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A methodology to evaluate human factors contributed to maritime accident by mapping fuzzy FT into ANN based on HFACS

机译：评估人为因素的方法通过将模糊FT绘制到基于HFACS

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摘要

A comprehensive accident analysis model is proposed to analyse the human factors that are involved in maritime accidents. The model integrates the superiorities of HFACS, Fuzzy FTA and ANN; HFACS is used to identify and classify human factors associated with 38 accidents, which are re-organized as Basic Events (BEs), Intermediate Events (IEs) and Top Event (TE), developing the architecture of FT; fuzzy aggregation analysis is employed to address expressions of experts, obtaining the failure probabilities of the BEs; and an ANN is established by matching the BEs with variables at the input layer, IEs with the variables in the hidden layers, and the TE with the variable at the output layer. The results show that the proposed ANN-based model has satisfactory performance in overcoming the drawbacks of FTA. The performance of the proposed model can be further improved through the introduction of newly available information/data and the adjustment of the numbers of neurons assigned at the first and second hidden layers according to the architecture of FT. The capability of ANNs-based model to effectively handle uncertainties, dynamics, nonlinear associated with human risk factors is also ascertained through the application for the developed maritime accident scenario.
机译：建议综合事故分析模型分析了涉及海事事故的人类因素。该模型集成了HFAC，模糊FTA和ANN的优势; HFACS用于识别和分类与38事故相关的人为因素，这些因素被重新组织为基本事件（BES），中级事件（IES）和顶级活动（TE），发展FT的架构;模糊聚集分析用于解决专家的表达，获得BES的失败概率;通过将BET与输入层的变量匹配，IES在隐藏层中的变量和输出层处的变量的变量来建立一个ANN。结果表明，拟议的基于Ann的模型具有令人满意的性能，克服了FTA的缺点。通过引入新可用的信息/数据以及根据FT的架构引入新可用的信息/数据以及调整在第一和第二隐藏层处分配的神经元数的调整，可以进一步提高所提出的模型的性能。通过申请发达的海事事故情景，还确定了基于ANNS的模型的能力，以有效处理与人类危险因素相关的不确定性，动态，非线性。

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