The functioning and status of the EC's major accident reporting system on industrial accidents

This paper describes the functioning and current status of the European Commission's Major Accident Reporting System (MARS), dedicated to collect in a consistent way data on major industrial accidents involving dangerous substances from the Member States of the European Union under the requirements of the `Seveso Directives', to analyse and statistically process them, and to distribute all non-confidential accidents data and analysis results to the Member States. This modern information exchange and analysis tool is made up of two connected parts: one for each local unit (i.e. for the Competent Authority of each Member State), and one central part for the European Commission. The local as well as the central parts of this network can serve both as data logging systems and, on different levels of complexity, as data analysis tools. The central database allows complex pattern analysis, identifying and analysing the succession of disruptive factors leading to an accident. On this basis, “lessons learned” can be formulated for the industry or regulatory bodies for further accident prevention. Results of various overall analyses of the contents of MARS are given. The availability of MARS data and analysis results is described.     

Technical note on the contribution of sociotechnical factors to accidents notified to MARS

Not least due to the new `Seveso II Directive', Safety Management Systems (SMS) have become a hot topic in the `safety business'. To reveal in a structured way how organisations manage safety at their plants, classification schemes can be applied to identify plant-specific precautions on different hierarchical levels, e.g. from `top level' equipment reliability to `bottom level' safety climate. In this paper, such a model is used to classify the accidents reported to the European Commission's Major Accident Reporting System (MARS) according to the levels on which failures resulting in actual accidents did occur, and to cross-compare the levels of causation with the overall `severity' of the accidents. It is analytically shown that ∼66% of the accidents are caused by latent SMS failures, and that especially those accidents which are caused by failures in the `deepest layers of latency' have significantly higher `severities' than accidents caused by more `immediate' failures. In other words, it is quite likely that the deeper the underlying causes of an accident, the more `severe' its consequences. Implementing SMS is thus worth the effort.     

Operational risk assessment of chemical industries by exploiting accident databases

Accident databases (NRC, RMP, and others) contain records of incidents (e.g., releases and spills) that have occurred in the USA chemical plants during recent years. For various chemical industries, [Kleindorfer, P. R., Belke, J. C., Elliott, M. R., Lee, K., Lowe, R. A., & Feldman, H. I. (2003). Accident epidemiology and the US chemical industry: Accident history and worst-case data from RMP*Info. Risk Analysis, 23(5), 865–881.] summarize the accident frequencies and severities in the RMP*Info database. Also, [Anand, S., Keren, N., Tretter, M. J., Wang, Y., O’Connor, T. M., & Mannan, M. S. (2006). Harnessing data mining to explore incident databases. Journal of Hazardous Material, 130, 33–41.] use data mining to analyze the NRC database for Harris County, Texas.Classical statistical approaches are ineffective for low frequency, high consequence events because of their rarity. Given this information limitation, this paper uses Bayesian theory to forecast incident frequencies, their relevant causes, equipment involved, and their consequences, in specific chemical plants. Systematic analyses of the databases also help to avoid future accidents, thereby reducing the risk.More specifically, this paper presents dynamic analyses of incidents in the NRC database. The NRC database is exploited to model the rate of occurrence of incidents in various chemical and petrochemical companies using Bayesian theory. Probability density distributions are formulated for their causes (e.g., equipment failures, operator errors, etc.), and associated equipment items utilized within a particular industry. Bayesian techniques provide posterior estimates of the cause and equipment-failure probabilities. Cross-validation techniques are used for checking the modeling, validation, and prediction accuracies. Differences in the plant- and chemical-specific predictions with the overall predictions are demonstrated. Furthermore, extreme value theory is used for consequence modeling of rare events by formulating distributions for events over a threshold value. Finally, the fast-Fourier transform is used to estimate the capital at risk within an industry utilizing the frequency and loss-severity distributions.     

Conducting in-depth accident studies

In 1983 the Accident Research Unit at the Institute for Consumer Ergonomics began a major study, sponsored by the UK Department of Transport and three motor companies, to investigate the causes of injury to occupants in car accidents. This study has been under way for nearly ten years and the database of information is one of the most comprehensive of its kind in the world. This information plays a key role in establishing the priorities for safety research in the UK, based on real life accident data. This paper is concerned with the practical aspects of the collection and analysis of real life accident data. Issues include the establishment and maintenance of collaboration with police forces, hospital consultants and coroners; recruitment and training of staff; accident sampling; data quality control and large scale project management. The case is also made for the need to establish a coordinated European database of accident information to facilitate the development of appropriate safety requirements and to set priorities for safety research in Europe.     

