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Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts

机译：使用神经网络从不安全行为的先决条件预测HFACS不安全行为

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摘要

Human Factors Analysis and Classification System (HFACS) is based upon Reason's organizational model of human error which suggests that there is a 'one to many' mapping of condition tokens (HFACS level 2 psychological precursors) to unsafe act tokens (HFACS level 1 error and violations). Using accident data derived from 523 military aircraft accidents, the relationship between HFACS level 2 preconditions and level 1 unsafe acts was modelled using an artificial neural network (NN). This allowed an empirical model to be developed congruent with the underlying theory of HFACS. The NN solution produced an average overall classification rate of ca. 74% for all unsafe acts from information derived from their level 2 preconditions. However, the correct classification rate was superior for decision- and skill-based errors, than for perceptual errors and violations. Practitioner Summary: A model to predict unsafe acts (HFACS level 1) from their preconditions (HFACS level 2) was developed from the analysis of 523 military aircraft accidents using an artificial NN. The results could correctly predict approximately 74% of errors.
机译：人为因素分析和分类系统（HFACS）基于Reason的人为错误组织模型，该模型表明条件标记（HFACS第2级心理先兆）与不安全行为标记（HFACS第1级错误和不安全行为）之间存在“一对多”映射违反）。使用从523架军机事故中得出的事故数据，使用人工神经网络（NN）对HFACS 2级前提条件与1级不安全行为之间的关系进行建模。这允许与HFACS的基础理论相适应地开发经验模型。 NN解决方案产生的平均总分类率为ca。所有不安全行为的74％来自其2级先决条件得出的信息。但是，对于基于决策和技能的错误，正确的分类率要优于对感知和错误的侵犯。从业人员摘要：通过使用人工神经网络对523架军机事故进行了分析，开发了一种根据其先决条件（HFACS等级2）预测不安全行为（HFACS等级2）的模型。结果可以正确预测大约74％的错误。

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