Understanding the process of community recovery impacted by various post-disaster decisions such as dynamic policies can effectively guide the recovery process and expedite recovery, thereby establishing a more resilient community. This paper proposes a methodology based on a multi-layer Monte Carlo simulation to model a two-stage recovery process for residential buildings: functional downtime due to delay and functional downtime due to repair. The delay portion of the model was modified based on the REDi framework and models the impeding factors that delay repairs such as post-disaster inspection, insurance claims, and building permits. Household income was examined to estimate the financing delay depending on different funding resources such as insurance and loans available to households at different income levels. The repair portion of the model followed the FEMA P-58 approach (which was originally for post-earthquake analysis) and was controlled by fragility functions. This study also investigates a series of policies to examine an illustrative example, namely the 2011 Joplin tornado. The residential recovery is modeled as a time-stepping process without any empirical data such that policies can be implemented pre-disaster (mitigation) and/or post-disaster. The ability to model hypothetical policy scenarios for residential recovery of a community will enable decision-makers to better understand collective community-wide impacts of their actions and policies, thereby improving community resilience planning.

了解受各种灾难后决策（例如动态策略）影响的社区恢复过程可以有效地指导恢复过程并加快恢复速度，从而建立一个更具弹性的社区。本文提出了一种基于多层蒙特卡洛模拟的方法来对住宅建筑的两阶段恢复过程进行建模：延迟造成的功能停机和维修造成的功能停机。该模型的延迟部分基于REDi框架进行了修改，并建模了延迟维修的障碍因素，例如灾后检查，保险索赔和建筑许可。对家庭收入进行了检查，以根据不同的资金来源（例如保险和可为不同收入水平的家庭提供的贷款）估算筹资延迟。模型的修复部分遵循FEMA P-58方法（最初用于地震后分析），并由脆弱性函数控制。这项研究还调查了一系列政策，以研究一个示例性例子，即2011年乔普林龙卷风。住宅恢复被建模为没有任何经验数据的时间步长过程，因此可以在灾难发生前（缓解）和/或灾难发生后实施策略。为社区的住宅恢复建立假设性政策情景的能力将使决策者能够更好地了解其行为和政策对社区的集体影响，从而改善社区的抗灾力规划。这项研究还调查了一系列政策，以研究一个示例性例子，即2011年乔普林龙卷风。住宅恢复被建模为没有任何经验数据的时间步长过程，因此可以在灾难发生前（缓解）和/或灾难发生后实施策略。为社区的住宅恢复建立假设性政策情景的能力将使决策者能够更好地了解其行为和政策对社区的集体影响，从而改善社区的抗灾力规划。这项研究还调查了一系列政策，以研究一个示例性例子，即2011年乔普林龙卷风。住宅恢复被建模为没有任何经验数据的时间步长过程，因此可以在灾难发生前（缓解）和/或灾难发生后实施策略。为社区的住宅恢复建立假设性政策情景的能力将使决策者能够更好地了解其行为和政策对社区的集体影响，从而改善社区的抗灾力规划。