Modelling accidental releases of dangerous gases into the lower troposphere from mobile sources

The article reports the results of different methods of modelling releases and dispersion of dangerous gases or vapours in cases of major accidents from road and rail transportation in urban zones. Transport accidents of dangerous substances are increasingly frequent and can cause serious injuries in densely inhabited areas or pollution of the environment. For quantitative risk assessment and mitigation planning, consequence modelling is necessary.

The modelling of dangerous substance dispersion by standard methods does not fully represent the behaviour of toxic or flammable clouds in obstructed areas such as street canyons. Therefore the predictions from common software packages as ALOHA, EFFECTS, TerEx should be augmented with computational fluid dynamics (CFD) models or physical modelling in aerodynamic tunnels, and further studies are planned to do this.

The goal of this article is to present the results of the first approach of modelling using these standard methods and to demonstrate the importance of the next development stage in the area of transport accident modelling of releases and dispersions of dangerous substances in urban zones in cases of major accident or terrorist attacks.     

Air Traffic Management accident risk. Part 1: The limits of realistic modelling

The prime goal of the Air Traffic Management (ATM) system is to control accident risk. Some key questions are posed, including: What do design safety targets really mean and imply for risk modelling? In what circumstances can future accident risk really be modelled with sufficient precision? If risk cannot be estimated with precision, then how is safety to be assured with traffic growth and operational/technical changes? This paper endeavours to answer these questions by an analysis of the nature of accidents, causal factors and practical collision risk modelling. The main theme is how best to combine sound safety evidence and real-world hazard analysis in a coherent and systematic framework.     

Dynamic risk analysis of offloading process in floating liquefied natural gas (FLNG) platform using Bayesian Network

The growing demand for natural gas has pushed oil and gas exploration to more isolated and previously untapped regions around the world where construction of LNG processing plants is not always a viable option. The development of FLNG will allow floating plants to be positioned in remote offshore areas and subsequently produce, liquefy, store and offload LNG in the one position. The offloading process from an FLNG platform to a gas tanker can be a high risk operation. It consists of LNG being transferred, in hostile environments, through loading arms or flexible cryogenic hoses into a carrier which then transports the LNG to onshore facilities. During the carrier's offloading process at onshore terminals, it again involves risk that may result in an accident such as collision, leakage and/or grounding. It is therefore critical to assess and monitor all risks associated with the offloading operation. This study is aimed at developing a novel methodology using Bayesian Network (BN) to conduct the dynamic safety analysis for the offloading process of an LNG carrier. It investigates different risk factors associated with LNG offloading procedures in order to predict the probability of undesirable accidents. Dynamic failure assessment using Bayesian theory can estimate the likelihood of the occurrence of an event. It can also estimate the failure probability of the safety system and thereby develop a dynamic failure assessment tool for the offloading process at a particular FLNG plant. The main objectives of this paper are: to understand the LNG offloading process, to identify hazardous events during offloading operation, and to perform failure analysis (modelling) of critical accidents and/or events. Most importantly, it is to evaluate and compare risks. A sensitivity analysis has been performed to validate the risk models and to study the behaviour of the most influential factors. The results have indicated that collision is the most probable accident to occur during the offloading process of an LNG carrier at berth, which may have catastrophic consequences.     

An analysis of motorcyclist injury severity under various traffic control measures at three-legged junctions in the UK

The effects various junction control measures have on accident frequencies among vehicles have been investigated by previous studies through the use of statistical modelling techniques but such effects on motorcyclist injury severity, given that a junction-type accident has occurred, have received little attention in literature. This paper attempts to estimate econometric models of motorcyclist injury severity under different control measures at three-legged junctions in the UK, as a function of demographic, vehicle and environmental factors. Separate ordered probit models were estimated for unsignalised and signalised junctions, using the data extracted from the STATS19 accident injury database (1999–2004). Also examined in the models are collision partners’ aggressiveness toward motorcyclists and the impacts various crash configurations have on injury severity. The modelling results uncover several combined factors that were deadly to motorcyclists: for example, injuries tended to be much more severe while motorcyclists involving in approach-turn collisions at signalised junctions than at unsignalised junctions. This study ultimately offers insights into potential countermeasures that could be undertaken to help lessen motorcyclist injury severity at three-legged junctions in the UK.     

