Quantitative Risk Assessment Framework Research Articles

Quantitative risk assessment for the introduction of bluetongue virus into mainland Europe by long-distance wind dispersal of Culicoides spp.: A case study from Sardinia.

Europe faces regular introductions and reintroductions of bluetongue virus (BTV) serotypes, most recently exemplified by the incursion of serotype 3 in the Netherlands. Although the long-distance wind dispersal of the disease vector, Culicoides spp., is recognized as a virus introduction pathway, it remains understudied in risk assessments. A Quantitative Risk Assessment framework was developed to estimate the risk of BTV-3 incursion into mainland Europe from Sardinia, where the virus has been present since 2018. We used an atmospheric transport model (HYbrid Single-Particle Lagrangian Integrated Trajectory) to infer the probability of airborne dispersion of the insect vector. Epidemiological disease parameters quantified the virus prevalence in vector population in Sardinia and its potential first transmission after introduction in a new area. When assuming a 24h maximal flight duration, the risk of BTV introduction from Sardinia is limited to the Mediterranean Basin, mainly affecting the southwestern area of the Italian Peninsula, Sicily, Malta, and Corsica. The risk extends to the northern and central parts of Italy, Balearic archipelago, and mainland France and Spain, mostly when maximal flight duration is longer than 24h. Additional knowledge on vector flight conditions and Obsoletus complex-specific parameters could improve the robustness of the model. Providing both spatial and temporal insights into BTV introduction risks, our framework is a key tool to guide global surveillance and preparedness against epizootics.

Comprehensive Threat Analysis in Additive Manufacturing Supply Chain: A Hybrid Qualitative and Quantitative Risk Assessment Framework

AbstractAdditive Manufacturing (AM) offers revolutionary design capabilities but introduces cybersecurity vulnerabilities due to its digital integration. While existing research focuses on cyber threats, a significant gap exists in addressing physical and cyber risks. The current research focuses on cyber threats, but more attention needs to be paid to addressing physical and cyber risks. This paper proposes a new risk assessment framework that considers both physical and cyber threats quantitatively and qualitatively and helps safeguard the AM supply chain. The framework identifies, analyses, and prioritises potential threats based on their characteristics. It also considers the trustworthiness of data and the frequency of reported threats to estimate the likelihood of each threat. The risks are then prioritised based on a combined impact-likelihood score. The framework has been validated on the standard benchmark MITRE CVEs dataset and has proven effective in identifying critical vulnerabilities. This approach empowers manufacturers to proactively deal with security concerns and navigate the complexities of the rapidly evolving AM landscape.

Quantitative risk assessment of a spill fire caused by the continuously leaked fuel in a sealed ship engine room

There are a large number of high-temperature heat sources in a ship engine room. Once the oil leakage occurs, it is likely to be ignited and form a spreading and burning spill fire. In this paper, spill fire experiments are conducted in a model-scale ship engine room to investigate their characteristic parameters. The quantitative models of flame height and thermal radiant surface emission power applicable to such fires are verified by the experimental data. Based on that, the classical quantitative risk assessment method is adopted to assess the risk of spill fires using thermal radiation hazard as the main reference, and a detailed framework for quantitative risk assessment of spill fires in ship engine room is established, which includes hazard identification, probability of failure calculation, consequence assessment, and risk quantification. The possible causes of spill fire accidents are briefly analyzed based on the bow-tie model. In cases where thermal threats above the risk threshold for larger leakage rates (70 mL/min), the probability method outperforms the threshold method in terms of accuracy in risk quantification. Additionally, the variation of thermal radiation with combustion diameter is analyzed, and some recommendations for fire emergency rescue are presented.

A comprehensive framework for quantitative risk assessment of organizational networks using FAIR-modified attack trees

Attack trees are a widely used method for threat modeling and analyzing cyber-attacks in organizational networks. Assessing the risk associated with each individual node of an attack tree is crucial for understanding the overall risk of the attack. This article presents a comparative study of different threat modeling methods and risk assessment approaches in organizational networks. The article also presents a novel comprehensive approach for quantifying risk assessment of organizational networks based on attack trees modified according to the factor analysis of information risk (FAIR) approach. Our results demonstrate the effectiveness of the novel approach in capturing the unique characteristics of different assets and their dependencies in an attack tree, leading to quantitative risk assessment.

