Center of Excellence for Occupational Health, Occupational Health and Safety Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia.
PLoS One. 2024 Aug 29;19(8):e0307883. doi: 10.1371/journal.pone.0307883. eCollection 2024.
This study aimed to propose a novel method for dynamic risk assessment using a Bayesian network (BN) based on fuzzy data to decrease uncertainty compared to traditional methods by integrating Interval Type-2 Fuzzy Sets (IT2FS) and Z-numbers. A bow-tie diagram was constructed by employing the System Hazard Identification, Prediction, and Prevention (SHIPP) approach, the Top Event Fault Tree, and the Barriers Failure Fault Tree. The experts then provided their opinions and confidence levels on the prior probabilities of the basic events, which were then quantified utilizing the IT2FS and combined using the Z-number to reduce the uncertainty of the prior probability. The posterior probability of the critical basic events (CBEs) was obtained using the beta distribution based on recorded data on their requirements and failure rates over five years. This information was then fed into the BN. Updating the BN allowed calculating the posterior probability of barrier failure and consequences. Spherical tanks were used as a case study to demonstrate and confirm the significant benefits of the methodology. The results indicated that the overall posterior probability of Consequences after the failure probability of barriers displayed an upward trend over the 5-year period. This rise in IT2FS-Z calculation outcomes exhibited a shallower slope compared to the IT2FS mode, attributed to the impact of experts' confidence levels in the IT2FS-Z mode. These differences became more evident by considering the 10-4 variance compared to the 10-5. This study offers industry managers a more comprehensive and reliable understanding of achieving the most effective accident prevention performance.
本研究旨在提出一种新的方法，使用基于贝叶斯网络（BN）的模糊数据进行动态风险评估，通过集成区间型 2 模糊集（IT2FS）和 Z 数，与传统方法相比，降低不确定性。采用系统危害识别、预测和预防（SHIPP）方法、顶事件故障树和障碍失效故障树构建蝴蝶结图。然后，专家们就基本事件的先验概率提供意见和置信度，然后利用 IT2FS 进行量化，并利用 Z 数进行组合，以降低先验概率的不确定性。利用记录的五年内需求和故障率数据，基于贝塔分布，获得关键基本事件（CBE）的后验概率。然后将此信息输入 BN。更新 BN 可以计算障碍失效和后果的后验概率。球罐被用作案例研究，以演示和确认该方法的显著优势。结果表明，在障碍失效概率之后，后果的整体后验概率在五年期间呈上升趋势。与 IT2FS 模式相比，IT2FS-Z 计算结果的上升斜率更平缓，这归因于 IT2FS-Z 模式下专家置信度的影响。与 10-5 相比，考虑到 10-4 的差异时，这些差异更加明显。本研究为行业经理提供了更全面、更可靠的理解，以实现最有效的事故预防性能。
