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This paper sets out the foundations for developing robust models of community recovery from earthquake disasters. Models that anticipate post-disaster trajectories are complementary to loss estimation models that predict damage and loss. Such models can serve as important decision support tools for increasing community resilience and reducing disas...
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... the literature on loss modeling has been growing rapidly, modeling of recov- ery processes and time frames has been largely neglected. The significance of this dis-tinction can be illustrated by the schematic diagram of recovery in Figure 1. Loss mod- els generally focus on initial loss caused by a disaster where initial loss is measured in terms of some indicator of community performance e.g., building stock or gross re- gional product relative to what would have occurred without the disaster. ...
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A novel distributed computing software solution, Simple Real-Time Infrastructure (SRTI), is developed to model the interdependencies that arise in community resilience simulations. SRTI is based on a client-server structure and uses a publish-subscribe pattern to realize data communication between the distributed simulators for different fields. Th...
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... However, early studies only made rough assumptions regarding recovery times [48], resulting in low accuracy. More complex recovery models have been proposed by Miles and Chang [49], they took into account the relationships between factors such as size, economic level, and neighborhood, and were used to assess a community's ability to recover from an earthquake. This model utilized three functions to quantify the recovery process under multiple factors: linear recovery function, exponential recovery function, and triangular recovery function [50]. ...
To address the problem of difficult performance assessment of train control on-board system after recovery from failures, we have proposed a resilience assessment methodology that uses reliability as an indicator of system resilience. Since the system failures are time-dependent, we adopted the Discrete Time Bayesian Network method to obtain the system's reliability before and after failure. Subsequently, we used an exponential recovery model to quantify the system's performance curve during the recovery phase, and finally utilized the resilient triangle area method to quantify its resilience size. Analyzing the CTCS3-300T train control on-board system, we found that the resilience of the system with cold standby redundancy design and hot standby redundancy design were 89.44 % and 87.34 %, respectively, indicating a slight decrease in system performance after recovery from failures compared to pre-failure levels. At that time, it was necessary to adjust operational plans based on actual conditions to avoid greater impact on the railway network. This paper realizes performance resilience of train control on-board system after failure recovery, which can be applied to similar systems and provide theoretical references for realizing intelligent maintenance of the high-speed train.
... The recovery process is oversimplified by using recovery functions that can fit the more accurate results obtained with the model by Miles and Chang [42]. The result is a complicated multidimensional performance limit state function that aims to provide a quantitative definition of resilience in a rational way based on an analytical function that can fit both technical and organizational aspects. ...
... The trend of R within the recovery time, T RE , defined as the range of time between t 0 and t 1 , is not easy to assess since the recovery process depends on multiple factors, such as time dimensions, spatial dimensions (e.g., different neighborhoods may have different recovery paths), and interdependencies between different economic sectors involved [3], [42]. In this work, the simplified approach proposed by Cimellaro et al. [3] has been assumed; it leads to choosing among three possible recovery functions depending on the system and the social response: (i) a linear equation, (ii) an exponential equation [62], and (iii) a trigonometric equation [63]. ...
... The trend of R within the recovery time, TRE, defined as the range of time between t0 and t1, is not easy to assess since the recovery process depends on multiple factors, such as time dimensions, spatial dimensions (e.g., different neighborhoods may have different recovery paths), and interdependencies between different economic sectors involved [3], [42]. In this work, the simplified approach proposed by Cimellaro et al. [3] has been assumed; it leads to choosing among three possible recovery functions depending on the system and the social response: (i) a linear equation, (ii) an exponential equation [62], and (iii) a trigonometric equation [63]. ...
In this work, the seismic resilience of the Emergency Department of a hospital complex located in Tuscany (Italy), including its nonstructural components and organizational features, has been quantified. Special attention has been paid to the ceilings, whose potential damage stood out in past earthquakes. A comprehensive metamodel has been set, which can relate all the considered parameters to the assumed response quantity, i.e., the waiting time of the yellow-code patients arriving at the Emergency Department in the hours immediately after the seismic event. The seismic resilience of the Emergency Department has been measured for potential earthquakes compatible with the seismic hazard of the area.