What would be the impact on the rabies risk of reducing the waiting period before dogs are imported? A modelling study based on the European Union legislation.

Lyssavirus rabies (RABV) is responsible for a major zoonotic infection that is almost always lethal once clinical signs appear. Rabies can be (re)introduced into rabies-free areas through transboundary dog movements, thus compromising animal and human health. A number of measures have been implemented to prevent this happening, one of which is the waiting period (WP) after anti-rabies vaccination and serological testing. This WP ensures that antibodies assessed through the serological test are due to the vaccine, not to infection. Indeed, if antibodies are due to RABV infection, the dog should display clinical signs within this WP and would not therefore be imported. Within a framework of quantitative risk assessment, we used modelling approaches to evaluate the impact of this WP and its duration on the risk of introducing rabies via the importation of dogs into the European Union. Two types of models were used, a classical stochastic scenario tree model and an individual-based model, both parameterised using scientific literature or data specifically applicable to the EU. Results showed that, assuming perfect compliance, the current 3-month waiting period was associated with a median annual number of 0.04 infected dogs imported into the EU. When the WP was reduced, the risk increased. For example, for a 1-month WP, the median annual number of infected dogs imported was 0.17 or 0.15 depending on the model, which corresponds to a four-fold increase. This in silico study, particularly suitable for evaluating rare events such as rabies infections in rabies-free areas, provided results that can directly inform policymakers in order to adapt regulations linked to rabies and animal movements.

A quantitative risk assessment framework for reverse logistics of plastic packaging wastes via a multi-stage intuitionistic fuzzy information: A case study of plastic recycling industry in Thailand

A quantitative risk assessment framework for reverse logistics of plastic packaging wastes via a multi-stage intuitionistic fuzzy information: A case study of plastic recycling industry in Thailand

A multidimensional quantitative risk assessment framework for dense areas of stay points for urban HazMat vehicles

A multidimensional quantitative risk assessment framework for dense areas of stay points for urban HazMat vehicles

A study on burst failure mechanism analysis and quantitative risk assessment of corroded pipelines with random pitting clusters

Corrosion defect is the primary inducement for pipeline burst failures. Natural-occurred defects appear in the form of pitting clusters, exhibiting randomness and irregularity in local geometrics, which influences the failure behaviors. Considering this effect, a finite element analysis model was firstly derived to simulate the isolated and interacting random pitting clusters and estimate the burst pressure pb. Reasonable consistence with the existing results was achieved with the mean absolute relative error of 2.32%. Subsequently, through a Monte-Carlo Simulation scheme, the failure mechanisms and the probabilistic characteristics of pbs were investigated. Three failure modes were detected to link the failure development with the local geometric morphologies; Weak correlation between material loss and pb was found and explained; The pb for pitting clusters exhibited pronounced variabilities and the mean value was higher than the residual strength from traditional methods, which was aggravated when interacting effects were involved. Finally, a model uncertainty factor-based quantitative risk assessment framework was developed to estimate the life-cycle failure probabilities Pf. The Pf in traditional methods was overestimated by 70%–150% maximumly; Influential factors on failure risk were evaluated and their evolution tendencies were discussed; Safety margins were provided to determine operation pressures and inspection intervals.

On the quantitative resilience assessment of complex engineered systems

Recent years have seen the increasing complexity of engineered systems. Complexity and uncertainty also exist in engineered systems’ interactions with human operators, managers, and the organization. Resilience, focusing on a system’s ability to anticipate, absorb, adapt to, and recover from disruptive situations, can provide an umbrella concept that covers reliability and risk-based thinking to ensure these complex systems' safety. This paper discusses the quantitative aspects of the notion of resilience. Like the quantitative risk assessment framework, a generic framework should be developed for quantitative resilience assessment. This paper proposes a framework based on a triplet resilience definition consisting of disruption, functionality, and performance. Uncertainty treatment is also considered. The proposed framework aims to answer the question of “resilience of what to what” and how it can be quantitively assessed.

