The multitude of uncertainties of both natural and man-made disasters have prompted an increased attention in resilience engineering and disaster management. To overcome the effects of disastrous events, such as economic and social effects, modern communities need to be resilient. Natural disasters are unpredictable and unavoidable. While it is not possible to prevent them and protect individuals and societies against such disasters, modern communities should be prepared by incorporating both pre-event (preparedness and mitigation) and post-event (response and recovery) resilience activities to minimize the negative effects after a severe event. Resilience indicators may be fundamental to help the planners and decision-makers to develop strategies and action plans for making communities more resilient. This chapter presents a quantitative approach to estimate the resilience and resilience-based risk at the state level. In the proposed method, the resilience-based risk is a function of resilience, hazard, and exposure. To evaluate the resilience parameter, data provided by the Sendai Framework for Disaster Risk Reduction (SFDRR) are used. The framework is developed using resilience indicators with the primary goal of achieving disaster risk reduction. To use those indicators in the resilience assessment, it is necessary to define the impact and the contribution of each indicator towards resilience. To do that, two possible methods to combine and weight the different SFDRR indicators are presented: Dependence Tree Analysis (DTA) and Spider Plot Weighted Area Analysis (SPA). The proposed approach allows the decision-makers and governments to evaluate the resilience and the related resilience-based risk (RBR) of their countries using available information.@en

This chapter introduces the role of machine learning (ML) in resilience engineering and discusses actual cases of emergencies in which ML contributed positively. To identify its benefits within the resilience-relevant aspects (social, economic, infrastructural, institutional, environmental, and communitywise), the role of ML in various disaster management applications is analyzed, including model identification, emergency detection, and solution generation. The problem of data scarcity in model identification is presented. The application of ML in different fields of emergency detection (e.g., physical, virtual) is highlighted. Finally, the effectiveness of ML in solution generation to support human decision making is evaluated. Real examples are included in which machines exceed humans in providing solutions.@en

Due to the increasing frequency of natural and man-made disasters, the scientific community has paid considerable attention to the concept of resilience engineering. On the other hand, authorities and decision-makers have been focusing their efforts on developing 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 the 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 a community. A fuzzy-based approach is implemented to quantify the PEOPLES indicators using descriptive knowledge instead of complex data, accounting for the uncertainties involved in the analysis. To demonstrate the applicability of the methodology, three cases with different levels of data availability are performed to obtain a resilience curve and resilience index of two out of seven dimensions of the PEOPLES framework. When numerical data does not exist, descriptive data based on expert knowledge is used as input. Results show that the proposed methodology can cope with both numerical and descriptive input data with different uncertainty levels providing good estimates of resilience. The methodology can be used as a decision-support tool to assist decision-makers and stakeholders in assessing and improving their communities' resilience for future events, focusing on specific indicators that suffer from resilience deficiencies and need improvements.

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Community and infrastructure resilience against natural and man-made hazards is paramount for the well-being of modern societies. To adapt to the fast-changing world, having communities that can effectively respond to the continuously changing (physical and social) environment is essential. Despite the existing literature on resilience definition and estimation, few frameworks and associated tools can effectively help decision-making. In addition, these tools are usually not well integrated into the community and infrastructure management processes so that decision-makers and authorities can effectively use them. This paper aims at developing a resilience-based risk assessment approach at the community level. It combines the risk analysis parameters with the intrinsic resilience of the community. The proposed approach offers essential insights into the quantitative resilience analysis of communities at different scales and for different natural hazards. It enables the combination of the risk parameters (hazard, exposure, and vulnerability) with the inherent resilience of all the systems that constitute a community. This paper also presents an easy-to-interpret tool for visualizing the resilience results obtained from the introduced approach. It translates the paper's scientific contribution into an interactive and visualization instrument that can ultimately support policymaking to ensure their communities' short and long-term resilience under known and unknown hazardous events.@en

