Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Sophia Nasca, Senior Addyson Wolfe
Lead Presenter's Name
Sophia Nasca
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Dumindu Samaraweera
Abstract
Uncovering the root causes of aviation accidents is a critical component of improving aviation safety. Traditional approaches are largely reactive, relying on post-incident analysis rather than proactively identifying risk factors. This project addresses the need for proactive safety by using a multi-source dataset that integrates aviation accident records, weather conditions, and maintenance data extracted from investigative reports. The objective of this work is to move beyond predicting broad probable causes and instead model the sequence of contributing factors that lead to aviation incidents. Using the Swiss Cheese Model, the study will capture layered failures across operational, environmental, and maintenance domains. The methodology involves data cleaning, dataset analysis, and the application of data mining techniques to identify patterns of latent conditions and active failures. As this project is ongoing, preliminary results indicate that incorporating maintenance and weather variables will improve the identification of high-risk scenarios compared to using accident data alone. The expected outcome is a predictive framework capable of detecting risk factors earlier in the failure chain, enabling more targeted and effective mitigation strategies. This work contributes to bettering aviation safety by shifting the focus from reactive investigation to proactive risk management and providing further insight into the complex interactions that precede accidents.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Classification of Sequential Factors in Aviation Accident Cause Prediction
Uncovering the root causes of aviation accidents is a critical component of improving aviation safety. Traditional approaches are largely reactive, relying on post-incident analysis rather than proactively identifying risk factors. This project addresses the need for proactive safety by using a multi-source dataset that integrates aviation accident records, weather conditions, and maintenance data extracted from investigative reports. The objective of this work is to move beyond predicting broad probable causes and instead model the sequence of contributing factors that lead to aviation incidents. Using the Swiss Cheese Model, the study will capture layered failures across operational, environmental, and maintenance domains. The methodology involves data cleaning, dataset analysis, and the application of data mining techniques to identify patterns of latent conditions and active failures. As this project is ongoing, preliminary results indicate that incorporating maintenance and weather variables will improve the identification of high-risk scenarios compared to using accident data alone. The expected outcome is a predictive framework capable of detecting risk factors earlier in the failure chain, enabling more targeted and effective mitigation strategies. This work contributes to bettering aviation safety by shifting the focus from reactive investigation to proactive risk management and providing further insight into the complex interactions that precede accidents.