Resilience Assessment Approach Research Articles

A simulation-based approach for resilience assessment of process system: A case of LNG terminal system

System resilience denotes the capacity to uphold desired system performance in the face of disruptions. Evaluating the resilience of a process system necessitates a thorough consideration of the intricate interplay between its components and the pivotal role of process parameters in reflecting the repercussions of disruptions on the system. This paper introduces an integrated methodology that takes into account component interactions and leverages process data for the resilience assessment of a process system. The proposed methodology comprises four key components: system structure analysis, disruption impacts analysis, process simulation, and resilience assessment. Firstly, the system structure is meticulously scrutinized using a P-graph model. This analysis encompasses the assessment of the significance and interplay of components, as well as the evaluation of how component failures affect the system's overall processes. Secondly, a Markov model is devised to examine the state transition process of components and quantifies the maintenance time needed for failed components. Subsequently, a simulation model is formulated to acquire real-time process parameters in the presence of disruptive events. Finally, the system's performance response function (PRF) is derived from the normalization of these process parameters. Building upon this foundation, a resilience assessment is conducted with a focus on the PRF. To illustrate the effectiveness of this methodology, an LNG terminal system is employed as an exemplar.

Spatial-Temporal Assessment of Urban Resilience to Disasters: A Case Study in Chengdu, China

Urban areas with an imbalanced vulnerability to disasters have garnered attention. Building an urban resilience index helps to develop a progressively favored instrument for tracking progress toward disaster-resilient cities. However, there remains a lack of empirical studies on measuring urban resilience, with limited focus on the spatial-temporal characteristics of urban resilience to disasters, particularly relevant in developing nations like China. Thus, a refined urban resilience index to disasters based on the subcomponents of infrastructure, environment, socio-economy, and institution is suggested in this study. This index-based assessment framework is applied and validated to measure the spatial-temporal resilience using a real-world case study in Chengdu, China. The main findings of this study indicate that: (1) the overall urban resilience of Chengdu has been growing toward better conditions, with infrastructural resilience accounting for the majority of this growth. (2) The distribution of urban resilience exhibits a regional disparity and a spatially polarized pattern. (3) The agglomeration characteristics of urban resilience are significant. (4) There is a clear regional mismatch in the distribution of urban resilience to disaster risk. The validated model offers a comprehensive and replicable approach for urban resilience assessment and planning, especially for disaster-frequent regions.

Enhancing resilience assessment and bus bridging service design for large‐scale rail transit systems under disruptions

AbstractThis paper proposes a more computationally efficient approach for resilience assessment of rail transit system under disruptions. An improved linear programming model is developed to depict commuter flows and estimate system statuses. To address the computational challenge caused by the complexity of system, a four‐step approach is proposed based on the proposed commuter flow model. In the first step, Origin‐Destination (OD) pairs are divided into smaller groups and their flows under normal conditions are estimated by the proposed model separately, with the assumption that the railway capacity is sufficient relative to demand. Next, overall system statuses under normal conditions, including commuters on each train and spare capacities of each train are calculated by integrating results obtained in the first step. In the third step, system statuses under disruptions are estimated. In this step, we assume that unaffected commuters will not change their routes and flows of all affected commuters are estimated by a modified commuter model with given spare space of trains. Based on these outputs, several critical measures are introduced and calculated to quantify the resilience, resistance, and recovery ability of rail network systematically. We also demonstrate how our approach could be used to facilitate design and evaluation of bus bridging service. The proposed approach is demonstrated on the core part of Hangzhou rail transit network.

Principles and practices of designing for resilient performance: An assessment framework

Although resilient performance is intrinsic to socio-technical systems it might be supported by design, an idea known as Design for Resilient Performance (DfRP). Considering that such design is usually a re-design, learning from existing systems is crucial. This article introduces a framework for assessing the extent to which a system uses practices and principles of DfRP. The framework allows for the assessment of 24 attributes of the principles, the analysis of their relationships (a model was devised based on a survey with experts), and the investigation of practices that operationalize the principles. A scoring system sheds light on the effectiveness of using the principles. The framework application is exemplified based on the study of an emergency department in which daily huddles stood out as a practice of DfRP. This study involved interviews, observations, and documentary analysis. Based on this, a knowledge structure of DfRP is presented, comprised of concepts, principles, and practices. Six propositions to guide the framework application are set out, addressing themes such as the need for cost-effective DfRP, short control cycles, and customized designs that meet preferences of designers. The study contributes to DfRP theory and offers a new approach for resilience assessment.

