Academic literature on the topic 'Reason's model of accident causation'
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Journal articles on the topic "Reason's model of accident causation"
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Ma, Xiaoli, Yingying Xing, and Jian Lu. "Causation Analysis of Hazardous Material Road Transportation Accidents by Bayesian Network Using Genie." Journal of Advanced Transportation 2018 (August 5, 2018): 1–12. http://dx.doi.org/10.1155/2018/6248105.
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With the increase of hazardous materials (Hazmat) demand and transportation, frequent Hazmat road transportation accidents had arisen the widespread concern in the community. Thus, it is necessary to analyze the risk factors’ implications, which would make the safety of Hazmat transportation evolve from “passive type” to “active type”. In order to explore the influence of risk factors resulting in accidents and predict the occurrence of accidents under the combination of risk factors, 839 accidents that have occurred for the period 2015–2016 were collected and examined. The Bayesian network structure was established by experts’ knowledge using Dempster-Shafer evidence theory. Parameter learning was conducted by the Expectation-Maximization (EM) algorithm in Genie 2.0. The two main results could be likely to obtain the following. (1) The Bayesian network model can explore the most probable factor or combination leading to the accident, which calculated the posterior probability of each risk factor. For example, the importance of three or more vehicles in an accident leading to the severe accident is higher than less vehicles, and in the absence of other evidences, the most probable reasons for “explosion accident” are vehicles carrying flammable liquids, larger quantity Hazmat, vehicle failure, and transporting in autumn. (2) The model can predict the occurrence of accident by setting the influence degrees of specific factor. Such that the probability of rear-end accidents caused by “speeding” is 0.42, and the probability could reach up to 0.97 when the driver is speeding at the low-class roads. Moreover, the complex logical relationship in Hazmat road transportation accidents could be obtained, and the uncertain relation among various risk factors could be expressed. These findings could provide theoretical support for transportation corporations and government department on taking effective measures to reduce the risk of Hazmat road transportation.
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Elliott, Malcolm, Karen Page, and Linda Worrall-Carter. "Reason’s accident causation model: Application to adverse events in acute care." Contemporary Nurse 43, no. 1 (December 2012): 22–28. http://dx.doi.org/10.5172/conu.2012.43.1.22.
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Zou, Xin, and Wen Long Yue. "A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica." Journal of Advanced Transportation 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/2525481.
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Based on an overall consideration of factors affecting road safety evaluations, the Bayesian network theory based on probability risk analysis was applied to the causation analysis of road accidents. By taking Adelaide Central Business District (CBD) in South Australia as a case, the Bayesian network structure was established by integrating K2 algorithm with experts’ knowledge, and Expectation-Maximization algorithm that could process missing data was adopted to conduct the parameter learning in Netica, thereby establishing the Bayesian network model for the causation analysis of road accidents. Then Netica was used to carry out posterior probability reasoning, the most probable explanation, and inferential analysis. The results showed that the Bayesian network model could effectively explore the complex logical relation in road accidents and express the uncertain relation among related variables. The model not only can quantitatively predict the probability of an accident in certain road traffic condition but also can find the key reasons and the most unfavorable state combination which leads to the occurrence of an accident. The results of the study can provide theoretical support for urban road management authorities to thoroughly analyse the induction factors of road accidents and then establish basis in improving the safety performance of the urban road traffic system.
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Fu, Gui, Ziqi Zhao, Chuanbo Hao, and Qiang Wu. "The Accident Path of Coal Mine Gas Explosion Based on 24Model: A Case Study of the Ruizhiyuan Gas Explosion Accident." Processes 7, no. 2 (February 2, 2019): 73. http://dx.doi.org/10.3390/pr7020073.
