Search

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]

Coal is an important energy resource internationally. However, accidents have severely restricted the clean and safe production of coal resources. Among such accidents, coal and gas outburst accidents are a kind of coalmine disaster with high destructive power. Previous research on coal and gas outburst accidents mainly focused on gas factors but ignored the role of human factors. This paper analyses the coal and gas outburst accidents in China from 2008 to 2018 and studies its macroscopic laws. To better understand the causes of coal and gas outbursts, this paper uses the 24modelel to analyse coal and gas accidents and suggest measures for accident prevention from the two aspects 'gas' (risk control) and 'humans' (behavioural safety). Macroscopic law research found the following: (1) March, May, July, and August are the predominant months for accidents. (2) The second to fourth hours of the working hours and the first hour before the end of work are the peak periods for accidents. (3) Guizhou, Hunan, Henan, Sichuan, Yunnan, and Chongqing are the provinces with the most coal and gas outburst accidents. (4) An overall 75.82 % of accidents occurred in the driving face, and 81.08 % of accidents occurred in coal and gas outburst mines. (5) Blasting, drilling, driving, and coal falling are the main inducing factors. Case analysis of accidents found the following: (1) Human error is the leading cause of accidents. Among the errors, the lack of strict enforcement of outburst prevention measures, illegal command, and the illegal operation of miners are the main unsafe acts. (2) Safety knowledge and awareness of miners is not generally high, and serious habitual violations and unsafe psychologies exist. (3) The gas comprehensive prevention system and supervision system in the coal mine safety management system (SMS) can be easily operated improperly, and the safety training system and emergency management system can be absent. (4) Coalmine enterprises seriously lack safety culture. [ABSTRACT FROM AUTHOR]

• HCLs show as similarities human errors while operating unsafe systems. • Unsafe systems assign to human performance a considerable contribution to the overall risk. • Structured human reliability analysis can improve offshore risk evaluations. • The regulator has an important influence on risk control performed by risk owners. • Early project's regulatory enforcement is challenging in the offshore industry. Despite laws, regulations, or risk management tools designed to avoid loss events, a hydrocarbon leak (HCL) in an offshore oil and gas platform 1 1 Also mentioned in this paper as 'offshore platform' or 'platform'. is a major hazard event that reveals failures in risk control. In addition, similarities between different HCLs challenge the efficiency of current improvement mechanisms and the completeness of applied risk management practices. Accident causation models sustain that different roles in different levels, including regulators, companies, and the work environment, create the necessary conditions to a loss event, and therefore should be part of the accident analysis. This paper analyzes similar HCLs occurred in Norwegian and Brazilian offshore platforms to compare the regulatory approaches to risk control, aiming to identify influences related to the recurrence of HCLs in functional regulatory regimes (FRRs). As a result, each HCL among case studies consists of human errors that occurred while offshore crews were operating unsafe systems created by improper design or changes to address operational demands. An inappropriate context may support the impairment of safeguards that may not always be available or reliable in an environment which has as particular aspects the remoteness and the need to be self-sufficient. As a consequence, unsafe systems impose safety rules to offshore personnel and more relevance to human performance in the overall risk picture. In FRRs, the combination of regulatory requirements and enforcement activities is part of the safety context and influence risk owners' behavior. The current comparison identifies differences between Brazilian and Norwegian regulatory regimes that may influence safety in offshore systems, as well as risk constraints related to human performance, which is strongly dependent on influencing factors such as safety culture. [ABSTRACT FROM AUTHOR]

• A new method is provided to classify safety regulatory functions. • The functions are divided into four levels. • Four functions with significant variability are identified. • Not only focusing on non-compliance behaviours, but safety decision-making. • A feedback mechanism between regulators and companies is more effective. Governmental safety regulatory functions play an important role in preventing major accidents by incorporating lessons from both the investigation of major accidents and the theory of system accident causation. However, to date, these functions have not been evaluated. In China, a unique aspect of accident prevention is that the government engages in macro-safety regulation. In this paper, the analysis process of the Functional Resonance Analysis Method (FRAM) is applied to the Chinese safety regulatory system to classify and evaluate governmental safety regulatory functions. The results show that there are nine main regulatory functions, and they are divided into four levels. The potential performance variability in the outputs of regulatory functions influences other functions through up-down coupling. The entire regulatory system could therefore undergo unexpected changes that could lead to major accidents. The two most important functions are F1, formulating a plan, and F2, enacting laws and regulations. This system reverses the past view of focusing only on the non-compliance behaviours of government regulators and companies. Some safety constraints are proposed in this paper, which mainly include monitoring and suppression, to reduce the unreasonable output of functions and unexpected changes. This paper provides a new perspective for other countries to further evaluate their governmental safety regulatory functions. [ABSTRACT FROM AUTHOR]

