Accident Risk Analysis Research Articles

Risk Analysis of Weather-Related Railroad Accidents in the United States

Risk Analysis of Weather-Related Railroad Accidents in the United States

Combination of two-fluid model and delayed equilibrium model for the critical flow in a slit

Accurately predicting the mass flux, pressure profile, and velocity profile of the critical flow in a slit is essential for analyzing the breaking process of the liquid phase and calculating the aerosol source term for leak-before-break (LBB) monitoring and Loss of Coolant Accident (LOCA) risk analysis. A new critical flow model combining Two-fluid Model (TFM) and Delayed Equilibrium Model (DEM) is built to get accurate profiles while avoiding the same phase velocity in DEM and the arbitrary critical flow criterion in TFM. The new model is verified using past experiments of the critical flow in a slit. It proves to be accurate in mass flux but not in critical pressure, with maximum relative errors of around 25% in mass flux and around 80% in critical pressure. The new model is optimized for higher accuracy in critical pressure. The empirical equation of saturated phase mass flow rate fraction gradient is optimized by conducting approximate pressure profile calculation and regression analysis. The maximum relative error decreases little while the ratio of critical pressure relative errors lying in the range of ±40% increases after optimization. In contrast, the difference in the average abstract relative error of pressure between original TFM-DEM and DEM is much larger, for the maximum relative error of mass flux and critical pressure are around 25% and 110%. The comparison between the original and optimized TFM-DEM proves that the new critical flow model is accurate in mass flux and can be optimized to raise pressure calculation accuracy. The comparison between the original TFM-DEM and DEM proves that the phase velocity difference is the major source of accuracy improvement in the pressure profile.

Road accident risk analysis of at- and near-port areas reflecting regional heterogeneous characteristics

ABSTRACT In port cities, road traffic safety management should consider various types of cargo that will shift in the trucks and containers. This applies not only in the case of ‘at port’ areas within a 2-km radius but also ‘near-port’ areas within a 4–8-km radius. This is important for traffic safety because the crash severity can be high as there are many trucks to the ports that can cause large-scale crashes. Therefore, to prepare traffic safety management strategies for port city, it is necessary to identify crash risk factors that affect the crash severity. This study developed a two-stage road crash severity model of ‘at-port’ and ‘near-port’ regions using a support vector machine and a Bayesian multinomial model. Additionally, a severity model suitable for Korean ports was proposed that considered spatial heterogeneity in the Bayesian multilevel multinomial model. Findings derived from this study can be used for policy development to increase road traffic safety in port cities. ‘This article is a revised and expanded version of a paper entitled [Accident Risk Analysis of At and Near-Port Areas Reflecting Regional Heterogeneous Characteristics] presented at [The 11th International Conference on Logistics and Maritime Systems, Korea, 4-7 September 2023]’.

Quantitative risk assessment of coal mine gas explosion based on a Bayesian network and computational fluid dynamics

A gas explosion is one of the most serious coal mine accidents. The complexity and dynamics of gas explosion accidents, resulting from both multiple risk factors and time-dependent physical parameters, necessitates the use of risk assessment techniques to analyze the accident. A quantitative risk assessment method is proposed for coal mine gas explosions based on a Bayesian Network (BN) and Computational Fluid Dynamics (CFD). The results show that, in addition to the problems of “low vigilance” and “substandard safety education”, the “irrational design of mine gas extraction” has become another major risk factor at the new stage of development of mine excavation technology. Three typical gas explosion areas are selected to analyze the possible consequences of an accident. Based on the probabilistic risk assessment model, time-dependent physical parameters such as accident conditions, location and time are analyzed. The probability of occurrence of time-dependent risk factors is quantified to calculate the risk values of the consequences of explosion accidents corresponding to different times. The proposed method helps to improve the calculation accuracy of updating the risk probability value over time and provides theoretical support for the risk analysis of coal mine gas explosion accidents.

