Proposed Risk Assessment Method Research Articles

Dynamic traffic graph based risk assessment of multivehicle lane change interaction scenarios

Vehicles' lane-changing behavior can potentially result in traffic conflicts and crash risks, particularly in scenarios with interactions among multiple vehicles. To assess the crash risk of multi-vehicle interaction lane-changing (MILC) scenarios, this study presents a dynamic traffic graph-based risk assessment method. First, a method for constructing dynamic scene graphs is proposed, along with graph-based indicators for assessing scene and scenario risks. Second, the Gaussian mixture model-latent Dirichlet allocation (GMM-LDA) algorithm is utilized to cluster risk sequences of MILC scenarios, enabling the classification of scenario risks into different levels. The method considers both dynamic and static elements in these scenarios, as well as the spatial relationships among these elements. A case study was conducted to illustrate this approach at a weaving area in China. The findings reveal that diverging segments have the highest probability of high-risk MILC scenarios. Furthermore, the likelihood of high-risk scenarios involving consecutive lane-changing maneuvers is higher compared to single lane changes. Left lane-changing behavior exhibits a higher probability of high-risk scenarios compared to right lane-changing. The proposed risk assessment method facilitates the identification and construction of high-risk MILC scenarios. It also enables the exploration of the risk evolution process.

A model of correlated interval–probabilistic conditional value-at-risk and optimal dispatch with spatial correlation of multiple wind power generations

The correlated uncertain variables with the probability density function have been conducted in previous studies. However, the probability density functions of random variables are not always available, but their boundaries are more readily obtained. Accordingly, this paper adopts the interval variables with spatial correlation in multiple wind generations to achieve more practical scheduling. First, a novel risk qualification model, that is, correlated interval-probabilistic-based conditional value-at-risk, can be proposed to reduce the conservativeness of the traditional risk assessment methods. Then, an uncertain optimization model is built that considers multiple uncertainties, including probabilistic uncertainties of the load and interval uncertainties of coupled adjacent wind farms. Moreover, two spinning reserves are developed and integrated into the dispatch model. Finally, the influence of the wind power generation's correlation and fluctuation levels on the dispatch results is also investigated. Simulations on IEEE-39 and IEEE-118 bus systems validate the accuracy of the proposed risk assessment method.

HGV fire risk assessment method in highway tunnel based on a Bayesian network

The complex characteristics of long-distance highway tunnels may lead to serious loss of life and property in fire incidents. According to the statistical analysis, heavy goods vehicle (HGV) is one of the primary risk factors for tunnel fires. In this study, a quantitative risk assessment model of HGV-involved tunnel fires is established based on the functional resonance analysis method (FRAM) and Bayesian network (BN). Using FRAM, the mechanisms of the incident occurrence and evolution and critical risk factors of HGV-involved tunnel fires are determined, and the BN model is used to quantify the risk based on a probabilistic analysis. Focusing on operational management, monitoring system construction, type of transporting goods, emergency rescue, and egress facilities, the incident severity is determined in terms of casualties and economic losses. The proposed risk assessment method is expected to assist the tunnel operational management and the fire services in identifying critical risk factors in HGV-involved tunnel fires.

Compliance Risk Assessment in the Banking Sector: Application of a Novel Pairwise Comparison-Based PRISM Method

Up-to-date compliance management uses a risk-based approach based on international standards. In addition to techniques and practices, implementing compliance measures is determined by principles and culture. Compliance risk assessment is an evolving field in theory and practice. Compliance risk management is complex and highly dependent on the decisions of experts. This article presents a new compliance risk assessment method based on a commercial banking case study. In the study, the Guilford method is used to extend the Partial Risk Map (PRISM) assessment technique, and the steps of the proposed pairwise comparison-based PRISM method are described in detail. Since risk assessment is critical to the operation and development of compliance management systems, the proposed risk assessment method involves testing individual evaluations’ consistency and the results’ robustness. The best-fitting and outlier experts can be identified based on testing the impact of individual expert rankings on the aggregated ranking. The main finding is that top partial risks can be identified by applying the proposed pairwise comparison-based PRISM technique; therefore, possible optimal risk mitigation strategies and measures can be designed.

