Quantification Of Resilience Research Articles

Flood Resilience Quantification Framework of Rural Communities: Case Study of Harlan County, Kentucky

Flood Resilience Quantification Framework of Rural Communities: Case Study of Harlan County, Kentucky

Resilience patterns in the analysis of social-ecological networks of small-scale fishing in a subtropical estuarine system

Network analysis concepts and tools are being increasingly applied to Social-ecological systems (SES) in human-environment and sustainability sciences. In this study, we used the Gao metric (βeff) to measure the resilience of networks in small-scale fisheries (SSF) in the Paranaguá Estuarine Complex (PEC). The purpose was to evaluate the variations and trends in network resilience across eight hydrological sectors of the PEC during different seasons. Our analysis revealed that the βeff metric varied significantly across the eight SES sectors, suggesting that resilience is not uniformly distributed. Notably, the sectors located internally the estuary exhibited lower resilience values than the others. The average trophic level of the fishery catches was the higher hierarchical position variable that most affected the network topological metrics, followed by variables magnitude of conflicts, water and sediment quality, and rainfall. Our hydrodynamic sectorization, reflecting ecological and social interactions, enabled us to identify distinct interaction networks within the PEC. This shows the complexity of managing these SES to ensure the sustainability and resilience of the system. While other variables and spatial and temporal scales could lead to different estimates and relationships, our findings shed light on the importance of resilience quantification and the interaction networks within SES to manage them effectively.

Integration and Quantification of Resilience and Sustainability in Engineering Projects

Integration and Quantification of Resilience and Sustainability in Engineering Projects

Quantification of the Seismic Resilience of Bridge Classes

Quantification of the Seismic Resilience of Bridge Classes

Resilience quantification and recovery strategy simulation for urban underground logistics systems under node and link attacks: A case study of Nanjing city

Urban Underground Logistics Systems (UULS) have become an emerging solution to mitigate urban surface traffic congestion, environmental pollution, and surface transport safety risks. However, during the operation of UULS, the use of advanced technologies such as the Internet of Things (IoT) introduces cybersecurity risks to the system. Moreover, severe natural disasters can also cause damage to underground transportation network links. Existing research and planning primarily concentrate on the system design and benefits of UULS, neglecting the system's service level under attack scenarios. This study outlines three representative UULS network prototypes and proposes a resilience quantification method centered on logistics efficiency. It also focuses on comparing the effectiveness of three recovery strategies. These strategies give priority to maximum flow, betweenness centrality, and regional importance, as well as the priority of node and link repairs. The resilience quantification method and recovery strategies are applied in a case study set in Nanjing City. The case study results reveal that the Two-echelon network shows exceptional resilience. Regarding the effectiveness of recovery strategies, the strategy based on maximum flow proves to be the most effective, and focusing on node repair can lead to higher system resilience. Based on these findings, this study offers recommendations to transportation and logistics management decision-makers, focusing on UULS resilience and recovery strategy selection. These recommendations are intended to provide valuable guidance for the planning and design of future UULS, ensuring their resilience and reliability.

Probabilistic dynamic resilience quantification for infrastructure systems in multi-hazard environments

Resilience has been evolving as a key criterion for infrastructure systems as it ensures the system's dynamic performance pre-, during, and post-hazard disruptions. However, estimating these performances is challenging due to system and operation complexities, and the probabilistic dynamic nature of infrastructure system. Moreover, infrastructure systems are usually exposed to multi-hazard environments, with their own probabilistic behavior, leading to additional complexity in terms of estimating the system response and, subsequently, the overall system resilience. As such, this study develops a probabilistic resilience-centric system dynamics modeling approach to quantify infrastructure dynamic resilience based on a holistic representation of infrastructure systems under multi-hazard scenarios, whereby the probabilistic natures of both the hazards and system are incorporated. Unlike the traditional resilience quantification approaches that represent system resilience by a single value calculated after the system's full recovery, the developed model focuses on tracking the temporal evolution of system resilience along the entire period of system performance deterioration and recovery. A real-world hydropower dam, as an example for infrastructure systems, in British Columbia, Canada is used as a demonstration application to show model utility in developing resilience-guided assessment plans for infrastructure systems. Overall, the developed approach empowers the decision-makers with insights into critical operational periods, the required time to reach specified resilience targets, and the efficiency of risk mitigation measures in real-time.

