Risk assessment of grain bin engulfment and entrapment using fault tree analysis and fuzzy fault tree analysis

Abstract

Grain bin safety remains a critical concern within the agricultural sector due to the prevalent incidence of grain entrapment and engulfment incidents. Despite existing guidelines and preventive measures, these hazards persist, underscoring the necessity for advanced analytical methodologies to enhance safety protocols. This research employs an integration of Fault Tree Analysis (FTA) and Fuzzy Fault Tree Analysis (FFTA) to address the complexities and uncertainties inherent in grain bin safety assessments comprehensively. The first project focused on using Fault Tree Analysis (FTA) to analyze 302 grain bin incidents from 1988 to 2024. A fault tree diagram was developed, with 30 basic events and 15 intermediate events identified as contributing to these accidents and categorized into engineering factors, enforcement factors, and education factors. The analysis provided a comprehensive visualization of the contributing factors, their interrelationships, and potential pathways leading to engulfment incidents. The second project employed Fault Tree Analysis (FTA) combined with fuzzy set theory. The integration of fuzzy set theory addresses uncertainties in expert judgments, enhancing the accuracy of risk factors assessment. The study identified key minimal cut sets and highlighted that Insufficient Availability of Rescue Gear, Insufficient Training for Specific Rescue Scenarios and Broken Grain Kernel were the most important risk factors among the concerns. This integrative approach facilitates a comprehensive understanding of the factors influencing grain bin incidents, providing a robust framework for risk assessment. Based on the findings, recommendations include enhancing emergency response training, improving the management of safety procedures, and ensuring regular maintenance of grain bin equipment. Future research should prioritize refining data collection methodologies to secure more contemporaneous and high-quality data, incorporating advanced technologies such as machine learning to augment predictive accuracy, and expanding the scope to encompass additional risk factors, including grain bin explosions. These endeavors will significantly contribute to the development of more effective risk management strategies, thereby enhancing safety outcomes in the agricultural sector. This research offers invaluable insights for policymakers and industry stakeholders, with the objective of mitigating the frequency and severity of grain bin entrapment and engulfment incidents through comprehensive and actionable recommendations.