Critical infrastructures are an integral part of our society and economy. Services like gas supply or water networks are expected to be available at all times since a service failure may incur catastrophic consequences to the public health, safety, and financial capacity of the society. Several resilience strategies have been examined to reduce disaster risk and evaluate the downtime of infrastructures following destructive events. This paper introduces an indicator-based downtime estimation model for buried infrastructures (i.e., water and gas networks). The model distinguishes the important aspects that contribute to determining the downtime of buried infrastructure following a hazardous event. The proposed downtime model relies on two inference methods for its computation, Fuzzy Logic (FL) and Bayesian Network (BN), which are adapted for the current application. Finally, through a case scenario, a comparison of the two inference methods, in terms of results and limitations, is presented. Results show that both methods incorporate intuitive knowledge and/or historical data for defining fuzzy rules (in FL) and estimating conditional probabilities (in BN). The difference stands in the interpretation of the outcome. The output of the FL is a membership that defines how well the downtime fits the fuzzy levels while the BN output is a probability distribution that represents how likely the downtime is in a certain state. Nevertheless, both approaches can be utilized by decision-makers to easily estimate the time to restore the functionality of buried infrastructures and plan preventive safety measures accordingly.

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Natural and human-made disasters can disrupt infrastructures even if they are designed to be hazard resistant. While the occurrence of hazards can only be predicted to some extent, their impact can be managed by increasing the emergency response and reducing the vulnerability of infrastructure. In the context of risk management, the ability of infrastructure to withstand damage and re-establish their initial condition has recently gained prominence. Several resilience strategies have been investigated by numerous scholars to reduce disaster risk and evaluate the recovery time following disastrous events. A key parameter to quantify the seismic resilience of infrastructures is the Downtime (DT). Generally, DT assessment is challenging due to the parameters involved in the process. Such parameters are highly uncertain and therefore cannot be treated in a deterministic manner. This paper proposes a Bayesian Network (BN) probabilistic approach to evaluate the DT of selected infrastructure types following earthquakes. To demonstrate the applicability of the methodology, three scenarios are performed. Results show that the methodology is capable of providing good estimates of infrastructure DT despite the uncertainty of the parameters. The methodology can be used to effectively support decision-makers in managing and minimizing the impacts of earthquakes in immediate post-event applications as well as to promptly recover damaged infrastructure.@en

Availability of resources is one of the primary criteria for communities to attain a high resilience level during disaster events. This paper introduces a new approach to evaluate resourcefulness at the community and national scales. Resourcefulness is calculated using a proposed composite resourcefulness index, which is a combination of several resourcefulness indicators. To build the resourcefulness index, resourcefulness indicators representing the different aspects of resourcefulness are collected from renowned literary publications. Every indicator is assigned a measure to make it quantifiable. Time-history data for the measures are needed to perform the analysis. While these data could be obtained from different sources, acquiring a full set of data is quite challenging. Hence, to account for missing data, the Multiple Imputation (MI) and the Markov Chain Monte Carlo (MCMC) data imputation methods are adopted. The data are then normalized, assigned weights, and aggregated to obtain the resourcefulness index. A case study is performed to demonstrate the applicability of the approach. The resourcefulness indexes of two countries, namely the United States and Italy, are evaluated. Results show that resourceful communities/countries are more resilient during disaster events as they have more tools to come up with solutions. It is also shown that knowing the current resourcefulness level helps in better identifying what aspects should be improved.@en

Resilience indicators are a convenient tool to assess the resilience of engineering systems. They are often used in preliminary designs or in the assessment of complex systems. This paper introduces a novel approach to assess the time-dependent resilience of engineering systems using resilience indicators. A Bayesian network (BN) approach is employed to handle the relationships among the indicators. BN is known for its capability of handling causal dependencies between different variables in probabilistic terms. However, the use of BN is limited to static systems that are in a state of equilibrium. Being at equilibrium is often not the case because most engineering systems are dynamic in nature as their performance fluctuates with time, especially after disturbing events (e.g. natural disasters). Therefore, the temporal dimension is tackled in this work using the Dynamic Bayesian Network (DBN). DBN extends the classical BN by adding the time dimension. It permits the interaction among variables at different time steps. It can be used to track the evolution of a system's performance given an evidence recorded at a previous time step. This allows predicting the resilience state of a system given its initial condition. A mathematical probabilistic framework based on the DBN is developed to model the resilience of dynamic engineering systems. Two illustrative examples are presented in the paper to demonstrate the applicability of the introduced framework. One example evaluates the resilience of Brazil. The other one evaluates the resilience of a transportation system.