Uncertainties and their treatment in the quantitative risk assessment of domino effects: Classification and review

Domino accidents are typical low-frequency and high-consequence events in chemical process industries. Applying quantitative risk assessment (QRA) in domino accident assessment is challenging due to the uncertainties in the escalation process. Meanwhile, the outcomes of QRA are subject to a certain degree of unreliability due to the inappropriate representation of uncertainty. This paper reviews the literature in the field of QRA of domino accidents that may happen in the chemical process industries. Firstly, the sources of uncertainty in risk assessment of domino effects are identified and categorized based on a fundamental structure of uncertainty and a QRA framework. Furthermore, the current methodologies and approaches applied for handling various uncertainties (input uncertainty, model parameter uncertainty, and model structure uncertainty) in the QRA related to domino effects are reviewed. Based on the literature review results, current challenges with respect to uncertainty handling in QRA of domino accidents are discussed, and recommendations for future research are given before the conclusions are presented. This study helps researchers to get insights into the interface between uncertainty fundamentals and the QRA framework and the current status of uncertainty handling in the QRA of domino effects. Furthermore, this study promotes the development of new approaches for handling uncertainty in domino accident analysis.

A Quantitative Risk Assessment Framework for the Cybersecurity of Networked Medical Devices

Medical devices are increasingly the source of cybersecurity exposure in healthcare organizations. Research and media reports demonstrate that the exploitation of cybersecurity vulnerabilities can have significant adverse impacts ranging from the exposure of sensitive and personally identifiable patient information to compromising the integrity and availability of clinical care. The results can include identity theft and negative health consequences, including loss of life. Assessing the risk posed by medical devices can provide healthcare organizations with information to prioritize mitigation efforts. However, producing accurate risk assessments in environments with both sparse historical data and a lack of validation regarding the accuracy of forecasts is particularly challenging.
 We present a risk assessment framework for quantifying the risk posed by connected medical devices in trusted healthcare networks. Our framework is built upon prominent existing frameworks and guidance for general risk assessment and cybersecurity risk assessment. We add a method for quantifying risk, which to our knowledge is novel in the context of medical devices on trusted networks. The framework provides a structure for combining publicly available information along with expert elicitation about threats, vulnerabilities, and consequences. The goal is to provide healthcare organizations with actionable information for prioritizing and mitigating risks in medical devices.

RAIN: Risk Assessment Framework Based on an Interdependent-Input Propagation Network for a 5G Network

It is essential for mobile network operators to provide effective protective measures for assets in 5G networks to mitigate various threats, and risk assessment plays an important role in decision-making for protective measures. Organizations refer to risk assessment to determine the priority for protective measures, and multiple studies have proposed assessments that take various aspects and methodologies into consideration. Nevertheless, these efforts are not sufficiently useful in a practical sense. Existing studies lack numerical results to make cost-efficient decisions and cannot be automatically updated when the security policy is altered. Finally, a unified assessment framework is necessary. Hence, we propose a quantitative risk assessment framework, RAIN, to solve these problems for the 5G network. A customized weighted network combined with an interdependent-input weighting method enables the framework to provide holistic and quantitative assessment results and can be used for any scenario in a 5G network. With the assessment results, organizations can prioritize the implementation of protective measures for the target assets. In addition, the framework is flexible for policy changes and suitable for different systems in 5G networks. We implement our framework on a 5G stand alone core network system with different scenarios.

Of critical importance: Toward a quantitative probabilistic risk assessment framework for critical infrastructure

AbstractCritical comments can be made to the current risk assessment framework for critical infrastructure used in The Netherlands: the Dutch National Risk Assessment (DNRA). The DNRA uses a qualitative approach to risk assessment, based on, for example, ordinal risk scoring and the risk matrix. Even though qualitative risk assessment methods are internationally popular, there is no scientific evidence that a qualitative approach to risk assessment actually works. In the case of the DNRA, the main points of criticisms relate to the usage of a subjective concept of risk, dependency on subjective risk experts, the use of risk matrices and the absence of decision rules. To combat these criticisms, this article proposes a quantitative probabilistic risk assessment framework for critical infrastructure based upon four design principles, that a methodologically justified risk assessment method for critical infrastructure should meet. The proposal made for the quantitative risk assessment framework uses a Bayesian approach, a standardized measure for negligible risk in the form of a yearly mortality probability of 10−6, and Disability Adjusted Life Years to quantify human life years for social cost–benefit analysis. Finally, the proposed quantitative method is demonstrated in a case study.

