetworks are classified using the k-means algorithm and resilience metric by simulating various disturbances occurring at each station or subnetwork. The proposed framework is validated via a case study of a BRTN in Beijing, China. Results indicate that the rail transit network (RTN) plays a crucial role in maintaining network function and resisting external disturbances in the interdependent BRTN. Furthermore, the coupling interactions between the RTN and bus transit network (BTN) exhibit distinct characteristics under infrastructure component disruption and functional disruption. These findings provide valuable insights into emergency management for PTNs and understanding the coupling relationship between BTN and RTN.

Resilience assessment of chemical processes using operable adaptive sparse identification of systems

Ensuring resilience in process systems is essential for safe and sustainable operations. Resilience is a property of the system which is characterized by the absorption, adaptation, and recovery performances of the system. Fault prognosis predicts the system's behavior after the occurrence of a fault and the time to failure which in-turn helps in determining the intervention strategies for restoring the system to its normal operating conditions. In the proposed framework, an adaptive modeling technique called operable adaptive sparse identification of system is implemented for fault prognosis. The time to failure of the system is determined based on the predicted system behavior. The system's absorption, adaptation, and recovery performances are modeled for different available intervention strategies, and they are evaluated based on a resilience metric. A case study is conducted on a batch reactor in thermal runaway condition and various intervention strategies are employed to demonstrate the applicability of the framework.

Resilience-oriented recovery of flooded road networks during mega-sport events: a novel framework

This research presents a novel framework that employs the resilience index and metrics, building on previous studies, to conduct a sequential evaluation of flooded road network performance at each timestep throughout the recovery stage. Subsequently, the mathematical integration of the performance-time diagram produces the Recovery Resilience Index (RRI) used to evaluate any recovery strategy/sequence. Furthermore, a sensitivity analysis ensures the consistency of the results. As a case study, this research evaluated three post-flood interventions: Flood-susceptibility-based; Centrality-based; and Critical-links-first recovery strategies, within the context of the FIFA World Cup in Qatar, 2018. The results showed that the best strategy to be dealing with the critical-links-first and flood-susceptibility strategy to be the worst. The suggested framework will expand the options for recovery planning and resource allocation. Also, it will provide the policymakers with a recommendation to improve the recovery process during FIFA World Cup in Qatar, 2018 and any future mega event.

Declining tree growth resilience mediates subsequent forest mortality in the US Mountain West.

Climate change-triggered forest die-off is an increasing threat to global forests and carbon sequestration but remains extremely challenging to predict. Tree growth resilience metrics have been proposed as measurable proxies of tree susceptibility to mortality. However, it remains unclear whether tree growth resilience can improve predictions of stand-level mortality. Here, we use an extensive tree-ring dataset collected at ~3000 permanent forest inventory plots, spanning 13 dominant species across the US Mountain West, where forests have experienced strong drought and extensive die-off has been observed in the past two decades, to test the hypothesis that tree growth resilience to drought can explain and improve predictions of observed stand-level mortality. We found substantial increases in growth variability and temporal autocorrelation as well declining drought resistance and resilience for a number of species over the second half of the 20th century. Declining resilience and low tree growth were strongly associated with cross- and within-species patterns of mortality. Resilience metrics had similar explicative power compared to climate and stand structure, but the covariance structure among predictors implied that the effect of tree resilience on mortality could partially be explained by stand and climate variables. We conclude that tree growth resilience offers highly valuable insights on tree physiology by integrating the effect of stressors on forest mortality but may have only moderate potential to improve large-scale projections of forest die-off under climate change.

Disruption-resilient supply chain entities with decentralized robust-stochastic capacity planning

This paper presents decentralized capacity planning models for four types of supply chain entities, classified based on functional similarities, to enhance their resilience. Novel resilience metrics are devised to quantify the proximity of backed-up, fortified, and recovered capacities to the amount of disrupted capacity by analyzing post-disruption events (i.e., absorption, adaptation, and restoration). Based on these metrics, bi-objective optimization models are developed to maximize resilience and cost-efficiency simultaneously to select proper business continuity plans contributing to supply chain entities’ proactive and reactive resilience. Finally, a robust-stochastic optimization method is tailored to address the uncertainties associated with the available time to recover and the impact and occurrence of disruptions. Also, disruption scenarios are simulated using a discrete-time Markov chain. Computational tests confirm the proposed models’ robustness, validity, and generality. The results describe essential features of business continuity plans, prescribe optimal decisions to enhance resilience, and predict how sensitive the resiliency and the imposed costs are to disruptions or proactive/reactive planning.

