Given the paramount importance of safety in the aviation industry, even minor operational anomalies can have significant consequences. Comprehensive documentation of incidents and accidents serves to identify root causes and propose safety measures. However, the unstructured nature of incident event narratives poses a challenge for computer systems to interpret. Our study aimed to leverage Natural Language Processing (NLP) and deep learning models to analyze these narratives and classify the aircraft damage level incurred during safety occurrences. Through the implementation of LSTM, BLSTM, GRU, and sRNN deep learning models, our research yielded promising results, with all models showcasing competitive performance, achieving an accuracy of over 88% significantly surpassing the 25% random guess threshold for a four-class classification problem. Notably, the sRNN model emerged as the top performer in terms of recall and accuracy, boasting a remarkable 89%. These findings underscore the potential of NLP and deep learning models in extracting actionable insights from unstructured text narratives, particularly in evaluating the extent of aircraft damage within the realm of aviation safety occurrences.

翻译：鉴于航空业安全至关重要，即使微小的操作异常也可能导致严重后果。对事故和事件进行全面记录有助于识别根本原因并提出安全措施。然而，事件叙述的非结构化特性给计算机系统解读带来了挑战。本研究旨在利用自然语言处理（NLP）和深度学习模型分析这些叙述，并对安全事件中飞机损伤等级进行分类。通过实施LSTM、BLSTM、GRU和sRNN深度学习模型，我们的研究取得了有希望的结果，所有模型均展现出具有竞争力的性能，在四分类问题中准确率超过88%，显著超越了25%的随机猜测阈值。值得注意的是，sRNN模型在召回率和准确率方面表现最佳，达到了89%的显著水平。这些发现凸显了NLP和深度学习模型在从非结构化文本叙述中提取可操作见解方面的潜力，特别是在评估航空安全事件中飞机损伤程度方面。