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In order to effectively prevent coal mine accidents, we selected the most serious type of accident in coal mines—gas explosions—as the research object. Based on the accident causation model (24Model), we propose an action path and analysis steps of accidents caused by different employees in the organization. A gas explosion coal mine accident was analyzed using the 24Model and the proposed action path, and 12 unsafe actions, 3 unsafe states, 4 habitual behaviors, 10 safety management systems, and 10 safety cultures were obtained. Case analysis results show that by using the 24Model and path analysis the proposed effect can help employees to clearly identify the cause of the accident, to better understand the logical relationship with the causes of the accident, improve the effectiveness of training, and effectively prevent similar accidents. The 24Model and the proposed path can be used to comprehensively analyze the reasons for and help to effectively prevent coal mine gas explosion accidents.
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Cookson, Simon. "Zagreb and Tenerife." Australian Review of Applied Linguistics 32, no. 3 (January 1, 2009): 22.1–22.14. http://dx.doi.org/10.2104/aral0922.
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The International Civil Aviation Organization (ICAO) is currently implementing a program to improve the language proficiency of pilots and air traffic controllers worldwide. In justifying the program, ICAO has cited a number of airline accidents that were at least partly caused by language factors. Two accidents cited by ICAO are analysed in this paper: the mid-air collision above Zagreb in 1976, and the runway collision at Tenerife in 1977. The paper examines the linguistic factors involved in each accident, such as code switching and L1 interference, and uses the ‘Swiss cheese’ model of accident causation developed by Reason (1990) and adapted by Wiegmann and Shappell (2003) to put these factors into a broader aviation context. It is shown that, while linguistic factors were in each case significant, both accidents occurred as the result of multiple causal factors, many of which were non-linguistic. Furthermore, stress and fatigue played a decisive role in exacerbating the linguistic factors in each accident. Finally, the paper suggests lessons that may be drawn from the analysis for the training of pilots and air traffic controllers, both nativespeaker and non native-speaker.
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Cookson, Simon. "Zagreb and Tenerife." Australian Review of Applied Linguistics 32, no. 3 (2009): 22.1–22.14. http://dx.doi.org/10.1075/aral.32.3.02coo.
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The International Civil Aviation Organization (ICAO) is currently implementing a program to improve the language proficiency of pilots and air traffic controllers worldwide. In justifying the program, ICAO has cited a number of airline accidents that were at least partly caused by language factors. Two accidents cited by ICAO are analysed in this paper: the mid-air collision above Zagreb in 1976, and the runway collision at Tenerife in 1977. The paper examines the linguistic factors involved in each accident, such as code switching and L1 interference, and uses the ‘Swiss cheese’ model of accident causation developed by Reason (1990) and adapted by Wiegmann and Shappell (2003) to put these factors into a broader aviation context. It is shown that, while linguistic factors were in each case significant, both accidents occurred as the result of multiple causal factors, many of which were non-linguistic. Furthermore, stress and fatigue played a decisive role in exacerbating the linguistic factors in each accident. Finally, the paper suggests lessons that may be drawn from the analysis for the training of pilots and air traffic controllers, both nativespeaker and non native-speaker.
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Jiang, Chengyue, Zhiyong Yin, Lihai Ren, Yuanzhi Hu, Xi Liu, and Hongxu Zhu. "Coupling Simulation of an Impact-Induced Rollover Accident and Evaluation of Curtain Airbag Effectiveness." International Journal of Computational Methods 17, no. 08 (June 26, 2019): 1950041. http://dx.doi.org/10.1142/s0219876219500415.
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The causes of fatal and serious injuries in rollover accidents have yet to be fully identified. This study aimed to reconstruct a complex accident, based on both video analysis and vehicle coupling simulation. Besides, the major fatal injury causation and curtain airbag’s effectiveness were analyzed. The coupling model, including finite element tire and multi-body vehicle body, was developed to estimate the vehicle trajectory. A validated curtain airbag (CAB) module, was introduced for occupant protection effectiveness evaluation. The vehicle trajectory from the simulation correlated well with that from video recordings and simulation result indicated that passenger’s head contact with road (maximum contact force 9834.9[Formula: see text]N) was assumed to be the main reason of the victim death. After introducing the CAB, the maximum head acceleration was reduced from 129.1[Formula: see text]g to 24.9[Formula: see text]g, and the neck Fz was reduced by 80.0% as well. The coupling simulation method turned out to be efficient to reconstruct certain complex accident case. Such study is beneficial for further similar accident reconstruction and restraint system evaluation.