This paper summarises the scientific evaluation of the performance of a novel modelling tool, the Accident Damage Assessment Module (ADAM), developed by the Joint Research Centre (JRC) of the European Commission (EC) to assess the consequences of an industrial accident resulting from an unintended release of a dangerous substance. The ADAM tool is specifically intended to assist Competent Authorities in the European Union (EU) and European Economic Area (EEA), and their supporting research institutions, responsible for the implementation of the Seveso Directive in their countries, as well as governmental and research organisations of EU Accession and Candidate Countries, and European Neighbourhood Policy countries involved in chemical accident prevention and preparedness. In particular, the tool provides decision-making support to various functions associated with industrial risk management, enforcement and oversight, including risk analysis, land-use and emergency planning, inspection and monitoring, and the preparation and review of safety reports. Consequence assessment models are characterised by high level of complexity and of uncertainty. It is therefore of paramount importance to assess their limits. The scientific evaluation was conducted across the entire consequence assessment cycle, including the source terms and the physical effects calculations associated with the concentration toxics after airborne dispersion, the thermal radiation of chemical fires, and the explosion of vapour flammable clouds. The evaluation described in this paper was conducted on a series of relevant scenarios, by benchmarking the outcome of ADAM with the results obtained by similar software tools and with the experimental data obtained on a series of reference field campaigns, as taken from the literature. [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]

• The role of human factors in the occurrence of accidents is presented. • Engineering ethics as an element of human factors is proposed. • The Challenger and Columbia space accidents as two case studies are discussed. • Negligence of engineering ethics as a gap in accident analysis models is described. • Ethical control is suggested in system boundary in systemic models in 3 levels. The purpose of this paper is to further investigate engineering ethics and its gap within accident analysis models. In this paper, at first, the role of human factors in the occurrence of accidents is presented. Then engineering ethics as an element of human factors is proposed. It is suggested that engineering ethics can provide engineers with the necessary guidelines to avoid possible accidents arising from their decisions and actions. In addition, the Challenger and Columbia space shuttle case studies that demonstrate the role of engineering ethics in the prevention and occurrence of accidents are discussed. Then sequential, epidemiological, and systemic accident analysis models are briefly investigated and negligence of engineering ethics as a gap in the accident analysis models is described. At the end, we suggest that by implementing engineering ethics as a controller within the system boundary in systemic accident models we may be able to identify and prevent the ethical causes of accidents. [ABSTRACT FROM AUTHOR]

Abstract Risk assessment has always been a serious concern in maritime transportation since shipping activities can pose potential harm to human life, commodity and maritime environment. Maritime safety researchers, in this context, are trying to enhance risk mitigation measures. This paper attempts to provide an insight into a process of accident development related to risks in ship mooring operation which presents a great challenge for ship's crew. A Fuzzy Fault Tree Analysis (FFTA) is adopted to perform systematic risk analysing on the case of ship mooring operation. The FFTA is capable of tackling limitations of conventional Fault Tree Analysis (FTA) where handling the uncertainties is possible with fuzz logic theory. A real maritime accident is analysed considering potential risks: the case of M/T Zarga which occurred during berthing manoeuvring to LNG (Liquefied Neutral Gas) terminal. The ship crew was seriously injured on his head during accident. To this end a risk model is developed and some risk control options are proposed. Besides handling data shortage in maritime risk assessment, this paper provides practical contributions to maritime professionals in course of risk mitigation and accident prevention. Highlights • The FTA method is adopted under fuzzy environment to conduct risk analysis. • A comprehensive risk analyzing to enhance maritime safety in operational aspects. • Practical contributions to risk analysis for ship owners and terminal managers are provided. [ABSTRACT FROM AUTHOR]

