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Au–Hg–Ag phases have been described from a variety of metallogenic orebodies and the placer deposits derived from them. In many documented placer deposits, the phases typically occur intergrown as 'secondary' rims to primary Au–Ag grains. The origin of these rims has been ascribed to supergene redistribution reactions during deposition or to the effects of amalgamation (i.e. use of mercury) during mining for gold. Difficulties in determining compositions and crystal structures on such a small scale have made full characterisation of these phases problematic. This paper describes a new occurrence of these phases, found by accident during investigation of a historical concentrate of 'osmiridium' containing a number of gold grains from beach sands at Waratah Bay, in southern Victoria, Australia. The phases occur as rims to gold grains and are intergrown on a scale of tens of micrometres or less. Application of electron microprobe analysis (EPMA) and limited electron back-scattered diffraction (EBSD) was required to characterise them. These techniques revealed the presence of the approved mineral weishanite (Au–Hg–Ag) and a phase with compositional range Au2Hg–Au3Hg surrounding primary Au–Ag (electrum) containing trace amounts of Hg. EBSD analysis showed weishanite is hexagonal P 63/ mmc and Au2Hg to be hexagonal P 63/ mcm. Comparison with published data from other localities (Philippines, British Columbia and New Zealand) suggests weishanite has a wide compositional field. Textures shown by these phases are difficult to interpret, as they might form by either supergene processes or by reaction with anthropogenic mercury used during mining. However, in the absence of any historical evidence for the use of mercury for gold mining at Waratah Bay, we consider the formation of the Au–Hg phases is most probably due to supergene alteration of primary Au–Ag alloy containing small amounts of Hg. In addition to revealing some of the reaction sequences in the development of these secondary Au–Hg–Ag rims, this paper illustrates methods by which these phases can be more fully characterised and thereby better correlated with the Au–Hg synthetic system. [ABSTRACT FROM AUTHOR]

Pipeline is the most common way for delivering natural gas to the urban areas. Nevertheless, pipeline leak accidents can pose significant threats to human life and property safety. This paper investigates the characteristics of urban natural gas pipeline leaks and the resulting gas dispersion in a semi-closed space using a 3D Computational Fluid Dynamics (CFD) model. The characteristics of a pipeline leak is examined considering the impact of shape, dimensions, and location of the aperture. The migration of natural gas in a semi-closed space is tracked, considering the impact of leak location and orientation. To illustrate the model, a real-life case study is conducted to simulate flammable gas release and dispersion resulting from an urban natural gas pipeline leak. The outcome can guide the monitoring and mitigating the consequences of urban natural gas pipeline leaks. [ABSTRACT FROM AUTHOR]

Safety is one of the most important factors taken into consideration in modern shipping industry. To reduce the number of accidents, it is crucial to deliver information about past disasters to maritime specialists. Conditions of work at sea demand to acquire the most knowledge in the shortest possible time. This paper evaluates various forms of presentation of such information in order to find how efficient they are in terms of data quality, usefulness and time required to comprehend them. Research was conducted among students of the Gdynia Maritime University. The students were presented with different material followed by a test. The best results were achieved with infographic and lecture presentation. Full reports were found too complex to be used on a daily basis despite their undeniable role in accident investigation. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles (CAVs) bring new risks and safety benefits to future road transport. This paper explores the impacts and consequences of CAVs on the organizational, governance and knowledge practices of vehicle accident investigation (VAI). A focus on these practices of VAI draws attention to its politics, a key aspect behind accident investigations that receives little attention. We do so through three lines of inquiry. First, we read the accident investigation and safety science literature to list the characteristics of a state-of-the-art ideal-typical VAI organization. Second, we draw from the literature to identify seven challenging characteristics from CAVs to existing VAI designs and practices: automation, mixed traffic, connectivity (including cyber security), data, regulatory gaps, safety culture and international connections. Lastly, we describe the current state of affairs of VAI in the United Kingdom, Japan and the United States, highlighting differences and commonalities. In the conclusion, we bring these lines of inquiry together, conclude that there is a need for further governance work on VAI and CAVs, and list several avenues of future study. [ABSTRACT FROM AUTHOR]

As an important consideration in the chemical industry, chemical process safety has received notable attention in China. However, catastrophic chemical accidents still occur. To better understand why accidents continue to occur, this paper presented a diagnostic analysis of 14 major chemical accidents in China from 2012 to 2022 based on VOSviewer software. The authors analysed the correlation between the accident causation and their relationship with the safety management elements. The study observed that inferior process safety culture, intentional violation (rule-breaking) of procedure, inadequate safety training, and illegal operations were the most frequent causes of accidents. These causes highlighted the prominent gaps in PSM in China in the process safety culture, compliance with standards, the conduct of operations, process safety competency, and training & performance assurance. The results based on co-occurrence analysis indicated a strong correlation between these gaps in PSM. Enterprises should pay attention to collaborative management among them. These deficiencies in the enterprise's PSM system showed that the essential defects in China's chemical industry are a poor safety culture, inadequate accident investigation, inadequate training, and a lack of chemical safety personnel. The study recommended that the chemical industry establish superior process safety culture and competency for all personnel, monitor leading and lagging process safety indicators, apply inherent safety, and practice advanced safety management concepts. We hope that the findings can provide China's perspectives and strengths for global chemical safety. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to evaluate the evolution of Accident Causation Models (ACMs) from the perspective of philosophy of science. I use insights from philosophy of science to provide an epistemological analysis of the ways in which engineering scientists judge the value of different types of ACMs and to offer normative reflection on these judgements. I review three widespread ACMs and clarify their epistemic value: sequential models, epidemiological models, and systemic models. I first consider how they produce and ensure safety ('usefulness') relative to each other. This is evaluated in terms of the ability of models to afford a larger set of relevant counterfactual inferences. I take relevant inferences to be ones that provide safety (re)design information or suggest countermeasures (safety-design-interventions). I argue that systemic models are superior at providing said safety information. They achieve this, in part, by representing non-linear causal relationships. The second issue is whether we should retire linear and epidemiological models. I argue negatively. If the goal is to assign blame, linear models are better candidates. The reason is that they can provide semantic simplicity. Similarly, epidemiological models are better suited for the goal of audience communication because they can provide cognitive salience. [ABSTRACT FROM AUTHOR]