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The capacity of a community to react to and resist during an emergency situation is highly related to the proper functioning of its infrastructure systems. Improving the infrastructure response and recovery capacity through management and adaptation strategies can help increase community resilience. Infrastructural assets are considered outdated in almost all countries in the world. This poses a clear vulnerability to infrastructure systems when subjected to disaster events such as earthquakes. This paper proposes a statistical probabilistic approach to quantify the resilience of large-scale Water Distribution Networks (WDN). The resilience of the network is evaluated using two indices: (1) the number of users without water, and (2) the drop in the total water supply, assuming that the local failure of the system occurs when the water flow and the water pressure go below a certain threshold. A series of earthquake scenarios are applied to the studied water network whose damage is determined using fragility functions that integrate the WDN characteristics with the seismic intensity. As an illustration, the proposed approach is used to quantify the resilience of a WDN in a virtual community testbed with 900,000 inhabitants. Results obtained show interesting correlation between the earthquake occurrence time, the water demand pattern, and the pipes material. The presented approach is the first step towards a systemic planning of the maintenance activities and budget allocation of pipeline networks, where both vulnerability and criticality of pipes are combined to obtain a more comprehensive resilience index of the network.@en

Recently, several attempts in the earthquake engineering field could find their ways into numerous innovative systems that provide the structure with a specific performance under a given earthquake level. Among others, the most known systems are: (a) seismic isolation systems, which uncouple the superstructure from its substructure leading to a “conceptual separation between the horizontal and vertical resisting systems” (Palermo et al. 2014b); (b) tuned mass damping systems, which are used to minimize the excitation of a structure caused by high lateral vibrations (Hoang et al. 2016); (c) active and semi-active systems, which adjust the mechanical properties of a structure in accordance with the measured response (Datta 2010b); (d) dissipative systems, which are inserted in the superstructure in order to minimize the seismic effects in the structure through their energy dissipation capacity (Chopra and Anil 2001). Although the listed systems have been well integrated into literature and practice, none of them could entirely fulfil the seismic performance@en

Natural and man-made disasters have caused a significant impact on residential buildings worldwide. The damaged buildings stay unoccupiable for a period of time, called the downtime. This chapter introduces a new methodology to predict the downtime and the resilience of building structures using Fuzzy logic. Generally, the downtime can be divided into three main components: downtime due to the structural and nonstructural damage (DT1); downtime due irrational delays (DT2); and downtime due to utilities disruption (DT3). DT1 is defined by assigning a pre-defined repair time to each building component given the number of workers assigned. DT2 and DT3 are estimated using the REDiTM Guidelines, which provide good estimates of the delays incurred by irrational components and utilities disruption. The Downtime of the building is finally obtained by combining all three components. Following the downtime estimation, the resilience of the building is estimated by combining the downtime of the building (DT) and the building damage level. The latter is assessed using a rapid visual screening form designed by the authors. As a case study, the methodology has been applied to a residential building where the 1994 Northridge earthquake is selected as the hazard event.@en

Residential buildings are designed to withstand earthquake damage because it causes the buildings to be inhabitable for a period of time, called the downtime. This paper introduces a method to predict the downtime of buildings using a Fuzzy logic hierarchical scheme. Downtime is divided into three components: downtime due to the actual damage (DT1); downtime due to irrational delays (DT2); and downtime due to utilities disruption (DT3). DT1 is evaluated by relating the damageability of the building's components to pre-defined repair times. A rapid visual screening is proposed to acquire information about the analyzed building. This information is used through a hierarchical scheme to evaluate the building vulnerability, which is combined with a given earthquake intensity to obtain the building damageability. DT2 and DT3 are estimated using the REDi™ Guidelines. DT2 considers irrational components through a specific sequence, which defines the order of components repair, while DT3 depends on the site seismic hazard and on the infrastructure vulnerability. The proposed method allows to estimate downtime combining the three components above, identifying three recovery states: re-occupancy; functional recovery; and full recovery. A case study illustrating the applicability of the methodology is provided in the paper. The downtime analysis is applied to buildings with low and medium damage levels. Results from the case study show that total repair time is higher in the medium damage case, as it is expected. In both evaluations, the downtime is influenced more by irrational components and it is different in the three recovery states.