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Joe-Asare, T., N. Amegbey, and E. Stemn. "Human Factor Analysis Framework for Ghana’s Mining Industry." Ghana Mining Journal 20, no. 2 (December 31, 2020): 60–76. http://dx.doi.org/10.4314/gm.v20i2.8.
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In an attempt to incorporate human factors into technical failures as accident causal factors, researchers have promoted the concept of human factor analysis. Human factor analysis models seek to identify latent conditions within the system that influence the operator’s action to trigger an accident. For an effective application of human factor analysis models, a domain-specific model is recommended. Most existing models are developed with category/subcategory peculiar to a particular domain. This presents challenges and hinders effective application outside the domain developed for. This paper sought to propose a human factor analysis framework for Ghana’s mining industry. A comparative study was carried out between three dominated accident causation models and investigation methods in literature; AcciMap, HFACS, and STAMP. The comparative assessment showed that HFACS is suitable for incident data analysis based on the following reason; ease of learning and use, suitability for multiple incident analysis and statistical quantification of trends and patterns, and high inter and intra-coder reliability. A thorough study was done on HFACS and its derivative. Based on recommendations and research findings on HFACS from literature, Human Factor Analysis, and Classification System – Ghana Mining Industry (HFACS-GMI) was proposed. The HFACS-GMI has 4 tiers, namely; External influence/factor, Organisational factor, Local Workplace/Individual Condition and, Unsafe Act. A partial list of causal factors under each tier was generated to serve as a guide during incident coding and investigation. The HFACS-GMI consists of 18 subcategories and these have been discussed. The HFACS-GMI is specific to the Ghanaian Mines and could potentially help in identifying causal and contributing factors of an accident during an incident investigation and data analysis.
Keywords: Human Factor Analysis, Causal Factor, Causation Model, Mining Industry
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Dolzhenko Nadezhda Aleksandrovna, Maylyanova Ekaterina Nikolaevna,, Toluev Y.,, and Assilbekova I.,. "INFLUENCE OF SYSTEM ERRORS IN METEOROLOGICAL SUPPORT ON FLIGHTS SAFETY." NEWS of National Academy of Sciences of the Republic of Kazakhstan 5, no. 443 (October 15, 2020): 81–88. http://dx.doi.org/10.32014/2020.2518-170x.107.
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There are many different systems of interaction in practical aviation activities. In particular, in a system consisting of an operator and a machine there are quite often failures due to errors of designers, operators, manufacturers, maintenance, etc. Errors are usually unintentional: a person performs erroneous actions, considering them as correct or most suitable due to insufficient information, neglect of rules and standards and even due to lack of such. The specialists of the American corporation Boeing calculated the share of aviation accidents related to incorrect decision-making. It turned out that of all aviation accidents, such incidents account for 75%. Back in 1990, Professor Reason developed a model describing the causation of an air accident. One of the main elements of this manufacturing system consists of decision-makers, another key element is decision-executors. For top-level decisions and line management actions to be implemented into effective and productive activities carried out directly by pilots and instructors, certain preconditions must be met. The Rison model explains how people contribute to the disruption of complex, interacting and well-protected systems (such as aviation), resulting in an aviation incident. This model reveals to us causal relationships that do not directly lead to an accident, but shows that, although there are lots of protection levels between risks and accidents, there are drawbacks in each layer of protection that, in the case of systemic “flaring-out”, can trigger an accident [3]. The drawbacks of one level of protection do not allow realizing the risk, since there are other protections to prevent a single point of failure, but with systemic accumulation of risks, catastrophic consequences are inevitable. This effect is sometimes called the "aggregate action effect." In our work, we tried to investigate the system of unprotected risks that led to disaster.
