Resilience Assessment Research Articles

Downscaling urban resilience assessment: A spatiotemporal analysis of urban blocks using the fuzzy Delphi method and K-means clustering

The spatiotemporal assessment of urban resilience is crucial for planning and policy-making toward addressing climate change and other societal challenges. However, our comprehension of urban resilience at smaller scales is limited. This study not only advances urban resilience research by developing an efficient measurement approach but also contributes to practice by evaluating resilience at the city block level, thereby informing targeted interventions. Our study aimed to downscale the unit of analysis for urban resilience assessment by focusing on the social, economic, housing and infrastructural, and environmental dimensions of blocks in the southern districts of Tehran between 2006 and 2016. The Fuzzy Delphi Method was utilized to determine the indicators for urban resilience assessment. The Analytic Hierarchy Process technique was employed to weigh these indicators. Subsequently, the PROMETHEE technique was applied to evaluate the resilience of urban blocks. Finally, the K-Means algorithm was utilized to cluster the urban blocks. The results indicate that from 2006 to 2016, the southern districts of Tehran saw a marked reduction in overall resilience. This decline affected 48 % of the urban blocks, encompassing 60 % of the area and 62 % of the population. The results of applying the K-means algorithm for urban block clustering did not align with the boundaries of the southern districts of Tehran. This finding highlights that assessing resilience on smaller scales could lead to more accurate conclusions.

Bridge Proxy Indicators for Risk-Based Asset Management and Resilience Assessment

Bridge Proxy Indicators for Risk-Based Asset Management and Resilience Assessment

A practical method for functional recovery analysis based on seismic resilience assessment of city building portfolios

Enhancing the seismic resilience of city building portfolios represents a pivotal strategy for mitigating the adverse effects of earthquakes. A practical method for functional recovery analysis is proposed to quantitatively assess the seismic resilience of city building portfolios. The proposed method involves quantifying delay time and developing repair scheduling schemes. The delay time considers the dependencies of normal functioning buildings on infrastructure service availability and road accessibility. It emphasizes the varying demands of buildings for critical versus general infrastructure services. The repair scheduling scheme, conducted in two steps, employs a rational repair priority index that integrates the post-earthquake residual functionality, service capacity, and relative importance of buildings. This scheme prioritizes the repair of life-rescue-related buildings, distinguishes the repair sequence for different occupancy types of buildings, and reflects the principles of local governments in repair decision-making. The effectiveness of this practical method is demonstrated through a case study. Recovery curves are compared under different earthquake scenarios and levels of labor force constraints. The concept of normalized time is introduced to allow for a comparative assessment of seismic resilience across different recovery scenarios in a more practical manner. The results can provide valuable guidance for formulating pre-earthquake risk reduction strategies, developing post-earthquake functional recovery schemes, and constructing more resilient cities.

A Review of Resilience Assessment and Recovery Strategies of Urban Rail Transit Networks

A Review of Resilience Assessment and Recovery Strategies of Urban Rail Transit Networks

Resilience evaluation and enhancing for China’s electric vehicle supply chain in the presence of attacks: A complex network analysis approach

Over the past decade, the electric vehicle (EV) industry has experienced rapid growth, necessitating a deeper understanding of its supply chain network’s structure and resilience. This study presents an analysis into China’s EV supply chain network through a two-tier network, employing complex network approach to explore its topological characteristics and network’s resilience. The results reveal that the nodes within China’s EV network follow a power-law distribution, with shorter average path lengths compared to other automobile networks, indicating a concentration of connections upstream. The resilience assessment shows that while the network is resilient against random attacks, it is significantly vulnerable to targeted attacks. Importantly, the High-Betweenness Addition (HBA) strategy is identified as a better strategy for improving the network’s overall resilience. This strategy of reinforcing the centrality of core nodes within the supply chain is highlighted as a crucial approach for enhancing the network’s resilience. Our findings underscore the importance of developing strategic connections among high-betweenness nodes to distribute these enhancements evenly, aiming to add one or two edges for enhancing network resilience. This study offers a lucid roadmap for enhancing the resilience of China’s EV supply chain network in the face of emerging challenges.

Quantifying urban spatial resilience using multi-criteria decision analysis (MCDA) and back propagation neural network (BPNN)

In the face of rapid urbanization and increasing environmental risks, building resilient cities has become a global priority. This study introduces a novel methodology for quantifying urban spatial resilience by integrating multi-criteria decision analysis (MCDA) and back propagation neural network (BPNN). The urban spatial resilience assessment system encompasses five critical criteria layers (urban spatial scale, structure, form, function, and network resilience) and 29 indicator layers. An empirical study of Yinchuan City, China, demonstrates the effectiveness of the MCDA-BPNN model in quantifying urban spatial resilience. The models identified significant disparities in resilience between central and peripheral urban zones, with central areas showing higher resilience scores compared to peripheral areas. This spatial pattern was most pronounced in structure, functional and network resilience. These results contribute to the field of urban environmental science by advancing the fundamental understanding of urban spatial resilience mechanisms and their spatial distribution. The study has significant implications for urban planners, policymakers, and risk management authorities, enabling data-driven decision-making and the development of targeted, resilience-oriented urban policies and strategies. The MCDA-BPNN approach offers a promising tool for comprehensive urban resilience assessment in diverse urban contexts.

