Next Article in Journal
The Growing Infrastructure Crisis: The Challenge of Scour Risk Assessment and the Development of a New Sensing System
Previous Article in Journal
Evaluating the Performance of Lateritic Soil Stabilized with Cement and Biomass Bottom Ash for Use as Pavement Materials
Previous Article in Special Issue
Resilience Indicator of Urban Transport Infrastructure: A Review on Current Approaches
 
 
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Resilience of Critical Infrastructure Systems: A Systematic Literature Review of Measurement Frameworks

1
Faculty of Engineering, South Eastern University of Sri Lanka (SEUSL), Oluvil 32360, Sri Lanka
2
Research Institute for Humanity and Nature, Kyoto 603-8047, Japan
3
School of Engineering, RMIT University, Melbourne 3000, Australia
*
Author to whom correspondence should be addressed.
Infrastructures 2022, 7(5), 67; https://doi.org/10.3390/infrastructures7050067
Received: 6 April 2022 / Revised: 28 April 2022 / Accepted: 29 April 2022 / Published: 2 May 2022
(This article belongs to the Special Issue Resilience of Infrastructures to Natural Hazards)

Abstract

:
Critical infrastructures such as transportation, power, telecommunication, water supply, and hospitals play a vital role in effectively managing post-disaster responses. The resilience of critical infrastructures should be incorporated in the planning and designing phase based on the risk assessment in a particular geographic area. However, the framework to assess critical infrastructure resilience (CIR) is variably conceptualised. Therefore, the objective of this study was to critically appraise the existing CIR assessment frameworks developed since the adoption of the Sendai Framework in 2015 with the hazard focus on earthquakes. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was used for the selection of the 24 most relevant studies, and these were analysed to delineate existing frameworks, models, and concepts. The study found that there are wide-ranging disparities among the existing frameworks to assess the infrastructure resilience, and it has become a key challenge to prioritise resilience-based investment in the infrastructure sector. Furthermore, key attributes such as performance indicators, emergency aspects, and damage assessment need to be considered for different disaster phases—ex-ante, during, and ex-post—to improve the long-term resilience of critical infrastructure. Subsequently, an integrated and adaptable infrastructure resilience assessment framework is proposed for proper critical infrastructure planning and resilience-based investment decision making.

1. Introduction

In 1950, only 30% of the world’s population lived in urban areas, which grew to 55% by 2018 and will be 68% by 2050 [1]. As urbanisation increases, people rely more on resilient infrastructure systems to access essential resources and facilities during and after a disaster [2,3]. Hence, strategic investments in infrastructure make it capable of withstanding multiple disasters and climate change risks are vital [4]. However, the impact of disasters may vary geographically due to differences in vulnerability, exposure, and capacity of the communities [5].
Disasters such as earthquakes, tsunamis, cyclones, floods, and landslides have negative consequences on a society and may trigger social, political, and economic instability [6,7,8]. Rapid onset disasters such as earthquakes have the potential to cause catastrophic infrastructure failures, which may result in fatalities and functional losses [9] compared to other slow onset disasters such as droughts. Although large magnitude earthquakes are not frequent (magnitude 8.0 or higher) [10], they can have a catastrophic impact on human lives, potentially resulting in fatalities and cascading disasters [11]. Therefore, building socioeconomic, infrastructure, and institutional resilience are crucial to preventing accumulated damages and ensuring the safety of communities [12,13].
In recent years, several frameworks, tools, strategies, and policies have been developed by researchers, policy makers, and key stakeholders in disaster management to support earthquake preparedness measures and to ensure the recovery of communities after earthquakes [14]. The resilience of infrastructure systems and critical services during crises needs to be assured by using measures such as safety margins in engineering design codes and guidelines [15]. In times of crisis, necessary services such as electricity, energy sources, transportation, telecommunication, water, and healthcare are usually interrupted [6]. These services are defined as critical needs that are provided by critical or lifeline infrastructure systems [16]. The term “infrastructure resilience” has no unified definition, and the definition varies depending on the key attributes used in the studies. Nonetheless, the primary consideration remains the same: to provide critical services to the people [17]. Typically, infrastructure resilience is defined as the ability of a critical infrastructure system (CIS) to withstand and recover from a potentially disruptive event [18].
Critical infrastructures play a key role in a nation’s and a community’s economic prosperity and have direct consequences on social development, civic participation, and environmental sustainability. Hence, their resilience to emerging disaster risks need to be strengthened [19]. Nevertheless, CISs are complex and often interconnected to one another [6]. This requires the CIS to be properly planned and designed to build a disaster-resilient community [20], which becomes the key priority for national and local governments [21].
Numerous approaches for qualitative and quantitative evaluations of a CIS have been employed to assess infrastructure resilience. Forcellini [22] used a quantitative method to reduce the earthquake risk to bridges through a geotechnical seismic isolation technique. The resilience of infrastructure systems and infrastructure interdependencies and the damage assessments are carried out with virtual city models for different seismic scenarios [23]. Some other frameworks have assessed the functionality of the critical infrastructure during and after disasters through performance-based design [24], a Bayesian network [25,26,27,28], fragility functions [27,28,29,30,31,32,33,34,35,36,37,38], and restoration curves [29]. Furthermore, a qualitative assessment can be carried out to assess the functionality of the critical infrastructures [39,40]. In general, many of the frameworks were developed with a geographic focus (for a specific county/region/city/community/coast) and in a specific socioeconomic setting. Despite the fact that these frameworks were successful in their application within the scope of a geographic region, they have limited application and replication potential in multiple contexts due to a lack of inclusive and adaptable indicators to different settings [12,13,41,42]. Such a framework with adaptable indicators is needed for consistent operationalisation in multiple contexts, which will then be helpful for making effective resilience investment decisions. Therefore, an integrated framework is required to assess the resilience of critical infrastructure using a set of potentially adaptable indicators in multiple geographic contexts and hierarchical levels (from a community to a national level).
In this study, an attempt has been made to critically appraise the available frameworks developed to assess CIR and to conceptualise a framework applicable in multiple contexts. Subsequently, a systematic critical review methodology using preferred reporting items for systematic reviews and meta-analyses (PRISMA) was adopted to select the relevant past studies. Consequently, 24 frameworks to assess critical/lifelines infrastructure for earthquake hazard that were analysed for their method of development and the indicators outlined were selected. A set of commonalities and differences between the frameworks such as geographic-specific and various hierarchical levels were studied. Finally, this study proposes an integrated and adaptable framework that can be used in different settings with proper contextualisation by key disaster management stakeholders at the policy, practice, and research levels.

2. Infrastructure Resilience in the Context of Seismic Hazards

2.1. Types of Critical Infrastructure

The degree of criticality of the infrastructure is measured by its effect on society in the event of its failure [6,43]. Protection and mitigation are two key CIR strategies [44]. Protection refers to the “necessity to protect the infrastructure from its collapse”, and mitigation means “necessity to reduce loss of life and damage to the property by lessening the impact due to disasters” [44] (p.33). Furthermore, most of the schools and community buildings are used as temporary shelters [45], which makes the performance analysis of these structures in the event of an earthquake essential.

2.2. Critical Infrastructure Resilience (CIR)

The resilience of infrastructure systems largely depends on the potential failure rate and the residual/restoration performance measure of infrastructure elements. The level of resilience can be primarily expressed with the robustness and rapidity components [28]. However, the ‘4R’ concept of resilience proposed by Bruneau et al. [46] and Tierney and Bruneau [47] is a good basis for assessing infrastructure resilience for earthquakes. The ‘4Rs’ include robustness (ability to withstand hazards without suffering the loss of functions), redundancy (capability of satisfying functional needs during disasters), resourcefulness (ability to use the resources), and rapidity (capable of recovery).
The resilience of the infrastructure depends on the robustness interval where critical infrastructure can provide the services without any interruption after an occurrence [46]. The functionality failure of critical infrastructure can vary according to the type and intensity of the hazards. Meanwhile, critical infrastructures can perform better/worse than the expected performance [28,48]. A performance-based infrastructure resilience curve for critical infrastructure is shown in Figure 1 for different phases of a disaster [ex-ante, during, and ex-post].
The performance of infrastructure can be dropped to a level lower than the targeted level or the preperformance level (β line) immediately after a disaster. The failure can occur in terms of functionality and serviceability as a sudden or progressive failure depends on the disaster intensity and the resilience to that disaster event (A, B, and C). The restoration performance of the infrastructure can be expected at different levels such as restored performance (α), expected performance (β), and regeneration performance (µ-higher performance than expected). The resilience of a critical infrastructure can be assessed with the key performance indicators such as functionality and serviceability. [49].
Cutter and Sherifi [12,50] evaluated many different community resilience assessment frameworks in which infrastructure resilience is one of the five key community dimensions. Marasco et al. [23] studied seismic resilience and vulnerability of critical infrastructures built at the urban level using large virtual models in which the recovery stage was not considered (ex-post phase). A number of disaster resilience assessment frameworks extantin the literature were developed in a multi-dimensional resilience context in which the infrastructure was one of the critical dimensions. There is still a gap in the knowledge and necessity to critically examine the distinct infrastructure resilience assessment frameworks developed in a disaster context. This critical analysis assists researchers in understanding the key attributes/indicators to assess infrastructure resilience that can be used in multiple contexts without much time and cost in conceiving such a framework.

