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Comparative evaluation of resilience quantification methods for infrastructure systems
Abstract
Resilience engineering has become an important field representing a new way of understanding and management of safety. The rising interest for resilience engineering and the rapid application of the respective theoretical basis to several application cases alters the traditional approach of infrastructure systems’ risk management. In this context, the quantification of resilience is a challenging issue, which is still far from considered as adequately addressed in the respective literature and, therefore, requires a better insight. The aim of this research is to provide such an insight into currently established and proposed methods for quantifying resilience of infrastructure systems and suggest a methodological framework that effectively responds to the requirements of resilience measuring. To achieve this aim, resilience engineering is shortly presented and discussed, in terms of definition and characteristics and an insight is provided into various resilience quantification methods, such as probabilistic, graph theory, fuzzy inference, and analytical methods. Discussion over these methods reveals their strengths and weaknesses in quantifying resilience. A major finding of this research is that current methods are, mostly, incomplete and largely dependent on concepts and approaches, which emanate from other well-established and well-elaborated methodological frameworks, thus failing to provide solutions in the context of resilience engineering. On the other hand, it is proposed that entropy theory constitutes a framework, which better captures the underlying interrelations of systems modules and, therefore, constitutes a more appropriate and effective framework for quantifying resilience of infrastructure systems.
... Resilience can be broken into three phases: anticipation, response, and recovery (also referred to as absorb, adapt, and restore/recover) [13], [14], [22], [48]. Not all metrics incorporate all three phases [14], [17], [55]. Some measurements do not incorporate the time over which recovery occurs [56]. ...
... Due to each study measuring different facets of resilience, researchers are unable to compare resilience between case studies [45]. Additionally, resilience measurement depends on the system, fault, and response strategy examined [17], [20], [55], [57]. Some approaches have limited use in the SoS design. ...
... For a full review of existing resilience metrics, see [22], [23], [55]. We classify current resilience metrics across three criteria. ...
... The overarching aim of the present work is to contribute to the sustainability of urban areas and infrastructures from an engineering-technical science driven perspective. Achieving sustainability requires the strengthening of resilience [12]. ...
... Tamvakis and Xenidis [12] give a comparative evaluation of different quantification methods and summarize that there are different approaches available, but they are mostly incomplete. Besides the earlier mentioned publications in the field of safety and security for built infrastructure, Tamvakis and Xenidis [12] review further work in the field of public transport network. ...
... Tamvakis and Xenidis [12] give a comparative evaluation of different quantification methods and summarize that there are different approaches available, but they are mostly incomplete. Besides the earlier mentioned publications in the field of safety and security for built infrastructure, Tamvakis and Xenidis [12] review further work in the field of public transport network. In this topic, the application of neural network models is common practice, e.g. ...
A growing urbanization, an increasing complexity of critical infrastructure and the formation of new threats are new challenges for urban areas and require a sustainable development and a stronger coping capacity with potential adverse events. Sustainability requires a strenghtening of resilience.
Within this work, an integrated mathematical approach for the quantification of resilience is defined. This method allows a comprehensive evaluation of urban areas and the identification of weak spots. Statistical data are combined with physical models to assess the occurrence of multiple threats and their potential consequences. This risk based assessment is combined with time dependent recovery models to result in a quantity for resilience. Results of this framework can be applied to evaluate the effectiveness of single resilience phases, like prepare, prevent, protect, response and recover.
Besides the mathematical formulation, application examples in this work assess exemplarily terroristic threats in urban surroundings with empirical information of historical events and engineering models to assess possible structural damage effects. The comparison of different urban footprints builds the basis for a resilient urban planning process.
... The economic impacts are assessed in various ways, including direct and indirect losses (Scawthorn et al. 2006), social costs (OECD 2008), cost-benefit analysis (Mechler 2005;White and Rorick 2010), and disaster resilience (Cutter et al. 2008; Committee on Increasing National Resilience to Hazards and Disasters 2012). In particular, considerable research interest exists on measuring community resilience from hazards/disasters (Cutter et al. 2008;Vugrin et al. 2010b;Tamvakis and Xenidis 2013). In general, resilience is defined as the ability to survive and cope with a disaster with minimum impact and damage in hazards research, while the global environmental change community defines it as a system's capacity to absorb disturbance and reorganize into a fully functioning system (Cutter et al. 2008). ...
... However, there is no broadly accepted single definition of resilience, and multiple definitions exist within the literature (Cutter et al. 2008). Various quantitative models have been suggested for measuring the resilience (Rose 2009;Vugrin et al. 2010b;Tamvakis and Xenidis 2013). In common, the definitions of resilience all include withstanding change, by reducing the impact of the change, adapting to the change, or recovering from the change (Vugrin et al. 2010b). ...
... The RCI is distinct from other resilience quantification methods (e.g., Tamvakis and Xenidis 2013) in that it includes RE and SI to quantify resilience. Thus, the RCI concludes that two systems have different resilience to a hazard when the two systems require different recovery efforts for the same loss recovery path. ...
This study estimated South Korea's resilience to volcanic eruption scenarios by summing direct and indirect losses in industrial sectors, health damages, and costs to clean roads of volcanic ash. This resilience assessment aimed to compare eruption scenarios from a socio-economic perspective and prepare mitigation strategies. Direct losses were estimated using vulnerability functions in agriculture and interruption times in air transportation and manufacturing. Indirect losses were estimated using input–output tables on South Korea. Health damages were estimated using the unit damage cost of particulate matter studied in the European Union and the United States. Resilience assessment results depended on eruption scenarios and the locations of vulnerable items. The industrial sectors had indirect losses that were larger than direct losses, suggesting the inclusion of indirect losses in economic impact assessments of natural hazards. This study indicated that the community resilience cost index (CRCI) is useful in assessing community resilience to hazards. The CRCI quantified the economic differences in eruption scenarios, and could lead an optimal hazard mitigation to enhance resilience. The CRCI can be applied to other hazards by defining and measuring losses and recovery strategies using appropriate data and to develop mitigation strategies by defining the interaction between losses and recovery costs.
... Based on the facts of increasing urbanization, growing complexity and the presence of new threats, there is a need to evaluate possible hazardous events and the corresponding consequences. Aim is the generation of more robust and sustainable cities. Achieving sustainability requires the strengthening of resilience [6]. To reach this goal, concepts have to be developed, to reduce the probability of occurrence and the consequences from those events. ...
... For each possible event location, the influence of all urban objects and their corresponding event frequencies is considered, distributed around the buildings using equation. (6). In particular between buildings 9 and 10 neighboring effects are nicely visible. ...
... Modulo normalization, equation (21) is a function of the building dimension and the distance of the event locations to the building. Possible locations with a closer distance to the urban object have a higher frequency of occurrence when using the susceptibility density function of equation (6). The bar diagram in Figure 8 shows the marginal distribution (sum of the two event probabilities below the bar) of the discrete density function to assess the susceptibility at different positions in case of a single threat type and threat intensity. ...