Highlights • A new way to research the work safety is proposed. • The work safety success theory based on the deductive method has been put forward. • The theoretical research of work safety has been deepen. • A dynamic work safety entropy model is constructed and applied. • The conclusion of the paper can be a reference of achieving the zero accident. Abstract Traditional work safety theory has played a significant role in promoting the development of work safety in that its application has effectively reduced the number of accidents and saved thousands of lives. However, as time goes on, the "marginal effect" of the theory is becoming more and more obvious. The world's statistics shows that the number of workplace accidents can only be reduced to a certain level, and cannot be reduced to zero. Also, as reflected from the increasing concerns and research of occupational diseases, the number of new occupational disease case has been found increasing year by year. This paper puts forward the work safety success theory based on the deductive method, which is different from the traditional work safety theory based on the inductive method. It deepens the theoretical research of work safety, and is an important improvement to the traditional theory. Combined with the theory of thermodynamics in physics, the entropy theory is used to explain the work safety success theory. And a dynamic work safety entropy model has been constructed and applied in specific cases, which further validates the work safety success theory. At present, technology and equipment have laid a good foundation for data collection and analysis of work safety research, and the work safety success theory provides a new methodology for the whole process work safety research with big data. The conclusion of this paper can provide reference for achieving the goal of zero accident in the workplace. [ABSTRACT FROM AUTHOR]

Risk prediction of disasters is one of the most effective ways to prevent accidents. To solve the problems in multi-factor complex disaster prediction, this paper proposes a new method for risk prediction and factorial risk analysis. Coal and gas outburst accidents were selected as research objects. First, a new coal and gas outburst prediction model was established that consists of 4 levels and 14 factors. Then, the Improved Fruit Fly Optimization Algorithm (IFOA) and the General Regression Neural Network (GRNN) algorithm were combined to establish the IFOA-GRNN prediction model. After that, the sensitivity analysis method was applied to the analysis of the sensitive factors of coal and gas outbursts. Finally, an apriori algorithm was used to mine the disaster information. The method proposed in this paper was applied to the Pingdingshan No. 8 Min. The application results show that the IFOA-GRNN algorithm proposed in this paper has an accuracy rate of 100% for the prediction of accident risk levels. Compared with the Back Propagation (BP), GRNN and FOA-GRNN algorithms, IFOA-GRNN has the characteristics of a smaller prediction error, higher stability and faster convergence. The sensitivity analysis method can judge the sensitive factors of coal and gas outbursts without knowing the mechanisms of the accident. The a priori algorithm can perform good data mining on the combination of high frequency factors leading to accidents and the relationships between the coal and gas outburst levels and factors. The data mining results are very helpful for the prevention and management of coal and gas outbursts. [ABSTRACT FROM AUTHOR]

Highlights • Accident information in SRs provide reliable and valid data for the statistical analysis. • There exist discernable patterns related to fatal accidents in building construction activities. • Accident patterns can help provide valuable direction for formulating accident prevention strategies. Abstract Fatal accidents occur frequently in building construction activities due to inherently hazardous nature. This paper aims to explore the fatal accident patterns in China's building construction activities by using frequency analysis, correlation coefficient analysis and variance analysis. The data presented in this study come from short reports of accidents published by the Ministry of Housing and Urban-Rural Development. The results are obtained by analyzing the factors related to month, day of week, time interval of day, province, type of accident, and severity of accident. It's mainly found that more fatal accidents occur (1) in July and August, (2) on Monday and (3) during the time intervals 10:00–11:00 and 15:00–16:00, and excessive laboring hours still exist in building construction activities. It's also notable that relatively underdeveloped provinces have experienced higher mortality rate per hundred million yuan of gross domestic product in the building industry, especially Qinghai, Hainan and Heilongjiang. Moreover, fall is the predominant type of fatal accidents, accounting for more than 55% overall. There exists the significant correlation between the types of accidents and the severities of accidents, and each collapse and hoisting damage can cause more fatalities compared to other types. The corresponding recommendations are ultimately put forward to prevent fatal accidents of building construction activities. The patterns found in this paper can provide valuable direction for formulating accident prevention strategies. [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]