Risk analysis of lithium-ion battery accidents based on physics-informed data-driven Bayesian networks

The catastrophic consequences of lithium-ion battery (LIB) accidents have attracted high attention from society and industry. Accordingly, risk analysis is indispensable for the risk prevention and control of LIBs. Nevertheless, it is difficult to establish a physics-informed risk analysis model due to the complex material characteristics and aging mechanisms of LIBs. Meanwhile, the data-driven approach requires historical information of LIBs and does not merely rely on knowledge of the internal mechanisms of LIBs. This study proposes a method integrating the physics-informed Bayesian network (BN) (i.e., mapping from fault tree) and data-driven BN (i.e., learning from data) to conduct risk analysis of LIBs. First, we establish physics-informed and data-driven BNs. Subsequently, we bridge physics-informed and data-driven BNs to establish a Bayesian network for risk analysis of LIB accidents. Second, we set up safety barriers in the system, including detectors, emergency response, and firefighting facilities. Third, we evaluate the performance of safety barriers. We validate the proposed model using data from LIBs in air transportation. The results indicate that safety barriers can reduce the accidental risk of LIBs. Eventually, we propose suggestions for the risk control of LIBs in air transportation. This study is supposed to provide theoretical basis for the risk prevention and control of LIB accidents.

A machine learning-based data-driven method for risk analysis of marine accidents

In view of the frequent occurrence of marine accidents and the complex interaction of various risk-influencing factors (RIFs), a data-driven method to risk analysis that combines association rule mining (ARM) and complex network (CN) analysis is proposed in this study. The efficient FP-Growth algorithm is applied to facilitate ARM to examine risk patterns that frequently occur in marine accidents. Subsequently, CN theory is employed to scrutinise the multifaceted role of various RIFs and their interactions in the complex marine accident system, which involves the basic characteristics of the network, the identification of key RIFs through the application of the weighted LeaderRank (WLR) algorithm, and a robustness analysis. The results of the study indicate that compared with random networks, marine accident networks exhibit a higher level of complexity, which brings challenges to safety prevention and control. Inadequate regulation, violations, and deficiencies in safety management systems are identified as key RIFs, stressing the urgency of improving supervision, strengthening law enforcement and strengthening the safety management system. This study may facilitate maritime safety management of maritime traffic and the development of risk analysis methods.

Framework for process risk analysis of maritime accidents based on resilience theory: A case study of grounding accidents in Arctic waters

The complexity and non-linear interactions involved in maritime accidents make it challenging to apply traditional models of risk analysis to them. Resilience is adopting a perspective focuses on holistic aspects of safety across the pre-accident, mid-accident, and post-accident phases. This paper proposes a framework for process risk analysis of maritime accidents based on resilience theory by applying the function resonance analysis method (FRAM). We first identify a three-layer maritime accident based on maritime accident investigation reports and then analyze the potential variability of all relevant functions. Subsequently, a FRAM model is constructed to analyze the accident's functional resonance of accidents by aggregating variations in the functions. Finally, we apply Monte Carlo simulations to compute the values of coupling of all interconnected functions. The ship grounding accidents in Arctic waters is chosen as the case to demonstrate the proposed framework. The relationships of influence between the functions were analyzed to obtain critical coupling pathways of coupling between them, and a time-weighted PageRank algorithm was used to identify key functions. The results show that the proposed approach can reveal vulnerabilities and instabilities in the system, and enables the proposal of targeted, long-term measures of risk control to enhance its resilience and safety.

Incorporation of a global perspective into data-driven analysis of maritime collision accident risk

Ship collision accidents are one of the most frequent accident types in global maritime transportation. Nevertheless, conducting an in-depth analysis of collision prevention poses a formidable challenge due to the constraints of limited Risk Influential Factors (RIFs) and available datasets. This paper aims to incorporate a global perspective into a new data-driven risk model, scrutinize the root causes of collision accidents, and advance measures for their mitigation. Additionally, it seeks to analyze the spatial distribution and conduct a comprehensive comparative study on collision characteristics for both pre- and post-COVID-19, utilizing the real accident dataset collected from two reputable organizations: Global Integrated Shipping Information System (GISIS) and Lloyd's Register Fairplay (LRF). The research findings and implications encompass several crucial aspects: 1) the constructed model demonstrates its reliability and accuracy in predicting collision accidents, as evident from its prediction performance and various scenario analysis; 2) the most hazardous voyage segment for collision accidents is identified to provide valuable guidance to different stakeholders; and 3) the hierarchical significance of various ship types in the context of collision accident is highlighted regarding the most probable scenario for collision occurrences; 4) During the pandemic, the rise in collision probabilities, particularly involving older vessels and bulk carriers, implies heightened operational challenges or maintenance issues for these ship types; (5) The prominence of favorable and adverse sea conditions in collision reports underscores the significant influence of weather on accidents during the pandemic. These findings and implications help enhance safety protocols, ultimately reducing the frequency of collision accidents in the global maritime domain.