Global resilience analysis of combined sewer systems under continuous hydrologic simulation

Managing and reducing combined sewer overflow (CSO) discharges is crucial for enhancing the resilience of combined sewer systems (CSS). However, the absence of a standardised resilience analysis approach poses challenges in developing effective discharge reduction strategies. To address this, our study presents a top-down method that expands the existing Global Resilience Analysis to quantify resilience performance in CSS. This approach establishes a link between threats (e.g., rainfall) and impacts (e.g., CSOs) through continuous and long-term simulation, accommodating various rainfall patterns, including extreme events. We assess CSO discharge impacts from a resilience perspective by introducing eight new metrics. We conducted a case study in Fehraltorf, Switzerland, analysing the performance of three green infrastructure (GI) types (bioretention cells, green roofs, and permeable pavements) over 38 years. The results demonstrated that GI enhanced all resilience indices, with variations observed in individual CSO performance metrics and their system locations. Notably, in Fehraltorf, green roofs emerged as the most effective GI type for improving resilience, while the downstream outfall displayed the highest resilience enhancement. Overall, our proposed method enables a shift from event-based to continuous simulation analysis, providing a standardised approach for resilience assessment. This approach informs the development of strategies for CSO discharge reduction and the enhancement of CSS resilience.

A review of power system resilience assessment and enhancement approaches by focusing on wind farms and wind turbines

AbstractRecently, due to the increase of the frequency of high impact low probability (HILP) events (manmade and natural events), assessment and enhancement of resilience has become very important in the operation and planning procedures of future power systems. In this regard, several researches have been conducted on operation and planning strategies of the power system to improve resilience. Here, assessment and enhancement approaches of power system resilience by focusing on the role of wind farms and wind turbines are reviewed. To do so, a comprehensive review of the related metrics, hazard types, modelling approaches, assessment methods, and enhancement solutions is presented. Then, the assessment and enhancement methods of power system resilience are categorized in three phases before event (preventive proactive actions), during event (corrective active actions) and after event (restorative reactive actions) from the point of view of operation and planning actions. Finally, the current challenges, research gaps and future trends are discussed.

Quantifying resilience of socio-ecological systems through dynamic Bayesian networks

Quantifying resilience of socio-ecological systems (SES) can be invaluable to delineate management strategies of natural resources and aid the resolution of socio-environmental conflicts. However, resilience is difficult to quantify and the factors contributing to it are often unknown. We provide a theoretical and conceptual framework to quantify resilience in a long-term context. Our approach uses elements from interdisciplinarity and network perspectives to establish links and causalities between social and ecological variables and resilience attributes. The evaluation and modeling of SES structure and function are established from the analysis of dynamic Bayesian networks (DBN). DBN models allow quantifying resilience through probabilities and offer a platform of interdisciplinary dialogue and an adaptive framework to address questions on ecosystem monitoring and management. The proposed DBN is tested in Monquentiva, a SES located in the high Andes of Colombia. We determined historical socio-ecological resilience from paleoecological evidence (palynological diversity, forest cover, fires, and precipitation) and social-economic factors (governance, social organization, and connectivity) between 1920 and 2019. We find that transformation processes in Monquentiva are mainly related to social change (e.g., social organization) and increased ecological diversity that in turn have fostered SES resilience between 1980 and 2019. The ability to predict the SES response over time and under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management. We conclude with a series of management and policy-relevant applications of the DBN approach for SES resilience assessment.

