ARAMIS project: a comprehensive methodology for the identification of reference accident scenarios in process industries

In the frame of the Accidental Risk Assessment Methodology for Industries (ARAMIS) project, this paper aims at presenting the work carried out in the part of the project devoted to the definition of accident scenarios. This topic is a key-point in risk assessment and serves as basis for the whole risk quantification.

The first result of the work is the building of a methodology for the identification of major accident hazards (MIMAH), which is carried out with the development of generic fault and event trees based on a typology of equipment and substances. The term “major accidents” must be understood as the worst accidents likely to occur on the equipment, assuming that no safety systems are installed.

A second methodology, called methodology for the identification of reference accident scenarios (MIRAS) takes into account the influence of safety systems on both the frequencies and possible consequences of accidents. This methodology leads to identify more realistic accident scenarios. The reference accident scenarios are chosen with the help of a tool called “risk matrix”, crossing the frequency and the consequences of accidents.

This paper presents both methodologies and an application on an ethylene oxide storage.     

A global view on ARAMIS, a risk assessment methodology for industries in the framework of the SEVESO II directive

The ARAMIS methodology was developed in an European project co-funded in the fifth Framework Programme of the European Commission with the objective to answer the specific requirements of the SEVESO II directive. It offers an alternative to purely deterministic and probabilistic approaches to risk assessment of process plants. It also answers the needs of the various stakeholders interested by the results of the risk assessment for land use or emergency planning, enforcement or, more generally, public decision-making. The methodology is divided into the following major steps: identification of major accident hazards (MIMAH), identification of the safety barriers and assessment of their performances, evaluation of safety management efficiency to barrier reliability, identification of reference accident scenarios (MIRAS), assessment and mapping of the risk severity of reference scenarios and of the vulnerability of the plant surroundings. The methodology was tested during five case studies, which provided useful information about the applicability of the method and, by identifying the most sensitive parts of it opened way to new research activity for an improved industrial safety.     

Towards a better reliability of risk assessment: Development of a qualitative & quantitative risk evaluation model (QREM) for different trades of construction works in Hong Kong

Since the safety professionals are the key decision makers dealing with project safety and risk assessment in the construction industry, their perceptions of safety risk would directly affect the reliability of risk assessment. The safety professionals generally tend to heavily rely on their own past experiences to make subjective decisions on risk assessment without systematic decision making. Indeed, understanding of the underlying principles of risk assessment is significant. In this study, the qualitative analysis on the safety professionals’ beliefs of risk assessment and their perceptions towards risk assessment, including their recognitions of possible accident causes, the degree of differentiations on their perceptions of risk levels of different trades of works, recognitions of the occurrence of different types of accidents, and their inter-relationships with safety performance in terms of accident rates will be explored in the Stage 1.     

Multiple-vehicle traffic accidents in Hong Kong

‘Multiple-vehicle traffic accident’ refers to a crash between two or more moving objects. Unlike single-vehicle accidents, not all drivers involving in a multiple-vehicle accident are responsible for the occurrence of the event. Accordingly, variables such as road type, speed limit and number of vehicles involved in the accident are expected to play a much more important role in association with injury severity in multiple-vehicle accidents. To study the factors influencing injury severity of multiple-vehicle traffic accidents, a population-based study was conducted. The traffic accident data was obtained from the Traffic Accident Data System (TRADS), which was developed by the Transport Department, Police Force and Information Technology Services Department, Hong Kong. Multiple-vehicle traffic accidents (N = 10,630) occurring during the 2-year period 1999/2000 were considered. Potential risk factors such as district, human, vehicle, safety, environmental and site factors were examined. Categorizing injury severity into “fatal/serious” and “slight”, a stepwise logistic regression model was applied to the population data set. The district board, time of the accident, driver's gender, vehicle type, road type, speed limit and the number of vehicles involved are significant factors influencing the injury severity. Identification of risk factors for severe traffic accidents provides valuable information to help with new and improved road safety control measures.     

