A Causal Correlations Analysis with Multi-sample Controlled Flight Into Terrain Incidents

To address the research gap regarding the causal correlations among incidents with varying consequence severities, this study adopts a Safety-Ⅱ perspective and takes controlled flight into terrain (CFIT), a high-risk aviation accident category, as the research object to analyze the causal factors and the interrelationships across samples with distinct outcome levels. A total of 128 CFIT accident investigation reports from the Aviation Safety Network (ASN) and 354 voluntary reports from the Aviation Safety Reporting System (ASRS) are selected as the analytical sample. Guided by the threat and error management (TEM) model, a systematic analysis is conducted to identify latent conditions, threats, flight crew errors, undesired aircraft states, and countermeasures inherent in the sampled accidents, which resulted in the identification of 3 367 causal factors and 2 169 causal-temporal relationships. Following semantic analysis and integration of these relationships, a Bayesian network (BN) model is constructed, resulting in an accident evolution network model comprising 46 BN nodes. Association rule mining is employed to compute conditional probabilities and inter-node correlations, so as to delineate the high-probability causal chains of the samples. Results demonstrate that: ①the primary high-risk pathway leading to CFIT accidents is: operational pressure → flight crew fatigue → loss of situational awareness → incorrect altimeter setting → aircraft deviation from the intended altitude profile. ②When negative factors including flight crew fatigue (accounting for 57% of the identified negative factors) and inadequate management by airlines/regulatory authorities (17%) are present, the probability of CFIT accidents increases by 24%. ③From the Safety-Ⅱ perspective, when positive factors, such as aviation system response measures (with an effectiveness rate of 78%) and the execution of recovery actions (84%) take effect, the probability of a stable approach is enhanced by 34%, thereby significantly mitigating the risk of CFIT occurrences.