Flammable gas leaked from pipelines is easy to cause explosion accident. The consequences of accident are amplified in strongly constrained environments near the pipelines. In this paper, a gas leakage and explosion accident in Beijing is analyzed using field investigation and relevant experimental analysis techniques, and the complete investigation process of accident cause based on physical evidence is displayed. Some theoretical analysis models are introduced to explore the causes of this type of accident from a system perspective. The laws of explosion damage of building structures in strongly constrained environments are also summarized. The results indicate that the gas leaked from the end of the hose was excited by an electric spark in the electrical box causing this explosion and a gas explosion with a TNT equivalent of 2.73–4.28 kg is sufficient to damage a large number of walls of civilian buildings. The pressure difference between the two sides of structures and the duration are decisive factors in the degree of damage. Cause analysis shows that human error needs to be avoided through education and supervision. The study can provide theoretical references for the investigation of explosion accidents, as well as improvement of energy management system. [ABSTRACT FROM AUTHOR]

The offshore petroleum industry faces continuous stress and threats of major accidents. A complete and well-designed spectrum of indicators could help identify and improve the understanding of the status of all relevant hazards for managing various risks on offshore installations. The Petroleum Safety Authority (PSA) in Norway develops a project of extended indicators, i.e., "Trends in risk level" (RNNP), which uses an assortment of risk indicators to reflect the status and trend of the risk level. However, a simple statistical approach is insufficient to see into the causes behind the risk indicators. This paper proposes an interpretable and augmented machine-learning approach for causation analysis of major accident indicators. The proposed methodology integrates the K-means SMOTE technique for data augmentation to promote the performance of machine-learning algorithms. Armed with the analytical approach, the problem of lack of sample data is solved and the importance of the features of risk indicators is analyzed. Furthermore, a case study is presented to augment and analyze the two types of risk indicators associated with major accidents recorded from 2005 to 2021. The effectiveness of the proposed approach is demonstrated by the case study. It can provide an effective tool to help find out which type of features deserves more attention in risk management and target risk reduction measures that could be taken to strengthen safety on offshore installations. [ABSTRACT FROM AUTHOR]

Anyone who owns a vehicle has certainly dealt with a dead battery at least several times in his/her life, however he/she might not know the hidden hazard of explosion of a battery. Therefore it is very important to know proper handling of battery to prevent a battery from exploding. This study is intended to inform people on proper battery handling, causes of an explosion, and how accident investigations can be conducted. To do this, this work will present information that informs people on causes, investigation, and corrective measures in battery use. The main methods used in this work are research, published literatures, and analysis that will aim to create awareness that is easy to understand. This paper will cover causes of battery explosions, techniques used in investigations, and preventative measures. The expected results of this paper are to find multiple reasons why a battery may explode, various techniques used in investigations and find ways that prevent a battery from exploding. [ABSTRACT FROM AUTHOR]

This paper used the data of automobile traffic accidents from 2018 to 2020 in the Chinese National Automobile Accident In-Depth Investigation System. The prediction features of traffic accident severity are innovated. Four accident features that did not participate in the importance ranking were added: accident location, accident form, road information, and collision speed. Eight accident features (engine capacity, hour of day, age of vehicle, month of year, day of week, age band of drivers, vehicle maneuver, and speed limit) have been used in previous studies. Random forest was used to rank the importance of 12 accident features, and 7 important accident features were finally adopted. By comparing the algorithms and optimizing the results, the prediction model of traffic accident degree with higher accuracy is finally obtained. [ABSTRACT FROM AUTHOR]

In the past few years, a number of incidents related to fire and explosions in temporary chemical storage facilities have occurred that have resulted in large numbers of casualties. This includes explosions at a warehouse near Tianjin Port, China, in 2015 and that at a warehouse facility storing ammonium nitrate at Beirut port, Lebanon, in 2020. Very recently, a similar incident occurred in a container depot in Bangladesh. On June 4, 2022, a massive fire broke out at BM Inland Container Depot, a temporary storage facility in the town of Sitakunda, Bangladesh. The fire and subsequent explosions resulted in at least 48 fatalities, including 10 firefighters, and injured more than 200 people. The fire, which took 86 h to completely extinguish, has resulted in financial losses of more than US$152 million according to authorities. In this paper, an investigative consequence analysis of the accident is presented with the goals to identify the possible reasons that culminated in the catastrophe, quantify the magnitude of the explosion, and explore the key lessons learned. This work also sheds light on the existing local legislation and international guidelines relating to the storage of hazardous materials. Furthermore, human and social consequences of an accident similar in magnitude have been assessed for two other inland container depots. This work, thus, may be considered a scientific exercise aimed at creating awareness of the gravity of accidents in temporary storage facilities and a lesson learned to prevent such catastrophes in the future. [ABSTRACT FROM AUTHOR]

