A Modified Accident Analysis and Investigation Model for the General Aviation Industry: Emphasizing on Human and Organizational Factors

Currently, there is a lack of specific analytical tools for general aviation accidents (GAAs). This has led to loopholes in the prevention of GAAs. A Swiss Cheese model for general aviation (SCM-GA) is proposed to identify the human and organizational factors involved in GAAs. In the proposed SCM-GA, 5 categories, 45 subcategories, a general aviation safety management system (GA-SMS) and safety culture were developed based on the classic accident causation models combined with the laws and regulations and safety management practices in the general aviation industry. 1 GAA was analyzed using SCM-GA. The human and organizational causes revealed by SCM-GA were more complete than the causes revealed through the accident report. The identification results of the deficiencies in the subcategories of GA-SMS and the safety culture were more consistent with the requirements in the general aviation laws and regulations than the organizational factors in the accident report. Based on the subcategories of SCM-GA, 41 GAAs that occurred between 1996 and 2010 in China were statistically analyzed and χ2 test analyses were performed to estimate the statistical strength of the association between 2 adjacent subcategories of SCM-GA. The results showed that 2 adjacent subcategories of SCM-GA were significantly associated. They helped to determine the hidden problems in the accident report based on the path of accident. SCM-GA is an accident analysis tool that can comprehensively analyze the human and organizational deficiencies involved in GAAs. The accident causes revealed by SCM-GA were more consistent with the general aviation safety management practices. General aviation companies should establish their own GA-SMS and safety culture based on the subcategories developed herein. Using SCM-GA for routine safety inspection and accident investigation will help the management and the staff make effective safety decisions to effectively prevent GAAs.


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  • Accession Number: 01683829
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 28 2018 3:04PM