2.3. Critical Infrastructure Resilience (CIR) in the Context of Seismic Hazards

The studies related to CIR in the context of earthquakes have been increasing in academic and policy studies [51,52]. The impact on the critical infrastructure from earthquakes is considerably high [9], leading to massive casualties and economic losses [53,54] compared to other disasters. Earthquakes are the most lethal natural disasters, killing almost 720,000 people worldwide between 2000 and 2018 [55] and causing significant damage to critical infrastructures around the world.
The resilience of systems to seismic hazards such as earthquakes is a measure mainly based on three attributes: the threat to a site; the vulnerability of people, structures, and infrastructure that makes them vulnerable to damage; and exposure to potential loss [56]. For example, the seismic risk of urban road networks included the study of land use, network connectivity and demands patterns [57]. The probability of road blockage by liquefaction and building collapse was assessed in Taiwan, based on direct and indirect damages during an earthquake [33]. In another study, the earthquake safety assessment was carried out for the reinforced buildings to check the applicability of rapid visual screening (RVS) methods in Turkey [58]. Similarly, Miles and Chang [59,60] studied the urban recovery of lifeline infrastructure during earthquakes using a conceptual framework and computer-based modelling. The currently available research on CIR have focused on specific places, whereas resilience standards relevant to specific regions have not demonstrated adaptability to other locations [58]. Hence, an adaptive and integrated framework to assess the CIR in the different phases of earthquakes is necessary.
The studies that focused on the impact of critical infrastructure due to earthquakes highlighted above addressed the recovery of lifeline services and planning, mitigation, and decision-making strategies. However, an integrated framework that can be used to assess the resilience of different infrastructure systems to earthquakes should be able to be practically operationalised in multiple contexts with consistent attributes. Although the probability of an earthquake may be low, the consequences of an earthquake are severe [7] and have the potential to trigger cascading disasters such as landslides, tsunamis, and fire. Therefore, much attention needs to be paid to assess the resilience of multiple CISs due to earthquakes, which is the key focus of this study.

3. Methodology

A systematic literature survey was performed in this study to critically analyse the extant frameworks for assessing infrastructure resilience to disasters. Research articles published in scholarly journals after 2015 were selected to analyse the development of the frameworks after the adoption of the Sendai Framework for Disaster Risk Reduction 2015–2030 [61]. The PRISMA method was used for the final selection of studies that proposed or applied infrastructure resilience frameworks in a disaster context [41] (Figure 2). PRISMA is an established method for guiding the systematic review of scholarly literature and is based on four key steps: (1) identification, (2) screening, (3) eligibility, and (4) inclusion. The process of the PRISMA includes:
Step 1: Identification phase—In this process, the keywords “infrastructure”, “resilien*”, and “disaster” were used to classify all relevant research and review publications on infrastructure resilience. The ‘AND’ Boolean search method was used for relevant studies [18]. In the identification step of this study, the search for relevant research articles published in peer-reviewed and indexed journals on infrastructure resilience in disaster management was conducted in the Scopus database [18] which is the world’s largest scholarly publication and citation database for peer-reviewed literature [62]. The search was conducted in title, abstract, and keywords, and the search yielded 9548 studies for initial analysis.
Step 2: Screening phase—The limiters were used again in this phase to further filter the database on the most specific area as engineering and the language as English. In this screening phase, 925 studies were found to be most relevant for more detailed analysis (Supplementary Table S1 in the supplementary information).
Step 3: Eligibility check—At this step, the 925 studies that were selected in Step 2 were further screened to exclude irrelevant articles and journals that are out of the scope of this review. The titles of the studies that were more relevant to the assessment of infrastructure resilience in a disaster context were sort listed. This process resulted in 228 more relevant studies for full-text analysis (Supplementary Table S2 in the supplementary information).
Step 4: Inclusion stage—By analysing the abstracts and conclusion sections, 43 articles were included (refer Supplementary Tables S3 and S4) for detailed content analysis to critically review the frameworks developed for assessing infrastructure resilience in disaster management. Among those articles, 24 studies were finally selected for critical review that focused on earthquake-hazard-specific infrastructure resilience assessment as shown in Table 1 (full details are given in Supplementary Table S5). The aim of this study is to analyse each of the 24 frameworks in detail as opposed to some reviews that aim to provide a macroview of the selected papers such as [63,64].

4. Key Findings and Discussion

The existing frameworks for assessing infrastructure resilience to earthquakes can be analysed in different ways in terms of geographic aspects, method of framework/model development, application of frameworks in different disaster phases (ex-ante, immediately after the disaster, or ex-post), and type of critical infrastructure focus. Table 1 shows the details of the 24 frameworks selected for assessing infrastructure resilience to earthquakes from 2015 to 2021. The details of the 24 frameworks are given in chronological order that includes the name of the framework, country developed, method adopted for developing the framework and the different phases of a disaster.
As shown in Figure 3, the majority of the frameworks focused on the housing and building infrastructure (32%). Other frameworks focused on transportation (26%), electricity and power (23%), water (11%), and telecommunication (8%) sectors.
The analysis of the year of publication of the study shows that more than 50% of the frameworks were published in 2020 and 25% in 2019 (Figure 4). The number of frameworks developed in 2015 was only 4%, 8% in 2016, and 9% in 2017. The trend shows that there has been increasing interest in developing and applying different infrastructure resilience assessment frameworks. As shown in Table 1 (Supplementary Table S5 for the details of earthquake-based critical infrastructure resilience assessment frameworks), the number of frameworks that considered the post-disaster phase is comparatively higher than the pre-disaster phase. Furthermore, the method of analysis and the indicators selected for the analysis also varied based on pre- and post-disaster phases. In addition, the analysis showed that most of the frameworks were compensated with the probabilisticbased study [25,27,28,33].

4.1. Types of Infrastructure Resilience Frameworks Evaluated in this Study

The frameworks to assess infrastructure resilience can be classified in many different ways. There are stand-alone frameworks [31,32,77,78] to assess infrastructure resilience. However, some other frameworks are multi-dimensional frameworks [25,26,27,79] in which the infrastructure resilience assessment is one among key resilience dimensions such as social, economic, ecological, and institutional. A number of stand-alone infrastructure resilience frameworks used different methods for their development. These included rapid visual screening [65], UN resilience scorecard [69], reconstruction conceptual framework [73], decision support framework [28,29,31], and probabilistic-based framework [25,26,28,35,36,79]. However, some frameworks have used multiple methods such as fragility functions and decision-making strategies [28,29]. Figure 5 summarises the approaches to develop infrastructure resilience frameworks and includes common indicators/attributes, sectors that used specific indicators, and context-specific indicators such as geography-focused or hazard-specific indicators.
The frameworks that focused on the resilience of housing and building infrastructures analysed the structural reliability during the earthquake, where they used time–history analysis and fragility functions to validate the frameworks [24,27,29,32,33,79]. Transportation, water, electricity, and power infrastructure resilience frameworks focused on the dependability of the road networks during unforeseen events and the reliability of the bridges [70,71,76,80,81,82]. However, some frameworks focused on more than one infrastructure [28,31,69], and some other frameworks used interconnectivity/interdependencies of infrastructure systems [23,28,37,74].
Each framework has its own set of constraints and methods of implementation. The analysis focused on different aspects such as geographic features, indicators used, and interdependencies of critical infrastructure. The variation of geographic scope includes community, city, region, country, and global-level frameworks as well as urban, rural, and coastal regions. The key differences between the existing frameworks are the number of variables used to assess infrastructure resilience and the method of their development/application. A wide range of methods were used to develop frameworks such as Bayesian networks, decision support frameworks, and damage modelling [26,27,28,83].

4.2. Frameworks Developed for a Specific Geographic Context

Most of the frameworks evaluated in this study were developed with a specific geographic focus, such as regional frameworks, rural/urban resilience frameworks, community frameworks, and coastal resilience frameworks. Figure 6 illustrates the number of frameworks developed in each country. Notably, a large number of frameworks (11) were developed with the focus on the United States of America (USA), whereas 3 among the 24 frameworks were general without any specific geographic focus.
Table 2 summarises the similarities and differences between various geographic-focused frameworks such as Global, USA, and China with probabilistic, decision support and conceptual model frameworks, respectively. The earthquake specification in terms of intensity, mitigation strategies, infrastructure specifications, guidelines of particular geographic context and community resilience are similar for different geographic contexts. However, the differences in resilience characteristics for different geographies include the selected infrastructure, their codes, and models. Furthermore, Kameshwar et al. [28] used the Cascadia Subduction zone (coastal zone) in the USA which is a specific area to assess infrastructure resilience.
Table 3 shows the similarities and differences in the example frameworks analysed with similar geographic context (three frameworks developed in USA regions). All the frameworks included fragility functions as an infrastructure resilience characteristic. The fragility curves can be empirical or analytical [36]. In addition, hazard specifications such as intensity and past records were considered seismic specifications. The differences are generally based on the infrastructure considered for the analysis. For example, Ranjbar and Naderpour [36] and Devendiran [32] focused on only one critical infrastructure: hospitals and bridges, respectively. Therefore, infrastructure-specific characteristics were used in the resilience assessments.

4.3. Approaches in Framework Development/Application

Subsequently, the review revealed that different approaches were used in the development/application of the framework. Kameshwar et al. [28] studied the resilience of critical infrastructure for earthquakes using a decision support framework that included Bayesian networks with performance-based indicators and guidelines for building, transportation, water, and electricity infrastructures in the coastal areas. Rowell and Goodchild [76] focused on the impact on road networks from pre- and post-disasters (earthquake and tsunami) perspectives, using travel demand models to predict passenger and forestry freight travel differences. This model suggested the key regional transportation centre. Liu et al. [29] investigated the wastewater services system losses after an earthquake using performance indicators to generate the decision support framework. This framework was primarily concerned with functional impacts, physical damage, and serviceability restoration modules. Farahmandfar et al. [38] examined the performance of water supply networks after hazards using primary indicators such as estimated network topology, hazard intensity, and pipeline response. The study proposed an optimisation framework to enhance the water network system after earthquakes.
Table 4 summarises the method or approach used in the development of the framework to assess infrastructure resilience to earthquakes (Supplementary Tables S6 and S7 in supplementary information provide details of the sectors/type of critical infrastructure and methods used for development). Many of the frameworks used probabilistic models, decision support models, Bayesian networks, damage modelling, and fragility curves for assessing infrastructure resilience. Some frameworks used more than one method/tool to create the framework [25,26,27,28,79]. For example, Hayat [73] evaluated infrastructure resilience using the literature and empirical evidence, semi-structured interviews and expert opinions.
The key factor considered in the assessment of CIR was the impact related to damage or failure to the infrastructure. Furthermore, it is essential to assess interdependencies of infrastructure systems so that the cascading effects of infrastructure failures can also be forecast/assessed [84]. If one or more critical elements of the infrastructure failed to provide services during and after the event, entire systems may fail, even if an alternative exists [28,31]. For instance, if there is an alternate arrangement for water (by truck) and the transport infrastructure fails, then other systems also collapse [28]. Moreover, the restoration process of critical services in the aftermath of hazards is important for providing effective performance for the other critical infrastructure [66,67].
The assessment frameworks of CIR can be categorised based on a specific context (i.e., geographic/hazard-specific); however, they may not take into account the time factors [41]. The challenges during the CIR may arise in terms of cooperation and communications between stakeholders, understanding of the system, and the involvement of citizens in resilience building [85]. In addition, evaluating and understanding the inter-dependencies of infrastructure is quite challenging [86], and mitigation of interdependencies of critical infrastructure is necessary to avoid cascade failures of other critical infrastructure services [87].