A quantitative risk assessment framework for domino accidents caused by double pool fires

Fires spreading at limited distances one to the other can cause changes in burning rates and flame shapes due to synergistic effects. These may result in more severe consequences and in the generation of specific and more intense escalation vectors, causing a higher probability of domino scenarios. In this study, a specific approach was developed to consider synergistic effects of double pool fires in a quantitative risk assessment (QRA) framework aiming at the assessment of domino accident scenarios. The double pool fire synergy (DPFS) model developed was validated against experimental results obtained using different fuels. A comprehensive framework was developed to address the quantitative risk assessment of escalation leading to domino scenarios, combining the DPFS model, escalation probability estimation for domino accident, and Bayesian network analysis. Three case studies were carried out to analyze the impact of synergistic effects on escalation probability. The results show that synergistic effect of simultaneous multiple pool fires significantly increase the intensity of the escalation vector, and the escalation probability increases up to three orders of magnitude. Furthermore, the inclusion of synergistic effect in the analysis of posterior probability improves the accuracy of identifying the critical units in domino scenarios, contributing to a better selection of the emergency response targets.

A generalized equipment vulnerability model for the quantitative risk assessment of horizontal vessels involved in Natech scenarios triggered by floods

Severe NaTech scenarios may be triggered by the impact of floods on chemical and process installations. The currently available equipment vulnerability models addressing the potential failure of equipment caused by floods suitable for use in a quantitative risk assessment framework are mostly limited to the specific case of storage tanks, not addressing other types of process equipment. The present study aims at filling this gap, proposing a generalized vulnerability model applicable also to other types of horizontal cylindrical vessels widely used in the chemical and process industry and in oil and gas installations (e.g., shell and tube heat exchangers, separators, etc.). The generalized vulnerability model developed allows a simplified assessment of the possible structural damage due to floating or displacement caused by floodwater, based on floodwater height and velocity, and on detailed geometrical data of the vessel. Shortcut correlations were also developed to provide a preliminary screening, allowing the identification of the more vulnerable equipment. The models were applied to the analysis of an extended case study, in order to assess their suitability and the interest of the results provided in the framework of quantitative risk assessment of NaTech events triggered by floods.

Assessment of wave overtopping risk for pedestrian visiting the crest area of coastal structure

Wave overtopping can be a great threat to the pedestrians visiting the coastal structure’s crest area, but a good tool for risk assessment is lacking. In this study, a quantitative risk assessment framework was developed, which is based on considering the overtopping impact force acting on a pedestrian’s body. The centerpiece of the framework is a model for the likelihood of overtopping accident, which is defined as being mobilized by overtopping flow at least once during a visit. The largest single wave of a given sea state at the structure’s toe is first evaluated, and the maximum overtopping force of this largest wave is then calculated using a force predictor (assuming non-breaking waves before the structure), in which the human body is approximated as a cylinder. A probabilistic instability model, which was calibrated based on real-human experiments in flood flows, was developed in this study. This model converts an instability number, i.e., the ratio of maximum impact force to ground friction, into the probability of instability, so the likelihood of overtopping accident can be quantified. By applying our model to an adult standing on a 1-on-3 sloped revetment, it was found that the model gives thresholds of tolerable overtopping conditions (in terms of discharge rate and maximum overtopping volume) very close to those suggested in EurOtop (2018). A risk assessment framework, which quantifies risk as the sum of likelihood and severity, was proposed. The merit of this framework is that it can account for different pedestrian’s body size, location of the pedestrian, structure’s slope and etc. As a demonstration, the risk assessment framework was applied to Singapore’s coastal structures. Limitations of this framework are given at the end of the paper.

