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Multi-variate and single-variable flood fragility and loss approaches for buildings

机译：建筑物的多变量和单变洪水脆弱和损失方法

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摘要

Propagating uncertainties in flood damage models is a critical step towards a risk-informed decision methodology that is based on quantitative assessment. Flood-related data scarcity and the use of deterministic models present challenges when seeking to include uncertainties in flood damage modeling. In this paper, a single-variable and multi-variate component-based flood fragility method is proposed. The method uses expert-based data derived from online sources that are applied within a Monte Carlo framework to divide the building into independent components and then assigns these components to five predefined damage states that describe the building damage as a whole. Using a series of Monte Carlo simulations, uncertainties in flood depth and flood duration that result in each damage level for each component were propagated. Their damage is then characterized using component fragility functions to be used to develop total building fragility and loss functions. The resulting fragilities can be used as a probabilistic vulnerability function to be assigned to a real community based on building archetype and occupancy. The ability to develop flood fragility curves for buildings without the need for empirical field data is the primary contribution of this work.
机译：在洪水损伤模型中传播不确定性是朝着风险明智的决策方法基于定量评估的关键步骤。与洪水相关的数据稀缺和使用确定性模型的使用在寻求在洪水损伤建模中包括不确定性时存在挑战。在本文中，提出了一种单变量和多变体组分的泛脆性方法。该方法使用从在Monte Carlo框架内应用的在线源中派生的基于专家的数据，以将建筑物划分为独立的组件，然后将这些组件分配给五个预定义的损害状态，以描述整个建筑物损坏。使用一系列蒙特卡罗模拟，洪水深度和洪水持续时间的不确定性传播了每个组件的每个损坏级别。然后，它们的损坏是使用要用于开发总建筑脆性和损耗功能的组件脆性函数的损坏。产生的磁带行动可以用作基于构建原型和占用的真实社区分配给真实社区的概率漏洞函数。在没有经验现场数据的情况下开发建筑物的洪水脆弱曲线的能力是这项工作的主要贡献。

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