4.3.1. Decision-Based Framework/Models

The decision-based frameworks were used by Nozhati et al. [31], Liu et al. [29], Kameshwar et al. [28], and Merschman et al. [70]. These frameworks focused on the post-disaster consequences and decision-making strategies. They used the progress of restoration of critical infrastructure after the disaster [28]. Figure 7 summarises the key aspects of the decision-based frameworks/models. The key findings were categorised based on social and serviceability measures, functional impact measures, indicators, and methods used.
Although the serviceability and functionality measures of the decision-based frameworks have similar attributes, the indicators used in these frameworks vary considerably. It is mainly due to the type of infrastructure focused on and the hazard specifications. The serviceability measures that were used in these frameworks include infrastructure interdependencies, response strategies based on lesson learned, and restoration functions. The functionality measures include mitigation strategies, short- and long-term restoration strategies, and functionality of interdependent infrastructure components. The assessment of system functionality included the key attributes of infrastructure such as materials, age, and construction method.

4.3.2. Probabilistic Models/Frameworks

Probabilistic models are more applicable in real life because they are better at forecasting uncertainty and interdependencies. [26,88]. Generally, the probabilistic-based hazard analysis was carried out in four different approaches: generation of the seismic hazard curve (ground motion intensity measures), use of structural response analysis, damage assessment/measures, and use of decision variables [42]. The probabilistic frameworks analysed in this study mainly used Bayesian networks/Bayesian Belief network [26,27,28], the Monte Carlo method [28,35,72,78] and the Markov chain model [78].
Figure 8 shows the key features of infrastructure resilience assessment frameworks developed using probabilistic models. The social/serviceability measures primarily focused on policies, early warning systems, disaster preparedness, and interdependencies, whereas the functionality measures focused on human resources, socioeconomic factors, damage analysis, and sustainability concerns. Discrete and continuous variables were used to analyse the downtime of power and telecommunication by Iuliis et al. [27] (Discrete variable is defined as the finite number such as earthquake intensity, and continuous variable is defined as the infinite element such as exposed structures and infrastructure types).

4.3.3. Damage-Modelling/Analysis Framework

As infrastructure systems are highly dependent on each other, the damage propagation on a particular infrastructure can have a direct or indirect impact on the other facilities [42]. One of the key aspects in this framework was the use of a fragility curve to estimate the various limit state/conditional probability. Most of the frameworks used the fragility functions to evaluate the fragility of the structures [27,28,29,31,32,33,34,35,36,37,38]. Figure 9 shows the key attributes of serviceability and functionality in the damage-modelling or damage analysis frameworks.
The damage analysis frameworks were specifically used for the pipeline networks and buildings. For example, Mazumder et al. [30] studied the damage assessment of pipelines during earthquakes using corrosion and seismic damage of pipelines. The significant social/serviceability measure was performed based on the time of occurrence of the event, societal response, life safety, and resources. The key attributes such as retrofitting methods, damage-based functionality, renewable strategies, and recovery functions were preliminary considerations in the analysis/modelling. In addition, building codes, design factors, and guidelines were used as key indicators in the damage modelling/analysis frameworks.

4.4. Analysis of Infrastructure Resilience Assessment Indicators

The selection of the most appropriate indicators to assess risk in critical infrastructure is critical [44]. In the assessment of infrastructure resilience, indicators represent the key components of the subject of assessment, such as functionality return time, redundancy, and resistance [89]. The selection of the indicators typically relies on the framework developed, and the reliability of the infrastructure systems in a disaster context needs to be analysed with the most relevant indicators [27]. The indicator analysis will provide benchmarks against the uncertainties and help to pre-determine the baseline status [7,89].
Resilience indicators should also be assessed in accordance with industry or system professionals for each specific industry [7]. Specific indicators and matrices can be developed for the specified critical infrastructure to investigate the risk to critical infrastructure, and they need to be validated for each organization by a particular sector and experts [44]. However, Petrović et al. [90] considered generic resilience indicators based on the criteria that the indicators should not be related to a specific hazard or specified critical infrastructure to enhance critical infrastructure interconnectivity. Increasing the number of indicators improves the coverage, but it becomes complex. During a disaster, it is assessed that humans can handle about three indicators [91].
In the frameworks analysed in this study, 140 variables/indicators were found, and they were iteratively categorised to appropriate sub-dimensions. As a result, a number of indicators were rejected, either because they were irrelevant or because they overlapped with other indicators [92]. For example, building components such as the number of stories, dimensions of the buildings/infrastructure, material usage, and building type, as well as pipe diameter and connectivity in the water network, were considered as infrastructure specifications. As a result, 24 indicators were selected to evaluate the seismic resilience of critical infrastructure (Supplementary Table S7 in the supplementary information for the results of indicator analysis).
Figure 10 shows the resulting map of the thematic analysis of critical infrastructure indicators used in the frameworks. In this study, two steps were followed to perform the word clustering: (1) The frequency of indicators and their associations were determined using the word link programme WORDij 3.0 [93,94]. WORDij is a content analysis software, and it was used to create the co-occurrence between the variables [95]. (2) The mapping was carried out using Gephi 0.9.2 software. Gephi is open source software, and the indicators created can be imported, analysed, specialised, filtered, and exported in a variety of networks [96,97]. The interconnection between the indicators is depicted by the arrow, and the size of the circle represents the frequency with which the indicators were mostly used in previous studies.
As shown in Figure 10, the size of the circle denotes the scalar values of the indicators used in the study [98]. It is also necessary to define the connections between the elements required for the analysis, as well as their characteristics in terms of direction and weight [92]. In this study, the mixed method of analysis was selected to evaluate correlations between critical infrastructure [86]. Moreover, a variable can be categorised as directed or undirected based on the graph theory [92]. The majority of the frameworks prioritised infrastructure specifications such as building type, number of stories in buildings, and distribution components in power infrastructure (e.g., distribution circuits and pole distribution) as well as seismic specifications such as seismic intensity, seismic cycling, and wave arrival time. In terms of connectivity in the analysis, the interdependencies are connected to infrastructure specifications, cascading hazards, socioeconomic factors, and infrastructure type (e.g., water networks, power networks, and transportation networks). Moreover, the indicators utilised in this study can help analyse various types of critical infrastructure as stated in the novel and adaptive framework (see Figure 11). The water network infrastructure can be analysed in different attributes: national guidelines and policies, geotechnical aspects (e.g., soil parameters), damage assessment (e.g., corrosion), interdependencies, specification of infrastructure (e.g., pipe diameter, distribution network, etc..), and emergency attributes (e.g., availability of water resources).

5. An Integrated and Adaptable Framework for Assessing Infrastructure Resilience

The critical analyses have shown that there is a need for an integrated and adaptable framework for global context for measuring resilience of infrastructure systems to earthquakes. The existing frameworks to assess seismic resilience of critical infrastructure were mostly based on a specific infrastructure network with limited assessment of indicators, specific phase of a disaster, and a specific spatial context. However, developing a context-specific framework is a time- and resource-intensive process [41]. To develop an adaptable and integrated framework, the similarities and differences in frameworks extant in the literature were analysed as highlighted in Section 4. Figure 11 shows a summary of the findings for different phases of a disaster—pre (ex-ante), during, post (ex-post), and the key factors/attributes are categorised under the key sections such as serviceability attributes, functionality measures, and key indicators.
As shown in Figure 11, the proposed framework will be focused on the different phases of hazards with key attributes of serviceability and functionality. There are two methodologies for assessing system performance: interconnectivity study and serviceability analysis [99]. In addition, the functionality of critical infrastructure also was focused on as other attributes. Throughout the content analysis of the 24 studies considered, the total numbers of attributes were classified as social/serviceability and functional measures. In the pre-disaster phase, the mitigation strategies and existing reliability of critical infrastructure were focused on as primary attributes. Moreover, interdependencies that lacked focus in past studies related to three different phases of a disaster were included as the other key feature in this framework. Emergency attributes and resourcefulness were also focused on during the disaster phases in our framework. In addition, system/interdependent functionality was added as an additional key attribute. The damage assessment and response strategies in the post-disaster phase were initially focused on restoring the critical infrastructure facility in a short period of time. As a result, the long-term restoration of the critical infrastructure can be achieved.