Microgrids and distribution system resilience assessment: A multi-objective robust-stochastic optimization approach considering social behavior using a deep learning modeling

In this paper, a new multi-objective two-stage robust-stochastic (MOTSRS) optimization approach for assessing microgrids and distribution system resilience is proposed. A novel approach based on deep learning is presented to model social behavior, specifically Social Reactions to Disasters (SRDs), in assessing microgrid and distribution system resilience. It is initially demonstrated that the proposed robust-stochastic approach is efficient for assessing the resilience of microgrids and distribution systems. A new resilience criterion is also proposed to be used for sensitivity analyses at the conservative level of the model. Subsequently, the effect of SRDs on system resilience is investigated through a supervised deep regression method. First, a large dataset is generated through a proposed nested Monte Carlo simulation. A numerical feature and five binary social behavior features are included in the dataset. Next, a feedforward neural network is utilized to predict the load during both normal and disaster circumstances based on the six features. The proposed supervised deep regression model with mean squared errors between 2e-5 and 2e-6 is capable of predicting electrical load during normal and disaster events. It is concluded that the resilience metrics of MGs, as well as expected operation costs and regret values, are influenced by the SRD effect.

The interdependent networked community resilience modeling environment (IN-CORE)

In 2015, the U.S National Institute of Standards and Technology (NIST) funded the Center of Excellence for Risk-Based Community Resilience Planning (CoE), a fourteen university-based consortium of almost 100 collaborators, including faculty, students, post-doctoral scholars, and NIST researchers. This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE - the Interdisciplinary Networked Community Resilience Modeling Environment (IN-CORE). IN-CORE enables communities, consultants, and researchers to set up complex interdependent models of an entire community consisting of people, businesses, social institutions, buildings, transportation networks, water networks, and electric power networks and to predict their performance and recovery to hazard scenario events, including uncertainty propagation through the chained models. The modeling environment includes a detailed building inventory, hazard scenario models, building and infrastructure damage (fragility) and recovery functions, social science data-driven household and business models, and computable general equilibrium (CGE) models of local economies. An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform.Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population, economics, physical services, and social services. An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas (CSA) that encompass an array of community resilience metrics (CRM) and support community resilience informed decision-making. Each testbed within IN-CORE has been developed by a team of engineers, social scientists, urban planners, and economists. Community models, begin with a community description, i.e., people, businesses, buildings, infrastructure, and progresses to the damage and loss of functions caused by a hazard scenario, i.e., a flood, tornado, hurricane, or earthquake. This process is accomplished through chaining of modular algorithms, as described. The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models, which have been the least studied area of community resilience but arguably one of the most important. Communities can then test the effect of mitigation and/or policies and compare the effects of “what if” scenarios on physical, social, and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.

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.

Measuring Resilience in Smart Infrastructures: A Comprehensive Review of Metrics and Methods

In today’s world, the safety, economic prosperity, and social well-being of nations depend heavily on highly interconnected critical infrastructures. These infrastructures encompass power networks, natural gas systems, communication networks, water treatment facilities, and transportation systems. Gaining insight into the behavior of these infrastructures, particularly during stress or attacks, has become crucial for both the private and public sectors. Ensuring an adequate level of functionality during emergencies, such as disasters, is also a priority, which can be attained by enhancing infrastructure resilience. Resilience metrics and models play a significant role in understanding the complex interplay between the behaviors and operational characteristics of interdependent critical infrastructures. Additionally, these models and metrics must demonstrate the interdependencies among infrastructures to provide a more comprehensive representation of infrastructure resilience. This paper reviews, categorizes, and presents resilience metrics and models for Smart Interdependent Critical Infrastructures (Smart ICIs). This paper provides a comprehensive evaluation of various resilience models and measurements tailored specifically for interdependent critical smart infrastructures. It includes the essential terminology and definitions related to the resilience of Smart ICIs, investigates the universally recognized phases and capabilities of resilience, and examines the various types of failures that could potentially affect Smart ICIs.

Risk‐averse optimization and resilient network flows

AbstractWe propose an approach to constructing metrics of network resilience, where resilience is understood as the network's amenability to restoring its optimal or near‐optimal operations subsequent to unforeseen (stochastic) disruptions of its topology or operational parameters, and illustrated it on the examples of the resilient maximum network flow problem and the resilient minimum cost network problem. Specifically, the network flows in these problems are designed for resilience against unpredictable losses of network carrying capacity, and the mechanism of attaining a degree of resilience is through preallocation of resources toward (at least partial) restoration of the capacities of the arcs. The obtained formulations of resilient network flow problems possess a number of useful properties, for example, similarly to the standard network flow problems, the network flow is integral if the arc capacities, costs, and so forth, are integral. It is also shown that the proposed formulations of resilient network flow problems can be viewed as “network measures of risk”, similar in properties and behavior to convex measures of risk. Efficient decomposition algorithms have been proposed for both the resilient maximum network flow problem and the resilient minimum cost network flow problem, and a study of the network flow resilience as a function of network's structure has been conducted on networks with three types of topology: that of uniform random graphs, scale‐free graphs, and grid graphs.

