使用 HFACS-UL 和模糊贝叶斯网络评估大学实验室中的不安全行为风险。
Risk assessment of unsafe behavior in university laboratories using the HFACS-UL and a fuzzy Bayesian network.
机构信息
School of Emergency Management and Safety Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China.
School of Emergency Management and Safety Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China.
出版信息
J Safety Res. 2022 Sep;82:13-27. doi: 10.1016/j.jsr.2022.04.002. Epub 2022 Apr 27.
INTRODUCTION
Risk assessment for unsafe behavior is an important task in the management of university laboratories. Yet related research activities are still in the early stages. This paper attempts to deepen the insight and provide a basis for further research.
METHOD
As traditional methods are inadequate in terms of quantitative assessment and uncertainty handling, this paper proposes a method to assess the risk of unsafe behavior in university laboratories using the human factor analysis and classification system for university laboratories (HFACS-UL)-fuzzy Bayesian network (BN) approach. A BN structure was established using the HFACS-UL model for the identification of factors influencing unsafe behavior. Using a fuzzy BN approach, parameters are learned based on prior knowledge and expert experience. The model is then applied for inference analysis to identify the main risk factors. The key agents were also analyzed along with meta-networks to determine further preventive and control measures.
RESULTS
Taking chemistry laboratories of a university as an example, the results show that the probability of unacceptable unsafe behavior in chemical laboratories is 86%, indicating that commitment and cooperation from different agents are required. Of the 24 risk factors, poor organizational climate, with a sensitivity value of 24.1%, has the greatest impact on unsafe behavior. The most fundamental factor contributing to the occurrence of unsafe behavior is inadequate legislation, which in turn results in unacceptable external factors and inadequate supervision, thus forming the most likely causal chain. The functional department, lab center director, and secondary faculty leadership team are the most critical agents.
CONCLUSIONS
Results from the chemistry laboratories demonstrate the credibility of the model.
PRACTICAL APPLICATIONS
This study may help provide technical support for risk management in university laboratories.
简介
对不安全行为进行风险评估是高校实验室管理的一项重要任务。然而，相关研究仍处于起步阶段。本文试图深入探讨这一问题，为进一步研究提供依据。
方法
由于传统方法在定量评估和不确定性处理方面存在不足，本文提出了一种使用高校实验室人为因素分析与分类系统（HFACS-UL）-模糊贝叶斯网络（BN）方法评估高校实验室不安全行为风险的方法。通过 HFACS-UL 模型建立 BN 结构，以识别影响不安全行为的因素。利用模糊 BN 方法，根据先验知识和专家经验进行参数学习。然后，应用该模型进行推理分析，以识别主要风险因素。还对关键代理进行了分析，并确定了元网络，以确定进一步的预防和控制措施。
结果
以某高校化学实验室为例，结果表明，化学实验室不可接受的不安全行为概率为 86%，这表明需要不同代理的承诺和合作。在 24 个风险因素中，组织氛围不佳（敏感度值为 24.1%）对不安全行为的影响最大。导致不安全行为发生的最根本因素是立法不足，进而导致不可接受的外部因素和监督不足，从而形成最有可能的因果链。职能部门、实验中心主任和二级教师领导团队是最关键的代理。
结论
化学实验室的结果表明了该模型的可信度。
实际应用
本研究可为高校实验室风险管理提供技术支持。
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