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The paper presented introduces an innovative accident prevention system that employs deep learning models and sensors to identify and alert drivers who lose concentration while driving for a variety of reasons. The system targets drowsiness, cell phone use, and alcohol consumption, which are among the leading causes of automobile accidents. The proposed solution employs neural networks to identify specific patterns associated with driver inattention and generates an audible alert to refocus the driver's attention on the road. In addition, the system utilizes alcohol-detection sensors, as alcohol consumption is another significant cause of accidents. This system seeks to reduce the number of casualties by enhancing road safety considering the high number of daily collisions, which are primarily caused by speeding. The paper describes the design, architecture, and implementation of the system on a modest scale to provide a lifesaving, cost-effective solution. [ABSTRACT FROM AUTHOR]

This study addresses challenges associated with hydrogen's physio-chemical characteristics and the need for safety and public acceptance as a precursor to the emerging hydrogen economy. It highlights the gap in existing literature regarding lessons learned from events in the green hydrogen production value chain. The study aims to use the documented lessons learned from previous hydrogen-related events to assist in enhancing safety measures and to guide stakeholders on how to avoid and mitigate future hydrogen-related events. Given the potential catastrophic consequences, robust safety systems are essential for hydrogen economy development. The work underscores the importance of human and operational factors as root causes of these events. The paper recommends establishing a specialized hydrogen-related event database to support risk assessment and risk mitigation, thus catering to the growing hydrogen industry's needs, and facilitating quick access to critical information for stakeholders in the private and public sectors. • 82 hydrogen-only events were identified from available databases. • From the identified hydrogen events, lessons learned are defined and recommendations provided. • Cause identification of events that could result in damaging consequences. • Maintenance operations, poor process management and human error are major causes of events. • Main value chain components related to hydrogen events are identified. [ABSTRACT FROM AUTHOR]

Confined space work is an important activity in various industries and a significant cause of workplace fatalities worldwide. Accidents and fatalities persist despite international standards, regulations, and efforts to improve safety in confined spaces. To address this issue, it is crucial to understand and disseminate knowledge on accident prevention strategies for confined spaces, to raise awareness among safety practitioners, and to harmonize communication between designers and end-users to anticipate OHS risks throughout a structure's lifecycle. Besides, confined spaces are unique and context-dependent, which affects the flexibility of their design and modifications. This paper introduces a Confined Space Permanent Collective Protection (CSPCP) knowledge base as a set of design-oriented principles and sub-principles that can be integrated into confined spaces to protect workers. It incorporates data from legislative and normative safety management frameworks, Inherently Safer Design (ISD) principles, literature-based solutions, expert knowledge, and contextual guidance on 112 CSPCP solutions based on the analysis of 19 case studies. This guidance is finally presented as a relational database, considering input from experts and client-side designers to understand the advantages and limitations of CSPCP for retrofitting confined spaces or designing and manufacturing new ones. Moreover, it provides owners, designers, and safety specialists access through flexible queries to help them balance safety, profitability, and investment. • The article outlines principles for designing new or retrofit confined spaces to minimize occupational risks. • Context of use determines flexibility in mitigating confined space risks and preventing fatalities. • An ISD knowledge base is introduced based on the contextual guidance of 19 case studies. • Applying effective ISD solutions in confined spaces is hindered by constraints in retrofitting existing structures. • Balancing risk reduction efficacity and feasibility, particularly in retrofitting settings, drives measures' adoption. [ABSTRACT FROM AUTHOR]

Hydrogen is becoming more popular as a fuel for vehicles. It is stored and dispensed at hydrogen refueling stations. Once the hydrogen in hydrogen refueling stations leaks, it easily forms a combustible cloud, and can explode by encountering a spark. It is therefore important for the safe and stable operation of hydrogen refueling stations to analyze the evolution of a leakage and explosion accident, clarify the causes and processes of the accident, and prevent the spread of risks. This paper proposes a model using multi-level variable weight fuzzy Petri net. On the basis of hierarchical consideration of the development of the accident, it adds a variable weight factor, which can quantify information in the development of the accident. According to the calculated results, the evolutionary path of risk and the most likely initial cause of the accident are deduced. Finally, taking the leakage and explosion accident of an urban hydrogen refueling station as an example, the usability and effectiveness of the model are verified. • This paper focus on the evolution and propagation path of potential accident at hydrogen refueling stations. • A multi-level variable weight fuzzy Petri net (MVWFPN) model is proposed to analyze dynamic risk analysis. • This method could vary the risk value according to real-time circumstances based on the hierarchy and weight adjustment. • This method puts forward targeted countermeasures for accident prevention at hydrogen refueling stations. [ABSTRACT FROM AUTHOR]

In a fire environment, liquid storage tanks are prone to integral cracking and eventually boiling liquid expanding vapor explosion (BLEVE), which a fireball, generates overpressure and ejected fragments with great impact on the safety of the tank area. Hence, it is important to reveal the influencing factors of the liquid storage tank failure process and the conditions that trigger integral cracking failure for the flammable liquid tank explosion accident prevention. This paper is aimed at the failure problem of metal storage tanks exposed to high temperature for a long time, through the fire experiment of small liquid tanks, taking the filling rate of tanks itself and the fire engulfment area as variables, the failure mechanism of tanks fire environment was explored from the aspects of pressure response, temperature response, failure form and tank deformation characteristics. A criterion was proposed to determine whether BLEVE will occur, which is based on the comprehensive analysis of the influence of tank internal pressure and high temperature damage of the material. [ABSTRACT FROM AUTHOR]

• Considering the entire confined space's life cycle in design is still very marginal. • Retrofitting structures usually cost more in the long term than implementing ISD earlier. • The notion of declassifying a confined space can give a false sense of safety. • Declassification was categorized into total elimination, hazard-oriented , and organizational.. • A risk reduction model involving ISD, declassification, and residual risks management is proposed. This paper aims to present solutions based on inherently safer design (ISD) principles to reduce the risks related to interventions in confined spaces and discuss their general applicability in a design situation or installations retrofit by exploring the client-designer relationship. A literature review on ISD solutions for confined spaces was followed by semi-structured interviews with 15 experts on confined space risk management in Quebec, Canada. The solutions based on the literature were confronted with experts' knowledge on the real-life conditions in confined space interventions. Our findings suggest that considering the entire confined space's life cycle in design is still very marginal (e.g., questioning the necessity to enter). As designers tend to replicate non intrinsically safe designs (e.g., inadequate entrances dimensions), end-users will then require retrofitting the structures over time, which usually costs more in the long term than implementing ISD solutions earlier. The notion of declassification of the confined space to eliminate confined spaces can give a false sense of safety. Its contribution to accident prevention depends on how end-users reduce inherent or task-induced risks as low as reasonably practicable. This paper categorized declassification into three types: total elimination, hazard-oriented declassification, and organizational declassification. Since the implementation guidelines of these risk reduction strategies are absent in the literature, this paper provides a risk reduction model that shows how end-users can identify application phases, declassify a confined space, and manage residual risks at the design phase in real-life environments. [ABSTRACT FROM AUTHOR]