Bayesian Network Analysis of Industrial Accident Risk for Fishers on Fishing Vessels Less Than 12 m in Length

The Marine Stewardship Council estimates that approximately 38 million people worldwide work in fisheries, and more than one-third of the global population is dependent on aquatic products for protein, highlighting the importance of sustainable fisheries. The FISH Safety Foundation reports that 300 fishers die every day. To achieve sustainable fisheries as a primary industry, the safety of human resources is of the utmost importance. The International Maritime Organization (IMO) and the International Labor Organization (ILO) have made efforts towards this goal, including the issuance of agreements and guidelines to reduce industrial accidents among fishing vessel workers. The criterion for applying these guidelines is usually a total ship length ≥12 m or ≥24 m. However, a vast majority of registered fishing vessels are <12 m long, and the fishers of these vessels suffer substantially more industrial accidents. Thus, we conducted a quantitative analysis of 1093 industrial accidents affecting fishers on fishing vessels <12 m in length, analyzed risk using a Bayesian network analysis (a method proposed by the Formal Safety Assessment of the IMO), and administered a questionnaire survey to a panel of experts in order to ascertain the risk for different types of industrial accidents and propose specific measures to reduce this risk.

Classification of traffic accidents’ factors using TrafficRiskClassifier

The TrafficRiskClassifier model introduced in this study adopts an innovative approach that incorporates migration learning, image classification, and self-supervised learning, aiming to significantly improve the accuracy and efficiency of traffic accident risk analysis. Compared with traditional traffic safety analysis techniques, this model focuses on utilizing contextual information and situational data of traffic accidents to achieve a higher level of risk classification accuracy. The core of this approach is to deeply mine and analyze the detailed information in the accident environment, to provide more scientific and effective support for traffic accident risk prevention and response. Initially, by integrating migration learning with image classification techniques, the model proficiently extracts pivotal features from intricate traffic scenarios and formulates initial assessments of accident risks. Subsequently, self-supervised learning is incorporated in this study, augmenting the model's capability to comprehend and categorize accident imagery. The TrafficRiskClassifier model exhibits a generalization ability of 91.82%, 85.16%, and 80.92% on individual classification tasks, respectively, signifying its robust learning capacity and proficiency in managing unseen data. Furthermore, the TrafficRiskClassifier model delineates a functional nexus between accident risk and variables such as weather, road conditions, and personal factors, employing a polynomial regression approach. This methodology not only amplifies the predictive precision of the model but also renders it versatile across diverse scenarios. Through the analysis of various polynomial functions, the model achieves enhanced accuracy in classifying disparate risk levels. The outcomes demonstrate that the TrafficRiskClassifier model can efficaciously amalgamate contextual information within traffic scenarios, thereby achieving more precise classification of traffic accident risks, and consequently serving as an invaluable instrument for urban traffic safety management.

Impact of Ship Noise on Seafarers’ Sleep Disturbances and Daily Activities: An Analysis of Fatigue Increase and Maritime Accident Risk through a Survey

This study delves into the impact of ship noise on seafarer well-being, emphasizing fatigue—a significant contributor to maritime accidents due to human error. The investigation, centered around the hypothesis that IMO ship construction standards may not adequately minimize noise levels in seafarer cabins, seeks to establish whether these levels are sufficient to ensure seafarer security and prevent sleep disturbances. According to current IMO regulations, noise levels are set at 55 dB for vessels under 10,000 gross tonnage and 60 dB for those over 10,000, yet WHO guidelines recommend a maximum of 40 dB in bedrooms to avoid sleep disruption. A comprehensive survey involving 221 cadets demonstrates that 79.6% of participants experience sleep disturbances, work disruptions, and stress due to noise, indicating that the present noise standards are insufficient. This paper argues that reducing noise levels in individual cabins to below 40 dB is critical for enhancing seafarer health and safety and could significantly reduce human error-related maritime accidents. The findings advocate for more stringent noise control measures and regulatory reforms to bridge the knowledge gaps and improve labor protection in the maritime industry.

