Resilience Assessment Methodology Research Articles

Comprehensive resilience assessment of bridge networks using ensemble learning method

The assessment of seismic resilience in bridge networks holds significant importance for urban disaster prevention and mitigation efforts. Unlike individual bridges, there has been limited efficiency in assessing bridge networks. A seismic resilience assessment methodology for bridge networks using ensemble learning methods is proposed in this paper. Initially, a comprehensive resilience index is proposed, integrating both structural and functional aspects of bridge networks. Using 3 ensemble learning methods, 9 parameters related to network structure and traffic characteristics are chosen as input variables for predicting the seismic resilience index. Finite element models of 18 bridges are constructed and combined to generate 3500 sets of virtual bridge networks for model training. The predictive accuracy of models trained using the 3 ensemble methods exceeds 89 %, and the expected values of peak ground acceleration (PGA) and functional loss rate are the most influential features. The methodology offers insights into the application of ensemble learning for bridge network seismic resilience assessment.

Predictive resilience assessment of road networks based on dynamic multi-granularity graph neural network

Due to the influence of global climate anomalies, abnormal weather conditions such as heavy rainfall have become more frequent in recent years, posing a significant threat to the operation of transportation systems. An effective assessment of the resilience of the transportation system before and during heavy rain can alert the transportation department to take necessary emergency actions. However, existing methods for assessing the rainfall resilience of transportation networks mostly suffer from the following problems: (1) Simulation methods for modeling rainfall impacts lack realism; (2) After-the-fact evaluations of resilience cannot offer advance warning prior to or during a heavy rain event. To address above problems, we present a novel resilience assessment methodology for evaluating the resilience of road networks in real-time during heavy rainfall scenarios. In this methodology, we propose the temporal decomposition-based dynamic multi-granularity graph neural network (TD2MG2NN) for long-term traffic speed forecasting, providing a perspective on the future evolution of traffic states for accurate resilience assessment. In addition, we construct a composite traffic resilience indicator, designed to comprehensively reflect changes in the spatial–temporal resilience of the transportation system during heavy rain. Experimental results on four publicly real datasets indicate that the prediction performance of TD2MG2NN outperforms state-of-the-art models. The assessment results for the transportation road network in California demonstrate that the comprehensive resilience indicator is superior to single functional resilience indicator and the real-time methodology for evaluating resilience can accurately depict and predict the operation of the road network system under heavy rainfall scenarios.

Research on resilience assessment method of train control on-board system

To address the problem of difficult performance assessment of train control on-board system after recovery from failures, we have proposed a resilience assessment methodology that uses reliability as an indicator of system resilience. Since the system failures are time-dependent, we adopted the Discrete Time Bayesian Network method to obtain the system's reliability before and after failure. Subsequently, we used an exponential recovery model to quantify the system's performance curve during the recovery phase, and finally utilized the resilient triangle area method to quantify its resilience size. Analyzing the CTCS3-300T train control on-board system, we found that the resilience of the system with cold standby redundancy design and hot standby redundancy design were 89.44 % and 87.34 %, respectively, indicating a slight decrease in system performance after recovery from failures compared to pre-failure levels. At that time, it was necessary to adjust operational plans based on actual conditions to avoid greater impact on the railway network. This paper realizes performance resilience of train control on-board system after failure recovery, which can be applied to similar systems and provide theoretical references for realizing intelligent maintenance of the high-speed train.

Broadening resilience: An evaluation of policy and planning for drinking water resilience in 100 US cities

Around the world, drinking water systems provide safe, accessible drinking water to the communities they serve. While they are faced with a growing number of short and long-term challenges, assessing the resilience of drinking water systems—or their ability to cope with disturbances and surprise and continuously adapt to stress and change—is an ongoing challenge. Many drinking water resilience assessment methodologies focus narrowly on the technical dimensions of the resilience of infrastructure systems, ignoring the human or environmental dimensions, and consider resilience to the present, ignoring resilience to future change. To fill this gap, we developed a conceptual framework and scoring methodology for evaluating municipal-scale policy and planning for drinking water system resilience. Our approach considers social, technical, and environmental elements of resilience at broad spatial and temporal scales. We then used this methodology to assess policy and planning for drinking water resilience in 100 U.S. cities. We found that municipalities are at very different stages in their policy and planning for drinking water resilience, particularly in terms of the attention they give to climate change and their consideration of the broader social dimensions of resilience. Overall, larger cities and those with more liberal populations are likely to have higher policy and planning scores. The findings highlight the variation in municipal policy and planning for drinking water system resilience, and the importance of community characteristics as drivers of resilience planning. Our approach is transferable to assessing resilience for drinking water systems within and beyond the U.S.