Towards an evaluation of accident investigation methods in terms of their alignment with accident causation models

The purpose of this paper is to reflect on accident causation models and accident investigation methods. Theories on accident causation and the modelling of accident mechanisms, as well as a number of methods for accident investigation have been developed and described in the literature. The evolution of accident causation models over time shows a shift from the sequence of events to the representation of the whole system. Respectively, the evolution of accident investigation methods over time reveals a gradual shift from searching for a single immediate cause, to the recognition of multiple causes. In order to evaluate the accident investigation methods, specific plausible requirements were established in order to verify that a specific accident investigation method fulfils the principles of a specific accident causation model or give evidence to the degree of alignment between them. Since different models approach accident causation in different ways, methods linked to these models provide fragmentary information regarding the accident. It is therefore expected that using a combination of model-method pairs could provide a more reliable platform for accident analysis.     

Risk analysis of deepwater drilling operations using Bayesian network

Deepwater drilling is one of the high-risk operations in the oil and gas sector due to large uncertainties and extreme operating conditions. In the last few decades Managed Pressure Drilling Operations (MPD) and Underbalanced Drilling (UBD) have become increasingly used as alternatives to conventional drilling operations such as Overbalanced Drilling (OVD) technology. These newer techniques provide several advantages however the blowout risk during these operations is still not fully understood. Blowout is regarded as one of the most catastrophic events in offshore drilling operations; therefore implementation and maintenance of safety measures is essential to maintain risk below the acceptance criteria. This study is aimed at applying the Bayesian Network (BN) to conduct a dynamic safety analysis of deepwater MPD and UBD operations. It investigates different risk factors associated with MPD and UBD technologies, which could lead to a blowout accident. Blowout accident scenarios are investigated and the BNs are developed for MPD and UBD technologies in order to predict the probability of blowout occurrence. The main objective of this paper is to understand MPD and UBD technologies, to identify hazardous events during MPD and UBD operations, to perform failure analysis (modelling) of blowout events and to evaluate plus compare risk. Importance factor analysis in drilling operations is performed to assess contribution of each root cause to the potential accident; the results show that UBD has a higher occurrence probability of kick and blowout compared to MPD technology. The Rotating Control Devices (RCD) failure in MPD technology and increase in flow-through annulus in UBD technology are the most critical situations for kick and blowout.     

大型群众性活动安全管理责任矩阵的研究探讨

针对突发事件未发生、发生前和发生中的3个不同阶段,把大型群众活动划分为日常、预警和应急3种管理状态。根据大型活动的组织机构和管理部门在策划、组织和运行过程中的安全管理责任和任务,设计了不同状态下活动承办者的安全责任矩阵。该研究对活动安全管理工作的落实和有效实施有重要的参考价值和指导意义。     

Data mining of tree-based models to analyze freeway accident frequency

INTRODUCTION: Statistical models, such as Poisson or negative binomial regression models, have been employed to analyze vehicle accident frequency for many years. However, these models have their own model assumptions and pre-defined underlying relationship between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimation of accident likelihood. Classification and Regression Tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems. METHOD: This study collected the 2001-2002 accident data of National Freeway 1 in Taiwan. A CART model and a negative binomial regression model were developed to establish the empirical relationship between traffic accidents and highway geometric variables, traffic characteristics, and environmental factors. RESULTS: The CART findings indicated that the average daily traffic volume and precipitation variables were the key determinants for freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies. IMPACT ON INDUSTRY: By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies.     

Dynamic accident modeling for high-sulfur natural gas gathering station

Dynamic accident modeling for a gas gathering station is implemented to prevent high-sulfur natural gas leakage and develop equipment inspection strategy. The progress of abnormal event occurring in the gas gathering station is modeled by the combination of fault tree and event sequence diagram, based on accident causal chain theory, i.e. the progress is depicted as sequential failure of safety barriers, then, the occurrence probability of the consequence of abnormal event is predicted. Consequences of abnormal events are divided into accidents and accident precursors which include incidents, near misses and so on. The Bayesian theory updates failure probability of safety barrier when a new observation (i.e. accident precursors or accidents data) arrives. Bayesian network then correspondingly updates failure probabilities of basic events of the safety barriers with the ability of abductive reasoning. Consequence occurrence probability is also updated. The results show that occurrence probability trend of different consequences and failure probability trend of safety barriers and basic events of the safety barriers can be obtained using this method. In addition, the critical basic events which play an important role in accidents occurrence are also identified. All of these provide useful information for the maintenance and inspection of the gas gathering station.     

Is there a need for new theories,models and approaches to occupational accident prevention?

This paper discusses occupational accident modelling challenges associated with a changing working life, and asks whether ideas from models developed for high-risk, complex socio-technical systems can be transformed and adapted for use in occupational accident prevention. Are occupational accidents mainly simple component failures or is a systemic approach to the phenomenon of some interest and value?     