Restored quality of life-based approach (REQUALIFE) for urban seismic resilience assessment: Quantitative method

With rapid urbanization, today's cities are increasingly vulnerable to unpredictable and catastrophic environmental hazards such as earthquakes. A quantitative approach for seismic resilience assessment at the urban scale is still a challenging task because of the extreme complexity of urban systems. This study proposes a Restored Quality of Life-based Approach to evaluate urban seismic resilience. First, extensive literature is reviewed and analyzed to determine the mathematical definition of urban seismic resilience. Performance-based methods are selected, and urban seismic resilience is calculated by comparing desired city performance with actual city performance after an earthquake. Second, the concept of post-disaster quality of life is applied to indicate the city performance. A set of indicators is constructed preliminarily based on literature to measure the quality of life and is then validated by a Delphi survey. Finally, a framework is recommended to apply the proposed approach. The Restored Quality of Life-based Approach offers an effective tool for evaluating and improving the urban seismic resilience of a city and lays a theoretical and methodological foundation for better insights into and analysis of the urban seismic resilience in the future.

Restored Quality of Life-based approach (REQUALIFE) for urban seismic resilience assessment: Case study

Management of urban seismic resilience relies on effective assessment approaches. However, developing an approach for the quantitative assessment at the urban scale remains to be a challenging task due to the complexity and heterogeneity of urban systems. Accordingly, Part I proposed a Restored Quality of Life-based approach to bridge this gap. As an extension of Part I, this study conducted a case study on a test city in China that experienced the devastating Wenchuan earthquake in 2008 to demonstrate the applicability of the proposed approach. The desired and actual performance curves of the city were delineated according to the collected data and materials of the field survey. The overall urban seismic resilience of the city was then assessed, and details from the temporal and spatial dimensions were investigated. The case study demonstrated that the need satisfactions of residents in different phases varied. Moreover, the resilience differences within the city were demonstrated. The causes for these differences were further analyzed. This case study calibrates the proposed approach and serves as a valuable reference for the effective assessment and improvement of urban seismic resilience.

Resilience of various innovative water management practices: The case of rice production in the Vietnamese Mekong Delta floodplains

The floodplains play an important role in agricultural development and rural livelihoods in the Vietnamese Mekong Delta. As an intensive rice production area of Vietnam, the floodplain has experienced significant changes in water management regime during the recent decades. Depending on specific locations and irrigation infrastructure investments, four main water management practices have been innovated, particularly a planting season of two crops per year (2C1Y), three crops per year (3C1Y), three years eight crops (3Y8C) and two years five crops (2Y5C). The 4R framework (Reform, Result, Resilience, and Return) was developed based on grounded theory approach for resilience assessment of various innovative water management practices. In terms of resilience, we found that each crop pattern involves pros and cons, and the intensive crop practices are less resilient systems, especially in social, environmental, and ecological aspects. The findings provide good lessons learned not only for Vietnam but also for the other rice-producing deltas implementing ecosystem resilience to adapt to global challenges such as flood, drought, and salinity intrusion.

A Bayesian Network Modeling Approach for Time-Varying Flood Resilience Assessment of Housing Infrastructure System

Natural hazard causes severe types of damage to infrastructure systems at regular intervals, and the occurrence of such events is inevitable. The concept of resilience is adopted to make infrastructure systems more reliable, adaptable, and recoverable against natural disasters. Resilience is defined as the ability of an infrastructure to resist the impact of a disaster and bounce back to its desirable performance level after the disaster. Recovery of infrastructure, which forms a part of resiliency, is a time-dependent process in nature. In this work, a dynamic Bayesian network (BN) model is developed for the resilience assessment of housing infrastructure against flood hazards. The proposed resilience model is then implemented in the testbed of Barak Valley in North-East India. An extensive field survey is performed to collect relevant indicator data for resilience assessment and validation. To assess the recovery process, the housing infrastructure resiliency of the Barak Valley testbed is evaluated and compared between multiple flood event time periods. Lastly, the most critical indicators of the proposed dynamic BN model are identified by performing sensitivity analysis. This study will help the planner, designers, policymakers, and stakeholders to provide resilience-based decisions on flood resiliency of housing infrastructure systems.

A Systematic Approach for Resilience Assessment in Road Transport Routes Involving Natural and Human Interruptions

This work presents a new approach for resilience assessment in road transport routes interrupted by natural causes (rain, earthquakes) as well as by human influence (accidents). In this way, by knowing the state of preservation of the elements located within a road, particularly bridges, it will be possible to identify which are those with the highest priority to be attended for their conservation, repair, and even replacement, thus relating the cost of maintenance and the number of people benefited. To test this methodology, a case study was proposed. The proposed systematic approach is applied in the federal highway 15 route, which is an international route that passes through seven states of Mexico, ending in Alberta Canada. The study is limited to the Michoacan state of Mexico, which corresponds to 426 kilometers. This study identified the highway bridges within the road and analyzed their deterioration, the cost of repair, and the benefited inhabitants. The most significant scenarios were obtained in terms of repair cost and people benefited, identifying which bridges have priority to be served, having a more accurate decision and distribution of adequate financial resources.