Assessing the resilience of sustainable autonomous shipping: New methodology, challenges, opportunities

This paper introduces a resilience assessment methodology for sustainable autonomous maritime transport networks developed by the European project entitled “Advanced, Efficient, and Green Intermodal Systems” (AEGIS). This problem being addressed in this paper concerns the investigation of threats, incidents, and risks in an autonomous- and sustainable shipping context, and the research question is the development of both preventive measures and reactive actions to maintain an acceptable level of operational constraints. The paper’s methodology aids in designing sustainable logistics systems for highly automated waterborne transport, identifying threats and barriers to mitigate event consequences, thereby facilitating a seamless green transition. To examine the usability, this methodology is applied in a case study for cargo transportation, where we in this paper consider the maritime corridor between Trondheim and Rotterdam. The findings encompass the spectrum of possible actions to prevent and mitigate unwanted events and enhance resilience and flexibility. This can be used as a tool to respond to unwanted threats, enhance safety, and introduce new strategies. These results are deemed important as resilience is one of the prerequisites for the development of a sustainable transport system. This is true both for the companies that are engaged in the operation of such systems and for policymakers.

Seismic fragility and resilience improvement of conventional bridges supported by double-column piers utilizing shear links

A bridge structure is one of the most vulnerable parts of the transportation network system subjected to earthquakes. It is paramount to the seismic capacity improvement and the rapid recovery function of bridges during earthquakes. To address these challenges, shear links (SLs) are designed between the columns supporting the continuous girder bridges based on the structural fuse concept. The impacts of SLs on the life-cycle performance and seismic resilience of continuous girder bridges were studied by comparing the continuous girder bridges supported by double-column piers with and without SLs. The incremental dynamic analyses were performed to evaluate the seismic fragility of continuous girder bridges supported by double-column piers with and without SLs under the far- and near-field seismic waves. The economic losses were assessed to compare the financial superiority of continuous girder bridges supported by double-column piers with SLs in the life cycle. A resilience assessment methodology was employed to evaluate the post-earthquake resilience of continuous girder bridges supported by double-column piers with and without SLs. The results showed that the SLs preferentially dissipated a certain amount of earthquake energy so that the continuous girder bridge supported by new-type double-column piers with SLs exhibited lower seismic fragility and life-cycle losses compared to the continuous girder bridge supported by traditional double-column piers. Also, the proposed continuous girder bridge supported by new-type double-column piers with SLs behaved with higher post-earthquake resilience under the far- and near-field seismic waves.

An integrated resilience assessment methodology for emergency response systems based on multi-stage STAMP and dynamic Bayesian networks

The interactions of external disruptions and technical-human-organizational factors in emergency operations are usually observed. Resilience assessment of emergency systems can improve emergency response capability and system functional recovery. The increasing complexity and coupling of factors in emergency response systems need to be investigated from a system resilience perspective. In this paper, we propose to integrate a multi-stage System-Theoretic Accident Model and Processes (STAMP) with a dynamic Bayesian network (DBN) for the resilience assessment of emergency response systems. In the proposed methodology, emergency response systems are viewed as multi-step emergency operations for STAMP to analyze the hierarchical control and feedback structures. The output of multi-stage STAMP in controllers, actuators, sensors, and controlled processes is applied to develop a DBN for resilience assessment. For known external shocks (e.g., natural disasters), the effects of external shocks on the system are decomposed into subsystems or components. System degradation and recovery models are established. Regarding unknown external disruption (e.g., unforeseen failure modes), degeneration and recovery are temporally integrated into the analysis of system functionality. System performance is evaluated through the combination of socio-technical factors and external disasters. Eventually, the resilience of emergency response systems is obtained from the performance curves. The results demonstrate that the proposed model can evaluate system resilience after the system suffers from external disasters.