Civil construction sites are considered as one of the riskiest environments where many potential hazards may occur. To protect construction workers and prevent accidents in such sites, this paper proposes a novel design for an autonomous system that monitors, localizes, and warns site laborers who avail within danger zones. The proposed system is user-friendly, and its architecture is based on Internet of Things (IoT). The heterogeneous components of this architecture are seamlessly integrated into a middleware backend online server. To accurately detect and identify construction workers, the proposed system employs three combined techniques. They are 1) the 868 MHz radio frequency, 2) directional antennas, and 3) the 40 kHz ultrasound waves. Vehicle's rear is secured by a sensing unit that ensures good coverage along with a wearable device for workers. The design of the wearable device includes a set of components which are a radio transceiver (transmitter/receiver), a wake-up sensor, an alarm actuator, and a GPRS module. The wearable device has a power saving scheme with a current consumption as low as 0.5 μA at 3 V supply; thanks to our RF wake-up sensor. Via proximity, this wearable device becomes hybrid (active/passive) in which it remains in deep sleep mode until the presence of a radio frequency (RF) field. Consequently, the rechargeable battery's life gets increased by up to 2 days of autonomy before recharging. Furthermore, the paper presents an implementation of wireless nodes that are powered by light energy using photovoltaic cells. These nodes adopt energy management and storage schemes for continuous operation for indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

Human factors are the primary factors leading to accidents. Therefore, managing human factors is an important way to prevent accidents. This paper aims to introduce a new method to assess and manage human factors. First, the accident causation model was improved based on Reason's "Swiss-cheese" model, which was then combined w ith the Human Factor Analysis and Classification System (HFACS) to establish the human factors risk assessment model. The evaluation model includes 5 levels (organization influence, unsafe supervision, preconditions for unsafe acts, unsafe acts and emergency influence) and 25 human factors. In the risk assessment process, the set pair analysis method was used to calculate the connection number and the partial connection number of each factor, level and whole system. The safety score and risk development interval were calculated by using the connection number and the risk grade is determined. Thus, the dynamic quantitative evaluation of human risk is realized. By using the partial connection number, the risk development trend of each factor is predicted. Due to the lack of human managed enterprises, the safety status of people is approximately discrete. Therefore, this paper establishes the SPA-Markov chain risk prediction model to predict human risk. The verification results show th a t the prediction error is less than 2%. This indicates that the prediction model can be applied in practice. To reduce human risk, ABC analysis and the "S-O-R" model were used for human risk management. The application results show that this method has a significant effect on improving human safety factors. Finally, this paper summarizes 12 common unsafe factors and their effective safety "stimulus" measure, researching the accident path. According to the organizational level and individual level of human factors, different kinds of human factors management methods are suggested. [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]

The paper presents theoretical approach for ensuring ADR safety. Whole spectrum of object constituting aspects is described in the light of the system methodology, with parallel identification of the system elements, relations between the elements as well as common aims and operational directions. Consequently, author's interpretation of ADR safety system is highlighted. Its implementation into relevant Polish determinants allows to analyze them in strictly practical dimensions and notice that main entities (institutions, services, entrepreneurs etc.) participating in ensuring ADR safety are connected by unique relations stated by law, trade and practical requirements. Such kind of research perspective is valuable especially from efficiency point of view. Accordingly to the Polish school of praxeology, the safety system can be analyzed by efficiency mechanisms, covering many different realms of safety-related operations (e.g. effectiveness, economy, rationality). In the paper a network methodology for identification of the crucial system efficiency determinants is used. Thus, practical development issues of ADR safety system are presented. [ABSTRACT FROM AUTHOR]

This paper deals with the application of Logical Analysis of Data (LAD) to machinery-related occupational accidents, using belt-conveyor-related accidents as an example. LAD is a pattern recognition and classification approach. It exploits the advancement in information technology and computational power in order to characterize the phenomenon under study. The application of LAD to machinery-related accident prevention is innovative. Ideally, accidents do not occur regularly, and as a result, companies have little data about them. The first objective of this paper is to demonstrate the feasibility of using LAD as an algorithm to characterize a small sample of machinery-related accidents with an adequate average classification accuracy. The second is to show that LAD can be used for prevention of machinery-related accidents. The results indicate that LAD is able to characterize different types of accidents with an average classification accuracy of 72–74%, which is satisfactory when compared with other studies dealing with large amounts of data where such a level of accuracy is considered adequate. The paper shows that the quantitative information provided by LAD about the patterns generated can be used as a logical way to prioritize risk factors. This prioritization helps safety practitioners make decisions regarding safety measures for machines. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid approach to assess potential operational contingencies in a real shipboard accident since safety at sea is of paramount significance in maritime transportation industry. The hybrid accident analysis model integrates an Analytical Network Process (ANP) method with Human Factors Analysis and Classification System (HFACS). To achieve this purpose, the HFACS model provides a schematic conceptual framework to investigate and analyse role of human error in marine accident and the ANP method provides correlation among the factors for assessment. Thus, the most important factors that contribute to the accident are revealed respectively. The novelty of this paper is to present a different perspective during marine accident analysis in which priority weights of accident causes related to the human error are calculated by ANP model. The hybrid accident analysis model is established to enhance safety and prevent loss of life or injury in maritime transportation industry. The proposed hybrid approach is illustrated with a real-ship incident case: a serious liquefied petroleum gas leak from the gas carrier ship. In conclusion, the research is expected to encourage safety researchers and ship-management companies to prevent similar accident occurrence. [ABSTRACT FROM AUTHOR]

