Accident Prevention Models - Report Example

Accident Analysis Name Institution Word Count: 2497 Task 1 Accident Prevention Models Increased innovations in equipment in areas such as aviation, maritime, nuclear power plants and petroleum industry among other sectors have brought about many opportunities for failure and the need for new kinds of safety systems. In recent years, much attention has shifted to the need for better strategies to prevent accidents given that most accidents not only lead to destruction of assets, but also to the loss of human lives. The realization that accidents have significant cost outcomes has been the main driving force behind the establishment of system safety, which characterize the use of an array of strategies to create safe systems. More than before, the primary focus of accident analysis has been how to determine the causes of an accident and use such insight into developing safety measures to minimize or reduce the impact of future accidents (Hollnagel, 2004). The need to analyze accidents has motivated the development of accident models, which helps in the conceptualization of accidents. Several accident models offer analysts with contemporary framework to describe the causes of an accident as well as its effect. This section describes common three prevention accident models and outlines the advantages and disadvantages of each model with relation to accident analysis. Sequential accident model The sequential accident model focuses on describing the development of an accident by emphasizing on the chain of events that lead to a negative outcome. This model conceptualizes accidents as an outcome that emanates from a series of events that may be a result of unsafe human acts or unsafe mechanical or physical condition. These causes are some of the possible attributes that lead to accidents. The domino theory is a useful framework that helps analysts understand how sequential accident model works. In this theory, one event leads to another event that could possibly lead to an accident. A significant component of the sequential accident model is its scope. The scope of this model is the operation level as this model concerns itself with operations that occur before an accident. Being that the focus of the theory is events that lead to other negative outcomes; the model provides attention to events that precede an accident. This implies that the model does not attempt to analyze the events that occur after an accident (Huang, 2005). In a typical road accident analysis, this accident model would focus on the activities of entities such as vehicle, road users, and road by behaviors. The critical aspect of the analysis would be to focus on the events that lead to an accident involving the entities under study. The primary component of the sequential accident model is events, which does not occur on its own but rather a result of an action. In most cases, events are a result of unexpected actions that have negative outcomes. The sequential accident model argues that events occur because of other events, which serve as antecedents. Such events are the ones, which cause other events that lead to unwanted outcome such as accident. The depiction of these events could be in a chain, with antecedent causing an event that leads to an accident (Oppe, 1990). The linking of one event to another is a primary attribute of sequential accident model, which underscores the need to prevent events from occurring in a chain to stop an accident from taking place. With sequential accident model linking a cause to an effect, analysts have the opportunity to work in reverse by observing the effect and making an inference to the cause of the accident. This accident model has several strengths and weaknesses. First, the model offers analysts an opportunity to describe accidents in a casual sequence (Huang, 2005). This is a vital approach to analyzing accidents because it offers analysts with an easy way of communicating findings. While this could be strength to this model of accident analysis, the model is limited in analyzing complex system. This is because the scope of the model focuses on the operational level only and does not take into account other additional factors that could lead to an accident. Nonetheless, this model offers a plausible way of reducing or eliminating accidents by breaking the chain of events that could lead to negative outcomes. The epidemiological accident model This model of accident analysis compares the occurrence of accidents to the process of disease infection. This model is grounded on the argument that disease infects their hosts when there are necessary conditions that allow the disease infection to spread. The main principle of this accident model is the fact that accidents occur due to the occurrence of factors that reside in the environment, agent, and the host. These occurrences correspond to latent failures, active failures and system failures, which lead to an accident. The epidemiological accident model is rather an extension of the sequential accident model because it posits that accidents occur due to active failures that take effect before an accident (Wiegmann & Shappell, 2003). However, the epidemiological accident model argues that active failures are a consequence of latent failures, which occur long before the accident takes place. An example of a latent failure would be a poorly maintained vehicle that could lead to an accident later. The scope of the epidemiological accident model touches on both the operational and design levels. Unlike the sequential accident model, the