Planning-Oriented resilience assessment and enhancement of integrated electricity-gas system considering multi-type natural disasters

A planning-oriented resilience assessment and enhancement approach is proposed that can efficiently deal with multi-type natural disasters. A unified disaster modelling framework is proposed to extract key information from various potential disaster scenarios, thus forming a disaster scenario database. The impact-increment-based enumeration method is applied, and a reusable impact-increment database is established to speed up the assessment process. The reusable database is also utilized to calculate component-level resilience indices and economic indices, so as to make enhancement strategies against potential disasters within planning time scale. Resilience assessment on an integrated electricity-gas system in Taiwan's coastal seismic statistical zone shows that the proposed method can significantly improve the computational efficiency as compared to existing methods. Numerical results indicate that the resilient planning considering the diversity of natural disaster types comprehensively improves the system resilience, which means it is not only concerned with the system performance under a single type of disaster. In addition, the most suitable resilience enhancement scheme with insufficient funds shall be developed according to the economic indices, instead of the component-level resilience indices that cannot balance the resilience enhancement effect with the implementation cost.

An agent-based modeling approach for community resilience assessment accounting for system interdependencies: Application on education system

This paper introduces an agent-based modeling (ABM) approach to model a community as a system of interdependent systems for studying education systems resilience, one of the least-studied components of communities in the quantitative disaster literature. In this ABM approach, autonomous entities, called agents, simulate the components of a system, while internal interactions among them shape the system and external interactions among systems shape the community. To study the education system resilience subject to tornado hazard, a library of agents is proposed including school, household, electric power network, water supply network, and construction companies agents. The proposed ABM approach is applied on the virtual Centerville community. A Monte Carlo sampling analysis for various tornado intensities is conducted to account for and quantify the inherent uncertainties. Moreover, an education system resilience measure based on the education quality and quantity is proposed and computed for Centerville. The probabilities of the education system falling in different resilience levels are also computed for different tornado intensities. Taking advantage of the comprehensive quantitative model proposed, decisions for enhancing the education system resilience can be evaluated, which is demonstrated by assessing the effect of providing backup utilities for schools on the education system resilience.

Stress-Testing Framework for Urban Water Systems: A Source to Tap Approach for Stochastic Resilience Assessment

Optimizing the design and operation of an Urban Water System (UWS) faces significant challenges over its lifespan to account for the uncertainties of important stressors that arise from population growth rates, climate change factors, or shifting demand patterns. The analysis of a UWS’s performance across interdependent subsystems benefits from a multi-model approach where different designs are tested against a variety of metrics and in different times scales for each subsystem. In this work, we present a stress-testing framework for UWSs that assesses the system’s resilience, i.e., the degree to which a UWS continues to perform under progressively increasing disturbance (deviation from normal operating conditions). The framework is underpinned by a modeling chain that covers the entire water cycle, in a source-to-tap manner, coupling a water resources management model, a hydraulic water distribution model, and a water demand generation model. An additional stochastic simulation module enables the representation and modeling of uncertainty throughout the water cycle. We demonstrate the framework by “stress-testing” a synthetic UWS case study with an ensemble of scenarios whose parameters are stochastically changing within the UWS simulation timeframe and quantify the uncertainty in the estimation of the system’s resilience.

