Search

This study aims to provide a better understanding of human factors and human performance mechanism in aviation accident analysis by visualisation and taxonomy analysis using mapping knowledge domain (MKD). An overview of aviation accident analysis involving human factors is firstly conducted, followed by an introduction of several conceptual models and human reliability analysis methods. Finally, a specific framework for risk assessment of human factors in aviation is proposed. The human factors analysis and classification system structure combined with the quantitative method is the most frequently used mode in aviation accident analysis. From a macro ergonomic perspective, systematic consideration has been emphasised frequently in existing research. Pilots or individuals are thought to be associated with the organisations and environment they located in. On the other hand, the micro ergonomic aspect prefers a quantitative and probabilistic way to reveal the individual's failure mode or error rate. Based on previous studies, the proposed framework provides a systematic view to help with the evaluation of human performance in aviation, taking into account human issues as well as management and environmental factors.

Liquefied Natural Gas (LNG) is a clean, efficient and economic energy, which is mainly transported by LNG carriers in the marine industry. Possible spilling of cryogenic LNG puts safety of crew in danger due to fire and explosion hazards. Taking into account various uncertainties, a modified fault tree model for LNG spill accident during LNG carriers’ handling operations is constructed in this study. Human factor analysis is introduced with a goal to predict human errors in LNG carriers’ handling operation. Finally, the results of the Fault Tree Analysis (FTA) and Human Reliability Analysis (HRA) are combined, so that risks can be assessed using Monte Carlo Simulation (MCS). Comparing the results of risk assessment with the traditional FTA and corresponding criterion set, an important reference for LNG carrier management is provided for administration.

Safety is crucial to ensure the sustainability of aviation growth. To better clarify the influences of human factors on aviation accident risks, this study developed a hybrid HFACS-BN model (HFACS: Human Factors Analysis and Classification System, BN: Bayesian Network). The authors designed and implemented a questionnaire survey based on the four-level HFACS framework and collected valid data from 180 out of 649 aviation professionals working in the Ulaanbaatar International Airport, Mongolian in 2017. The model identified 35 major human factors out of 129 factors. The model validation was performed in terms of content validity and predictive validity. The results showed that even though a majority of respondents perceived that many human factors had a middle- or high-effect on aviation accident risks, the probability of the risks caused by human factors was estimated to be just 1.37%. The Unsafe Acts level is most influential to the risks among the four levels, while the Unsafe Supervision level contributes least. It is revealed that enhancing aviation professionals&rsquo, awareness of human factors should make full use of causal chaining effects among human factors. Finally, this study contributes to the literature from the perspectives of both methodological development and important empirical analysis.