epidemiological accident model includes the notion of latent failures making analysis based on this model to touch on operation and design levels. The scope of the epidemiological accident model thus covers both the temporal and spatial sense of accidents. Most important, this model argues that there could be more than one chain of events that lead to an accident. This implies that accidents could occur because of more than one antecedent, which underscore the prevention of an accident in this model by focusing on ways of preventing latent failures. The epidemiological accident model has a wider scope than the sequential model making it superior to the latter. While the sequential method focuses on events prior to an accident, the epidemiological accident model places additional emphasis on latent events, which are critical in analyzing accidents. Moreover, this model allows analysts to analyze complex systems, which could not be possible with a simple model like the sequential model (Wiegmann & Shappell, 2003). Nonetheless, this method lacks detail because it focuses on general events. It is also less effective when it comes to analysis of small accidents that do not allow isolation of system failure. This limiting factor makes the model less useful in small and less adverse events. Systemic accident model The systemic accident model offers a radical approach to analyzing accidents, a new approach that does not focus on deterministic causal mechanisms. Rather than attempting to determine the causal mechanism of a failure, the system accident model concerns itself with the dynamic behavior of the system. This new approach offers a view that accidents occur due to occurrence of several factors that contribute to a negative outcome. For instance, the coincidental existence of several causal factors has the ability to cause an accident. When analyzing accidents, the systemic accident model identifies some of the potential causal factors such as human, environmental and technical (Kecklund, 1998). The main principle that guides the systemic accident model is that the existence of these causal factors in a system could be coincidental and may lead to an accident or degradation of performance. This is because the performance of the system and its health depends on the close interaction between the various components of the system. This means that a mismatch between the performance of the system and the requirement of the environment could cause an accident. The notion that several variables contribute to an accident is a fundamental factor, which denotes that the systemic accident model eliminates the possibility that one single component can cause an accident. More important, this model emphasizes on the role of system failure where many components work to cause an accident. Unlike the other models of accident analysis, the systemic accident model offers a new paradigm of analyzing system failure since it looks as the ensemble performance that arises from the performance of individual components. Being that the model focuses on system behavior, several aspects of the system that contribute to effective analysis of an accident (Perrow, 1984). The first aspect is the cognitive part of the system while the other aspect is the environmental conditions that interact with the latter to affect the health of a system. In the systemic accident model, accidents are likely to occur when there is a mismatch between the performance of the system and the system demands from its environment. Based on the focus of the model, the systemic accident model emphasizes on events that could work to cause an accident. These events could occur in a combination to cause an accident and preventing an accident would focus on limiting the occurrence of these factors. The understanding rests on the notion that one event is less likely to cause an accident from taking place and that a combination of factors can cause an accident. This technique of analyzing accidents has several weaknesses and strengths. The most important advantage of this model is the fact that it focuses on the characteristic of the system rather than emphasizing on individual components. This is a better approach to analyzing accidents because it focuses on the whole system behavior, but not a series of events that culminate to an accident (Hoang et al., 2004). On the other hand, this accident model lacks simplicity given that it is a powerful model for analyzing ideal complex systems. In addition, this accident analysis does not offer a graphical representation of accident analysis despite being a powerful process. Task 2 Analysis of the Glenbrook Train Crash using sequential and systemic accident models Accident prevention models offer a conceptual way of analyzing accidents to determine the cause(s) of an accident not to mention developing effective strategies that could reduce or prevent future accidents. This section presents analyses of the Glenbrook Train Crash of 1999 using two-accident prevention model—the sequential and system accident model (Hopkins, 2005). The Glenbrook accident occurred in 1999 in the New South Wales when an intercity passenger train collided with Indian Pacific train that was stationery (Stanton, 2012). Just before the accident, the State Rail Authority intercity passenger train had arrived at the rail station at Glenbrook, but it had to stop because of a failed light. The driver of the train passed the red light after the signaler told him he was right to go past the red light. However, another train, the Indian-Pacific had passed the first light, but stopped at the second