Indicator-based method to evaluate community resilience

The capacity of a community to react and resist to an emergency is strictly related to the proper functioning of its own infrastructure systems. A better understanding of critical infrastructure architecture is necessary for defining measures to achieve a better resilience against threats (natural and human threats) in an integrated manner. For this purpose, indicators are perceived as important instruments to measure the resilience of infrastructure systems. Many research activities have been focusing on developing reliable indicators that could be applied at different scales, but research on resilience, which is a multidimensional and transformative concept, is still in the early stages of development. Developing a comprehensive, standardized set of resilience indicators is obviously difficult for such a dynamic, constantly re-shaping and context-dependent concept, Previous studies have highlighted the importance of conceptual frameworks to guide the selection of the indicators, so following the same trend this paper describes the procedure for selecting the proper indicators for community resilience within the PEOPLES framework (Cimellaro et. al 2009). PEOPLES is a holistic framework for defining and measuring disaster resilience of communities at various scales. It is divided into seven dimensions, and each dimension is further divided into several components. An integrated approach is presented that combines both quantitative and qualitative as well as outcome and process indicators, addressing a broad variety of issues such as the security, the geo-politics, the sociology, economy, etc. The methodology classifies the indicators’ location within the seven PEOPLES dimensions and provides a structure for creating a condensed list of indicators. Each indicator is linked to a measure allowing it to be quantified. The measures are expressed by serviceability functions rather than scalar values in order to allow a dynamic measurement of the indicators. Finally, the proposed indicators are weighted and then aggregated into a single serviceability function that describes the functionality of the community in time. The developed methodology has been tested on the critical infrastructures of San Francisco, USA, in order to assess their level of resiliency. Results of the case study show that the methodology introduced to compute the resiliency allows decision makers to derive key-indicators of community resilience that are applicable on a higher level of societal resilience, across different contexts and hazard types (attacks, accidents, etc.). The present work contributes to this growing area of research as it provides a universal tool to quantitatively assess the resilience of communities at multiple scales

Resilience assessment at the state level

This paper presents an analytical approach to evaluate the level of post-disaster adaptation (Bounce-Back) of communities based on their resilience. While resilience is the intrinsic characteristics of a system, adaptation considers external agents in its assessment. The presented work is to some extent a parallelism to the risk assessment concept. Generally, risk is a function of vulnerability, exposure, and hazard, whereas adaptation considers resilience instead of vulnerability in its estimation. This leads to the evaluation of a system’s ability to cope with after-shock consequences and to return back to a functional state rather than the likelihood of a system to experience damage. The paper also proposes a quantitative framework for assessing resilience at the state level based on the Hyogo Framework for Action (HFA), a work done by the UN. HFA has succeeded in assessing the resilience of every state in a quantifiable fashion. HFA estimates the resilience of countries based on a number of indicators that are weighted equally. Those indicators, however, do not contribute equally to the resilience output; therefore, it is necessary to weigh those indicators according to their contribution towards resilience. To do so, we are introducing the Dependence Tree Analysis (DTA), which identifies the strength of relationships between the indicators and the resilience, giving weights to the indicators accordingly. A full case study composed of 37 countries is presented in this paper, where the resilience and the Bounce Back indices of each country are evaluated

Intervention Grouping Strategy for Multi-component Interconnected Systems:A Scalable Optimization Approach

The well-being of modern societies depends on the functioning of their infrastructure networks. During their service lives, infrastructure networks are subject to different stresses (e.g., deterioration, hazards, etc.). Interventions are performed to ensure the continuous fulfillment of the infrastructure's functional goals. To guarantee a high level of infrastructure availability and serviceability with minimal intervention costs, preventive intervention planning is essential.Finding the optimal grouping strategy of intervention activities is an NP-hard problem that is well studied in the literature and for which various economic models and optimization approaches are proposed. This research focuses on a new efficient optimization model to cope with the intervention grouping problem of interconnected multi-component systems. We propose a scalable two-step intervention grouping model based on a clustering technique. The clustering technique is formulated using Integer Linear Programing, which guarantees the convergence to global optimal solutions of the considered problem. The proposed optimization model can account for the interactions between multiple infrastructure networks and the impact on multiple stakeholders (e.g., society and infrastructure operators). The model can also accommodate different types of intervention, such as maintenance, removal, and upgrading.We show the performance of the proposed model using a demonstrative example. Results reveal a substantial reduction in net costs. In addition, the optimal intervention plan obtained in the analysis shows repetitive patterns, which indicates that a rolling horizon strategy could be adopted so that the analysis is only performed for a short time horizon