The dynamic growth and evolution of urban areas generate new challenges for safety and security driving societal, economic, and ecological developments at local and worldwide levels. Cities comprise a high degree of critical infrastructure with an increasing complexity and interdependency. In addition, modified and new threats ranging from natural to man-made and malicious hazards ask for more robust and sustainable cities. This work combines and extends existing empirical, engineering, and simulative methods to define and determine quantities for resilience assessment of urban areas in a comprehensive approach. Based on a multitude of possible events in a city quarter with a larger number of infrastructures, susceptibilities, vulnerabilities, and averaged risks are analyzed in a systematic and quantitative way. The use of an established empirical-historical database gives first insights to identify susceptible elements or endangered areas in the considered urban environments. It is coupled to an approach for consequences where state-of-the-art physical-engineering hazard and damage propagation and quantification models are integrated for vulnerability assessment. The consideration of multiple threats and multiple possible locations cumulates in an object- and location-dependent quantification of averaged risks to visualize the most critical regions and infrastructure aspects in densely populated areas. In this article, the approach is exemplarily applied to terroristic threats. The integration of the three-dimensional visualized approach into existing risk assessment and management processes will help to create cities that are more resilient.
... However, formally quantifying the abstract concepts embodied in resilience theory and in its notion of an adaptive cycle has been exceedingly difficult (Walker and Salt, 2012;Tamvakis and Xenidis, 2013). Given the sheer number of interacting system attributes that contribute to resilience, Walker and Salt (2012: 92) assert that it is "difficult, if not impossible, to quantify general resilience". ...
... Those attempts that have been made to measure urban resilience typically estimate disaster-related risks of financial and other losses. Even in those cases, resilience measures are primarily of engineered infrastructure such as transportation networks, energy and water delivery systems, or the built environment (for example, Chang and Shinozuka, 2004;Jha et al., 2013;Tamvakis and Xenidis, 2013). Similarly, others have attempted to operationalize urban resilience but these efforts typically resolve to more risk assessment or qualitatively clarifying the contributing factors to resilience (for example, Hill et al., 2008;Wardekker et al., 2010;Sudmeier et al., 2013;Keating et al., 2014;Martin and Sunley, 2015). ...
... The opposite is true for negative ζ′s, in which case the occupations may be in competition for similar labour skills or may otherwise hinder each other's presence. Essentially, our metric is an information-based metric, which is an increasingly preferred approach to quantifying resilience, sustainability and similar attributes of complex adaptive social systems (Ulanowicz et al., 2009;Bossomaier et al., 2013, Tamvakis andXenidis, 2013;Mayer et al., 2014). Thus our initial inspiration for this metric was based on Hidalgo et al.'s (2007) measure of national production interdependencies. ...
Resilience is increasing rapidly as a framework to understand and manage coupled human-natural systems. Yet the concept of resilience is rarely quantified. Here we quantify system resilience by operationalising the notion of system tightness. Multiple resilience frameworks recognize the strong relationship between system tightness and resilience, though they differ on the directionality of that relationship. Thus, by measuring the system tightness we ultimately measure urban economic resilience, with the added benefit of empirically determining the directionality of the relationship between tightness and resilience. We then assess how well this measure predicts the response of urban economies to the recent so-called Great Recession. Results show that cities with lower tightness (higher resilience) fared better during the recession with respect to several economic productivity measures. However, in the absence of shocks, those with higher tightness (lower resilience) exhibit superior economic performance. Thus, a tradeoff between efficiency and resilience is nicely reflected in the empirical data. Though this study deals with economic shocks, quantitative metrics based on its methodology may help anticipate a city’s response to shocks more generally, such as natural disasters, climate change, social unrest, or significant policy shifts.
... While many of the examples below are sector-specific, the methodologies used could be applicable across other sectors. In addition to sectoral-based approaches, much work has been done to develop models and methods capable of analysing interdependent infrastructure systems (for a more detailed overview, see for example Yusta et al. 2011;Tamvakis and Xenidis 2013;Huang et al. 2014;Ouyang 2014). Johansson and Hassel (2008) suggest these methods can be divided into two categories: empirical and predictive approaches. ...
... This can become a bottleneck for the evolution of resilience engineering, as theory building would benefit from the observation of experiences of large-scale 'building in' of resilience engineering by an infrastructure provider (Righi et al. 2015). Tamvakis and Xenidis (2013) note that "current methods [of resilience quantification] are mostly incomplete and largely dependent on concepts and approaches which emanate from other well-established and well-elaborated methodological frameworks, thus failing to provide solutions in the context of resilience engineering". ...
The economy and well-being of modern societies relies on complex and interdependent infrastructure systems to enable delivery of utilities and movement of goods, people and services. This complexity has resulted in an increased potential for cascading failures, whereby small scale initial failures in one system can result in events of catastrophic proportions across the wider network. Resilience, and the emerging concept of resilience engineering within infrastructure, are among the main concerns of those managing such complex systems. However, the disparate nature of resilience engineering development in various academic and industrial regimes has resulted in a diversity of definitions and characterisations. These are discussed in this paper, as are the commonalities between sectors and between different engineering disciplines. The paper also highlights the various methodologies used as part of resilience engineering implementation and monitoring, current practices including existing approaches and metrics, and an insight into the opportunities and potential barriers associated with these methodologies and practices. This research was undertaken for the Resilience Shift initiative to shift the approach to resilience in practice for critical infrastructure sectors. The programme aims to help practitioners involved in critical infrastructure to make decisions differently, contributing to a safer and better world.
... The higher entropy of a variable leads to more information from its definitive observation. Entropy theory is used for various resilience assessments, including engineering, society, economics, etc. (Tamvakis and Xenidis 2013). We used the Shannon entropy to calculate the entropy value and evaluate the reliability and feasibility of the indicators' weight obtained from the ANP model. ...
The physical structure of urban settlements has become increasingly vulnerable to hazards following the growing trends of natural hazards, including earthquakes. The concept of resilience has gained momentum to facilitate better planning and response to such hazards. This research seeks to develop a conceptual spatial framework considering different phases of disaster risk management to evaluate urban physical resilience. Twenty indicators that define urban structure are identified and included in an Interpretive Structural Modeling—Analytic Network Process (ISM-ANP) hybrid model for analysis. The model and the indicator weights are adjusted using statistical and optimization techniques. District 4 of Tehran has been selected as the study area, and the proposed evaluation framework is applied to several zones with different physical urban structures. According to the results, the most important indicators of urban structure are the Robustness of Buildings, Street Width, Building Density, and Aspect Ratio. Sensitivity analysis and scenario-making are performed to explore the desired state of urban physical resilience for each zone. The results of the case study indicate moderate levels of urban physical resilience. The study provides more clear and practical insights into the concept of resilience to help urban planners and decision-makers improve urban physical resilience.
... Henry and Ramirez-Marquez (10) propose that the key parameters in resilience quantification are as follows: disruptive events, component restoration, and overall resilience strategy. In a comparative study to highlight the importance of resilience in system infrastructure, Tamvakis and Xenidis (11) propose a process to evaluate the system's resilience based on the entropy theory. This process is similar to how resilience is quantified, as both aim to demonstrate the system's vulnerability to disruptions. ...