The Role of Delay Time in the Preliminary Assessment of the Seismic Resilience (SR) of a Bridge: A Case Study

The seismic resilience of bridges has become an important concept in civil engineering since these systems need to remain operative during and after earthquakes. In this regard, the definition of recovery needs to consider the delay time (named as the time between an event and the beginning of the recovery process). The original concept of seismic resilience has been expanded herein in order to account for the delay time of bridge configurations. Its role in the quantification of seismic resilience has been investigated by performing a case study of a Californian highway bridge subjected to an ensemble of 100 input motions. The results demonstrate that the delay time may significantly reduce the seismic resilience of bridges.

Breeding for resilience in finishing pigs can decrease tail biting, lameness and mortality

BackgroundPrevious research showed that deviations in longitudinal data are heritable and can be used as a proxy for pigs’ general resilience. However, only a few studies investigated the relationship between these resilience traits and other traits related to resilience and welfare. Therefore, this study investigated the relationship between resilience traits derived from deviations in longitudinal data and traits related to animal resilience, health and welfare, such as tail and ear biting wounds, lameness and mortality.ResultsIn our experiment, 1919 finishing pigs with known pedigree (133 Piétrain sires and 266 crossbred dams) were weighed every 2 weeks and scored for physical abnormalities, such as lameness and ear and tail biting wounds (17,066 records). Resilience was assessed via deviations in body weight, deviations in weighing order and deviations in observed activity during weighing. The association between these resilience traits and physical abnormality traits was investigated and genetic parameters were estimated. Deviations in body weight had moderate heritability estimates (h2 = 25.2 to 36.3%), whereas deviations in weighing order (h2 = 4.2%) and deviations in activity during weighing (h2 = 12.0%) had low heritability estimates. Moreover, deviations in body weight were positively associated and genetically correlated with tail biting wounds (rg = 0.22 to 0.30), lameness (rg = 0.15 to 0.31) and mortality (rg = 0.19 to 0.33). These results indicate that events of tail biting, lameness and mortality are associated with deviations in pigs’ body weight evolution. This relationship was not found for deviations in weighing order and activity during weighing. Furthermore, individual body weight deviations were positively correlated with uniformity at the pen level, providing evidence that breeding for these resilience traits might increase both pigs’ resilience and within-family uniformity.ConclusionsIn summary, our findings show that breeding for resilience traits based on deviations in longitudinal weight data can decrease pigs’ tail biting wounds, lameness and mortality while improving uniformity at the pen level. These findings are valuable for pig breeders, as they offer evidence that these resilience traits are an indication of animals’ general health, welfare and resilience. Moreover, these results will stimulate the quantification of resilience via longitudinal body weights in other species.

Resilience quantification model for cyber‐physical power systems

Resilience quantification model for cyber‐physical power systems

Watershed Resilience: Quantification Methods and Future Perspectives

In recent years, watershed resilience has garnered a substantial interest driven by the need to sustainably manage vital ecosystems in the face of increasing pressures such as climate change and land-use alterations, leading to assortment of definitions and assessment approaches. This overabundance has occasionally manifested in ambiguity and, at times, contributed improper implementations. This review evaluates the capacities, and frameworks employed in quantifying watershed resilience across various geographical contexts. It synthesizes the current state of knowledge to identify trends, limitations, and areas requiring further investigation. Due to the limited number of prior researches synthesizing watershed resilience quantification methods, the primary contribution of this study lies in its consolidation and synthesis of diverse research efforts, shedding light on the evolving landscape of watershed resilience quantification. By critically examining the strengths and weaknesses of existing definitions and adopted frameworks, we aim to provide a roadmap for future research in this field. Additionally, this review emphasizes the importance of developing standardized indicators and frameworks to facilitate more robust and comparative assessments of watershed resilience. A critical research gap is the lack of a universally accepted assessment framework for watershed resilience, hindering comparability and decisionmaking. We advocate for interdisciplinary collaboration to establish a common framework integrating ecological, hydrological, and social aspects of resilience. In conclusion, this review underscores the urgent need to advance watershed resilience quantification and offers a clear research agenda. Addressing research gaps and fostering interdisciplinary collaboration can significantly contribute to the evolving field of watershed resilience assessment and management.