Resilience assessment of airport aircraft area network operations under thunderstorm weather

Thunderstorms can cause a decrease in airports' dynamic capacity, and taxiway runway capacity is a key element of airport airfield operations. In this paper, based on the cell transmission model (CTM), the meteorological operation parameters of thunderstorms are set according to the airport aircraft taxiing rules, and a CTM is proposed for evaluating the resilience of the airport aircraft area operation network under thunderstorm weather. Based on the CTM, the network performance parameters are obtained, and the number of aircraft waiting for the network and the network operation efficiency are used as the basic performance indicators to establish a resilience index that reflects the dynamic change in the resilience of the thunderstorm meteorological interference network. Finally, this paper collects and analyzes the operation data of Tianjin Binhai International Airport in 2019 to build a CTM for aircraft operation in the flight area. The results show that the model's simulated airport departure operation data is almost consistent with the trend of the actual departure operation data, which verifies that the model can be used to simulate traffic for the airport airfield operation network. In addition, based on the resilience index, the sensitivity analysis of different parameters to network performance under thunderstorm weather was also studied. Based on the evaluation results of different parameter scenarios and indicators, we drew the resilience map of the airport ground operation network, and found that the network shows different resilience levels in different thunderstorm level scenarios. This study provides a great method and evaluation index for modeling and resilience evaluation of aircraft operation networks in the airfield, which is expected to improve airport operations, reduce flight delays, and improve service quality.

Urban Climate Change Resilience and Adaptation Assessment in Semi-arid Region Based on TOPSIS Analysis Method

Urban Climate Change Resilience and Adaptation Assessment in Semi-arid Region Based on TOPSIS Analysis Method

Quantitative assessment of machine learning reliability and resilience.

Advances in machine learning (ML) have led to applications in safety-critical domains, including security, defense, and healthcare. These ML models are confronted with dynamically changing and actively hostile conditions characteristic of real-world applications, requiring systems incorporating ML to be reliable and resilient. Many studies propose techniques to improve the robustness of ML algorithms. However, fewer consider quantitative techniques to assess changes in the reliability and resilience of these systems over time. To address this gap, this study demonstrates how to collect relevant data during the training and testing of ML suitable for the application of software reliability, with and without covariates, and resilience models and the subsequent interpretation of these analyses. The proposed approach promotes quantitative risk assessment of ML technologies, providing the ability to track and predict degradation and improvement in the ML model performance and assisting ML and system engineers with an objective approach to compare the relative effectiveness of alternative training and testing methods. The approach is illustrated in the context of an image recognition model, which is subjected to two generative adversarial attacks and then iteratively retrained to improve the system's performance. Our results indicate that software reliability models incorporating covariates characterized the misclassification discovery process more accurately than models without covariates. Moreover, the resilience model based on multiple linear regression incorporating interactions between covariates tracks and predicts degradation and recovery of performance best. Thus, software reliability and resilience models offer rigorous quantitative assurance methods for ML-enabled systems andprocesses.

Spatiotemporal assessment of post-earthquake road network resilience using a data-driven approach

An accurate assessment of the spatial and temporal changes in the post-earthquake road network is essential for scientifically establishing emergency rescue programs and recovery strategies. This paper utilizes high-resolution traffic data to evaluate the resilience of city-to-city road networks to earthquakes. Additionally, it elucidates the distribution of link-level resilience in both temporal and spatial dimensions. Through a case study of the Luding earthquake, the results indicate that the road network's recovery duration exceeds five days, emphasizing the persistent and temporally delayed impact of earthquakes on traffic flow. Specifically, earthquakes disrupt link resilience, causing it to fluctuate around normal operational behavior. In the immediate aftermath of the earthquake, travel time from the epicenter to Luding surged by 132 %, triggering congestion that spread to neighboring cities. At the spatial level, spatial autocorrelation in link resilience is evident, with concentrated clusters of low-resilience links observed in segments such as G248 and S211. Our findings offer valuable and novel insights into the resilience of road networks to earthquakes from an empirical perspective.