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Grey, E., and P. Wilkinson. "APPLYING WHAT WE KNOW ABOUT HUMAN ERROR: FROM THEORY INTO PRACTICE." APPEA Journal 44, no. 1 (2004): 885. http://dx.doi.org/10.1071/aj03050.
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Human error is often said to be at the heart of the majority of incidents and the developing discipline of human factors a way of understanding how these errors occur. There is little debate about this. But do we practise what we preach and are we reaping the benefits of applying the insights? Anecdotal evidence suggests not. Human error is too often interpreted as people being reckless, careless or just ignorant in discharging their duties. This so-called careless worker approach was the unstated assumption behind early moves to improve health and safety. It could be argued in the petroleum industry that we have adopted a more sophisticated approach, emphasising the importance of the engineering integrity of process systems and the role of formal management systems. However, there remains a need to better integrate what we know about human and organisational error. Reason’s (1997) organisational accident model has had a profound effect on how accidents are viewed and how we can learn from them. The clarity with which the model is presented does not, however, necessarily translate directly into ease of application. The model is a description of accident causation, but does not provide a method for making assessments about organisational resilience in its own right. As such, individuals wanting to use the model need to be well trained if benefits are to be realised. This paper describes a practical and applied approach to human error training based on principles of adult learning that is designed to tap into trainees’ existing knowledge and experiences.
Dissertations / Theses on the topic "Reason's model of accident causation"
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McKeon, Christine. "Psychological factors influencing unsafe behaviour during medication administration." University of Southern Queensland, Faculty of Sciences, 2004. http://eprints.usq.edu.au/archive/00001473/.
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The health system in Australia delivers safe and effective health care to millions of patients each year. However, health care is not as safe as it could be with research indicating that errors involving medications are a leading cause of unintended harm to patients both in Australia and internationally. Historically, hospital authorities have attempted to reduce incidents by focusing on the actions of individuals. However, the health system is now taking advantage of research carried out in other complex industries which indicates that error is inevitable and that identifying individuals as the ultimate cause of adverse incidents is of limited value unless the context in which the incident occurred is well understood. This series of studies used Reason's (1990) model of accident causation as the basis for the search into possible contributing factors to unsafe behaviour by nurses during medication administration. Structural equation modelling was used to operationalise Reason's theory by developing a model linking organisational and individual factors to unsafe behaviour in the hospital system. Study 1 in this series was a preliminary investigation of the role of organisational factors in contributing to violations by nurses in rural and remote areas in Queensland, Australia. Data were collected using a self-report questionnaire with this instrument being used to develop a structural model wherein organisational variables predicted 23% of the variance in self-reported violations. Study 2 extended the number of organisational factors measured by using a validated instrument that is widely used in public sector hospitals in Queensland. This instrument measures organisational climate and also a number of individual factors. In addition to the outcome variable, violation behaviour, a measure of errors was included. Data were collected from nurses working in two rural health service districts. A structural model was developed from this instrument wherein organisational variables predicted 7% of the variance in selfreported violations and 24% of the variance in errors. The hypothesised relationships between the individual factors and errors were not supported in this study. Study 3 investigated the impact of individual factors and a specific type of organisational climate, that is, safety climate on unsafe behaviour. The violation behaviour and error scales were extended and improved in this study, for example, the error scale was expanded to include near misses. In addition, a new scale measuring reporting behaviour was developed and included. Data were collected from nurses working in a large rural centre. The structural model developed from the instrument indicated that safety climate predicted 27% of the variance in violation behaviour, 61% of the variance in errors and near misses, and 20% of the variance in willingness to report. This series of studies identified underlying contributing factors to unsafe behaviour during medication administration, indicated the strength of the relationships among the various elements, and illustrated how the various parts of the system link together to influence safety outcomes. By identifying which elements are important by the use of structural equation modelling, this research provides the basis for predicting unsafe organisational conditions and leads to suggestions for suitably targeted interventions to reduce unsafe behaviour and adverse incidents.