Resilience analysis of large scale networks using the D-spectrum method

Infrastructure systems are crucial for the development of communities because they provide essential services to the habitants. Here we focus on the transportation network, which is designed to provide a continuous service to the community. Due to its decisive role in the economy, governments and policy makers have been investing in developing strategies to increase the resilience of this kind of infrastructure against disruptive events. In the literature, several methods to evaluate networks’ reliability and resilience can be found. The applicability of these methods is limited to small networks due to the computational complexities. In this paper, the case of city-scale road transportation networks is tackled. The case study considered in this work is the transportation network of a virtual, city called ‘Ideal City’. First, the road map of the city is transformed into an undirected graph with 15012 nodes and 19614 edges. A non-random gradual removal of the edges has been applied until the network’s failure point is reached. The edge removal process is related to the failure probabilities of the edges when the network is exposed to a certain hazard. In fact, the effect of hazards on the transportation network is not direct. The hazard exposes the building structures on the road sides to a failure risk. These structures if collapsed would cause the adjacent roads to be blocked and thus lose functionality due to the debris falling from the structures. For this purpose, a building infrastructure is modeled and the relationship between the level of damage of building and the amount of debris falling on the adjacent roads is developed. A Monte Carlo approach is used to generate failure permutations of edges considering their failure probabilities. The network reliability is then calculated using the Destruction Spectrum (D-spectrum) approach. In addition, the network’s edges have been ranked from the most to the least important by applying the Birnbaum Importance Measure (BIM). Due to the large size of the network, a number of computational problems have arisen. Therefore, several coding algorithms have been developed to allow evaluating both the reliability and the BIM indexes while avoiding computational errors. The results obtained in this study are used to identify the vulnerable components of the network. The vulnerable components are the ones that should be focused on to improve the overall resilience of the infrastructure. The analysis concept adopted in this study is applicable to all network-based infrastructure systems such as water, gas, transportation, etc. Future work will be oriented towards applying the methodology to other network-based infrastructure systems

DOWNTIME ESTIMATION AND ANALYSIS OF LIFELINES AFTER AN EARTHQUAKE

This paper provides an empirical probabilistic model for estimating the downtime of lifelines following earthquakes. Generally, the downtime of infrastructure varies according to several factors, including the characteristics of the exposed structure, the earthquake intensity, and the amount of available human resources. Having so many variables makes the process of estimating the downtime even harder. Therefore, it is necessary to have a simple and practical model to estimate the downtime of infrastructure systems. To do so, a large database has been collected from literature, which includes damage data for many earthquakes that took place in the last century. The database has been used to create restoration curves for four types lifelines: Water distribution network, Gas network, Power system, and Telecommunication network. Different restoration curves have been developed based on several criteria, such as the earthquake magnitude, development level of the affected country, and countries with enough data. The restoration curves have been presented in terms of probability of recovery and time; the longer is the time after the disaster, the higher is the probability of the infrastructure to recover its functions

A New Resilience Rating System for Countries and States

This research presents a quantitative method to assess resilience at the state level. The approach introduced in this work is an evolution of the risk assessment concept. Risk is mainly a function of vulnerability, hazard, and exposure; on the other hand, resilience focuses more on the internal characteristics of a system rather than its vulnerability. To tackle this difference, a new formulation has been introduced for the evaluation of resilience. In this formulation, resilience is a function of hazard, exposure, and intrinsic resilience. Generally, intrinsic resilience deals with the internal characteristics of a system, and it differs from the traditional resilience index that takes into account external factors in its assessment, such as the disaster intensity and the level of exposure. The paper also provides a method to compute the intrinsic resilience of countries. This method is based on the data provided by Hyogo Framework for Action (HFA), which is a work developed by the United Nations (UN). HFA evaluates the inherent resilience of countries based on a number of equally weighted indicators. However, further analysis has shown that the contribution made by each of those indicators toward the intrinsic resilience is different. This discrepancy has necessitated weighting the indicators based on their individual contribution towards the intrinsic resilience. To do that, we introduce the Dependence Tree Analysis (DTA). DTA is a method that determines the correlation between a component and its sub-components (i.e., between intrinsic resilience and its indicators), enabling us to orderly allocate new weights to the indicators to obtain a more representative output for the intrinsic resilience. Finally, a case study composed of 37 states has been conducted in order to illustrate the methodology in all details. Both intrinsic resilience and resilience indexes for each of the states were assessed. This was followed by a comparative analysis in order to test the applicability of the methodology, and the results were in line with the predictions