• We introduce eight years' worth of New Zealand forestry incident data. • We analyse forestry incidents from the perspective of worker-failure and fatigue. • 70% of incidents in this data are attributed, at least in part, to worker-failures. • 78% of worker-failure based causes show indications of fatigue. Reports of incidents in dangerous work environments can be analysed to identify common hazards, in turn aiding in the prevention of future accidents. Whilst studies exist that do this, most focus on causes that involve physical risks. In this paper we propose an alternative approach, and illustrate causes of forestry incidents from the perspective of worker-failure and fatigue. This paper outlines the analysis of eight years' worth of New Zealand forestry incident data, with a focus on the cause of, and time that, incidents occur. This has resulted in two main findings. First, 70% of incidents can be attributed, at least in part, to worker-failures. Second, 78% of worker-failure based causes show indications of fatigue. This indicates that a significant number of forestry incidents are caused by worker-fatigue. Finally, this dataset showed inconsistencies in data quality, similar to those that exist in other datasets. This did not affect our analysis. However, these types of errors have the potential to affect the data quality in the national reporting system. The results from this study will be used in a larger project on detecting fatigue in forestry workers for injury and incident prevention. It is also our hope that other researchers may find these results of interest for further fatigue prevention research in hazardous industries. [ABSTRACT FROM AUTHOR]

• Syncretic K-shell is developed to identify the node importance of urban rail transit network. • The invulnerability of urban rail transit network is evaluated. • Cumulative core value is proposed considering both global and local centrality of nodes. • Syncretic core value is the node importance identification criterion of the syncretic K-shell. • Relative size of maximal connected subgraph and relative network efficiency are applied to evaluate the invulnerability performance. In this paper, the Syncretic K-shell algorithm is developed and applied to identify the node importance and evaluate the invulnerability in an urban rail transit network, which is based on the idea that the nodes removed later are more important than those removed former, the nodes with the same core value also have different importance. It has a very important strategic significance for optimizing network structure, improving operational efficiency, and providing targeted protection and designated accident prevention measures for stations with different importance. Syncretic Core Value, as the node importance recognition criterion of Syncretic K-shell algorithm, considers the influence of node degree, cumulative core value and syncretic weight of adjacent nodes on node importance. Firstly, we use space L to establish the indirect and non-weighted urban rail transit network. Then, the node importance recognition steps of Syncretic K-shell algorithm are introduced. Then, a numerical study of the proposed Syncretic K-shell algorithm is conducted, and the Degree Centrality, Betweenness Centrality, Closeness Centrality, Mixed Degree Decomposition and Classical K-shell are used as comparison approaches. The node anti-destruction under node failure (including intentional attack and random attack) is evaluated, and finally, an example study is conducted with the background of Chengdu Metro Network. The results show that the important nodes identified by Syncretic K-shell have a greater impact on anti-destruction performance. The research found that Chengdu Metro Network is more vulnerable to attack and easy to be destroyed under intentional attack. Finally, two coping strategies are provided. According to the research results, we can conclude that compared with the previous node importance recognition methods, Syncretic K-shell algorithm has five advantages: (i) it can distinguish the importance between nodes with the same core value; (ii) considering the global and local centrality of nodes, the Syncretic Core Value of nodes can better reflect the importance of nodes; (iii) it can recognize the importance of nodes in networks with the same degree value or without obvious aggregation characteristics; (iv) the time complexity of Syncretic K-shell algorithm can be used for the recognition of node importance in large-scale networks; (v) it can better distinguish and amplify the differences between nodes, and quickly and effectively rank the importance of nodes. [ABSTRACT FROM AUTHOR]

• This paper proposes a hazards correlation analysis method for railway accidents. • The knowledge graph theory and big data techniques are applied into the method. • Real-world case studies based on the decade-long UK railway accident data are conducted. With the continuous development and construction of railway transportation, the railway accidents occur frequently which greatly threatens the life safety of passengers and the further development of railway industry. Discussing and summarizing experience from past accidents is benefit to improve the safety of railway. This paper proposes a modeling method for the correlation analysis of hazards in railway accidents based on the knowledge graph theory. By describing the association between accidents and hazards in the knowledge graph network, the potential law of accident occurrence is revealed. The innovation of this study is that it considers the correlations between hazards. In addition, the hazards are further refined, and new topology indexes that adapt to the heterogeneous structure characteristics of knowledge graph are presented. Based on actual railway accident data in the UK, a number of key hazards have been identified using the methods proposed in this paper. The experimental results show that by controlling key hazards one by one, the harmful consequence caused by hazards to accidents is also continuously decreasing. Finally, based on the experimental exploration of the correlation between key hazards, corresponding preventive measures were developed. The method based on knowledge graph is expected to be applied to explore the relationship between hazards in railway accidents and provide additional decision-making information for the prevention of railway accidents. [ABSTRACT FROM AUTHOR]

• A railway operational hazard knowledge graph modelling method is developed. • A knowledge graph embedding-based hazard prediction method is proposed. • A procedure of formulating targeted hazard control measures is shown. • An application to real accident data demonstrates the effectiveness of the proposed approach in predicting hazards and assisting in making accident prevention decisions. Railway operational accidents are usually caused by the domino effects of hazards. Predicting such hazards before accidents is an essential way to prevent operational accidents of railways. Various railway operational hazards constitute a heterogeneous relationship network due to their interactions. It is useful to predict hazards from such a network. In this paper, a novel knowledge graph-based hazard prediction approach is proposed, aiming to capture hazards in advance for blocking potential accident causation paths. This approach serves as a powerful supplement to classical ways of predicting railway accident information. Its originality lies in the application of knowledge graph embedding-based link prediction theory to railway operational hazard prediction, by means of a translation-based embedding method adapting to the relational features of hazards. It also provides a feasible way to construct the railway operational hazard knowledge graph. The outcomes of this approach could offer railway operators the basis of decision regarding accident prevention. An example of application to a set of real railway operational accident data in the UK is presented. The results show that this approach is effective in terms of predicting hazards and assisting in developing targeted hazard control measures. [ABSTRACT FROM AUTHOR]

With the rapid development of global hydrogen energy industry, the issue of hydrogen energy safety has emerged, it is prone to cause a serious explosion accident once hydrogen leaks. Therefore, it is urgent to develop efficient suppression media to reduce the severity of hydrogen explosion accidents. In this paper, a new compounded powder composed of ordinary ultra-fine dry powder extinguishing agent (ODPEA) and zinc hydroxystannate (ZHS) is prepared. On the basis of material preparation, an experimental system of hydrogen explosive suppression is established to explore the inhibitory effect of ODPEA-ZHS. In terms of explosion overpressure, the results show that the peak overpressure suppression rates of ODPEA-ZHS are 90.12 %, 92.31 %, and 93.04 % for hydrogen release pressures of 2.0, 4.0, and 6.0 MPa, respectively. Besides, the flame front propagation velocity under the action of ODPEA-ZHS reaches a suppression rate of 40.55 %. Furthermore, the overpressure suppression efficiency of ODPEA-ZHS exceeds 90 % when ignites 1.0 m axially from leak port. Ultimately, the physicochemical synergistic inhibition mechanism of ODPEA-ZHS is revealed by combining material characterization and pyrolysis results. This study can provide effective countermeasures for accident prevention in hydrogen energy industry, and also contribute to the establishment of hydrogen energy safety standards and norms. [ABSTRACT FROM AUTHOR]

