Quantitative Assessment of Human Factors in Civil Aircraft Flight Test Risks Based on Fuzzy Bayesian Network and Improved HFACS Model.
Saved in:
| Title: | Quantitative Assessment of Human Factors in Civil Aircraft Flight Test Risks Based on Fuzzy Bayesian Network and Improved HFACS Model. |
|---|---|
| Authors: | Wang, Liang1 (AUTHOR), Feng, Yongbo1 (AUTHOR), Ye, Lu1 (AUTHOR) yelucafuc@163.com, Mou, Yihao2 (AUTHOR), Wang, Yongzheng2 (AUTHOR), Habib, Mohammad Rezwan (AUTHOR) mohabib@wiley.com |
| Source: | International Journal of Aerospace Engineering. 1/3/2026, Vol. 2025, p1-19. 19p. |
| Subjects: | Flight testing, Risk assessment, Accident prevention, Fuzzy logic, Ergonomics, Psychological factors, Commercial aeronautics |
| Abstract: | Human factors play an important role in civil aviation safety, which is also vital in flight tests. To significantly reduce the possibility of accidents induced by human errors during the flight test, systematic assessment of human factor risks, determination of the critical causes of accidents, and designs of targeted preventive strategies are imperative. The scarcity and confidentiality of flight test accident data pose significant challenges to the quantitative assessment of human factor risks. This paper proposes a novel model to assess human‐induced risks in flight tests, which enables the quantitative analysis of risk factors. The HFACS model has been adaptively improved to more comprehensively identify human‐related risk factors in flight tests. By integrating the fault tree model with the fuzzy Bayesian network, this study calculates the quantitative value of the probability of hazard occurrence. Furthermore, this study incorporates the impacts of individual differences on the consistency of expert ratings via the improved similarity aggregation method. The paper concludes with a case analysis that identifies the most critical human factors in civil aircraft flight tests, thereby providing valuable insights for formulating preventive measures and optimizing risk management strategies. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Aerospace Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Human factors play an important role in civil aviation safety, which is also vital in flight tests. To significantly reduce the possibility of accidents induced by human errors during the flight test, systematic assessment of human factor risks, determination of the critical causes of accidents, and designs of targeted preventive strategies are imperative. The scarcity and confidentiality of flight test accident data pose significant challenges to the quantitative assessment of human factor risks. This paper proposes a novel model to assess human‐induced risks in flight tests, which enables the quantitative analysis of risk factors. The HFACS model has been adaptively improved to more comprehensively identify human‐related risk factors in flight tests. By integrating the fault tree model with the fuzzy Bayesian network, this study calculates the quantitative value of the probability of hazard occurrence. Furthermore, this study incorporates the impacts of individual differences on the consistency of expert ratings via the improved similarity aggregation method. The paper concludes with a case analysis that identifies the most critical human factors in civil aircraft flight tests, thereby providing valuable insights for formulating preventive measures and optimizing risk management strategies. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 16875966 |
| DOI: | 10.1155/ijae/8879803 |