Optimization of Leakage Risk and Maintenance Cost for a Subsea Production System Based on Uncertain Fault Tree

Traditional fault tree analysis is an effective tool used to evaluate system risk if the required data are sufficient. Unfortunately, the operation and maintenance data of some complex systems are difficult to obtain due to economic or technical reasons. The solution is to invite experts to evaluate some critical aspect of the performance of the system. In this study, the belief degrees of the occurrence of basic events evaluated by experts are measured by an uncertain measure. Then, a system risk assessment method based on an uncertain fault tree is proposed, based on which two general optimization models are established. Furthermore, the genetic algorithm (GA) and the nondominated sorting genetic algorithm II (NSGA-II) are applied to solve the two optimization models, separately. In addition, the proposed risk assessment method is applied for the leakage risk evaluation of a subsea production system, and the two general optimization models are used to optimize the leakage risk and maintenance cost of the subsea production system. The optimization results provide a theoretical basis for practitioners to guarantee the safety of subsea production system.

Research on Risk Assessment and Suppression Measures for Ice-Shedding on 500 kV Compact Overhead Lines

Overhead line ice-shedding causes line breakage and results in electrical faults occurring between transmission lines, which seriously affects the safe operation of the power grid. In this paper, a risk assessment method for ice-shedding on 500 kV compact overhead lines is proposed. Two ratios were used to assess the discharge risk and mechanical failure risk of a line. A 500 kV compact overhead line in southern China was taken as an example, the proposed risk assessment method was used to carry out a risk assessment of the ice-shedding line with an ice thickness of 15 mm, and the suppression effects of different spacer arrangement schemes on the ice-shedding line were analyzed. The results showed that the impact coefficient of the conductor reached 1.88, the maximum jump height of the lower phase conductor reached 22.596 m, and the interphase clearance of the conductor decreased sharply when the lower phase conductor of 500 kV case line underwent full-span ice-shedding synchronously. The ice-shedding line displayed both a discharge risk and a mechanical failure risk. When interphase spacers were installed at L/3 from both ends of the span for the upper-left phase and the lower phase, the upper-right phase, and the lower phase, the impact coefficient of the line became 1.76, which was lower than the recommended value of 1.8; there was no mechanical failure risk; and the maximum jump height became 10.266 m. This study verified the effectiveness of this spacer arrangement scheme.

Real-time urban rainstorm and waterlogging disaster detection by Weibo users

Abstract. With the process of urbanization in China, the urban waterlogging caused by rainstorms occurs frequently and often leads to serious damage to the natural environment, human life, and the city economy. Rapid detection of rainstorm and urban waterlogging disasters is an essential step to minimize these losses. Weibo, a popular microblog service in China, can provide many real-time Weibo posts for rapid detection. In this paper, we propose a method to identify microblogs with rainstorm and waterlogging information and apply them to waterlogging risk assessment. After pre-processing the microblog texts, we evaluate the performance of clustering (k-means) and classification (support vector machine, SVM) algorithms in the classification task. Apart from word vector features, we also introduce sentiment and publisher features for more real-time and accurate results. Furthermore, we build a waterlogging intensity dictionary to assess the waterlogging risk from the Weibo texts and produce a risk map with ArcGIS. To examine the efficacy of this approach, we collect Weibo data from two rainstorms and waterlogging disasters in Beijing city as examples. The results indicate that the SVM algorithm can be applied for a real-time rainstorm and waterlogging information detection. Compared to official-authentication and personal-certification users, the microblogs posted by general users can better indicate the intensity and timing of rainstorms. The location of waterlogging points is consistent with the risk assessment results, which proves our proposed risk assessment method can be used as a reference for timely emergency response.

Assessment of red tide risk by integrating CRITIC weight method, TOPSIS-ASSETS method, and Monte Carlo simulation

This study proposes a red tide risk assessment method based on intercriteria correlation (CRITIC), technique for order preference by similarity to an ideal solution (TOPSIS), assessment of estuarine trophic status (ASSETS) methods and Monte Carlo simulation (MCS) to calculate the probability of each risk level. The integrated TOPSIS-ASSETS method is used to calculate the risk levels of each year, where index weight is determined by CRITIC method. MCS method is employed to calculate the probability of each risk level. The results showed that level III to level V indicates high possibility of red tides in the case study area (Tolo Harbor). The highest risk rating was level V in 1988. The change of the risk level of red tide is consistent with the real situation of the occurrence of red tide. Another case of the east part of Skagerrak Strait shows that the results of this method are consistent with field situation. When there is an error between the evaluation results and the real situation, MCS can further suggest the probability of error in the evaluation results. Meanwhile, sensitivity analysis was used to test the performance of the evaluation model and two comparative methods. The results show that the proposed risk assessment method has better performance than other methods and can provide an effective risk evaluation for red tide management.

