Dynamic Risk Assessment Model Research Articles

Quantitative assessment rules and models for dynamic disaster risk in high-density gas gathering stations: Practical application in a largest CBM gathering station

Coalbed Methane (CBM) gathering stations involve multiple engineering disciplines, with a concentration of pressure vessels and strong production continuity. Once accidents occur, they can easily result in severe casualties, property losses, and environmental pollution. Therefore, prior to the large-scale promotion and urban application of CBM, this paper proposes a dynamic quantitative risk assessment model for the operational and task-related risks in CBM gathering stations. Firstly, various methods are employed for risk factor identification. Then, a dynamic Bayesian network (DBN) model is constructed to assess the dynamic operational risks and task-related risks of CBM gathering stations under different maintenance conditions. This model provides the initial probability of operational risk occurrence at stations, predicts changes in the probability of equipment multi-state operational risks under different maintenance conditions over 10 years, and forecasts the probability of task-related risks during operations. Finally, a combined weighting model is established, incorporating instance information to comprehensively evaluate safety management and derive safety management correction coefficients. These coefficients are integrated with the probabilities of station operation and task-related risk occurrence to ultimately determine the likelihood of operational risk occurrence during station production, completing the dynamic quantitative assessment of operational risks in CBM gathering stations.

Characteristics of Overburden Damage and Rainfall-Induced Disaster Mechanisms in Shallowly Buried Coal Seam Mining: A Case Study in a Gully Region

Shallow coal mining in gully regions has resulted in significant subsidence hazards and increased the risk of surface water inflow into mining panels, compromising the sustainability of surface water management and underground resource exploitation. In this study, the chain disaster process caused by shallow coal seam mining and heavy rainfall is quantitatively analyzed. The findings reveal that shallow coal seam mining leads to the formation of caved and fractured zones in the vertical direction of the overlying rock. The fractured zone can be further classified into a compression subsidence zone and a shear subsidence zone in the horizontal direction. The shear subsidence zone is responsible for generating compression and shear deformations, intercepting rainfall runoff, and potentially triggering landslides, necessitating crack landfill treatments, which are critical for promoting sustainable mining practices. The HEC-RAS program was utilized to integrate annual maximum daily rainfall data across different frequencies, enabling the establishment of a dynamic risk assessment model for barrier lakes. Numerical simulations based on unsaturated seepage theory provide insights into the infiltration and seepage behavior of rainfall in the study area, indicating a significant increase in saturation within lower gully terrain. Precipitation infiltration was found to enhance the saturation of the shallow rock mass, reducing matric suction in unsaturated areas. Finally, the disaster chain is discussed, and recommendations for managing different stages of risk are proposed. This study offers a valuable reference for the prevention and control of surface water damage under coal mining conditions in gully regions.

Study on risk assessment models for the aggregation of vehicles transporting hazardous chemicals

Aggregation of vehicles transporting hazardous chemicals is part of a complex system of hazardous chemical transportation with frequent accidents that threaten public safety. In order to avoid catastrophic consequences in the vehicle aggregation areas, dynamic risk assessment models have been studied in this paper. The macro quantitative risk assessment (M-QRA) model determines the accident probability through the probability of leakage and the probability of induced secondary accidents and takes into account the domino effect to determine the risk in conjunction with the consequence of pool fire, but some data are not readily available. Therefore, in order to accurately assess the risk in case of incomplete data, a rapid quantitative risk assessment (R-QRA) model considering the number of vehicles and the critical mass of hazardous chemicals was developed. In this paper, the M-QRA and R-QRA model were used to calculate the real-time risk of aggregation of vehicles transporting hazardous chemicals respectively, and the two models were compared in terms of calculation accuracy and assessment efficiency. The results show that the risks obtained from the two models are basically consistent, but the R-QRA model is faster in calculation, which is of great practical significance for actual engineering applications and helps safety management.