Proposed Integrated and Adaptable Framework

Based on the critical analysis of existing frameworks, an integrated and adaptable framework for assessing the resilience of critical infrastructures for earthquakes has been developed in this study (Figure 12). This framework is aligned with the three key phases of a disaster (ex-ante, during, and ex-post), as shown in Figure 1. In this framework, the serviceability and functionality measures in different phases of earthquake risk were considered as key attributes. “The serviceability is expressed as the ratio of the available demand to required demand corresponding to a seismic damage scenario” [100] (p. 07). Moreover, the focus on the pre-disaster serviceability measures is necessary due to the post-disaster scenario causing high demand for critical infrastructure services [101]. The decrease in resilience is deemed equivalent to the degeneration of the infrastructure throughout the period of recovery [102]. The functionality measures define the degree of functionality and service life required by stakeholders [103]. Furthermore, infrastructure resilience depends on the capacities of facilities and systems to maintain a sufficient degree of functionality during and after the disruptions and to recover full functionality within a specified time frame [104].
As shown in Figure 1 (Section 2.2), the ‘during’ disaster time phase is very short for earthquakes [105], despite the fact that the impacts are severe in the aftermath of the disaster. As a result of such a crisis, it is necessary to investigate the attributes in the pre-disaster phase for mitigation [106]. In this proposed framework, the resilience of critical infrastructure before an earthquake (ex-ante) will be evaluated based on the interdependencies between CISs and their connectivity as a functionality attribute. It is necessary to assess the interdependencies in the early stage (pre-disaster) to provide continued services during and after (ex-post) the disasters [3,105] and with the cascade hazards/failures of critical infrastructure [107]. Moreover, sevaluating critical infrastructure interdependencies resilience at the regional level would yield reliable results for serviceability and functionality losses [108]. The evaluation of proactive and reactive requirements of interdependencies between infrastructures is necessary [23,109]. The risk and reliability analysis will also need to be carried out based on the critical infrastructure’s risk assessment in the ex-ante phase. The critical infrastructure’s existing risk management policies, design aspects, and mitigation/improving structural reliability will be evaluated in this attribute. The critical infrastructure should be able to continue offering services during and after a disaster. For instance, corrosion in the water pipe line distribution system will be severely impacted by an earthquake [34]. Moreover, the recovery of critical infrastructure, such as transportation, from an interruption will help enhance performance [17,110].The results will exhibit the existing critical infrastructure’s performance, improvement techniques, and mitigation strategies such as early warning systems.
During the disaster phase, emergency attributes, applications of the safety protocols, resourcefulness, and contingencies will be assessed. The contingencies [111] and resourcefulness [112] are primary attributes to ensure the functionality of critical infrastructure during earthquakes. In the immediate aftermath of an earthquake, financial conditions will be critical [113], and it is also important the government/private sector to allocate funds for critical infrastructure to withstand economic impacts of disasters [14]. Furthermore, efficient resource allocation will improve the resilience of critical infrastructure for a quick recovery [114]. In this phase, the availability of human resources, alternative essential needs (for example, alternative power supply systems after the loss of power), temporary housing facilities (e.g., schools and house of worship), and food services will be considered as major resources. Furthermore, the destruction of infrastructure has a significant influence on local emergency response, resulting in a shortfall of rescue resources for disaster relief [75]. The emergency attributes and safety protocols of system efficiency have to be evaluated throughout the disaster phase to assess the effectiveness of the emergency services (fire services, rescue services, and armed forces).
Damage assessment has to be carried out and should progress to allow critical infrastructure to recover quickly to provide services. The infrastructure can then be further improved in the longer-term recovery as depicted in the Figure 1 (Section 2.2) (α-curve line). Moreover, the applications of safety protocols must be restudied as lessons learned to ensure the adequate performance of designed safety systems. Subsequently, the long-term CIR for earthquakes in terms of the concept of restoration [46] and regeneration [48] has to be assessed to improve performance and services. Another aspect of the ex-post phase is to plan an earthquake debris management strategy to improve long-term performance [115]. In each phase of the analysis, the stakeholders, engineers, architects, and decision makers need to select appropriate tools to perform the analysis needed for the specific stage of the disaster. For example, they need to improve the mitigation strategies (e.g., retrofitting and health monitoring systems) in pre-disaster phases. During the hazard phase, they need to focus on emergency management plans (e.g., short-recovery, warning systems, and temporary shelters). Finally, during the post-disaster phase, they should focus on short-term and long-term recovery of critical infrastructure services [31,48,67,116,117]. Thus this proposed framework is adaptable for the user to select the most appropriate tool and a method for application based on the user requirements [118,119].

6. Summary and Conclusions

This paper proposes an adaptive and integrative framework for assessing critical infrastructure and buildings in the event of an earthquake. To assess the seismic resilience of critical infrastructure, an integrated and adaptable framework with possible indicator applications is necessary. A total of 24 infrastructure resilience assessment frameworks developed for earthquake risks were critically reviewed using the PRISMA methodology. The frameworks were selected from the articles published in the Scopus database between 2015 and January 2021, which is the period coinciding with the implementation of the 2015 Sendai framework for disaster risk reduction (SFDRR). The critical assessment conducted in this study revealed the following key findings:
  • There is a lack of systematic configurations to assess CIR for seismic hazards. It is mostly due to the fact that the majority of the frameworks were primarily focused on a specific context such as within a geographic scope or in a selected community. Therefore, it is challenging to use one of these frameworks as a general, but adaptable tool for assessing seismic risks in any other context. This research gap needs to be addressed by developing an integrated and adaptable infrastructure resilience assessment framework. Such a framework will provide a consistent approach to develop a uniform method to make resilience investment decisions.
  • The serviceability and functionality of critical infrastructure are the key attributes to provide uninterrupted services during a disaster. Therefore, it is vital that any disaster framework establishes a set of key resilience performance indicators. Such performance indicators can be relied upon in different phases of a disaster to consistently measure progress before, during, and after earthquakes and to make well informed resilience investment decisions for future risks.
  • The frameworks evaluated in this study emphasise risk/reliability assessment in the ex-ante phase, resourcefulness as disaster impact mitigation strategies, and short/long-term restoration strategies of critical infrastructure in the ex-post phase. In contrast, the proposed framework focuses on the socioeconomic and emergency protocols during and after the disasters. Therefore, governments should maintain contingencies for unforeseen events. Policymakers and stakeholders can use the framework to reduce the vulnerability of critical infrastructures and ensure community safety before, during, and after disasters. The seismic hazard level has the greatest influence on the robustness of critical infrastructure networks immediately after the disasters occur.
  • An integrated and adaptive framework for assessing critical infrastructure for earthquake hazards was developed based on the key findings of a critical evaluation of the 24 selected frameworks develop over the past five years. This framework is helpful for policy makers, engineers/practitioners, and other key stakeholders involved in developing critical infrastructure in earthquake risk-prone geographic areas.
However, this framework only focuses on the preliminary attributes of seismic resilience, which can be further expanded by taking into account the interconnectivity of critical infrastructures and the cascading failures/hazards. The proposed conceptual framework needs to be validated in all three key phases of a disaster—pre, during, and post disaster phases and needs to be tested in various geographic settings.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/infrastructures7050067/s1, Table S1: Scopus-Extracted; Table S2: Screening Stage-01; Table S3: Screening Stage-02; Table S4: Multiple Haz IR frameworks; Table S5: Seismic IR Framework detail; Table S6: Sectors of infrastructure; Table S7: Framework type and content.

Author Contributions

Conceptualization, A.S. and J.T.; methodology, A.S. and M.S.; data curation, M.S.; investigation, M.S.; Analysis, M.S.; writing—original draft, M.S. and A.S.; writing—review and editing, J.T., M.H. and S.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The information is available on request to the corresponding author with justifiable reason.