Urban transportation systems' structure and functionality can be affected by unexpected disruptions for several reasons, such as natural hazards, intentional attacks, accidents, and so forth. The conventional definition of resilience is the capacity to withstand, assimilate, adjust, and expeditiously recuperate from various forms of perturbations such as shocks, disturbances, and deliberate attacks. Though multiple studies in the literature focus on resilience assessment and improving the resilience level of mobility services before disruption, few studies offer solutions for the operators of transportation systems during disruptions to alleviate their negative effects, such as reducing the recovery time. In this context, a new paradigm called ``resilience as a service'' (RaaS) has emerged in the field of operations management. The idea of RaaS is to integrate the available resources of different service providers to manage disruptions and maintain the system's resilience. This paper proposes a definition of RaaS dedicated to transportation systems. To provide a methodological example for the RaaS paradigm, we formulate a bi-level optimization problem to represent a solution example that RaaS providers can deliver. The upper-level model formulates the resource reallocation problem during disruption from the perspective of RaaS providers, while the lower-level model considers user perspectives. We provide a numerical example in a real test case of a French city to illustrate the benefits of implementing a RaaS solution. The results show that we can reduce the average travel delay of all users by 69%, including the delay results from the proposed RaaS strategy compared with the absence of RaaS.
... Critical functionality. With respect to critical technical infrastructures such as power grids or communication networks, the design of resilient systems has been studied extensively [4,21,83,161,164,166]. Here resilience, sometimes also called "resiliency", refers to a system's capacity to "maintain an acceptable level of service in the presence of [. . . ...
Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, non-equilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) \emph{delimitation}, i.e., narrowing down the target systems, (ii) \emph{conceptualization}, .e., identifying how to approach social organizations, (iii) formal \emph{representation} using a combination of agent-based and network models, (iv) \emph{operationalization}, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the \emph{robustness} of social organizations and their \emph{adaptivity}, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation.
... Übergreifend lässt sich festhalten, dass trotz zahlreicher Versuche nach wie vor keine standardisierte Methode zur Quantifizierung von Resilienz existiert (Ferris et al. 2018, Tamvakis/Xenidis 2013. In den meisten Fällen verweisen die Autoren zwar mehr oder weniger explizit auf Bruneaus Ideen und ein Verständnis von Resilienz als Kombination aus Schadensausmaß und Zeit zur Wiederherstellung, entwickeln aber davon ausgehend eigenständige Metriken, die eine Vergleichbarkeit erschweren (Hosseini et al. 2016: 56). ...
Die zunehmende Komplexität unserer Welt macht das Auftreten disruptiver Ereignisse mit unvorhersehbaren Folgen wahrscheinlicher. Die Zukunft ist durch Unsicherheit gekennzeichnet. Deshalb brauchen Gesellschaften und ihre kritische Infrastrukturen Resilienz, verstanden als generische Anpassungsfähigkeit dank Flexibilität und loser Ressourcen. Der Autor argumentiert in diesem Buch, dass Resilienz durch das Zusammendenken von individueller Freiheit und Sicherheit normativ wünschenswert sein kann. Davon ausgehend entwickelt er ein neues Resilienz-Konzept für die zivile Sicherheitsforschung und zeigt, wie die Ingenieurwissenschaften dieses durch Systemprinzipien wie Diversität, Modularität, Dezentralität und Redundanz umsetzen können.
... Authors have posited that resilience and sustainability are linked performance characteristics, with of each suitable for different challenges [20], [23], [24]. One approach of [26], [28], [29]. ...
Systems of Systems combine constituent systems (such as financial, transportation, energy, food, and water) to improve performance. System of Systems employment, however, often requires additional material and infrastructure investment. Due to their widespread use, it is essential that Systems of Systems are sustainable. One approach to increasing Systems of Systems sustainability is to increase its resilience (e.g. a less-resilient System of Systems would require additional resources to recover from a fault). The need for increased Systems of Systems resilience inspired our research question: How does SoS resilience change environmental impact during post-fault operation? To examine this research question, a model of an electric motor supply chain is examined. The supply chain architecture is divided into two parallel supply chains servicing four distribution centers. If supply to a distribution center is disrupted, the non-impacted supply chain can provide motors, but at a changed use-phase impact due to new shipping routes (e.g. further geographic distance). To answer our research question, each of the 14 constituents were disrupted one at a time, supply rerouting occurred as necessary, and the change in operational impact is reported (additional km of supply chain travel per week. This study provides key evidence that even if System of System infrastructure repair could occur without impact, changes in operational impact is an important factor for decision makers. Additionally, this paper provides case study evidence supporting the claim that improved resilience can improve System of Systems sustainability.
... Early in 2003, Bruneau et al. (2003) has put forward a conceptual framework of community resilience and identified four dimensions namely: a) technical, b) organizational, c) social, and d) economic. They also attempted to move towards a qualitative conceptualization of resilience to a more comprehensive quantitative but end up with an undefined quantification of community resilience due to uncertain components in the system (Tamvakis and Xenidis, 2013). In recent years, some research has also begun to focus on quantity community resilience measurement, indicators such as asset losses (Cimellaro et al. 2010), and loss of gross regional product (GRP) (Ouyang & Duenas-Osorio 2014) have been proposed. ...
... The great variety of methods aiming at quantifying the resilience of infrastructure systems can be grouped in probabilistic methods (Miller-Hooks et al., 2012;Queiroz et al., 2013), graph theory methods (Berche et al., 2009;Dorbritz, 2011), fuzzy methods (Heaslip et al., 2010, and analytical methods (Cimellaro et al., 2010a;Tamvakis and Xenidis, 2013). ...
Wastewater and water distribution networks (WDNs) are among the critical infrastructures (CIs) in our communities. Their service failure due to the increased frequency of natural disasters and man-made catastrophes may incur catastrophic consequences to public health, economic security, and social welfare. Maintaining proper operation of CIs and reducing the vulnerability of the structures, systems, and components, through physical and organizational restoration plans are, therefore, a primary challenge that has prompted attention to the seismic safety of lifelines systems. This paper proposes a resilience index (R) of a WDN as the product of three indices: (1) the number of users temporarily without water, (2) the water level in the tank, and (3) the water quality. The functionality of a WDN can be evaluated through the resilience index by considering: (1) delivering a certain demand of water with an acceptable level of pressure and quality, and (2) the restoration process following an extreme event. To demonstrate the applicability of the R index, different disruptive scenarios have been implemented in a small town in the south of Italy. Results show that the lack of services can be reduced if the network is divided into districts. Moreover, it is also necessary to consider the proposed indices independently to find trends that cannot be captured by the global index. The proposed indices can be used to effectively support decision-makers and governmental agencies in including the environmental and social aspects in their design strategy.
... Various frameworks and models have been proposed for quantification and studying resilience in different fields [19][20][21]. Several methodologies have been proposed for the proper quantification of resilience, such as probabilistic methods [14,22,23], graph theory methods [24,25], fuzzy logic methods [26], and analytical methods [27,28]. A "PEOPLES" (Population and demographics, Environmental and ecosystem, Organized governmental services, Physical infrastructure, Lifestyle and community competence, Economic development, Social cultural capital) factor-based framework for resilience quantification at different scales was also proposed [7,8]. ...