This paper explores concerns regarding the design, implementation, and management of alarms in DP vessels that, while in operation, need an incredibly high level of accuracy along with high reliability and safe operations. The Human, Technological, and Organizational factors (HTO) method is primarily used as analysis tool to find weaknesses in alarm handling during DP operations. The research focuses on results collected from Dynamic Positioning Operators (DPO) and instructors. Findings from the survey are presented and compared to the results from past accidents and technical requirements from Petroleum Safety Agency Norway via YA 711. Three accidents from past are referred to picturize the findings from the survey results. Furthermore, the conclusion is given with recommendations reflecting the findings from the survey. The main findings are an urgency to establish a centralized marine accident investigation system which enforces learning and recommendation to make operations safer. In addition, the survey also suggests that prohibition of clients or limiting their access to the bridge is necessary. Manufacturers could focus on research and development of alarm prioritization, on structuring and presentation, and profiting by taking feedback from end-users to make DP operations safer. [ABSTRACT FROM AUTHOR]

This paper uses the concept of inherent simplicity stemming from the Theory of Constraints to explain whether safety at work is a complex or an exceedingly simple matter. In this context, the study seeks to explore the causalities that govern safety at work, identifying its constructs and presenting logic propositions based on the theory-building blocks: classification, correlation, and causal consistency. To support the research, a dataset composed of 46 work-related accident investigation reports from an elevator industry in Latin America was carefully analyzed using association rules. Moreover, direct observations grounded on inductive reasoning were used to speculate plausive causes concerning the effect of work-related accidents. The research strategy followed common strategies of theory building to reach common sense: theory-to-practice and practice-to-theory. As a result, a conceptual proposition is postulated based on the reasoning that safety at work is governed by very few constructs, and that its complexity is explained through the two elements from inherent simplicity: degrees of freedom (interdependencies between constructs) and harmony (conflicts resolution within the work environment). From the practitioners' perspective, the study also offers directions towards safety improvements at the organizational level by considering the impact of the interdependencies between constructs in safety at work. [ABSTRACT FROM AUTHOR]

A severe accidental fire with 'explosion' resulting from spraying colored corn flour powder occurred at a water fun park in Taiwan. The possible fire scenarios were studied in this paper using flame acceleration simulator (FLACS). Environmental conditions including wind action and solid boundary conditions were deduced based on government fire investigation and video records, and were used as the input parameters in simulation. Simulation results indicate that upon ignition of the sprayed powder, the maximum overpressure in the open space was only 0.03 bar gauge, with a dust flash fire generated without having an explosion. These environmental conditions or parameters and the simulation results together would give a fire scene that agrees with the accident observed, indicating that appropriate environmental parameters had been identified. Therefore, CFD simulation with carefully selected parameters can be used to reproduce explosion scenarios and to identify key factors in supporting accident investigations. [ABSTRACT FROM AUTHOR]

A rise in traffic accidents has led to both traffic congestion and subsequent secondary accidents. Effectively addressing this issue requires rapid accident investigation and management. In this paper, we aim to improve the efficiency of traffic accident assessment and investigation with the aid of drone technologies. Our approach involves strategically pre-positioning drones, enabling traffic supervisory agencies to dispatch drones immediately upon receiving an accident report. Methodology-wise, we present a data-driven robust stochastic optimization (RSO) model, which encapsulates the uncertainty of traffic accidents within a scenario-wise Wasserstein ambiguity set. To the best of our knowledge, this is the first study that incorporates covariates, i.e., weather conditions, into the Wasserstein ambiguity set with the CVaR metric. We demonstrate that the proposed RSO model can be reformulated into a mixed-integer programming model, allowing an efficient solution approach. Via a real-world dataset of London traffic accidents, we validate the practical applicability of the RSO model. Across various parameter settings, our RSO model exhibits superior out-of-sample performance compared with various benchmark models. The numerical results yield valuable insights for traffic supervisory agencies. • We propose a drone pre-positioning problem for traffic accident rapid assessment. • We propose an RSO model that incorporates the uncertain traffic accident. • We demonstrate that the RSO model admits a tractable MILP reformulation. • A real world instance is used to verify the practicality of our model. [ABSTRACT FROM AUTHOR]

This research was based on a rare case involving an explosion accident of liquefied natural gas (LNG) bus that occurred in the second week of December 2019 in China. The author of this paper participated in the whole accident investigation process as expert. Through the nonlinear elastoplastic analysis of the bursting tank by finite element simulation, this explosion accident was caused by the rapid phase transition of residual LNG during the deicing of the frozen pipeline by water, and the human factor was the direct cause of the accident. To understand the accident causation in depth, human factors involving safety issues in the LNG vehicle maintenance operation were identified using a modified human factors analysis and classification system and the analytic hierarchy process method. Furthermore, the lessons learned, and recommendations were revealed to prevent such accidents in the field of liquefied natural gas vehicle. [ABSTRACT FROM AUTHOR]

• Recent advances in experimental and numerical studies on the suppression of thermal runaway of Li-ion batteries are reviewed. • The research progress of battery management system with thermal control and energy control is summarized. • The latest research results on li-ion battery safety devices are classified and analyzed and recommendations are made. • Electrolyte additives that are commercially available and have research potential are summarized. • Analyzed the advantages and disadvantages of various security strategies,proposed development prospects and directions. With its high energy density and long lifespan, li-ion battery (LIB) is now dominating the power source market for portable electronics such as smartphones, laptops, and electric vehicles. However, LIB electrolyte is flammable, and the diaphragm has low stability, making it easy to cause thermal runaway (TR). This paper reviews the research progress on TR propagation characteristics and prevention strategies. The study reviewed the analysis of practical measures of battery management systems, safety devices, flame retardants, electrolyte additives, and thermal absorption materials. It also discusses accidents related to TR of LIBs to emphasise the significance of TR to the entire accident investigation and analysis process. This paper can provide useful information on how to avoid TR-related accidents. [ABSTRACT FROM AUTHOR]