Power Distribution System Outage Management Using Improved Resilience Metrics for Smart Grid Applications

Smart grid systems play a significant role in improving the resilience of distribution systems (DSs). In this paper, two strategies are proposed for implementation of a smart grid application: (a) a network reconfiguration and (b) a network reconfiguration with mobile emergency generator (MEGs) deployment. An improved set of resilience metrics to quantify and enhance the resiliency of distribution systems (DSs) is developed for the proposed optimization. The metrics aim to determine a suitable strategy and the optimal number and capacity of MEGs to restore the disconnected loads through the development of several microgrids. These metrics are then aggregated with the proposed strategy to develop an automated solution provider. The objective is to maximize system resilience considering the priority loads. The proposed resilience metrics are tested on the IEEE 33-Bus radial DSs. The case studies conducted proved the performance of the proposed power outage management strategy and resilience metrics in maximizing system resiliency for smart grids.

Resiliency Metrics for Monitoring and Analysis of Cyber-Power Distribution System With IoTs

The electric grid operation is constantly threatened with natural disasters and cyber intrusions. The introduction of Internet of Things (IoTs) based distributed energy resources (DERs) in the distribution system provides opportunities for flexible services to enable efficient, reliable and resilient operation. At the same time, IoT based DERs comes with cyber vulnerabilities and requires cyber-power resiliency analysis of the IoT-integrated distribution system. This work focuses on developing metrics for monitoring resiliency of cyber-power distribution system, while maintaining consumers’ privacy. Here, resiliency refers to the system’s ability to keep providing energy to the critical load even with adverse events. In the developed cyber-power Distribution System Resiliency (DSR) metric, the IoT Trustability Score (ITS) considers the effects of IoTs using a neural network with federated learning. ITS and other factors impacting resiliency are integrated into a single metric using Fuzzy Multiple-Criteria Decision Making (F-MCDM) to compute Primary level Node Resiliency (PNR). Finally, DSR is computed by aggregating PNR of all primary nodes and attributes of distribution level network topology and vulnerabilities utilizing game-theoretic Data Envelopment Analysis (DEA) based optimization. The developed metrics will be valuable for i) monitoring the distribution system resiliency considering a holistic cyber-power model; ii) enabling data privacy by not utilizing the raw user data; and iii) enabling better decision-making to select the best possible mitigation strategies towards resilient distribution system. The developed ITS, PNR, and DSR metrics have been validated using multiple case studies for the IoTs-integrated IEEE 123 node distribution system with satisfactory results.

Regional seismic risk and resilience assessment: Methodological development, applicability, and future research needs – An earthquake engineering perspective

Given the devastating losses incurred by past major earthquake events together with the ever-increasing global seismic exposures due to population growth and urbanization, strategic decision-support tools are required to help stakeholders make more contemplated decisions that promote seismic resilience of the built environment. Such decision-support is enabled by regional seismic risk and resilience assessment, which holistically incorporates the various underlying physics processes and uncertainties for quantitative and probabilistic assessment of regional seismic hazard impacts, and has thereby attracted increasing research focus over the past twenty years. As a significant departure from the traditional site-specific assessment, where only an individual structure and site-specific seismic hazard are of interest, such regional-level assessment introduces additional dimensions and complexity. To date, there is a lack of review studies summarizing the related research advancements in seismic risk and resilience assessment from a regional-level perspective in the context of earthquake engineering. This study fills this gap by conducting a systematic review covering: the methodological development of regional seismic risk assessment (RSRiA) across its key modules, including hazard analysis, exposure modeling, fragility assessment, and consequence evaluation, as well as the associated uncertainty quantification and propagation; the development of resilience metrics, restoration modeling and planning in regional seismic resilience assessment (RSReA); and the applicability of existing computational workflows. Insights into the features, applicability, compatibility, and limitations of existing models and tools are provided. This study also highlights the challenges and future directions toward further advancing the research frontiers of RSRiA and RSReA.

Loss of growth resilience towards the alpine shrubline

The loss of resilience and prolonged recovery times after extreme climate events can be used as early warning signals of impending tipping points or abrupt irreversible changes. However, evidence of such critical slowing down in growth series is still lacking in terrestrial ecosystems under harsh environmental conditions such as alpine shrublands. Alpine shrublines, at which shrublands shift into grasslands under climate thresholds, constitute unique settings to look for critical slowing down phenomena. On the northeastern Tibetan Plateau, we sampled Salix oritrepha Schneid. (willow shrubs) in three elevational sites along each of two transects (QL, Qilian; HSX, Huashixia) running from alpine treeline position to shrubline. We then quantified the radial growth resilience and recovery indices of alpine willow shrubs to cold spells along elevational gradients by using dendrochronology. Willow growth was primarily constrained by low winter temperatures in QL and low summer temperatures in HSX. Cold spell events were detected in 2008 and 2011 in HSX and QL sites, respectively. Because temperature decreases with elevation, both growth resilience and recovery indices differed within each transect, showing a decreasing trend with elevation. Growth in 78% and 56% willow shrubs recovered back to the level before cold spell within two years in QL and HSX sites, respectively. Moreover, the average growth recovery period was longer at the shrubline (ca. 2–4 years) than near the treeline position (ca. 1–2 years). Critical slowing down theory could thus be applied to assess growth resilience towards the alpine shrubline, a thermal threshold for shrub growth. Our results and framework highlight the priority to directly measure the resilience metrics and also offer a promising perspective that the critical slowing down phenomenon may be universal in various ecosystems.