... The post-disaster functionality restoration process is filled with various uncertainties and can be regarded as a function of a series of random variables. In line with the definition of resilience, it can be concluded that the resilience of a bridge depends on both its resistance capacity against disasters and the subsequent functionality restoration process [5]. Therefore, establishing a well-defined restoration model is crucial for conducting the research on bridge resilience. ...
Bridges are critical components of transportation systems and are susceptible to various natural and man-made disasters throughout their lifecycle. With the rapid development of the transportation industry, the frequency of vehicle-induced disasters has been steadily increasing. These incidents not only result in structural damage to bridges but also have the potential to cause traffic interruptions, weaken social service functions, and impose significant economic losses. In recent years, research on resilience has become a new focus in civil engineering disaster prevention and mitigation. This study proposes a concept of generalized bridge resilience and presents an evaluation framework for cable-stayed bridges under disasters. The framework includes a resilience evaluation indicator system from multiple dimensions, including safety, society, environment, and economy, which facilitates the dynamic and comprehensive control of bridge resilience throughout its entire lifecycle with the ultimate goals of enhancing structural safety and economic efficiency while promoting the development of environmentally friendly structural ecosystems. Furthermore, considering the influence of recovery speed, the study evaluates various repair strategies through resilience assessment, revealing the applicable environments and conditions for different repair strategies. This methodology offers significant implications for enhancing the safety, efficiency, and environmental sustainability of infrastructure systems, providing valuable guidance for future research in this field.
... The authors proposed a SR deterministic metric to evaluate hospitals. Miles and Chang (2006) conducted a conceptual model to investigate the urban relationships of households, businesses, lifeline networks, and neighborhoods. Subsequently, Cimellaro et al. (2010b, a) further proposed a resilience metric in terms of rapidity and robustness. ...
Seismic resilience (SR) plays a vital role in evaluating and improving performance losses along with saving repair costs of structures from potential earthquakes. To further explore the developments, hotspots, and trend directions of SR, a total of 901 articles are obtained from the Web of Science (WoS) database. CiteSpace software is used to conduct a bibliometric analysis, which indicates an upward trend of publications in SR and explores the relationship of countries, journals, cited references, and keywords based on visual maps and detailed tables. Then, based on the results of the bibliometric analysis, a state-of-the-art review is conducted to further explore the current challenges and trend directions of SR. The trend directions can be divided into five categories: (a) SR assessments of infrastructure structures, (b) multi-hazard quantifications of SR, (c) seismic resilient structures, (d) refining and calibrating analytical models, and (e) multi-criteria decision-making frameworks for sustainability and SR.
... The authors performed three measures of resilience: reduction of failure probabilities, lowering of consequences from failures, and minimization of recovery time. They measures were incorporated into the four dimensions of community resilience-technical, organizational, social, and economic to quantify the resilience of various physical and organizational systems [41] proposed a model of community recovery from disasters such as earthquakes, providing a structured framework for empirical research into the key factors and relationships involved in the recovery process. The researchers also looked at the differences between systems with varying levels of catastrophe preparedness and mitigation strategies. ...
... The resilience (R) is defined as an overall measure of structural performance that summarizes several aspects that are usually employed to judge seismic performance, such as economic losses, casualties, and recovery time. In this perspective, resilience (R) is the capability to sustain a predefined level of functionality Q t ð Þ during a control time (T LC ) for an infrastructure that is determined by the owner, decision-makers or community (Miles & Chang, 2006). In this context, several studies have been conducted to practically define and calculate the resilience index of various infrastructure systems and identify the foundational parameters for measuring resilience (Bruneau & Reinhorn, 2007;Chang & Shinozuka, 2004;Cimellaro, Reinhorn, & Bruneau, 2010). ...