• Solar industry is experiencing notable growth in response to renewable energy demand. • There is limited knowledge and data understanding the risks to solar installers. • 365 articles were selected via systematic literature review and screened to 31 papers. • Emerging areas are heat stress, manual handling, and musculoskeletal disorders. • Future ergonomic studies are vital to measure and mitigate risks in PV installations. The adoption of solar photovoltaic (PV) technology and infrastructure are increasing rapidly to meet the ever-growing global need for renewable energy sources. An obstacle to solar PV growth is the severity of the occupational safety risks associated with their installation. Although PV installers are known to experience some of the most significant and widespread construction-related occupational safety risks, PV installer accident investigation research, reporting, and verification are limited. To contribute to this literature gap, this paper conducts a systematic literature review to understand and present the occupational safety risks, mitigation measures, and current and potential safety research areas associated with PV installations. A systematic literature search for relevant articles was conducted from October 2021 to January 2022 using the Scopus, Web of Science, Science Direct, and PubMed databases identifying 365 articles by their title. After screening these articles with various selection criteria, 31 articles were selected as relevant to the research questions of this study. These selected articles identified electrical and fire risks, heat stress, manual handling risks, and fall risks as the major occupational safety risk categories associated with PV installations. This study can aid solar installation companies, occupational safety professionals, and policymakers in gaining a deeper understanding of the safety risks and mitigation measures associated with PV installations as they develop protocols and guidelines to safely support the solar workforce's development. [ABSTRACT FROM AUTHOR]

This paper presents a novel framework for system-theoretic risk management of maritime transportation systems (MTS) based on the principles of formal safety assessment. The proposed framework comprises four essential steps: (1) risk identification: identifying accident scenarios and associated risk-influencing factors in the MTS through text mining of maritime accident investigation reports; (2) risk analysis: determining the probabilities and consequences of accident scenarios in the MTS using Bayesian network modeling; (3) risk evaluation: assessing the risks of various accident scenarios and the comprehensive risk of the MTS, and comparing the results with risk acceptance criteria to make informed decisions; and (4) risk treatment: proposing effective risk control options (RCOs) for accident scenarios and the comprehensive risk of the MTS by incorporating expert judgment, fuzzy sets, and Bayesian network modeling. To demonstrate the applicability of the proposed framework, the risk management of the Yangtze River Estuary is presented as a case study. By adopting a comprehensive approach to risk management, stakeholders gain a more holistic view of risk and achieve more effective decision-making processes for RCOs. The framework may help improve the safety and security of maritime operations, enhance the efficiency and competitiveness of the industry. • A novel framework for system-theoretic risk management of MTS based on the principles of FSA. • The framework can be used for proposing effective RCOs for reducing the comprehensive risk of the MTS. • The risk management of the Yangtze River Estuary is presented as a case study for reducing the navigational risks. [ABSTRACT FROM AUTHOR]

This paper presents a novel framework for system-theoretic risk management of maritime transportation systems (MTS) based on the principles of formal safety assessment. The proposed framework comprises four essential steps: (1) risk identification: identifying accident scenarios and associated risk-influencing factors in the MTS through text mining of maritime accident investigation reports; (2) risk analysis: determining the probabilities and consequences of accident scenarios in the MTS using Bayesian network modeling; (3) risk evaluation: assessing the risks of various accident scenarios and the comprehensive risk of the MTS, and comparing the results with risk acceptance criteria to make informed decisions; and (4) risk treatment: proposing effective risk control options (RCOs) for accident scenarios and the comprehensive risk of the MTS by incorporating expert judgment, fuzzy sets, and Bayesian network modeling. To demonstrate the applicability of the proposed framework, the risk management of the Yangtze River Estuary is presented as a case study. By adopting a comprehensive approach to risk management, stakeholders gain a more holistic view of risk and achieve more effective decision-making processes for RCOs. The framework may help improve the safety and security of maritime operations, enhance the efficiency and competitiveness of the industry. • A novel framework for system-theoretic risk management of MTS based on the principles of FSA. • The framework can be used for proposing effective RCOs for reducing the comprehensive risk of the MTS. • The risk management of the Yangtze River Estuary is presented as a case study for reducing the navigational risks. [ABSTRACT FROM AUTHOR]

• Risk influencing factors and accident scenarios are investigated in ice-covered Arctic waters. • An object-oriented Bayesian network model (OOBN) is proposed for the quantitative risk assessment. • The proposed OOBN model decomposes navigational accidents based on accident causation theory. • The proposed OOBN mode quantifies multiple navigational accidents in ice-covered Arctic waters. Merchant ship operations in the ice-covered Arctic waters may encounter traditional navigational accident risks (i.e., grounding, collision, etc.) and risks from sea ice, such as ship besetting in ice. However, describing, modeling, and quantifying the multiple risks in ice navigation are challenges from maritime risk assessment perspective. This paper proposes an object-oriented Bayesian network (OOBN) model for the quantitative risk assessment of multiple navigational accidents in ice-covered Arctic waters. The OOBN model makes use of the accident database from Lloyd's intelligence and maritime accident investigation reports. The proposed model decomposes navigational accidents into five levels based on accident causation theory: environment, unsafe condition, unsafe act, probability of navigational accident, and consequence of the navigational accident. Consequently, collision, grounding, ship besetting in ice, and ship–ice collision accidents are selected as the cases to interpret the quantitative risk assessment for navigational risk factors identification, risk analysis, and evaluation. The results demonstrate that (1) the risk is the highest in grounding accidents, followed by besetting in ice, collision, and ship–ice collision in ice-covered Arctic waters; (2) unsafe speed and unsafe condition are the critical mutual factors of these four accident scenarios; (3) and the critical risk influencing factors for the specific navigational accidents are identified to propose corresponding risk control options. The proposed OOBN model can be used for quantitative risk assessment of navigational accidents in ice-covered Arctic waters. [ABSTRACT FROM AUTHOR]