Resilience is the capability of a system to resist any hazard and revive to a desirable performance. The consequences of such hazards require the development of resilient infrastructure to ensure community safety and sustainability. However, resilience-based housing infrastructure design is a challenging task due to a lack of appropriate post-disaster datasets and the non-availability of resilience models for housing infrastructure. Hence, it is necessary to build a resilience model for housing infrastructure based on a realistic dataset. In this work, a Bayesian belief network (BBN) model was developed for housing infrastructure resilience. The proposed model was tested in a real community in Northeast India and the reliability, recovery, and resilience of housing infrastructure against flood hazards for that community were quantified. The required data for resilience quantification were collected by conducting a field survey and from public reports and documents. Lastly, a sensitivity analysis was performed to observe the critical parameters of the proposed BBN model, which can be used to inform designers, policymakers, and stakeholders in making resilience-based decisions.
... Based on a review of available works, there are four major types of methods used to evaluate transportation resilience in the literature: probabilistic methods, fuzzy inference systems methods, analytical methods, and graph (network) theory approach (Tamvakis and Xenidis 2013). A more detailed introduction of these four types of methods is presented below. ...
This paper examines the vulnerability of road networks to different types of disruptions by modeling the percolation dynamics in road networks under different disruption scenarios. The objective of this paper is threefold: (1) to examine if the theoretical network robustness measure proposed in the literature is applicable to road networks;
(2) to unveil the impacts of network size on the overall vulnerability of road networks; (3) to compare the performance profile of road networks to random and non-random types of disruptions. To that end, this study first modeled the road system in a community as a planar graph. Then, the percolation dynamic in the road network during the flood is captured by assigning different removal probabilities to nodes in the road network according to Bayesian rules that take floodplain types, node-elevation, and street-grade as inputs. In the end, an overall road network robustness measure and its temporal changes were obtained and for random and non-random scenarios, using road networks with different sizes. The results were compared in
order to characterize the vulnerability of road networks under different scenarios. The proposed method was applied to the road network in Houston during Hurricane Harvey. The results show that: (1) The theoretical network robustness measure is applicable to assess the road network robustness. (2) Compared to the random percolation model, the probability (Bayesian-rule) based percolation could lead to a greater decrease in the network robustness. (3)The percolation profiles of the road networks with different sizes are not significantly different. The findings of this study could not only inform resilience enhancing decisions by the stakeholders but also could serve as a foundation for future vulnerability related research.
... That is, the rate and intensity of the variations along the performance curve could be used to characterize the difference in resilience as a performance indicator. In addition to the area of the resilience triangle, other characteristics of the triangle (associated with different stages of loss and recovery) have been individually studied in different engineering disciplines [40,41]. However, in most complex systems, such as transportation systems, a single dimension might not be effective in characterizing the resilience and differentiating the relative performance of different components in a system. ...
As demonstrated for extreme events, the resilience concept is used to evaluate the ability of a transportation system to resist and recover from disturbances. Motivated by the high cumulative impact of recurrent perturbations on transportation systems, we have investigated resilience quantification as a performance assessment method for high-probability low-impact (HPLI) disturbances such as traffic congestions. Resilience-based metrics are supplementary to conventional travel-time-based indices in literature. However, resilience is commonly quantified as a scalar variable despite its multi-dimensional nature. Accordingly, by hypothesizing increased information gain in performance assessment, we have investigated a multi-dimensional approach (mD-Resilience) for resilience quantification. Examining roadways’ resilience to recurrent congestions as a contributor to sustainable mobility, we proposed to measure resilience with several attributes that characterize the degradation stage, the recovery stage, and possible recovery paths. These attributes were integrated into a performance index by using Data Envelopment Analysis (DEA) as a non-parametric method. We demonstrated the increased information gain by quantifying the performance of major freeways in Los Angeles, California using Performance Measurement System (PeMS) data. The comparison of mD-Resilience approach with the method based on area under resilience curves showed its potential in distinguishing the severity of congestions. Furthermore, we showed that mD-Resilience also characterizes performance from the lens of delay and bottleneck severities.
... In order to cope with disruptions efficiently and take active precautionary measures, it is critical to understand the mechanisms and patterns with which the disruptions unfold in the transportation network. Due to the planar nature of transportation networks, they tend to lend themselves readily to being represented as graphs, and therefore graph theory-based approaches have been one of the standard tools to study the vulnerability in transportation systems [32]. Graph theory reduces a road network to a mathematical matrix where the vertices (nodes) represent road intersections and the edges are the road sections between these nodes [18]. ...
This study aims to characterize the vulnerability of road networks to fluvial flooding using a network diffusion-based method. Various network diffusion models have been applied widely for modeling the spreading of contagious diseases or capturing opinion dynamics in social networks. By comparison, their application in the context of physical infrastructure networks has just started to gain some momentum, although physical infrastructure networks also exhibit diffusion-like phenomena under certain stressors. This study applies a susceptible-impacted-susceptible (SIS) diffusion model to capture the impact of flooding on the road network connectivity. To that end, this paper undertook the following four steps. First, the road network was modeled as primal graphs and nodes that were flood-prone (or the origins of the fluvial flood) were identified. Second, temporal changes in the flood depth within the road network during a flooding event were obtained using a data-driven geospatial model. Third, based on the relationship between vehicle speed and flood depth on road networks, at each time step, the nodes in the road network were divided into two discrete categories, namely functional and closed, standing for Susceptible and Impacted in the SIS diffusion model, respectively. Then, two parameters of the SIS model, average transition probabilities between states, were estimated using the results of the hydraulic simulation. Fourth, the robustness of the road network under various SIS diffusion scenarios was estimated, which was used to test the statistical significance of the difference between the robustness of the road network against diffusions started from the randomly chosen nodes and nodes with different high centrality measures. The methodology was demonstrated using the road network in the Memorial super neighborhood in Houston. The results show that diffusive disruptions that start from nodes with high centrality values do not necessarily cause a more significant loss to the connectivity of the road network. The proposed method has important implications for applying link predictions on road networks, and it casts significant insights into the mechanism by which cascading disruptions spread from flood control infrastructure to road networks, as well as the diffusion process in the road networks.
... Berdica (2002) provides a definition of the vulnerability of road networks that are widely cited in the literature: "Vulnerability in the road transportation system is susceptibility to incidents that can result in considerable reductions in road network serviceability." There are four major types of methods used to evaluate resilience identified in the literature: probabilistic methods, fuzzy inference systems method, analytical methods, and graph theory-based methods (Tamvakis & Xenidis, 2013). Graph theory in transportation is commonly used to study issues related to routing and networks (Monteiro, Robertson, & Atkinson, 2012). ...