Book chapters on the topic "Reason's model of accident causation"
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Asan, Amran, and Zainal Abidin Akasah. "Developing an Accident Causation Model for Accident Prevention at Building Construction Sites." In InCIEC 2014, 273–85. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-290-6_24.
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Yuanyuan, Qian, Xu Jie, Mi Chuanmin, and Peng Qiwei. "Research on Electricity Falling Accident Based on Improved Bode Accident Causation Model." In Smart Service Systems, Operations Management, and Analytics, 127–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30967-1_12.
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Marsh, William, and George Bearfield. "Using Bayesian Networks to Model Accident Causation in the UK Railway Industry." In Probabilistic Safety Assessment and Management, 3597–602. London: Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_575.
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Gao, P., G. Fu, and W. Yin. "The implications of behavior-based accident causation “2-4 model” in the prevention of coal mine roof accident." In Progress in Mine Safety Science and Engineering II, 741–44. CRC Press, 2014. http://dx.doi.org/10.1201/b16606-141.
Full textConference papers on the topic "Reason's model of accident causation"
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Mitropoulos, Panagiotis, Gregory A. Howell, and Tariq S. Abdelhamid. "Accident Prevention Strategies: Causation Model and Research Directions." In Construction Research Congress 2005. Reston, VA: American Society of Civil Engineers, 2005. http://dx.doi.org/10.1061/40754(183)8.
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Smith, Doug, Brian Veitch, Faisal Khan, and Rocky Taylor. "An Accident Model for Arctic Shipping." In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/omae2015-41415.
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This paper examines historical Arctic marine accidents from 1995–2004. It was seen during this time period that sinking and grounding of (fishing) vessels was the most common type of Arctic marine accident. A comprehensive accident model is presented to describe Arctic shipping accidents and their causation factors. The accident model is based on epidemiological concepts which explain how non-sequential factors result in an unwanted outcome, analogous to disease spreading through a human body. The causation factors are non-sequential and non-linearly dependent. The applicability of the model is demonstrated through examination of two past accidents: the Kolskaya and the Kulluk. Detailed description of how the accident model could be used for predictive accident modelling and risk analysis of Arctic shipping scenarios is also presented.
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Abreu, Danilo Taverna Martins Pereira de, Marcos Coelho Maturana, and Marcelo Ramos Martins. "Development of Accidental Scenarios Involving Human Errors for Risk Assessment in Restricted Waters." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18996.
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Abstract The navigation in restricted waters imposes several challenges when compared to open sea navigation. Smaller dimensions, higher traffic density and the dynamics of obstacles such as sandbanks are examples of contributors to the difficulty. Due to these aspects, local experienced maritime pilots go onboard in order to support the ship’s crew with their skills and specific regional knowledge. Despite these efforts, several accidents still occur around the world. In order to contribute to a better understanding of the events composing accidental sequences, this paper presents a hybrid modelling specific for restricted waters. The main techniques used are the fault tree analysis and event tree analysis. The former provides a framework to investigate the causes, while the latter allows modelling the sequence of actions necessary to avoid an accident. The models are quantified using statistical data available in the literature and a prospective human performance model developed by the Technique for Early Consideration of Human Reliability (TECHR). The results include combined estimates of human error probabilities and technical failure probabilities, which can be used to inform the causation factor for a waterway risk analysis model. In other words, given that the ship encounters a potential accidental scenario while navigating, the proposed models allow computing the failure probability that of the evasive actions sequence. The novelty of this work resides on the possibility of explicitly considering dynamicity and recovery actions when computing the causation factor, what is not a typical feature of similar works. The results obtained were compared with several results available in the literature and have been shown to be compatible.