Acknowledgments

Authors acknowledge the South Eastern University of Sri Lanka for providing the resources required for undertaking this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. United Nations Department of Economic and Social Affairs. World Urbanization Prospects 2018: Highlights; United Nations: San Francisco, CA, USA, 2019. [Google Scholar] [CrossRef]
  2. Ali, H.M.; Desha, C.; Ranse, J.; Roiko, A. Planning and Assessment Approaches towards Disaster Resilient Hospitals: A Systematic Literature Review. Int. J. Disaster Risk Reduct. 2021, 61, 102319. [Google Scholar] [CrossRef]
  3. Benmokhtar, A.; Benouar, D.; Rahmoune, A. Modeling the Propagation of the Effects of a Disturbance in a Critical Infrastructure System to Increase Its Resilience. Urban. Arhit. Construcţii 2020, 11, 157–178. [Google Scholar]
  4. Yang, Y.; Tatano, H.; Huang, Q.; Liu, H.; Yoshizawa, G.; Wang, K. Evaluating the Societal Impact of Disaster-Driven Infrastructure Disruptions: A Water Analysis Perspective. Int. J. Disaster Risk Reduct. 2021, 52, 101988. [Google Scholar] [CrossRef]
  5. United Nations Infrastructure and Disaster: A Contribution by the United Nations to the Consultation Leading to the Third UN World Conference on Disaster Risk Reduction; Japan. 2014. Available online: https://www.preventionweb.net/publication/infrastructure-and-disaster-contribution-united-nations-consultation-leading-third-un (accessed on 19 April 2021).
  6. Bach, C.; Gupta, A.K.; Nair, S.S.; Birkmann, J. Training Module, Critical Infrastructures and Disaster Risk Reduction (in the Context of Natural Hazards); National Institute of Disaster Management and Deutsche Gesellschaft für internationale Zusammenarbeit GmbH (GIZ): New Delhi, India, 2013. [Google Scholar]
  7. Bertocchi, G.; Bologna, S.; Carducci, G.; Carrozzi, L.; Cavallini, S.; Lazari, A.; Oliva, G.; Traballesi, A. Guidelines for Critical Infrastructures Resilience Evaluation; AIIC: Rome, Italy, 2016. [Google Scholar]
  8. Satheeskumar, N. Wind Load Sharing and Vertical Load Transfer from Roof to Wall in a Timber-Framed House; James Cook University: Townsville, Australia, 2016. [Google Scholar]
  9. Mara, S.; Vlad, S.-N. Global Climatic Changes, a Possible Cause of the Recent Increasing Trend of Earthquakes Since the 90’s and Subsequent Lessons Learnt. In Earthquake Research and Analysis—New Advances in Seismology; IntechOpen: London, UK, 2013. [Google Scholar]
  10. Incorporated Research Institutions for Seismology How Often Do Earthquakes Occur? Available online: https://www.iris.edu/hq/inclass/fact-sheet/how_often_do_earthquakes_occur (accessed on 17 June 2021).
  11. Brunsdon, D. Critical Infrastructure and Earthquakes: Understanding the Essential Elements of Disaster Management; Australian Earthquake Engineering Society: Adelaide, Australia, 2002; p. 9. [Google Scholar]
  12. Cutter, S.L. The Landscape of Disaster Resilience Indicators in the USA. Nat. Hazards 2016, 80, 741–758. [Google Scholar] [CrossRef]
  13. Cutter, S.L.; Barnes, L.; Berry, M.; Burton, C.; Evans, E.; Tate, E.; Webb, J. A Place-Based Model for Understanding Community Resilience to Natural Disasters. Glob. Environ. Chang. 2008, 18, 598–606. [Google Scholar] [CrossRef]
  14. Osei-Kyei, R.; Tam, V.; Ma, M.; Mashiri, F. Critical Review of the Threats Affecting the Building of Critical Infrastructure Resilience. Int. J. Disaster Risk Reduct. 2021, 60, 102316. [Google Scholar] [CrossRef]
  15. Kim, Y.; Chester, M.V.; Eisenberg, D.A.; Redman, C.L. The Infrastructure Trolley Problem: Positioning Safe-to-Fail Infrastructure for Climate Change Adaptation. Earth’s Futur. 2019, 7, 704–717. [Google Scholar] [CrossRef][Green Version]
  16. President’s Commission on Critical Infrastructure Protection. Critical Foundations: Protecting America’s Infrastructures; The Report of the President’s Commission on Critical Infrastructure Protection; President’s Commission on Critical Infrastructure Protection: Washington, DC, USA, 1997; Volume 1, Available online: https://sgp.fas.org/library/pccip.pdf (accessed on 25 January 2022).
  17. Chan, R.; Schofer, J.L. Measuring Transp ortation System Resilience: Response of Rail Transit to Weather Disruptions. Nat. Hazards Rev. 2016, 17, 05015004. [Google Scholar] [CrossRef]
  18. Berkeley, A.R., III; Wallace, M. NIAC A Framework for Establishing Critical Infrastructure Resilience Goals: Final Report and Recommendations; National Infrastructure Advisory Council: Washington, DC, USA, 2010. [Google Scholar]
  19. Tang, L.Y.; Shen, Q.; Cheng, E.W.L.; Damijan, S.; Padilla, S.B.; Flyvbjerg, B.; Tobing, J.; Prambudi, S.E. OECD Towards a Framework for the Governance of Infrastructure. Int. J. Proj. Manag. 2015, 28, 683–694. [Google Scholar] [CrossRef][Green Version]
  20. RECIPE Project Team. Resilience of Critical Infrastructure Protection: Guidelines; European Commission, Humanitarian Aid and Civil Protection: Brussel, Belgium, 2015. [Google Scholar]
  21. Fernando, M.J.; Kulasinghe, A.N.S. Seismicity of Sri Lanka. Phys. Earth Planet. Inter. 1986, 44, 99–106. [Google Scholar] [CrossRef]
  22. Forcellini, D. Assessment on Geotechnical Seismic Isolation (GSI) on Bridge Configurations. Innov. Infrastruct. Solut. 2017, 2, 9. [Google Scholar] [CrossRef]
  23. Marasco, S.; Cardoni, A.; Zamani Noori, A.; Kammouh, O.; Domaneschi, M.; Cimellarof, G.P. Integrated Platform to Assess Seismic Resilience at the Community Level. Sustain. Cities Soc. 2021, 64, 102506. [Google Scholar] [CrossRef]
  24. Hadigheh, S.A.; Mahini, S.S.; Setunge, S.; Mahin, S.A. A Preliminary Case Study of Resilience and Performance of Rehabilitated Buildings Subjected to Earthquakes. Earthq. Struct. 2016, 11, 967–982. [Google Scholar] [CrossRef]
  25. Sen, K.M.; Dutta, S. An Integrated GIS-BBN Approach to Quantify Resilience of Roadways Network Infrastructure System against Flood Hazard. J. Risk Uncertain. Eng. Syst. Part A Civ. Eng. 2020, 6, 04020045. [Google Scholar] [CrossRef]
  26. Kammouh, O.; Gardoni, P.; Cimellaro, G.P. Probabilistic Framework to Evaluate the Resilience of Engineering Systems Using Bayesian and Dynamic Bayesian Networks. Reliab. Eng. Syst. Saf. 2020, 198, 106813. [Google Scholar] [CrossRef]
  27. De Iuliis, M.; Kammouh, O.; Cimellaro, G.P.; Tesfamariam, S. Quantifying Restoration Time of Power and Telecommunication Lifelines after Earthquakes Using Bayesian Belief Network Model. Reliab. Eng. Syst. Saf. 2020, 208, 107320. [Google Scholar] [CrossRef]
  28. Kameshwar, S.; Cox, D.T.; Barbosa, A.R.; Farokhnia, K.; Park, H.; Alam, M.S.; van de Lindt, J.W. Probabilistic Decision-Support Framework for Community Resilience: Incorporating Multi-Hazards, Infrastructure Interdependencies, and Resilience Goals in a Bayesian Network. Reliab. Eng. Syst. Saf. 2019, 191, 106568. [Google Scholar] [CrossRef]
  29. Liu, M.; Giovinazzi, S.; Beukman, P. Post-Earthquake Performance Indicators for Sewerage Systems. Proc. Inst. Civ. Eng. Munic. Eng. 2016, 169, 74–84. [Google Scholar] [CrossRef]
  30. Pang, Y.; Wang, X. Cloud-IDA-MSA Conversion of Fragility Curves for Efficient and High-Fidelity Resilience Assessment. J. Struct. Eng. 2021, 147, 04021049. [Google Scholar] [CrossRef]
  31. Nozhati, S. A Resilience-Based Framework for Decision Making Based on Simulation-Optimization Approach. Struct. Saf. 2021, 89, 102032. [Google Scholar] [CrossRef]
  32. Devendiran, D.K.; Banerjee, S.; Mondal, A. Impact of Climate Change on Multihazard Performance of River-Crossing Bridges: Risk, Resilience, and Adaptation. J. Perform. Constr. Facil. 2020, 35, 04020127. [Google Scholar] [CrossRef]
  33. Lo, I.T.; Lin, C.Y.; Yang, C.T.; Chuang, Y.J.; Lin, C.H. Assessing the Blockage Risk of Disaster-Relief Road for a Large-Scale Earthquake. KSCE J. Civ. Eng. 2020, 24, 3820–3834. [Google Scholar] [CrossRef]
  34. Mazumder, R.K.; Fan, X.; Salman, A.M.; Li, Y.; Yu, X. Framework for Seismic Damage and Renewal Cost Analysis of Buried Water Pipelines. J. Pipeline Syst. Eng. Pract. 2020, 11, 04020038. [Google Scholar] [CrossRef]
  35. Anwar, G.A.; Dong, Y.; Zhai, C. Performance-Based Probabilistic Framework for Seismic Risk, Resilience, and Sustainability Assessment of Reinforced Concrete Structures. Adv. Struct. Eng. 2019, 1–19. [Google Scholar] [CrossRef]
  36. Rezaei Ranjbar, P.; Naderpour, H. Probabilistic Evaluation of Seismic Resilience for Typical Vital Buildings in Terms of Vulnerability Curves. Structures 2020, 23, 314–323. [Google Scholar] [CrossRef]
  37. Yu, P.; Wen, W.; Ji, D.; Zhai, C.; Xie, L. A Framework to Assess the Seismic Resilience of Urban Hospitals. Adv. Civ. Eng. 2019, 2019, 7654683. [Google Scholar] [CrossRef]
  38. Farahmandfar, Z.; Piratla, K.R.; Andrus, R.D. Resilience Evaluation of Water Supply Networks against Seismic Hazards. J. Pipeline Syst. Eng. Pract. 2016, 8, 04016014. [Google Scholar] [CrossRef]
  39. Vugrin, E.D.; Warren, D.E.; Ehlen, M.A.; Camphouse, R.C. A Framework for Assessing the Resilience of Infrastructure and Economic Systems. In Sustainable and Resilient Critical Infrastructure Systems: Simulation, Modeling, and Intelligent Engineering; Springer: Berlin/Heidelberg, Germany, 2010; pp. 77–116. ISBN 9783642114045. [Google Scholar]
  40. Perrone, D.; Aiello, M.A.; Pecce, M.; Rossi, F. Rapid Visual Screening for Seismic Evaluation of RC Hospital Buildings. Structures 2015, 3, 57–70. [Google Scholar] [CrossRef]
  41. Saja, A.M.A.; Goonetilleke, A.; Teo, M.; Ziyath, A.M. A Critical Review of Social Resilience Assessment Frameworks in Disaster Management. Int. J. Disaster Risk Reduct. 2019, 35, 101096. [Google Scholar] [CrossRef]
  42. Cimellaro, G.P. Urban Resilience for Emergency Response and Recovery; Geotechnical, Geological and Earthquake Engineering; Springer International Publishing: Cham, Switzerland, 2016; Volume 41, ISBN 978-3-319-30655-1. [Google Scholar]
