5 Papers
Gui Fu is an academic researcher from China University of Mining and Technology. The author has contributed to research in topics: Accident analysis & Coal mining. The author has an hindex of 4, co-authored 5 publications.
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Papers
The development history of accident causation models in the past 100 years: 24Model, a more modern accident causation model
TL;DR: In this paper, a new method for classifying accident causation models is proposed, and the method divides the accident causal models into linear and non-linear models, and further divides the latter models into human-based, statistics-based and energy-based models.
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Root causes of coal mine accidents: Characteristics of safety culture deficiencies based on accident statistics
TL;DR: Wang et al. as discussed by the authors identified the root causes of coal mine accidents and identified the characteristics of safety culture deficiencies driving typical coal accidents using the accident analysis pathway of the 24Model and the logical thought of Why Because Analysis (WBA).
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Accidents analysis and prevention of coal and gas outburst: Understanding human errors in accidents
TL;DR: Wang et al. as mentioned in this paper used the 24modelel to analyze coal and gas accidents and suggest measures for accident prevention from the two aspects "gas" (risk control) and "humans" (behavioural safety).
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Risk prediction and factors risk analysis based on IFOA-GRNN and apriori algorithms: Application of artificial intelligence in accident prevention
TL;DR: The application results show that the IFOA-GRNN algorithm proposed has an accuracy rate of 100% for the prediction of accident risk levels and has the characteristics of a smaller prediction error, higher stability and faster convergence.
105
LPG leakage and explosion accident analysis based on a new SAA method
TL;DR: This analysis proved the coupling relationship between accident causes involving multiple organisations and multiple factors and showed that the output of 24Model is reliable and has a more understandable framework, which can be used as reference for the development of other linear accident cause models and application of system accident analysis methods to real applications.
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