Simulation and risk assessment of power system with cascading faults caused by strong wind weather

Global warming has led to the frequent occurrence of extreme weather events and threatened the stable operation of the power system. The faults caused by extreme weather events are of long time and massive scale characteristics, which pose new challenges to assessing power system operation risk. The model of the relationship between strong wind information and power grid faults was constructed considering the uncertain of line galloping and complexity of line trajectory. A meteorological-electric hybrid simulation approach was constructed with alternative and adaptive simulation strategies to simulate the cascading faults scenarios under the extreme weather of strong wind. The risk assessment method and index combining risk and fragility were proposed based on the proposed hybrid simulation approach. Case studies were performed to verify the feasibility of the proposed risk assessment method.

A Novel Risk Assessment Approach Using a Hybrid Method Based On Fine–Kinney and Extended MCDM Methods Under Interval-Valued Intuitionistic Fuzzy Environment

Subway construction is one of the most dangerous industries due to the highest number of occupational accidents. Risk assessment is an effective tool to evaluate risk factors and reduce the number of accidents in such industries. In this study, a novel risk assessment method is proposed using three-stage hybrid method based on Fine–Kinney risk assessment method to evaluate the risk factors encountered in subway constructions and take necessary precautions against possible accidents. Accordingly, in order to deal with the drawbacks of the Fine–Kinney risk assessment method, a novel hybrid approach, which includes Fine–Kinney, analytic hierarchy process (AHP), and multiple multi-objective optimization by ratio analysis (MULTIMOORA) methods, is suggested under interval-valued intuitionistic fuzzy (IVIF) environment to handle uncertainty in the risk evaluation. To demonstrate the applicability and benefits of the proposed hybrid risk assessment method, a real-life case study in subway construction is presented. To determine the weights of three risk parameters in Fine–Kinney method and the risk priority of occupational risk factors in subway construction, IVIF-AHP and IVIF-MULTIMOORA methods are performed, respectively. Then, a sensitivity analysis is applied to confirm the robustness of the proposed method. Finally, a comparative analysis is carried out to check the consistency of the ranking of risk factors obtained in proposed method. The results prove that the proposed risk assessment method generates consistent and reliable results.

Evaluation of M&A Risk Based on Adaptive Immune Genetic Algorithm

In order to improve the convergence and accuracy of M&A risk assessment method, an M&A risk assessment method based on adaptive immune genetic algorithm is proposed. The risk of M&A is divided into M&A preparation stage, M&A implementation stage, and M&A integration stage. The event tree analysis method is used to clarify the inducement of M&A risk events. We optimize the traditional genetic algorithm, design adaptive immune genetic algorithm to ensure the convergence of risk assessment algorithm, select the risk assessment indicators of enterprise M&A, determine the weight of the assessment indicators, and construct the quantitative model of enterprise M&A risk assessment. The experimental results show that the proposed risk assessment method takes less time, has higher accuracy, and has less probability of enterprise data imbalance.

Logical-linguistic method for assessing the risk of specialized lending (on the example of project financing)