Accidents (such as hazardous chemical accidents and fires) have adverse effects on urban safety and sustainable development. Many countries, such as China, are making great efforts to develop better urban safety strategies to prevent and mitigate accidents in urban areas and to keep cities safer. At national level, and from an accident prevention perspective, this study uses an evidence-based safety approach to propose potential strategies for China's urban safety. The contributions include: (1) the identification and discussion of major urban safety problems and challenges in China, and (2) the development of China's urban safety strategies based on available evidence. This study attempts to provide useful evidence and suggestions for future urban safety management, both within China and in other countries. However, because this paper discusses China's urban safety at macro level, more detailed studies on China's urban safety should be carried out for a deeper understanding and a more targeted solution for China's urban safety. [ABSTRACT FROM AUTHOR]

Accident prevention on the manufacturing shop floor is essential. This paper aims to develop a proximity warning system capable of reducing accidents on the manufacturing shop floor, by notifying employers of any possible danger within the workplace. The system consists of an ultrasonic sensor, Arduino microcontroller, alarm system and an easy to understand user interface that can detect and alert workers within the danger zone. The system was developed using the Unified Modelling Language, python programming language and the Arduino software. Preliminary test conducted on volunteer participants revealed that the system is easy to use and provides real-time easy-to-understand audio and visual feedback to workers. The system is useful for reducing accidents on shop floors. [ABSTRACT FROM AUTHOR]

• We develop a novel cleaning algorithm to remove the unnecessary features based on SIFT extractor. • An accurate object detection model is proposed by adopting the G-RCNN to process vehicle image data. • We develop an evolutionary algorithm to intelligently explore the hyper-parameters space of the IRCNN-VD. This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed using the SIFT extractor. The region convolution neural network is then used to detect the vehicles. We propose a new hyper-parameters optimization model based on evolutionary computation that can be used to tune parameters of the deep learning framework. The proposed solution was tested using the well-known boxy vehicle detection data , which contains more than 200,000 vehicle images and 1,990,000 annotated vehicles. The results are very promising and show superiority over many current state-of-the-art solutions in terms of runtime and accuracy performances. [ABSTRACT FROM AUTHOR]

Analyzing the causes of accidents, excavating accident paths, and applying accident prevention are important tasks in safety management. Focusing on coal and gas outburst accidents, this study examined the primary accident path and conducted applied research on the reasoning of the accident case. First, combined with the obtained accident causes, a coupling analysis of the causes of coal and gas outburst accidents was conducted. Second, using the method of data mining coupled with Apriori algorithm, the coupling relationship between each cause module of the coal and gas outburst accident was obtained, and consequently, a path map of the coal and gas outburst accident was drawn. Third, a Bayesian network model for the causes of coal and gas outburst accidents was established based on the accident path map and the probability of occurrence of each cause. Finally, considering the safety concept element (SC1) as an example, the Bayesian network model was used to conduct a sensitivity analysis of accident causes. Thereafter, considering the coal and gas outburst accident of the Sanjia Coal Mine in Guizhou Province as an example, probabilistic reasoning research on the cause of the accident was conducted. The application results showed that (1) under normal conditions, there are approximately 797,280 accident paths for coal and gas outbursts. Following data mining, 188 main accident paths were found. (2) Sensitivity analysis determined 19 factors that were sensitive to safety concept elements (SC1), of which the three most sensitive factors were (i) resource management system procedures (SM7), (ii) safety policy (SM1), and (iii) safety training system procedure (SM8). 13 paths exhibited a sensitivity ≥0.5%, of which 7 exhibited strong sensitivity. (3) The absolute accuracy rate of accident cause reasoning in the Sanjia Coal Mine in Guizhou Province was 71.43%, while the relative accuracy rate was close to 100%. Thus, it was concluded that: (1) the accident path mining method proposed in this paper is feasible for main accident path mining. (2) The Bayesian network model for the causes of coal and gas outburst accidents established in this study can be practically applied for the sensitivity analysis of accident causes and exhibits high reliability in the probabilistic reasoning of accident causes. The results of this study is expected to aid in the prevention of coal and gas outburst accidents, and provide reference and help for the path mining of other accident causes and the probabilistic reasoning of accident causes. [ABSTRACT FROM AUTHOR]

• An active accident prevention method for the railway system is proposed. • A reinforcement learning method is introduced to learn the pattern of accidents. • Multi-modal data, including text and numerical data, is used for accident prediction. • The influence of human factors is analyzed, and related safety measures are advised. Railway systems are entering an era of highly intelligent automation where stability and safety are becoming increasingly important. However, there is still a lack of intelligent and effective ways for railway accident prevention, especially active accident prevention strategies. This paper presents a railway accident prevention method based on the reinforcement learning model and multi-modal data to achieve active railway accident prevention strategies. Three metrics are designed to show the performance of active prevention methods. Based on the three metrics and the data from Federal Railroad Administration, the effectiveness of the proposed method is verified in the case study by introducing two methods as baselines. The results also show that nearly 30% of accidents can be effectively prevented through active preventive measures with the proposed method. Finally, this paper analyzes the influence of personal skills on the proposed model and makes relevant suggestions for improving railway safety based on the analysis of the results. [ABSTRACT FROM AUTHOR]

Many accidents occur owing to decision-making failures in accident prevention. From an information perspective, decision-making failures in accident prevention are due to a lack of necessary information. Moreover, the research-practice gap is a long-standing and common problem in accident prevention. To solve the abovementioned problems to improve accident prevention, this study proposes a new accident prevention approach referred to as evidence-based accident prevention. This approach emphasizes the use of the best accident prevention evidence in the accident prevention decision-making process. It is regarded as a practical approach to avoid the lack of safety information to improve the quality of accident prevention decision-making and narrow the research-practice gap vis-A-vis accident prevention. First, this paper explains that evidence-based accident prevention is based on accident prevention failures from a safety information perspective. Second, this study proposes a model for evidence-based accident prevention. Finally, this study applies an evidence-based accident prevention approach to the prevention of hazardous chemical storage accidents in a paint manufacturing plant in Tianjin, China, as a case study. This study aims to help researchers and practitioners to understand the evidence-based accident prevention approach and lay a foundation for the future study and practice of evidence-based accident prevention. [ABSTRACT FROM AUTHOR]

As an accident prevention mechanism, Port State Control (PSC) is essential for the development of maritime transportation and security. To evaluate the effectiveness of PSC and provide recommendations for its enhancement, we develop Dynamic Bayesian Network (DBN) models using the latest five inspection records from the Tokyo and Indian Memorandum of Understandings (MoUs). These models incorporate a hidden variable called "ship condition" to represent the overall influence of four ship characteristic factors. Additionally, a "risk level" hidden variable is determined by two inspection results during each inspection: detention and the number of deficiencies. Two analyses are conducted in this paper. One aims to identify changes in risk levels, thereby investigating the effectiveness of dynamic inspections. The other involves comparing inspected and uninspected ships, with the goal of assessing the impact of PSC and ship condition on accident type, consequences, and severity. The results indicate that the inspection effects of the two MoUs demonstrate the same changing pattern, with the ship risk levels initially rising and then falling. However, the higher detention rate of the Indian MoU justifies its lower level of risk. Ships with inspection records are less likely to encounter capsizing accidents and have a lower probability of being taken out of service, while collision accidents and casualties are more likely to occur. The findings provide valuable insights and practical suggestions on how to enhance the effectiveness of PSC. • It investigates the dynamic changes of ships' risk levels and their impacts on ship accidents. • Dynamic BNs are developed considering "ship condition" and "risk level" measuring by five consecutive PSC inspections. • It analyzes and discusses the effectiveness of dynamic inspections on ship risk and accident consequences. • It discloses the risk level pattern of rising first and then decreasing with the increase of inspection times. [ABSTRACT FROM AUTHOR]