Global maximum flow based time-domain simulation method for evaluating power system resilience

With climate change, extreme weather is occurring frequently, which can cause serious damage to energy supply systems. It is of great significance to study the performance of power transmission system under extreme weather for prevention and reinforcement before and after disasters. This paper proposes a time-domain simulation model for quantifying power system resilience under extreme typhoon scenario. Firstly, the power system resilience assessment method based on global maximum flow (GMF) is presented, which is applied to map the overall system operation performance via creating a network indicator while considering component fragility, system topology, and power flow. A specific resilience expression (RES) is proposed to describe percentage improvement of each component in the resilience metrics by considering the overall system performance. Secondly, power system vulnerability model and time-domain simulation methodology are established. The proposed method is demonstrated by four case studies using the IEEE-30 node test system under specific typhoon scenarios. From the GMF based RES index, the component repair strategy is formulated accordingly. The proposed restoration strategy, compared with the traditional expert strategy can effectively measure the resilience contribution of each component to the whole power system, and minimizes the power system performance losses due to extreme typhoon. The proposed approach quantitatively evaluates real-time system resilience to extreme typhoons, and it can also identify the system critical areas and assess the benefits of different resilience enhancement strategies. The resilience assessment framework can also be applied to other extreme scenarios and contribute to planning and construction of strong smart grid in the future.

Why Do We Need Food Systems Informatics? Introduction to This Special Collection on Smart and Connected Regional Food Systems

Public interest in where food comes from and how it is produced, processed, and distributed has increased over the last few decades, with even greater focus emerging during the COVID-19 pandemic. Mounting evidence and experience point to disturbing weaknesses in our food systems’ abilities to support human livelihoods and wellbeing, and alarming long-term trends regarding both the environmental footprint of food systems and mounting vulnerabilities to shocks and stressors. How can we tackle the “wicked problems” embedded in a food system? More specifically, how can convergent research programs be designed and resulting knowledge implemented to increase inclusion, sustainability, and resilience within these complex systems, support widespread contributions to and acceptance of solutions to these challenges, and provide concrete benchmarks to measure progress and understand tradeoffs among strategies along multiple dimensions? This article introduces and defines food systems informatics (FSI) as a tool to enhance equity, sustainability, and resilience of food systems through collaborative, user-driven interaction, negotiation, experimentation, and innovation within food systems. Specific benefits we foresee in further development of FSI platforms include the creation of capacity-enabling verifiable claims of sustainability, food safety, and human health benefits relevant to particular locations and products; the creation of better incentives for the adoption of more sustainable land use practices and for the creation of more diverse agro-ecosystems; the wide-spread use of improved and verifiable metrics of sustainability, resilience, and health benefits; and improved human health through better diets.

Resilience and sustainability-informed probabilistic multi-criteria decision-making framework for design solutions selection

In this paper, a novel probabilistic multi-criteria decision-making (MCDM) framework for sustainable and resilient building design solutions selection is developed to optimize alternatives or rehabilitation strategies in a life cycle context. The proposed framework holistically takes into account the buildings' resilience metrics, environmental sustainability, and energy consumption. The life cycle economic cost, global warming potential (GWP), primary energy use (PEU), and social impact are used to represent the buildings’ resilience and sustainability. The second-generation Performance-based Earthquake Engineering procedure and FEMA P-58 method for time-based seismic performance analyses are used to evaluate earthquake-induced impact. The whole-building energy modeling (BEM) is adopted to assess energy use during the operational phase. In addition, life cycle analysis (LCA) and life cycle cost analysis (LCCA) are carried out to assess the life cycle performance of different design alternatives regarding environmental impacts and cost, respectively. Aiming to consider the various uncertainties in the decision-making process, a novel MCS-MCDM method is developed by combining the Monte Carlo simulation (MCS) with the traditional MCDM method. The proposed framework is applied to analyze hypothetical cases considering different geographic locations, structural systems, and building envelope systems, and recommendations are given according to the analysis results. The case study demonstrates the advantages of the proposed framework over traditional frameworks and its guidance for architectural design decisions.