  43. Yellman, T.W.; Murray, T.M. Vulnerability and Resilience. Risk Anal. 2013, 33, 753. [Google Scholar] [CrossRef] [PubMed]
  44. Petit, F.; Bassett, G.; Buehring, W.; Collins, M.J.; Dickinson, D.C.; Haffenden, R.A.; Huttenga, A.A.; Klett, M.S.; Phillips, J.A.; Veselka, S.N.; et al. Protective Measures Index and Vulnerability Index: Indicators of Critical Infrastructure Protection and Vulnerability; Argonne National Laboratory: Lemont, IL, USA, 2013. [Google Scholar]
  45. Petal, M.; Wisner, B.; Kelman, I.; Alexander, D.; Cardona, O.-D.; Benouar, D.; Bhatia, S.; Bothara, J.K.; Dixit, A.M.; Green, R.; et al. School Seismic Safety and Risk Mitigation. In Encyclopedia of Earthquake Engineering; Springer: Berlin/Heidelberg, Germany, 2015; pp. 1–20. [Google Scholar]
  46. Bruneau, M.; Chang, S.E.; Eguchi, R.T.; Lee, G.C.; O’Rourke, T.D.; Reinhorn, A.M.; Shinozuka, M.; Tierney, K.; Wallace, W.A.; Von Winterfeldt, D. A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities. Earthq. Spectra 2003, 19, 733–752. [Google Scholar] [CrossRef][Green Version]
  47. Tierney, K.; Bruneau, M. Conceptualizing and Measuring Resilience: A Key to Disaster Loss Reduction. TR News 2007, 250, 14–17. [Google Scholar]
  48. Kondo, T. Post-Disaster Recovery Strategies for Neighborhood Built Environment Regeneration after Mega Disaster: Case Study of Hurricane Katrin. In Proceedings of the 4th International Conference on Urban Disaster Reduction Sustainable Disaster Recovery: Addressing Risks and Uncertainty, Christchurch, New Zealand, 17–20 October 2016; p. 4. [Google Scholar]
  49. Keogh, D.U.; Apan, A.; Mushtaq, S.; King, D.; Thomas, M. Resilience, Vulnerability and Adaptive Capacity of an Inland Rural Town Prone to Flooding: A Climate Change Adaptation Case Study of Charleville, Queensland, Australia. Nat. Hazards 2011, 59, 699–723. [Google Scholar] [CrossRef][Green Version]
  50. Sharifi, A. A Critical Review of Selected Tools for Assessing Community Resilience. Ecol. Indic. 2016, 69, 629–647. [Google Scholar] [CrossRef][Green Version]
  51. Cantelmi, R.; Di Gravio, G.; Patriarca, R. Reviewing Qualitative Research Approaches in the Context of Critical Infrastructure Resilience. Environ. Syst. Decis. 2021, 41, 341–376. [Google Scholar] [CrossRef] [PubMed]
  52. Mottahedi, A.; Sereshki, F.; Ataei, M.; Nouri Qarahasanlou, A.; Barabadi, A. The Resilience of Critical Infrastructure Systems: A Systematic Literature Review. Energies 2021, 14, 1571. [Google Scholar] [CrossRef]
  53. Zhang, X.; Zeng, X.; Nie, G.; Fan, X. An Empirical Method to Estimate Earthquake Direct Economic Losses Using Building Damages in High Intensity Area as a Proxy. Nat. Hazards Res. 2021, 1, 63–70. [Google Scholar] [CrossRef]
  54. Rashid, M.; Ahmad, N. Economic Losses Due to Earthquake—Induced Structural Damages in RC SMRF Structures. Cogent Eng. 2017, 4, 16. [Google Scholar] [CrossRef]
  55. Moitinho de Almeida, M.; Schlüter, B.S.; van Loenhout, J.A.F.; Thapa, S.S.; Kumar, K.C.; Singh, R.; Guha-Sapir, D.; Mahara, D.P. Changes in Patient Admissions after the 2015 Earthquake: A Tertiary Hospital-Based Study in Kathmandu, Nepal. Sci. Rep. 2020, 10, 400043. [Google Scholar] [CrossRef][Green Version]
  56. Giuliani, F.; De Falco, A.; Cutini, V. Unpacking Seismic Risk in Italian Historic Centres: A Critical Overview for Disaster Risk Reduction. Int. J. Disaster Risk Reduct. 2021, 59, 102260. [Google Scholar] [CrossRef]
  57. Caiado, G.; Oliveira, C.; Ferreira, M.A.; Sá, F. Assessing Urban Road Network Seismic Vulnerability: An Integrated Approach. In Proceedings of the 15th World Conference on Earthquake Engineering, Lisbon, Portugal, 24–28 September 2012; p. 10. [Google Scholar]
  58. Harirchian, E.; Lahmer, T.; Buddhiraju, S.; Mohammad, K.; Mosavi, A. Earthquake Safety Assessment of Buildings through Rapid Visual Screening. Buildings 2020, 10, 51. [Google Scholar] [CrossRef][Green Version]
  59. Miles, S.B.; Chang, S.E. Urban Disaster Recovery: A Framework and Simulation Model; Technical Report MCEER-03-0005; University at Buffalo: Buffalo, NY, USA, 2003; p. 102. [Google Scholar]
  60. Miles, S.; Chang, S.E. A Simulation Model of Urban Disaster Recovery and Resilience: Implementation for the 1994 Northridge Earthquake; Technical Report MCEER-07-0014; University at Buffalo: Buffalo, NY, USA, 2007; p. 130. [Google Scholar]
  61. United Nations Office for Disaster Risk Reduction. Sendai Framework for Disaster Risk Reduction 2015–2030. Aust. J. Emerg. Manag. 2015, 30, 9–10. [Google Scholar]
  62. Scopus about Scopus—Abstract and Citation Database | Elsevier. Available online: https://www.elsevier.com/solutions/scopus (accessed on 4 March 2021).
  63. Gupta, S.; Starr, M.K.; Farahani, R.Z.; Matinrad, N. Disaster Management from a POM Perspective: Mapping a New Domain. Prod. Oper. Manag. 2016, 25, 1611–1637. [Google Scholar] [CrossRef]
  64. Altay, N.; Green, W.G. OR/MS Research in Disaster Operations Management. Eur. J. Oper. Res. 2006, 175, 475–493. [Google Scholar] [CrossRef][Green Version]
  65. Harirchian, E.; Lahmer, T. Developing a Hierarchical Type-2 Fuzzy Logic Model to Improve Rapid Evaluation of Earthquake Hazard Safety of Existing Buildings. Structures 2020, 28, 1384–1399. [Google Scholar] [CrossRef]
  66. Tomar, A.; Burton, H.V.; Mosleh, A.; Yun Lee, J. Hindcasting the Functional Loss and Restoration of the Napa Water System Following the 2014 Earthquake Using Discrete-Event Simulation. J. Infrastruct. Syst. 2020, 26, 04020035. [Google Scholar] [CrossRef]
  67. Tomar, A.; Burton, H.V. Risk-Based Assessment of the Post-Earthquake Functional Disruption and Restoration of Distributed Infrastructure Systems. Int. J. Disaster Risk Reduct. 2020, 52, 102002. [Google Scholar] [CrossRef]
  68. Aslani, F.; Amini Hosseini, K.; Fallahi, A. A Framework for Earthquake Resilience at Neighborhood Level. Int. J. Disaster Resil. Built Environ. 2020, 11, 557–575. [Google Scholar] [CrossRef]
  69. Whitworth, M.R.Z.; Moore, A.; Francis, M.; Hubbard, S.; Manandhar, S. Building a More Resilient Nepal—The Utilisation of the Resilience Scorecard for Kathmandu, Nepal Following the Gorkha Earthquake of 2015. Lowl. Technol. Int. 2020, 21, 229–236. [Google Scholar]
  70. Merschman, E.; Doustmohammadi, M.; Salman, A.M.; Anderson, M. Postdisaster Decision Framework for Bridge Repair Prioritization to Improve Road Network Resilience. Transp. Res. Rec. 2020, 2674, 81–92. [Google Scholar] [CrossRef]
  71. Chen, C.; Buylova, A.; Chand, C.; Wang, H.; Cramer, L.A.; Cox, D.T. Households’ Intended Evacuation Transportation Behavior in Response to Earthquake and Tsunami Hazard in a Cascadia Subduction Zone City. Transp. Res. Rec. 2020, 2674, 99–114. [Google Scholar] [CrossRef]
  72. Koc, A.C.; Demir, U.S.; Toprak, S. Water Distribution System Postearthquake Service Ratio Prediction with Repair Rate and Graph Index. J. Pipeline Syst. Eng. Pract. 2019, 10, 04019032. [Google Scholar] [CrossRef]
  73. Hayat, E.; Haigh, R.; Amaratunga, D. A Framework for Reconstruction of Road Infrastructure after a Disaster. Int. J. Disaster Resil. Built Environ. 2019, 10, 151–166. [Google Scholar] [CrossRef]
  74. Sun, L.; Stojadinovic, B.; Sansavini, G. Resilience Evaluation Framework for Integrated Critical Infrastructure-Community Systems under Seismic Hazard. J. Infrastruct. Syst. 2019, 25, 1–11. [Google Scholar] [CrossRef][Green Version]
  75. Wang, S.; Tang, W.; Qi, D.; Li, J.; Wang, E.; Lin, Z.; Duffield, C.F. Understanding the Role of Built Environment Resilience to Natural Disasters: Lessons Learned from the Wenchuan Earthquake. J. Perform. Constr. Facil. 2017, 31, 04017058. [Google Scholar] [CrossRef]
  76. Rowell, M.; Goodchild, A. Effect of Tsunami Damage on Passenger and Forestry Transportation in Pacific County, Washington. Transp. Res. Rec. 2017, 2604, 88–94. [Google Scholar] [CrossRef]
  77. Dunn, S.; Gonzalez-Otalora, S. Development of an Adaptive Solution to Increase Infrastructure System Resilience Based upon a Location-Allocation Methodology. J. Infrastruct. Syst. 2021, 27, 04020043. [Google Scholar] [CrossRef]
  78. Sutley, E.J.; Hamideh, S. Postdisaster Housing Stages: A Markov Chain Approach to Model Sequences and Duration Based on Social Vulnerability. Risk Anal. 2020, 40, 2675–2695. [Google Scholar] [CrossRef]
  79. Nofal, O.M.; van de Lindt, J.W.; Do, T.Q. Multi-Variate and Single-Variable Flood Fragility and Loss Approaches for Buildings. Reliab. Eng. Syst. Saf. 2020, 202, 106971. [Google Scholar] [CrossRef]
  80. Creutzig, F.; Jochem, P.; Edelenbosch, O.Y.; Mattauch, L.; Van Vuuren, D.P.; McCollum, D.; Minx, J. Transport: A Roadblock to Climate Change Mitigation? Science 2015, 350, 911–912. [Google Scholar] [CrossRef] [PubMed][Green Version]
  81. Kenworthy, J.R. The Eco-City: Ten Key Transport and Planning Dimensions for Sustainable City Development. Environ. Urban. 2006, 18, 67–85. [Google Scholar] [CrossRef]
  82. Dong, S.; Mostafizi, A.; Wang, H.; Gao, J.; Li, X. Measuring the Topological Robustness of Transportation Networks to Disaster-Induced Failures: A Percolation Approach. J. Infrastruct. Syst. 2020, 26, 04020009. [Google Scholar] [CrossRef]
  83. Laursen, M. International Journal of Disaster Resilience in the Built Environment Article Information. Int. J. Disaster Resil. Built Environ. 2015, 6, 102–116. [Google Scholar]
  84. Fekete, A. Common Criteria for the Assessment of Critical Infrastructures. Int. J. Disaster Risk Sci. 2014. [Google Scholar] [CrossRef][Green Version]
  85. Bach, C.; Bouchon, S.; Fekete, A.; Birkmann, J.; Serre, D. Adding Value to Critical Infrastructure Research and Disaster Risk Management: The Resilience Concept. Surv. Perspect. Integr. Environ. Soc. 2013, 6, 1–12. [Google Scholar]