The complex and variable operation environment poses a challenge to risk control of electrical work. Many existing methods have difficulty in dynamically controlling the risk of time-varying causes using accident investigation reports. This paper proposes a dynamic risk control method for electrical personal accidents, and evaluates the risk control measures. According to the occurrence time, accident reports are rolling divided into training sets and test sets, and two accident causation networks are constructed respectively. Next, a set-based index is proposed to measure the similarity between the training ranking and test ranking. Finally, two dynamic risk control strategies, without and with cause constraint, are proposed respectively, and quantitative risk analysis is given. The former calculates the cause control compliance rate under an expected risk transmission decline rate, and the latter calculates the accident prevention rate under a maximum cause control rate. Experiments on 155 electrical personal accidents verify the effectiveness of the proposed method, and discuss the influence of network update delay. • A dynamic risk control method for electrical personal accidents is proposed. • A set-based index is proposed to measure the ranking similarity. • Two dynamic risk control strategies are proposed. [ABSTRACT FROM AUTHOR]

Large open-plan compartment fires in modern buildings may exhibit a local burning region travelling across the floor plan as a 'travelling fire'. This phenomenon has been found in the forensic investigations of fire accidents and in the large compartment fire tests. The fire impact in a large compartment is spatially non-uniform and time-variant, which can cause severe local damage to structural components. Advanced from the previous models assuming constant travelling, the natural fire model established in this paper comprises time-variant and test-based travelling behaviour models and localised fire models of various modes. It is demonstrated with the fast-spread Veselí fire test and the slow-spread Malveira fire test. A generic structural model is set up within OpenSees for fire to examine the thermal impact on structural members under various travelling fire scenarios of different travelling parameters, fire travelling directions, and beam sizes. Locally much higher thermal responses are represented after introducing behaviour models while adopting the same design fire load. Based on the work in this paper, a library of design fire models can be potentially enabled to examine the fire safety performance of structures regarding the realistic fire load and fire impact aiming for discovering unknown worse fire scenarios. • Time-variant travelling behaviour models are proposed for large compartment fire scenarios with transitions of fire modes. • The travelling behaviour models are demonstrated with a fast-spread fire and a slow-spread fire. • The thermal responses of the steel beam are dominated by the "stay time" and intensity of the near field. • By introducing travelling behaviour models, design fire scenarios of locally high fire impact can be found. [ABSTRACT FROM AUTHOR]

• A semi-automatic model is developed for hazard and accident cause identification. • We explore and analyze the semantic relationship between the hazards of marine accidents. • The importance of both hazards and accident causes is calculated and ranked. • We gain accident causes distribution in 40 different accident scenarios by using topic model. In the past decades, marine accidents brought the serious loss of life and property and environmental contamination. With the accumulation of marine accident data, especially accident investigation reports, compared with subjective reasoning based on expert experience, data-driven methods for analysis and accident prevention are more comprehensive and objective. This paper aims to develop a content-aware corpus-based model for the analysis of marine accidents to mine the accident semantic features. The general research framework is established to combine accident data, expert prior knowledge, and semi-automated natural language processing (NLP) technology. The NLP models are optimized, fused, and applied to the case study of ship collision accidents. The results show that the proposed model can accurately and quickly extract hazards, accident causes, and scenarios from the accident reports, and perform semantic analysis for the latent relationships between them to extend the accident causation theory. This study can provide a powerful and innovative analysis tool for marine accidents for maritime traffic safety management departments and relevant research institutions. [ABSTRACT FROM AUTHOR]

• A detailed review and analysis of techniques for HOFs analysis in maritime transportation during 2000–2020. • Developed HOFs analysis methods are analysed and compared in accident investigation, risk analysis and emergency situations. • Study also reviewed potential challenges for HOFs analysis in maritime transportation. • Study provides deep insight into use of alternative techniques for HOFs analysis. This paper summarises the advanced techniques adopted for the analysis of human and organizational factors, which are the predominant factors in maritime accidents, and the various attempts that have been made to reduce human errors by identifying the existing challenges. Advanced techniques for human and organizational factor modelling, including human error identification in accident investigation, human error probability quantification in risk analysis, and human and organizational factor analysis for emergency situations, are comprehensively analysed and discussed. The most widely used modelling technique for human error identification is the Human Factors Analysis and Classification System (HFACS), and preconditions and unsafe acts exert the most important impacts on maritime accidents in previous studies. Moreover, Cognitive Reliability Error Analysis (CREAM) is the most widely used technique for human error probability quantification, and fuzzy, evidential reasoning and Bayesian networks are often incorporated for common performance condition (CPC) quantification and synthesis processes. In the future, other techniques should be introduced and developed for modelling HOFs for maritime transportation. Moreover, the challenges for human and organizational factors, including data collection, individual factors, and autonomous shipping, are identified for future studies. Consequently, this paper provides insight into human and organizational factors for maritime transportation, including quantification modelling, solutions to data collection and future research directions. [ABSTRACT FROM AUTHOR]

