The risk of accidents at workplaces, particularly in the sensitive locations with unsafe behaviors, have increased substantially, needing to be managed accurately. To ameliorate the safety in such systems, enhancing the integrated resilience engineering and macro-ergonomics concepts is of pivotal importance. In this sense, this paper unveils a novel method based on Bayesian network and artificial neural network models to enhance safety of such systems considering both mentioned concepts. Exploiting the Bayesian network, the effects of the indicators on the system safety efficiency is evaluated according to the expert’s opinions. The Artificial neural network examines these effects based on the operator’s opinions. Thereinafter, to decrease the uncertainty and bias of results and also augment the robustness and accuracy of them, the combination of the results of these models is considered as the final criterion. For analyzing the efficacy of the proposed method, a case study in a gas refinery in Ilam, Iran is conducted. The results corroborate the validity and efficacy of the proposed method and draw outstanding managerial insights.

中文翻译：

---

基于弹性工程和宏观人机工程学指标的天然气精炼厂安全改进：贝叶斯网络-人工神经网络方法

在工作场所，特别是在具有不安全行为的敏感地区，发生事故的风险已大大增加，需要进行准确管理。为了改善此类系统的安全性，增强集成的弹性工程和宏观人体工程学概念至关重要。从这个意义上讲，本文提出了一种基于贝叶斯网络和人工神经网络模型的新颖方法，以考虑到上述两个概念来增强此类系统的安全性。利用贝叶斯网络，根据专家的意见评估指标对系统安全效率的影响。人工神经网络根据操作员的意见检查这些影响。此后，为了减少结果的不确定性和偏差，并提高结果的鲁棒性和准确性，这些模型结果的组合被认为是最终标准。为了分析该方法的有效性，在伊朗伊兰的一家天然气精炼厂进行了案例研究。结果证实了所提方法的有效性和有效性，并得出了出色的管理见解。