Credit risk assessment is associated with uncertainty of many factors which are hard to describe mathematically. This reduces the probability of success of specialized lending as well. Among the various risk assessment methods suitable for specialized lending risk assessment, it’s possible to single out a scenario approach that uses the methods of fuzzy set theory to calculate the values of membership functions. However, the problem of ranking the set of project implementation scenarios for which a loan is requested is not studied enough. Despite the existing technical difficulties of risk factors management, the ranking of the risks of project implementation can be carried out by comparing with the ones from the database using the logical-linguistic classification method. The purpose of the study is to develop new methods of credit risk assessment based on the logical and linguistic classification of specialized lending projects as well as algorithms for assessing the credit risk of the project with determining its rating. The definition of a Project is used according to the concept of Project Finance as one of the classes of specialized lending. Methods: the assignment of weight factors for all subclasses of credit requirements, the assignment of risk levels for all values of the types of criteria, introduction of a table of the risk value assessment or the creditworthiness of the project, the formation of a database of reference attribute strings for all classes of projects, generating attribute strings for the analyzed project, assigning the analyzed project to the benchmark using the logical-linguistic classification method, assigning risk degree values for the analyzed project for all values of the criteria (attribute) types of the selected reference project, calculating the credit risk assessment of the analyzed project and determining the rating of the analyzed project for decision making. Results: based on the proposed risk assessment method, an algorithm for calculating the risk assessment of lending to the analyzed project with determining its rating was developed. Practical significance: the results of the research can be used in the development of computer program allowing to accelerate risk assessment of project financing.

A Cybersecurity Risk Assessment Method and its Application for Instrumentation and Control Systems in Nuclear Power Plants

As the digitalization of instrumentation and control (I&C) systems in nuclear power plants (NPPs), the stability of systems has been increased and the economic benefits have been improved. However, the cybersecurity issues are emerging and a practical risk assessment method for I&C systems is in urgent need. In this paper, a risk assessment method combining quantitative and qualitative analysis for I&C systems in NPPs is proposed, referring to a Chinese national standard GB/T 36466-2018. According to the standard, the protection capability is considered as the fourth element contributing to the risk along with the asset, the threat, and the vulnerability. We also apply the proposed risk assessment method to an actual I&C system. The results show that the method provides an applicable and effective means of risk assessment for I&C systems in NPPs.

Absolute mortality risk assessment of COVID-19 patients: the Khorshid COVID Cohort (KCC) study

BackgroundAlready at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis.MethodsWe used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead.ResultsCause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC = 0.872 [CI 95%: 0.835–0.910]).ConclusionsThis study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.

Inspection and assessment of concrete pipe culverts under highways in Tatarstan

The experience of examining small culverts of artificial structures, carried out by a number of specialized organizations, shows that on some highways of the country up to 80% of pipes have some degree of damage. This article provides data from field surveys of pipes located on the main highways of Tatarstan. Survey data is needed to identify common problems at concrete culverts in Tatarstan. Field data were also analyzed using statistical software to identify factors contributing to the degradation of concrete culverts. The article presents a method for assessing the risk of developing negative phenomena when maintaining concrete pipes. The proposed risk assessment method allows predicting and preventing the occurrence of such phenomena.

Network Attack Path Selection and Evaluation Based on Q-Learning

As the coupling relationship between information systems and physical power grids is getting closer, various types of cyber attacks have increased the operational risks of a power cyber-physical System (CPS). In order to effectively evaluate this risk, this paper proposed a method of cross-domain propagation analysis of a power CPS risk based on reinforcement learning. First, the Fuzzy Petri Net (FPN) was used to establish an attack model, and Q-Learning was improved through FPN. The attack gain was defined from the attacker’s point of view to obtain the best attack path. On this basis, a quantitative indicator of information-physical cross-domain spreading risk was put forward to analyze the impact of cyber attacks on the real-time operation of the power grid. Finally, the simulation based on Institute of Electrical and Electronics Engineers (IEEE) 14 power distribution system verifies the effectiveness of the proposed risk assessment method.

Water environmental nexus-based quality and safety risk assessment for fish (Carassius auratus) in aquaculture

Abstract Freshwater aquaculture is the main form of aquaculture in inland areas, and pollution is a significant challenge. The aim of this work was to propose an effective risk assessment method for aquaculture to predict pollutant bioaccumulation in fish tissue based on water environment variables. This method was designed to assess the risk of pollutant levels in fish tissue that exceed China’s food safety standards. Firstly, field observation and investigation were conducted to analyze the migration mechanisms of the main pollutants between fish tissue and the water environment. Secondly, a classification and regression tree (CART) and generalized additive model (GAM) nexus method was used to analyze the environmental variables related to pollutants in the water environment and establish prediction models. Finally, models of the bio-reaction kinetics were established to predict the pollutant bioaccumulation in fish tissue. The experimental results indicate that the GAM models could explain 73.7%, 84.4%, and 84.5% of the deviations for the three predicted pollutants. For 90% of the sample data, the accuracy of the bioaccumulation prediction was 93.44% for volatile phenol bioaccumulation, and 90.16% for Cu and coliform bioaccumulation. The proposed risk assessment method could be extended for aquaculture applications that require monitoring of additional parameters to safeguard aquaculture quality and facilitate effective safety risk management.