The objective of this study is to identify vulnerable sections in the transportation network with the help of machine learning classifiers. Many network-theory based frameworks have been proposed to assess the vulnerability of transportation networks using network centrality based measures; however, those measures can not be directly translated into the actual vulnerability of transportation networks as many studies seem to proclaim. This is because vulnerability is not only about the failure consequence but also failure probability, and there are clear heterogeneities in disaster-exposure levels of the individual nodes due to the spatially embedded nature of transportation networks. It is possible to study and characterize this heterogeneity with the help of classification tools in machine learning. First, the road network at a super neighborhood level is modeled as a primal graph. Then, a new measure for flood exposure of the nodes in a road network was proposed and treated as the dependent variable. Two independent variables, namely elevation and the shortest distance from flood control infrastructure were identified for each individual node. A classification algorithm was trained and tested in order to predict the flood exposure of individual nodes in the road network. In the end, connectivity of the road network was estimated after removing nodes (which are predicted using the best performing classification algorithm) that are particularly vulnerable to fluvial flooding. The results indicated that the K-means clustering algorithm had the highest prediction accuracy. The proposed methodology was then applied to assess the vulnerability of other super neighborhoods in Houston during Hurricane Harvey. The proposed framework expands the scope of traditional vulnerability assessment analysis for the road networks by effectively making use of machine learning tools, as well as publicly available data. Results from this study could be used to inform resilience enhancement decisions.
... In complex coupled systems such as infrastructures, resilience also deals with improving the ability of organizations from public, private, and civic sectors to work together to anticipate hazards and respond and adaptively recover in ways that reduce future risk. A rich body of research focuses on the physical aspect of resilience [1,[5][6][7][8]. Numerous research studies have been conducted to capture the role of interdependencies among various Interdependent Infrastructure Systems (IISs), all of which have used various analytical and simulation techniques [9][10][11][12][13][14]. ...
Institutions are important elements in human systems influencing interactions among organizations that contribute to resilience management in interdependent infrastructure systems (IISs). In particular, the existence of institutional congruence among actors across these complex systems is important for the successful operation of such systems. Institutional congruence is the extent to which organizations have similar, agreed upon, or harmonized institutions. Institutional congruence has remained an understudied aspect of the management of resilience in IISs. This paper assesses three significant aspects of resilience management of IISs through the evaluation of organizational institutions: organizations' involvement in hazard mitigation planning and use of hazard mitigation plans; organizations' view regarding the responsibility of hazard mitigations and contributors to hazard; and organizations’ support of policies for hazard risk mitigation. Organizational institutions within a sector and across different sectors that are involved in hazard risk mitigation in the context of the Harris County during Hurricane Harvey were investigated. The impacts of the extent to which organizational institutions were congruent on the cooperative management of resilience and the vulnerability of infrastructures were discussed. Findings show that, although the use of non-structural solutions is supported by most of the organizations, the existing plans did not specifically focus on such solutions. Additionally, the results indicate the existence of congruence across sectors about the importance of responsibility sharing among governments at different levels in mitigating the risk of hazard. Different organizations across sectors have varying perceptions regarding suitable approaches for flood risk mitigation that contribute to the vulnerability of physical infrastructures.
... Entropy theory has been applied to wide range of system resilience assessments from engineering and economics to anthropology and social ones (Comfort, Oh, & Ertan, 2009;Comfort, Siciliano, & Okada, 2011;Greco, Di~Nardo, & Santonastaso, 2012;Zurlini, Li, Zaccarelli, & Petrosillo, 2015). Entropy indicates the degree of disorder, uncertainty or lack of information about the configuration of system modules (Tamvakis & Xenidis, 2013). The lower the entropy value, the higher is the information utility it has. ...
... The conditions of any disruptive event can be broadly characterized as intense, unsettling, and severe under both pre-and post-disaster applications. The complex nature and the dynamic interactions between the system components challenge the achievement of optimal operations for system infrastructure, and this causes economic loss [5] . For instance, statistics show that the economic losses caused by natural disasters from 2000 to 2017 were around $3,312 billion across the world [6] . ...
This research utilizes Bayesian network to address a range of possible risks to the electrical power system and its interdependent networks (EIN) and offers possible options to mitigate the consequences of a disruption. The interdependent electrical infrastructure system in Washington, D.C. is used as a case study to quantify the resilience
using the Bayesian network. Quantification of resilience is further analyzed based on different types of analysis such as forward propagation, backward propagation, sensitivity analysis, and information theory. The general insight drawn from these analyses indicate that reliability, backup power source, and resource restoration
are the prime factors contributed towards enhancing the resilience of an interdependent electrical infrastructure system.
... Entropy theory has been applied to wide range of system resilience assessments from engineering and economics to anthropology and social ones [30][31][32][33]. Entropy indicates the degree of disorder, uncertainty, or lack of information about the configuration of system modules [34]. The lower the entropy value, the higher is the information utility it has. ...
... The overarching aim is to contribute from an engineering-technical science driven perspective to the sustainability of urban areas and infrastructures. Achieving sustainability requires the strengthening of resilience [7]. ...
The formation of new threats and the increasing complexity of urban built infrastructures underline the need for more robust and sustainable systems, which are able to cope with adverse events. Achieving sustainability requires the strengthening of resilience. Currently, a comprehensive approach for the quantification of resilience of urban infrastructure is missing. Within this paper, a new generalized mathematical framework is presented. A clear definition of terms and their interaction builds the basis of this resilience assessment scheme. Classical risk-based as well as additional components are aligned along the timeline before, during and after disruptive events, to quantify the susceptibility, the vulnerability and the response and recovery behavior of complex systems for multiple threat scenarios. The approach allows the evaluation of complete urban surroundings and enables a quantitative comparison with other development plans or cities. A comprehensive resilience framework should cover at least preparation, prevention, protection, response and recovery. The presented approach determines respective indicators and provides decision support, which enhancement measures are more effective. Hence, the framework quantifies for instance, if it is better to avoid a hazardous event or to tolerate an event with an increased robustness. An application example is given to assess different urban forms, i.e., morphologies, with consideration of multiple adverse events, like terrorist attacks or earthquakes, and multiple buildings. Each urban object includes a certain number of attributes, like the object use, the construction type, the time-dependent number of persons and the value, to derive different performance targets. The assessment results in the identification of weak spots with respect to single resilience indicators. Based on the generalized mathematical formulation and suitable combination of indicators, this approach can quantify the resilience of urban morphologies, independent of possible single threat types and threat locations.
... Attempts have been made to evaluate the system performance under changing environment from resilience perspective. For instance, Suweis et al. (2015) found that the resilience of a coupled population-food system was losing due to the increasing dependency of food security on global trade. The negative impacts of policy changes on system resilience have also been identified. ...
The water-energy-food nexus has gained increasing attention in the research communities as the security of water, energy and food becomes a very high concern due to future uncertainties. Studies pertaining to calculations of flows and dependencies between different resources, assessments of technology and policy applications, and quantifications of system performance have been conducted to understand their interlinkages and develop management options. This paper provides a state-of-the-art review on the concepts, research questions and methodologies in the field of water-energy-food. First, two types of nexus definition are compared and discussed to understand the nature of nexus research issues. Then, nexus research questions are summarized into three themes: internal relationship analysis, external impact analysis, and nexus system evaluation. Eight nexus modelling approaches are discussed in terms of their advantages, disadvantages and applications, and guidance is provided on the selection of an appropriate modelling approach. Finally, future research challenges are identified, including system boundary, data uncertainty and modelling, underlying mechanism of nexus issues and system performance evaluation. This review helps bring research efforts together to address the challenging questions in the nexus research and develop sustainable and resilient water, energy and food systems.