Evolutionary model and risk analysis of ship collision accidents based on complex networks and DEMATEL

To manage and control ship collision risk, this paper aims to develop a quantitative risk analysis approach based on complex networks and Decision-making Trial and Evaluation Laboratory (DEMATEL), to study the evolution of ship collision accidents. Firstly, through the search, selection and analysis of accident investigation reports of ship collisions in the world, 46 Risk Influential Factors (RIFs) were identified, and a network model for the evolution of ship collision was established. Secondly, a risk analysis of the evolution model for ship collision accidents was conducted by utilizing the topology characteristics of the complex network theory. Thirdly, a robustness analysis was used to identify 10 key RIFs involved in ship collisions from a global network perspective, and the grey relation analysis technique was employed to verify the key RIFs. Finally, the DEMATEL method was utilized to identify 11 key RIFs from a perspective of causality, and corresponding risk control measures were proposed to mitigate those key RIFs. The research results provide theoretical support for identifying the key RIFs and controlling the risk transmission of ship collision, and have practical significance for preventing the occurrence of ship collision accidents and ensuring the safe navigation of ships.

Risk Analysis of Work Accidents in the Construction Project of Class D Pratama Hospital Building Construction of Nias Regency

In the construction project of RSU D Pratama in Nias Regency, this study aims to identify variables that increase the risk of work accidents. The author of this study used quantitative methods. The aspects used by researchers to identify the risk of work accidents include Factors of Work Accidents, Factors Causing Disease, Factors Causing Work Accident Hazards in Development Construction Projects, and Protection Factors for the Application of Occupational Safety and Health. Based on the results of the study, almost all aspects of the Class D Primary Hospital building in Nias Regency have occupational accident risks that have an impact on Occupational Safety and Health (K3).

Work Accident Risk Analysis in Tukad Ayung Bridge Replacement Project

Analysis of the risk of work accidents in the Tukad Ayung Bridge replacement project needs to be carried out by identifying, assessing, and controlling risks using the HIRARC method. This study aims to conduct a work accident risk analysis in the Tukad Ayung Bridge replacement project, precisely in the implementation of concrete girder installation. The data used are qualitative and quantitative data with primary data sources and secondary data. The data collection techniques used are observation, interviews, questionnaires, literature studies, and documentation studies. The data analysis technique used is HIRARC. The results showed that; 1) There are 24 types of work accident risks identified in the concrete girder installation work in the Tukad Ayung bridge replacement project in seven stages of work, namely, the concrete girder preparation stage, girder truss making, erection (segmental girder setting), prestressed steel installation (strand), strand cable stressing work, girder concrete tendon grouting, and diaphragm installation stage, 2) The results of risk analysis on concrete girder installation work on the project The replacement of the Tukad Ayung Bridge, is in the moderate category, which is as many as 12 risks with a percentage of 50%. Furthermore, as many as 11 risks are included in the high category with a percentage of 45.837%. Meanwhile, in the extreme category as much as one risk with a percentage of 4.17%, 3) Risk control efforts that can be carried out from the three levels of risk found, namely moderate, high, and extreme, namely by reducing risk through administrative control, engineering control, and warning system.

Analysis of occupational accident risk in breakwater construction and floating piers

In every implementation of the project, work accidents can occur which result in injury or other losses. One of the projects that has the potential for work accidents is the floating dock construction work. The purpose of writing this paper is to find out the dominant hazard variable in floating piers and breakwater construction work, to know the causes and effects of the dominant hazard variable, and to know the right dominant risk control risk control in floating wharf construction work. This research begins with hazard identification using several sources and also direct discussions with resource persons who understand this work. Then a questionnaire was distributed to obtain the likelihood and severity values of the existing hazard variables. From the results of distributing the questionnaires, tests were carried out using validity and reliability tests on each hazard variable that might occur. The results of the tests carried out obtained a valid hazard variable and were then analysed using HIRA which was assisted by a risk matrix. So that the results of the analysis are known to be the dominant hazard variable, namely workers falling and sinking into the sea. Furthermore, an analysis of the two variables is carried out to determine the causes and also the impact of risks that can occur using a bow-tie diagram to reduce the occurrence of dominant potential hazards.

Analysis of the Risk of Work Accidents for Field Officers PT PLN West Tanjung Jabung Regency, Jambi Province using the Hiradc Method

The aim of this research is to analyze the risk of work accidents among PT field officers. PLN West Tanjung Jabung Regency, Jambi Province using the HIRADC method. This type of research is based on the philosophy of post-positivism and is carried out to study the state of natural objects (not experiments). There were 7 research informants including 1 key informant, namely the K3L Implementing Officer of PT. PLN Kuala Tungkal, 2 main informants, namely the Head of PT. PLN Kuala Tungkal, and PT Performance Analysis. PLN Kuala Tungkal, 4 supporting informants, namely PT field employees. PLN Kuala Tungkal. Based on the results of the research, conclusions related to the risk analysis of work accidents among PT field officers. PLN West Tanjung Jabung Regency found 11 potential dangers after evaluating the risk ranking of accidents in the implementation of electricity production work in the operations division. There are 15 types of risk (37.5%) classified as medium risk, 22 types of risk (55%) classified as high risk, and 3 types of risk (7.5%) classified as very high risk (extreme risk). , and After analyzing the classification of work procedures, there are several work procedures, namely inappropriate work procedures, not being disciplined in using PPE when working.