The Zero Accident Vision (ZAV) is a promising approach developed in industry, but not so much addressed by the safety science research community. In a discussion paper in Safety Science (2013) a call was made for more research in this area. Three years later is a good time to take status of developments in this field. A first set of empirical studies has been published, several authors see new perspectives with the vision, while misunderstandings still flourish with a focus on ‘zero incidents’ as a ‘goal’, rather than the ‘vision’ that all occupational incidents are preventable. This has thus given rise to fundamental criticism of ZAV with some authors seeing ZAV as an unjustified and misleading pretention that is counter-productive for safety. In this paper an overview is given of the knowledge developments in this respect, as well as on the discourse on the controversial aspect of ZAV. There appears to be consensus that merely promoting traditional safety management or accident prevention will not lead to significant new improvements in safety. Six innovative perspectives associated with ZAV are identified and presented in this paper, which together offer a range of possibilities for both industry and for the safety science community to develop new practices and knowledge that may provide significant improvements in safety. The call for more empirical research into this challenging area is relevant for the advocates of ZAV as well as for its critics. [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]

• Confined space risks are mainly managed through the use of PPE. • Safety measures depend on confined space design, need for entry and hazard sources. • Design-oriented solutions could help prevent confined space fatalities. • A knowledge base of design-oriented solutions is proposed for stakeholders. The design of most industrial facilities incorporates confined spaces. Although they are not designed for human occupation, many workers penetrate them to perform tasks such as repairs, cleaning, and inspections while facing the risks of asphyxiation, drowning, and toxicity from chemical exposure. Legislative and normative frameworks recommend adopting Inherently Safer Design (ISD) principles as the primary accident prevention strategy However, confined space fatalities occur mainly during improvised interventions, emphasizing the need for designers to consider these accidents' underlying factors in their future or present projects. This paper provides an applied perspective, suggesting how practitioners can employ ISD principles to effectively overcome the inherent design deficiencies and hazards associated with confined space fatalities. For this purpose, ten confined space fatalities in six different sectors of activity were analyzed using the Causal Tree Method (CTM). Industrial process cases are included. Our results show that alternative safer designs of confined spaces or permanent adapted equipment could eliminate the need for a confined space entry or confined space work. A first model of a design-oriented solutions knowledge base, called Confined Space Permanent Collective Principles (CSPCP), is proposed. This model provides stakeholders from across various industries with an overview of safety measures that can be integrated at different life cycle phases to address confined space hazards. [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]

Systems thinking is a philosophy currently prevalent within construction safety literature that is applied to understand and improve safety in sociotechnical systems. Among systems, the site-project organizational system is of particular interest to this paper. Using focus group and survey feedback research to learn about how safety incidents effect levels of construction workers engagement this paper reveals how a safety incident provides an opportunity to create a potential quality (productivity) upgrade within an organization. The research approach involved a qualitative study involving 27 frontline supervisors and a follow-up survey completed by 207 frontline workers in the Australian Asphalt and Pavement Industry. The focus group interviews supported the articulation of the concepts of tacit safety, explicit safety, situational awareness, foresight ability, practical intelligence and crew synergy. Our findings indicate that having regular shift changes and other job site workers being fatigued are influential on perceptions of tacit safety. An individual's foresight ability was found to be the most potent predictor of worker perceptions of work engagement. The paper explains that relatively small improvements in worker perceptions of safety can bring about significant improvements in employee engagement and productivity. [ABSTRACT FROM AUTHOR]

