Quantifying Generalized Residential Fire Risk Using Ensemble Fire Models with Survey and Physical Data
Quantifying Generalized Residential Fire Risk Using Ensemble Fire Models with Survey and Physical Data
复制标题
使用带有调查和物理数据的整体火灾模型量化广义住宅火灾风险
作者：
A. Anderson;O. Ezekoye
了解和量化财产损失火灾风险对于火灾现场的决策者做出明智的决策至关重要。例如，消防部门所做的各种决定对社区火灾风险的影响，虽然大多数当事人都有定性的了解，但没有很好地量化。缺乏量化可能导致对服务价值的错误判断和误解，从而导致低于标准的资源分配，对服务对象产生负面影响。此外，在公开的火灾数据中，消防部门的表现和火灾风险之间的混杂效应可能会导致标准回归模型基于有效的相关性（由于某些难以收集的关键变量，因此无法观察到）提供反直觉的结论。提出了一种方法，利用美国的住房，房间布局，火灾事件的数据，以及实验热释放速率，材料降解速率，热物理性质的数据结合物理火灾模型来估计社区平均程度的火灾损失的家庭。单户住宅的房屋布局数据来自美国住房调查。代表45%的美国家庭的五个家庭类别被选中进行分析。美国消防局国家火灾事故报告系统数据库用于选择初始燃料的分布和点火位置。调查数据被用来指定家庭内的家具布局。总共有5167个场景被开发为家庭几何形状，第一个项目点燃，和家具布局的组合。火灾演变预测这些情况下使用CFAST和耦合到一个热解启发的损害模型，使用热通量的目标在家中。针对家居和家具布局以及点火分布的整体构建了损伤演化概率函数。这种损害模型行使在一个决策分析问题，以证明社区规模的资源分配的方法的效用。敏感性研究的模型同样进行，表明不确定性的最大来源是与选择的替代材料性能计算时，家庭的损害。与最佳可用数据进行比较，以评估模型的稳健性。
Understanding and quantifying property loss fire risk is critical to enabling decision makers in the fire field to make informed decisions. For example, the impact of various decisions made by fire departments on the fire risk in their community, while qualitatively understood by most parties, is not well quantified. Lack of quantification can lead to misdiagnosis and miscommunication on the value of services that result in sub-par resource allocations that negatively impact constituencies. Additionally, the confounding effects between fire department performance and fire risk in publicly available fire data can cause standard regression models to supply counter-intuitive conclusions based upon valid correlations (due to certain critical variables that are difficult to collect and thus unobserved). A methodology is presented that utilizes United States housing, room layout, and fire incident data, as well as experimental heat release rates, material degradation rates, and thermophysical property data in conjunction with physical fire models to estimate a community-averaged extent of fire damage in homes. Housing layout data for single family residential homes were collected from the American Housing Survey. Five home categories representing 45% of U.S. homes were selected for analysis. The U.S. Fire Administration National Fire Incident Reporting System database was used to select the distribution of initial fuels and ignition locations. Survey data was taken to specify furniture layout within the homes. A total of 5167 scenarios were developed for the combinations of home geometry, first item ignited, and furniture layout. Fire evolution was predicted for these scenarios using CFAST and coupled to a pyrolysis-inspired damage model using heat flux to targets in the homes. A damage evolution probability function was constructed for the ensemble of home and furnishing layouts and ignition distributions. This damage model was exercised in a decision analysis problem to demonstrate the utility of the methodology for community-scale resource allocation. Sensitivity studies on the model are likewise performed, indicating the largest source of uncertainty to be linked to the choice of surrogate material properties when calculating home damage. Comparison to best available data is made to assess the robustness of the model.