Probabilistic seismic resilience quantification of a reinforced masonry shear wall system with boundary elements under bi-directional horizontal excitations

The concept of resilience is gaining increased attention in disaster management due to the recent awareness of the need to reduce the detrimental post-event effects of natural disasters, e.g., earthquakes. Resilience is a practical concept that includes pre-event (preparedness and mitigation) and post-event (response and recovery) activities. Quantitative resilience assessment approaches are needed to compare the available mitigation strategies to decide on the most suitable strategy and provide better support for decision-making procedures. In this study, a methodology for quantifying the seismic resilience of reinforced masonry shear wall (RMSW) buildings with end-confined masonry boundary elements is implemented. The uncertainties associated with structural and non-structural losses and estimated recovery time uncertainties are considered while quantifying the resilience index of RMSW buildings. The archetype buildings studied have 8-, 10-, and 12-storey heights and are located in Vancouver, representing a high seismic zone in Canada. First, a numerical model was developed using OpenSees to derive the fragility surface for the studied archetypes subjected to bi-directional horizontal excitation. Second, a Monte Carlo simulation was performed to quantify the resilience index of each archetype considering the above-mentioned uncertainties. The results prove the robustness of ductile RMSW buildings having end-confined MBEs in mitigating the losses associated with disaster events. Additionally, the findings provide comprehensive and valuable information for earthquake mitigation measures and disaster risk reduction programmes.

Resilience assessment approach of mechanical structure combining finite element models and dynamic Bayesian networks

Resilience notionally means the ability to adapt changing conditions and recover rapidly from disruptions, which is vital for mechanical structure. Structure failure is usually caused by sudden changes of the fatigue mechanical property. Mechanical properties of structures should be considered when assessing resilience. The work proposes a general resilience assessment approach for mechanical structure through combining finite element models and dynamic Bayesian networks (DBNs). Resilience assessment process is divided into two parts, namely degradation process and recovery process. Degradation states in different time points can be analyzed by the finite element model, which can further provide data when establishing the DBN model of the degradation process. Recovery process is composed of fault diagnosis, resource allocation and maintenance. Fault diagnosis capability and resource allocation capability are calculated as quantitative coefficients, which can influence the maintenance activity. The maintenance capability is simulated by a DBN model through physical model mapping. The DBN model for the recovery process is finally established by integrating the quantitative coefficients and the maintenance model. Subsea wellhead connector attacked by the internal wave is adopted to demonstrate the application of the proposed assessment approach.

System approach for flood vulnerability and community resilience assessment at the local level – a case study of Sakon Nakhon Province, Thailand

System approach for flood vulnerability and community resilience assessment at the local level – a case study of Sakon Nakhon Province, Thailand

A comprehensive assessment of power system resilience to a hurricane using a two-stage analytical approach incorporating risk-based index

Sustainability of power systems is a vital need for modern societies. The occurrence of extreme weather events, such as hurricanes, may lead to blackouts. Hence, power systems resilience is a critical issue for experts. The main focus of this paper is on how to assess power system resilience comprehensively. In this regard, a two-stage framework is proposed. In the first stage, an approach is presented to evaluate power system resilience against a single intensity of a hurricane, which is called snapshot resilience assessment. The Cost of Energy Not Supplied (CENS) is regarded as a primary criterion. A risk measure called Conditional Value at Risk (CVaR) is incorporated into this approach to manage the risk of experiencing unfavorable failure scenarios. Accordingly, CVaR of CENS is proposed as an index in this stage. In the second stage, an approach for comprehensive resilience assessment is proposed, which is based on the trend of changing the values of snapshot resilience indices over a range of intensities of the event. The applicability of the proposed framework is tested in the IEEE 24-bus system. Finally, to examine the accuracy of the framework, the resilience of the test system is re-evaluated after applying a resilience improvement method.

A Dynamic-Bayesian-Networks-Based Resilience Assessment Approach of Structure Systems: Subsea Oil and Gas Pipelines as A Case Study

Under unanticipated natural disasters, any failure of structure components may cause the crash of an entire structure system. Resilience is an important metric for the structure system. Although many resilience metrics and assessment approaches are proposed for engineering system, they are not suitable for complex structure systems, since the failure mechanisms of them are different under the influences of natural disasters. This paper proposes a novel resilience assessment metric for structure system from a macroscopic perspective, named structure resilience, and develops a corresponding assessment approach based on remaining useful life of key components. Dynamic Bayesian networks (DBNs) and Markov are applied to establish the resilience assessment model. In the degradation process, natural degradation and accelerated degradation are modelled by using Bayesian networks, and then coupled by using DBNs. In the recovery process, the model is established by combining Markov and DBNs. Subsea oil and gas pipelines are adopted to demonstrate the application of the proposed structure metric and assessment approach.