Since 1927 more than 242 formal inquiries and reviews into Australian natural disasters, and 62 international post-incident investigations following firefighter fatalities or injuries during wildfire entrapment and burnover have been completed. Despite the significant number of completed inquires, evidence suggests emergency services continue to repeat mistakes of the past when preparing for, preventing, responding to, and recovering from disasters. Where ongoing mistakes occur, the interventions themselves (including analysis methods) must be questioned as the significant cost of repeated inquiries may divert funding from improving both the capabilities and capacities of the emergency services being investigated. The objective of this paper is to work towards addressing limitations of investigative processes within the context of Australian fire and emergency services. It does this by building upon the Incident Causation Analysis Model (ICAM), and proposes the resultant PESTLE Incident Analysis Model (PIAM). The PIAM is designed to enhance analysis of adverse incidents, and to assist emergency services both communicate and implement recommendations from post-incident investigations and analysis. The PIAM is validated using a historical firefighter entrapment. In doing so this paper demonstrates the suitability of the PIAM technique for both emergency service and more traditional occupational safety investigations, making the critical connection between research and practice that is essential within emergency services worldwide. [ABSTRACT FROM AUTHOR]

Global shipping flows through strategically important restricted waters such as Panama and Suez Canals, and hence accidents occurring in the Canals will cause serious disruptions to global supply chains. In this paper, a new data-driven Bayesian network (BN) based risk model is developed to investigate how risk factors jointly generate impact on different types of maritime accidents within restricted waters. A new risk database involving 25 factors has been developed by manual analysis of all the recorded accidents from 2005 to 2021 that occurred in the world's important restricted waters including key maritime canals, channels and straits. A data-driven BN model is constructed to analyse the key risk influential factors (RIFs) contributing to such accidents. The model is verified by sensitivity analysis and real accident cases. Further, it is tested by the already known information of the 2021 Suez Canal blockage case to generate useful insights and draw the lessons to learn. In a retrospective analysis using the currently available limited information on the Suez Canal case, the implication of the case study shows a plausible explanation for the observed findings by scenario analysis. The findings can be applied to backward risk cause diagnosis for accident investigation and forward risk prediction for accident prevention in restricted waters to avoid the reoccurrence of similar accident to the Suze Canal blockage. • A new data-driven risk model enables the systematic analysis of the risk factors of maritime accidents in restricted waters. • Analysis of the dependencies between RIFs for maritime accidents in restricted waters. • Forward risk prediction for accident analysis and prevention. • Backward risk diagnosis for root cause analysis of maritime accidents. • Analysis of the Suez Canal blockage case study to provide insights for accident investigation and prevention. [ABSTRACT FROM AUTHOR]

• A framework is proposed for the quantitative causation analysis of grounding accidents in Arctic waters. • Potential risk factors are identified for grounding accidents in Arctic shipping. • The interdependency of the risk factors is reflected using the AcciMap model. • The critical risk factors are identified for quantitative analysis using the BN model. • RCOs are proposed for decreasing the risk of ship grounding in Arctic shipping. The melting of Arctic Sea ice has significantly facilitated Arctic shipping. However, such increased shipping has brought about higher maritime accidents in Arctic waters, especially for grounding and fire/explosion accidents. The paper presents a framework for quantitative analysis of the causation of grounding accidents in Arctic shipping by developing an accident map (AcciMap) - Bayesian network (BN) model. First, the potential risk factors for grounding accidents in the Arctic shipping were identified according to 322 maritime accident investigation reports (MAIRs) - 299 global MAIRs of grounding accidents (including 5 in Arctic waters) and 23 MAIRs (except grounding accidents) in Arctic waters and related literature. Consequently, an AcciMap model is developed for describing the evolution of grounding accident scenarios and reflecting the interdependency of the identified risk factors. Then, a probabilistic model is proposed to evaluate the probability and severity of the grounding accident for presenting a convincing justification for risk control options (RCOs). The framework is applied for the quantitative analysis of a cruise ship grounding accident in Arctic waters. Results demonstrate (1) improved understanding of cruise ship grounding risk factors related to government supervision, shipping company management, technical and operational management, unsafe incidents and behaviors, and environmental conditions; (2) quantitative analysis of the evolution of grounding accident and better identification of the critical risk factors; (3) determination of RCOs for risk management in Arctic shipping. [ABSTRACT FROM AUTHOR]

In March 2011, severe nuclear accident happened at the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) after the gigantic earthquake and following huge tsunami wave. A lot of investigations to assess environmental and radiological impacts of released radionuclides have been conducted by domestic and international organizations. Environmental radioactivity research related to the FDNPP accident has spread widely over different scientific fields due to specific features of the accident, and specifically its impact on the marine environment. The present paper summarizes major lessons learned from the environmental investigations of the FDNPP accident. Environmental radioactivity studies have typical interdisciplinary character; especially physics and chemistry are fundamental as a base of process studies in the environment. In this sight, we review chemical aspects regarding FDNPP-derived radiocesium transfer within and between compartments (atmosphere, ocean and land). We also discuss future trends in investigations of behavior of anthropogenic radionuclides in the environment, important not only for a better understanding of impacts of the FDNPP accident on the environment, but also for improving our general knowledge of the total environment in the Anthropocene era and its protection for the future. • 10 years after the FDNPP accident, the environment has been contaminated mainly by Cs-137. • Understanding of Cs-transfer within and between compartments has considerably improved. • Chemical processes are a key factor of Cs-transfer between aquatic and solid phases. • Migration of Cs-137 in terrestrial and marine ecosystems will dominate in future studies. [ABSTRACT FROM AUTHOR]

