This paper introduces a new methodology to evaluate the resilience of communities. The methodology is based on the PEOPLES framework and it makes use of resilience indicators to evaluate community resilience. The methodology requires data for the indicators as input and returns a resilience function as an output. The resilience function shows the serviceability of the community for a given period of time following the disaster. This methodology has been implemented in the form of two software tools. The first one is a web app that is accessible at http://www.resiltronics.org/PEOPLES/login.php or http://borispio.ddns.net/PEOPLES/login.php while the other is a desktop software. The output quality provided by the tools is not compromised with their usage simplicity. Both softwares are meant to assist the user to use the introduced resilience framework by offering a user-friendly interface. As a case study, the resilience of the city of San Francisco city has been evaluated using both tools.@en

The capacity of a community to react and resist to an emergency situation is strictly related to the proper functioning of its own infrastructure systems. This paper proposes a simulation oriented approach to evaluate the resilience of large scale water distribution networks. The case study used in this research is the water network of a large scale virtual city. The water network is modeled using the software EPANET 2.0 with the help of an integrated Matlab toolbox. The network consists of 16000 junctions and 19000 water pipes buried under the road network of the city. A series of earthquake scenarios is applied to the water network and the damage induced by the earthquakes has been determined using fragility function. The failure of the system occurs when the water flow and the water pressure go below a certain threshold. The resilience of the network is then evaluated using two indices: (1) the number of users without water, (2) the loss of total water supply.@en

The multiple uncertainties of both natural and human-caused disasters have attracted increased attention to the topic of resilience engineering. In this paper, an indicator-based method for measuring urban community resilience is proposed. The method is based on the PEOPLES framework, which is a hierarchical framework for defining the disaster resilience of communities at various scales. It consists of seven dimensions, summarized by the acronym PEOPLES, which stands for population, environmental and ecosystem, organized governmental services, physical infrastructures, lifestyle, economic development, and social capital. Each of the dimensions is split into several components and indicators, which were derived by the authors or collected from a wide range of literature. Each indicator is represented using a performance function, which portrays the functionality of the indicator in time. A higher functionality of the indicator denotes a higher resilience of the community. These functions can be constructed in a systematic manner using damage and restoration parameters. The aggregation of the performance functions passing through the different hierarchical levels of PEOPLES framework leads to one function that represents the dynamic performance of the analyzed community. This paper also introduces a matrix-based interdependency technique that serves as a weighting scheme for the different indicators. As a case study, the proposed methodology is applied to the city of San Francisco for which a resilience curve and resilience metric have been computed.@en

Community resilience is becoming a growing concern for authorities and decision makers. This paper introduces two indicator-based methods to evaluate the resilience of communities based on the PEOPLES framework. PEOPLES is a multi-layered framework that defines community resilience using seven dimensions. Each of the dimensions is described through a set of resilience indicators collected from literature and they are linked to a measure allowing the analytical computation of the indicator’s performance. The first method proposed in this paper requires data on previous disasters as an input and returns as output a performance function for each indicator and a performance function for the whole community. The second method exploits a knowledge-based fuzzy modeling for its implementation. This method allows a quantitative evaluation of the PEOPLES indicators using descriptive knowledge rather than deterministic data including the uncertainty involved in the analysis. The output of the fuzzy-based method is a resilience index for each indicator as well as a resilience index for the community. The paper also introduces an open source online tool in which the first method is implemented. A case study illustrating the application of the first method and the usage of the tool is also provided in the paper.@en

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 of 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.@en

To improve the resilience of critical infrastructure systems, their intrinsic properties need to be understood and their resilience state needs to be identified. In the literature, several methods to evaluate networks’ reliability and resilience can be found. However, the applicability of these methods is usually restricted to small-size networks. In this paper, the transportation network of a large-scale virtual city is considered as a case study. A random removal strategy of the links (roads) is applied to simulate the network’s failure. The network reliability is then calculated using the Destruction Spectrum (D-spectrum) method and a Monte Carlo approach has been developed to generate failure permutations that are necessary for the evaluation of the D-spectrum. In addition, the Birnbaum Importance Measure (BIM) has been adopted in this study to determine the importance of the network’s components. The methodology adopted in this study can be also extended to all network-based systems. The paper also introduces resilience indicators as a soft tool to predict the performance and the serviceability of the transportation networks.@en

This work aims at establishing laboratory requirements and testing conditions in order to understand the physical and emotional stability of people during a ground shaking. Several individuals with different human characteristics (gender, age, height, etc.) are considered in the test. Two different laboratory setups are presented. A position device (Kinect) and video targetless tracking algorithm has been used to collect the human body position during the shaking. A two and a three dimensional shaking tables are used to generate artificial earthquakes with different frequency bands. In addition, a well detailed virtual reality setting is applied to the testing site in order to illustrate the real environment. During the experiment, a special attention has been given to the factor of “surprise”, which is necessary to ensure a natural reaction of the individuals. The result of the experiment proved common behaviors among the individual samples during the shaking. This work is considered a first step towards a large test campaign, which is necessary to obtain comprehensive statistics on this topic.@en

In recent years, the concept of resilience has been introduced to the field of engineering as it relates to disaster mitigation and management. However, the built environment is only one element that supports community functionality. Maintaining community functionality during and after a disaster, defined as resilience, is influenced by multiple components. This report summarizes the research activities of the first two years of an ongoing collaboration between the Politecnico di Torino and the University of California, Berkeley, in the field of disaster resilience. Chapter 1 focuses on the economic dimension of disaster resilience with an application to the San Francisco Bay Area; Chapter 2 analyzes the option of using base-isolation systems to improve the resilience of hospitals and school buildings; Chapter 3 investigates the possibility to adopt discrete event simulation models and a meta-model to measure the resilience of the emergency department of a hospital; Chapter 4 applies the meta-model developed in Chapter 3 to the hospital network in the San Francisco Bay Area, showing the potential of the model for design purposes Chapter 5 uses a questionnaire combined with factorial analysis to evaluate the resilience of a hospital; Chapter 6 applies the concept of agent-based models to analyze the performance of socio-technical networks during an emergency. Two applications are shown: a museum and a train station; Chapter 7 defines restoration fragility functions as tools to measure uncertainties in the restoration process; and Chapter 8 focuses on modeling infrastructure interdependencies using temporal networks at different spatial scales. @en

This paper presents a new methodology to predict the potential damage and physical impacts of an earthquake on the built environment. A new methodology to the urbanized systems and large-scale simulations within a seismic scenario are explored, by evaluating multipurpose codes for numerical simulation. A 3-D building shape of a standard virtual city is developed for evaluating the seismic effects at increasing intensities. Once the buildings are integrated into the city, parallel simulations are applied to compute the global behavior of buildings after a disruptive scenario. Monte Carlo Simulations (MCS) are applied to take into account the epistemic uncertainties associated with geometry and mechanical properties within the range of observations. For each set of buildings' data, the nonlinear dynamic analysis is performed through SAP2000 Application Programming Interface (API) in order to assess the dynamic response of the buildings in an organized and automatic fashion. Accordingly, the city is mapped into different zones representative to the possibility of having different levels of damage (complete, extensive, moderate, and slight). This tool supports decision-makers to explore how their community will respond to a disruptive event, to develop different strategies for monitoring and control the emergency in urbanized areas.@en