An Integrated Infrastructure Resilience Approach, from the Geotechnical Asset to the Transport Network

The main objective of this work is to present a comprehensive and integrated methodology to enhance the resilience of transportation critical infrastructure (TCI), focusing on the interplay between geotechnical assets and the transport network. Societies are greatly dependent on transport infrastructure systems, and as the mobility of passengers and the transport of freight is continuously growing, a disruption due to natural or man-made hazards creates significant impacts and dysfunctionalities on their operation and necessitates response measures to minimize vulnerability and ensure continuous functionality and robustness through resilience. Therefore, resilience quantification allows the design of ad hoc operation action plans before, during, and after a disruption, considering the dynamics of societal, ecological, and technological (SET) environments. The current work focuses on resilience quantification methodologies for TCIs and on the influence of single geotechnical asset (i.e., slope failure) resilience capacity on the overall system (i.e., national road network) resilience. Two case studies of unexpected transport network disruptions that took place in Greece are presented, and resilience metrics and performance indicators are applied to quantify the influence of the recovery stage.

Towards a Cyber Resilience Quantification Framework (CRQF) for IT infrastructure

Cyber resilience quantification is the process of evaluating and measuring an organisation’s ability to withstand, adapt to, and recover from cyber-attacks. It involves estimating IT systems, networks, and response strategies to ensure robust defence and effective recovery mechanisms in the event of a cyber-attack. Quantifying cyber resilience can be difficult due to the constantly changing components of IT infrastructure. Traditional methods like vulnerability assessments and penetration testing may not be effective. Measuring cyber resilience is essential to evaluate and strengthen an organisation’s preparedness against evolving cyber-attacks. It helps identify weaknesses, allocate resources, and ensure the uninterrupted operation of critical systems and information. There are various methods for measuring cyber resilience, such as evaluating, teaming and testing, and creating simulated models. This article proposes a cyber resilience quantification framework for IT infrastructure that utilises a simulation approach. This approach enables organisations to simulate different attack scenarios, identify vulnerabilities, and improve their cyber resilience. The comparative analysis of cyber resilience factors highlights pre-configuration’s robust planning and adaptation (61.44%), buffering supported’s initial readiness (44.53%), and network topologies’ robust planning but weak recovery and adaptation (60.04% to 77.86%), underscoring the need for comprehensive enhancements across all phases. The utilisation of the proposed factors is crucial in conducting a comprehensive evaluation of IT infrastructure in the event of a cyber-attack.

A Systematic Approach of Global Sensitivity Analysis and Its Application to a Model for the Quantification of Resilience of Interconnected Critical Infrastructures

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities.

Robustness, redundancy, inclusivity, and integration of built environment systems: resilience quantification from stakeholders’ perspectives

The built environment faces a growing number of challenges due to changing climates. A resilient built environment system (BES) can withstand disruptions and shocks, and resilient design allows communities to bounce back quickly. Considering present and future needs, BESs can be oriented to adapt to new uses or modified to handle changing climates. This study examines the resilience qualities (RQs) of built environment systems (BESs) in responding to and recovering from climate change disruptions effectively. A survey was designed to capture the views of various stakeholders about the different indicators to assess the four RQs: robustness (Rb), redundancy (Rd), inclusivity (Ic), and integration (It). Regulatory and engineering stakeholders participated in the survey, and the results were analyzed using statistical methods. Stakeholders generally agree on the need to enhance transformative capacity for addressing uncertainties and climate challenges. While stakeholders trust the role of BESs’ robustness against climate impacts, some suggest improving standards for better resilience. There is consensus on the importance of regulatory measures mandating emergency resources in BESs. The study highlights the need to enhance adaptive capacities and tools within BESs. Incorporating reconfigurability and spare capacity in BESs is crucial to prevent disruptions. Participants tend to think promoting good practices at the community level is essential to address climate impacts effectively. The analysis highlights the importance of inclusive community consultation and involvement in fostering a shared responsibility for enhancing urban ecosystems against climate change impacts. This involves aligning processes across various city systems to support cohesive decision-making and strategic investments. The study suggests developing objective engineering techniques to establish a standardized approach for evaluating the RQs of BESs.