• Analyzed the cause of secondary explosion accident in Hushan Gold mine in China. • Clarified the direct and indirect causes of the accident. • Calculated and verified the direct cause of the secondary explosion accident. • Put forward the suggestions for preventing secondary explosion accidents in mines. Explosion accidents often occur during mining, especially sudden secondary explosions, which pose a serious threat to the life and property safety of employees. Based on the development process and consequences of typical secondary explosion accidents in Hushan Gold Mine, Qixia City, Shandong Province, China, this paper uses accident analysis model and cone measurement calculation method to determine the direct cause of secondary explosion accidents. The main conclusions are as follows: The HFACS-CM model is used to analyze the causes of secondary explosion from five aspects: external environment, organizational influence, unsafe leadership, the prerequisite conditions of unsafe behavior and unsafe behavior. It is found that the CO ignition explosion in the mining process is the direct cause of secondary explosion. Then, combined with the experimental data of the cone measuring instrument, the field CO amount was calculated, and the M CO ≥ 101.4 m3 was obtained, indicating that the CO production did reach the condition of explosion range, and the analysis results of the model were verified. Through HFACS-CM model analysis and calculation verification, the key causes of secondary explosion accidents in such mines are found and lessons are learned, which provides an effective reference for the effective prevention of deflagration accidents in the field of mine safety. [ABSTRACT FROM AUTHOR]

• One hundred ninety-four data-driven articles are comprehensively examined from the past decade in four aspects: Paradigm, Model, Data Source, and Purpose. • The mapping between ML approaches and safety purposes is established. • Some future research directions of ML in the field of safety are proposed. • This paper describes the possible bias in the past data-driven safety research. Data-driven intelligent technologies are promoting a disruptive digital transformation of human society. Industrial accident prevention is also amid this change. Although many emerging technologies, such as machine learning (ML), are extensively employed in workplace safety, these approaches need to fit the intended safety purpose of accident analysis, risk assessment, adverse outcome prediction, or anomaly detection. Hence, examining the "real-world" need for accident prevention and the advantages of emerging data-driven methodologies to better integrate them is necessary. This study provides a systematic review to clarify the current research status, existing problems, and future insights into these evolving technologies in accident prevention. We present notable gaps and barriers in data-driven accident prevention by analyzing 194 published studies from four perspectives: Paradigm, Model, Data Source, and Purpose. The results demonstrate (1) lack of a systematic framework to guide the application of Big Data (BD) in the field of safety; (2) few prior studies have considered model interpretability; (3) more proactive data needs to be incorporated into accident analysis; (4) safety-related data and domain knowledge need to be further integrated; (5) some recent data-driven techniques are unexplored in safety science. Further, the future research opportunities are discussed based on these findings. Such review may help clarify the mapping of data-driven tasks to safety goals to accelerate the uptake of data-driven technologies in safety or accident analysis research. [ABSTRACT FROM AUTHOR]

Considering the complexity of the urban rail transit system (URTS), it is necessary to systematically identify the critical factors and their relationships to avoid or prevent urban rail transit accidents. In response to the problems few previous studies have modeled networks for the whole system components of URTS and have focused too much on the identification of specific component states or behaviors without quantitative analysis of inter-component relationships. This paper proposed a risk network construction and analysis method based on complex network theory to identify the critical accident causative factors and their relationships of the URTS system. Firstly, the accident is defined as the combination of a sequence of risk incidents occurring in a certain order and their resulting consequences, and the definitions of risk point and risk incident were given. Secondly, a risk network construction and analysis method based on the combination of accident reports and complex network theory was proposed to obtain the critical factors and their relationships in the process of the accident. At last, this paper constructed the urban rail transit risk point set containing 39 risk points, built the risk network based on 201 accident reports of URTS in China and analyzed the critical risk points and risk relationships. The results show that this study can provide a new perspective for identifying the critical causative factors of URTS accidents and their relationships for practical application in risk analysis and accident prevention. • The new definitions of risk points and incidents are put forward. • A critical risk analysis framework are proposed by complex network theory. • Critical risk points and relationships are identified in the urban rail transit system by the risk network. [ABSTRACT FROM AUTHOR]

The acquisition and maintenance of non-technical skills by the pilots are fundamental factors for the prevention of aviation accidents. The aviation authorities are promoting that air crew training be carried out through simulator sessions using scenarios specifically designed to develop and assess the global performance of pilots in such skills. When designing custom flight training scenarios, choosing the correct events and conditions from the myriad of possible combinations with respect to their potential utility in training specific competencies is a costly task that depends entirely on highly specialized expert knowledge. In this paper, we present EBTOnto, an OWL DL ontology that allows to formalize this knowledge and other useful data from real cases, laying the foundations for a semantic knowledge base of scenarios for airline pilots training. Previous advances in this matter and possible applications of this system are reviewed. EBTOnto is built on top of a source validated by experts, the Evidence-Based Training Implementation Guide by the International Air Transport Association, and then checked using an automatic reasoner and a database of 37,568 aviation safety incidents, extracted from the widely regarded Aviation Safety Reporting System by the U.S. National Aeronautics and Space Administration. The results suggest that it is possible to classify real aviation scenarios in terms of non-technical competencies and filter useful incident reports for design and enrichment of these training scenarios. EBTOnto opens up new possibilities for interoperability between incident databases and training organizations, and smoothes the path to represent, share and generate custom simulation training scenarios for pilots based on real data. [ABSTRACT FROM AUTHOR]

• Accident modification functions for traffic law enforcement are compared. • All functions show that more enforcement improves safety. • Differences-in-differences estimates capture both increases and decreases in enforcement. • A decrease in enforcement worsens road safety. Traffic law enforcement is a road safety measure whose effects on accidents or injuries is best described by means of a function rather than a point estimate. An informative function should comprise both increases and decreases in enforcement. Currently available accident modification functions cannot serve this need. A fruitful approach to developing accident modification functions covering both increases and decreases in enforcement is differences-in-differences estimates based on multivariate accident prediction models. The paper explains how to develop such estimates and illustrates them. The interpretation of the results of empirical studies can be informed by a game-theoretic model of the effects of enforcement, previously published in Accident Analysis and Prevention (Bjørnskau and Elvik 1992, 507–520). [ABSTRACT FROM AUTHOR]