Road safety is influenced by many factors; these factors include characteristics of the road, climate, traffic and, most importantly, vehicle speeds. Previous research shows that increases in speed are typically associated with an increased collision risk. Moreover, previous studies have also found relationships between road and traffic characteristics and collisions. In addition, these features have also been found to affect speeds. This paper aims to model all the aforementioned relationships simultaneously using a Structural Equation Modelling approach. More specifically, the paper attempts to model the relationship between average speed and collision frequency, while taking into account the effects of factors that confound the relationship. Moreover, the analysis attempts to assess the mediated effects that some variables have on collisions through their effects on speed. The data used in this study originated from 353 two-lane urban roads in the city of Edmonton, Canada. The average speeds were obtained from 35 million speed survey observations collected over a five-year period. The speed data are linked to the crash frequency at each location during the same time frame, along with the other factors (road, traffic and climate). The results show that, among others, average speed, volume, segment length, medians and horizontal curves all have statistically significant effects on collisions. On the other hand, shoulders, speed limits and vehicle-lengths are some variables that significantly influence speeds. The results also show that the effects of some variables on safety are indeed mediated through speeds (both partial and full mediation is observed). These findings provide valuable insight that may assist decision makers in choosing and developing alternative speed management strategies, which, in turn, could help improve safety. [ABSTRACT FROM AUTHOR]

Behavior-Based Safety (BBS) has been used in construction to observe, analyze and modify workers’ behavior. However, studies have identified that BBS has several limitations, which have hindered its effective implementation. To mitigate the negative impact of BBS, this paper uses a case study approach to develop a Big-Data-based platform to classify, collect and store data about workers’ unsafe behavior that is derived from a metro construction project. In developing the platform, three processes were undertaken: (1) a behavioral risk knowledge base was established; (2) images reflecting workers’ unsafe behavior were collected from intelligent video surveillance and mobile application; and (3) images with semantic information were stored via a Hadoop Distributed File System (HDFS). The platform was implemented during the construction of the metro-system and it is demonstrated that it can effectively analyze semantic information contained in images, automatically extract workers’ unsafe behavior and quickly retrieve on HDFS as well. The research presented in this paper can enable construction organizations with the ability to visualize unsafe acts in real-time and further identify patterns of behavior that can jeopardize safety outcomes. [ABSTRACT FROM AUTHOR]

This paper analyses driving behaviour in car-following conditions, based on extensive individual vehicle data collected during experimental field surveys carried out in Italy and the UK. The aim is to contribute to identify simple evidence to be exploited in the ongoing process of driving assistance and automation which, in turn, would reduce rear-end crashes. In particular, identification of differences and similarities in observed car-following behaviours for different samples of drivers could justify common tuning, at a European or worldwide level, of a technological solution aimed at active safety, or, in the event of differences, could suggest the most critical aspects to be taken into account for localisation or customisation of driving assistance solutions. Without intending to be exhaustive, this paper moves one step in this direction. Indeed, driving behaviour and human errors are considered to be among the main crash contributory factors, and a promising approach for safety improvement is the progressive introduction of increasing levels of driving automation in next-generation vehicles, according to the active/preventive safety approach. However, the more advanced the system, the more complex will be the integration in the vehicle, and the interaction with the driver may sometimes become unproductive, or risky, should the driver be removed from the driving control loop. Thus, implementation of these systems will require the interaction of human driving logics with automation logics and then an enhanced ability in modelling drivers’ behaviour. This will allow both higher active-safety levels and higher user acceptance to be achieved, thus ensuring that the driver is always in the control loop, even if his/her role is limited to supervising the automatic logic. Currently, the driving mode most targeted by driving assistance systems is longitudinal driving. This is required in various driving conditions, among which car-following assumes key importance because of the huge number of rear-end crashes. The increased availability of lower-cost information and communication technologies (ICTs) has enhanced the possibility of collecting copious and reliable car-following individual vehicle data. In this work, data collected from three different experiments, two carried out in Italy and one in the UK, are analysed and compared. The experiments involved 146 drivers (105 Italian drivers and 41 UK drivers). Data were collected by two instrumented vehicles. Our analysis focused on inter-vehicular spacing in equilibrium car-following conditions. We observed that (i) the adopted equilibrium spacing can be fitted using lognormal distributions, (ii) the adopted equilibrium spacing increases with speed, and (iii) the dispersion between drivers increases with speed. In addition, according to different headway thresholds (up to 1 second) a significant number of potentially dangerous behaviours is observed. Three different car-following paradigms are also applied to each of the experiments, and modelling parameters are calibrated and compared to obtain indirect confirmation about the observed similarities and differences in driving behaviour. [ABSTRACT FROM AUTHOR]