A dynamic risk assessment model based on multidimensional and quantitative inference theory

With the continuous increase in petroleum consumption and the trend towards more refined management, crude oil storage tanks are playing an increasingly crucial role in the petrochemical industry. However, the corrosion issue of crude oil storage tanks has consistently been a major factor limiting their service life, and leaks in crude oil tanks are often attributed to corrosion of the tank bottom. Due to the high cost and complexity associated with tank opening inspections, in this paper, directly observable information and physical quantities are utilized as indirect evidence, combined with Bayesian network, to indirectly determine the probability of tank corrosion. This approach allows for selective internal tank inspections, enabling effective risk assessment while also facilitating cost savings. Some parameters in the Bayesian network model are obtained through a probability estimation model combining expert experience and fuzzy set theory. Finally, the feasibility of the method is demonstrated by citing examples. The current model can offer a relatively reliable reference for evaluating the risks associated with tanks and their ancillary facilities, while also reducing the costs of tank inspections to some extent.

Agent-based risk modeling of domino effects in urban LNG stations

As the transfer hub between natural gas and users, urban LNG stations (ULSs) are the important link of the whole LNG industry chain. Due to special geographical location and various process equipment, potential risks in ULSs, especially domino effect accidents, usually lead to catastrophic consequences and social impacts. In this study, a dynamic risk assessment model for analyzing domino effects in urban LNG stations (DRADL) based on agent modelling and simulation is proposed. Tanks and tank cars as well as vaporizers and boilers in ULSs are modeled. A set of physical process and effect models are employed as the simulation process in this paper. Considering the lightweight nature and narrow flammability limit of LNG vapor cloud, a cyclic process is applied to judge the consequences of vapor cloud dispersion, which could avoid overestimating the results. A real urban LNG station is taken as a case study for modeling its potential domino effect risk. The results demonstrate the effectiveness and the advantages of the proposed ABMS approach for LNG dynamic risk assessment. The proposed DRADL model takes into account the combined effects of explosion and fire and LNG characteristics, which can provide guidance for risk management and rescue operation of domino effect accidents in urban LNG stations.

A Dynamic Collision Risk Assessment Model for the Traffic Flow on Expressways in Urban Agglomerations in North China

Expressways in urban agglomerations are important in connecting cities, thus attracting great attention from researchers in the expressways risk assessment. However, there is a lack of safety assessment models suitable for the characteristics of expressways in Chinese urban agglomerations, and the nature and mode of dynamic risks on Chinese highways are still unclear. Therefore, this study adopts the Adaptive Neural Fuzzy Inference System (ANFIS) and the method of decision tree, combined with data from the Beijing section of the Beijing Harbin Expressway, to model the risk of accident-prone highways in urban agglomerations. To determine the optimal model, we evaluated the model’s bias at different time intervals. In addition, key factors affecting highway safety were analyzed, providing scientific support for the risk prevention of highways in urban agglomerations in China.

Research on eVTOL Air Route Network Planning Based on Improved A* Algorithm

With the continuous opening of low-altitude airspace and the development of aircraft such as electric vertical takeoff and landing (eVTOL) vehicles, urban air traffic has become a sustainable and green development direction for future transportation. Air route networks, as a mainstream design scheme for air traffic, are able to provide prerequisites for eVTOL and other green aircraft to enter urban airspace for safe operation, among which air route planning is a fundamental component of air route network design. Currently, most of the research on aircraft path planning is performed in free airspace, lacking the analysis and processing for the complex operation environment, which has led to the high risk and large operation cost of path planning results, failing to meet the demand for safe and efficient development in the future. Aiming at the above problems, eVTOL-oriented air route planning research was carried out. Firstly, the urban low-altitude airspace structure was planned, and the operational levels of eVTOL were clarified; this was followed by introducing the urban dynamic air–ground risk factors and constructing a dynamic risk assessment model containing risk level information; finally, the improved A* algorithm based on the risk cost was employed to plan the eVTOL air route network, which finally realized the purpose of short path length and low total risk. The simulation results showed that the route generated by the improved A* algorithm could reduce the risk cost by at least 30% with a relatively small path cost, which ensured the operation efficiency and safety of eVTOLs and laid the foundation for the further sustainable and green development of urban airspace in the future.