A new accident model for engineering safer systems

New technology is making fundamental changes in the etiology of accidents and is creating a need for changes in the explanatory mechanisms used. We need better and less subjective understanding of why accidents occur and how to prevent future ones. The most effective models will go beyond assigning blame and instead help engineers to learn as much as possible about all the factors involved, including those related to social and organizational structures. This paper presents a new accident model founded on basic systems theory concepts. The use of such a model provides a theoretical foundation for the introduction of unique new types of accident analysis, hazard analysis, accident prevention strategies including new approaches to designing for safety, risk assessment techniques, and approaches to designing performance monitoring and safety metrics.     

Exploring motorcyclist injury severity resulting from various crash configurations at T-junctions in the United Kingdom--an application of the ordered probit models

OBJECTIVES: The fact that motorcycle users tend to be more vulnerable to injuries than those using other motorized vehicles may act synergistically with the complexity of conflicting movements between vehicles and motorcycles to increase injury severity in a junction-type accident. A junction-type collision tends to be more severe than a non-junction case due to the fact that some of the injurious crashes such as angle-collision commonly occur. Existing studies have applied several statistical modeling techniques to examine influential factors on the occurrences of different crashes among motorized vehicles but surprisingly very little has empirically explored whether a particular crash type, resulting from a junction-type accident, is more injurious to motorcyclists. This article attempts to investigate whether a particular collision is more deadly to motorcyclists conditioned on crash occurrence at T-junctions in the U.K., while controlling for environment, vehicle, and demographic factors. METHODS: The statistical modeling technique employed is the ordered probit models using the data extracted from the STATS19 accident injury database (1999-2004). RESULTS: The modeling found determinants of injury severity among motorcyclists at T-junctions in the U.K. For example, an approach-turn/head-on collision is much more injurious to motorcyclists; and, those riding in early morning (i.e., 0000-0659) are more likely to be severely injured. CONCLUSIONS: This study offers a guideline for future research, as well as insight into potential prevention strategies that might help moderate motorcyclist injuries.     

Time series analysis of coal mine accident experience

This study investigates several forecasting techniques that can be useful to mine safety managers for studying mine accident rate behavior. Three time series models were studied for extrapolation of accident rates. These models are applied to historical accident incidence data from a coal mine. Further, a method is presented for evaluating the three models for the selection of an appropriate model. For this particular mine application, it is concluded that the more complex Box-Jenkins ARMA model as well as first order autoregressive model do not give better results than the simple exponential smoothing model. However, when the random variations or autocorrelations in the accident experience rates between periods are different, the models may predict differently. As such, specific models must be developed for each mine on the basis of statistical analysis of the mine accident experience data over time. Moreover, the importance of incorporating human judgement to interpret the results of statistical forecasting cannot be overemphasized. Integration of policy or operating changes, which may impact mine safety performance, with statistical forecasting techniques is essential to arrive at a realistic prediction of future performance.     

Assessment of the Consequences of Accident Scenarios Involving Dangerous Substances

This paper highlights major steps in the procedure for evaluating the consequences of accidents involving dangerous substances, especially during the storage, and loading/unloading activities. The procedure relies on identifying accident scenarios that could be encountered at particular plants, followed by a modelling of these scenarios by means of available modelling systems. Finally, the resultant outcomes are identified, together with their effects on both people and property. The resources needed to perform this procedure are discussed, in order to clarify the roles of plant operators, external experts and other institutions when evaluating any accident consequences. Four examples, all relevant in industrial practice, are given in order to illustrate the procedure: the releasing of liquified petroleum gas, flammable organic solvents, toxic chlorine, and oil fuels. The results of these studies may be used for a quick order-of-magnitude estimation of accidents consequences.     

Modelling of discharge and atmospheric dispersion for carbon dioxide releases

This paper discusses the modelling of the discharge and subsequent atmospheric dispersion for carbon dioxide releases using extensions of models in the consequence modelling package Phast. Phast examines the progress of a potential incident from the initial release to the far-field dispersion including the modelling of rainout and subsequent vaporisation. The original Phast discharge and dispersion models allow the released chemical to occur only in the vapour and liquid phases. As part of the current work these models have been extended to also allow for the occurrence of liquid to solid transition or vapour to solid transition. This applies both for the post-expansion state in the discharge model, as well as for the thermodynamic calculations by the dispersion model. Solid property calculations have been added where necessary. The above extensions are generally valid for fluid releases including CO₂. Using the extended dispersion formulation, a sensitivity study has been carried out for mixing of solid CO₂ with air, and it is demonstrated that solid effects may significantly affect the predicted concentrations.     