Resilience assessment methodology of natural gas network system under random leakage

Natural gas accounts for more than 20% of global primary energy demand. Potential natural or man-made disasters can cause natural gas supply shortages, with severe economic and social consequences. System resilience is difficult to achieve because of many factors affecting the natural gas network system (NGNS) and the uncertainty of the timing, location, and shock strengths. A new methodology for evaluating the resilience of NGNS under random leakage conditions is developed by combining Monte Carlo simulation and hydraulic analysis of an unsteady flow. A resilience metric based on gas supply capacity and reliability is proposed. The extensive time delay and nonlinear dynamic characteristics of the system caused by the compressibility of the natural gas are considered in combination with hydraulic analysis. System uncertainty in damage and recovery states is resolved by batch simulation of the random leakage. The resilience value is calculated by integrating the performance curve. The proposed methodology is demonstrated on the NGNS of Shandong Province, China, and results show that it can effectively assess the resilience of the NGNS under random leakage conditions.

Probabilistic Assessment of Power Systems Resilience Under Natural Disasters

Extreme weather can have a substantial influence on a power system's operational resilience since they are high-impact, low-probability (HILP) events. Therefore, Power systems must be resilient to HILP incidents in addition to being reliable against widely spread and credible threats. Despite the rarity of such events, the severity of their potential impact necessitates the development of appropriate resilience assessment tools to capture their implications and enhance the resilience of energy infrastructure systems. In this paper, a probabilistic strategy is proposed to assess and evaluate the operational resilience of power distribution networks against the impacts of HILP events depending on value-at-risk and conditionally value-at-risk quantitative risk-based assessments. With several scenarios built on sequentially Monte Carlo simulations, the consequence of a windstorm on a distribution network can be assessed using a probability-based resilience assessment methodology. The presented method is examined on an IEEE 37-bus system. Different case studies based on detailed data are presented and analyzed to demonstrate the proposed method's usefulness.

Assessing the seismic resilience of a healthcare system: A hybrid modeling

Healthcare systems play a key role in providing emergency medical services during earthquake events. However, the healthcare systems themselves are also subjected to the impact of earthquakes. Assessing and enhancing the seismic resilience of healthcare systems contributes to the improved performance of medical rescue during earthquakes. Despite many studies in this field, a methodology for resilience assessment that considers both the pre- and in-hospital rescue phases is still lacking, which makes it challenging to assess the seismic resilience of a healthcare system comprehensively. This paper addresses the gaps by proposing mathematical expressions for the indicators of healthcare system functionalities, developing a hybrid model to simulate and compute the functionalities, and providing methods for resilience assessment. A case study is carried out in China to demonstrate the effectiveness of the proposed approach. This paper contributes to the body of knowledge to understand and model the functionality of a healthcare system after an earthquake better. The proposed approach can serve as an effective tool for decision-makers to assess the seismic resilience of a healthcare system comprehensively and to compare the efficacy of different resilience enhancement measures.

Methodology for Resilience Assessment of Oil Pipeline Network System Exposed to Earthquake

The oil pipeline network system (OPNS) is an essential part of the critical infrastructure networks (CINs), and is vulnerable to earthquakes. Assessing and enhancing the resilience of the OPNS can improve its capability to cope with earthquakes or to recover the system’s performance quickly after the disturbance. This study defines the concept of OPNS resilience in the resistive ability, the adaptive ability, and the recovery ability. Then, the quantitative resilience assessment model is established considering the earthquake intensities, the role of safety barriers, the time-variant reliability, and the importance coefficient of each subsystem via a Monte Carlo simulation. Combining the model with GIS technology, a new methodology to evaluate OPNS resilience is proposed, and the resilience partition technology platform is developed, which can visualize the results of the resilience assessment. Finally, a case study is implemented to demonstrate the developed methodology, and a discussion is provided to identify the sensitive variables. The proposed resilience methodology can provide a framework for the probabilistic resilience assessment of OPNS, and could be expanded to other lifeline network systems.