• A propagation method with risk entropy as the information carrier is proposed based on the improved system dynamics model. • Two different types of dynamic risk nodes, energy diffusion and energy conduction are identified. • The prediction accuracy of dynamic risk nodes of the improved model is increased. • The prediction of the path of energy-conducting type risk evolution direction is increased. The LNG unloading system is an important transit station for LNG storage, processing and outgoing transmission. The main processes include ship unloading, storage, gasification and outgoing transmission, BOG condensation, flare venting and other processes, which involve intensive personnel operation and safety management activities. Once LNG is leaked, frostbite will happen to personnel and fire and explosion will occur when encountering ignition sources, which will cause serious consequences that are difficult to bear. The risk path of the static safety evaluation method is based on qualitative judgment, and the dynamic evolution direction of the LNG unloading system risk factors is not accurately predicted, so how to accurately predict and warn the risk path during the risk evolution is a key issue. In this paper, based on the improved system dynamics model, a propagation method with risk entropy as the information carrier is established to identify two different types of dynamic risk nodes, energy diffusion and energy conduction. Compared with the traditional dynamics model, the prediction accuracy of dynamic risk nodes of the improved model is increased by 22%, 20% and 14% for the unloading process, storage process and loading and transfer process, respectively. The prediction of the path of energy-conducting type risk evolution direction is increased by 29.3%, 32.4% and 30%, respectively. The simulation results are beneficial to the dynamic risk control and accident prevention of LNG unloading system, and provide new ideas for natural gas energy safety and security. [ABSTRACT FROM AUTHOR]

• Timely and correct identification of NPP transients could either prevent an accident or mitigate the consequences of an accident. • A hybrid deep learning technique is proposed for the online identification of nuclear power plant transients. • A CNN-LSTM network was used for the transient identification, which transformed the input matrices into vectors using a CNN and then LSTM layers predicted the probability of each transient by sequence processing. • The results were shown that the proposed technique performs better than common deep learning techniques in terms of accuracy, identification time, and computational cost. Identification of NPP transients plays an important role in the prevention of accidents and mitigation of their consequences. NPP parameters may follow different patterns during each transient. So the transients can be identified by monitoring the operating parameters. It has been shown in several studies that data-driven methods, especially deep learning approaches, have a desirable performance in NPP transient identification. A hybrid deep learning technique is proposed in the present paper, in which transient identification is done using a CNN-LSTM neural network. The training data set is taken from a VVER-1000 full-scope simulator and the most important operating parameters are determined by feature selection techniques. According to the results, the proposed technique has identified the NPP transients in a short time, with high accuracy, and with a reasonable computational cost. The effective performance of the technique makes it possible to use it as a practical tool for online transient identification. [ABSTRACT FROM AUTHOR]

• Core melting prevention and mitigation are compared for SFR and GFR. • Both reactor concepts have been developed at CEA (GFR2400 and ASTRID). • Core melting prevention relies on systems operating under pressure for GFR. • Large thermal inertia of SFR gives them good inherent prevention features. • Gaseous coolant makes unlikely large mechanical energy release for GFR. • Low void worth core and mitigation devices of ASTRID makes unlikely energy release. CEA has developed Generation IV fast neutron reactors with gas and sodium coolants in the two last tenth of years. Namely these reactor projects were the GFR2400 (gas-cooled fast reactor of 2400 thermal megawatts) and ASTRID (sodium-cooled fast reactor demonstrator of 1500 thermal megawatts). The objective of this paper is to provide a cross-comparison of the severe accident prevention and mitigation capability of these reactor concepts based on the work done during a significant time period and taking benefit of the distance since these studies. This comparison can highlight both generic trends resulting from a common study approach used for both concepts and from very detailed results obtained during their conceptual design studies. Despite their power difference and their fuel element design specificities, the main study results and conclusions are quite generical of the two concepts as explained in the paper. Thus they could be extrapolated to other reactor design providing main reactor features are kept (core materials, coolant, neutron spectrum, etc.). The assessment of core melting prevention relies on the study of the natural behavior of the GFR2400 and of ASTRID when facing the various accident sequence families. Then, the efficiency and the features of the systems to be foreseen for core degradation prevention are presented. As far as mitigation is concerned, all the consequences of core melting are investigated (i.e. the induced loadings in terms of nature and of range) by considering various core degraded states. Based on the magnitude of these loadings, the needs of mitigation means are assessed for each concept. Among other trends, the presented work shows the very good prevention capability of the SFR concept but the necessity to mitigate the fast vaporization and expansion of degraded core materials. Conversely, the limited coolant capability of the GFR concept and its low thermal inertia require a pressurized gas circulation into the core, limiting its prevention capability whereas its core melting should not induce substantial mechanical loadings of the reactor structures. However, for this last concept, thermochemical interactions between the core materials are an issue deeply investigated in order to understand and simulate core degradation. [ABSTRACT FROM AUTHOR]

• Calculating the Safety Level of a system using a mathematical model. • Implementing the severity of accidents to the calculations of the Safety Level. • Assisting decision makers in maintaining safety in acceptable levels. • Utilizing a new accident causation model based on systems theory. The RealTSL methodology is based on STAMP and aims at determining the safety level of complex systems in real-time. RealTSL utilizes accident scenarios derived from STPA, together with: (a) The time durations required between system state transitions, (b) Managerial decisions regarding the sensitivity of the mathematical model, and (c) Real-time system data. This manuscript proposes an extension of the RealTSL mathematical model that allows analysts to distinguish system-level accidents according to their severity, and introduces a new expression of the safety level. In addition, this paper shows how the information contained in the graphical form of the acyclic diagram can be used as an interface so that managers can: (a) Comprehend the current systems state against possible accidents, (b) Interpret the results of the mathematical model, and (c) Help them decide whether mitigation actions are needed. The safety level of a system in time t, therefore, is defined as SL = p → w A i > p → w A j > ⋯ > p → w A m ordering, where m is the number of system accidents and p → w A i are the most detrimental to safety sequences of safety constraint violations for every accident and ordered according to the severity of their corresponding accidents. The extended mathematical model is demonstrated using a fictitious system, providing a graphic rendition of how unsafe situations progress to accidents through time. This paper aims to open a new perspective in the STAMP literature, on discussions on the problem of measuring what the actual safety level of a system is at a certain moment in time and in a certain context. [ABSTRACT FROM AUTHOR]

The emergence of Intelligent Connected Vehicles (ICVs) is expected to drastically change various fields in the transportation system—especially traffic safety of road users. Therefore, this special issue aims to facilitate a forum for transportation researchers fostering an exchange of research ideas and experience in traffic safety with a specific focus on operations, planning and management of ICVs. The issue contains thirty-six papers from seven different countries. Topics are classified into seven categories: (1) Driving behavior in the ICV environment; (2) Safety evaluation of ICVs; (3) ICV driving/management strategies; (4) New framework for ICV safety analysis; (5) ICV safety for vulnerable road users; (6) Perception towards ICVs; and (7) Security issues relating to ICVs. The papers are concisely introduced in this editorial. All the papers were invited to present at the International Symposium on Accident Analysis & Prevention in 2021 (ISAAP 2021) and the symposium was successfully held. The research conducted in these articles reveal challenges and future directions in the area of ICVs that include further developing novel methodologies and algorithms for collision-free trajectories of ICVs, testing diverse scenarios in complex environments with mixed traffic, and addressing inherent safety risks of specific vulnerable road users (e.g., older road users, bicyclists, riders of micro-mobility vehicles). [ABSTRACT FROM AUTHOR]