Risk factors affecting the severity of single vehicle traffic accidents in Hong Kong

A population-based case-control study was conducted to examine factors affecting the severity of single vehicle traffic accidents in Hong Kong. In particular, single vehicle accident data of three major vehicle types, namely private vehicles, goods vehicles and motorcycles, which contributed to over 80% of all single vehicle accidents during the 2-year-period 1999-2000, were considered. Data were obtained from the newly implemented traffic accident data system (TRADS), which was developed jointly by the Transport Department, Police Force and Information Technology Services Department, Hong Kong. The effect of district, human, vehicle, safety, environmental and site factors on injury severity of an accident was examined. Unique risk factors associated with each of the vehicle types were identified by means of stepwise logistic regression models. For private vehicles, district board, gender of driver, age of vehicle, time of the accident and street light conditions are significant factors determining injury severity. For goods vehicles, seat-belt usage and weekday occurrence are the only two significant factors associated with injury severity. For motorcycles, age of vehicle, weekday and time of the accident were determined to be important factors affecting the injury severity. Identification of potential risk factors pertinent to the particular vehicle type has important implications to relevant official organisations in modifying safety measures in order to reduce the occurrence of severe traffic accidents, which would help to promote a safe road environment.     

Bayesian-network-based safety risk assessment for steel construction projects

There are four primary accident types at steel building construction (SC) projects: falls (tumbles), object falls, object collapse, and electrocution. Several systematic safety risk assessment approaches, such as fault tree analysis (FTA) and failure mode and effect criticality analysis (FMECA), have been used to evaluate safety risks at SC projects. However, these traditional methods ineffectively address dependencies among safety factors at various levels that fail to provide early warnings to prevent occupational accidents. To overcome the limitations of traditional approaches, this study addresses the development of a safety risk-assessment model for SC projects by establishing the Bayesian networks (BN) based on fault tree (FT) transformation. The BN-based safety risk-assessment model was validated against the safety inspection records of six SC building projects and nine projects in which site accidents occurred. The ranks of posterior probabilities from the BN model were highly consistent with the accidents that occurred at each project site. The model accurately provides site safety-management abilities by calculating the probabilities of safety risks and further analyzing the causes of accidents based on their relationships in BNs. In practice, based on the analysis of accident risks and significant safety factors, proper preventive safety management strategies can be established to reduce the occurrence of accidents on SC sites.     

Measuring the effect of police surveillance on the prevention of traffic accidents

An approach for estimating the effect of preventive measures taken against traffic accidents is presented. A simple stochastic model as to the process of accident occurrence is provided and the relationship among vulnerable behavior, collisions, and police surveillance is formalized. Here vulnerable behavior is defined as the state prior to the collision in the process of accident occurrence, and police surveillance is selected as one of the preventive measures against the accident.

The procedures by which the model can be applied in practice are shown for the case of intersection accidents, and the effects of some intersection surveillance forms on accident prevention are calculated. The results suggest that police surveillance which affects vulnerable behavior reduces accidents and that each collision category differently has the most effective surveillance form.

It is concluded that vulnerable behavior, which increases the likelihood of accident occurrence, provides a more sensitive safety measure than accidents.     

Cannabis use and traffic accidents in a birth cohort of young adults

Objective: to examine linkages between cannabis use and traffic accident risks in a birth cohort of 907 young New Zealanders studied from 18 to 21 years. Methods: during the course of a 21-year longitudinal study of a birth cohort of 907 New Zealand born children information was gathered on (a) annual frequency of cannabis use over the period from 18 to 21 years; (b) annual rates of traffic accidents during the period 18–21 years; (c) measures of driver behaviours and characteristics. The association between cannabis use and traffic accident risk was examined among the 907 sample members who reported driving a motor vehicle between the ages of 18 and 21 years. Results: there were statistically significant relationships between reported annual cannabis use and annual accident rates. This association was present only for ‘active’ accidents in which driver behaviours contributed to the accident; those using cannabis more than 50 times per year had estimated rates of active accidents that were 1.6 (95% CI 1.2–2.0) times higher than the rate for non-users. However, statistical control for driver behaviours and characteristics related to cannabis use (drink driving behaviour; risky/illegal driving behaviours; driver attitudes; gender) eliminated the association between cannabis use and traffic accident risks. Conclusions: although cannabis use was associated with increased risks of traffic accidents among members of this birth cohort, these increased risks appear to reflect the characteristics of the young people who used cannabis rather than the effects of cannabis use on driver performance.     