BANKRUPTCY RISK ASSESSMENT FOR INSURANCE COMPANIES UNDER UNCERTAINTY

The insurance market is an integral part of the country's financial system and is therefore extremely important for economic development. Any shocks in the insurance market may cause a slowdown in economic growth and development and have a negative impact on the country's financial system. The stability of the insurance market is ensured by the companies operating in it. That is why our analysis focuses on assessing the risk of insurance companies' bankruptcy and analyzing the main factors that affect the situation in the insurance market.This article analyzes the main approaches to assessing the risk of insurance companies depending on the size of the risk premium. A simple dynamic risk assessment model was applied.Our analysis of the literature has shown that general approaches to assessing the risk of insurance company failure depend on the overall situation in the country and the market itself. In countries with more frequent force majeure events, more complex and sophisticated models have been introduced to assess the required amount of risk premium to avoid bankruptcy.The market situation in Ukraine had been stable for a long time, but with the onset of the pandemic, certain problems began to arise. An analysis of one of the leading insurance companies in the market showed that before the war started, it had a bankruptcy risk close to zero. Unfortunately, it should be noted that military actions, which are force majeure circumstances, complicate the quantitative analysis, as Ukrainian companies do not have effective mechanisms to cover this type of risk.It is established that a company cannot arbitrarily determine the amount of risk premium based on its own needs. This value will also be influenced by external macroeconomic factors that will affect not only the overall economic situation but also all participants in the insurance market, including consumers of insurance services.

Laboratory Risk Assessment Based on SHELL-HACCP-Cloud Model

With the increasing demand and expanding scale of laboratories in colleges and universities, laboratory accidents frequently occur, seriously impacting personal health, schools, society, and the environment. Scientific and effective risk assessment is crucial to prevent accidents. Therefore, in order to effectively reduce the risk of chemical laboratories and minimize the frequency of accidents. This study employs the combination of the SHELL model and HACCP system to analyze the potential sources of hazards in hazardous chemical laboratories and establish a risk assessment index system. Based on the Cloud model, a dynamic risk assessment model for the laboratory is established to quantitatively evaluate the risk level of the evaluation results. In order to ensure the rationality of the assessment results, the subjective and objective weights are combined by the principle of minimizing information entropy. Case analysis proves the scientific validity of the evaluation results of the model, which can assist laboratory managers in formulating emergency plans and risk management mechanisms to reduce or eliminate the occurrence of experimental accidents. This approach ensures the safe and sustainable development of schools and laboratories, which is conducive to the progress of researchers’ scientific research results.

Dynamic assessment method for human factor risk of manned deep submergence operation system based on SPAR-H and SD

Due to the complex and changeable contextual environment, the human factor risk of the manned deep submergence operating system shows dynamic characteristics. Compared with the traditional static human reliability analysis (HRA) method, dynamic HRA method can better simulate the dynamic characteristics and the nonlinear information feedback mechanism of the operating system. This paper proposed a dynamic risk assessment model based on System Dynamics and SPAR-H. The cognitive load was introduced into the Performance Shaping Factor (PSF) network to make it more suitable for the task and environment of manned deep submergence. In addition, in order to measure the compensation effect of PSF on the work efficiency of oceanauts, eight compensation functions were constructed between PSFs and work efficiency. Finally, key risk tasks and sensitive PSFs were identified. Taking the 12 h manned deep diving mission as an example, the dynamic quantification of human error probability and work efficiency of oceanauts was simulated. The results indicate that the dynamic simulation results of the constructed risk assessment model are consistent with the actual situation, and can effectively predict the changes of dynamic risk.

