State-dependent models can be used to represent the system recovery process as a series of stochastic transitions from lower to higher functional states. However, the applications of these models have been limited in scope and there is a lack of a generalized recovery modeling framework. A generalized framework would permit a robust forecasting of systems and system-of-systems recovery under multiple hazards, and more broadly, would contribute to community disaster preparedness. This paper develops a generalized post hazard-event recovery modeling framework based on state-dependent Markov-type processes. We then apply the proposed framework to solve a spectrum of problems that range from hind-casting single-system recovery following a single hazard event to forecasting post-event trajectories under multiple hazards and modeling the recovery of a system-of-systems. First, Markov chains are used to hind-cast the observed recovery for a portfolio of buildings affected by the 2014 South Napa, California, earthquake. Next, Markov processes are used to formulate a parametric post hazard-event recovery model, which can be updated using Bayesian statistics when relevant datasets become available. Semi-Markov processes are then used to develop a more general model of single hazard recovery, which accounts for the intensity of the loading and level of damage caused by the event. Semi-Markov processes with non-renewal features are then used to account for multihazard interactions in a post-event recovery model, and applied to a case study that involves a community in Charleston, South Carolina. Lastly, Markov-type processes are combined with Bayesian networks to model the recovery of residential, commercial, educational, and industrial buildings (system-of-systems) following a hazard event. These applications demonstrate the versatility of the Markov framework towards handling recovery problems with varying levels of complexity.

状态相关模型可以用来表示系统恢复过程，即从较低功能状态到较高功能状态的一系列随机过渡。但是，这些模型的应用范围受到限制，并且缺少通用的恢复建模框架。一个通用的框架将允许对多种危害下的系统和系统的恢复进行有力的预测，并且更广泛地讲，将有助于社区备灾。本文建立了一个基于状态依赖的马尔可夫型过程的广义灾后事件恢复建模框架。然后，我们将所提出的框架用于解决一系列问题，这些问题的范围从单个灾害事件之后的后向铸造单系统恢复到预测多种危害下的事后轨迹并为系统的系统恢复建模。首先，马尔可夫链用于预测受2014年加利福尼亚州南纳帕地震影响的一组建筑的观测恢复。接下来，使用马尔可夫过程来制定参数化的危险事件后恢复模型，当相关数据集可用时，可以使用贝叶斯统计信息对其进行更新。然后，使用半马尔可夫过程来开发更通用的单一危害恢复模型，该模型考虑了事件造成的负荷强度和破坏程度。然后将具有非更新特征的半马尔可夫过程用于事件后恢复模型中的多灾种相互作用，并应用于涉及南卡罗来纳州查尔斯顿社区的案例研究。最后，将马尔可夫类型的过程与贝叶斯网络相结合，以对灾害事件后住宅，商业，教育和工业建筑物（系统的系统）的恢复进行建模。这些应用程序展示了Markov框架在处理复杂程度不同的恢复问题方面的多功能性。发生危险事件后的工业建筑物（系统的系统）。这些应用程序展示了Markov框架在处理复杂程度不同的恢复问题方面的多功能性。发生危险事件后的工业建筑物（系统的系统）。这些应用程序展示了Markov框架在处理复杂程度不同的恢复问题方面的多功能性。