Li Hua, Apostolakis George E, Gifun Joseph, VanSchalkwyk William, Leite Susan, Barber David
Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA.
Risk Anal. 2009 Mar;29(3):438-56. doi: 10.1111/j.1539-6924.2008.01164.x. Epub 2008 Dec 8.
Natural hazards, human-induced accidents, and malicious acts have caused great losses and disruptions to society. After September 11, 2001, critical infrastructure protection has become a national focus in the United States and is likely to remain one for the foreseeable future. Damage to the infrastructures and assets could be mitigated through predisaster planning and actions. A systematic methodology was developed to assess and rank the risks from these multiple hazards in a community of 20,000 people. It is an interdisciplinary study that includes probabilistic risk assessment (PRA), decision analysis, and expert judgment. Scenarios are constructed to show how the initiating events evolve into undesirable consequences. A value tree, based on multi-attribute utility theory (MAUT), is used to capture the decisionmaker's preferences about the impacts on the infrastructures and other assets. The risks from random failures are ranked according to their expected performance index (PI), which is the product of frequency, probabilities, and consequences of a scenario. Risks from malicious acts are ranked according to their PI as the frequency of attack is not available. A deliberative process is used to capture the factors that could not be addressed in the analysis and to scrutinize the results. This methodology provides a framework for the development of a risk-informed decision strategy. Although this study uses the Massachusetts Institute of Technology campus as a case study of a real project, it is a general methodology that could be used by other similar communities and municipalities.
自然灾害、人为事故和恶意行为给社会造成了巨大损失和破坏。2001年9月11日之后，关键基础设施保护已成为美国的一项国家重点工作，并且在可预见的未来可能仍将如此。通过灾前规划和行动，可以减轻对基础设施和资产的损害。开发了一种系统方法，用于评估和排序一个拥有20000人的社区中这些多重灾害带来的风险。这是一项跨学科研究，包括概率风险评估（PRA）、决策分析和专家判断。构建情景以展示初始事件如何演变成不良后果。基于多属性效用理论（MAUT）的价值树用于捕捉决策者对基础设施和其他资产影响的偏好。随机故障带来的风险根据其预期性能指标（PI）进行排序，PI是情景的频率、概率和后果的乘积。由于无法获得攻击频率，恶意行为带来的风险根据其PI进行排序。采用审议过程来捕捉分析中无法解决的因素并审查结果。该方法为制定基于风险的决策策略提供了一个框架。尽管本研究以麻省理工学院校园作为一个实际项目的案例研究，但它是一种通用方法，其他类似的社区和城市也可以使用。