An in-depth study of accidents involving collisions with utility poles

This paper summarizes the objectives, methodology, findings, and recommendations of a large-scale study of automobile crashes involving utility poles. In an 8-month survey of accidents, information on site characteristics and accident severity was obtained for a sample of 879 crashes. Randomly-selected samples of 795 sites and 627 vehicles provided control information. An accident-predictor model which identifies accident risk on the basis of site measurements has been derived. The model reveals a range of relative risks in the population of exposed poles of the order of 1000:1, and enables the identification of the relatively small proportion of poles which account for the majority of accidents. The model was used in conjunction with estimates of the costs of accidents to show that a number of remedial treatments are warranted. The hazardous effects of low tyre tread depths and improper tyre inflation pressures are also demonstrated.     

Major hazard risk indicators for monitoring of trends in the Norwegian offshore petroleum sector

The Petroleum Safety Authority Norway (PSA, formerly Norwegian Petroleum Directorate) took in 1999 the initiative to develop a method in order to assess trends and status for the risk levels in the Norwegian offshore petroleum industry. A method was developed, and a pilot study report was issued in April 2001, covering the period 1996–2000. Annual updates have been performed since then, and the latest report covers the period 1996–2004. The statistical approach is based on recording occurrence of near misses and relevant incidents, performance of barriers and results from risk assessments. Of similar importance is an evaluation of safety culture, motivation, communication and perceived risk. This is covered through the use of social science methods, such as questionnaire surveys and a number of interviews, audit and inspection reports as well as accident and incident investigations. There are also indicators for occupational accidents and occupational illness/-physical working environment factors.The focus is on the major hazard risk components for personnel staying on the offshore installations. An overview of the indicators used to illustrate these risk aspects is presented, followed by a discussion of the analytical approach used for these indicators. Results from the risk assessment for the Norwegian Continental Shelf in the period 1996–2004 are used throughout for illustration, and discussion of challenges.     

Overview of critical risk factors in Power-Two-Wheeler safety

Power-Two-Wheelers (PTWs) constitute a vulnerable class of road users with increased frequency and severity of accidents. The present paper focuses of the PTW accident risk factors and reviews existing literature with regard to the PTW drivers’ interactions with the automobile drivers, as well as interactions with infrastructure elements and weather conditions. Several critical risk factors are revealed with different levels of influence to PTW accident likelihood and severity. A broad classification based on the magnitude and the need for further research for each risk factor is proposed. The paper concludes by discussing the importance of dealing with accident configurations, the data quality and availability, methods implemented to model risk and exposure and risk identification which are critical for a thorough understanding of the determinants of PTW safety.     

Augmenting defense-in-depth with the concepts of observability and diagnosability from Control Theory and Discrete Event Systems

Defense-in-depth is a fundamental principle/strategy for achieving system safety. First conceptualized within the nuclear industry, defense-in-depth is the basis for risk-informed decisions by the U.S. Nuclear Regulatory Commission, and is recognized under various names in other industries (e.g., layers of protection in the Chemical industry). Accidents typically result from the absence or breach of defenses or violation of safety constraints. Defense-in-depth is realized by a diversity of safety barriers and a network of redundancies. However, this same redundancy and the intrinsic nature of defense-in-depth - the multiple lines of defense or “protective layers” along a potential accident sequence - may enhance mechanisms concealing the occurrence of incidents, or that the system has transitioned to a hazardous state (accident pathogens) and that an accident is closer to being released. Consequently, the ability to safely operate the system may be hampered and the efficiency of defense-in-depth may be degraded or worse may backfire. Several accidents reports identified hidden failures or degraded observability of accidents pathogens as major contributing factors.In this work, we begin to address this potential theoretical deficiency in defense-in-depth by bringing concepts from Control Theory and Discrete Event Systems to bear on issues of system safety and accident prevention. We introduce the concepts of controllability, observability, and diagnosability, and frame the current understanding of system safety as a “control problem” handled by defense-in-depth and safety barriers (or safety constraints). Observability and diagnosability are information-theoretic concepts, and they provide important complements to the energy model of accident causation from which the defense-in-depth principle derives. We formulate a new safety-diagnosability principle for supporting accident prevention, and propose that defense-in-depth be augmented with this principle, without which defense-in-depth can degenerate into a defense-blind safety strategy. Finally, we provide a detailed discussion and illustrative modeling of the sequence of events that lead to the BP Texas City Refinery accident in 2005 and emphasize how a safety-diagnosable architecture of the refinery could have supported the prevention of this accident or mitigated its consequences. We hope the theoretical concepts here introduced and the safety-diagnosability principle become useful additions to the intellectual toolkit of risk analysts and safety professionals and stimulate further interaction/collaboration between the control and safety communities.     