... A relatively large variety of propositions for the modeling and quantification of systems resilience are available in the literature, see e.g., Tamvakis & Xenidis (2013), Cimellaro, ...
The paper proposes a novel decision analysis framework and corresponding probabilistic systems representations allowing for the consistent and integral quantification of systems resilience and sustainability. This facilitates – to the knowledge of the authors, for the first time – that decisions relating to the governance of socio-ecologic-technical systems may be optimized with due consideration of their impacts at both local and short-term time scales as well as on global and long-term time scales. The resilience performance of the interlinked system is modeled through the formulation of resilience failure events which occur if one or more of the capacities of the interlinked system are exhausted. Sustainability failure is analogously introduced as the event that one or more of the Planetary Boundaries are exceeded. A principal example shows there is a trade-off between resilience, generation of benefits, consumption of materials, and emissions to the environment. Resilience provides benefits to society but at the same time imposes material consumption and emissions to the environment. Systems can, however, be designed such that resource consumption and associated environmental impacts are reduced and the resilience performance is increased simultaneously. The example further illustrates that social governance system failure may follow from inadequate design and governance of infrastructure.
... Thereafter, the annual resilience is quantified as the mean ratio of the area underneath the real performance curve to the area under the target performance curve during a year. Other methodologies have been also utilized for quantification of resilience, such as graph theory, fuzzy inference, probabilistic methods, and entropy theory [20]. ...
Abstract. This paper puts forward a comprehensive framework for probabilistic quantifica-tion of community resilience considering multiple interdependent infrastructure systems. The proposed framework integrates various dimensions of resilience including technical, organi-zational, social, and economic. To this end, first the post-hazard status of the components of the community, e.g., infrastructure systems, is determined through casualty and damage mod-els. Next, discrete events simulation is employed to quantify the recovery of the community, and the infrastructures thereof. For this purpose, the community restoration capacity, com-prising workforce, material, and equipment, is assigned to the damaged components, which produces repair events. Once a component restored, the status of all components is updated considering interdependencies. At this point, the framework quantifies the costs incurred by the community comprising direct costs, i.e., restoration and relocation costs, and indirect costs, i.e., business interruption and socioeconomic costs due to absence of services, during the pre-repair period. Thereafter, the released restoration capacity is reassigned to another unrestored component, producing another event. This process continues until all components reach the intended functionality. The total community cost, which is the accumulated cost over the entire recovery period, is regarded as an indicator of the community resilience. The func-tionality of different infrastructure systems as well as different dimensions of resilience is in-corporated in this single global indicator. This, in turn, provides the ability to determine the importance of each component based on the extent of contribution to this indicator. Therefore, the proposed framework provides decision makers with a decision support tool to identify the optimal resource allocation strategy to achieve a resilient community. The proposed frame-work is showcased by an application to a community with a building portfolio, commercial units, transportation network, healthcare facilities, and a power distribution network.
... The values in the black circles represent the actual shift and have three position possibilities with respect to the reference – towards the boundary, from the boundary and similar distance to the boundary. The combination of all boundaries enables the understanding of their interrelationships and trade-offs (Cooke et al., 2001; Qureshi, 2008; Tamvakis and Xenidis, 2013). For reaching the specific details from system level values, we designed for each boundary a three-click search (see an example with explanation in the results section Fig. 7 ). ...
Resilience engineering concepts can complement proceduralization of complex sociotechnical systems (STS). Proceduralization aims at defining precise and quantified system objectives, and at defining a process that describes and prescribes how to achieve those objectives. Although proceduralization has been successfully implemented to capture knowledge and experience, it is limited when the unexpected and unforeseen occurs. Resilience engineering focuses on this drawback and seeks for concepts to enable adaptive responses in these situations. We propose a team reflection process to enhance resilience of a rail STS, complementing its proceduralization. In the present study, we describe how rail signallers used team reflection, supported by a tool that allowed in-depth post-shift inspection of train movements. A near accident, occurring during a one-week observation, is described and used for two purposes. First, it was used as an example to explain the usage of the support tool. Second, it was used as a reference case of topics playing a role in evolving accidents. The analysis showed that the topic categories discussed during the team reflections were similar to the incident categories. This means that relevant topics are available , when things go right, to learn from and anticipate on. In addition, we showed that rail signallers, over the course of the observations, increasingly analysed and reasoned about their work. This enriched knowledge beyond procedures, enhancing the ability to cope with the unexpected and unforeseen.
... (This is the only direct attempt to apply entropy that I have found in the entire literature on projects.) This work by Bushuyev & Sochnev was recently refined by Tamvakis & Xenidis (2013). Entropy is proposed there as a unified measure of uncertainty which allows the comparison of various types and sources of risk. ...
The present research develops a new measure of projects " complexity: It analyzes the structural sophistication of PERT networks through Information Theory and utilizes Shannon " s first-order joint binary entropy. Higher entropy means that a project is more complex. A complex project is a project which is neither sure nor impossible. Unsurprisingly, project entropy (or structural complexity) increases with time (i.e., longest path or number of arrows that follow each other), together with the number of connections among the tasks. The main discovery of this research is that later occurring risks increase project entropy significantly more than equivalent risks occurring earlier.
... To date, the implementation of the engineering resilience concept has been widely spotted in various engineering disciplines. Many of the engineering resilience implementations are associated with large-interconnected-complex systems, such as transportation systems [11][12][13][14][15][16][17][18][19], power systems [5,[20][21][22][23][24], production systems [25][26][27][28][29][30], multitier supply chains [3,25,[31][32][33][34][35][36][37][38][39], general infrastructure systems [5,20,[40][41][42][43][44][45][46][47], health care systems [48][49][50][51], and many more. The implementation of engineering resilience is not only limited to complex systems applications, but the engineering resilience concept could also be implemented to single-mechanicaldesign system, such as aircraft actuators [52], aircraft controllers [53][54][55], or computer numeric control machining systems [56]. ...
A resilient system is a system that possesses the ability to survive and recover from the likelihood of damage due to disruptive events or mishaps. The concept that incorporates resiliency into engineering practices is known as engineering resilience. To date, engineering resilience is still predominantly application-oriented. Despite an increase in the usage of engineering resilience concept, the diversity of its applications in various engineering sectors complicates a universal agreement on its quantification and associated measurement techniques. There is a pressing need to develop a generally applicable engineering resilience analysis framework, which standardizes the modeling, assessment, and improvement of engineering resilience for a broader engineering discipline. This paper provides a literature survey of engineering resilience from the design perspective, with a focus on engineering resilience metrics and their design implications. The currently available engineering resilience quantification metrics are reviewed and summarized, the design implications toward the development of resilient-engineered systems are discussed, and further, the challenges of incorporating resilience into engineering design processes are evaluated. The presented study expects to serve as a building block toward developing a generally applicable engineering resilience analysis framework that can be readily used for system design.
