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In recent years, the concept of resilience has been introduced to the engineering field in particular related to disaster mitigation and management. However, the built environment is only part of the elements that support community functions. Maintaining community functionality during and after a disaster, defined as resilience, is influenced by multiple components. The paper 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. Several applications of part of such framework can already be found in literature for different types of infrastructures, physical and organizational (e.g. gas network, water distribution networks, health care facilities etc.). The proposed framework can be used as decision support by stakeholders and managers and it can help planners in selecting the optimal restoration strategies that enhance the community resilience index.

3The increased frequency of natural disasters and man-made catastrophes has caused major disruptions to 4 critical infrastructures (CI) such as Water Distribution Networks (WDNs). Therefore, reducing the 5 vulnerability of the systems through physical and organizational restoration plans are the main concern 6 for system engineers and utility managers that are responsible for the design, operation, and protection 7 of WDNs. In this paper, a Resilience Index (R) of a WDN has been proposed which is the product of 8 three indices: (i) the number of users temporary without water, (ii) the water level in the tank, and (iii) 9 the water quality. The Resilience Index is expected to help planners and engineers to evaluate the 10 functionality of a WDN which includes: (1) delivering a certain demand of water with an acceptable 11 level of pressure and quality; (2) the restoration process following an extreme event. A small town in the 12 South of Italy has been selected as a case study to show the applicability of this index using different 13 disruptive scenarios and restoration plans. The numerical results show the importance of the partition of 14 the network in districts to reduce the extension of disservices. It is also shown the necessity to consider 15 the indices separately to find trends that cannot be captured by the global index. Advantages and 16 disadvantages of the different restoration plans are discussed. The proposed indices can be implemented 17 in a decision support tool used by governmental agencies which want to include the restoration process, 18 the environmental and social aspects in their design procedure. 20The water distribution networks and the Critical Infrastructures (CI) in general provide services by 24 allowing flows of fuels, materials, information, electric power etc.. The disruptions change the 25 operability state of parts of the network (e.g. nodes and/or links), and then the recovery actions restore 26 the functionality of the damaged parts of the network, allowing the performance of the system to return 27 to the nominal levels as fast as possible. In the past, emphasis was given to the physical protection of 28 water distribution networks, but now attention is shifting toward the infrastructure resilience, defined as 29 the ability of infrastructure systems to withstand, adapt to, and rapidly recover from the effects of a 30 disruptive event. This concept is becoming increasingly important in the context of CIs and defining 31 infrastructure functionality is essential for evaluating its resilience (Cimellaro et al., 2014a). Although 32 several authors (Holling, 1973; Mileti, 1999; Fiksel, 2003) have worked in the field of Disaster 33 Resilience, Bruneau et al. (2003) offered the first broad definition of this quantity including the effects 34 of losses, mitigation and rapid recovery. In their study, they identify four dimensions of community 35 resilience, namely: i) technical, ii) organizational, iii) social, and iv) economic. However, in their work 36 they did not provide a d...

SUMMARYA resilience index is used to quantify preventive measures, emergency measures, and restoration measures of complex systems, such as physical infrastructures, when they are subjected to natural disasters like earthquakes, hurricanes, floods, etc. Interdependencies among these systems can generate cascading failures or amplification effects, which can also affect the restoration measures right after an extreme event and generate a reduction of the resilience index. In this article, a method is proposed to evaluate the physical infrastructure resilience of a region affected by a disaster considering infrastructure interdependency. It is illustrated using available restoration curves from the March 11 2011 Tohoku Earthquake in Japan. The weights assigned to each infrastructure, which are used to determine resilience, are evaluated using the degree of interdependency indices which are obtain by time series analysis. Results show that the weight coefficients thus obtained do not influence the resilience index significantly; however, the methodology proposed is unbiased from subjective judgment and is able to identify the critical lifelines. Furthermore, the results of the case study presented here suggest that to obtain meaningful estimation of the weight coefficients, it is necessary to consider the period range between two perturbations (e.g., main shock and aftershock). Future infrastructure disruption data (from this and other earthquakes) would be needed to generalize this finding that will allow also to quantify the changes in the restoration curves caused by the magnitude and distance of the shocks from the epicenter, as well as the intrinsic properties of the physical infrastructures.

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SUMMARYThis paper introduces an organizational model describing the response of the Hospital Emergency Department (ED). The metamodel is able to estimate the hospital capacity and the dynamic response in real time and to incorporate the influence of the damage of structural and non-structural components on the organizational ones. The waiting time is the main parameter of response and it is used to evaluate the disaster resilience index of healthcare facilities. Its behaviour is described using a double exponential function and its parameters are calibrated based on simulated data. The metamodel covers a large range of hospital configurations and takes into account hospital resources, in terms of staff and infrastructures, operational efficiency and existence of an emergency plan, maximum capacity and behaviour both in saturated and over-capacitated conditions. The sensitivity of the model to different arrival rates, hospital configurations, and capacities and the technical and organizational policies applied during and before the strike of the disaster has been investigated. This model becomes an important tool in the decision process either for the engineering profession or for the policy makers.

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