Application of classification algorithms for analysis of road safety risk factor dependencies

Transportation continues to be an integral part of modern life, and the importance of road traffic safety cannot be overstated. Consequently, recent road traffic safety studies have focused on analysis of risk factors that impact fatality and injury level (severity) of traffic accidents. While some of the risk factors, such as drug use and drinking, are widely known to affect severity, an accurate modeling of their influences is still an open research topic. Furthermore, there are innumerable risk factors that are waiting to be discovered or analyzed. A promising approach is to investigate historical traffic accident data that have been collected in the past decades. This study inspects traffic accident reports that have been accumulated by the California Highway Patrol (CHP) since 1973 for which each accident report contains around 100 data fields. Among them, we investigate 25 fields between 2004 and 2010 that are most relevant to car accidents. Using two classification methods, the Naive Bayes classifier and the decision tree classifier, the relative importance of the data fields, i.e., risk factors, is revealed with respect to the resulting severity level. Performances of the classifiers are compared to each other and a binary logistic regression model is used as the basis for the comparisons. Some of the high-ranking risk factors are found to be strongly dependent on each other, and their incremental gains on estimating or modeling severity level are evaluated quantitatively. The analysis shows that only a handful of the risk factors in the data dominate the severity level and that dependency among the top risk factors is an imperative trait to consider for an accurate analysis.     

Modeling secondary accidents identified by traffic shock waves

The high potential for occurrence and the negative consequences of secondary accidents make them an issue of great concern affecting freeway safety. Using accident records from a three-year period together with California interstate freeway loop data, a dynamic method for more accurate classification based on the traffic shock wave detecting method was used to identify secondary accidents. Spatio-temporal gaps between the primary and secondary accident were proven be fit via a mixture of Weibull and normal distribution. A logistic regression model was developed to investigate major factors contributing to secondary accident occurrence. Traffic shock wave speed and volume at the occurrence of a primary accident were explicitly considered in the model, as a secondary accident is defined as an accident that occurs within the spatio-temporal impact scope of the primary accident. Results show that the shock waves originating in the wake of a primary accident have a more significant impact on the likelihood of a secondary accident occurrence than the effects of traffic volume. Primary accidents with long durations can significantly increase the possibility of secondary accidents. Unsafe speed and weather are other factors contributing to secondary crash occurrence. It is strongly suggested that when police or rescue personnel arrive at the scene of an accident, they should not suddenly block, decrease, or unblock the traffic flow, but instead endeavor to control traffic in a smooth and controlled manner. Also it is important to reduce accident processing time to reduce the risk of secondary accident.     

Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian network

Highway traffic accidents all over the world result in more than 1.3 million fatalities annually. An alarming number of these fatalities occurs in developing countries. There are many risk factors that are associated with frequent accidents, heavy loss of lives, and property damage in developing countries. Unfortunately, poor record keeping practices are very difficult obstacle to overcome in striving to obtain a near accurate casualty and safety data. In light of the fact that there are numerous accident causes, any attempts to curb the escalating death and injury rates in developing countries must include the identification of the primary accident causes.This paper, therefore, seeks to show that the Delphi Technique is a suitable alternative method that can be exploited in generating highway traffic accident data through which the major accident causes can be identified. In order to authenticate the technique used, Korea, a country that underwent similar problems when it was in its early stages of development in addition to the availability of excellent highway safety records in its database, is chosen and utilized for this purpose. Validation of the methodology confirms the technique is suitable for application in developing countries. Furthermore, the Delphi Technique, in combination with the Bayesian Network Model, is utilized in modeling highway traffic accidents and forecasting accident rates in the countries of research.     