Resilience assessment at the state level

This paper presents an analytical approach to evaluate the level of post-disaster adaptation (Bounce-Back) of communities based on their resilience. While resilience is the intrinsic characteristics of a system, adaptation considers external agents in its assessment. The presented work is to some extent a parallelism to the risk assessment concept. Generally, risk is a function of vulnerability, exposure, and hazard, whereas adaptation considers resilience instead of vulnerability in its estimation. This leads to the evaluation of a system’s ability to cope with after-shock consequences and to return back to a functional state rather than the likelihood of a system to experience damage. The paper also proposes a quantitative framework for assessing resilience at the state level based on the Hyogo Framework for Action (HFA), a work done by the UN. HFA has succeeded in assessing the resilience of every state in a quantifiable fashion. HFA estimates the resilience of countries based on a number of indicators that are weighted equally. Those indicators, however, do not contribute equally to the resilience output; therefore, it is necessary to weigh those indicators according to their contribution towards resilience. To do so, we are introducing the Dependence Tree Analysis (DTA), which identifies the strength of relationships between the indicators and the resilience, giving weights to the indicators accordingly. A full case study composed of 37 countries is presented in this paper, where the resilience and the Bounce Back indices of each country are evaluated

Resilience assessment at the state level

This paper presents an analytical approach to evaluate the level of post-disaster adaptation (Bounce-Back) of communities based on their resilience. While resilience is the intrinsic characteristics of a system, adaptation considers external agents in its assessment. The presented work is to some extent a parallelism to the risk assessment concept. Generally, risk is a function of vulnerability, exposure, and hazard, whereas adaptation considers resilience instead of vulnerability in its estimation. This leads to the evaluation of a system’s ability to cope with after-shock consequences and to return back to a functional state rather than the likelihood of a system to experience damage. The paper also proposes a quantitative framework for assessing resilience at the state level based on the Hyogo Framework for Action (HFA), a work done by the UN. HFA has succeeded in assessing the resilience of every state in a quantifiable fashion. HFA estimates the resilience of countries based on a number of indicators that are weighted equally. Those indicators, however, do not contribute equally to the resilience output; therefore, it is necessary to weigh those indicators according to their contribution towards resilience. To do so, we are introducing the Dependence Tree Analysis (DTA), which identifies the strength of relationships between the indicators and the resilience, giving weights to the indicators accordingly. A full case study composed of 37 countries is presented in this paper, where the resilience and the Bounce Back indices of each country are evaluated

Measuring and improving community resilience: a Fuzzy Logic approach

Due to the increasing frequency of natural and man-made disasters worldwide, the scientific community has paid considerable attention to the concept of resilience engineering in recent years. Authorities and decision-makers, on the other hand, have been focusing their efforts to develop strategies that can help increase community resilience to different types of extreme events. Since it is often impossible to prevent every risk, the focus is on adapting and managing risks in ways that minimize impacts to communities (e.g., humans and other systems). Several resilience strategies have been proposed in the literature to reduce disaster risk and improve community resilience. Generally, resilience assessment is challenging due to uncertainty and unavailability of data necessary for the estimation process. This paper proposes a Fuzzy Logic method for quantifying community resilience. The methodology is based on the PEOPLES framework, an indicator-based hierarchical framework that defines all aspects of the community. A fuzzy-based approach is implemented to quantify the PEOPLES indicators using descriptive knowledge instead of hard data, accounting also for the uncertainties involved in the analysis. To demonstrate the applicability of the methodology, data regarding the functionality of the city San Francisco before and after the Loma Prieta earthquake are used to obtain a resilience index of the Physical Infrastructure dimension of the PEOPLES framework. The results show that the methodology can provide good estimates of community resilience despite the uncertainty of the indicators. Hence, it serves as a decision-support tool to help decision-makers and stakeholders assess and improve the resilience of their communities