  86. Rinaldi, S.M.; Peerenboom, J.P.; Kelly, T.K. Identifying, Understanding, and Analyzing Critical Infrastructure Interdependencies. IEEE Control Syst. Mag. 2001, 21, 11–25. [Google Scholar] [CrossRef]
  87. National Research Council. National Earthquake Resilience: Research, Implementation, and Outreach; The National Academies Press: Washington, DC, USA, 2011. [Google Scholar]
  88. Pourret, O.; Na¿m, P.; Marcot, B. Bayesian Networks: A Practical Guide to Applications; John Wiley: Hoboken, NJ, USA, 2008; ISBN 978-0-470-06030-8. [Google Scholar]
  89. Prior, T. Measuring Critical Infrastructure Resilience: Possible Indicators; CSS Risk and Resilience Reports; Center for Security Studies (CSS): Zürich, Switzerland, 2015; 14p. [Google Scholar]
  90. Petrovic, N.; Stranjik, A.; Peternel, R. Generic Resilience Indicators of Critical Infrastructures. EU-CIRCLE A Pan Eur. Framew. Strength. Crit. Infrastruct. Resil. Clim. Chang. 2018, 1, 97–103. [Google Scholar]
  91. Jovanović, A.S.; Jelic, M.; Chakravarty, S. Resilience and Situational Awareness in Critical Infrastructure Protection: An Indicator-Based Approach. In Issues on Risk Analysis for Critical Infrastructure Protection; IntechOpen: London, UK, 2021; ISBN 978-1-83962-621-0. [Google Scholar]
  92. Arosio, M.; Martina, M.L.V.; Figueiredo, R. Natural Hazard Risk of Complex Systems—The Whole Is More than the Sum of Its Parts: I. A Holistic Modelling Approach Based on Graph Theory. Nat. Hazards Earth Syst. Sci. 2018, 1–23. [Google Scholar] [CrossRef]
  93. Danowski, J.A.; Yan, B.; Riopelle, K. A Semantic Network Approach to Measuring Sentiment. Qual. Quant. 2021, 55, 221–255. [Google Scholar] [CrossRef]
  94. Zhu, Y.; Cheng, M.; Wang, J.; Ma, L.; Jiang, R. The Construction of Home Feeling by Airbnb Guests in the Sharing Economy: A Semantics Perspective. Ann. Tour. Res. 2019, 75, 308–321. [Google Scholar] [CrossRef]
  95. Kim, J.; Lee, Y.O.; Park, H.W. Delineating the Complex Use of a Political Podcast in South Korea by Hybrid Web Indicators: The Case of the Nakkomsu Twitter Network. Technol. Forecast. Soc. Chang. 2016, 110, 42–50. [Google Scholar] [CrossRef]
  96. Bastian, M.; Heymann, S.; Jacomy, M. Gephi: An Open Source Software for Exploring and Manipulating Networks. In Proceedings of the Third International ICWSM Conference, San Jose, CA, USA, 17–20 May 2009; pp. 361–362. [Google Scholar]
  97. Cherven, K. Mastering Gephi Network Visualization Produce Advanced Network Graphs in Gephi and Gain Valuable Insights into Your Network Datasets; Packt Publishing: Birmingham, UK, 2015; ISBN 9781783987344. [Google Scholar]
  98. Cyberinfrastructure Technology Integration Visualization Group: An Introduction of Gephi; Clemson University, CITI Group: San Jose, CA, USA, 2009; Available online: https://visualization.sites.clemson.edu/visualization/wp-content/uploads/2016/10/Gephi-UserGuide.pdf (accessed on 25 November 2021).
  99. Kongar, I.; Giovinazzi, S. Damage to Infrastructure: Modeling. In Encyclopedia of Earthquake Engineering; Springer: Berlin/Heidelberg, Germany, 2014; pp. 1–14. [Google Scholar]
  100. GIRAFFE. GIRAFFE User’s Manual; Cornell University, School of Civil & Environmental Engineering: Ithaca, NY, USA, 2008. [Google Scholar]
  101. Choi, J.; Deshmukh, A.; Naderpajouh, N.; Hastak, M. Dynamic Relationship between Functional Stress and Strain Capacity of Post-Disaster Infrastructure. Nat. Hazards 2017, 87, 817–841. [Google Scholar] [CrossRef]
  102. Kammouh, O.; Marasco, S.; Noori, A.Z.; Cimellaro, G.P. Peoples: Indicator-Based Tool to Compute Community Resilience. In Proceedings of the 11th National Conference on Earthquake Engineering 2018, NCEE 2018, 25–29 June 2018, Los Angeles, CA, USA; 2018; Volume 6, pp. 3699–3710. [Google Scholar]
  103. Szigeti, F.; Davis, G. Using the ASTM/ANSI Standards for Whole Building Functionality and Serviceability for Major Asset and Portfolio Decisions. In Proceedings of the CIB W070 2002 Global Symposium; University of Cape Town: Capa Town, South Africa, 2002; pp. 507–521. [Google Scholar]
  104. Therese, P. McAllister Developing Guidelines and Standards for Disaster Resielince of the Built Environment: A Research Needs Assessment (NIST TN 1795). Natl. Inst. Stand. Technol. Publ. 2013, 1–142. [Google Scholar] [CrossRef][Green Version]
  105. Zhao, T.; Sun, L. Seismic Resilience Assessment of Critical Infrastructure-Community Systems Considering Looped Interdependences. Int. J. Disaster Risk Reduct. 2021, 59, 102246. [Google Scholar] [CrossRef]
  106. Hosseini, M. Earthquake Disaster Risk Management Planning in Schools. Disaster Prev. Manag. 2006, 15, 649–661. [Google Scholar] [CrossRef]
  107. Pescaroli, G.; Nones, M.; Galbusera, L.; Alexander, D. Understanding and Mitigating Cascading Crises in the Global Interconnected System. Int. J. Disaster Risk Reduct. 2018, 30, 159–163. [Google Scholar] [CrossRef]
  108. Freddi, F.; Galasso, C.; Cremen, G.; Dall’Asta, A.; Di Sarno, L.; Giaralis, A.; Gutiérrez-Urzúa, F.; Málaga-Chuquitaype, C.; Mitoulis, S.A.; Petrone, C.; et al. Innovations in Earthquake Risk Reduction for Resilience: Recent Advances and Challenges. Int. J. Disaster Risk Reduct. 2021, 60, 102267. [Google Scholar] [CrossRef]
  109. Kong, J.; Zhang, C.; Simonovic, S.P. Optimizing the Resilience of Interdependent Infrastructures to Regional Natural Hazards with Combined Improvement Measures. Reliab. Eng. Syst. Saf. 2021, 210, 107538. [Google Scholar] [CrossRef]
  110. Janić, M. Modelling the Resilience of Rail Passenger Transport Networks Affected by Large-Scale Disruptive Events: The Case of HSR (High Speed Rail). Transportation 2018, 45, 1101–1137. [Google Scholar] [CrossRef][Green Version]
  111. Raikes, J.; Smith, T.F.; Jacobson, C.; Baldwin, C. Pre-Disaster Planning and Preparedness for Floods and Droughts: A Systematic Review. Int. J. Disaster Risk Reduct. 2019, 38, 101207. [Google Scholar] [CrossRef]
  112. Zona, A.; Kammouh, O.; Cimellaro, G.P. Resourcefulness Quantification Approach for Resilient Communities and Countries. Int. J. Disaster Risk Reduct. 2020, 46, 101509. [Google Scholar] [CrossRef]
  113. de Vries, H.P.; Hamilton, R.T. Smaller Businesses and the Christchurch Earthquakes: A Longitudinal Study of Individual and Organizational Resilience. Int. J. Disaster Risk Reduct. 2021, 56, 102125. [Google Scholar] [CrossRef]
  114. Aros-Vera, F.; Gillian, S.; Rehmar, A.; Rehmar, L. Increasing the Resilience of Critical Infrastructure Networks through the Strategic Location of Microgrids: A Case Study of Hurricane Maria in Puerto Rico. Int. J. Disaster Risk Reduct. 2021, 55, 102055. [Google Scholar] [CrossRef]
  115. Marchesini, G.; Beraud, H.; Barroca, B. Quantification of Disaster Waste: Review of the Available Methods. Int. J. Disaster Risk Reduct. 2021, 53, 101996. [Google Scholar] [CrossRef]
  116. Pezzica, C.; Cutini, V.; Bleil de Souza, C. Mind the Gap: State of the Art on Decision-Making Related to Post-Disaster Housing Assistance. Int. J. Disaster Risk Reduct. 2021, 53, 101975. [Google Scholar] [CrossRef]
  117. Bulajić, B.; Todorovska, M.I.; Manić, M.I.; Trifunac, M.D. Structural Health Monitoring Study of the ZOIL Building Using Earthquake Records. Soil Dyn. Earthq. Eng. 2020, 133, 106105. [Google Scholar] [CrossRef]
  118. Oktarina, R.; Bahagia, S.N.; Diawati, L.; Pribadi, K.S. Artificial Neural Network for Predicting Earthquake Casualties and Damages in Indonesia. In Proceedings of the IOP Conference Series: Earth and Environmental Science; Institute of Physics Publishing: Solo, Indonesia, 2020; Volume 426, p. 012156. [Google Scholar]
  119. Xie, Y.; Ebad Sichani, M.; Padgett, J.E.; DesRoches, R. The Promise of Implementing Machine Learning in Earthquake Engineering: A State-of-the-Art Review. Earthq. Spectra 2020, 36, 1769–1801. [Google Scholar] [CrossRef]
Figure 1. Performance based resilience components of a CIS (adapted from Refs. [28,48]).
Figure 1. Performance based resilience components of a CIS (adapted from Refs. [28,48]).
Infrastructures 07 00067 g001
Figure 2. PRISMA flowchart to identify and select studies relevant to infrastructure resilience frameworks.
Figure 2. PRISMA flowchart to identify and select studies relevant to infrastructure resilience frameworks.
Infrastructures 07 00067 g002
Figure 3. The summary of studies evaluated based on the infrastructure types.
Figure 3. The summary of studies evaluated based on the infrastructure types.
Infrastructures 07 00067 g003
Figure 4. The summary of studies evaluated by the year of publication.
Figure 4. The summary of studies evaluated by the year of publication.
Infrastructures 07 00067 g004
Figure 5. Summary of infrastructure resilience framework development approaches.
Figure 5. Summary of infrastructure resilience framework development approaches.
Infrastructures 07 00067 g005
Figure 6. The geographic scope of the infrastructure resilience frameworks evaluated.
Figure 6. The geographic scope of the infrastructure resilience frameworks evaluated.
Infrastructures 07 00067 g006
Figure 7. Attributes of the decision-based framework from past studies in terms of social, functional, and physical metrics, taking into account the indicators and methodology used.
Figure 7. Attributes of the decision-based framework from past studies in terms of social, functional, and physical metrics, taking into account the indicators and methodology used.
Infrastructures 07 00067 g007
Figure 8. Attributes of the probabilistic-based framework from past studies in terms of social, functional, and physical metrics, taking into account the indicators and methodology used.
Figure 8. Attributes of the probabilistic-based framework from past studies in terms of social, functional, and physical metrics, taking into account the indicators and methodology used.