An algorithm for assessing the risk of traffic accident

INTRODUCTION: This study is aimed at developing an algorithm to estimate the number of traffic accidents and assess the risk of traffic accidents in a study area. METHOD: The algorithm involves a combination of mapping technique (Geographical Information System (GIS) techniques) and statistical methods (cluster analysis and regression analysis). Geographical Information System is used to locate accidents on a digital map and realize their distribution. Cluster analysis is used to group the homogeneous data together. Regression analysis is performed to realize the relation between the number of accident events and the potential causal factors. Negative binomial regression model is found to be an appropriate mathematical form to mimic this relation. Accident risk of the area, derived from historical accident records and causal factors, is also determined in the algorithm. The risk is computed using the Empirical Bayes (EB) approach. A case study of Hong Kong is presented to illustrate the effectiveness of the proposed algorithm. RESULTS: The results show that the algorithm improves accident risk estimation when comparing to the estimated risk based on only the historical accident records. The algorithm is found to be more efficient, especially in the case of fatality and pedestrian-related accident analysis. IMPACT ON INDUSTRY: The output of the proposed algorithm can help authorities effectively identify areas with high accident risk. In addition, it can serve as a reference for town planners considering road safety.     

Factors associated with fatal accident involvement among California drivers

The primary objectives of this study were to examine characteristics of drivers involved in fatal accidents and to determine if those drivers could be distinguished from California's general driving population on the basis of prior driving record. A sample of drivers involved in 1970–1971 fatal accidents was analyzed and compared to a sample of drivers from the general driving population during the same time period. Drivers who had been drinking prior to the accident, who were considered at-fault for the accident, or whose accident occurred at night were found to have worse prior driving records than other fatal accident-involved drivers. The results also indicated that, as a whole, drivers involved in fatal accidents had worse violation and/or accident records, as well as different demographic and license characteristics than drivers in the general population. The classification functions derived to predict fatal accidents, however, did not differ greatly from regression equations that have been constructed to predict total accidents. It was therefore concluded that prediction systems keyed to total accidents will, to a large extent, also identify high-risk fatal accident drivers.     

Reference criteria for the identification of accident scenarios in the framework of land use planning

Land use planning (LUP) around industrial sites at risk of major accidents requires the application of sound approaches in the selection of credible accident scenarios. In fact, the ‘technical’ phase of LUP is based on the identification and assessment of relevant accident scenarios. An improper choice of scenarios may critically affect both the ‘technical’ phase of risk assessment and the following ‘policy’ phase concerning decision making on land-use restrictions and/or licensing. The present study introduces a procedure aimed at the systematic identification of reference accident scenarios to be used in the gathering of technical data on potential major accidents, which is a necessary step for LUP around Seveso sites. Possible accident scenarios are generated by an improved version of the MIMAH methodology (Methodology for the Identification of Major Accident Hazards). The accident scenarios are then assessed for LUP relevance considering severity, frequency and time scale criteria. The influence of prevention and mitigation barriers is also taken into account. Two applications are used to demonstrate the proposed procedure. In both case-studies, the proposed methodology proved successful in producing consistent sets of reference scenarios.     

A study of maintenance-related major accident cases in the 21st century

This paper is based on a review of 183 detailed, major accident investigation and analysis reports related to the handling, processing and storage of hydrocarbons and hazardous chemicals over a decade from 2000 to 2011. The reports cover technical, human and organizational factors. In this paper, the Work and Accident Process (WAP) classification scheme is applied to the accident reports with the intention of investigating to what extent maintenance has been a cause of major accidents and what maintenance-related causes have been the most frequent.The main objectives are: (1) to present more current overall statistics of maintenance-related major accidents, (2) to investigate the trend of maintenance-related major accidents over time, and (3) to investigate which maintenance-related major accident causes are the most frequent, requiring the most attention in the drive for improvement.The paper presents statistical analysis and interpretation of maintenance-related major accidents’ moving averages as well as data related to the types of facility, hazardous substances, major accidents and causes. This is based on a thorough review of accident investigation reports.It is found that out of 183 major accidents in the US and Europe, maintenance was linked to 80 (44%) and that the accident trend is decreasing. The results also show that “lack of barrier maintenance” (50%), “deficient design, organization and resource management” (85%) and “deficient planning/scheduling/fault diagnosis” (69%) are the most frequent causes in terms of the active accident process, the latent accident process and the work process respectively.     