Assessing risk: Characterizing language performance in pediatric patients with intractable epilepsy pre- and post-surgical resection

Neuropsychologists play an important role in assessing risk for post-surgical cognitive decline in pediatric patients with medically refractory epilepsy. Families, neurologists, and neurosurgeons are particularly concerned about the possibility for language decline for patients with a dominant, most often left, hemisphere epileptic focus and planned surgical resection. This study aims to describe language functioning in pediatric epilepsy patients following resection and evaluate the accuracy of a clinical approach of assessing risk. This study proposes a risk assessment method that considers a patient’s pattern of lateralized dysfunction across cognitive domains, suspected neuroanatomical reorganization of language functions, and planned site of resection. Pediatric patients (N = 47) were dichotomized as being at minimal risk or at greater risk for post-surgical language decline based on the proposed risk assessment method. Retrospective chart review was utilized to obtain neuropsychological (Boston Naming Test and Weschler Vocabulary subtest) and clinical variables of interest. Patients in the minimal risk group demonstrated significantly improved BNT scores at post-surgery. Most patients remained stable in their Vocabulary knowledge. The proposed risk assessment method correctly classified patients 77% of the time based on BNT performance. Cluster analysis examining the individual components of the proposed method revealed three distinct patient subgroups. Clinical implications are discussed.

Risk assessment of chemical process considering dynamic probability of near misses based on Bayesian theory and event tree analysis

With the development of modern automatic control systems, chemical accidents are of low frequency in most chemical plants, but once an accident happens, it often causes serious consequences. Near-misses are the precursor of accidents. As the process progresses, near misses caused by abnormal fluctuation of process variables may eventually lead to accidents. However, variables that may lead to serious consequences in the production process cannot update the risk in the life cycle of the process by traditional risk assessment methods, which do not pay enough attention to the near misses. Therefore, this paper proposed a new method based on Bayesian theory to dynamically update the probability of key variables associated with process failure risk and obtain the risk change of the near-misses. This article outlines the proposed approach and uses a chemical process of styrene production to demonstrate the application. In this chemical process, the key variables include flow rate, liquid level, pressure and temperature. In order to study the dynamic risk of the chemical process with consideration of near misses, according to the accumulated data of process variables, firstly the abnormal probability of the variables and the failure rate of safety systems associated with the variables were updated with time based on Bayesian theory. On the basis of the dynamic probability of key process variables, an event tree of possible consequences caused by variable anomalies was established. From the logical relationship of the event tree, the probability of different consequences can be obtained. The results show that the proposed risk assessment method based on Bayesian theory can overcome the shortcomings of traditional analysis methods. It shows the dynamic characteristics of the probability of different near misses, and achieves the dynamic risk analysis of chemical process accidents.

Data learning and expert judgment in a Bayesian belief network for aiding human reliability assessment in offshore decommissioning risk assessment

Decommissioning of offshore facilities involve changing risk profiles at different decommissioning phases. Bayesian Belief Networks (BBN) are used as part of the proposed risk assessment method to capture the multiple interactions of a decommissioning activity. The BBN is structured from the data learning of an accident database and a modification of the BBN nodes to incorporate human reliability and barrier performance modelling. The analysis covers one case study of one area of decommissioning operations by extrapolating well workover data to well plugging and abandonment. Initial analysis from well workover data, of a 5-node BBN provided insights on two different levels of severity of an accident, the ’Accident’ and ’Incident’ level, and on its respective profiles of the initiating events and the investigation-reported human causes. The initial results demonstrate that the data learnt from the database can be used to structure the BBN, give insights on how human reliability pertaining to well activities can be modelled, and that the relative frequencies from the count analysis can act as initial data input for the proposed nodes. It is also proposed that the integrated treatment of various sources of information (database and expert judgement) through a BBN model can support the risk assessment of a dynamic situation such as offshore decommissioning.