A Three-Stage Dynamic Risk Model for Metro Shield Tunnel Construction

The complex construction process of the metro shield method often leads to safety accidents. The various construction stages of shield tunnel construction comprise different construction activities and are accompanied by different safety risk factors. However, traditional risk assessment often evaluates the risk factors as a whole before shield tunnel construction and does not evaluate the risk factors dynamically by construction stages and by construction activities. To fill this gap, this paper aims to construct a dynamic Bayesian-based safety risk assessment model for shield tunnel construction from the perspective of changing construction stages and activities. First, safety risk factors were identified using the work breakdown structure-risk breakdown structure (WBS-RBS) method. Then, a three-stage dynamic assessment model of safety risks was constructed to depict the shield launch, shield tunnel, and shield reach. The dynamic Bayesian network (DBN) was improved to address the model with triangular fuzzy numbers and the leaky noisy-or-gate extension model. Finally, a case study was conducted. The model proposed in this paper is able to reveal the dynamic evolution of safety risks triggered by different construction activities. It offers a new simulated model for the prevention of safety accidents in the construction of metro shields.

Dynamic risk analysis of evolving scenarios in oil and gas separator

Risk assessment models in the oil and gas (O&G) industry has begun to transform from time-static models like fault tree, event tree or bowtie to dynamic risk assessment (DRA) models, as the latter is able to better capture the real-time-dependent risk behavior of safety barriers. Currently, most existing works on DRA in O&G industry mainly rely on event-driven data, such as failures or accident data from similar systems for risk updating. These data can be collected only when accidents or near-misses have occurred, which limits the prediction capability of the DRA model. To address this drawback, a DRA model is developed in this paper that uses condition-monitoring data for risk updating. A significant advantage of using condition-monitoring data is that the risk can be updated before accidents or failures occur, giving the operation team more time to take preventive actions.The developed approach comprises of an offline and an online phase. In the offline phase, a conventional risk assessment is performed based on fault tree models to calculate the risks at the beginning of the operation. Through the offline analysis, we can also identify the most critical safety barriers by examining their contribution to the risk indexes. Critical safety barriers are selected for condition-monitoring. In the online phase, the condition-monitoring data are used to update the reliability of the safety barriers in real-time based on a sequential Markov Chain Monte Carlo (MCMC) algorithm using a Bayesian framework. The updated reliabilities of the safety barriers are, then, used in the offline risk assessment model for a DRA.The developed approach is applied for a DRA of liquid carryover from an oil and gas separator to downstream equipment, using real-time data from the separator's safety barriers. The results show that the developed method provides a more accurate representation of the system's performance, enabling early detection of potential failures and reducing uncertainty in risk estimates. The proposed DRA model demonstrated its effectiveness in predicting failures of safety barriers in real-time, giving operational teams ample opportunity to take corrective action. This leads to improved decision-making in the O&G industry, enabling timely response to changes in risk levels. The use of condition-monitoring data enhances the accuracy of risk estimations, representing a crucial advance in DRA applications in the O&G sector.

Dynamic risk assessment model for third-party damage to buried gas pipelines in urban location class upgrading areas

Buried gas pipeline leakage accidents caused by third-party interference in urban location class upgrading areas are time-varying and can cause serious consequences. This study proposed an integrated dynamic risk assessment model to address this issue. First, a bow-tie model combining a fault tree (FT) with an event tree (ET) was used to construct an overall evolutionary process from basic events to accidents and thus mapped into a Bayesian network (BN). The prior probabilities of basic events in FT and safety barriers in ET were determined by four types of methods, and the critical basic events were identified by sensitivity analysis. By integrating the Hierarchical Bayesian analysis method with time-varying precursor data, the dynamic probability updating of critical basic events and safety barriers was achieved, enabling adaptation of accident probabilities using BN. Then, a dynamic calculation method for accident consequences was proposed by integrating the appropriate leakage rates with a new time-varying method for calculating fatalities. Finally, a new risk matrix method was proposed to dynamically evaluate the risk level by integrating the accident probabilities with the consequences. A case study showed that the proposed model could dynamically evaluate the integrated accident risk, providing explicitly quantified support for risk management and emergency rescue of buried gas pipeline networks in urban location class upgrading areas.

