Wang Xinan, Hu Xiaofeng
Guanghua School of Management, Peking University, Beijing 100871, China.
School of Information Technology and Cybersecurity, People's Public Security University of China, Beijing 100038, China.
Heliyon. 2023 Feb 11;9(2):e13674. doi: 10.1016/j.heliyon.2023.e13674. eCollection 2023 Feb.
Because of the more emerging risks and stronger risk interactions, the risk of college campuses as well as students and staff received more and more attention. Current works on campus risk mostly focus on single-category factors, and few of them considered risk interactions. Therefore, an integrated model for assessing comprehensive risks on the campus is proposed to put forward risk reduction strategies. First, a comprehensive risk identification of the college campus is conducted by integrating the modified egg model and the fault tree. Then, DEMATEL (Decision-Making Trial and Evaluation Laboratory) is applied to quantify the complex risk interactions and determine the influential causes for further modelling. Finally, the Bayesian network is established for cause diagnosis, consequence prediction, and risk reduction. The identified most sensitive cause is alcohol use. In the case of the four sensitive causes simultaneously occurring, the probability of high campus risk will increase from 21.9% of the original to 39.4%. Moreover, an efficiency analysis of different risk reduction strategies is performed to determine the most efficient risk reduction strategy. The results indicate that the proposed methodology may of great significance for the risk reduction of the college campus in the changing age.
由于新出现的风险越来越多且风险相互作用更强,大学校园以及学生和教职员工的风险受到越来越多的关注。当前关于校园风险的研究大多集中在单一类别因素上,很少有人考虑风险相互作用。因此,提出了一种用于评估校园综合风险的集成模型,以提出风险降低策略。首先,通过整合改进的鸡蛋模型和故障树对大学校园进行全面风险识别。然后,应用决策试验与评价实验室(DEMATEL)对复杂的风险相互作用进行量化,并确定有影响力的原因以便进一步建模。最后,建立贝叶斯网络用于原因诊断、后果预测和风险降低。识别出的最敏感原因是饮酒。在四个敏感原因同时出现的情况下,校园高风险概率将从原来的21.9%增加到39.4%。此外,对不同风险降低策略进行效率分析,以确定最有效的风险降低策略。结果表明,所提出的方法对于变化时代的大学校园风险降低可能具有重要意义。