Fuzzy FMEA-Resilience Approach for Maintenance Planning in a Plastics Industry ‎

The productivity and efficiency of industrial systems are highly affected by failures and machine breakdowns. Further, in asset-intensive industries, unexpected failures are considered the primary source of operational risk. In response, the maintenance department strives to calculate reliable estimates of the risk levels associated with such failures and develop resilient maintenance strategies that enable it to respond effectively to equipment failures. The research developed a framework for integrating fuzzy failure mode and effects analysis (FFMEA) with resilience engineering (RE) concepts for maintenance planning. The framework consists of four main stages: FFMEA, Risk iso-surface (RI), resilience assessment, and maintenance planning. In FFMEA, multiple sub-factors were considered for each main risk factor and evaluated using fuzzy logic. Then, in the RI stage, the risk priority number (RPN) was calculated through a fuzzy approach that considered the order of the importance of the main three risk factors. The fuzzy resilience assessment was applied through a survey of fifty-one questions related to the main four RE potentials to determine the need for resilient maintenance strategies. Finally, the RPN-Resilience diagram was employed to classify maintenance activities into six main maintenance strategies. A case study from a production line of plastic bags was used for illustration. The main advantage of the proposed FFMEA is that it divides the main risk criteria into sub-criteria to increase the accuracy of risk assessment and evaluate resilience potentials under fuzziness. In conclusion, the integration of the risk-resilience evaluation is a valuable tool for effectively planning maintenance activities.

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.

Framework for lifecycle resilience assessment of earthquake-damaged coastal RC structures considering non-uniform corrosion

Reinforced concrete (RC) structures in seismic coastal areas often suffer from the coupled effects of corrosion and earthquakes due to prolonged exposure to harsh environmental conditions. Previous research on the seismic performance of buildings primarily focused on uniform corrosion(UC), disregarding the impact of varying concentrations of chloride-ions on corrosion rate. This discrepancy will overestimate the seismic performance of building structures. The framework proposed in this study aims to evaluate the resilience of RC structures to non-uniform story corrosion(NUSC). The framework encompasses an innovative functional recovery function and considers the influence of chloride-ions concentration on the corrosion rate, time-varying initial functional function, and degradation model for corroded reinforced concrete. NUSC model is established within the proposed framework via OpenSees software. The proposed framework integrates the seismic hazard, fragility, resilience, and recovery models. A nonlinear time history analysis method is employed to simulate the structural response to the seismic action. The findings demonstrate that NUSC leads to a reduction in seismic resilience by 7.3% and 10% compared to UC when considering service times of 35 and 55 years, respectively. This indicates an escalation in seismic risk as well as a decline in structural seismic capacity.

Correlated spatiotemporal downscaling of Euro‐CORDEX climatic data for infrastructure resilience assessment

AbstractA methodology is presented for downscaling the Euro‐CORDEX climatic projections in order to derive spatially and temporally correlated weather fields that can be used for risk and resilience assessment of large‐scale asset portfolios or interconnected infrastructure. The temporal resolution of the Euro‐CORDEX data is downscaled to a 10 min basis by employing a modified analogue‐type approach that utilizes the k‐NN algorithm along with measurements from weather stations. The aim is to generate composite “Frankenstein” days comprising 144 jigsaw pieces of observed 10 min timeseries that are scaled and/or shifted, and matched together to form a continuous daily record. These point‐estimates, valid only at the locations of the weather stations, are expanded spatially by employing high‐fidelity weather intensity measure fields that provide variable yet synchronous patterns of weather parameters at all locations of interest. As a case study, the Euro‐CORDEX projections for wind, temperature, and precipitation are downscaled for the Metsovo‐Panagia segment of Egnatia Odos highway in Greece, by employing high‐fidelity Computational Fluid Dynamic simulations that account for the topography of the site to simulate turbulent wind flows. These are combined with measurements of two local weather stations to generate the Frankenstein timeseries and corresponding weather fields that can be used for estimating operability, recovery and direct/indirect loss statistics on an event‐by‐event basis for an ensemble of interconnected highway assets.

Developing an airport resilience assessment model for climate change

Climate change is affecting the stability of all types of transportation systems, including air transport. Ensuring the disaster preparedness capability of an airport has become a crucial issue. Prior studies on assessing airport resilience and risk analysis have failed to consider the impact of the disaster at various stages and the interdependence between the factors or criteria. This study proposes airport resilience model that considers the various stages of disaster response. First, we collect criteria affecting airport resilience through literature review and experts' survey to develop a novel evaluation framework. Second, this study applies the modified DEMATEL (Decision Making Trial and Evaluation Laboratory) which was modified by introducing the concept of heterogeneous impact analysis and combined with the concept of centrality weighting. Then, the modified DEMATEL is further combined with the Dombi Weighted Aggregator method to explore the interdependence relationships among the criteria and effectively integrate expert opinions. Finally, modified VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) analysis is applied to assess the resilience performance of Taiwan's major international airports and provide suggestions for improvement. The results indicate that pre-disaster preparation, with a weight of 0.338 among four stages, is the most crucial stage for enhancing airport resilience. This can be achieved through the implementation of effective emergency response procedures and evacuation plans in the event of a disaster. Additionally, this study suggests various improvement strategies based on the assessment results to enhance disaster resistance and resilience.

Community Resilience Assessment: Implementation of a Novel Resilience Framework in Four Different European Case Study Areas

Community Resilience Assessment: Implementation of a Novel Resilience Framework in Four Different European Case Study Areas

The Role of Delay Time in the Preliminary Assessment of the Seismic Resilience (SR) of a Bridge: A Case Study

The seismic resilience of bridges has become an important concept in civil engineering since these systems need to remain operative during and after earthquakes. In this regard, the definition of recovery needs to consider the delay time (named as the time between an event and the beginning of the recovery process). The original concept of seismic resilience has been expanded herein in order to account for the delay time of bridge configurations. Its role in the quantification of seismic resilience has been investigated by performing a case study of a Californian highway bridge subjected to an ensemble of 100 input motions. The results demonstrate that the delay time may significantly reduce the seismic resilience of bridges.

Establishing resilience-targeted prediction models of rainfall for transportation infrastructures for three demonstration regions in China

Rainstorm is one of the global meteorological disasters that threaten the safety of transportation infrastructure and the connectivity of transportation system. Aiming to support the resilience assessment of transportation infrastructure in three representative regions: Sichuan–Chongqing, Yangtze River Delta, and Beijing-Tianjin-Hebei-Shandong, rainfall data over 40 years in the three regions are collected, and the temporal distribution of rainfall are analyzed. Prediction equations of rainfall are established. For the purpose of this, the probabilistic density function (PDF) is assigned to the rainfall by fitting the frequency distribution histogram. Using the assigned PDF, the rainfall data are transformed into standard normal space where regression of prediction equations is performed and the prediction accuracy is tested. The results show that: (1) The frequency of rainfall in the three regions follows a lognormal distribution based on which the prediction equations of rainfall can be established in standard normal space. The error of regression shows no remarkable dependence on self-variables, and the significance analysis indicates that the equations proposed in this paper are plausible for predicting rainfalls for the three regions. (2) The Yangtze River Delta region has a higher risk of rainstorm disaster compared to the other two regions according to the frequency of rainfall and the return period of precipitation concentration. (3) Over the period of 1980–2021, the Sichuan–Chongqing region witnessed an increase in yearly rainfall but a decrease in rainstorm disasters, whereas the other two regions experienced a consistent rise in both metrics.

Integrating flood and earthquake resilience: a framework for assessing urban community resilience against multiple hazards

Floods and earthquakes are the most common natural disasters, causing significant damage and casualties in urban communities. Although academic research has focused on assessing cities' ability to withstand these natural disasters, there is a lack of established frameworks for evaluating resilience against multiple hazards. This research integrates the earthquake and flood resilience indicators to create a comprehensive resilience assessment framework. This study integrated seismic and flood resilience indicators and categorized them into 27 clusters. It also used the DEMATEL (Decision-Making Trial and Evaluation Laboratory) method to determine the significance of clusters and prioritize them. This research identified 13 cause clusters and 14 effect clusters of indicators for seismic and flood resilience. The ‘response capacity’ cluster has the most impact on seismic and flood resilience, while the ‘transportation and accessibility’ cluster is the most affected cluster. This article presents a framework for evaluating flood and seismic resilience and makes suggestions for future research.

Turning peril into opportunity: Construction and validation of a resilience assessment system for China's coastal megacities

Enhancing urban resilience and strengthening risk resistance capacities have emerged as pivotal concerns in the construction and governance of contemporary urban landscapes globally. Given the uniqueness of coastal megacities in terms of geography and population, a more targeted and effective resilience assessment is urgently needed. This investigation develops a comprehensive, multidimensional index system to assess the resilience life cycle of China's coastal megacities. First, we creatively introduce the process perspective of the 4R model of crisis management into urban resilience assessment, revealing the preventive, defensive, recovery, and transformative resilience of China's coastal megacities. Second, we propose a spatio-temporal assessment matrix that integrates four processes of urban resilience and six representative subsystems in coastal megacities: social, economic, ecological, infrastructural, technological, and institutional. Third, we select 59 indicators to construct a three-level assessment index system for China's coastal megacities. This index system is strictly screened and optimized through qualitative and quantitative methods. Fourth, we validate the assessment system using Qingdao's data by calculating an urban resilience index and analyzing trends. We find the most important improvement goals and resilience enhancement suggestions for Qingdao by analyzing the indicator sensitivity. This study contributes to the development of urban resilience theories and identifies weaknesses in different dimensions to strengthen and improve the resilience of coastal megacities.