• The INRNE perform investigation of core degradation progression during severe accident. • The INRNE simulate two calculations of M B L O CA scenario with different break locations using ASTECv2.1.1.6 computer code; • The INRNE simulate a MBLOCA scenario with investigation of core degradation progression with MBLOCA accident at cold and hot legs; The presented paper discuses a study of core degradation behavior where a Medium Break Loss of Coolant Accident (MB LOCA) with a150 mm internal diameter along with station blackout (SBO) was the initiating event. One of the goals of this work was the examination of core degradation at different primary side break locations. Two simulations of MB-LOCA scenarios were performed. The first analysis was performed with the break located in the hot leg and the other with the break in the cold leg. The purpose of the selected study was to investigate plant response with breaks of similar sizes located in different positions in the reactor. This study was focused on in-vessel phase phenomena, such as: core uncovery, core heat up, the beginning of hydrogen generation based on the steam zirconium reaction and the oxidation of other core materials, core degradation, fuel cladding failure, melting of core materials with the formation of a molten pool in the reactor core, relocation of core materials to the bottom of the reactor vessel, and the formation of a molten pool containing corium in the lower head with further vessel failure. The investigation demonstrates that the scenario with a break in the hot leg leads to faster HAs depletion and faster core degradation during first part of the accident with delays in the accidents progression during the later stages of the accident resulting in a later failure of the reactor vessel head due to better core cooling when the results are compared to the evaluation of the accident with a cold leg break. The investigation of the severe accident sequence induced by a MB-LOCA was performed using the computer code ASTEC (Accident Source Term Evaluation Code) v2.1.1.6. The referenced nuclear power plant considered for this analysis is a water-water energetic (WWER)-1000 reactor. The results of the paper could be used for improving severe accident management guidlines (SAMG) as well as for level 2 probabilistic safety analysis (PSA). [ABSTRACT FROM AUTHOR]

[Display omitted] • A real-scenario-oriented research on an overcharge-caused thermal runaway accident. • Multi-factor modeling theory is demonstrated based on electrothermal coupling analysis. • Multi-stage pre-accident characterization are analyzed referring to accident investigations. • A variable evaluation for the state of health degradation under overcharge is implemented. • A rudimentary probability model of overcharge-caused thermal runaway is proposed. Overcharge is one of the most typical triggers leading to battery thermal runaway during real-world vehicular operation. An in-depth exploration of the real-scenario-oriented failure mechanism of battery overcharge is critical to safeguard the safe and durable operation of electric vehicles. With environmental changes and drivers' random behaviors under real occasions considered, this paper demonstrates the multi-factor overcharge-to-thermal-runaway modelling theory based on electro-chemical thermal coupling analysis. A case study of a real-world electric vehicle that experienced an overcharge-caused thermal runaway accident is presented, and the pre-accident battery overcharge characteristics are dissected referring to the accident investigation reports. Furthermore, the multi-stage pre-accident characterization is performed by dividing overcharge into three feature stages, such as charge, fully-charged instant, and overcharge. Various characterization parameters directly measurable and indirectly calculated are extracted, then a variable evaluation of the state of health degradation under overcharge is implemented. Moreover, a rudimentary probability model of overcharge-caused thermal runaway is proposed combining all potential motivating factors, including environments and drivers' behaviors. This is the first of this kind to apply the actual accident case on overcharge-caused thermal runaway research, which can guide the battery overcharge protection to safeguard drivers and passengers. [ABSTRACT FROM AUTHOR]

In order to manage the performance of socio-technical systems in a safe and sustainable way, the importance of looking at human and organisational factors (HOF) and their contribution to adverse events is widely recognised. In reality, however, the scope of accident and incident investigations stays usually limited to investigating the immediate causes and decision-making processes related to the accident sequence (e.g. Antonsen, 2009). Important factors, including design and planning decisions, contributing to accidents are hereby often overlooked and the weaknesses in the Safety Management System are hardly ever analysed. The SAfety FRactal ANalysis (SAFRAN) method (Accou and Reniers, 2019) can guide investigators in an intuitive and logic way, to ask questions that help to gain deeper understanding of the capability of organisations to monitor and manage safety critical variability. The essence of using the SAFRAN method for evaluating the performance of the different processes in a socio-technical system, is to approach them in a similar way, building on the generic elements that compose a SMS and systematically looking at the HOF that influenced actions and decision making, regardless of the hierarchical level they are situated at. This paper presents the SAFRAN method, specifying its HOF taxonomy and sharing examples of supporting HOF questions. The approach enables non-experts in HOF to systematically identify the different elements that introduce critical variability in performance and to recognise what additional expertise can be called upon when needed. [ABSTRACT FROM AUTHOR]

• VR and MR are successful in operationalising authenticity and groupwork factors. • The perceived guidance factor influences the perceived effectiveness of the lesson. • The motivation of the students is influenced by the perceived authenticity and groupwork factors. • The study derived three proposed guidelines for using VR and MR in safety management education. Effective safety management education is essential for improving safety within the architecture, engineering, and construction (AEC) industry. While new information technologies such as Virtual Reality (VR) and Mixed Reality (MR) are increasingly explored to improve safety management education and training, they are currently limited by the lack of pedagogies to guide the design and development of learning content. This paper aims to address this issue by utilising the authentic learning framework to guide the design of a VR/MR lesson for safety management education. The study developed an immersive VR/MR simulation-based activity based on the authentic learning framework. The effectiveness of the safety management lesson using an immersive VR/MR simulation is compared to a recorded video lesson. The results showed that the VR/MR simulation is more beneficial to the participants' motivation than the video lesson, but not for knowledge improvement and perceived lesson effectiveness. The results can be attributed to the difficulties in operationalising the guidance factor in the authentic learning framework. The key contributions of this study are threefold: (1) the study demonstrates that the authentic learning framework can be used for guiding the development of VR/MR simulation and recorded video lessons for safety management education; (2) the analysis finds that the VR/MR simulation improves motivation for learning, but not knowledge and perceived effectiveness of the lesson; and (3) three guidelines are suggested to improve the design of VR/MR educational applications. [ABSTRACT FROM AUTHOR]