Infrastructures 07 00067 g008
Figure 9. Attributes of the damage-analysis-based framework from past studies in terms of social, functional, and physical metrics, taking into account the indicators and methodology used.
Figure 9. Attributes of the damage-analysis-based framework from past studies in terms of social, functional, and physical metrics, taking into account the indicators and methodology used.
Infrastructures 07 00067 g009
Figure 10. CIR indicators/variables map view The arrows represent the correlation between the indicators, while the size indicates the importance of the indicators used to measure critical infrastructure resilience.
Figure 10. CIR indicators/variables map view The arrows represent the correlation between the indicators, while the size indicates the importance of the indicators used to measure critical infrastructure resilience.
Infrastructures 07 00067 g010
Figure 11. Key features for assessing infrastructure resilience in different phases of a disaster (pre-, during, and post-disaster) to develop an integrated and adaptable framework.
Figure 11. Key features for assessing infrastructure resilience in different phases of a disaster (pre-, during, and post-disaster) to develop an integrated and adaptable framework.
Infrastructures 07 00067 g011
Figure 12. An overview of an integrated and adaptive framework for assessing infrastructure resilience for earthquake hazards.
Figure 12. An overview of an integrated and adaptive framework for assessing infrastructure resilience for earthquake hazards.
Infrastructures 07 00067 g012
Table 1. Infrastructure Resilience Frameworks Assessed in this study.
Table 1. Infrastructure Resilience Frameworks Assessed in this study.
#Author (Year)FrameworkCountryHazardMethod AdoptedDisaster PhaseRef.
1Nozhati (2021)Optimisation formulation-based frameworkUSAEarthquakeParallel rollout method, dynamic
programming algorithms along with heuristics and case study
Post disaster[31]
2Iuliis et al. (2020)Probabilistic approachGlobalEarthquakeLiterature study, experts’ opinionsPost-disaster[27]
3Devendiran et al. (2020)Integrated approach Hydrological modelUSAFlood and Earthquakemodel simulations in conjunction with a macro-scale hydrological model and bridge structural components (case study)Post-disaster[32]
4Lo et al. (2020)Complete model building type (Combined probabilistic)TaiwanEarthquakeCascade failure due to soil liquefications and building collapse, peak ground motions, and fragility curve evaluationDuring the disaster[33]
5Harirchian and Lahmer (2020)Index-based frameworkTurkeyEarthquakeRapid visual screening (RVS) Type-2 fuzzy system, fragility functions, and vulnerability indexPre- and post-disaster[65]
6Tomar et al. (2020)Discrete-event simulation framework (probabilistic-based framework)USAEarthquakePipe damage and repair (napa water system) and case studyPost-disaster[66,67]
7Kammouh et al. (2020)Probabilistic-based frameworkBrazilNatural and ManmadeExpert knowledgeAll phase[26]
8Aslani et al. (2020)4R based frameworkIranEarthquakeLiterature study, analytical hierarchy process (hybrid approach), experts’ opinions and case study (analytical maps), SWOT analysisAll phase[68]
9Whitworth et al. (2020)UN Resilience ScorecardNepalEarthquakeDisaster cycle, operational capacity, and resilience of the societyPre-disaster[69]
10Merschman et al. (2020)Decision frameworkUSANatural HazardFunctional, topological, and social measurespost disaster[70]
11Ranjbar and Naderpour(2020)Seismic resilience index (Index-based framework)USAEarthquakeCase study, seismic hazard analysis, dynamic analysis (fragility and vulnerability functions)Pre- and post-disaster[36]
12Chen et al. (2020)Residents’ perceptions and intended evacuation behaviours (Static-based framework)USATsunami and earthquakeSurvey data, bivariate chart, intercorrelation table, and regression analysesPost-disaster[71]
13Mazumder et al. (2020)Damage-based frameworkUSA, Italy BangladeshEarthquakeScenario-based seismic damage analysis, Python-based open-source libraries, SeismoPiDuring and post-disaster[34]
14Kameshwar et al. (2019)Probabilistic decision supportUSAMulti-hazardPerformance goals, case study, hazard models, and system topologyPre- and post-disaster[28]
15Koc et al. (2019)medium articulation graph index (Probability-based framework)GlobalEarthquakePolynomial equations, hypothetical water distribution systemsPost-disaster[72]
16Hayat (2019)Reconstruction conceptual frameworkIndonesiaEarthquake and tsunamiLiterature, empirical evidence (structured interviews), and case studiesPost-disaster[73]
17Sun et al. (2019)Agent-based modelling frameworkGlobalEarthquakeParametric investigation and virtual system and case studyPost-disaster[74]
18Yu et al. (2019)Seismic resilience assessment frameworkChinaEarthquakeFault tree analysis and case studiesPost-disaster[37]
19Anwar et al. (2019)Performance-based probabilistic frameworkChinaEarthquakeThree-dimensional inelastic fibre-based numerical modelling approachesAll phase[35]
20Wang et al. (2017)Conceptual model of the role of built environmentChinaEarthquakeTriangulation method was utilized for collecting data, drones field trips, lesson learnedDuring and post-disaster[75]
21Rowell and Goodchild (2017)Travel demand modelUSAEarthquakeCommunity-based disaster recovery planningPost-disaster[76]
22Liu et al. (2016)Decision support frameworkEnglandEarthquakeexperiences and lesson learnedPost-disaster[29]
23Hadigheh et al. (2016)Resilience-based design framework (RBD)AustraliaEarthquakeCapacity spectrum method and retrofitting methodsPre-disaster[24]
24Farahmandfar et al. (2016)Resilience and optimisation frameworkUSAEarthquakeNode degree formulation and demandDuring the disaster[38]
Table 2. Example of key infrastructure resilience characteristics developed for specific geographic scope.
Table 2. Example of key infrastructure resilience characteristics developed for specific geographic scope.
FrameworkKey Infrastructure Resilience CharacteristicsAuthorsGeographic Scope
SimilaritiesDifferences
Probabilistic modelInfrastructure specifications, earthquake specification, human resources/available resources, type of recovery technologyLifeline infrastructure (power and telecommunication), hierarchical model, anti-seismic technology of structureIuliis et al. [27]Global
Decision-
support
framework
Community-planning guidelines/standards, specification of the infrastructure, earthquake specifications, performance-based guidelinesCritical infrastructure (interconnectivity) seaside, economic damages, restoration goals.Kameshwar et al. [28]United Status of America
Conceptual modelPlanning and natural hazard resilient technologies, assessment of hazards, rural community mitigation.Lesson-learned techniques, setting appropriate design codes, construction process managementWang et al. [75]China
Table 3. The similarities and differences in the framework with similar geographic scope.
Table 3. The similarities and differences in the framework with similar geographic scope.
FrameworkKey Infrastructure Resilience CharacteristicAuthorsGeographic Scope
SimilaritiesDifferences
Decision-
support
framework
Community planning guidelines/standards, specification of the infrastructure, earthquake specifications, performance based-guidelines, fragility curveCritical infrastructure (electricity, water, and transportation) located in seaside, economic damages.Kameshwar et al. [28]USA
Probability modelSeismic specifications, hospital structural details, fragility curveHospital building, dynamic analysis, vulnerability curveRanjbar and Naderpour [36]USA
Integrated approachGround motions, bridge structural details, seismic demand parameters, guidelines, fragility curveBridge seismic vulnerability in flood-induced scour, flood inundation details, riprapsDevendiran et al. [32]USA
Table 4. Methods/approaches used to develop the infrastructure resilience frameworks 4.3.1 Decision-based framework/models.
Table 4. Methods/approaches used to develop the infrastructure resilience frameworks 4.3.1 Decision-based framework/models.
#ReferenceIntegrated ApproachDecision-BasedDamage BasedFragility Based EvaluationProbabilistic Model4RStatic-BasedReconstructionIndex-BasedResilience AssessmentTravel Demand ModelOptimisation BasedConceptual ModelAgent-Based Model
1Nozhati [31]🞨🞨
2Iuliis et al. [27]🞨🞨
3Devendiran et al. [32]🞨🞨
4Lo et al. [33]🞨🞨
5Harirchian and Lahmer [65]🞨
6Tomar et al. [66,67]🞨
7Kammouh et al. [26]🞨
8Anwar et al. [35]🞨🞨
9Aslani et al. [68]🞨
10Whitworth et al. [69]
11Merschman et al. [70]🞨
12Ranjbar and Naderpour [36]🞨🞨
13Chen et al. [71]🞨
14Kameshwar et al. [28]🞨🞨🞨🞨
15Koc et al. [72]🞨🞨
16Mazumder et al. [34]🞨🞨
17Hayat [73]🞨🞨
18Sun et al. [74]🞨
19Yu et al. [37]🞨🞨
20Wang et al. [75]🞨
21Rowell and Goodchild [76]🞨
22Liu et al. [29]🞨🞨
23Hadigheh et al. [24]🞨🞨
24Farahmandfar et al. [38]🞨🞨🞨
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Sathurshan, M.; Saja, A.; Thamboo, J.; Haraguchi, M.; Navaratnam, S. Resilience of Critical Infrastructure Systems: A Systematic Literature Review of Measurement Frameworks. Infrastructures 2022, 7, 67. https://doi.org/10.3390/infrastructures7050067

AMA Style

Sathurshan M, Saja A, Thamboo J, Haraguchi M, Navaratnam S. Resilience of Critical Infrastructure Systems: A Systematic Literature Review of Measurement Frameworks. Infrastructures. 2022; 7(5):67. https://doi.org/10.3390/infrastructures7050067

Chicago/Turabian Style

Sathurshan, Mathavanayakam, Aslam Saja, Julian Thamboo, Masahiko Haraguchi, and Satheeskumar Navaratnam. 2022. "Resilience of Critical Infrastructure Systems: A Systematic Literature Review of Measurement Frameworks" Infrastructures 7, no. 5: 67. https://doi.org/10.3390/infrastructures7050067

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop