Vulnerability Analysis Method Research Articles

A systematic framework for dynamic nodal vulnerability assessment of water distribution networks based on multilayer networks

Nodal demands vary throughout the day, as such any vulnerability analysis based on static networks, which considers daily average demands cannot realistically represent the criticality of nodes in the network. This study presents a systematic framework, which couples multilayer networks, structural reducibility and a Demand Adjusted Vulnerability Measure for dynamic nodal vulnerability assessment of water distribution networks (WDNs) under extended period simulation. Within this framework, we present the novel idea of characterizing the dynamics of WDNs with multi-slice networks, which captures the state of the network within a predefined temporal window taking into consideration the directional flow in pipes and the operational status of pumps, valves etc. Using a benchmark WDN, Net 3, as a case study we have demonstrated the importance of demand variations and operational status of various components, no matter how minuscule their operational time, on nodal vulnerability assessment in WDNs. The results indicated that the framework evaluates the criticality of all types of nodes, even intermediary nodes with zero base demand, within any temporal window much more realistically than conventional vulnerability analysis methods based on single (static) networks. Structural reducibility unearthed correlations between the operational status of source nodes and pumps on the general dynamics of the distribution system. The multilayer framework opens a new frontier in vulnerability analysis of WDNs and could serve as a tool for stakeholders in accessing node criticality, impact of various failure scenarios and optimal scheduling of maintenance routines.

Vulnerability analysis method based on risk assessment for gas transmission capabilities of natural gas pipeline networks

The crucial part of vulnerability analysis is identifying the critical components of a pipeline network. In this study, we proposed a novel analysis method- “Risk-Vulnerability,” which combines the characteristics of risk assessments and vulnerability analyses methods. Risk-Vulnerability identifies the critical components of a pipeline network from three perspectives: pipeline operating status, transmission performance, and network characteristics. The formulas of the importance value of each component were established. Then the component risk indicators were established, and the component risk values were calculated. And a utility theory was introduced to calculate the severity of the consequences. Finally, the component importance and risk values were multiplied to obtain the vulnerability of the component. The feasibility and effectiveness of the method were verified by comparing the identification results of this method to those from the weighted flow capacity rate (WFCR). The Risk-Vulnerability method provides an improved insight into the pipeline criticality compared to the WFCR and improves the calculation formulae based on the identification content, which can not only be used to identify critical components, but also can be used to formulate research on risk reduction measures for pipeline network systems.

Power system vulnerability analysis based on topological potential field theory

Evaluating the vulnerability of power system effectively and accurately is of great significance for predicting cascading failures and preventing blackouts. Therefore, we propose a power system vulnerability analysis method based on the topological potential field theory in this paper. Firstly, according to the overload risk and operation risk of transmission lines in cascading failures, we construct a fault chain graph considering two-level cascading failures. Secondly, we define the generalized node quality, and then propose a power system vulnerability evaluation method based on the topological potential theory of the data field by using the network edge weights to quantify the node entropy degree. At the topological structure level, this method pays attention to the influence between lines and that of each line on the entire power system, which can more reflect the important lines in the power system accurately. Finally, we do vulnerability simulations on the IEEE-39 bus system and a certain regional power grid in China respectively. The results show that the method has higher identification accuracy and lower time complexity on the basis of ensuring the integrity of the power grid topology.

Vulnerability analysis of pile-reinforced slope under the condition of near-fault impulse ground motions

In order to analyze the impact of the near-fault pulse ground motions on the slope, the method of seismic vulnerability analysis based on IDA is introduced into the stability evaluation. Taking the slope reinforced by stabilizing piles as an example, a typical analysis model is established. The seismic performance is divided into four stages: basically intact, partial damage, severe damage and overall instability. A large number of calculations are used to compare the instability probability of slopes with near-fault and far-field ground motions. The results show that: 1) The calculation model which combines the Newmark and the limit analysis method can reflect the difference between the near-fault and the far-field ground motions. 2) The IM-DM curve with indexs of PGA and displacement has a better fitting effect. The vulnerability analysis based on IDA can be used to evaluate the slope failure risk under seismic loads. 3) The IDA curve shows a monotonous increasing trend. When the seismic intensity is lower(GPA=0∼0.5), there is no obvious difference between the vulnerability curves of the near-fault and far-field. As the intensity increases(GPA=0.5∼1.2), the failure probability of near-fault is significantly higher than the far-field. Meanwhile, the failure mode changed from the partial failure to the complete failure. It is clear that near-fault ground motions have the characteristics of large energy and strong impact, which are more likely to cause slope instability. If the impact of the near-field earthquake is ignored, the disaster may be underestimated.

A new day-to-day dynamic network vulnerability analysis approach with Weibit-based route adjustment process

The disruption of critical components in a transportation network can bring about severe network performance degradation and requires a relatively long period to recover, which would lead to commuters’ day-to-day route choice adjustment. Under disruptions, there would be greater travel time variability (objective uncertainty) and travelers’ perception error uncertainty (subjective uncertainty) in the transportation network. However, no vulnerability analysis method in the literature can consider the day-to-day network performance fluctuation under uncertainties. In this paper, we develop a new day-to-day dynamic network vulnerability analysis approach that allows the consideration of day-to-day network performance fluctuation based on a new day-to-day dynamic model considering both objective travel time uncertainty and subjective perception error uncertainty. Compared to most existing day-to-day models that either adopt User Equilibrium (UE) or Logit-based route choice criterion, the new day-to-day model has two advantages: (1) the Weibit model is used to capture travelers’ subjective perception error uncertainty, which does not have the perfect information assumption in the UE model, or the identically distributed perception error assumption in the Logit model; and (2) the mean-excess travel time (METT) concept is used to capture the objective travel time uncertainty, which handles the inconsideration of travel time variability in most day-to-day models while remaining computational tractability. Based on the proposed day-to-day dynamic model, we develop a new component importance metric for network vulnerability analysis. This new metric characterizes the post-disruption day-dependent consequences to alleviate the limitation of only assessing the final static equilibrium consequence as in the existing studies of vulnerability analysis. Numerical examples are provided to demonstrate the features of the proposed day-to-day dynamic model and the new component importance metric, as well as their applicability in identifying the critical bridges in the Winnipeg network. The proposed approach provides a new decision support tool for planners and managers in assessing the consequences of disruptions, identifying the critical components, and determining the recovery schedules after disruptions.

A Graph-based Method for Vulnerability Analysis of Renewable Energy integrated Power Systems to Cascading Failures

A large amount of uncertainty introduced into power systems may affect the system vulnerability to cascading failures. The objectives of this study are to analyze the power system vulnerability to cascades considering higher security criteria and to analyze the variation of the power system vulnerability considering renewable energy integration. To this end, we use a graph-based model to reflect cascades propagation, which is constructed by a thermal inertia-based cascades model incorporating an N-k contingency sampling algorithm. Based on the graph model, comprehensive evaluation indices are proposed to analyze the power system vulnerability in terms of its variable operation state and different generation uncertainty levels. Results show that the graph-based method can reveal cascades propagation, and provides an effective way to visualize and analyze the system vulnerability. Results also show that power systems’ vulnerability is sensitive to the variation of renewable generation, the system would be highly vulnerable to cascades when facing a large uncertainty level that exceeds a certain range. This implies that it is important to limit power systems to operate within the allowed uncertainty range. The proposed approach can help operators to test the system resilience and to analyze the scenario-based risk of systems.

Electricity grid resilience amid various natural disasters: Challenges and solutions

Electricity grid vulnerabilities can lead to outages with prolonged load interruptions. Research activities on the impact of natural disasters on power system are underway to figure out the outage reasons, identify the ways to prepare for preventive actions, and increase the resiliency for the corrective and preventive actions under such scenarios. This paper presented a review of the methods for the electricity grid vulnerability analysis, pre-disaster recovery planning, and post-disaster restoration models.

Integrated Empirical-mechanical Seismic Vulnerability Analysis Method for Masonry Buildings in Timișoara: Validation based on the 2009 Italian Earthquake

Background: Timisoara is one of the most important Romanina cities, located in the Banat area and characterised by shallow earthquakes with high ground motion vertical component. The seismicity of the area, in accordance with the P-100 Romanian Code, is affected by medium-high hazard level with an expected maximum PGA of 0.20 g. From a historical-artistic point of view, the city of Timisoara is full of monumental buildings of inestimable values and it was declared as the European Capital of Culture for 2021. Aims: The present work aims to investigate the seismic vulnerability of masonry buildings located in the districts of Fabric and Iosefin, which were grouped in typology classes based on the EMS-98 scale according to their geometrical and structural characteristics. Methods: The performed vulnerability analysis is based on a combined empirical-mechanical procedure to estimate the propensity at the damage of the buildings surveyed. Results: A specific formulation was proposed for typological vulnerability curves of building classes of the examined area, which were calibrated on the basis of the damages detected after occurred earthquakes. Conclusion: The proposed formulation was validated from the application to an urban sector of the municipality of San Pio delle Camere (Italy), which was damaged after the 2009 Abruzzo earthquake.

Vulnerability analysis of aqueduct structure based on boundary method

The damage forms of aqueduct under earthquake are mainly reflected in column of bent and rubber bearing. Taking a typical aqueduct as an example, the uncertainty of structural materials of aqueduct is considered by Latin hypercube sampling method; the uncertainty of ground motion is considered by stratified sampling method; the aqueduct sample and seismic wave are randomly combined to carry out time history analysis to obtain structural response; the limit damage states of column of bent and rubber bearing are defined respectively; the vulnerability curves of components are established by classical vulnerability analysis method. The system vulnerability curve of aqueduct structure is obtained by boundary method. The results show that the rubber bearing of aqueduct is more vulnerable to damage than bent frame. The failure probability interval obtained by the second-order limit method is smaller than that obtained by the first-order limit method, and the second-order limit method is more reasonable to evaluate the seismic performance of aqueduct structure.

Ranking the critical sections of railway networks

Railway systems are now facing an increasing number of threats such as aging infrastructures and climate changes. The identification of critical network sections provides infrastructure managers with the ability to understand the impact of a disruption and creates a suitable preventive strategy to counter such threats. To this end, various vulnerability analysis methods have been proposed for railway networks. Two main types of methods, network topological analysis and network flow-based analysis, have been developed. Both approaches are constructed based on macroscopic models, which take only some railway properties such as network structure, train and passenger flow into account. Thus, the results obtained are high level approximations. This study proposes a new analysis method, which is developed based on the stochastic-microscopic railway network simulation model. The method can be applied to identify the critical sections of a railway network. The effect of impact levels and occurrence times of a disruption on the network section criticality is presented. An application of the proposed model is demonstrated using the Liverpool railway network in the UK.

Assessing potential likelihood and impacts of landslides on transportation network vulnerability

As one of the devastating natural disasters, landslide may induce significant losses of properties and lives area-wide, and generate dramatic damages to transportation network infrastructure. Accessing the impacts of landslide-induced disruptions to roadway infrastructure can be extremely difficult due to the complexity of involved impact factors and uncertainties of vulnerability related events. In this study, a data-driven approach is developed to assess landslide-induced transportation roadway network vulnerability and accessibility. The vulnerability analysis is conducted by integrating a series of static and dynamic factors to reflect the landslide likelihood and the consequences of network accessibility disruptions. The analytical hierarchy process (AHP) model was developed to assess and map the landslide likelihood. A generic vulnerability index (VI) was calculated for each roadway link in the network to identify critical links. Spatial distributions of landslide likelihood, consequences of network disruptions, and network vulnerability degrees were fused and analyzed. The roadway network on Oahu Island in Hawaii is utilized to demonstrate the effectiveness of the proposed approach with all the geo-coded information for its network vulnerability analysis induced by area-wide landslides. Specifically, the study area was classified into five categories of landslide likelihood: very high, high, moderate, low, and stable. About 34% of the study area was assigned as the high or very high categories. The results of network vulnerability analyses highlighted the importance of three highway segments tunnel through the Ko‘olau Range from leeward to windward, connecting Honolulu to the windward coast including the Pali highway segment, Likelike highway segment, and Interstate H-3 highway segment. The proposed network vulnerability analysis method provides a new perspective to examine the vulnerability and accessibility of the roadway network impacted by landslides.

A stochastic vulnerability analysis method for armored vehicles with active protection systems (APS)

A stochastic vulnerability analysis method for armored vehicles with APS was proposed. A two-processes threat-target interaction, including an interception process and a damage process, replaced the one-process of threat-target interaction in the traditional vulnerability method. Depending on the vulnerability results of the incoming threat, various residues-after-interception because of detection or interception failure, deflection, explosion and/or decomposition of the threat and collateral damage was generated and treated as input in the damage process. After randomization of the interception point, the residual penetrator deflection, warhead performance, behind armor debris, and component vulnerability characterization, a stochastic vulnerability analysis was performed. The stochastic model could provide damage states of all the critical components of interest, more practical and informative compared to the expected model.

Intelligent Vulnerability Analysis for Connectivity and Critical-Area Integrity in IoV

The large-scale connectivity of Internet of Vehicles (IoV) is an important challenge for the Intelligent Transportation Systems (ITS). Intelligence vulnerability analysis is an excellent solution. However, existing methods for analyzing connectivity vulnerability have ignored the existence of critical areas in the system. Due to the heterogeneities of the IoV environments and services, the failure of some specific areas may seriously damage connectivity and system performance. To this end, in this paper we focus on both the dynamic connectivity and the critical-area integrity, and propose an intelligent vulnerability analysis method to effectively identify the critical area of extreme vulnerability. Specifically, we consider an intelligent analysis scenario in which roadside servers continuously learn IoV heterogeneous environment and dynamic topology, and then translate the learning results into a flexible disruption cost problem. Based on this, we utilize the spectral partitioning method to identify the minimum-cost set of topological elements whose failure not only severely damages system connectivity but also disrupts its critical areas. Furthermore, we confirm that the identified set can be used to optimize disruption cost problem, thus intelligently improving vulnerability. Simulation results show that our proposed method can effectively identify vulnerable elements and prevent significant loss in the IoV system connectivity and performance.

Cyber Vulnerability Intelligence for Internet of Things Binary

Internet of Things (IoT) integrates a variety of software (e.g., autonomous vehicles and military systems) in order to enable the advanced and intelligent services. These software increase the potential of cyber-attacks because an adversary can launch an attack using system vulnerabilities. Existing software vulnerability analysis methods used to be relying on human experts crafted features, which usually miss many vulnerabilities. It is important to develop an automatic vulnerability analysis system to improve the countermeasures. However, source code is not always available (e.g., most IoT related industry software are closed source). Therefore, vulnerability detection on binary code is a demanding task. This article addresses the automatic binary-level software vulnerability detection problem by proposing a deep learning-based approach. The proposed approach consists of two phases: binary function extraction, and model building. First, we extract binary functions from the cleaned binary instructions obtained by using IDA Pro. Then, we employ the attention mechanism on top of a bidirectional long short-term memory for building the predictive model. To show the effectiveness of the proposed approach, we have collected datasets from several different sources. We have compared our proposed approach with a series of baselines including source code-based techniques and binary code-based techniques. We have also applied the proposed approach to real-world IoT related software such as VLC media player and LibTIFF project that used on Autonomous Vehicles. Experimental results show that our proposed approach betters the baselines and is able to detect more vulnerabilities.

A fuzzy-based vulnerability assessment model for infrastructure networks incorporating reliability and centrality

Purpose The purpose of this paper is to attempt to shift away from an exclusive probabilistic viewpoint or a pure network theory-based perspective for vulnerability assessment of infrastructure networks (INs), toward an integrated framework that accounts for joint considerations of the consequences of component failure as well as the component reliability. Design/methodology/approach This work introduces a fuzzy inference system (FIS) model that deals with the problem of vulnerability analysis by mapping reliability and centrality to vulnerability. In the presented model, reliability and centrality are first fuzzified, then 16 different rules are defined and finally, a defuzzification process is conducted to obtain the model output, termed the vulnerability score. The FIS model developed herein attempts to explain the linkage between reliability and centrality so as to evaluate the degree of vulnerability for INs elements. Findings This paper compared the effectiveness of the vulnerability score in criticality ranking of the components against the conventional vulnerability analysis methods. Comparison of the output of the proposed FIS model with the conventional vulnerability indices reveals the effectiveness of the vulnerability score in identifying the criticality of components. The model result showed the vulnerability score decreases by increasing reliability and decreasing centrality. Practical implications Two key practical implications for vulnerability analysis of INs can be drawn from the suggested FIS model in this research. First, the maintenance strategy based on the vulnerability analysis proposed herein will provide an expert facilitator that helps infrastructure utilities to identify and prioritize the vulnerabilities. The second practical implication is especially valuable for designing an effective risk management framework, which allows for least cost decisions to be made for the protection of INs. Originality/value As part of the first contribution, we propose a novel fuzzy-based vulnerability assessment model in building a qualitative and quantitative picture of the vulnerability of INs. The second contribution is especially valuable for vulnerability analysis of INs by virtue of offering a key to understanding the component vulnerability principle as being constituted by the component likely behavior as well as the component importance in the network.

Vulnerability analysis method of vegetation due to groundwater table drawdown induced by tunnel drainage

Tunnel drainage demonstrates a behavior that constantly drains the high-ground subsurface water that feeds the overlying vegetative cover. The environmental impact of construction process on groundwater table drawdown can be evaluated using groundwater models. However, considering only the effects of drawdown is insufficient to evaluate the vulnerability between the terrain surface and groundwater table. We proposed a vulnerability analysis method based on soil water pressure by including the Soil-Plant-Atmosphere Continuum (SPAC) approach. We defined the vulnerability based on the wilting point of the terrain vegetation. A theoretical case study representing the groundwater level alterations that induce permanent wilting was investigated. The vulnerability analysis method takes account of two important factors; vegetation type and soil textures. The results show that wilting is more infiltration dependent, therefore, it does not occur immediately once the water table is lower than rooting depth. Soil water pressure losses caused by groundwater table drawdowns are compensated when sufficient infiltration occurs. Without sufficient infiltration, soil texture is an important factor for the resilience of vegetation cover under the influence of groundwater table drawdown. In our case study, layered soils lead to a flow behavior which makes the vegetation reach permanent wilting earlier than the homogeneous counterpart. Vulnerability analysis of vegetation cover can be regarded as an important step for an environment-friendly tunnel drainage design.

Assessment and Design of Critical Infrastructures against Intentional Attacks Based on Degraded States Vulnerability Methodology

A crucial issue in today’s counterterrorism environment is how to assess critical infrastructure damage and develop a more appropriate design rapidly against intentional attacks, since it has become unacceptable to ignore the high impact of disruptions caused by these attacks. Degraded states vulnerability methodology (DSVM) is used to explore the vulnerability analysis and design method of critical infrastructures. After the terrorist attack, the damage status of the components at all levels of the target is quickly determined, and the probability of component level damage is simulated. Using this method to judge the functional damage condition and improve the planning and design under multi-hit. Respond quickly to the damage effect of each hit, and put forward multi-hit’s degraded states transition tables and state-effect tables. It provides the basis for rapid assessment, planning and design of critical infrastructure in modern local wars where terrorists are more intensive and frequent.

Entropy of centrality values for topological vulnerability analysis of water distribution networks

Purpose The increased complexity of water distribution networks (WDNs) emphasizes the importance of studying the relationship between topology and vulnerability of these networks. However, the few existing studies on this subject measure the vulnerability at a specific location and ignore to quantify the vulnerability as a whole. The purpose of this paper is to fill this gap by extending the topological vulnerability analysis further to the global level. Design/methodology/approach This paper introduces a two-step procedure. In the first step, this work evaluates the degree of influence of a node by employing graph theory quantities. In the second step, information entropy is used as a tool to quantify the global vulnerability of WDNs. Findings The vulnerability analysis results showed that a network with uniformly distributed centrality values exhibits a lower drop in performance in the case of partial failure of its components and therefore is less vulnerable. In other words, the failure of a highly central node leads to a significant loss of performance in the network. Practical implications The vulnerability analysis method, developed in this work, provides a decision support tool to implement a cost-effective maintenance strategy, which relies on identifying and prioritizing the vulnerabilities, thereby reducing expenditures on maintenance activities. Originality/value By situating the research in the entropy theory context, for the first time, this paper demonstrates how heterogeneity and homogeneity of centrality values measured by the information entropy can be interpreted in terms of the network vulnerability.

Study on Assessment Method of Satellite Damaged by Space Debris

The damage assessment model of space debris was established to satellite, included the description of space debris distribution field, the analysis of function and structure of satellite components, the rendezvous analysis between space debris and satellite, the damage criterion and so on. NASA ORDEM model and Shot-line method were used to describe the probability distribution field of millimeter and centimeter debris in space. The equivalent model of satellite was established by energy conservation principle and basic body method. The failure probability of satellite system was calculated under the impact of space debris. Establishing the vulnerability analysis method of satellite target is great significance to the risk assessment of space debris impacting satellite, and provides reference for the optimization design of satellite protection structure.

A physics-based framework for analyzing the resilience of interdependent civil infrastructure systems: A climatic extreme event case in Hong Kong

Operationalizing resilience concepts and principles, in particular when interdependency is considered, remains a major challenge for the sustainable development of urban infrastructures and future cities. From theoretical perspective, not only does it call for sector-specific infrastructure asset management knowledge, traditional risk-based disaster analysis and emergency management techniques, but it also demands quality data and stakeholders’ competency to holistically integrate the information and analyze the data. To address these limitations, this research provides a physics-based synthesis framework for interdependent civil infrastructure system resilience analysis. The framework consists of four major components: (i) setting resilience analysis boundary through the prescribed criteria; (ii) establishing an infrastructure resilience management lifecycle; (iii) defining the physics-based modeling of infrastructure system functions; and (iv) designing the interfaces between interdependent infrastructure systems. The feasibility and applicability of the proposed framework are tested through a case study involving two interconnected infrastructure systems (i.e. stormwater drainage system and road transport system) located in Hong Kong. It incorporates relevant infrastructure information by using the ArcGIS platform which is integrated with the physics-based vulnerability analysis methods from the two distinct knowledge domains. Cascading effects of adverse events propagating through the two infrastructures are delineated using an empirical nominal damage function.