Simulation and factor analysis for post-earthquake recovery of densely populated urban residential communities in China

Post-earthquake recovery simulation is crucial for resilient community planning, and different interconnected factors are required to be considered holistically. To identify the key infrastructural characteristics affecting the recovery of densely populated urban residential communities (URCs) in China, a comprehensive methodology for resilience assessment and analysis is established on a systematic integration of multiple analysis tools (e.g. population-based functionality indicators, post-earthquake recovery simulations, and infrastructural dependence analyses). The methodology can consider the dependence among residential buildings, supporting buildings, and utility networks, as well as the relationships between their functionalities and resident outmigration; it also includes infrastructural repair sequences to allow flexible repair plans to be simulated. A case study is employed to conduct a factor analysis to clarify the impacts of three important infrastructural characteristics. Results show that improvements on the seismic performance of residential buildings facilitate recovery more significantly than utility networks, and the use of redundant utility pipelines can hardly impact the recovery of URCs. In long-term community recoveries, utility networks play more important role due to the cascading effects arising from the extension of repair durations. The proposed methodology can promote the understanding of community recovery, and the results demonstrate the effectiveness of identifying key factors.

A model and methodology for resilience assessment of interdependent rail networks – Case study of Great Britain's rail network

Rail networks entail multiple interdependencies which can initiate or propagate network failures with serious consequences for the movement of trains and passengers. In this study, we present a rail infrastructure system-of-systems model which can be used to simulate disruptions to the network's operations and propose a performance metric based on train and passenger delay minutes. We demonstrate the applicability of our model by evaluating the resilience of the southern region of Great Britain's rail network in scenarios of failure initiated in the traction power supply system. The results highlight the sensitivity of the rail network to a small number of traction assets concentrated in the London area, where we find that failure of one of the region's most critical electricity traction power grid could disrupt 75% of all trains in the southern region and 25% of all trains nationally. Our analysis demonstrates how the resilience of the rail network is influenced by the speed of backup restoration following a failure event as well as the network's vulnerability to failure at peak travel times with a delay during peak travel resulting in about 63% increase in passenger delay minutes compared with off-peak travel times for the same duration.

A test-based methodology for the probabilistic assessment of system resilience under random disturbances

Considering the random characteristics of the disturbances that may occur on the engineering system, this paper proposes a test-based probabilistic resilience assessment methodology. It is a sampling-based test method, including five main steps: (1) the bottom-up disturbance identification; (2) the sample size determination according to the law of large numbers and the central limit theorem; (3) the sample stratification and selection based on proportion stratified sampling method; (4) resilience test and performance data collection; and (5) probabilistic resilience assessment, consisting of point estimates and confidence intervals. Besides, two probabilistic resilience measures are defined to reflect the average and the percentile characteristics of the system resilience under random disturbances, and the traditional performance normalization method is extended to adapt to different types of parameters. A wireless DC servo motor is used to verify the effectiveness of our methodology, and this generic methodology can be further applied in other engineering systems.

Comprehensive functional resilience assessment methodology for bridge networks using data-driven fragility models

This paper proposes a probabilistic seismic resilience assessment methodology for bridge networks subjected to spatially correlated earthquakes. The proposed method integrates the effects of influencing factors (e.g., regional seismic hazards, bridge fragilities, and traffic flow) and their uncertainties in the seismic performance assessment of bridge networks. However, there are two major challenges in applying such a methodology that need to be addressed: rapid seismic damage assessment of regional bridges and comprehensive resilience quantification of bridge networks. Therefore, a data-driven fragility scheme based on an artificial neural network is developed to predict the damage states of regional bridges under earthquakes, which can support the simulation of the functionalities of bridges and roadways by Monte Carlo simulation. Furthermore, a multi-dimensional functional resilience vector is explored to quantify the ability of bridge networks to maintain the holistic system function, important subsystem function, and emergency response function. Finally, the proposed methodology is illustrated on the Sioux Falls bridge network, which shows the quantitative effects of regional bridge fragilities, traffic flow, and epicenter location on the functional resilience of bridge networks.

Resilience Assessment for Microgrid with Pre-Position and Reconfiguration of Emergency Distribution Generations under Natural Hazard

Recently, increasing the number and severity of the natural hazards requires the resilience assessment and enhancement of the power system, especially the microgrid system. The emergency distribution generations have great potential to enhance the resilience of microgrid against blackouts under the emergency environment. This paper investigates the resilience assessment of the microgrid system under natural hazard, where emergency distribution generations are firstly pre-positioned on system nodes and then reconfigured after system damage occurs. A new resilience metric index and an efficient approximation computation method are provided for the resilience assessment of the focused problem. A new pre-position strategy and a new reconfiguration strategy on emergency distribution generations are proposed for the microgrid system emergency restoration under natural hazard. Also, a framework of resilience assessment is provided for problem-solving. The results of extensive experiments on the modified IEEE 30-Bus system and modified IEEE 118-Bus system confirm the effectiveness of the resilience assessment methodology and the superiority of proposed restoration strategies.