Power system resilience quantification and enhancement strategy for real-time operation

Power system resilience quantification and enhancement strategy for real-time operation

Traffic resilience quantification based on macroscopic fundamental diagrams and analysis using topological attributes

Transportation system disruptions significantly impair transportation efficiency. This paper proposes new indicators derived from the Macroscopic Fundamental Diagram (MFD) dynamics before and after a disruption to evaluate its impact on traffic resilience. Considering that MFD is an intrinsic property of a homogeneously congested transportation network, the resilience losses due to congestion and network disruption are measured separately. The resilience loss is defined as the reduction in trip completion rate, comparing congested cases to uncongested cases or disrupted cases to undisrupted cases. The resilience loss hence also exists for an undisrupted network and is measurable by the proposed method. A Simulation of Urban MObility (SUMO) model is calibrated by real origin–destination patterns, to allow for experiments in scenarios of different demand variations and supply disruptions. Case studies are conducted in Munich, Germany and Kyoto, Japan to test the usefulness of the newly proposed indicators. We furthermore explore the relationship between resilience loss and network topological attributes such as centrality and connectivity from a variety of synthetic disruption experiments in Munich and Kyoto. We find that the resilience loss in a grid-like network as in Kyoto is less dependent on the degradation of network connectivity than in a ring-like network as in Munich.

Reconfiguration and transformation for resilience: building service organizations towards sustainability

PurposeAmidst unpredictable and turbulent periods, such as the COVID-19 pandemic, service organization’s responses are required to be innovative, adaptable and resilient. The purpose of this study is to explore the utilization of both reconfiguration and transformational strategies as instruments for cultivating resilience and advancing sustainability in service organizations.Design/methodology/approachThe study examines a proposed resilience model using fuzzy logic. The research also used a semantic differential scale to capture nuanced and intricate attitudes. Finally, to augment the validity of the resilience model, a measurement scale was formulated using business mathematics and expert opinions.FindingsAlthough investing in resilience training can help organizations gain control and maintain their operations in times of crisis, it may not directly help service organizations understand the external turmoil, seek available resources or create adaptive remedies. Conversely, high levels of reconfiguration and transformation management vigour empower a service organization’s revolutionary, malleable vision, organizational structure and decision-making processes, welcoming talented and innovative employees to enhance capabilities during crises.Research limitations/implicationsThe resilience model bestows a comprehensive understanding of the pertinence of building resilience for service organizations identifying the antecedents that influence the adoption of these strategies and introduces a range of theoretical perspectives that empowers service organizations to conceptualize and plan for building resilience. The research guides service organizations to become more resilient to external shocks and adapt to changing circumstances by diversifying their offerings, optimizing their resources and adopting flexible work arrangements. The study elaborates on the enhancement of resilience, increasing innovation, improving efficiency and enhancing customer satisfaction for service organizations to remain competitive and contribute to positive social and economic outcomes through the adoption of both reconfiguration and transformational strategies.Practical implicationsThe study also guides the service organizations to become more resilient to external shocks and adapt to changing circumstances by diversifying their offerings, optimizing their resources and adopting flexible work arrangements. Rapid innovation and business model innovation are essential components, enabling service organizations to foster a culture of innovation and remain competitive. In addition, the adoption can lead to improved financial performance, job creation and economic growth, contributing to positive social and economic impacts.Social implicationsThe resilience model bestows a comprehensive understanding of the pertinence of building resilience for service organizations. It identifies the antecedents that influence the adoption of these strategies and introduces a range of theoretical perspectives that empowers service organizations to conceptualize and plan for building resilience. The research also provides a foundation for further investigation into the effectiveness of these strategies and their impact on organizational performance and sustainability. By better preparing service organizations for disruptions and uncertainties, this research triggers ameliorated organizational performance and sustainability.Originality/valueWithin the realm of the service industry, the present investigation has undertaken the development, quantification and scrutiny of both resilience and tenacity. In addition, it has delved into the intricate dynamics surrounding the influencing factors and antecedents that bear upon resilience, elucidating their consequential impact on the operational performance and outlook of service-oriented organizations. The findings derived from this research furnish valuable insights germane to enhancing operational efficacy and surmounting impediments within the sector.