red light because it was also not working. With the Indian Pacific train stopped and the driver trying to call the signaler, the busy commuter train collided into the stationery train. The above narrative presents each of the two prevention models a perspective to analyze the accident. The following is the analysis of the accident using the sequential prevention model. The sequential accident prevention model, places focus on events that occur before an accident. In the Glenbrook rail accident of 1999, this model would identify the failure of the red light as the main cause of the accident (Hopkins, 2005). The fundamental principle of this accident prevention model is the argument that accidents occur because of antecedents events. Being that the two trains collided because of a faulty red light; the event of stopping at the red light is the main event while the brief stop was the antecedent. Had the red light not shown up (antecedent event), the main event (stopping at the red light), would not have made the Indian Pacific train to stop. It is because the Indian Pacific train stopped, which made the oncoming busy commuter train collide into the train. In preventing this kind of accident, the sequential accident model would focus on breaking the chain of events. For instance, it would be vital to prevent future failure of red lights as a way of preventing future accidents from taking place. In an event where it is possible to prevent the red lights from turning on in the railroad, another strategy would be to prevent train from stopping because of defective rail lights. This strategy of preventing accident rests on the elimination of attributed causes that led to an accident. The systemic accident prevention model is a powerful hazard analysis tool. When applied in the Glenbrook train accident, the results have a significant difference from that of the sequential accident prevention model. This model of accident prevention analyzes accident as a series of events that are links to other events, which contribute to an accident. This approach differs from that of sequential model, which focus on a chain of events as causal factors leading to an accident. The main thrust of this accident model is the notion that an accident occurs when there is a mismatch between the components making the system with the environment. This will be the fundamental tools for analyzing the Glenbrook train accident. With this accident prevention model, it is possible to note that several aspects of the system failed and lead to the accident. Rather than focusing on an individual chain of events, the systemic accident model emphasize on evaluating an ensemble of performances issues. The hardware failure is the first component under review. When Indian Pacific arrived at the station, the first red light was not functioning, but the train proceed toward the second light after receiving the signal from the signaler. However, the train stopped at the second light that was not working. The same event repeated when the busy interstate train arrived at the station. This event alone, according to the systemic accident prevention model, could not have led to the accident. Further, the action of the driver of the interstate train to speed towards the second light compounded the risk of an accident, as he could not stop in time when he saw. The failure of the interstate train to stop before colliding with the Indian Pacific was another event that led to the accident. More important, driving at high speed the intention to beat the deadlines was a contributing factor to the accident. Seen from another perspective, the lack rules that disallowed the use of telephone within the train prevented the driver of Indian Pacific from inquiring about the second red light. Within the perspective of this model, the factor interacted to cause the accident. Comparison of findings with Hopkins The sequential accident prevention model has little similarly with Hopkins analysis. While it mentions individual events as the cause of the accident, it fail to embrace a systemic approach to the analysis. However, the systemic model has significant similarity with Hopkins analysis. Systemic model identifies more than one issue as the cause of the problem. Similar to the above systemic analysis, Hopkins highlights culture and demand of work as causes of the accident. In addition, it also notes that rules made the drivers disempowered creating an environment maladjustment that culminated to the accident. References Hopkins, A. (2005). Safety, culture, and risk: The organizational causes of disasters. Sydney: CCH Australia. Huang, Y. (2005). A systemic traffic accident model (Licentiate thesis). Linköping: Linköping University. Huang, Y., Ljung, M., Sandin, J. & Hollnagel, E. (2004). Accident Modelsfor Modern Road Traffic: Changing Times Creates New Demands. In Proceedings of the International Conference on Systems, Man and Cybernetics: The Hague, The Netherlands. Kecklund, L. (1998). Studies of Safety and Critical Work Situations in Nuclear Power Plants: a Human Factors Perspective (PhD thesis), Stockholm: Stockholm University. Oppe, S. (1990). Discussion on accident analysis methodology. IATSS Research, 14 (1), 50-54. Perrow, C. (1984). Normal accidents: living with high-risk technologies. Princeton: Princeton University Press. Stanton, N. (2012). Advances in Human Aspects of Road and Rail Transportation. Florida: CRC Press. Wiegmann D. & Shappell, S. (2003). A human error approach to aviation accident analysis- The human factors analysis and classification method. Aldershot: Ashgate. Read More