What-You-Look-For-Is-What-You-Find – The consequences of underlying accident models in eight accident investigation manuals

Accident investigation manuals are influential documents on various levels in a safety management system, and it is therefore important to appraise them in the light of what we currently know – or assume – about the nature of accidents. Investigation manuals necessarily embody or represent an accident model, i.e., a set of assumptions about how accidents happen and what the important factors are. In this paper we examine three aspects of accident investigation as described in a number of investigation manuals. Firstly, we focus on accident models and in particular the assumptions about how different factors interact to cause – or prevent – accidents, i.e., the accident “mechanisms”. Secondly, we focus on the scope in the sense of the factors (or factor domains) that are considered in the models – for instance (hu)man, technology, and organization (MTO). Thirdly, we focus on the system of investigation or the activities that together constitute an accident investigation project/process. We found that the manuals all used complex linear models. The factors considered were in general (hu)man, technology, organization, and information. The causes found during an investigation reflect the assumptions of the accident model, following the ‘What-You-Look-For-Is-What-You-Find’ or WYLFIWYF principle. The identified causes typically became specific problems to be fixed during an implementation of solutions. This follows what can be called ‘What-You-Find-Is-What-You-Fix’ or WYFIWYF principle.     

Comparison of techniques for accident scenario analysis in hazardous systems

In this paper, three accident scenario analysis techniques are presented and compared regarding their efficiency vs. the demanded resources. The complexity of modern industrial systems has prompted the development of accident analysis techniques that should thoroughly investigate accidents. The idea of criteria classification to fulfill this requirement has been proposed by other researchers and is examined here too. The comparison is done through the application of Event Tree analysis, Fault Tree analysis and Petri Nets technique—two relatively simple and a more demanding methodology—on the same hazardous chemical facility in view of analyzing an accident scenario of a hazardous transfer procedure. Accident scenario analysis techniques are essential not only in learning lessons from unfortunate events in the chemical industry but also in preventing the occurrence of such events in the future and in communicating risk more efficiently.     

PLR事故隐患分级法在上海市化工企业中的应用

PLR事故隐患分级法针对火灾、爆炸、毒物泄漏重大事故类型 ,根据事故隐患评价的基本原理 ,结合上海市事故发生及隐患普查现状 ,提出了上海市“重大事故隐患”的定义和辨识方法 ,确定了事故隐患易发性(P)、事故导致损失程度 (L)和事故后果影响范围 (R0 .5)三项评价指标 ,并将P ,L ,R三项指标组合成“PLR事故隐患分级评价表” ,由PLR事故隐患分级评价表得出事故隐患分级结果。新方法通过在上海市典型化工企业的试点应用 ,验证了PLR事故隐患分级法在化工企业中的可操作性和有效性 ,为上海市重大事故隐患的辨识、评价和整改工作提供了科学的管理方法     

Modeling accident occurrence at signalized tee intersections with special emphasis on excess zeros

In implementing effective remedial treatments at hazardous intersections, it often is necessary to identify the geometric and traffic factors that lead to accident occurrence. However, one particular problem frequently encountered in accident studies is how to distinguish virtually safe intersections with little likelihood of accident occurrence from those that have happened to have no accident due to the random process. To deal with this problem, the "excess" records of zero accident, the zero-inflated negative binomial was used to assign the probability to the accident outcome. Accident data at 104 signalized tee intersections in Singapore over a period of 9 years were employed for model development. The model indicates that uncontrolled left-turn slip road, permissive right-turn phase, existence of a horizontal curve, short sight distances, large number of signal phases, total approach volume, and left-turn volume may increase accident occurrence. On the other hand, right-turn channelization, acceleration section on the left-turn lane, median railings, and more than 5% approach gradient may reduce accident occurrence. Moreover, there is a trend of reducing accidents over the years.     