A Novel Loss Model to Include the Disruption Phase in the Quantification of Resilience to Natural Hazards

Resilience of systems to natural hazards has become an interesting concept in civil engineering and it is based on the determination of the losses due to the impacts of natural hazards. In the last decades, many contributions have focused on the assessment of losses that may occur at the time of the event, as generally assumed for earthquakes. However, this assumption may be incorrect when the interval between the time of occurrence and the time when the system functionality reaches the minimum value needs to be considered. This paper aims to propose a novel method to quantify this interval, which is called disruption time, by proposing a novel formulation of the loss model based on infrastructure redundancy. The proposed method was herein applied to a case study that considers landslides in Sri Lanka. The main goal of the paper is to propose a formulation that can be implemented in a more comprehensive framework to calculate more realistically the resilience of systems to natural hazards.

Fragility modeling practices and their implications on risk and resilience analysis: From the structure to the network scale

Although fragility function development for structures is a mature field, it has recently thrived on new algorithms propelled by machine learning (ML) methods along with heightened emphasis on functions tailored for community- to regional-scale application. This article seeks to critically assess the implications of adopting alternative traditional and emerging fragility modeling practices within seismic risk and resilience quantification to guide future analyses that span from the structure to infrastructure network scale. For example, this article probes the similarities and differences in traditional and ML techniques for demand modeling, discusses the shift from one-parameter to multiparameter fragility models, and assesses the variations in fragility outcomes via statistical distance concepts. Moreover, the previously unexplored influence of these practices on a range of performance measures (e.g. conditional probability of damage, risk of losses to individual structures, portfolio risks, and network recovery trajectories) is systematically evaluated via the posed statistical distance metrics. To this end, case studies using bridges and transportation networks are leveraged to systematically test the implications of alternative seismic fragility modeling practices. The results show that, contrary to the classically adopted archetype fragilities, parameterized ML-based models achieve similar results on individual risk metrics compared to structure-specific fragilities, promising to improve portfolio fragility definitions, deliver satisfactory risk and resilience outcomes at different scales, and pinpoint structures whose poor performance extends to the global network resilience estimates. Using flexible fragility models to depict heterogeneous portfolios is expected to support dynamic decisions that may take place at different scales, space, and time, throughout infrastructure systems.

Ranking the Impact of Interdependencies on Power System Resilience Using Stratified Sampling of Utility Data

It is well known that interdependence between electric power systems and other infrastructures can impact energy reliability and resilience, but it is less clear which particular interactions have the most impact. There is a need for methods that can rank the relative importance of these interdependencies. This paper describes a new tool for measuring resilience and ranking interactions. This tool, known as Computing Resilience of Infrastructure Simulation Platform (CRISP), samples from historical utility data to avoid many of the assumptions required for simulation-based approaches to resilience quantification. This paper applies CRISP to rank the relative importance of four types of interdependence (natural gas supply, communication systems, nuclear generation recovery, and a generic restoration delay) in two test cases: the IEEE 39-bus test case and a 6394-bus model of the New England/New York power grid. The results confirm industry studies suggesting that a loss of the natural gas system is the most severe specific interdependence faced by this region.