[Display omitted] • A multi-method traffic system simulation model of rear-end collisions with a powered two-wheeler (PTW) is developed for the purpose of testing different collision warning algorithms. • A combined Emergency Stop Signal (ESS) & Rear-End Collision Alert Signal (RECAS) system, named Motorcycle Emergency Braking Warning System (MEBWS), reduced total number of rear-end collisions with leading PTW by approximately 30%. • In unavoidable collisions, MEBWS reduced severity of injuries by over 10%. • The estimated annual reduction in economic crash costs of PTW rear-end collisions in the EU is estimated to over 43 million EUR (45 million USD). Every year, several thousand powered two-wheeler (PTW) i.e., motorcycle, moped and scooter drivers and passengers die in traffic accidents in the EU. Despite the much higher risk of death and injuries for PTW vs. car users, there is a three-fold lack regarding collision warning technologies for PTWs: lack of research, lack of regulation and lack of availability in the market. Many injuries occur in rear-end collisions, when PTW is struck from the rear by another vehicle. In this paper we present a hybrid, multi-method simulation model that allows simulation of various situations in which a vehicle may collide with the rear end of a PTW. We have used this model to estimate the potential impact of market penetration of a novel PTW ESS + RECAS system, named MEBWS (Motorcycle Emergency Braking Warning System), within the EU on the number of traffic accidents and their consequences, which would contribute to the EU "Vision Zero" goal: "reduce road deaths to almost zero by 2050". MEBWS has been developed at the Faculty of Information Studies in Novo mesto and patented. Simulation results using EU traffic accident data show that with 100% market penetration of the MEBWS system in the EU, the total number of PTW rear-end collisions would decrease by 29.50%. This reduction would result in fewer injuries and a decrease in economic crash costs by €43,145,172, according to the standard EU methodology. With the MEBWS system enabled, the number of traffic accidents in the standard rear-end collision emergency braking scenarios Moto, normal drive , Moto, emergency stop and Moto, not moving decreased by 33.15%, 27.76% and 28.76%, respectively. In cases where the collision could not be prevented due to slow response of the following driver or very high relative speed of the vehicles, MEBWS reduced the relative speed at impact, resulting in a reduction of injury severity by up to 11.198%, as estimated by the amount of kinetic energy released at collision. [ABSTRACT FROM AUTHOR]

• Improved text segmentation techniques can automatically and accurately generate more complete phrases. • Data pre-processing techniques that have undergone oversampling and correlation coefficient measurements enable more complete processing of sample data. • The outcomes of the accident severity prediction in distinct scenarios demonstrate the reliable performance of the feature attribute-based prediction model. • The ranking results of feature importance can adequately identify and explain the critical attribute factors of occupational accidents. Machine learning algorithms are capable of handling complex non-linear problems related to the prediction domain, but further exploration is required for automated, semi-supervised outcome prediction of occupational accidents employing unstructured textual data. It has been demonstrated that the injury severity can be predicted from the equipment, scenario and environmental attributes in the workplace, so this paper aims to enhance text data pre-processing and optimize machine learning algorithms to create an attribute factor-based occupational accident severity prediction framework, mapping characteristic attributes to accident severity categories (i.e., casualties and property damage). The reliability validation of the prediction framework and analysis of critical attribute components are performed using the collapse accidents data in construction engineering as a case study, which is the third most serious occupational problem. The findings indicate that the dataset obtained after addressing the class imbalance issue and improving the text segmentation procedure can be utilized as a training sample to accurately predict injury severity. The accuracy of the prediction model is evaluated in three simulated scenarios, and it can reach 82%, confirming the robust performance of the prediction model based on RF machine learning. Additionally, the outcomes of the measured ranking of feature importance enable the identification of critical attributes that can credibly explain the causal relationships resulting in injury severity findings, and provide managers with accident prevention strategies to minimize occupational injuries and losses. [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 Value of (imperfect) Prediction (VoP) model based on information value theory is proposed to calculate the optimal response time to threats. • The time to respond is the time when VoP does not increase anymore. • Involving partial predictability in decision-making pushes down risk acceptance level and raises up the risk level to be precautionary. • Ignoring partial predictability is found to be improper. This paper presents a novel value of (imperfect) prediction (VoP) model to estimate optimal response time to a threat that may result in an accident. The proposed VoP model is based on information value theory and considers both prediction accuracy and action failure probability over time. The optimal response time is dependent on parameters: the ratio between the accident cost and response action cost, accident probability, action failure probability, prediction performance, and response strategy (a series of sequential responses or a single response). A case study of iceberg management is presented to demonstrate the proposed approach; a sensitivity study is done to evaluate how optimal response time changes with those parameters. The case study show that it is reasonable to respond as early as possible if the threat can lead to a serious accident, while the response can be postponed when the potential consequence is moderate. In addition, the proposed VoP model is proven able to calculate accuracy requirements, thresholds for tolerating risk and acting precautionarily, and maximum investment in accident prevention. Imperfect prediction can lower risk acceptance threshold and higher the threshold of being precautionary; and it is reasonable to increase action cost. [ABSTRACT FROM AUTHOR]

• Machine learning method is used to predict probable factors impacting fatal falls from height accidents. • The analysis uses 3321 fatal fall incidents from Malaysia. • This study establishes the feasibility of machine learning in the field of construction safety management. The focus of this paper is to use machine learning to create a prediction model that detects the probable factors impacting fatal falls from heights accidents at the Malaysia construction industry. The dataset used in this study was imported from the Department of Occupational Safety and Health of Malaysia's industrial accident database. The dataset details 3321 accident scenarios which include; the date, the activity, the region, the summary of the accident, the direct cause, the root cause, and the factors. Seven machine learning models were tested to determine which model fits the dataset better, and as a result, the Random Forest Classification model was selected for this work. Random Forest classification tested several contributing factors such as: site conditions, management factors, individual characteristics and agent factors separately to determine their accuracy. Management factors and individual characteristics factors recorded the highest accuracy in every other prediction model; while agent factors recorded the highest accuracy in the random forest model. Additionally, this approach created ensemble predictions based on all of the dataset's characteristics. As a result, this study establishes the feasibility of machine learning in the field of construction safety management. The provided results can aid in accident prevention by increasing awareness of potential safety hazards, quantitatively predicting fatal accidents, and implementing the findings in potential safety management systems. [ABSTRACT FROM AUTHOR]

• Radio frequency identification (RFID) has been leveraged to proactively prevent accidents on construction sites. • Proximity detection empowered by the RFID is highly flexible for creating various detection zones by programming the devices. • Semi-passive tags (EPC UHF Gen2) have been employed to identify and warn trespassers. • A long-range wireless communication system enabled by LoRa is able to deliver the data to a remote server. • The RFID subsystem shows 100% successful detection rate, and the wireless network can efficiently disseminate the information to the server for safety management and data analytics. In recent years, emerging sensing and detection technologies have been adopted to enhance safety in many construction sites. In this paper, we proposed to use a radio frequency identification (RFID) system, which consists of RFID readers, semi-passive tags and an Internet of Things (IoT) network, to prevent accidents on construction sites. The proposed system with multiple functions is versatile for different situations. Firstly, it can effectively detect the entry/exit activities when workers carrying tags access a site. Such a record is essential in daily project management. Secondly, the system can be programmed to flexibly create various virtual zones of different sizes and shapes. The virtual zones are usually hazardous ones that anyone cannot approach and enter. The semi-passive tag will warn an intruder if someone intentionally or accidentally intrudes the zones. A warning message is generated and sent to the remote server in real-time via the IoT network. Lastly, by adopting the advanced LoRa technology in the IoT network, the sensed data can be wirelessly transmitted over a longer distance than other wireless technologies, such as WiFi and Bluetooth, thereby reducing the number of transmission hops and improving the transmission efficiency. [ABSTRACT FROM AUTHOR]