... The analytical expression of Q t ð Þ, considering Eq. (2), constitutes a loss function and a recovery function of infrastructure system performance during the period of system interruption because of the occurrence of an extreme event, such as an earthquake. From the mathematical viewpoint, Q t ð Þ can be expressed as follows (Miles & Chang, 2006) ...
Each of the total Life-Cycle Cost (LCC) and resilience index are valuable indicators of infrastructure management against hazard events during its service lifetime. In this study, the proposed multi-objective framework provides a systematic methodology for decision-makers to select the optimal retrofit strategies that minimize the LCC while satisfying a given level of resilience, for which various retrofit strategies are chosen. For each case, the fragility curves are established through IDA for calculating the resilience and LCC, which incorporates the effects of complete or incomplete repair actions of damage conditions induced by multiple occurrences of previous hazard events. This study employs a well-known 'NSGA II' to identify the optimal set of solutions. The various aspects of the optimal retrofit strategies are thoroughly investigated and discussed for an actual structure in the refinery sites as a case study infrastructure. ARTICLE HISTORY
... As the repair process progresses, the system components are gradually repaired, and the system functionality is gradually restored. After the repair process with a total time of T RE , the system functionality reaches a new level, which may be equal to the pre-earthquake level or lower or higher, depending on the invested resources, the repair strategy, and other factors [10,29]. Urban water supply systems are multicomponent systems and consist of general infrastructure categories such as supply, transmission, treatment, pumping, storage, and distribution. ...
A quantitative evaluation model is proposed to assess the seismic resilience of water supply systems. The water supply system is divided into three parts: water sources, aboveground infrastructures, and underground pipeline network, and importance factors for the different parts are quantified. Resilience demand is expressed as the desirable functionality loss and the recovery time of the water supply system after an earthquake. First, seismic fragility models are established for the different components of the water supply system. A water quality index is utilized to represent the impact of earthquakes on the water sources, the seismic performances of aboveground infrastructures are represented by fragility curves, and the repair rate in terms of number of repairs per kilometer is adopted for the pipeline network. Then, the post-earthquake functionality of the water supply system is quantified based on seismic fragility analysis. Changes in the water quality index are used to indicate the functionality losses related to water sources, the functionality losses of aboveground infrastructures are represented by the economic losses derived from component fragility curves, and post-earthquake functionality losses in the underground pipeline network are quantified by hydraulic simulations. The functionalities of the three parts are calculated separately, and then the overall system functionalities are obtained as the sum of the weighted functionalities of the three parts. Finally, a repair strategy is developed and the recovery time is calculated considering the system damage scenarios, system functionality analyses, and resource reserves. The proposed resilience assessment model considers all components of the water supply system, and the results are reliable when the basic information is complete and accurate.
... A resilience simulation model called ResilUs was implemented and run inside a modelling software to help educate users about the alternative approaches to enhance recovery and resilience [14]. Such a model was built upon the prototype model of Miles and Chang [9]. It defines hazard-related damage and recovery over time of buildings and lifelines using parameters calibrated on the basis of the 1994 Northridge earthquake data. ...
... The seminal work of Bruneau et al. [8] built a general framework to quantify the seismic resilience of communities through quantitative measures of robustness, rapidity, resource availability and redundancy. Based on this work, several models and conceptual frameworks have been proposed (e.g., [9]) by including socioeconomic aspects, housing issues and infrastructure systems (electricity, transportation and water). The main objective is to analyze the recovery of community functionality and related operational levels. ...
... The main objective is to analyze the recovery of community functionality and related operational levels. The integration of economic, organizational, social and technical aspects together with dimensions of infrastructure and facility systems (such as critical facilities, energy, transportation, water) is conceptually considered to have a key role in resilience assessment (e.g., [9]). the resilience of a reinforced concrete (RC) bridge retrofitted with fiber-reinforced polymer. ...