Resilience assessment methodologies and enhancement strategies of multi‐energy cyber‐physical systems of the distribution network

Natural disasters and cyber intrusions threaten the normal operation of the critical Multi-Energy Systems (MESs) infrastructures. There is still no universally-accepted definition of MESs resilience under the integration of cyber and physical, and lack of a widely accepted methodology to quantify and assess the resilience in MESs. Hence, this paper introduces an extensive review of the state-of-the-art research of power systems’ resilience. Then, this work proposes the definition of the Multi-Energy Cyber-Physical Systems (MECPSs) resilience and its related characteristics. To improve the resilience of MECPSs, this paper investigates extreme natural disaster models and analyses the vulnerability of the system to find the key constraint factors. Furthermore, this work presents the qualitative assessment curve, quantitative indexes, and assessment framework of the MECPSs resilience. Finally, the key improvement measures for the planning and operation of MECPSs resilience and the focus of future research are presented.

General resilience: Conceptual formulation and quantitative assessment for intervention development in the urban wastewater system

General resilience addresses the resilience of a water system to any threat including unknowns, in contrast to specified resilience to individual identified threats. However, quantification of general resilience is challenging and previous assessments have typically been qualitative or based on system properties that are assumed to be indicative of resilient performance. Here we present a General Resilience Assessment Methodology (GRAM), which uses a middle-state based approach to decompose general resilience into contributing components to provide a quantitative and performance-based resilience assessment. GRAM enables the accounting of the effects of any threat if all modes of system failure are identifiable. It is applied to an integrated urban wastewater system where five interventions are explored. The results obtained show that whilst substantial improvements in specified resilience are achieved, increasing the general resilience of the system is challenging. However, general resilience analysis enables identification of system failure modes to which level of service is least resilient and highlights key opportunities for intervention development. GRAM is beneficial as it can inform the development of interventions to increase the resilience of a system to unknowns such as unforeseeable natural hazards in a quantifiable manner.

Improved model for continuous, real-time assessment and monitoring of the resilience of systems based on multiple data sources and stakeholders

Faced with an increasing level of disruption from natural disasters, terrorist attacks or internal failures, organisations need to ensure their business continuity. Ensuring this continuity depends, among other things, on the continuous assessment, monitoring, and management of their resilience based on the variations of the functionalities. Resilience-assessment methodologies are nowadays used to (1) prepare stakeholders for future crisis management situations and (2) help stakeholders assess past levels of resilience in the aftermath of the crisis. However, continuous, real-time monitoring and assessment of resilience is generally either outside the scope of such methods or limited to raw data representation, lacking effective filtering, interpretation, or integration in the evolving context of the organisation’s activities. This paper enhances previous works on resilience assessment. The result is a complementary methodology for continuous, real-time resilience assessment and monitoring based on multiple data-sources and stakeholders. The novelty is (1) in the context of use of the methodology, (2) in the way the functionality analysis model is obtained and (3) in the way the resilience is continuously assessed.

Fast Resilience Assessment of Distribution Systems With a Non-Simulation-Based Method

Traditional simulation-based method for resilience assessment of distribution systems is very time consuming since the network topology is characterized with a large number of scenario-specific optimization models. In this paper, we propose a methodology for fast resilience assessment of distribution systems with a non-simulation-based method, which can significantly improve the assessment accuracy and computational efficiency. First, a probabilistic metric is proposed to assess the system resilience against extreme events, which quantifies the system performance starting from the pre-event stage to the post-event stage. Then, a mixed-integer linear programming (MILP) is proposed to model the energization paths (EPs) with binary decision variables. Subsequently, the resilience metric-related probability events are built using the EPs. Last, the probabilistic resilience metric is explicitly expressed based on the total probability formula, conditional probability formula and EP-topology simplification methods. In the proposed method, the topology evolution along with the system degradation, restoration (part healed) and recovery (all healed) is characterized with a non-simulation-based method, rather than the multiple scenarios in traditional methods. The numerical tests validate the effectiveness of the proposed method and superiority over the simulation-based approach.