Dynamic Risk Assessment in Cybersecurity: A Systematic Literature Review

Traditional information security risk assessment (RA) methodologies and standards, adopted by information security management systems and frameworks as a foundation stone towards robust environments, face many difficulties in modern environments where the threat landscape changes rapidly and new vulnerabilities are being discovered. In order to overcome this problem, dynamic risk assessment (DRA) models have been proposed to continuously and dynamically assess risks to organisational operations in (near) real time. The aim of this work is to analyse the current state of DRA models that have been proposed for cybersecurity, through a systematic literature review. The screening process led us to study 50 DRA models, categorised based on the respective primary analysis methods they used. The study provides insights into the key characteristics of these models, including the maturity level of the examined models, the domain or application area in which these models flourish, and the information they utilise in order to produce results. The aim of this work is to answer critical research questions regarding the development of dynamic risk assessment methodologies and provide insights on the already developed methods as well as future research directions.

A Runway Overrun Risk Assessment Model for Civil Aircraft Based on Quick Access Recorder Data

The quick access recorder (QAR), as an airborne device used to monitor and record flight parameters, has been widely installed on various types of aircraft. Based on QAR data, research on runway overrun, a typical flight safety incident, has attracted widespread attention in recent years. However, existing runway overrun risk models generally suffer from oversimplified risk metrics or insufficient consideration of risk dynamics. In this paper, we propose a new dynamic runway overrun risk assessment model based on QAR data. We first consider the noise of aircraft trajectory data in the QAR parameters and present a landing trajectory correction method combining ground speed and runway position information. Second, to improve the accuracy of the risk assessment model, we design an algorithm to automatically recognize the aircraft autobrake level during the landing phase, based on which a new dynamic risk assessment model is developed. Finally, feature engineering is performed to extract the relevant contributing factors of runway overrun risk, based on which classification and regression models are applied to identify risky flights and predict the risk values. The proposed risk assessment model was evaluated using QAR data from an airline in China. The results show that the automatic deceleration rate, the way that the aircraft approaches the runway, the touchdown distance, and the kinetic energy at 50 ft are key factors in the risk of runway overrun during the landing phase.

Dynamic risk assessment of urban flood disasters based on functional area division—A case study in Shenzhen, China

The assessment of urban flood risk plays a vital role in disaster prevention and mitigation. This work aims to assess the dynamic risk of urban flood triggered by population movements through dividing urban functional zoning from the perspective of collective cognition. Firstly, the urban functional areas are identified using Points of Interest data and then the population movements mobile is detected based on functional areas using mobile signaling big data. Then, one-dimensional and two-dimensional hydrodynamic models are employed to simulate the 50-year flood scenario in Futian District, Shenzhen. Finally, a spatio-temporal dynamic assessment model for urban flood risk is constructed based on the extent of inundation, water depth, population density, and the disaster-bearing capacity of functional areas. The research findings are as follows: (1) Futian District's urban planning showcases harmonious integration of single-function and mixed-function areas. Utilizing the 50% perception standard efficiently identifies distinct functional types across diverse urban zones. The results are highly consistent with the actual situation. (2) During morning peak hours, the population exhibits a nuanced pattern of dispersal, concentration, and transition. Lunchtime witnesses multiple central clusters forming and gradually dispersing, while the evening peak witnesses population regrouping, covering broader geographical extents. Dynamic utilization of functional areas and mobile phone signaling data outperforms static population metrics, offering deeper insights into the complexities of human activity. (3) Between 12:00 and 13:00, lunchtime movements lead to a surge of 6 high-risk zones in the central area and 5 in the Meiling area. The dynamic flood risk assessment model, based on functional area delineation, effectively identifies disparities and fluctuations in flood risk across diverse functional areas during rainfall scenarios, ensuring heightened precision and accuracy in risk assessment.

Risk Assessment of Single-Gully Debris Flow Based on Dynamic Changes in Provenance in the Wenchuan Earthquake Zone: A Case Study of the Qipan Gully

