The Implementation of AI in Aviation Accidents Investigations

Abstract: Accident investigation is fundamental to aviation safety, serving to identify causal factors and prevent the recurrence of accidents. Traditionally, such investigations have depended on systematic methodologies like the SHELL model and Fault Tree Analysis, drawing on data from flight data recorders, cockpit voice recorders, and eyewitness accounts. However, the rapid integration of digital technologies, the increasing complexity of modern systems, and the challenges posed by globalized operations have created an urgent need for more sophisticated investigative tools. Artificial intelligence offers capabilities such as pattern recognition, predictive modeling, and real-time data analysis, which can significantly augment the investigative process and improve outcomes. This research explores the application of AI in aviation accident investigations with a specific focus on several areas. First, the literature review examines the use of AI for human error analysis, investigating behavioral patterns, decision-making processes, and cognitive workload during incidents. It also evaluates the potential of AI tools to assess system reliability by detecting latent failures and interdependencies in avionics and mechanical systems. Furthermore, the research considers how AI-driven applications - simulations can be used for resilience modeling by reconstructing accidents and assessing system responses to cascading failures. In addition, the study evaluates how AI can enhance investigative efficiency through the automation of data sorting, analysis, and hypothesis testing. A multi-disciplinary approach was employed, integrating theoretical frameworks with AI-driven simulations and case study analyses. The methodology began with an extensive literature review of existing accident investigation methodologies, emphasizing the role of technology and data analytics. Building on this review, an AI-powered investigative framework was developed that incorporates machine learning algorithms for anomaly detection, natural language processing for analyzing cockpit communications and maintenance logs, and predictive analytics for modeling potential accident scenarios based on historical data. The framework was then tested through the “Aviation Human Factors Analyst” Open AI application simulations that replicated past aviation accidents to validate its ability to identify causal factors and suggest preventive measures. Finally, the framework was applied to real-world aviation accidents, such as controlled flight into terrain and loss of control in flight, to assess its effectiveness in uncovering human, technical, and environmental contributors. By augmenting traditional methodologies with advanced AI-driven tools, investigators can achieve greater accuracy and efficiency in uncovering causal factors, ultimately enhancing overall aviation safety. Future research should address cybersecurity considerations to protect AI systems from cyber threats, explore the transferability of AI frameworks to other transportation sectors such as rail and maritime, develop AI tools capable of real-time incident analysis to support immediate corrective actions, and advance methods in explainable AI to ensure transparency and accountability in AI-driven findings. Integrating AI into aviation accident investigations promises a more resilient and adaptive safety ecosystem, paving the way for safer skies.

Keywords: Aviation Safety, Artificial Intelligence (AI), Accident Investigation, Human Error, Reliability, Resilience, Performance

DOI: 10.54941/ahfe1006499

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