• A methodology and taxonomy for FPSO accident analysis are proposed. • The contribution of human and organizational factors to the accidents is addressed. • The chain of accidental events and their root causes are considered. • The application of the proposed taxonomy is demonstrated. This paper proposes a taxonomy, named CASFOP (Casualty Analysis Methodology for FPSO Operations), for the analysis and codification of accidents in Floating Production Storage and Offloading units. The taxonomy incorporates the contribution of human and organizational factors to the accidental events, derived from the CASMET (Casualty Analysis Methodology for Maritime Operations) methodology that has been developed for accidents of conventional ships. The objective of the taxonomy for FPSO accident analysis is to integrate various causal factors (or elements of codification) related to the FPSO system and to formulate an event-orientated common framework for the representation of the chain of accidental events that lead to the accident and their root causes. The application of the suggested methodology is demonstrated with the analysis of the Cidade de São Mateus FPSO accident. The events involved and the underlying factors are coded according to the proposed CASFOP taxonomy. The application of the present taxonomy is also tested against a well-known human factor analysis taxonomy and the relevance of both taxonomies is discussed. The proposed taxonomy has revealed a greater number of refined human factors and root causes responsible for the accidents. It is also found to better capture the various dimensions of events from event initiation to the consequence of the accident. This approach can be used as an efficient tool for accident analysis, safety assessment and improving the reporting system in FPSOs and other floating installations such as Tension-leg Platforms and Semi-submersibles and Jack-up units. [ABSTRACT FROM AUTHOR]

• Adaptive non-conform behaviour cuts across all levels of Heavy Goods transport. • Accident investigation of transport systems seldom recognize nonconform behaviour. • Meta-analysis of accident and crash investigations should be commonplace. This paper presents findings from a study of 29 accident investigations in the heavy goods transportation sector in Norway. The investigations are performed by the Norwegian Safety Investigation Authority (NSIA). The NSIA has extensive multi-modal experience with accident investigation studying the holistic systems involved. Their approach is non-blame, in which the major goal is to provide information that triggers the involved actors' learning. It is reasonable to assume that the NSIA will challenge system characteristics at the sharp end, close to the accident site, as well as characteristics towards the blunt end involving planning, managing and regulatory enterprises. The term adaptive non-conform behaviour cuts across all levels in the system and covers the outright violation of safety-related rules and regulations and activities that deviate from established good praxis. Non-conform behaviour can include strategic adaptations to external and internal socioeconomic pressures. Actors in the industry claim non-conform behaviour is a prominent characteristic of the day-to-day activities. Non-conformity with safety-related rules and regulations is currently common in the transport industry as a reaction to increased international competition. It can be regarded as an adaption to the local business environment. Thus, we subsequently claim that the NSIA investigations should reflect this context in order to reveal important risk influencing factors. NSIA rarely reflects non-conform behaviour in investigations. When non-conform behaviour is touched upon, it is not described as adaptations and established praxis in the industry. Adaptive non-conform behaviour seems not to be a systemic problem based on the aggregated analysis of the NSIA-investigations. [ABSTRACT FROM AUTHOR]

Tire's yaw marks are characterized by oblique striations which contain important information about the braking and steering maneuvers performed by drivers in road accidents. This paper introduces an original method for realistic numerical and graphic simulation of yaw marks striations (scuffs) for the purpose of accidents reconstruction and analysis. The introduced method includes authors' implementation of an existing 3D vehicle dynamics model using a non-rigid wheel/ground interaction model. Results are compared to commercial accident reconstruction software concerning vehicle dynamics implementation, and then examples of lateral sliding situations are discussed to illustrate the application of the proposed yaw marks striations model in road accidents reconstruction. [ABSTRACT FROM AUTHOR]

Due to the unobserved heterogeneity inherent in the severity data of maritime accidents, traditional methods used to investigate the severity of maritime accidents always result in hiding some underlying relationships. This paper proposes a methodology for analysing the factors affecting the severity of maritime accidents from an emergency response perspective. A two-step clustering method is first used to classify maritime accidents into two homogeneous clusters. Subsequently, multinomial ordered logit models are developed for each cluster to analyse the unobserved heterogeneity that affects the severity of the three accident consequences: ship damage, casualties, and environmental damage. Analyses are performed based on data collected from China's Maritime Safety Administration reports on maritime accidents that occurred in sea lanes of the country's maritime transportation system. The model estimation results indicate that a wide spectrum of factors affect the severity of accident consequences, including natural environmental factors, ship characteristics, accident characteristics and rescue conditions. However, the factors have different effects on the severity of accident consequences. Furthermore, variations in estimated coefficients across clusters reveal unobserved heterogeneity. The proposed methodology can effectively explore the factors affecting the severity of maritime accidents, and the results provide a decision-making strategy in maritime emergency rescue measures. • Develop a cluster-based multinomial ordered logit model to analyse the severity of maritime accidents. • Identify the factors affecting the severity of the accident from the perspective of emergency rescue. • Consider the unobserved heterogeneity of maritime accident data. • Provide an analysis tool for maritime emergency rescue decision-making. [ABSTRACT FROM AUTHOR]