Dynamic risk assessment using failure assessment and Bayesian theory

To ensure the safety of a process system, engineers use different methods to identify the potential hazards that may cause severe consequences. One of the most popular methods used is quantitative risk assessment (QRA) which quantifies the risk associated with a particular process activity. One of QRA's major disadvantages is its inability to update risk during the life of a process. As the process operates, abnormal events will result in incidents and near misses. These events are often called accident precursors. A conventional QRA process is unable to use the accident precursor information to revise the risk profile. To overcome this, a methodology has been proposed based on the work of Meel and Seider (2006). Similar to Meel and Seider (2006) work, this methodology uses Bayesian theory to update the likelihood of the event occurrence and also failure probability of the safety system. In this paper the proposed methodology is outlined and its application is demonstrated using a simple case study. First, potential accident scenarios are identified and represented in terms of an event tree, next, using the event tree and available failure data end-state probabilities are estimated. Subsequently, using the available accident precursor data, safety system failure likelihood and event tree end-state probabilities are revised. The methodology has been simulated using deterministic (point value) as well as probabilistic approach. This Methodology is applied to a case study demonstrating a storage tank containing highly hazardous chemicals. The comparison between conventional QRA and the results from dynamic failure assessment approach shows the significant deviation in system failure frequency throughout the life time of the process unit.     

The development of a more risk-sensitive and flexible airport safety area strategy: Part I. The development of an improved accident frequency model

This two-part paper presents the development of an improved airport risk assessment methodology aimed at assessing risks related to aircraft accidents at and in the vicinity of airports and managing airport safety areas (ASAs) as a risk mitigation measure. The improved methodology is more quantitative, risk-sensitive, flexible and transparent than standard risk assessment approaches. As such, it contributes to the implementation of Safety Management Systems at airports, as stipulated by the International Civil Aviation Organisation.The first part of the paper presents the methodological advances made in the development of accident frequency models; namely the building of a single comprehensive database of all relevant accident types, the collection and use of normal operations data in quantifying the criticality of a series of risk factors, and modelling accident frequency using multivariate logistic regression. The resulting models have better goodness-of-fit, sensitivity and specificity than standard risk assessment methodologies.     

Systems-based accident analysis methods: A comparison of Accimap, HFACS, and STAMP

Three accident causation models, each with their own associated approach to accident analysis, currently dominate the human factors literature. Although the models are in general agreement that accidents represent a complex, systems phenomenon, the subsequent analysis methods prescribed are very different. This paper presents a case study-based comparison of the three methods: Accimap, HFACS and STAMP. Each was used independently by separate analysts to analyse the recent Mangatepopo gorge tragedy in which six students and their teacher drowned while participating in a led gorge walking activity. The outputs were then compared and contrasted, revealing significant differences across the three methods. These differences are discussed in detail, and the implications for accident analysis are articulated. In conclusion, a modified version of the Accimap method, incorporating domain specific taxonomies of failure modes, is recommended for future accident analysis efforts.     

Simulating the relative influence of tire,vehicle and driver factors on forward collision accident rates

Introduction: There is currently a strong focus within the automotive industry centered on traffic safety, with topics such as distracted driving, accident avoidance technologies, and autonomous vehicles. These papers tend to focus on the possible improvements from a single factor. However, there are many factors that are present in each accident, and it is important to understand the influence of each factor on the relative accident risk in order to identify the most effective approaches for improving driver safety. Rear-end accidents tend to be the most common accident type with approximately 1.8 M cases, or 31% of all accidents, in 2012, according to NHTSA. Of the rear-end accident scenarios, approximately 18–23% occur on wet surfaces. Method: A Monte Carlo Forward Collision Simulation models the conditions of a wet rear-end accident and estimates the relative impact of various vehicle collision parameters. The model takes distributions of these parameters as inputs, and outputs a risk of collision relative to a known reference case. The parameters that can be studied include: tire grip level, road grip level, vehicle velocity, following distances, and the presence of vehicle technologies (ABS, FCW & AEB). Distributions of some of these parameters have been improved thanks to Naturalistic Driving Study data from SHRP2. Results: This study shows that these vehicle systems have a large impact on safety and can change the amount of influence attributed to other parameters such as tire grip levels. As the use of automated vehicle systems expands, so will the influence of tire grip performance levels on collision risks. Practical Applications: It is more important than ever for consumers and auto manufacturers to consider tire performance levels. Therefore, the tire industry should continue to focus on wet grip as a key performance related to safety and should strive to continue to improve tire performance.