利用贝叶斯回归建立洪水易损性模型

A. Wells, C. Pope
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引用次数: 0

摘要

传统的组件通过/失败设计分析和测试协议驱动了过于保守的操作限制和设定值,以及不必要的大安全裕度。组件性能测试与故障概率模型开发相结合,可以支持更灵活的操作限制和设定值的选择,以及软化纵深防御元素。本章讨论了利用马尔可夫链蒙特卡罗方法建立贝叶斯回归脆弱性模型的过程,以及使用三种贝叶斯p值的模型检验协议。本章还讨论了模型开发和测试技术的应用,通过与工业钢门受到水位上升情景相关的组件淹水性能实验。这些组件测试为脆弱性模型开发提供了必要的数据,同时为测试协议的开发提供了洞察力,测试协议将为脆弱性模型开发提供有意义的数据。最后,本章讨论了工业钢门在受水上升情景影响时的脆弱性模型的开发和选择。
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Flooding Fragility Model Development Using Bayesian Regression
Traditional component pass/fail design analysis and testing protocol drives excessively conservative operating limits and setpoints as well as unnecessarily large margins of safety. Component performance testing coupled with failure probability model development can support selection of more flexible operating limits and setpoints as well as softening defense-in-depth elements. This chapter discuses the process of Bayesian regression fragility model development using Markov Chain Monte Carlo methods and model checking protocol using three types of Bayesian p-values. The chapter also discusses application of the model development and testing techniques through component flooding performance experiments associated with industrial steel doors being subjected to a rising water scenario. These component tests yield the necessary data for fragility model development while providing insight into development of testing protocol that will yield meaningful data for fragility model development. Finally, the chapter discusses development and selection of a fragility model for industrial steel door performance when subjected to a water-rising scenario.
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