| Affiliations: | STEM Research Leadership Program |
| Project Leader: | Srihari Menon srimenon@tamu.edu |
| Faculty Mentor: | Nancy Currie-Gregg, Ph.D. |
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Meeting Times:
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TBD |
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Team Size:
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2 |
| Open Spots: | 0 |
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Special Opportunities:
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In developing this model, you would be: -assisting in improving aviation safety and investigating airplane accidents by developing a dynamic modeling tool -contributing to ongoing research and coauthor publications in aviation and aerospace safety -gaining appreciation for, and knowledge of, aviation safety research and state of the art modeling tools.
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Team Needs:
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-Graduate or upper-level undergraduate students from any engineering major; CSCE, ISEN and AERO majors preferred.– Programming experience to include natural language processing, using any relevant package.-Knowledge of Bayesian network modeling and analysis not required but preferred. |
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Description:
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AThe Systems Analysis and Functional Evaluation (SAFE) Laboratory conducts research to provide a system engineering perspective on the safety and reliability of aerospace systems. One area of interest is determining the proximate and root causes of general aviation accidents. We intend to develop a Bayesian network to model causes of general aviation accidents, using accident data from National Transportation Safety Board (NTSB) investigation reports.Previous efforts have yielded root cause models of the two leading causes of general aviation accidents – loss of control and loss of thrust. However, the current models do not address the relative weights of the multiple causes. Thus, it is necessary to mine accident data, such as that contained in the extensive NTSB database, to determine approximate weightings for the factors identified in these causal models. In order to go beyond the initial NTSB classification of proximate and contributing causes, analysis of the data contained in accident reports using natural language processing (NLP) and other data analysis techniques. This data will then be used to determine the relative frequencies or weights for the root and contributing causes of general aviation accidents which is necessary to create a dynamic fault trees of accident scenarios using Bayesian network modeling.Results and products created from this research may be used to inform mitigations and barriers to aviation accidents and improve general aviation safety. |