... The aspects of traditional safety can enhance control structure based on report and analysis of events, incidents, and accidents. 1 Organizations and systems can be informed of the incidents and accidents through the reports, but error analysis and reporting incidents and accidents cannot improve safety to a higher level in socio-technical complex systems and hazardous environments. 2 The socio-technical approach brings about collective and joint techniques and provides an analysis closer to the complex reality of reciprocal interactions and conformity between people, technology, and work. ...
Resilience engineering is a new and proactive attitude toward improving the safety and reliability of complex industrial systems such as oil and gas companies and petrochemical plants. This study aims to evaluate the effect of redundancy on a resilient system and determine which redundancy in different aspects including human resources and equipment items (hardware, software, and information) is more efficient in a resilient system through data envelopment analysis. Data collection was performed in a petrochemical plant through a questionnaire. Then, reliability of the data was calculated through Cronbach’s alpha test. Next, the effects of human redundancy and equipment redundancy on resilience engineering are examined by means of data envelopment analysis approach. The results show that efficiency of the resilient system is improved by considering both equipment redundancy and human redundancy. It is also revealed that human redundancy is more effective in resilient system performance compared to equipment redundancy. Hence, human redundancy plays a vital role in efficiency enhancement of resilient systems. This study is among the first ones that investigate the impact of the redundancy in resilient systems in different aspects including human and equipment redundancy by means of data envelopment analysis approach and highlights the gaps between managers and staff about the impact of equipment redundancy and human redundancy on resilience engineering.
... References [44], [45] are focused on how to carry out long-term planning in consideration of highly interdependence among various energy transportation infrastructures. Since Sandy hurricane in the United States in 2012, interdependence studies on energy integrated transport system have attracted more attention due to analysis of disaster resistance [46], [47]. ...
... However, without a clear understanding of what manifestations of resilience look like (Back et al. 2008), it will be difficult to identify such manifestations in practice and quantify the theoretical models developed, creating a research-practice gap (Underwood and Waterson 2013). This is especially true when focusing on quantifying resilience for infrastructural systems, in which the current quantification methods used (e.g., graph theory: Berche et al. 2009; fuzzy interference: Heaslip et al. 2010) emanate from other well-established and well-elaborated methodological frameworks but as such are not fully capable of capturing the underlying interrelations of system modules (Tamvakis and Xenidis 2013). Research aimed at operationalizing theoretical resilience models and prospective analysis frameworks for quantifying resilience of infrastructure systems is required (e.g., Madni and Jackson 2009). ...
... Beyond all these discrepancies, recent research has aimed to develop a methodology to quantify the resilience of given system at some point in time, and represent it with an index. Despite Holling's statement that "measures of resilience require an immense amount of knowledge of the system and it is unlikely that we will often have all that is necessary", Yan [21] and Tamvakis [22] provide a description of the main methodologies proposed to quantify the resilience of a system. Both conclude by pointing out the lack of a common basis and the fact that all of these methodologies are applied to a particular case and that is difficult to generalize the methods to other situations. ...
Cost-benefit analysis (CBA) has emerged as one of the most widely used methodologies in environmental policy analysis, with many governments applying it in their decision-making procedures and laws. However, undertaking a full CBA is expensive, and conclusions must be drawn on which project or policy impacts to include in the analysis. Based on the ideas of resilience, vulnerability and risk, we suggest a method for prioritizing project impacts for inclusion in a CBA, which includes both expert assessment and citizen preferences. We then illustrate how the method can be applied in the context of land use change decisions, using a real application.
... In a recent paper, Tamvakis and Xenidis (2013) provided a comparative review of several methods for resilience quantification, pointing out that most of the existing approaches might actually have quite limited applicability, due to the fact that these methods usually rely on some ad-hoc assumptions and often focus on specific subsystems, like telecommunication or power distribution networks. As a result, although some of the concepts and methodologies are interesting and potentially powerful, they cannot be straightforwardly extended to quantify the resilience of a urban system as a whole, which normally consist of several interconnected and interdependent subsystems. ...
One of the most important tasks of urban and hazard planning is to mitigate the damages and minimize the costs of the recovery process after catastrophic events. In this context, the capability of urban systems and communities to recover from disasters is referred to as resilience. Despite the problem of resilience quantification having received a lot of attention, a mathematical definition of the resilience of an urban community, which takes into account the social aspects of an urban environment, has not yet been identified. In this article, we provide and test a methodology for the assessment of urban resilience to catastrophic events which aims at bridging the gap between the engineering and the ecosystem approaches to resilience. We propose to model an urban system by means of different hybrid social–physical complex networks, obtained by enriching the urban street network with additional information about the social and physical constituents of a city, namely citizens, residential buildings, and services. Then, we introduce a class of efficiency measures on these hybrid networks, inspired by the definition of global efficiency given in complex network theory, and we show that these measures can be effectively used to quantify the resilience of an urban system, by comparing their respective values before and after a catastrophic event and during the reconstruction process. As a case study, we consider simulated earthquakes in the city of Acerra, Italy, and we use these efficiency measures to compare the ability of different reconstruction strategies in restoring the original performance of the urban system.
... In a recent paper, Tamvakis and Xenidis (2013) provided a comparative review of several methods for resilience quantification, pointing out that most of the existing approaches might actually have quite limited applicability, due to the fact that these methods usually rely on some ad-hoc assumptions and often focus on specific subsystems, like telecommunication or power distribution networks. As a result, although some of the concepts and methodologies are interesting and potentially powerful, they cannot be straightforwardly extended to quantify the resilience of a urban system as a whole, which normally consist of several interconnected and interdependent subsystems. ...
One of the most important tasks of urban and hazard planning is to mitigate
the damages and minimize the costs of the recovery process after catastrophic
events. The rapidity and the efficiency of the recovery process are commonly
referred to as resilience. Despite the problem of resilience quantification has
received a lot of attention, a mathematical definition of the resilience of an
urban community, which takes into account the social aspects of a urban
environment, has not yet been identified. In this paper we provide and test a
methodology for the assessment of urban resilience to catastrophic events which
aims at bridging the gap between the engineering and the ecosystem approaches
to resilience. We propose to model a urban system by means of different hybrid
social-physical complex networks, obtained by enriching the urban street
network with additional information about the social and physical constituents
of a city, namely citizens, residential buildings and services. Then, we
introduce a class of efficiency measures on these hybrid networks, inspired by
the definition of global efficiency given in complex network theory, and we
show that these measures can be effectively used to quantify the resilience of
a urban system, by comparing their respective values before and after a
catastrophic event and during the reconstruction process. As a case study, we
consider simulated earthquakes in the city of Acerra, Italy, and we use these
efficiency measures to compare the ability of different reconstruction
strategies in restoring the original performance of the urban system.
Las inundaciones son uno de los efectos de los huracanes que se han evidenciado más en las zonas costeras, ocasionando pérdidas de vidas humanas y económicas. Esta situación ha puesto a prueba la capacidad de respuesta de los sistemas de infraestructura de drenaje pluvial. El objetivo de la investigación que precedió a este trabajo fue determinar la resiliencia ante inundaciones asociadas a huracanes del sistema de drenaje pluvial de la ciudad de Chetumal, Quintana Roo, México. Se consideraron cinco componentes, cada uno con un número variable de indicadores mostrados entre paréntesis: robustez (8), redundancia (7), recursos (2), rapidez (2) y capacidad adaptativa (13). El valor de cada indicador se normalizó; luego, se ponderó mediante la consulta de expertos; y, por último, se integró el índice de resiliencia, obteniéndose un valor de 0.2982 para la ciudad de Chetumal. Este valor indica una resiliencia baja, asociada principalmente a los componentes recursos y redundancia. Con base en lo anterior, se deben reforzar ambos componentes para elevar la resiliencia con énfasis en la aplicación de normativas e instrumentos de planeación territorial, planes de emergenciaen caso de inundaciones, planes de mantenimiento de la infraestructura pluvial, así como inversión enprogramas de prevención.
Rapid global urbanization, urban renewal and changes in people's lifestyles have led to both an increase in waste generation and more complex waste types. In response to these changes, many local governments have invested in municipal solid waste infrastructure (MSWI) to implement circular strategies. However, matching and bridging the costly and logistically complex MSWI with the dynamic social context is a central challenge. In this paper we aim to explore the interdependencies between MSWI and the local social system, and then conceptualize and empirically validate the systemic nature of MSWI. We first review the current MSW treatment methods, corresponding infrastructure, and the challenges facing them. Then, we interrogate system-oriented concepts and use two key insights to set up a conceptual model for mapping the interdependencies in a MSWI system (MSWIS). Finally, a case study of the Dutch city of Almere is used to empirically validate the MSWIS model and identify the social systems that contribute to the development of the MSWIS. The analysis reveals that the development of MSWIS is beyond the municipality's control: efficient resource recovery facilities established by businesses under market rules and waste reuse facilities constructed by social organizations/individuals based on their own needs are key pieces of the puzzle to complete the MSWIS. This highlights the ability of the framework to capture interdependencies that go further than just the formal municipal sphere of influence.
The increasing complexity of our world makes the occurrence of disruptive events with unforeseeable consequences more likely. The future is characterised by uncertainty; therefore, societies and their critical infrastructures need resilience, understood as generic adaptability thanks to flexibility and loose resources. In this book, the author argues that resilience can be normatively desirable if it is used to combine individual freedom and security. Using this as his premise, he develops a new concept of resilience for civil security research and shows how the engineering sciences can implement it through system principles such as diversity, modularity, decentralisation and redundancy.
Housing infrastructure is the basic need of living, and due to disaster, many houses got damaged. Therefore, assessing resilience for housing infrastructure against a flood hazard is an important task for any community as it gives the real scenario of the capability to resist and recover from the disaster after the occurrence of the hazard. The process of resilience quantification requires a different type of information from different sources, and due to which uncertainty and incomplete data may get involved. There is significantly limited literature available focusing on housing infrastructure resilience; however, the available literature has not incorporated such uncertainty and incomplete information. Therefore, in this work, a resilience assessment framework for housing infrastructure is proposed using a combination approach of Best Worst Method and a Hierarchical Evidential Reasoning based on the Dempster-Shafer theory against flood hazard. The proposed framework is then implemented in Barak valley North-East India to quantify that valley's resilience and evaluate the model. Initially, different resilience attributes are selected, and based on experts' opinion, the Best Worst Method rates the criteria to find the weightage. After finding the weightage, flood resilience is evaluated by using the Dempster-Shafer rule of combination. Lastly, sensitivity analysis is also performed to investigate the sensitivity of all the attributes of the proposed hierarchical housing infrastructure resilience model. The proposed flood resilience assessment model generates satisfactory results which indicate the condition state of resilience along with the unassigned degree of belief or uncertainty.
This chapter is proposing a framework for measuring community resilience at different spatial and temporal scales. Seven dimensions are identified for measuring the community resilience: Population and Demographics, Environmental/Ecosystem, Organized Governmental Services, Physical Infrastructures, Lifestyle and Community Competence, Economic Development, and Social-Cultural Capital. They are summarized with the acronym PEOPLES. Each dimension is characterized by a corresponding performance metric that is combined with the other dimensions using a multi-layered approach. Therefore, once a hybrid model of the community is defined, the proposed framework can be applied to measure its performance against any type of extreme event during emergency and in long term post-disaster phases. A resilience index can be determined to reflect all, or part, of the dimensions influencing the events.
This paper aims to enhance tangibility of the resilience engineering concept by facilitating understanding and operationalization of weak resilience signals (WRSs) in the rail sector. Within complex socio-technical systems, accidents can be seen as unwanted outcomes emerging from uncontrolled sources of entropy (functional resonance). Various theoretical models exist to determine the variability of system interactions, the resilience state and the organization’s intrinsic abilities to reorganize and manage their functioning and adaptive capacity to cope with unexpected and unforeseen disruptions. However, operationalizing and measuring concrete and reliable manifestations of resilience and assessing their impact at a system level have proved to be a challenge. A multi-method, ethnographic observation and resilience questionnaire, were used to determine resilience baseline conditions at an operational rail traffic control post. This paper describes the development, implementation and initial validation of WRSs identified and modeled around a ‘performance system boundary.’ In addition, a WRS analysis function is introduced to interpret underlying factors of the performance WRSs and serves as a method to reveal potential sources of future resonance that could comprise system resilience. Results indicate that performance WRSs can successfully be implemented to accentuate relative deviations from resilience baseline conditions. A WRS analysis function can help to interpret these divergences and could be used to reveal (creeping) change processes and unnoticed initiating events that facilitate emergence that degrades rail-system resilience. Establishing relevant change signals in advance can contribute to anticipation and awareness, enhance organizational learning and stimulate resilient courses of action and adaptive behavior that ensures rail operation reliability.
The impacts of extreme events continue long after the emergency response has terminated. Effective reconstruction of supply chain strategic infrastructure (SCSI) elements is essential for post-event recovery and the reconnectivity of a region with the outside. This study uses an interdisciplinary approach to develop a comprehensive framework to model resilience time. The framework is tested by comparing resilience time results for a simulated EF-5 tornado with ground truth data from the tornado that devastated Joplin, Missouri, on May 22, 2011. Data for the simulated tornado were derived for Overland Park, Johnson County, Kansas, in the greater Kansas City, Missouri, area. Given the simulated tornado, a combinatorial graph considering the damages in terms of interconnectivity between different SCSI elements is derived. Reconstruction in the aftermath of the simulated tornado is optimized using the proposed framework to promote a rapid recovery of
the SCSI. This research shows promising results when compared with the independent quantifiable data obtained from Joplin, Missouri, returning a resilience time of 22 days compared with 25 days reported by city and state officials
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