• Prevention is a fragmented object, shared by a network of activity systems. • This network is embodied in institutions and organizations in charge of prevention. • Its development is driven by inner historical contradictions. • Contradictions emerged as symptoms of incompatibilities in the system. • Understanding contradictions provides key information about limits and challenges. This article aims to trace some of the key trends in the historical development of Occupational Accident and Disease Prevention in France. For this purpose, we have used the analytical concept of contradiction derived from the Cultural-Historical Activity Theory framework (CHAT). We consider prevention as a fragmented object shared by a network of Activity Systems (AS). We conduct a scoping review to identify contradiction hypotheses on how the French activity system of prevention of occupational accidents and diseases have evolved over time. This review includes 72 theoretical and empirical studies published between 1988 and 2019. The results obtained highlight five contradiction hypotheses related to (i) prevention and remediation, (ii) technological and occupational risks, (iii) employment and improvement of working conditions, (iv) workers' participation or social dialogue and expertise, (v) individual and collective approaches. These contradictions have arisen over time and have emerged as symptoms of incompatibilities in the network of AS that have persisted. The paper ends with a discussion on future prospects and learning challenges for prevention. [ABSTRACT FROM AUTHOR]

• Generate a novel database of maritime accidents capturing the characteristics of 23 risk factors. • Develop a data-driven BN-based risk analysis model for global maritime accidents. • Incorporate new risk influencing factors (RIFs) and states into the risk analysis model. • Explore the new characteristics of maritime accidents through sensitivity and scenario analysis. • Analyze the real accidents to verify the model and provide implications for accident prevention. Maritime risk research often suffers from insufficient data for accurate prediction and analysis. This paper aims to conduct a new risk analysis by incorporating the latest maritime accident data into a Bayesian network (BN) model to analyze the key risk influential factors (RIFs) in the maritime sector. It makes important contributions in terms of a novel maritime accident database, new RIFs, findings, and implications. More specifically, the latest maritime accident data from 2017 to 2021 is collected from both the Global Integrated Shipping Information System (GISIS) and Lloyd's Register Fairplay (LRF) databases. Based on the new dataset, 23 RIFs are identified, involving both dynamic and static risk factors. With these developments, new findings and implications are revealed beyond the state-of-the-art of maritime risk analysis. For instance, the research results show ship type, ship operation, voyage segment, deadweight, length, and power are among the most influencing factors. The new BN-based risk model offers reliable and accurate risk prediction results, evident by its prediction performance and scenario analysis. It provides valuable insights into the development of rational accident prevention measures that could well fit the increasing demands of maritime safety in today's complex shipping environment. [Display omitted] [ABSTRACT FROM AUTHOR]

• A review of safety climate research is provided based on data mining. • Hot topics include safety culture, safety performance, safety behavior. • The limitations of past research on safety climate are discussed. • Recommendations for future research on safety climate are provided. It is well known that safety climate (SC) has paramount significance in safety science and accident prevention. In this paper, a bibliometric data mining is conducted to systematically review the research domain of SC. Overall, 1624 documents on SC are obtained, covering 4830 authors, 473 journals, 89 countries/regions, and 1766 institutions between 1980 and 2021. SC has obtained increasing attention since the number of publications related to SC grew from 1 in 1980 to 188 in 2020. Based on the bibliometric data, network analysis was carried out to understand the relationship among different countries/regions, authors, and keywords. Safety Science, Journal of Safety Research, and Accident Analysis and Prevention are the major sources of SC publications, and the USA, Australia, and China lead scientific collaboration production on SC research. Then, text mining of publication keywords is used to identify the hot topics and the evolution of mainstream research over time in the SC domain. The dominant topics in SC research include culture, performance, safety behavior, and model. Meanwhile, the limitations of past research on SC are analyzed and the differences between SC and safety culture are discussed. Moreover, recommendations for future research on SC are also given based on the results of bibliometric analysis and existing literature reviews. [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]

This paper utilizes a fuzzy fault tree analysis and Noisy-OR gate Bayesian network to estimate the occurrence likelihood of navigational accidents. The kernel of this proposed method is first to construct the fault tree from investigation reports of navigational accidents, to calculate the occurrence probability of basic events using fuzzy set, to transform the fault tree to Bayesian network using the Noisy-OR gate. The merit of the developed model can overcome the problem of absolute description of the relationships between basic events and intermediate events. Finally, the model is applied to Qinzhou Port, the results are reasonable by comparing the results in other waterways. Moreover, the key influencing factors are identified from minimum cut set analysis and sensitive influencing factors are quantified sensitivity analysis. Consequently, the findings are beneficial for the maritime authorities to take countermeasures for navigational accidents prevention. • Fuzzy fault tree analysis and Noisy-OR gate Bayesian network to estimate the occurrence likelihood of navigational accidents. • The absolute description of the relationships between BEs and IEs can be overcome in the new model. • Newly developed model is applied to estimate the occurrence likelihood of five regions in Qinzhou Port. • Key influencing factors are identified and quantified by using minimum cut set and sensitivity analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • 1. The explosion pressure curve of the hybrids appear double-peaked structure. • 2. Hybrids have greater explosion severity and temperature than single combustible. • 3. Mg particle is more prone to homogeneous shrinking core reaction in hybrid explosion. • 4. After the explosion of any hybrid system, there will be H 2 remaining. • 5. Oxygen content of hybrid explosion products is lower than that of magnesium powder. The explosion of magnesium powder/hydrogen hybrid is a complex reaction process, which will cause more serious harm than the explosion of a single combustible medium. The research on the mechanism of the hybrid explosion can provide corresponding reference for the prevention of such accidents. A 20L explosion device was used to study its explosion mechanism, and the results show that the magnesium particles in the hybrid explosion underwent homogeneous shrinking core reaction. It is found that the explosion pressure curve of the hybrid will appear bimodal structure under certain conditions, so this paper will explore it separately. When the hybrid system is in the "oxygen-poor" state, the explosion characteristic parameters of the first stage will decrease with the increase of combustible materials. And the hydrogen content and oxygen consumption in the gaseous products are also increase gradually, and unreacted magnesium particles are also found. In the "oxygen-rich" state, the opposite is true, and the number of cubic particles and the oxidation degree in the solid explosion products increasing with the increasing combustible materials. In addition, even under "oxygen-rich" conditions, hydrogen remains in the gaseous product of the hybrid. Interestingly, even under the condition of 30%vol-50%vol hydrogen/magnesium powder hybrid in the "oxygen-poor" state. As the combustible materials increase, the explosion characteristic parameters and maximum explosion temperature in the second stage show a change trend of increase first and then decrease. [ABSTRACT FROM AUTHOR]