This paper analyzes the factors influencing safety investment decisions by the government and enterprises from an opportunity cost perspective, which consists of shortage cost due to the occurrence of accidents and excess cost due to investing more than required for accident prevention. Analytical models are built by minimizing expected total opportunity costs. The result of government decision reveals that optimal government safety investments vary positively with unit shortage cost, while they vary inversely with unit excess cost. The mandatory safety investment level established by government for enterprises, which is exactly the same as the optimal government safety investment level, can compel the enterprise to invest more in safety measures when it is higher than the optimal enterprise safety investment level without the mandatory standard. Based on the analytical models, factors influencing safety investment decisions are analyzed through consideration of a case study of China. The results of the case study indicate that the ineffective administrative accountability system, performance appraisal system of local government and officials and government–enterprise relations all contribute to the high accident frequency in China by leading to a safety investment decision with a lower safety level, which also verify that the analytical models proposed in this paper are reasonable. [ABSTRACT FROM AUTHOR]

Accidents involving dangerous substances still happen and establishing an effective proactive methodology to provide safe daily operation remains a challenge. Findings about the limitations of the existing approaches with scenario presentation for the purpose of demonstrating that appropriate precautionary actions are taken to ensure a high level of protection, are the basis for the development of the new methodology, presented in this paper. The emphasis of the methodology is on effective daily operation, based on the identification of latent weaknesses; it takes into account that with dynamic processes safety changes from one moment to another. With the new approach, operators are encouraged to think continuously about the issues that have an impact on safety. Another challenge is the fact that understanding what effective daily operation means may differ from country to country and from legislation level to the operator. The guidance in this paper illustrates what effective daily operation means. The assessment of effective daily operation is also used as the risk acceptance criteria for Seveso establishments, and the presented approach is one of methodological guidance on how to implement the Article 19 of the Directive, 2012/18/EU of the European Parliament and of the Council (the Seveso III Directive). [ABSTRACT FROM AUTHOR]

Management of health and safety in micro-enterprises (less than 10 employees) is a critical issue due to the relatively high level of injuries in such firms. It is also an under-researched area in the international safety literature. The present paper is based on a nationwide survey with the cooperation of the relevant health and safety enforcement authority among micro-firms in several economic sectors. Micro-enterprises comprise a major percentage (95.4%) of business firms in a small economy like Cyprus. The survey examined the perceptions and work attitudes of workers and their relationship with safety performance of the particular firms. This paper presents the results related to the perceptions of employees. A previous paper presented the results focusing on employers. Information was collected on their demographic characteristics and major influencing factors of safety performance like employee participation in safety decisions and safety training. The safety performance measure, an index comprising several safety performance aspects was assessed by the health and safety (labour) inspectors, in order to overcome potential problems of common method bias when all data are obtained from the same group of respondents. The findings suggest that safety performance has a positive and statistically significant correlation with safety training provided by the firm. It has also a positive and statistically significant association with the safety information available to employees and with the perceived safety conditions. The effect of the participation of workers in safety decisions and their organizational commitment, although significantly correlated with safety performance as individual variables, are not statistically significant after controlling for the effect of the other variables mentioned above. The results show the important relationship between safety training and other good practices (like provision of information and improved safety conditions) on safety performance in micro-firms. [ABSTRACT FROM AUTHOR]

In the last two decades, several serious accidents at large-scale technological systems that have had grave consequences, such as that at Bhopal, have primarily been attributed to human error. However, further investigations have revealed that humans are not the primary cause of these accidents, but have inherited the problems and difficulties of working with complex systems created by engineers. The operators have to comprehend malfunctions in real time, respond quickly, and make rapid decisions to return operational units to normal conditions, but under these circumstances, the mental workload of operators rises sharply, and a mental workload that is too high increases the rate of error. Therefore, cognivitive human features such as situation awareness (SA)--one of the most important prerequisite for decision-making--should be considered and analyzed appropriately. This paper applys the SA Error Taxonomy methodology to analyze the role of SA in three different accidents: (1) A runaway chemical reaction at Institute, West Virginia killing two employees, injuring eight people, and requiring the evacuation of more than 40,000 residents adjacent to the facility, (2) The ignition of a vapor cloud at Bellwood, Illinois that killed one person, injured two employees, and caused significant business interruption, and (3) An explosion at Ontario, California injuring four workers and caused extensive damage to the facility. In addition, the paper presents certain requirements for cognitive operator support system development and operator training under abnormal situations to promote operators' SA in the process industry. [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]