The importance of near miss reporting to further improve safety performance

Dealing with accidents implies that such events have in common the potential to affect people and the environment in a significant way. Therefore, all parties involved in industrial risk management processes, i.e. industry, regulatory authorities, public as well as scientific and technical institutions, are well aware of the importance of considering and analysing such type of events for the purposes of accident prevention. Also, the methods of Quantitative Risk Assessment (QRA) have large experience in numerically expressing the various degrees of risk related to accidents. On the other hand, the topic of including `near misses' (i.e. any event which could have escalated to an accident) in safety management systems with the aim to prevent major accidents and the occurrence of similar events in the future is relatively new. Although its importance has more and more been recognised in the last few years, it is not yet a commonly accepted fact that near miss reporting and investigation of near misses should be an integral part of a safety management system in industrial facilities. In the European Council's new `Seveso II Directive' 96/82/EC, there is—in addition to the mandatory requirements of major accident reporting—an explicit recommendation to report near misses to the Commission's Major Accident Reporting System (MARS) on a voluntary basis. In this paper, examples of current experience in the chemical industry with the collection and analysis of data on near misses are presented and discussed with regard to industry-wide conclusions. In addition to this more qualitative discussion, quantitative arguments are put forward regarding the impact of near misses on risk estimates derived from QRA.     

PDE accident model from a safety information perspective and its application to Zhangjiakou fire and explosion accident

Accident models can provide theoretical frameworks for determining the causes and mechanisms of accidents, and thus are theoretical bases for accident analysis and prevention. The role of safety information in accident causation is profound. Thus, safety information is an important and essential perspective for developing accident models. This study presents a new accident model developed from a safety information perspective, called the Prediction—Decision—Execution (PDE) accident model. Because the PDE accident model is an emerging accident model that was proposed in 2018, its analysis logic and viability remain to be discussed. Thus, the main contributions of this study include two aspects: (i) detailed explanation of the analysis logic of the PDE accident model, and (ii) case-study examination of the Zhangjiakou fire and explosion accident, a serious accident that occurred in China in 2018, to demonstrate the viability of the PDE accident model. Results show that this is a safety-information-driven accident model that can provide a new and effective methodology for accident analysis and prevention, and safety management.     

水上交通事故分析研究进展

为更好地开展水上交通事故分析研究,提高我国水上交通安全水平,从船舶风险评估与事故预测、事故分析以及事故及通航安全数据的组织与数据库建立3个方面对国内外的相关研究进行论述和分析。提出以建立水上交通事故时空数据平台为基础,结合数据挖掘和安全工程的理论方法研究事故发生机理,评估事故风险,并将研究事故模拟再现技术作为事故分析的重要技术手段。     

Role of gender in road accidents

Influence of driver sex on road accidents is assessed in this article. Accident records for 3 years and for three different income regions were analyzed. Annual distance traveled, social and economic participation, and effect of public vehicle accidents were considered. Effects of environmental factors and driver age were also included. Driver faults analysis identified possible reasons for accident differences. Analysis of accident severity was used to assess degree of harm. Statistical analysis at the 5% significance level was used to evaluate all differences. The results show that male accident rates are significantly higher. This trend is consistent through all the analyses. Accident differences are significant only in normal driving conditions. Drivers over age 50 had the lowest accident rates. Accident rate differences were caused by lack of attention and impatience among male drivers. Appropriate means of communication should alert concerned populations to these findings.     

基于 MIMAH 的工业事故场景辨识

介绍了事故场景概念,并根据欧盟ARAMIS项目框架下提出的MIMAH（辨识重要事故危险方法）,即从危险设备的角度来辨识与设备相关的关键事件,并利用事故树（ FTA ）、事件树（ ETA）,建立一个以关键事件为中心的蝴蝶结结构图来描述事故场景。通过运用这种方法,能够对事故场景的辨识更加具有系统性、针对性。最后,以液氨储罐装置作为示例进行说明。     

A safety barrier-based accident model for offshore drilling blowouts

Blowout is one of the most serious accidents in the offshore oil and gas industry. Accident records show that most of the offshore blowouts have occurred in the drilling phase. Efficient measures to prevent, mitigate, and control offshore drilling blowouts are important for the entire offshore oil and gas industry. This article proposes a new barrier-based accident model for drilling blowouts. The model is based on the three-level well control theory, and primary and secondary well control barriers and an extra well monitoring barrier are established between the reservoir and the blowout event. The three barriers are illustrated in a graphical model that is similar to the well-known Swiss cheese model. Five additional barriers are established to mitigate and control the blowout accident, and event tree analysis is used to analyze the possible consequence chains. Based on statistical data and literature reviews, failures of each barrier are presented. These failures can be used as guidance for offshore drilling operators to become aware of the vulnerabilities of the safety barrier system, and to assess the risk related to these barriers. The Macondo accident is used as a case study to show how the new model can be used to understand the development of the events leading to the accident. The model can also be used as an aid to prevent future blowouts or to stop the escalation of events.