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Resilience indicators are a convenient tool to assess the resilience of engineering systems. They are often used in preliminary designs or in the assessment of complex systems. This paper introduces a novel approach to assess the time-dependent resilience of engineering systems using resilience indicators. The temporal dimension is tackled in this work using the Dynamic Bayesian Network (DBN). DBN extends the classical BN by adding the time dimension. It permits the interaction among variables at different time steps. It can be used to track the evolution of a system’s performance given an evidence recorded at a previous time step. This allows predicting the resilience state of a system given its initial condition. A mathematical probabilistic framework based on the DBN is developed to model the resilience of dynamic engineering systems. A case study is presented in the paper to demonstrate the applicability of the introduced framework.@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

Sensor-enabled infrastructure systems are destined to empower resilient communities with more intelligence and sustainability, and therefore enhancing the operational safety of physical infrastructures. Through online and onboard monitoring, the infrastructure components incorporating appropriate analytic and predictive modeling tool not only provides real-Time insight into the operational status of every system and its components, but also enables the trend prediction and timely prognosis of failure before it happens as well as early-stage diagnosis of damage in its incipiency. This paper gives an overview of the importance of structural health monitoring (SHM) for critical infrastructures. It highlights the recent shift from infrastructure monitoring to infrastructure resilience using SHM. The paper also introduces the Infrastructure Resilience Framework (IRF) that encourages the incorporation of innovative techniques, such as optical fiber sensors and machine learning techniques, in infrastructure monitoring. It defines the steps that need to be taken throughout the life cycle of infrastructure.

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The efficient movement of goods is crucial to the economic growth of communities. This makes the existence of seaports essential for the marine transportation system. Due to their natural location, ports are continuously threatened by natural hazards such as wind action, which necessitates a continuous monitoring and assessment for their performance. The work presented here aims at assessing the resilience of ports against natural disasters. This is done by identifying the performance and the recovery rate of such infrastructure during the period following the event. The research commenced with gathering information about the port’s main components that are influenced by natural hazards. The collected data has been compiled in the form of indicators, which have been filtered and grouped under four dimensions in the proposed “PORT framework”. Each of the indicator has been allocated a measure to enable its quantitative evaluation. The aggregation of the indicators’ values allows identifying the port resilience.

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Modeling the performance of critical infrastructures and their interdependencies is an important task in the resilience assessment. In this paper, restoration curves for four critical lifelines (power, water, gas, telecommunication, and transportation) have been developed using a probabilistic approach. To do that, a large database on infrastructure downtime has been collected for most of the earthquakes that occurred in the past century. The restoration curves have been grouped based on the earthquake magnitude and the level of development of the country in which the earthquake occurred. The curves are 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

The loss of functionality of road networks during the past Canterbury (2010-2011) and Kaikōura (2016) earthquakes has questioned New Zealand's established seismic resilience. In both events, overall bridge performance was satisfactory from a life-safety perspective. However, based on the observed undesirable sub-system performance of the damaged bridges and on the direct and indirect costs due to downtime and non-structural damage, an investigation into possible improvements of the current design philosophy was warranted. Resilience can be considered as a performance indicator that quantifies the residual functionality along with the effort in responding to the seismic event. Resilience is not being considered in the seismic codes, as traditionally their main objective has been to prevent collapse and ensure life-safety. Performance-based design, as a supplement to code objectives, does not include explicit verification of the expected functionality of the structure after the earthquake. On the other hand, resilience-based design appears as a holistic design process, which identifies and mitigates earthquake-induced risks to enable rapid recovery in the aftermath of major earthquakes. This paper presents an overview of the recovery process of the Inland Route in the aftermath of the Kaikōura earthquake. The most severely damaged bridges in the route are introduced as case studies, and the main performance and functionality issues are highlighted. Based on this, a framework to incorporate resilience concepts and measures, as key design criteria and indicators, into the structural design process is also introduced and conceptually exemplified. Applying the proposed framework during the design phase will allow the estimation of final recovery times and preliminary recovery costs of the bridge after an earthquake.@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.

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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.

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