After the Wenchuan earthquake on 12 May 2008, a huge amount of loose deposits was generated on the mountain surface in the earthquake zone, and vegetation was severely damaged, providing a rich source of material for debris flow, greatly increasing the danger. For many years, researchers have mainly considered the recovery of slope vegetation in assessing the risk of debris flow post-earthquake. However, field investigations have found that large amounts of the dynamic reserve materials in the gully have an important impact on the risk. Thus, based on field survey data, this paper takes the Qipan gully in Wenchuan County as an object and uses multi-source and multi-scale monitoring methods (Landsat series, Quickbird, and Unmanned Air Vehicle) to analyze and statistically study the provenance of the slope and gully both pre- and post- the earthquake. By comprehensively using game theory combination weighting and the cloud model, a dynamic risk assessment model for debris flow was constructed to evaluate the risk of debris flow from 2005 to 2019. The results show that the slope provenance post-earthquake was 7.7 times that of pre-earthquake, and by 2019 the slope provenance had recovered to the pre-earthquake level. Based on the statistical estimation of the amount of debris flow outbreak and the dredging of the blocking dam recorded in relevant data, the dynamic provenance of debris flow had decreased by about 781.3 × 104 m3 by 2019. Compared with considering slope provenance only, the assessment result of debris flow risk considering both slope and gully provenance is more realistic. The results are expected to provide reference and guidance for dynamic assessment of the risk of debris flow faced by increasing projects in high-seismic-intensity mountainous areas and to ensure the safety of people’s lives and property effectively.

A Cyber-Physical Power System Risk Assessment Model Against Cyberattacks

With the integration of cyber devices into power systems, such as information communication technology devices and intelligent electronic devices, there are currently a large number of software vulnerabilities (SVs) in cyber devices, which are probably used to obtain the authority of cyber devices for malicious attackers and be carried out subsequently severe attacks on power systems. Thus, it is inappropriate to only consider the power risks as the risk assessment for the system with the strong coupling between cyber systems and power systems. In this article, we investigate a dynamic risk assessment model for the cyber-physical power system (CPPS) against cyberattacks, combining the network security vulnerability of the supervisory control and data acquisition (SCADA) system in a substation and physical consequences in power systems caused by it under malicious control. By construction, a dynamic network security vulnerability assessment method for the SCADA system is well studied by considering SVs and deployed network security defense measures, which is also corrected by the propagation characteristics. Moreover, we estimate physical consequences by minimum shedding loads in N-1 contingencies caused by the SCADA system in a substation under malicious control. Finally, simulations on modified IEEE 14-bus and IEEE 118-bus systems verify the proposed model's effectiveness, highlighting the importance of network security in CPPS.

Chronicles of security risk assessment in process industries: Past, present and future perspectives

Security incidents around the world causes enormous loss in terms of life, economy, and environment. The 9/11 ′triggered the serious onset of research initiatives in the field of security with a more specific focus on the protection of high sensitive and hazardous facilities. It is well known that technology is advancing in a very rapid phase, proportionally increasing the security risk of the facilities. Conventional deterministic risk techniques widely found their applications in the earlier stage but owing to the increasing dynamic nature of the security risk, probabilistic dynamic risk assessment models were developed over time. In this article, the authors attempted to present the evolution of security risk concepts, their present status, and their future scope in a precise and consolidated form. The last 20 years of development in the security risk assessment concept involving deterministic and dynamic risk assessment models and applications in security risk assessment relevant to physical security are discussed in this article. The objective of the article is to outline the past, current and future directions of security risk highlighting their strength, weakness and limitations.

Cloud Enterprise Dynamic Risk Assessment (CEDRA): a dynamic risk assessment using dynamic Bayesian networks for cloud environment

Cloud computing adoption has been increasing rapidly amid COVID-19 as organisations accelerate the implementation of their digital strategies. Most models adopt traditional dynamic risk assessment, which does not adequately quantify or monetise risks to enable business-appropriate decision-making. In view of this challenge, a new model is proposed in this paper for assignment of monetary losses terms to the consequences nodes, thereby enabling experts to understand better the financial risks of any consequence. The proposed model is named Cloud Enterprise Dynamic Risk Assessment (CEDRA) model that uses CVSS, threat intelligence feeds and information about exploitation availability in the wild using dynamic Bayesian networks to predict vulnerability exploitations and financial losses. A case study of a scenario based on the Capital One breach attack was conducted to demonstrate experimentally the applicability of the model proposed in this paper. The methods presented in this study has improved vulnerability and financial losses prediction.