Development of a dynamical systems model of plant programmatic performance on nuclear power plant safety risk

Application of probabilistic risk assessment (PRA) techniques to model nuclear power plant accident sequences has provided a significant contribution to understanding the potential initiating events, equipment failures and operator errors that can lead to core damage accidents. Application of the lessons learned from these analyses has resulted in significant improvements in plant operation and safety. However, this approach has not been nearly as successful in addressing the impact of plant processes and management effectiveness on the risks of plant operation. The research described in this paper presents an alternative approach to addressing this issue. In this paper we propose a dynamical systems model that describes the interaction of important plant processes on nuclear safety risk. We discuss development of the mathematical model including the identification and interpretation of significant inter-process interactions. Next, we review the techniques applicable to analysis of nonlinear dynamical systems that are utilized in the characterization of the model. This is followed by a preliminary analysis of the model that demonstrates that its dynamical evolution displays features that have been observed at commercially operating plants. From this analysis, several significant insights are presented with respect to the effective control of nuclear safety risk. As an important example, analysis of the model dynamics indicates that significant benefits in effectively managing risk are obtained by integrating the plant operation and work management processes such that decisions are made utilizing a multidisciplinary and collaborative approach. We note that although the model was developed specifically to be applicable to nuclear power plants, many of the insights and conclusions obtained are likely applicable to other process industries.     

Prediction of road accidents: A Bayesian hierarchical approach

In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks in order to represent non-linearity between risk indicating and model response variables, as well as different types of uncertainties which might be present in the development of the specific models.     

Barrier and operational risk analysis of hydrocarbon releases (BORA-Release). Part I. Method description

Investigations of major accidents show that technical, human, operational, as well as organisational factors influence the accident sequences. In spite of these facts, quantitative risk analyses of offshore oil and gas production platforms have focused on technical safety systems. This paper presents a method (called BORA-Release) for qualitative and quantitative risk analysis of the platform specific hydrocarbon release frequency. By using BORA-Release it is possible to analyse the effect of safety barriers introduced to prevent hydrocarbon releases, and how platform specific conditions of technical, human, operational, and organisational risk influencing factors influence the barrier performance. BORA-Release comprises the following main steps: (1) development of a basic risk model including release scenarios, (2) modelling the performance of safety barriers, (3) assignment of industry average probabilities/frequencies and risk quantification based on these probabilities/frequencies, (4) development of risk influence diagrams, (5) scoring of risk influencing factors, (6) weighting of risk influencing factors, (7) adjustment of industry average probabilities/frequencies, and (8) recalculation of the risk in order to determine the platform specific risk related to hydrocarbon release. The various steps in BORA-Release are presented and discussed. Part II of the paper presents results from a case study where BORA-Release is applied.     

Design for safety of engineering systems with multiple failure state variables

Since possible failure events of large engineering systems with a higher level of innovation may not be identified by experience or from previous accidents and incident reports of similar systems, and since ‘design for safety’ of such systems requires no omission of failure causes associated with possible system failure events, a top-down approach is not always satisfactorily applied in the risk identification and risk estimation phases and a more objective and flexible bottom-up approach may be more effective.

This paper proposes an inductive bottom-up risk identification and estimation methodology combining Failure Mode, Effects and Criticality Analysis (FMECA) and the Boolean Representation Method (BRM). This methodology can be used to identify all possible system failure events and associated causes, and to assess the probabilities of occurrence of them particularly in those cases where multiple state variables and feedback loops are involved. The Boolean representation method is presented together with its use in modelling cause and effect relationships. The overall model and the algorithms are described and tested in association with the associated computer software. The applications of this methodology in association with other formal safety modelling methods are discussed. An illustrative example is presented to demonstrate the methodology.     

A risk analysis of winter navigation in Finnish sea areas

Winter navigation is a complex but common operation in north-European sea areas. In Finnish waters, the smooth flow of maritime traffic and safety of vessel navigation during the winter period are managed through the Finnish–Swedish winter navigation system (FSWNS). This article focuses on accident risks in winter navigation operations, beginning with a brief outline of the FSWNS. The study analyses a hazard identification model of winter navigation and reviews accident data extracted from four winter periods. These are adopted as a basis for visualizing the risks in winter navigation operations. The results reveal that experts consider ship independent navigation in ice conditions the most complex navigational operation, which is confirmed by accident data analysis showing that the operation constitutes the type of navigation with the highest number of accidents reported. The severity of the accidents during winter navigation is mainly categorized as less serious. Collision is the most typical accident in ice navigation and general cargo the type of vessel most frequently involved in these accidents. Consolidated ice, ice ridges and ice thickness between 15 and 40 cm represent the most common ice conditions in which accidents occur. Thus, the analysis presented in this article establishes the key elements for identifying the operation types which would benefit most from further safety engineering and safety or risk management development.