Elsevier

Engineering Structures

Volume 32, Issue 11, November 2010, Pages 3639-3649
Engineering Structures

Framework for analytical quantification of disaster resilience

https://doi.org/10.1016/j.engstruct.2010.08.008Get rights and content

Abstract

The concepts of disaster resilience and its quantitative evaluation are presented and a unified terminology for a common reference framework is proposed and implemented for evaluation of health care facilities subjected to earthquakes. The evaluation of disaster resilience is based on dimensionless analytical functions related to the variation of functionality during a period of interest, including the losses in the disaster and the recovery path. This evolution in time including recovery differentiates the resilience approach from the other approaches addressing the loss estimation and their momentary effects. The recovery process usually depends on available technical and human resources, societal preparedness, public policies and may take different forms, which can be estimated using simplified recovery functions or using more complex organizational and socio-political models. Losses are described as functions of fragility of systems that are determined using multidimensional performance limit thresholds. The proposed framework is formulated and exemplified for a typical Californian Hospital building using a simplified recovery model, considering direct and indirect losses in its physical system and in the population served by the system. A hospital network is also analyzed to exemplify the resilience framework. Resilience function captures the effect of the disaster, but also the results of response and recovery, the effects of restoration and preparedness. Therefore, such a function becomes an important tool in the decision process for both the policy makers and the engineering professionals.

Introduction

Over the past years the natural and man-made disasters with which the human society had to cope with had stressed the necessity to be prepared and to be able to recover in a short time from a sudden and unexpected change in the community’s technical, organizational, social and economical condition. The concepts of ‘risk reduction’, ‘vulnerability’, ‘recovery’ and ‘resilience’ have become keywords when dealing with hazardous events, but there is a need to go beyond the intuitive definition and provide a quantitative evaluation of them. When a disaster strikes, the community affected requires immediate help to survive, resources, and efforts to recover in a short time. In other words, the community needs to be “prepared” and less “vulnerable”, in order to achieve a high ‘resilience’.

The outcomes of the 2005 World Conference on Disaster Reduction (WCDR) confirmed the importance of the entrance of the term resilience into disaster discourse and gave birth to a new culture of disaster response. Resilience, according to the dictionary, means “the ability to recover from (or to resist being affected by) some shock, insult or disturbance” and the root of the term has to be found in the Latin word ‘resilio’ that literary means ‘to jump back’. Manyena [1], evaluating all the possible definitions provided from the 90’ to nowadays, suggests that Resilience could be viewed as the “intrinsic capacity of a system, community or society predisposed to a shock or stress to adapt and survive by changing its non-essential attributes and rebuilding itself”. As regards its relationship with the concept of vulnerability, it can be accepted that the latter is closely associated to the level of resilience, but it is a complementary aspect of the community preparedness.

Emphasizing the concept of resilience means to focus on the quality of life of the people at risk and to develop opportunities to enhance a better outcome. In contrast, the vulnerability approach places stress on the production of nature [2] to resist the natural hazard. Engineers, guided by legislation, play a guiding role in the quantification of vulnerability. In order to understand better the relationship between vulnerability and fragility, it is convenient to focus on the field of seismic engineering. Given a certain control parameter (e.g. the shaking intensity), vulnerability defines the loss while fragility gives the probability of some undesirable event (e.g. collapse). Thus, fragility functions may assess the probability that a building will collapse, as well as that a factory may release hazardous materials into the atmosphere, given a certain seismic intensity. On the other side, vulnerability functions would provide as a function of the same control parameter the damage factor for the building (e.g. valuated as the repair cost divided by the replacement cost) or the quantity of hazardous materials released. In the last years, as the idea of the necessity of building disaster-resilient communities gains acceptance, new methods have been proposed to quantify resilience beyond estimating losses. Because of the vastness of the definition, resilience necessarily has to take into account its entire complex and multiple dimensions, which includes technical, organizational, social, and economic facets. Bruneau et al. [3], [4] offered a very broad definition of resilience to cover all actions that reduce losses from hazard, including effects of mitigation and rapid recovery. However, Bruneau et al. [3], [4] defined a fundamental framework for evaluating community resilience without a detailed quantification and definition.

After the general framework provided by Bruneau et al. [3] various studies have been carried out, with the goal of practically evaluating the concept of resilience and identifying the main units of measurement of it.

Miles and Chang [5] present a comprehensive conceptual model of recovery, which establishes the relationships among a community’s household business, lifeline networks, and neighborhoods. The primary aim is to discuss issues of community recovery and to attempt to operationalize it. Even if a measure of resilience is not provided in their work, the paper points out the necessity to correlate the concept of recovery to real factors, such as the household object, whose attributes are the income, the year the building of residence was built, and the possible existence of any retrofit building.

Davidson and Cagnan [6] developed a model of the post-earthquake restoration processes for an electric power system. A discrete event simulation model based on available data was built, with the goal of improving the quantitative estimates of restoration times that are required to evaluate economic losses, and identify ways to improve the restoration processes in future earthquakes.

Chang and Shinozuka [7] contribute to the literature on disaster resilience discussing a quantitative measure of resilience based on the case study of the Memphis water system. They explored the extent to which loss estimation models can be used to measure resilience.

Cimellaro et al. [8], attempted to formulate the first framework to quantify resilience, however only the uncertainties of the intensity measure I were considered, whereas in the framework proposed in this work all other uncertainties are involved.

Bruneau and Reinhorn [4] for the first time relate probability functions, fragilities and resilience in a single integrated approach for acute care facilities. After having defined the main properties and concepts of resilience, two different options to quantify the disaster resilience of acute care facilities are exposed as the percentage of healthy population and as the number of patients/day that can receive service.

While this literature survey is by no mean comprehensive, it is presented here to highlight several distinct techniques, and set the stage for future developments in this work.

The goal of this paper has been to provide a framework for quantitative definition of resilience using an analytical function that may fit both technical and organizational issues showing two applications to health care facilities of the methodology.

Section snippets

Definitions and formulations

To establish a common framework for resilience, a unified terminology is proposed, while the fundamental concepts are analyzed and presented in this paper.

Definition 1

Resilience (R) is defined as a function indicating the capability to sustain a level of functionality or performance for a given building, bridge, lifeline networks, or community, over a period defined as the control time (TLC) that is usually decided by owners, or society (usually is the life cycle, life span of the system etc.).

Definition 2

The

Numerical examples

Two case studies are illustrated in this section to show the implementation of the procedure for evaluating disaster resilience. The first case is a loss estimation study of a specific hospital; it is aimed to provide a more accurate evaluation of economic losses for buildings located at specific sites. In this case, an accurate analysis was performed using nonlinear dynamic analysis with an adequate description of limit state thresholds and their variability.

The second case is a regional loss

Remarks and conclusions

The definition of disaster resilience combines information from technical and organizational fields, from seismology and earthquake engineering to social science and economics. Many assumptions and interpretations have to be made in the study of disaster resilience. However, the final goal is to integrate the information from these different fields into a unique function leading to results that are unbiased by uninformed intuition or preconceived notions of risk. The goal of this paper has been

Acknowledgements

The research leading to these results has received funding from the European Community’s Seventh Framework Programme (Marie Curie International Reintegration ActionsFP7/2007-2013 under the Grant Agreement no PIRG06-GA-2009-256316 of the project ICRED–Integrated European Disaster Community Resilience. The research is sponsored also by MCEER-A Center for Excellence on Earthquake Engineering to Extreme Events, which is supported by NIST. Any opinions, findings and conclusions or recommendations

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