Buried pipeline is the most common mode of natural gas transportation. The analysis of methane diffusion characteristics in leakage accident is of great significance for accident detection and secondary explosion accident prevention. The existing numerical simulation and experimental studies focused on the diffusion of methane in the soil from the leakage of buried natural gas pipeline, and the coupling process of methane passing through the soil and entering the atmosphere was rarely mentioned. However, the surface atmosphere is the main area of human activities, which increases the possibility of fire and other risk factors. In this paper, the coupling process of methane leakage passing through soil and entering the atmosphere was studied by numerical simulation, and the effects of various factors on the methane concentration in the first danger zone (FDZ) and the second danger zone (SDZ) were analyzed. Based on the least square method and multiple regression theory, combined with MATLAB mathematical calculation software, a methane concentration prediction model on the high-risk side of the FDZ was established. The results showed that after methane entered the atmosphere, the diffusion velocity increased significantly. The downstream building surface of the FDZ was the high-risk side of methane distribution, and the upstream building surface of the SDZ was the high-risk side. The average calculation error of the methane concentration prediction equation on the high-risk side of the FDZ was 9.97%, which can effectively evaluate the methane concentration under any leakage working condition. When the buildings floors were different, the high building surface between high building and low building was the high-risk side of methane distribution. This study provides guidance for the design of buried natural gas pipelines in residential buildings, and supplies basis for improving detector installation and gas leakage maintenance specifications. • The soil-atmosphere coupling process of methane diffusion in the cavity of buildings is studied. • The influence of underground and aboveground factors on methane diffusion is considered. • The prediction equation of methane concentration in the atmosphere is established. • Provide suggestions for leakage monitoring of buried natural gas pipeline. • Provide guidance for the laying of buried natural gas pipelines in residential buildings. [ABSTRACT FROM AUTHOR]

Safety is the precondition and guarantee of railway development. However, due to equipment failure, poor management, misoperation and so on, accidents on the railway occur from time to time. China and Japan, as leading countries in Railway Technology in the world, have done a lot of work in accident prevention and safety. Through the collection and comparison of transport data and typical casualty cases in China and Japan, this paper summarizes the main causes of accidents and the experience of accident prevention in the two countries. And further, puts forward suggestions for preventing accidents and large-scale casualty. [ABSTRACT FROM AUTHOR]

• Information on accidents in facilities for processing and storage of selected agricultural products are contained in the work. • A unique accident database was created containing 195 items. • The causes, consequences and possible measures are researched and discussed in the work. • The information contained in the work can be used to increase the level of safety in the agricultural sector. Process safety in the storage and processing of agricultural products is an important aspect related not only to reducing the risk of damage to the human health or property, but is also strategical to national food safety. A special emphasis must therefore be placed on risk assessment of the operation of these facilities. One of the cornerstones of the risk assessment process is the use of knowledge of past accidents. As the agricultural industry is rather marginal in the field of safety engineering compared to other sectors of the economy, available publications dealing with this topic are rather an exception. The aim of this paper is to conduct research on the available information on accidents that have occurred in agricultural processing and storage facilities in the past. The purpose is to provide professionals and operators with a comprehensive source of information useful for activities related to the prevention of these accidents. The output of the work is then a previously unrealised representative database of systematically sorted information on accidents that have occurred in the process of treatment or storage of agricultural products. This information was subsequently analysed and evaluated. The created database consists of 195 records of accidents that occurred between 1989 and 2018. To create a unique representative database, mainly publicly available databases of industrial accidents or professional publications were used. The author's database contains information on accident locations, accident causes and accident prevention measures. A total of 21 persons died and 32 persons were injured during the accidents. The analysis shows that fires are the most frequent manifestation of accidents (86%) and accidents in material handling facilities are the most frequent (33%). According to the information available in the database, organisational factors were identified as the most frequent causative factors of accidents (47%). For a better understanding of the accident process, a description of selected accident scenarios was also provided in the work. A linear model was used for this purpose. Due to the strategic importance of the evaluated facilities for processing and storage of agricultural products, efforts are made to prevent accidents from recurring or to reduce their impact on the surrounding area. Therefore, an integral part of the work is the description of possible measures against the recurrence of similar accidents. [ABSTRACT FROM AUTHOR]

[Display omitted] • New 48 cases of major tunnel construction accidents (MTCA) in China 2010–2020. • Analysis of varied classification and time-space distribution of MTCA database. • Novel accident data-based resilient network of management for tunnel construction. The increase of transport mileage and expansion of underground space foster rapid development in tunnel engineering, particularly in China. China has the largest number of existing tunnels, and the growth rate has doubled over the past decade; whereas varied geographical environments, complex geological conditions, and incomplete management systems result in frequent tunnel construction accidents. The existing studies mostly focused on an individual accident type or operation stage, and the databases built decades ago are becoming obsolete to match developing construction concepts and technologies. Motivated by this concern, this paper conducts the statistical analysis on 48 cases of major tunnel construction accidents (MTCA, defined as an accident that results in more than three deaths) in China from 2010 to 2020, and examines the accident characteristics, including classification, time, region, location, construction method, and risk source. Based on the above time-space distributions of MTCA, two critical scientific issues concerning the safe construction of underground tunnels are discussed, including how to accurately evaluate the resilient network of hazards -vulnerable tunnel engineering, and how to build an efficient monitoring-mitigation-rescue system. The purpose is to provide a theoretical foundation for developing a database-driven management system that improves the resilience of tunnel construction to multiple hazards. [ABSTRACT FROM AUTHOR]

• Safety and security research based on system dynamfics was systematically reviewed. • Three main knowledge bases were visually captured using co-citation analysis. • Three research hotspots were explored using keywords co-occurrence analysis. • The research frontiers were identified using keywords timeline view analysis. Safety and security (SS) research based on system dynamics (SD) play a greater theoretical and technical support for hazard identification and accident prevention, which meant SD has become an important method to study the system safety, also that SS research based on SD has entered the application stage. It is a relatively macroscopic understanding of the application of SD to SS research by scholars, and difficult to capture the changes and characteristics of specific topics from the understanding. To get an in-depth understanding of the current research characteristics and development in the research domain, and help researchers dig into the more information from a quantitative perspective, we retrieved 1091 documents about SS-related research based on SD published in the period 2000–2020 from the Web of Science Core Collection database in this paper, the bibliometric analysis and science mapping were carried out. The USA, China, UK, Germany and Australia were the five most contributions to the literature associated with SS research based on SD. In terms of the major sources and co-citation of SS-related research publications, IEEE Transactions on Power System , Safety Science , Energy Policy , System Dynamics Review and Reliability Engineering & System Safety are the five most active journals in this domain. The knowledge base of SS research based on SD focuses on "safety management", "risk analysis" and "network and information security". There are some significant differences between the safety and security domains while they are very similar in "risk analysis". Currently, hotspot topics include "safety management", "energy security", "resource utilization" and "industry applications". "system safety and reliability", "energy consumption and security" and "power system dynamics and stability" have become the research frontier in the field. [ABSTRACT FROM AUTHOR]