Coupling analysis of crane accident risks based on Bayesian network and the N-K model

Crane usage is pervasive on construction sites, however, it is associated with a notably high accident rate. The analyzing of crane accident risks is essential for accident prevention, control, and ensuring the safety of lifting operations. Hence, significant emphasis should be placed on understanding the interaction among various risk factors. This paper proposes a quantitative coupling method for human, machine, management, and environmental risk factors in crane accidents, leveraging Bayesian networks (BN) and the N-K model. Firstly, text mining technology and fault tree analysis are employed to analyze the causes of crane accidents and categorize the associate risk factors. Secondly, the types of risk coupling resulting from human, machine, management, and environmental risk factors are defined. Thirdly, the BN model is developed based on the analysis of crane accident risksand its N-K model. Fourthly, the parameters of the risk coupling nodes in the developed BN are determined based on the calculation results of the N-K model. Finally, for the risk coupling types with high coupling values and the first-level node and second-level node, the failure probability is analyzed through posterior probability and sensitivity analysis. The results indicate that factors related to man and management significantly impact crane accidents and warrant enhanced attention. The interplay among multiple risk factors significantly influences the probability of crane accidents, necessitating careful attention.

Risk Analysis of Oil Tanker Accidents in Southeast Asia by using Data Driven Bayesian Network

Risk Analysis of Oil Tanker Accidents in Southeast Asia by using Data Driven Bayesian Network

Модель мониторинга охраны опасных производственных объектов от лесных пожаров

The methodological analysis of the requirements for the prevention of forest fire onset and spread has demonstrated that at the design, development, and maintenance of forest sites, it is required to apply various proven methods enabling the assessment of risk of damage to hazardous production facilities protected from forest fires (upper and ground fires) via the selection of fire protection measures in forests, such as forest fire prevention, fire hazard and forest fire monitoring in forests, and forest fire extinguishing. The expert method, the system analysis methods, and the methods of management theory and decision-making were applied in order to solve the problems. The topicality of the study covered in the article is the analysis of fire safety requirements in forests and the development of informational and analytical support for the decision-making when evaluating the probability of the onset of forest fire and the risk of damage to the adjacent hazardous production facilities caused by a forest fire (upper fire, ground fire) in accordance with the calculation methodologies applied for specific situations, based on the expert method. The industrial safety management system and the industrial safety requirements must ensure the identification, analysis, and forecast of accident risk at hazardous production facilities, as well as the planning and implementation of fire safety measures and risk reduction. The scope of studies is industrial facilities, the Federal Forestry Agency, expert activities, activities of insurance companies, scientific activities, and individuals competent in forest fire safety (experts). To ensure the fire safety of hazardous production facilities, it is required to assess the risk of the onset and spread of a forest fire (upper fire, ground fire). In order to determine a safe allocation of forest stands and ensure the safe operation of firefighting subdivisions during forest fire extinguishing, it is required to apply the proposed expert method associated with the control of activities of the body responsible for the implementation of measures of planning, development and maintenance of forest stands at the exercise of official powers.

Evaluation of OHS practices based on work accident risk analysis using HIRA, FTA, and WISE methods (Case study: Plastic recycling business X)

The lack of OHS implementation is particularly notable in Small and Medium Industries (SMI), primarily due to limited knowledge and motivation concerning OHS application in their work systems. SME X, a plastic recycling business, has experienced several work accidents and necessitated hazard identification and OHS implementation for hazards with high and extreme risks. The HIRA method was employed to assess the extent of risk posed by various types of hazards. After identifying these hazards, the root causes of high and extreme-risk hazards were determined using FTA. Subsequently, measures were taken to mitigate the risks associated with high and extreme-risk hazards through WISE. The results revealed 28 potential hazards, with improvements focused on six high and extreme-risk categories. Proposed WISE interventions include the provision of Personal Protective Equipment (PPE), the use of ramps, the utilization of wheeled aids, and other enhancements to techniques and methods at each workstation.