Quantitative risk assessment for ammonia ship‐to‐ship bunkering based on Bayesian network

AbstractThe maritime industry is getting prepared for using ammonia as a fuel to meet the decarbonization goal. However, ammonia is toxic, corrosive, and flammable, which poses specific safety challenges during bunkering compared with conventional fuels. The corrosion can be prevented by selecting suitable materials. However, the impact of toxic gas dispersion and fire has high uncertainties, thus risk assessment should be conducted. Currently, there are insufficient risk assessment guidelines for ammonia bunkering available. Therefore, this paper proposes a Bayesian network (BN) based quantitative risk assessment framework to investigate the potential risks of ammonia in ship‐to‐ship bunkering considering the toxicity and flammability. The study validates the utility of the proposed framework and demonstrates the BN as an efficient model in performing the probabilities calculations and flexible in conducting causal diagnosis. The results show that toxicity has the greatest impact on the risks of ammonia bunkering compared with flammability. The main innovation of this work is realizing the efficient quantification of risks for ammonia ship‐to‐ship bunkering by using the BN.

Risk Assessment of Terrestrial Transportation Infrastructures Exposed to Extreme Events

Keeping transport links open in adverse conditions and being able to restore connections quickly after extreme events are important and demanding tasks for infrastructure owners/operators. This paper is developed within the H2020 project SAFEWAY, whose main goal is to increase the resilience of terrestrial transportation infrastructure. Risk-based approaches are excellent tools to aid in the decision-making process of planning maintenance and implementation of risk mitigation measures with the ultimate goal of reducing risk and increasing resilience. This paper presents a framework for quantitative risk assessment which guides an integrated assessment of the risk components: hazard, exposure, vulnerability and consequences of a malfunctioning transportation infrastructure. The paper guides the identification of failure modes for transportation infrastructure exposed to extreme events (natural and human-made) and provides models for and examples of hazard, vulnerability and risk assessment. Each assessment step must be made in coherence with the other risk components as an integral part of the risk assessment.

Preliminary Quantitative Risk Assessment of Norovirus in Shellfish in the Yellow Sea and Bohai Sea of China

Norovirus (NoV) is a main foodborne pathogen of acute gastroenteritis in the world. A preliminary quantitative risk assessment (QRA) was conducted to evaluate the health risk caused by this virus in shellfish in the Yellow Sea and Bohai Sea of China. The QRA framework was established from the process of shellfish at retail through cooking at home to consumer consumption. The prevalence and quantity of NoVs in shellfish, cooking methods, internal temperature and time of shellfish in different cooking conditions, shellfish consumption frequency, and consumption amount were analyzed in the exposure assessment. The results of exposure assessment were introduced into the beta-Poisson dose-response model, and Monte Carlo analysis was used to calculate the risk of gastroenteritis caused by shellfish consumption in the cities around the Yellow Sea and Bohai Sea of China. The results showed that the probability of illness caused by NoVs due to shellfish consumption per year (Pill,yr) was 1.86 × 10-5. It was estimated that the annual number of patients with gastroenteritis per 1,000,000 general population (Nexp,mil) was 0.10, 1.23, 16.90, and 0.38 for population aged 0-4, 5-18, 19-64, and >65 years, respectively. This assessment provides valuable information such as the probability of illness associated with the consumption of shellfish and it also provides a reference for further in-depth QRA of NoVs in shellfish or other foods.

The study of burning behaviors and quantitative risk assessment for 0# diesel oil pool fires

The liquid fuel safety issues on fuel storage, transportation and processing have gained most attention because of the high fire risk. In this paper, some 0# diesel pool fire experiments with different diameters (0.2–1 m) were conducted with initial fuel thicknesses of 2 cm and 4 cm, respectively, to obtain liquid fuel combustion characteristics. Some key parameters including mass burning rate, flame height and the flame radiative heat flux, associated with fire risk, were investigated and determined. Subsequently, a detail quantitative risk assessment framework for 0# diesel pool fire is proposed based on the 0# diesel burning characteristics. In the framework, the probability of personal dead and the facility failure are calculated by the vulnerability models, respectively. In the end, 10 special tank fire scenarios were selected to show the whole risk calculation process. The tank diameter and the distance to pool fires were paid more attention in the cases. The safety distances in the cases are provided for the persons and nearby facilities, respectively. The paper enriches the basic experimental data and the provided framework is useful to the management of 0# diesel tank areas.