In recent decades, one of the most interesting innovations has undoubtedly been the application of resilience principles to the study and mitigation of seismic risk. However, although new rigorous mathematical models have become available in the context of seismic resilience assessment, their applications to real case studies focus on a local scale, or even single structures. Consequently, new models and procedures are absolutely necessary to adopt resilience measurements in the formulation of mitigation strategies on a national or subnational scale. Given the crucial role of residential buildings in the global resilience of Italian cities against major earthquakes, a new framework for large-scale applications is proposed to roughly measure the seismic resilience of communities through the integration of an empirical recovery function based on the reconstruction process of housing systems in the aftermath of the 2012 Northern Italy Earthquake. As a first attempt, the framework is applied to housing systems in the southern regions of Italy by modelling their physical damage with vulnerability curves defined on the basis of macroseismic approaches. The main results are presented and discussed in terms of average functionality levels over time in order to compare and understand the recovery capacity of the considered housing systems.
... However, seismic risk is not constant but dynamic (varying in time, space, and context) due to changes in short-and long-term temporal variation of the hazard (e.g., occurrence of earthquake sequences, secondary effects such as tsunamis, fires or landslides), exposure (e.g., population growth and displacements, time of the day), and vulnerability (e.g., retrofitting, structural degradation) as well as complex interactions between individual and social vulnerabilities (e.g., Orru et al., 2022). To address these dynamics and related challenges, different approaches are needed in different phases of the disaster cycle (i.e., before, during, and after an earthquake sequence) such as operational earthquake forecasting (Jordan et al., 2011), dynamic exposure and vulnerability modelling (Schorlemmer et al., 2020;Orlacchio et al., 2021;Pittore et al., 2016), earthquake early warning (Allen and Melgar, 2019; Cremen and Galasso, 2020), rapid loss assessment (Erdik et al., 2011), and recovery and rebuilding efforts (Miles and Chang, 2006); see also Supplement S2a. The data, models, products, and services (hereafter referred to as assets) that have been produced in RISE contribute to all phases of the disaster cycle (Carr, 1932) and, taken together, address dynamic seismic risk (Alexander, 2018). ...
Reducing the seismic risk for societies requires a bridge between scientific knowledge and societal actions. In recent years, three subjects that facilitate this connection gained growing importance: open science, transdisciplinarity, and ethics. We outline their relevance in general and specifically at the example of `'dynamic seismic risk' as explored in a dedicated workshop. We argue that these reflections can be transferred to other research fields for improving their practical and societal relevance. We provide recommendations for scientists at all levels to make science more open, transdisciplinary, and ethical. Only with a transition can we, as scientists, address current societal challenges and increase societies' resilience to disasters.
... Source: (Cutter et al., 2008) Figure 3 presents the DROP model, which is showing the connectivity of risk and resilience. Miles and Chang (2006) developed the comprehensive Conceptual Model of Recovery is developing the relationship between households, neighbours, businesses, and infrastructure systems. The particular model is focused on the investigation of community recovery and operational levels including household income, businesses, building construction and building retrofit. ...
... The particular model is focused on the investigation of community recovery and operational levels including household income, businesses, building construction and building retrofit. Source: (Miles & Chang, 2006) ...
Disasters are inevitable and unique; however, their impact on livelihood can be minimised. Improving disaster resilience is used as one of the key approaches to minimise the impact of disasters. Several disaster resilience models were presented during the last 20 years. However, these disaster resilience models have a vast diversity and research has not been conducted to assess the connectivity of the available models and frameworks. Therefore, this study critically reviews the disaster resilience models and frameworks to identify their positive and negative aspects that support the development of community resilience. The research is following a narrative literature review methodology while using selected journal and conference papers from the last 20 years. The models and frameworks were critically reviewed using the characteristics and availability of different concepts concerning disaster resilience context. the study summarises 10 disaster resilience models and frameworks utilised in different contexts. The outcome illustrates that DROP and Regional Resilience of Process and Outcome frameworks are comprehensive based on the availability of concepts. Moreover, the Regional Resilience of Process and Outcome framework signifies the suitability of the particular framework for disaster resilience based on concepts and characteristics. This study enhances the existing level of knowledge on disaster resilience and its understanding based on diversified discussion.
